TW202309299A - Methods of identifying drug sensitive genes and drug resistant genes in cancer cells - Google Patents

Abstract

The present application relates to methods of identifying target genes in cancer cells whose mutations make the cancer cells sensitive or resistant to anti-cancer drugs. Also provided are methods of treating cancer and selecting patients based on aberrations (e.g., mutations) in target genes identified herein. Modified cancer cells that are sensitive or resistant to anti-cancer drugs, and methods and kits for generating thereof are also provided.

Description

Method for identifying drug-sensitive and drug-resistant genes in cancer cells

This application claims priority to International Patent Application PCT/CN2021/105822 filed on July 12, 2021 and International Patent Application PCT/CN2021/105816 filed on July 12, 2021, the contents of each of which are incorporated by reference in their Incorporated into this article as a whole.

The present application relates to methods of identifying target genes in cancer cells whose mutations render the cancer cells sensitive or resistant to anticancer drugs. Also provided are methods of treating cancer and selecting patients based on aberrations (eg, mutations) in target genes identified herein. Also provided are modified cancer cells sensitive or resistant to anticancer drugs, methods and kits for producing the same.

When mutations occur, cancer cells can acquire resistance to targeted therapeutics. Resistance to anticancer drugs has become a major obstacle to the successful treatment of cancer. Take colorectal cancer as an example. It is the third most common cancer in the world, the second leading cause of cancer-related death, and the leading cause of death from gastrointestinal cancer. Traditional pathological staging divides colorectal cancer into stage 0, stage I, stage II, stage III, and stage IV according to the depth of tumor invasion into the intestinal wall, metastasis to lymph nodes, or distant metastasis. Currently, early-stage colorectal cancer is usually treated with surgery or radiation therapy. In addition to surgery and radiotherapy, patients with advanced disease are usually treated systematically with chemotherapy and targeted drug therapy (such as PARP inhibitors). With current treatments, the five-year survival rate for early-stage colorectal cancer exceeds 90%; however, the five-year survival rate for advanced metastatic colorectal cancer is only 14% (H. Sung et al ., CA Cancer J Clin . May 2021 ; 71(3):209-249; R. Dienstmann et al ., Nat Rev Cancer . 2017;17(2):79-92; EJ Kuipers et al ., Nat Rev Dis Primers . 2015;1:15065; C . Joachim et al ., Medicine (Baltimore). 2019; 98(35):e16941).

After the onset of cancer, specific genetic mutations may occur. Mutated genes are often associated with specific pathogenesis and/or therapeutic pathways. Among these mutated genes, some may be drug-sensitive genes (that is, the mutated cancer cells are more sensitive to the therapeutic effect of anticancer drugs), and some may be drug-resistant genes (that is, the mutated cancer cells are more sensitive to the treatment effect of anticancer drugs). effect is more resistant). Identifying drug-sensitive genes and drug-resistant genes involved in multiple therapeutic pathways is of great significance for patient selection and treatment design for drugs targeting the corresponding therapeutic pathways to achieve better therapeutic effects.

The clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 (CRISPR-associated protein 9) (CRISPR/Cas9) system enables editing at targeted genomic loci with high efficiency and specificity. One of its broad applications is the identification of the function of coding genes, non-coding RNAs, and regulatory elements through high-throughput pooled screens combined with next-generation sequencing ("NGS") analysis. By introducing pooled single-guide RNA (“sgRNA”) or paired-guide RNA (“pgRNA”) libraries into cells expressing Cas9 or catalytically inactive Cas9 fused to an effector domain (dCas9), researchers can generate distinct mutations for multiple genetic screens, large genomic deletions, transcriptional initiation, or transcriptional repression.

In order to generate high-quality gRNA cell libraries for any given pooled CRISPR screen, low multiplicity of infection (“MOI”) must be used during cell library construction to ensure that each cell contains on average less than one sgRNA or pgRNA to minimize screen false discovery rate (FDR). In order to further reduce FDR and improve data reproducibility, in-depth coverage of gRNAs and multiple biological replicates are usually required to obtain hit genes with high statistical significance, resulting in increased workload. Additional difficulties may arise when one performs extensive genome-wide screens, when cellular material for library construction is limited, or when performing more challenging screens (i.e., in vivo screens) where experimental replicates or MOI control are difficult to obtain. The applicant's previously developed "internal barcoding (iBAR)" approach (see WO2020125762, the content of which is incorporated herein by reference in its entirety) provides a reliable and efficient screening strategy for large-scale target identification in eukaryotic cells, free of false positives and The false negative rate is much lower and allows the use of high MOI to generate cell banks. For example, compared to traditional CRISPR/Cas screens with a low MOI of 0.3, the iBAR approach can reduce starting cell numbers by more than 20-fold (e.g., MOI of 3) to more than 70-fold (e.g., MOI of 10), while maintaining High efficiency and accuracy. The iBAR system is particularly suitable for cell-based screening where the number of cells is limited, or for in vivo screening where viral infection of specific cells or tissues is difficult to control at low MOI.

The disclosures of all publications, patents, patent applications, and published patent applications mentioned herein are incorporated by reference in their entirety.

One aspect of the invention provides a method of identifying a target gene in a cancer cell whose mutation renders the cancer cell sensitive or resistant to an anticancer drug, the method comprising: a) providing a cancer cell library comprising a plurality of cancer cells, wherein the Each of the plurality of cancer cells has a mutation in a hit gene ("hit gene mutation"), wherein the hit genes in at least two of the plurality of cancer cells are different from each other; wherein the cancer cell library is Generated by contacting a naive cancer cell population with i) comprising multiple sgRNA constructs under conditions that allow introduction of the sgRNA construct and the Cas module into the naive cancer cell population and generation of the mutation in the hit gene A single-stranded guide RNA ("sgRNA") library, wherein each sgRNA construct comprises or encodes a sgRNA, and wherein each sgRNA comprises a target site complementary (e.g., at least about 50%, 60%, 70%, 80%, 90%, 95%, 96%, 97%, 98%, 99% or 100% complementary) guide sequence; and ii) Cas comprising Cas protein or nucleic acid encoding Cas protein element (e.g., Cas9); b) contacting the library of cancer cells with the anticancer drug; c) growing the library of cancer cells to obtain a population of treated cancer cells (e.g., viable and resistant to the anticancer drug drug); and d) identifying the target gene based on the difference between the profiles of the sgRNA or hit gene mutations in the treated cancer cell population and the control cancer cell population. In some embodiments, the control population of cancer cells is obtained from a library of cancer cells cultured under the same conditions and not exposed to the anticancer drug.

In some embodiments according to any one of the methods above, the target gene is identified based on a difference between the sgRNA profiles in a population of treated cancer cells and a population of control cancer cells. In some embodiments, the sgRNA profiles in the treated and control cancer cell populations are identified by next generation sequencing. In some embodiments, the method comprises comparing sgRNA sequence counts obtained from a treated cancer cell population (e.g., viable and resistant to an anticancer drug) to sgRNA sequence counts obtained from a control cancer cell population, wherein: i ) whose corresponding sgRNA guide sequence is identified as enriched in the treated cancer cell population compared to the control cancer cell population and has a hit gene with FDR ≤ 0.1, whose mutations are identified to render the cancer cells resistant to the anticancer drug and/or ii) hit genes whose corresponding sgRNA guide sequences are identified as depleted in the treated cancer cell population compared to the control cancer cell population and have FDR ≤ 0.1, are identified as mutations that make the Target genes sensitive to anticancer drugs in cancer cells.

In some embodiments according to any of the methods above, the sgRNA library and the Cas module are introduced sequentially into the initial cancer cell population. In some embodiments, the Cas element is introduced into the initial population of cancer cells and then introduced into the sgRNA library.

In some embodiments according to any one of the methods above, the Cas protein is Cas9. In some embodiments, each sgRNA comprises a guide sequence fused to a second sequence comprising a repeat-inverter stem-loop that interacts with Cas9. In some embodiments, the second sequence of each sgRNA further comprises stem-loop 1, stem-loop 2, and/or stem-loop 3.

In some embodiments according to any of the methods above, each sgRNA further comprises an internal barcode (iBAR) sequence ("sgRNA ^iBAR "), wherein each sgRNA ^iBAR operates with a Cas protein (e.g., Cas9) to modify the A hit gene (eg, cleavage of the hit gene, or modulation of hit gene expression). In some embodiments, each sgRNA ^iBAR comprises a first stem-loop and a second stem-loop in the 5'-to-3' direction, wherein the first stem-loop sequence hybridizes to the second stem-loop sequence to form an interaction with the Cas protein. An active double-stranded RNA (dsRNA) region, and the iBAR sequence is located between the 3' end of the first stem-loop sequence and the 5' end of the second stem-loop sequence. In some embodiments, the Cas protein is Cas9, and the iBAR sequence of each sgRNA ^iBAR is inserted into the loop region of the repeat-invert repeat stem-loop. In some embodiments, each iBAR sequence comprises about 1 to about 50 nucleotides (eg, about 6 nucleotides). In some embodiments, the sgRNA library is a sgRNA ^iBAR library, wherein the sgRNA ^iBAR library comprises multiple sets of sgRNA ^iBAR constructs, wherein each set of sgRNA ^iBAR constructs comprises four sgRNA ^iBAR constructs, each of which comprises or encoding sgRNA ^iBAR , wherein the guide sequences of the four sgRNA ^iBAR constructs are identical, wherein the iBAR sequences of each of the four sgRNA ^iBAR constructs are different from each other, and wherein the guide sequences of each set of sgRNA ^iBAR constructs Complementary (e.g., at least about 50%, 60%, 70%, 80%, 90%, 95%, 96% to different target sites of a hit gene (e.g., different target sites in the same hit gene or different hit genes) %, 97%, 98%, 99% or 100% complementary). In some embodiments, the sgRNA ^iBAR library comprises at least about 100 sets (eg, at least about any of 1,000, 10,000, 50,000, or more sets) of sgRNA ^iBAR constructs. In some embodiments, the iBAR sequences of at least two sgRNA ^iBAR constructs in different sets of sgRNA ^iBAR constructs are identical (e.g., the first and second set of sgRNA ^iBAR constructs in the two sets of sgRNA ^iBAR constructs have at least 1, 2, 3, 4 or more consensus iBAR sequences). In some embodiments, the iBAR sequences of at least two sets of sgRNA ^iBAR constructs are identical. In some embodiments, the cancer ^cell library (e.g., a Cas9 ⁺ sgRNA ^iBAR cancer cell library) has an average of at least about 100-fold (e.g., at least about 200-, 400-, 500-, 1,000 -, 5,000-fold or more) coverage, such as an average of about 100-fold to about 1000-fold, or an average of about 1000-fold coverage for each sgRNA ^iBAR . In some embodiments, the cancer cell library (e.g., a Cas9 ⁺ sgRNA ^iBAR cancer cell library) has an average of at least about 400-fold (e.g., at least about 800-, 1000-, 2000-, 4000-fold) for each set of sgRNA ^iBARs . -, 16,000-fold or more) coverage, such as an average of about 400-fold to about 4000-fold, or an average of about 4000-fold coverage for each set of sgRNA ^iBARs . In some embodiments, the cancer cell library (e.g., a Cas9 ⁺ sgRNA ^iBAR cancer cell library) has an average of at least about 400-fold (e.g., at least about 800-, 1000-, 1200-, 2000-fold) per hit gene. -, 3000-, 4000-, 10,000-, 12,000-, 16,000-fold or more) coverage, such as an average of about 1200-fold to about 12,000-fold coverage for each hit gene, or for Each gene hit averaged about 12,000-fold coverage.

In some embodiments according to any of the methods above, of the sgRNA library (or sgRNA ^iBAR library), at least about 95% (eg, at least about 96%, 97%, 98%, 99%, or 100%) The sgRNA construct (or sgRNA ^iBAR construct) of any of ) was introduced into the initial cancer cell population.

In some embodiments according to any one of the methods above, for each sgRNA (or sgRNA ^iBAR ), the cancer cell library (e.g., a Cas9 ⁺ sgRNA cancer cell library, or a Cas9 ⁺ sgRNA ^iBAR cancer cell library) has at least about 400-fold (eg, at least about any of 600-, 800-, 1,000-, 2,000-, 8,000-, 12,000-fold, or more) coverage.

In some embodiments according to any of the methods above, the sgRNA (or gRNAiBAR) library comprises at least about 400 (e.g., at least about 400, 600, 1000, 5000, 10,000, 50,000, 100,000 or more) Either) sgRNA (or gRNAiBAR) constructs, such as about 6000 to about 18,000 sgRNA (or gRNAiBAR) constructs.

In some embodiments according to any one of the methods above, each sgRNA (or sgRNA ^iBAR ) construct in the sgRNA (or sgRNA ^iBAR ) library is RNA. In some embodiments, each sgRNA (or sgRNA ^iBAR ) construct in the sgRNA (or sgRNA ^iBAR ) library is a plasmid. In some embodiments, each sgRNA (or sgRNA ^iBAR ) construct in the sgRNA (or sgRNA ^iBAR ) library is a viral vector, such as a lentiviral vector. In some embodiments, each sgRNA (or sgRNA ^iBAR ) construct in the sgRNA (or sgRNA ^iBAR ) library is a virus, such as a lentivirus. In some embodiments, the sgRNA (or sgRNA ^iBAR ) library is contacted with the initial cancer cell population at a multiplicity of infection (MOI) of at least about 2 (eg, 3).

In some embodiments according to any of the methods above, each guide sequence comprises about 17 to about 23 nucleotides.

In some embodiments according to any one of the methods above, step b) comprises contacting the library of cancer cells with the anticancer drug at a concentration of about IC50 to about IC70 for about 9 to about 10 doubling times. In some embodiments, step b) comprises contacting the library of cancer cells with the anticancer drug at a concentration of about IC50 to about IC70 for about 15 to about 16 doubling times.

In some embodiments according to any of the methods above, the sgRNA (or sgRNA ^iBAR ) sequence counts undergo median ratio normalization followed by mean-variance modeling. In some embodiments, the sgRNA library is a sgRNA ^iBAR library, and the variance of each guide sequence is adjusted based on the data identity between the iBAR sequences among the sgRNA ^iBAR sequences corresponding to the guide sequences. In some embodiments, the profile identity between the iBAR sequences in the sgRNA ^iBAR sequences corresponding to each guide sequence is determined based on the direction of the fold change of each iBAR sequence, wherein if the fold change of the iBAR sequences is relatively In different directions relative to each other, the variance of the guide sequences is increased (eg, increased relative to decreased, increased relative to unchanged, or decreased relative to unchanged).

In some embodiments according to any one of the methods above, the method comprises: subjecting the cancer cell library from step a) to at least two different treatments with the anticancer drug in step b); growing the library to obtain a population of treated cancer cells from each treatment (e.g., viable and resistant to an anticancer drug); identifying one or more hit genes in the population of treated cancer cells obtained from each treatment; and The one or more hit genes identified from all treatments are combined, thereby identifying target genes in the cancer cells whose mutations render the cancer cells sensitive or resistant to the anticancer drug. In some embodiments, i) its corresponding sgRNA (or sgRNA ^iBAR ) guide sequence is identified as enriched in a post-treatment cancer cell population (e.g., viable and resistant to an anticancer drug) compared to a control cancer cell population Hit genes with FDR ≤ 0.1 in at least one treatment identified as target genes whose mutations render said cancer cells resistant to anticancer drugs; and/or ii) their corresponding sgRNA (or sgRNA ^iBAR ) guides Hit genes identified as depleted in post-treatment cancer cell populations (e.g., viable and resistant to anticancer drugs) compared to control cancer cell populations and having an FDR ≤ 0.1 in at least one treatment were identified as Mutations target genes that sensitize the cancer cells to anticancer drugs.

In some embodiments according to any one of the methods above, the method comprises: subjecting the cancer cell library from step a) to two separate treatments b1) and b2): b1) making the cancer cell library from step a) contacting the anticancer drug at a concentration of about IC50 to about IC70 for about 9 to about 10 doubling times; b2) contacting the cancer cell library from step a) with the anticancer drug at a concentration of about IC50 to about IC70 Contacting lasts about 15 to about 16 doubling times; c1) growing the cancer cell library from treatment b1) to obtain a post-treatment cancer cell population (e.g., viable and resistant to an anticancer drug); c2) growing the cancer cell library from treatment b2 ) to obtain a post-treatment cancer cell population (e.g., viable and resistant to an anticancer drug); d1) identifying one or more hit genes in the post-treatment cancer cell population from treatment b1), d2 ) identifying one or more hit genes in the post-treatment cancer cell population from treatment b2), and d3) combining the one or more hit genes identified from all treatments b1) and treatment b2), thereby identifying said cancer cells Target genes whose mutations render the cancer cells sensitive or resistant to anticancer drugs. In some embodiments, i) its corresponding sgRNA (or sgRNA ^iBAR ) guide sequence is identified as enriched in a post-treatment cancer cell population (e.g., viable and resistant to an anticancer drug) compared to a control cancer cell population Hit genes with FDR ≤ 0.1 in at least one treatment identified as target genes whose mutations render said cancer cells resistant to anticancer drugs; and/or ii) their corresponding sgRNA (or sgRNA ^iBAR ) guides Hit genes identified as depleted in post-treatment cancer cell populations (e.g., viable and resistant to anticancer drugs) compared to control cancer cell populations and having an FDR ≤ 0.1 in at least one treatment were identified as Mutations target genes that sensitize the cancer cells to anticancer drugs.

In some embodiments according to any one of the methods above, the method comprises: i) identifying a set of one or more target genes, respectively, whose mutations render the cancer cells anti-cancer agents that, when treated alone, target both or more (e.g., 2, 3, 4, 5 or more) different anticancer drugs; ii) obtain one or more targets present in each set of target genes identified for each anticancer drug genes, thereby identifying target genes whose mutations render said cancer cells susceptible to combined treatment with two or more different anticancer drugs; and/or i) identifying a set of one or more target genes, respectively, whose mutations rendering the cancer cells resistant to two or more (e.g., 2, 3, 4, 5 or more) different anticancer drugs when treated alone; ii) acquiring One or more target genes in the combination of anticancer drug identified target genes, thereby identifying target genes whose mutations render the cancer cells resistant to the combination treatment of two or more different anticancer drugs. In some embodiments, the two or more different anticancer drugs target the same cancer target. In some embodiments, the two or more different anticancer drugs target different cancer targets.

In some embodiments according to any one of the methods above, the method further comprises ranking the identified target genes, wherein the sgRNA (or sgRNA ^iBAR ) guide is based on the post-treatment cancer cell population compared to the control cancer cell population The degree of enrichment or depletion of the sequence (eg, fold enriched, fold depleted, FDR enriched, or FDR depleted) was used to rank the target genes. In some embodiments, the sgRNA library is a sgRNA ^iBAR library, and the ranking of the target genes is further adjusted based on the data identity between the iBAR sequences in the sgRNA ^iBAR sequences corresponding to the guide sequences of the target genes. In some embodiments, the method further comprises assigning a sensitivity score or a drug resistance score to the identified target gene, wherein the sgRNA (or sgRNA ^iBAR ) in the post-treatment cancer cell population is compared to the control cancer cell population Based on the enrichment factor of the guide sequence (or based on the enrichment FDR - the smaller the FDR, the higher the ranking; or based on the degree of data consistency - the higher the data consistency, the higher the ranking), the mutation will make the cancer cells The target genes for anticancer drug resistance are ranked from high to low, and each target gene is assigned a drug resistance score accordingly; and/or wherein the cancer cell population is The multiple of the depletion of the sgRNA (or sgRNA ^iBAR ) guide sequence described in (or based on the depletion FDR - the smaller the FDR, the higher the ranking; or based on the degree of data consistency - the higher the data consistency, the higher the ranking), the The target genes whose mutations sensitize the cancer cells to the anticancer drug are ranked from high to low, and each target gene is assigned a sensitivity score accordingly in the sequence from high to low.

In some embodiments according to any one of the methods above, the anticancer drug is a PARP inhibitor.

In some embodiments according to any one of the methods above, the cancer cells are colorectal cancer cells.

In some embodiments according to any one of the methods above, the method further comprises culturing the same library of cancer cells under the same conditions without contacting the anticancer drug, and optionally undergoing the same acquisition method in step c) to obtain the control cancer cell population.

In some embodiments according to any one of the methods above, the method further comprises verifying the target gene by: a) modifying the target gene by generating a mutation (eg, an inactivating mutation) in the cancer cell said cancer cells; and b) determining the sensitivity or resistance of said modified cancer cells to said anticancer drug.

Another aspect of the invention provides a method of identifying a target gene in a cancer cell whose mutation sensitizes the cancer cell to a combination therapy comprising a first anticancer drug and a second anticancer drug, the method comprising: i) identifying a first set of one or more target genes in cancer cells whose mutation renders the cancer cells sensitive to the first anticancer drug according to any one of the methods herein above; ii) identifying a second set of one or more target genes in cancer cells whose mutations sensitize the cancer cells to a second anticancer drug according to any one of the methods described above; and iii) obtaining the presence of One or more target genes in both the first set of target genes and the second set of target genes, thereby identifying target genes whose mutations render said cancer cells sensitive to said combination therapy.

Another aspect of the invention provides a method of treating cancer in an individual (e.g., a human) comprising administering to the individual an effective amount of an anti-cancer drug, wherein the individual is selected for treatment based on: ("Drug Sensitivity Gene") has an aberration (e.g., carries a mutation) that renders the cancer cell sensitive to an anticancer drug ("Drug Sensitivity Aberration"), and wherein the Drug Sensitivity Gene is identified according to the target gene identification method described above any of them to be identified.

Another aspect of the invention provides a method of excluding an individual (e.g., a human) with cancer from treatment comprising administering to the individual an effective amount of an anticancer drug, wherein if the individual has a target gene (" drug resistance gene") that has an aberration (e.g., carries a mutation) that renders the cancer cell resistant to an anticancer drug ("drug resistance aberration"), the individual is excluded, and wherein the drug resistance gene is Any one of the above target gene identification methods for identification.

Another aspect of the invention provides a method of treating cancer in an individual (e.g., a human), comprising administering to the individual an effective amount of an anticancer drug, wherein the individual is selected based on: i) one or more an aberration (eg, a mutation) in a target gene ("drug-sensitivity gene") that makes said cancer cell sensitive to an anticancer drug ("drug-sensitivity aberration", such as a "drug-sensitivity mutation"), and ii) a or aberrations (eg, mutations) in multiple target genes ("drug resistance genes") that render the cancer cells resistant to anticancer drugs ("drug resistance aberrations" such as "drug resistance mutations"), wherein the The drug-sensitive gene and the drug-resistant gene are identified according to any one of the above target gene identification methods, and wherein if the drug-sensitive aberration (for example, drug-sensitive mutation) and the drug-resistant aberration (for example, drug-resistant mutation) If the composite score is higher than the composite score threshold level, the individual is selected for treatment. In some embodiments, the composite score is obtained by: (i) (the absolute value of the total number of sensitivity scores for the drug-sensitive genes) minus (the absolute value of the total number of drug resistance scores for the drug-resistant genes value), or (ii) formula I described herein, wherein the individual is selected for treatment if the composite score is above zero.

In another aspect, there is also provided a method of producing a modified cancer cell comprising inactivating a target gene identified by any of the above target gene identification methods.

Also provided is a modified colorectal cancer cell comprising a mutation (eg, an inactivating mutation) in a target gene, wherein the target gene is: i) selected from the group consisting of ARID2, ATM, BIRC6, BRCA1, BRCA2, CCNA2 , CCND1, CDK2, FBXW7, HRAS, KAT2B, NBN, PBRM1, PTEN, SKP2, SMAD7, TGFB2, TSC1, TSC2, ATR, RIF1, POLQ, AXIN1, GSK3A, GSK3B, CHD7, SCAF4, FANCM, NIPBL, ATRX, STAG1 , RAD51, RAD51B, RAD51C, RAD51D, FANCL, EXO1, DIDO1, LRBA, FAM71A, HDAC2, PMS2, MSH6, MSH2, MLH1 and WEE1; or ii) selected from the group consisting of AKT1, CDKN1A, CKS1B, CKS2, CTNNB1, DLG5 , E2F3, E2F4, HDAC1, MAPK1, MYC, RAC1, RAF1, RICTOR, SMAD4, TP53, BRAF, HSP90B1, PARP2, PARP1, PIK3CA, EIF3A, CCNA1, RBL1, ZMYND8, MED12, GCN1, Kras, TP53BP1, CHD2, DOCK5 , IGF1R, ILK, IRS1, RAPGEF1, EP300, TCF7L2, KMT2B, CDKN2A, CHEK1, CHEK2, RHEB, SPTA1, PKMYT1, SIDT2, APC, and SETD2.

Also provided is a sgRNA (or sgRNA ^iBAR ) library comprising one or more sgRNA (or sgRNA ^iBAR ) constructs, wherein each sgRNA (or sgRNA ^iBAR ) construct comprises or encodes a sgRNA (or sgRNA ^iBAR ), and wherein each The sgRNA (or sgRNA ^iBAR ) comprises a target site complementary to (e.g., at least about 50%, 60%, 70%, 80%, 90%, 95%, 96%, 97%, 98%, 99% or 100% complementary), the target gene i) is selected from the group consisting of ARID2, ATM, BIRC6, BRCA1, BRCA2, CCNA2, CCND1, CDK2, FBXW7, HRAS, KAT2B, NBN, PBRM1, PTEN, SKP2, SMAD7, TGFB2, TSC1, TSC2, ATR, RIF1, POLQ, AXIN1, GSK3A, GSK3B, CHD7, SCAF4, FANCM, NIPBL, ATRX, STAG1, RAD51, RAD51B, RAD51C, RAD51D, FANCL, EXO1, DIDO1, LRBA, FAM71A, HDAC2, PMS2, MSH6, MSH2, MLH1, and WEE1; or ii) selected from the group consisting of AKT1, CDKN1A, CKS1B, CKS2, CTNNB1, DLG5, E2F3, E2F4, HDAC1, MAPK1, MYC, RAC1, RAF1, RICTOR, SMAD4, TP53, BRAF, HSP90B1, PARP2, PARP1, PIK3CA, EIF3A, CCNA1, RBL1, ZMYND8, MED12, GCN1, Kras, TP53BP1, CHD2, DOCK5, IGF1R, ILK, IRS1, RAPGEF1, EP300, TCF7L2, KMT2B, CDKN2A, CHEK1, CHEK2, RHEB, SPTA1, PKMYT1, SIDT2, APC, and SETD2.

Also provided are kits and articles of manufacture for use in the methods described herein, such as kits for generating modified cancer cells that are sensitive or resistant to anticancer drugs.

When mutations occur, cancer cells can acquire resistance to targeted therapy drugs. Resistance to anticancer drugs has become a major obstacle to successful cancer treatment. Certain genetic mutations may make cancer cells more likely to be killed by anticancer agents. Treating cancers harboring such drug-sensitive mutations with anticancer agents may lead to higher rates of treatment success. For patients with resistance mutations, alternative treatment options may be sought. As a result, "drug sensitivity genes" (i.e., mutated cancer cells are more sensitive to anticancer drugs) and "drug resistance genes" (i.e., mutated cancer cells are more responsive to anticancer drugs) are identified that are involved in multiple therapeutic pathways. Drug resistance), which has important implications for patient selection and treatment design (eg, selection of drugs or combination therapy targeting selected therapeutic pathways) to achieve better therapeutic outcomes. Engineered cancer cells carrying these drug-sensitive or drug-resistant mutations can also be used in new drug design and screening, such as de novo design, or by modifying chemical groups of existing compounds that are resistant to certain drug-resistant mutations.

For example, poly(ADP ribose) polymerase (PARP) inhibitor (PARPi) is a cancer drug that targets a specific therapeutic pathway. Once PARP detects a single-strand break (SSB), PAPR binds DNA and catalyzes the synthesis of polymerized adenosine diphosphate-ribose (poly(ADP-ribose) or PAR) chains on protein substrates. Through this catalysis, PARP can recruit other DNA damage repair (DDR) proteins to the site of damage to collectively repair DNA damage. PARPi binds to the PARP catalytic site, preventing poly ADP ribosylation (PARylation) and the recruitment of other DDR proteins; more importantly, PARP becomes trapped on damaged DNA and cannot be shed. PRAP trapped at the site of DNA damage causes the DNA replication fork to stall, DNA replication cannot proceed, resulting in DNA double-strand breaks. When this happens, cells typically trigger homologous recombination repair (HRR). BRCA plays an important role in HRR. In tumors with HRR deficiency, such as those with BRCA mutations, the HRR of double-strand DNA (dsDNA) breaks is impaired, and tumor cells are directed to use other DNA repair methods, such as error-prone non-homologous end joining (NHEJ ), which often introduce large-scale genome recombinations, leading to genetic instability and cell death. Therefore, the combination of PARPi and BRCA loss of function may greatly inhibit tumor cell DDR and promote tumor cell apoptosis.

In order to provide more efficient patient selection and treatment design, and to achieve better therapeutic outcomes for cancers, especially difficult-to-treat cancer types and/or stages (such as advanced metastatic colorectal cancer), the present invention uses high-throughput screening to identify leading Mutations in drug-sensitivity and/or resistance phenotypes to certain anticancer drugs, to obtain the relationship between gene function and drug response, and to explore the use of these drug-sensitivity and resistance genes as a basis for patient selection and treatment design Biomarkers. This will greatly facilitate the accurate selection of patient populations and improve the efficacy of anticancer drugs such as PARPi in the treatment of cancers such as colorectal cancer. Engineered cancer cells carrying mutations in these drug-sensitive or drug-resistant genes will also serve as promising tools for new drug design and screening.

The present application provides methods for identifying target genes in cancer cells whose mutations render the cancer cells sensitive or resistant to anticancer drugs. A target gene whose mutation renders cancer cells sensitive to an anticancer drug is hereinafter referred to as a "drug-sensitive gene", and a mutation in this gene is hereinafter referred to as a "drug-sensitive mutation". A target gene whose mutation renders cancer cells resistant to an anticancer drug is hereinafter referred to as a "drug resistance gene", and a mutation in this gene is hereinafter referred to as a "drug resistance mutation". The method comprises: a) providing a cancer cell library comprising a plurality of cancer cells, wherein each of the plurality of cancer cells has a mutation (e.g., an inactivating mutation) in a hit gene (“hit mutation”), wherein said hit genes in at least two of said plurality of cancer cells are different from each other; b) contacting said library of cancer cells with said anticancer drug; c) growing said library of cancer cells to obtain post-treatment a population of cancer cells (e.g., viable and resistant to an anticancer drug); and d) identifying the target gene based on the difference between the profile of the hit gene mutation in the treated cancer cell population and a control cancer cell population (e.g., obtained from the same cancer cell library cultured under the same conditions without exposure to the anticancer drug). In some embodiments, one or more mutations (e.g., inactivating mutations) of one or more hit genes are generated by: a CRISPR/Cas guide RNA (e.g., a single-stranded guide RNA, "sgRNA") or A construct encoding a CRISPR/Cas guide RNA (eg, a vector such as a viral vector, or a virus such as a lentivirus), such as an sgRNA comprising the iBAR sequence described herein (sgRNA ^iBAR ). Thus, target genes can be identified based on the direct difference (e.g., by DNA sequencing) between the profiles of hit gene mutations in the treated and control cancer cell populations, or based on the Differences between the profiles of sgRNAs or sgRNA ^iBARs that produce mutations in the hit genes (eg, by identifying sgRNA guide sequences and thus corresponding hit genes). Screening assays using the sgRNA ^iBAR molecules, constructs, panels or libraries described herein provide a robust and efficient screening strategy for large-scale target identification in eukaryotic cells (e.g., cancer cells) with much lower false positives and false negative rates, and allows the generation of cell libraries using high MOI. The target genes identified herein are particularly useful in patient selection/exclusion in cancer therapy. For example, patients who carry mutations (e.g., inactivation) and/or reduced or absent expression of drug-sensitive genes (e.g., mRNA or protein) in drug-sensitive genes identified herein, compared to healthy individuals, and/or with healthy individuals In contrast, the activity of the expression product (for example, mRNA or protein) of the drug-sensitive gene is reduced or disappeared, which is especially suitable for treatment with corresponding anticancer drugs.

Accordingly, one aspect of the present invention provides a method of identifying a target gene in a cancer cell whose mutation renders the cancer cell sensitive or resistant to an anticancer drug, the method comprising: a) providing a gene comprising an sgRNA library or an sgRNA ^iBAR library and a targeting A cancer cell library of Cas elements (e.g., Cas9) of one or more hit genes; b) contacting the cancer cell library with the anticancer drug; c) growing the cancer cell library to obtain treated cancer cells population (e.g., viable and resistant to an anticancer drug); and d) identifying the target gene based on the difference between the profile of the sgRNA, sgRNA ^iBAR , or hit gene mutation in the treated and control cancer cell populations . In some embodiments, the Cas element comprises a Cas protein or a nucleic acid encoding the Cas protein. In some embodiments, the sgRNA library comprises one or more sgRNA constructs, wherein each sgRNA construct comprises or encodes an sgRNA, and wherein each sgRNA comprises a target site complementary to a corresponding hit gene (e.g., at least A guide sequence that is about any of 50%, 60%, 70%, 80%, 90%, 95%, 96%, 97%, 98%, 99% or 100% complementary). In some embodiments, the sgRNA ^iBAR library comprises multiple sets of sgRNA ^iBAR constructs, wherein each set of sgRNA ^iBAR constructs comprises 3 or more (e.g., 4) sgRNA ^iBAR constructs, each of which comprises or encoding sgRNA ^iBAR , wherein each sgRNA ^iBAR comprises a guide sequence and an iBAR sequence, wherein the guide sequences for the 3 or more (4) sgRNA ^iBAR constructs are identical and target the same in the corresponding hit gene The sites are complementary (e.g., at least about any of 50%, 60%, 70%, 80%, 90%, 95%, 96%, 97%, 98%, 99%, or 100% complementary), wherein the The iBAR sequence of each of 3 or more (4) sgRNA ^iBAR constructs differs from each other, where the guide sequence of each set of sgRNA ^iBAR constructs is identical to the hit gene (e.g., a different gene, or a different site within the same gene). ) are complementary to different targets, and wherein each sgRNA ^iBAR is operable with a Cas protein (eg, Cas9) to modify (eg, cut or regulate expression) the target site. In some embodiments, more than one (eg, 2, 3, 4 or more, such as 3) guide sequences are designed for each hit gene. In some embodiments, the method comprises comparing sgRNA (or sgRNA ^iBAR ) sequence counts obtained from a population of treated cancer cells to sgRNA (or sgRNA ^iBAR ) sequence counts obtained from a control cancer cell population. In some embodiments, its corresponding sgRNA (or sgRNA ^iBAR ) guide sequence is identified as an enriched hit in a post-treatment cancer cell population (e.g., alive and resistant to an anticancer drug) compared to a control cancer cell population Genes (eg, with FDR ≤ 0.1) were identified as drug resistance genes. In some embodiments, its corresponding sgRNA (or sgRNA ^iBAR ) guide sequence is identified as a depleted hit gene in a post-treatment cancer cell population (e.g., alive and resistant to an anticancer drug) compared to a control cancer cell population (eg, with FDR ≤ 0.1), were identified as drug-sensitive genes.

Also provided is a method of identifying a target gene in a cancer cell whose mutation sensitizes said cancer cell to a combination therapy comprising two or more (e.g., 2, 3, 4, 5 or more) anticancer drugs, comprising separately identifying a set of target genes whose mutations sensitize said cancer cells to an anticancer drug when treated alone, using any of the methods described herein, and obtaining the number of genes present in each set of target genes identified for each anticancer drug. One or more target genes, whereby target genes whose mutations render said cancer cells sensitive to said combination therapy are identified.

Another aspect of the invention provides a method of treating cancer in an individual (e.g., a human), comprising administering to the individual an effective amount of an anticancer drug, wherein the individual is selected for treatment based on: compared to healthy individuals, the The individual has a drug-sensitive aberration of a drug-sensitive gene (for example, carries a drug-sensitive mutation for an anticancer drug, and/or has abnormal (for example, reduced or absent) expression (for example, mRNA or protein), and/or has a corresponding Abnormal (e.g., reduced or abolished) activity (e.g., RNA or protein activity, such as due to epigenetic or post-translational modifications) of a drug sensitive gene compared to healthy individuals. The invention also provides an individual who will suffer from cancer A method excluding treatment comprising administering to the individual an effective amount of an anticancer drug, wherein the individual has a drug resistance aberration of a drug resistance gene (e.g., carries a resistance mutation to an anticancer drug) if compared to a healthy individual , and/or have abnormal (e.g., reduced or absent) expression (e.g., mRNA or protein), and/or have abnormal (e.g., reduced or eliminated) activity of drug resistance genes compared to healthy individuals (e.g., RNA or protein activity, such as due to epigenetic or post-translational modification), the individual is excluded. The present invention also provides a method of treating cancer in an individual, comprising administering to the individual an effective amount of an anticancer drug, wherein The individual is selected based on: a drug sensitivity aberration (e.g., a drug sensitivity mutation) and a drug resistance aberration (e.g., a drug resistance mutation), wherein if the composite score of the drug sensitivity aberration and drug resistance aberration Above a composite score threshold level (eg, total mutations that sensitize the cancer cell to the anticancer drug), the individual is selected for treatment.

Also provided are sgRNA or sgRNA ^iBAR molecules, constructs, panels or libraries that can be used to perform the screening methods described herein. Also provided are modified cancer cells comprising the sgRNA or sgRNA ^iBAR molecules, constructs, panels or libraries, and methods of producing the same. Also provided are target genes whose mutations (eg, inactivation such as knockouts) render cancer cells more sensitive or resistant to killing by one or more anticancer drugs. Also provided are sgRNA or sgRNA ^iBAR molecules, constructs, panels or libraries directed against drug sensitive or resistant genes identified herein, modified cancer cells comprising the same, pharmaceutical compositions thereof, and kits.

I. Definition

The present invention will be described with respect to particular embodiments and with reference to certain drawings but the invention is not limited thereto. Any reference signs in the claims should not be construed as limiting the scope. In the drawings, the size of some of the elements may be exaggerated and not drawn on scale for illustrative purposes. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. In case of conflict, this document (including definitions) will control. The preferred methods and materials are described below, although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention. All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. The materials, methods, and examples disclosed herein are illustrative only and not restrictive.

As used herein, "internal barcode" or "iBAR" refers to an index inserted or appended to a molecule that can be used to track the identity and properties of the molecule. An iBAR can be, for example, a short nucleotide sequence inserted or appended to a guide RNA for a CRISPR/Cas system, as exemplified by the present invention. Multiple iBARs can be used to track the performance of ss-guide RNA-seq in a single experiment, thus providing replicates for statistical analysis without the need to repeat experiments.

A "CRISPR system" or "CRISPR/Cas system" collectively refers to transcripts and other elements involved in the expression and/or directing of the activity of a CRISPR-associated ("Cas") gene. For example, a CRISPR/Cas system can include a sequence encoding a Cas gene, a tracr (trans-initiating CRISPR) sequence (e.g., tracrRNA or active part tracrRNA), a tracr-pair sequence (e.g., containing a "direct repeat" and an endogenous tracrRNA-processed partial direct repeats in CRISPR systems), guide sequences (also called "spacers" in endogenous CRISPR systems), and other sequences and transcripts derived from CRISPR loci.

In the context of the formation of a CRISPR complex, a "target sequence" refers to a sequence to which a guide sequence is designed to be complementary, wherein hybridization between the target sequence and the guide sequence facilitates the formation of the CRISPR complex. It is not necessary to have perfect complementarity, but sufficient complementarity to cause hybridization and facilitate the formation of the CRISPR complex. A target sequence may comprise any polynucleotide, such as a DNA or RNA polynucleotide. The CRISPR complex can comprise a guide sequence that hybridizes to the target sequence and complexes with one or more Cas proteins.

The term "guide sequence" refers to the continuous nucleotide sequence in the guide RNA, which has partial or complete complementarity with the target sequence in the target polynucleotide, and can hybridize with the target sequence through the alkali pairing promoted by the Cas protein. In the CRISPR/Cas9 system, the target sequence is adjacent to the PAM site. Together, the PAM sequence and its complement on the other strand form the PAM site.

The terms "single-stranded guide RNA", "synthetic guide RNA" and "sgRNA" are used interchangeably to refer to a gene comprising a guide sequence and any other required for sgRNA function and/or sgRNA interaction with one or more Cas proteins to form a CRISPR complex. The polynucleotide sequence of the sequence. In some embodiments, the sgRNA comprises a guide sequence fused to a second sequence comprising a tracr sequence derived from a tracrRNA and a tracr mate sequence derived from a crRNA. The tracr sequence may comprise all or part of the sequence of a tracrRNA from a naturally occurring CRISPR/Cas system. The term "guide sequence" refers to a nucleotide sequence specifying a target site in a guide RNA and is used interchangeably with the terms "guide" or "spacer". The term "tracr mate" is also used interchangeably with the term "direct repeat". As used herein, "sgRNA ^iBAR " refers to a single-stranded guide RNA having an iBAR sequence.

The term "operable with the Cas protein" means that the guide RNA can interact with the Cas protein to form a CRISPR complex.

As used herein, the term "wild type" is a term understood by those skilled in the art, and refers to a typical form of an organism, strain, gene or characteristic existing in nature that is different from a mutant or variant form.

As used herein, the term "variant" should be understood to mean the expression of qualities that deviate from patterns that occur in nature.

"Complementarity"refers to the ability of a nucleic acid to form hydrogen bonds with another nucleic acid sequence through conventional Watson-Crick base pairing or other non-traditional types. Percent complementarity represents the percentage of residues in a nucleic acid molecule that can form hydrogen bonds (e.g., Watson-Crick base pairing) with a second nucleic acid sequence (e.g., 5, 6, 7, 8, 8 out of 10). 9, 10, 50%, 60%, 70%, 80%, 90% and 100% complementary). "Perfectly complementary" means that all contiguous residues of a nucleic acid sequence will form hydrogen bonds with the same number of contiguous residues in a second nucleic acid sequence. As used herein, "substantially complementary" means at 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, Within a region of 35, 40, 45, 50 or more nucleotides, the degree of complementarity is at least 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 97%, 98%, 99%, or 100%, or refers to two nucleic acids that hybridize under stringent conditions.

As used herein, "stringent conditions" for hybridization refers to conditions under which a nucleic acid having complementarity to a target sequence hybridizes primarily to the target sequence and does not substantially hybridize to non-target sequences. Stringent conditions are generally sequence-dependent and depend on many factors. In general, the longer the sequence, the higher the temperature at which the sequence will specifically hybridize to its target sequence. Non-limiting examples of stringent conditions are described in detail in, Tijssen (1993), Laboratory Techniques In Biochemistry And Molecular Biology-Hybridization With Nucleic Acid Probes Part 1, Second Chapter "Overview of principles of hybridization and the strategy of nuclear acid probe assay", Elsevier, N.Y.

"Hybridization"refers to a reaction in which one or more polynucleotides react to form a complex that is stabilized by hydrogen bonding between alkyl groups of nucleotide residues. Hydrogen bonding can occur through Watson-Crick base pairing, Hoogstein bonding, or in any other sequence-specific manner. The complex may comprise two strands forming a duplex structure, three or more strands forming a multi-stranded complex, a single self-hybridizing strand, or any combination of the foregoing. A hybridization reaction can constitute a step in a wider process, such as the initiation of PCR, or the enzymatic cleavage of a polynucleotide. A sequence capable of hybridizing to a given sequence is called the "complement" of the given sequence.

"Doubling time" or "population doubling time" (PDT) herein refers to the time it takes for a population of cells to double in size. Cell doubling time = ln(2)/(growth rate). Growth rate (gr) refers to the amount of doubling per unit time. gr = ln⁡(N(t)/N(0))/t, where N(t) is the number of cells at time t, N(0) is the number of cells at time 0, and t is time (usually in hours h unit). When a cell population is an exponentially growing population, i.e. each individual cell doubles in each cell cycle, the growth rate depends only on the length of the cell cycle, gr = log2(N(t)/N(0))/ t.

As used herein, "construct" refers to a nucleic acid molecule (eg, DNA or RNA), or a vector capable of delivering such a nucleic acid molecule. For example, when used in the context of an sgRNA, a construct refers to an sgRNA molecule, a nucleic acid molecule encoding an sgRNA (e.g., an isolated DNA or viral vector), or a vector capable of delivering a nucleic acid molecule encoding an sgRNA, e.g., carrying a nucleic acid molecule encoding an sgRNA. Nucleic acid molecules of lentivirus. When used in the context of a protein, a construct refers to a nucleic acid molecule comprising a nucleotide sequence that can be transcribed into RNA or expressed as a protein. The construct may contain the necessary regulatory elements operably linked to the nucleotide sequence that permit transcription or expression of the nucleotide sequence when the construct is present in the host cell.

As used herein, "operably linked" means that the expression of a gene is under the control of a regulatory element (eg, a promoter) to which it is spatially linked. A regulatory element may be located 5' (upstream) or 3' (downstream) of the gene under its control. The distance between the regulatory element (eg, a promoter) and a gene can be about the same as the distance between the regulatory element (eg, promoter) and the gene it naturally controls and from which the regulatory element is derived. Variations in this distance can be accommodated without loss of function of the regulatory element (eg, promoter), as is known in the art.

The term "vector" is used to describe a nucleic acid molecule that can be engineered to contain a cloned polynucleotide or polynucleotide for possible propagation in a host cell. Vectors include, but are not limited to: nucleic acid molecules that are single-stranded, double-stranded, or partially double-stranded; nucleic acid molecules comprising one or more free ends, without free ends (e.g., circular); nucleic acid molecules comprising DNA, RNA, or both; and other types of polynucleotides known in the art. One type of vector is a "plasmid," which refers to a circular double-stranded DNA circle into which other DNA segments can be inserted, such as by standard molecular cloning techniques. Certain vectors are capable of autonomous replication in the host cell into which they are introduced (eg, bacterial vectors with a bacterial origin of replication and episomal mammalian vectors). After introduction into the host cell, other vectors (eg, non-episomal mammalian vectors) are integrated into the genome of the host cell and are thereby replicated along with the host genome. In addition, certain vectors are capable of directing the expression of genes to which they are operably linked. Such vectors are referred to herein as "expression vectors." The recombinant expression vector may contain the nucleic acid of the present invention in a form suitable for expressing the nucleic acid in a host cell, which means that the recombinant expression vector includes one or more regulatory elements, which can be selected according to the host cell used for expression, i.e. The expressed nucleic acid sequences are operably linked.

"Host cell" refers to a cell that may be or has become a recipient for a vector or isolated polynucleotide. Host cells may be prokaryotic or eukaryotic. In some embodiments, the host cells are eukaryotic cells that can be cultured in vitro and modified using the methods described herein. The term "cell" includes the primary subject cell and its progeny.

"Multiplicity of infection" or "MOI" are used interchangeably herein to refer to the ratio of an agent (eg, phage, virus or bacteria) to its target (eg, cell or organism) it infects. For example, when referring to a group of cells inoculated with viral particles, the multiplicity of infection or MOI is the ratio between the number of viral particles (eg, viral particles comprising a library of sgRNAs) and the number of target cells present in the mixture during viral transduction.

As used herein, a "phenotype" of a cell refers to an observable characteristic or characteristic of a cell, such as its morphology, development (eg, growth, proliferation, differentiation, or death), biochemical or physiological properties, phenology, or behavior. Phenotypes may be caused by the expression of genes in cells, the influence of environmental factors, or an interaction between the two. In some embodiments, the phenotype is resistance or sensitivity to killing (eg, by an anticancer drug). In some embodiments, the phenotype is inhibition of growth or proliferation. In some embodiments, the phenotype is death.

An "isolated" nucleic acid molecule as described herein refers to a nucleic acid molecule identified and separated from at least one contaminant nucleic acid molecule with which it is normally associated in the environment in which it is produced. Preferably, the isolated nucleic acid is not associated with all components associated with the environment in which it was produced. An isolated nucleic acid molecule that encodes the polypeptides and antibodies herein is in a form other than that in which it is found in nature or in an environment. Isolated nucleic acid molecules thus are distinguished from nucleic acid encoding the polypeptides and antibodies herein that occur naturally in cells.

Unless otherwise stated, a "nucleotide sequence encoding an amino acid sequence" includes all nucleotide sequences that are degenerate versions of each other and that encode the same amino acid sequence. A phrase nucleotide sequence that encodes a protein or RNA may also contain introns, so that a nucleotide sequence that encodes a protein may contain introns in some versions.

As used herein, the term "transfected" or "transformed" or "transduced" refers to the process of transferring or introducing exogenous nucleic acid into a host cell (eg, a cancer cell). A "transfected" or "transformed" or "transduced" cell is a cell that has been transfected, transformed or transduced with an exogenous nucleic acid. The cells include primary test cells and their progeny.

As used herein, "treatment/treating" is a method used to obtain beneficial or desired results, including clinical results. For the purposes of this invention, beneficial or desired clinical outcomes include, but are not limited to, one or more of the following: alleviation of one or more symptoms caused by the disease, reduction of the extent of the disease, stabilization of the disease (e.g., prevention or delay of progression of the disease) , to prevent or delay the spread (e.g., metastasis) of disease, to prevent or delay the recurrence of disease, to delay or slow the progression of disease, to ameliorate disease state, to provide remission (partial or total) of disease, to reduce one or more other conditions needed to treat disease The dose of the drug slows disease progression, improves quality of life, and/or prolongs survival. "Treatment" also includes reducing the pathological consequences of cancer.

As used herein, an "individual" or "subject" refers to a mammal including, but not limited to, a human, bovine, equine, feline, canine, rodent, or primate. In some embodiments, the individual is a human.

As used herein, "patient" includes anyone suffering from a disease (eg, cancer). The terms "subject", "individual" and "patient" are used interchangeably herein.

When the term "comprising" is used in the present description and claims, it does not exclude other elements or steps.

It is to be understood that embodiments of the present application described herein include "consisting of" and/or "consisting essentially of" embodiments.

Reference herein to "about" a value or parameter includes (and describes) variations to that value or parameter itself. For example, description referring to "about X" includes description of "X."

As used herein, reference to "not" a value or parameter generally means and describes "other than" a value or parameter. For example, the method is not used to treat type X cancer, meaning that the method is used to treat cancer other than type X.

As used herein, the term "about X-Y" has the same meaning as "about X to about Y".

For the recitation of numerical ranges for nucleotides herein, each intervening number therebetween is expressly contemplated. For example, for the range 19-21nt, the number 20nt is considered in addition to 19nt and 21nt, while for the range of MOI, every intermediate digit in between (whether integer or decimal) is explicitly considered.

As used herein and in the appended claims, the singular forms "a/an", "or" and "the" include plural referents unless the context clearly dictates otherwise.

II. Methods of Identifying Target Genes in Cancer Cells whose Mutations Make Said Cancer Cells Sensitive or Resistant to Anticancer Drugs

The present application provides methods for identifying target genes in cancer cells that regulate the activity of said cancer cells, such as the response to anticancer drug treatment.

In some embodiments, there is provided a method of identifying a target gene in a cancer cell whose mutation renders the cancer cell sensitized or resistant to an anticancer drug, the method comprising: a) providing a cancer cell library comprising a plurality of cancer cells, wherein each of the plurality of cancer cells has a mutation (e.g., an inactivating mutation) in a hit gene ("hit gene mutation"), wherein the hit gene in at least two of the plurality of cancer cells different from each other; b) contacting the cancer cell library with an anticancer drug (e.g., at a concentration of about IC50 to about IC70); c) growing the cancer cell library to obtain a treated cancer cell population (e.g., viable and anticancer drug resistance); and d) identifying the target gene based on the difference between the profile of the hit gene mutation in the treated cancer cell population and the control cancer cell population. In some embodiments, the control population of cancer cells is obtained from a library of cancer cells cultured under the same conditions and not exposed to the anticancer drug. In some embodiments, the profile of hit gene mutations in the treated cancer cell population and the control cancer cell population is identified by next generation sequencing. In some embodiments, the method comprises comparing a sequence count of a sequence comprising the hit gene mutation from a treated cancer cell population to a sequence count of a sequence comprising the hit gene mutation from a control cancer cell population, wherein: i) its corresponding hit mutation sequence is identified as enriched in a post-treatment cancer cell population (e.g., viable and resistant to an anticancer drug) compared to a control cancer cell population and has an FDR ≤ 0.1 (and/or A hit gene having at least about 2-fold enrichment) identified as a target gene whose mutation renders said cancer cell resistant to the anticancer drug; and/or ii) its corresponding hit gene mutated sequence compared to a control cancer cell population Hit genes identified as depleted and having a FDR ≤ 0.1 (and/or having at least about 2-fold depletion) in post-treatment cancer cell populations (e.g., viable and resistant to anticancer drugs) are identified as mutated Target genes that sensitize the cancer cells to anticancer drugs. In some embodiments, the cancer cell library has at least about 100-fold (e.g., at least about 200-, 600-, 1000-, 2000-, 4000-, 8000-, 10000-fold, or Any of more multiples) coverage, such as about 600-fold to about 1200-fold, or about 1200-fold to about 12,000-fold coverage for each hit gene. In some embodiments, at least 2 (e.g., 2, 3, 4, 5, 6 or more, such as 3, or 6-12) different hit gene mutations (e.g., Different target sites targeting hit genes) Target each hit gene. In some embodiments, steps b) and c) comprise contacting the library of cancer cells with the anticancer drug at a concentration of about IC50 to about IC70 for about 9 to about 10 doubling times while allowing viable cancer cells to The cancer cells are grown, optionally passaged every about 3 doubling times. In some embodiments, steps b) and c) comprise contacting the library of cancer cells with the anticancer drug at a concentration of about IC50 to about IC70 for about 15 to about 16 doubling times while allowing viable cancer cells to The cancer cells are grown, optionally passaged every about 3 doubling times. In some embodiments, the coverage of each hit gene against the cancer cell library remains the same or similar (eg, within about 10% difference) after serial anticancer drug treatment passages. In some embodiments, sequence counts of sequences comprising hit mutations are subjected to median ratio normalization followed by mean variance modeling. In some embodiments, the variance of each sequence comprising a mutation in a hit gene (eg, an inactivating mutation) is adjusted based on the consistency of the data in the same gene. In some embodiments, the data concordance between sequences of different hit mutations (e.g., inactivating mutations) corresponding to the same gene is determined based on the direction of the fold change of each hit mutation sequence, wherein if the sequence for the same hit gene The fold changes of the different hit gene mutation sequences are in different directions relative to each other (for example, increased relative to decreased, increased relative to unchanged, or decreased relative to unchanged, all are considered as different directions), then The variance of the hit gene mutation sequence is increased.

In some embodiments, the frequency of DNA mutations in cancer patients (e.g., based on literature or databases) is at least about 5% (e.g., at least about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or higher) genes were selected as hit genes. In some embodiments, RNA expression levels are up-regulated or down-regulated at least about 1.2-fold (e.g., at least about 1.5, 2, 3, 4, 5, 6, 7, Any of 8, 9, 10, 50, 100-fold or more, such as at least about 2-fold) genes are selected as hit genes. In some embodiments, the frequency of DNA mutations in cancer patients (e.g., based on literature or databases) is at least about 5% (e.g., at least about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or higher), and its RNA expression level is up-regulated or down-regulated by more than about 2-fold (for example, more than about 2.5, 3, 4, 5, 6, 7, 8, 9 , 10, 50, 100 times or higher) genes were selected as hit genes. In some embodiments, the hit genes are further selected based on whether the encoded mRNA or protein is expressed in the cell, or the encoded protein is expressed on the cell surface in healthy or cancer cells. In some embodiments, hit genes are selected based on: i) having a DNA mutation frequency of at least about 5% (e.g., at least about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or higher), ii) in cancer patients (for example, based on literature or databases) its RNA expression level is up-regulated or down-regulated by more than about 2-fold (for example, by more than about 2.5, 3, 4 , 5, 6, 7, 8, 9, 10, 50, 100 times or more); and iii) the mRNA or protein encoded by it is expressed in the cell, or the protein encoded by it is expressed on the cell surface (in cancerous or healthy cells).

In some embodiments, the library of cancer cells is generated by contacting an initial population of cancer cells with a mutagen.

In some embodiments, the library of cancer cells is generated by subjecting an initial population of cancer cells to gene editing (eg, a whole genome, or a subset of genes). In some embodiments, the cancer cell library is obtained by contacting the initial cancer cell population with Generated by: i) a sgRNA library comprising multiple sgRNA constructs, wherein each sgRNA construct (e.g., lentiviral vector or lentivirus) comprises or encodes an sgRNA, and wherein each sgRNA comprises a target in the corresponding hit gene a guide sequence that is site complementary (e.g., at least about any of 50%, 60%, 70%, 80%, 90%, 95%, 96%, 97%, 98%, 99%, or 100% complementary); And ii) a Cas assembly comprising a Cas protein or a nucleic acid encoding a Cas protein. In some embodiments, the sgRNA library and Cas elements are simultaneously introduced into the naive cancer cell population. In some embodiments, the sgRNA library and the Cas module are introduced sequentially into the initial cancer cell population. In some embodiments, the naive cancer cell library comprises a Cas element (eg, Cas9). In some embodiments, the cancer cell library is obtained by combining the initial cancer cell population comprising Cas9 with sgRNA libraries comprising multiple sgRNA constructs (e.g., lentiviral vectors or lentiviruses) comprising or encoding sgRNAs are generated by contacting each sgRNA construct, and wherein each sgRNA comprises a target site associated with a corresponding hit gene A guide sequence that is complementary (eg, at least about any of 50%, 60%, 70%, 80%, 90%, 95%, 96%, 97%, 98%, 99%, or 100% complementary). In some embodiments, the Cas element is introduced into the naive cancer cell population followed by the sgRNA library. In some embodiments, the cancer cell library is produced by: i) combining an initial cancer cell population with a Cas protein or a nucleic acid encoding a Cas protein under conditions that allow the introduction of a Cas component into the initial cancer cell population. Cas element (e.g., lentiviral vector or lentivirus encoding Cas9) contact; ii) optionally obtain a population of cancer cells comprising the Cas element ("Cas ⁺ cancer cell population"; e.g. by FACS sorting, e.g., using markers on vectors of Cas); iii) under conditions that allow the introduction of sgRNA constructs into cancer cells (e.g., Cas ⁺ cancer cells) and the mutation of the hit gene, the Cas+ cancer cell population is combined with the Cas ⁺ cancer cell containing A sgRNA library contact of multiple sgRNA constructs, wherein each sgRNA construct (e.g., lentiviral vector or lentivirus) contains or encodes an sgRNA, and wherein each sgRNA contains a target site complementary to the corresponding hit gene (e.g., A guide sequence that is at least about any of 50%, 60%, 70%, 80%, 90%, 95%, 96%, 97%, 98%, 99%, or 100% complementary). In some embodiments, the Cas protein is Cas9. In some embodiments, each sgRNA comprises a guide sequence fused to a second sequence comprising a repeat-inverter stem-loop that interacts with Cas9. In some embodiments, the second sequence of each sgRNA further comprises stem-loop 1, stem-loop 2, and/or stem-loop 3. In some embodiments, each sgRNA further comprises an iBAR sequence ("sgRNA ^iBAR "), wherein each sgRNA ^iBAR is operable with a Cas protein to modify (eg, cleave or regulate expression) the hit gene. In some embodiments, each sgRNA ^iBAR comprises a first stem-loop sequence and a second stem-loop sequence in the 5'-to-3' direction, wherein the first stem-loop sequence hybridizes with the second stem-loop sequence to form a Cas A protein-interacting dsRNA region, and wherein the iBAR sequence is located between the 3' end of the first stem-loop sequence and the 5' end of the second stem-loop sequence. In some embodiments, the Cas protein is Cas9, and the iBAR sequence of each sgRNA ^iBAR is inserted into the loop region of the repeat-invert repeat stem-loop. In some embodiments, each guide sequence comprises about 17 to about 23 nucleotides. In some embodiments, at least about 95% (e.g., at least about any of 96%, 97%, 98%, 99% or higher), such as at least about 99% of the sgRNA constructs in the sgRNA library are constructed Introduced into the initial cancer cell population. In some embodiments, each hit gene within the cancer cell library or the sgRNA library is identified by at least about 3 (eg, about 6 to about 12) different target gene loci of the hit gene. About 3 (eg, about 6 to about 12) different sgRNA constructs target. In some embodiments, the cancer cell library has at least about 100-fold (eg, about 600-fold to about 1200-fold) coverage for each sgRNA. In some embodiments, the cancer cell library has at least about 300-fold coverage for each hit gene, such as about 600-fold to about 1200-fold coverage for each hit gene.

In some embodiments, the cancer cell library is obtained by making the ^initial generated by contacting a population of cancer cells with: i) a sgRNA ^iBAR library comprising multiple sets of sgRNA ^iBAR constructs, wherein each set of sgRNA ^iBAR constructs comprises 3 or more (e.g., 4) sgRNA ^iBAR constructs ( For example, a lentiviral vector or a lentivirus), each of the constructs comprises or encodes a sgRNA ^iBAR , wherein each sgRNA ^iBAR comprises a guide sequence and an iBAR sequence, wherein the three or more (4) sgRNA ^iBAR constructs The guide sequences of the clones are identical and complementary to the same target site of the hit gene (e.g., at least about 50%, 60%, 70%, 80%, 90%, 95%, 96%, 97%, 98%, 99% or 100% complementary), wherein the iBAR sequences of each of the 3 or more (4) sgRNA ^iBAR constructs are different from each other, wherein the guide sequence of each set of sgRNA ^iBAR constructs is identical to the hit Different target sites in a gene (e.g., different hit genes, or different sites within the same hit gene) are complementary, and wherein each sgRNA ^iBAR is operable with a Cas9 protein to modify the target site; and ii) comprises Cas Cas (for example, Cas9) element of the nucleic acid of albumen or coding described Cas albumen. In some embodiments, the naive cancer cell library comprises a Cas element (eg, Cas9). In ^some embodiments, the cancer cell library is obtained by making the initial cancer cell population containing Cas9 Cell populations are produced by contacting sgRNA ^{iBAR libraries} comprising sets of sgRNA iBAR constructs, wherein each set of sgRNA ^iBAR constructs comprises 3 or more (e.g., 4) sgRNA ^iBAR constructs (e.g., lentiviral vectors or lentivirus), each of which constructs contains or encodes a sgRNA ^iBAR , wherein each sgRNA ^iBAR contains a guide sequence and an iBAR sequence, wherein the guide sequences for the three or more (4) sgRNA ^iBAR constructs are the same and is complementary (e.g., at least about 50%, 60%, 70%, 80%, 90%, 95%, 96%, 97%, 98%, 99%, or 100% complementary to the same target site of the hit gene any of ), wherein the iBAR sequences of each of the 3 or more (e.g., 4) sgRNA ^iBAR constructs are different from each other, wherein the guide sequence of each set of sgRNA ^iBAR constructs is identical to the hit gene (e.g., Different target sites in different hit genes, or different sites within the same hit gene), and wherein each sgRNA ^iBAR is operable with the Cas9 protein to modify the target site. In some embodiments, the cancer cell library is generated by: i) combining an initial cancer cell population with a Cas protein or encoding Cas elements (for example, lentiviral vectors or lentiviruses encoding Cas9) of nucleic acids of Cas protein contact; ii) optionally obtain a population of cancer cells comprising the Cas element ("Cas ⁺ cancer cell population"; e.g. by FACS sorting, For example, using a marker on a vector encoding Cas); iii) under conditions that allow introduction of the sgRNA ^iBAR construct into cancer cells (e.g., Cas ⁺ cancer cells) and generation of the mutation in the hit gene, A population of Cas ⁺ cancer cells is contacted with a sgRNA ^{iBAR library} comprising sets of sgRNA iBAR constructs, wherein each set of sgRNA ^iBAR constructs comprises 3 or more (e.g., 4) sgRNA ^iBAR constructs (e.g., lentiviral vectors or lentivirus), each of which constructs comprises or encodes a sgRNA ^iBAR , wherein each sgRNA ^iBAR comprises a guide sequence and an iBAR sequence, wherein the guide for said 3 or more (for example, 4) sgRNA ^iBAR constructs The sequences are identical and complementary (e.g., at least about 50%, 60%, 70%, 80%, 90%, 95%, 96%, 97%, 98%, 99%, or 100% complementary), wherein the iBAR sequences of each of the 3 or more (e.g., 4) sgRNA ^iBAR constructs are different from each other, wherein the guide sequence of each set of sgRNA ^iBAR constructs is identical to the hit gene (eg, different hit genes, or different sites within the same hit gene), and wherein each sgRNA ^iBAR is operable with the Cas9 protein to modify the target site. In some embodiments, ^the cancer cell library is obtained by making naive cancer cells The population is produced by contacting: i) a sgRNA ^iBAR library comprising multiple sets of sgRNA ^iBAR constructs, wherein each set of sgRNA ^iBAR constructs comprises 3 or more (e.g., 4) sgRNA ^iBAR constructs, each of which The construct comprises or encodes a sgRNA ^iBAR , wherein each sgRNA ^iBAR comprises a guide sequence, a second sequence and an iBAR sequence, wherein the guide sequences for the 3 or more (e.g., 4) sgRNA ^iBAR constructs are identical , and is complementary to the same target site of the hit gene (e.g., at least about 50%, 60%, 70%, 80%, 90%, 95%, 96%, 97%, 98%, 99%, or 100% complementary in any of ), wherein the iBAR sequences of each of the 3 or more (e.g., 4) sgRNA ^iBAR constructs are different from each other, wherein the guide sequence is fused to a second sequence, wherein the second sequence Comprising a repeat-inverse repeat stem-loop that interacts with the Cas9 protein, wherein the iBAR sequence is inserted into the loop region of the repeat-inverse repeat stem-loop, wherein the guide sequence of each set of sgRNA ^iBAR constructs is aligned with the hit gene (e.g. , different hit genes, or different target gene loci of the same hit gene), and wherein each sgRNA ^iBAR is operable with the Cas9 protein to modify the target site; and ii) comprises the Cas9 protein or A Cas9 component of a nucleic acid encoding a Cas9 protein. In some embodiments, the Cas element (eg, Cas9) is introduced into the cancer cell and then introduced into the sgRNA ^iBAR library. In some embodiments, the sgRNA ^iBAR library is introduced into cancer cells followed by the introduction of the Cas element (eg, Cas9). In some embodiments, the Cas element (eg, Cas9) and the sgRNA ^iBAR library are introduced into the cancer cell simultaneously. In some embodiments, each iBAR sequence comprises about 1 to about 50 (eg, 6) nucleotides. In some embodiments, each set of sgRNA ^iBAR constructs comprises 4 sgRNA ^iBAR constructs, and the iBAR sequences for each of the 4 sgRNA ^iBAR constructs are different from each other. In some embodiments, the sgRNA ^iBAR library comprises at least about 100 sets of sgRNA ^iBAR constructs. In some embodiments, the iBAR sequences for at least two sgRNA ^iBAR constructs in different sets of sgRNA ^iBAR constructs are identical (e.g., in two sets of sgRNA ^iBAR constructs, the sgRNA ^iBAR sequences of the first set and the second set Constructs have at least 1, 2, 3, 4 or more consensus iBAR sequences). In some embodiments, the iBAR sequences for at least two sets of sgRNA ^iBAR constructs are identical. In some embodiments, the sgRNA ^iBAR library is contacted with the initial population of cancer cells at an MOI of greater than about 2 (eg, at least about 3, 5, or 10), such as 3. In some embodiments, a sgRNA ^iBAR library comprising a plurality of sgRNA ^iBAR constructs comprises or encodes a sgRNA ^iBAR with a guide sequence complementary to a target site of a cancer-associated gene. In some embodiments, at least about 95% (e.g., at least about any of 96%, 97%, 98%, 99% or more) of the sgRNA ^iBAR library, such as at least about 99% of the sgRNA ^iBAR Constructs are introduced into the initial cancer cell population. In some embodiments, each hit gene in the cancer cell library or the sgRNA ^iBAR library is targeted by 3 different sets of sgRNA ^iBAR constructs for 3 different target loci of the hit gene. In some embodiments, the library of cancer cells has at least about 100-fold coverage for each sgRNA ^iBAR , such as about 100-fold to about 1000-fold coverage for each sgRNA ^iBAR . In some embodiments, the cancer cell library has at least about 400-fold coverage for each set of sgRNA ^iBARs , such as about 400-fold to about 4000-fold coverage for each set of sgRNA ^iBARs . In some embodiments, the cancer cell library has at least about 400-fold coverage for each hit gene, such as about 1200-fold to about 12,000-fold coverage for each hit gene.

In some embodiments, using the screening methods for sgRNA ^iBAR libraries described herein, target recognition and data reproducibility can be improved and FDR reduced by statistical analysis. In traditional CRISPR/Cas-based screening methods using pooled sgRNA libraries, high-quality cell libraries expressing gRNAs are generated using low MOI during cell library construction to ensure that each cell has on average less than one sgRNA or paired guide RNA ("pgRNA"). Since the sgRNA molecules in the library are randomly integrated into the transfected cells, a sufficiently low MOI ensures expression of a single sgRNA per cell, thereby minimizing the FDR of the screen. To further reduce FDR and improve data reproducibility, deep coverage of gRNAs and multiple biological replicates are usually required to obtain hit genes with high statistical significance. Traditional screening methods face difficulties when large genome-wide screens are required, when cellular material for library construction is limited, or when performing more challenging screens (i.e., in vivo screens) where it is difficult to schedule experimental replicates or control the MOI. Using the screening method for sgRNA ^iBAR libraries described here, the difficulty was overcome by including an iBAR sequence in each sgRNA, which enabled the collection of internal duplications in each set of sgRNAs with the same guide sequence but different iBAR sequences. This iBAR approach reduces experimental noise. For example, as shown in WO2020125762, iBARs with four nucleotides per sgRNA can provide enough internal repeats to evaluate the data consistency between different sgRNA ^iBAR constructs targeting the same genomic locus. The high agreement between two independent experiments in WO2020125762 suggests that for CRISPR/Cas screening using the iBAR approach, one experimental replicate is sufficient. Since library coverage increases significantly with high MOI during viral transduction of host cells, the number of cells in the initial cell population can be reduced by more than 20-fold to achieve the same library coverage as the genome-wide human constructed in WO2020125762 shown in the library. Likewise, the workload per genome-wide screen using sgRNA ^iBARs can be proportionally reduced. Using sgRNAs with different iBAR sequences, and then by calculating the guide sequences and the corresponding iBAR nucleotide sequences, the performance of each guide sequence can be tracked multiple times in the same experiment, thereby greatly reducing FDR and increasing efficiency and probability. Transduction efficiency and library coverage can be further improved by using high virus titers during the viral transduction step, e.g., MOI > 1 (e.g., MOI > 1.5, MOI > 2, MOI > 2.5, MOI > 3, MOI > 3.5 , MOI>4, MOI>4.5, MOI>5, MOI>5.5, MOI>6, MOI>6.5, MOI>7, MOI>7.5, MOI>8, MOI>8.5, MOI>9, MOI>9.5 or MOI >10; such as MOI is about any one of 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5 or 10).

In some embodiments, there is provided a method of identifying a target gene in a cancer cell whose mutation renders the cancer cell sensitized or resistant to an anticancer drug, the method comprising: a) providing a gene comprising targeting one or more hit genes A cancer cell library of an sgRNA ^iBAR library described herein; b) contacting the cancer cell library with an anticancer drug (e.g., for about 9 to about 10 doubling times, or about 15 to about 16 doubling times, for or without cell passaging); c) growing the library of cancer cells to obtain a population of treated cancer cells (e.g., viable and resistant to an anticancer drug); and d) based on the population of treated cancer cells and control cancer cells Differences between profiles of sgRNA ^iBAR or hit gene mutations in the population identified the target genes. In some embodiments, there is provided a method of identifying a target gene in a cancer cell whose mutation renders the cancer cell sensitized or resistant to an anticancer drug, the method comprising: a) providing a gene comprising targeting one or more hit genes A cancer cell library of the sgRNA ^iBAR library described herein; b/c) contacting the cancer cell library with an anticancer drug while allowing viable cancer cells to grow (e.g., for about 9 to about 10 doubling times, or about 15 to about 16 doubling times, with or without cell passage), harvest the cancer cells by removing the cell culture medium (and dead floating cells) containing the anticancer drug and collect the remaining adherent cancer cells (e.g., by trypsinization digestion) to obtain treated cancer cell populations (e.g., viable and resistant to anticancer drugs); and d) based on the comparison between profiles of sgRNA ^iBAR or hit gene mutations in treated and control cancer cell populations The target genes were differentially identified. In some embodiments, the sgRNA ^iBAR library targets cancer-associated genes. In some embodiments, the cancer cell library has about 100-fold to about 1000-fold coverage for each sgRNA ^iBAR , such as about 1000-fold coverage for each sgRNA ^iBAR . In some embodiments, the cancer cell library has at least about 400-fold coverage for each hit gene, eg, about 1200-fold to about 12,000-fold coverage for each hit gene. In some embodiments, the control cancer cell population is obtained from a library of cancer cells cultured under the same conditions and not exposed to the anticancer drug, and optionally subjected to the same method of obtaining in step c). In some embodiments, sequence counts obtained from a treated cancer cell population are compared to corresponding sequence counts obtained from the control cancer cell population to provide a fold change (e.g., an actual fold change, or a derivative of a fold change such as log2 or log10 fold change). In some embodiments, the target gene is identified based on the difference between the profiles of sgRNA ^iBARs in a population of treated cancer cells and a population of control cancer cells. In some embodiments, the profile of the sgRNA ^iBAR of a treated cancer cell population and a control cancer cell population is identified by next generation sequencing. In some embodiments, identifying the target gene in step d) comprises: comparing the sgRNA ^iBAR (or its guide sequence) sequence count obtained from the treated cancer cell population with the sgRNA ^iBAR (or guide sequence) obtained from the control cancer cell population ( or its guide sequence) sequence counts where i) its corresponding sgRNA ^iBAR guide sequence is identified as enriched in a treated cancer cell population (e.g., viable and resistant to an anticancer drug) compared to a control cancer cell population. A set of hit genes with FDR ≤ 0.1 (and/or with at least about 2-fold enrichment), identified as target genes whose mutations render said cancer cells resistant to the anticancer drug; and/or ii) their corresponding sgRNA ^iBAR guide sequences were identified as depleted in a post-treatment cancer cell population (e.g., viable and resistant to an anticancer drug) and having an FDR ≤ 0.1 (and/or having at least about 2-fold compared to a control cancer cell population) depletion) were identified as target genes whose mutations sensitized the cancer cells to anticancer drugs. In some embodiments, identifying the target gene in step d) comprises: i) identifying a sgRNA ^iBAR sequence in the treated cancer cell population; and ii) identifying a hit gene corresponding to the guide sequence of the sgRNA ^iBAR . In some embodiments, identifying the target gene in step d) comprises: i) obtaining the sgRNA ^iBAR sequence in the cancer cell population after treatment; ii) sorting the corresponding guide sequence of the sgRNA ^iBAR sequence based on sequence counting, wherein the Ranking comprises adjusting the ranking of each guide sequence based on data concordance between iBAR sequences in the sgRNA ^iBAR sequences corresponding to guide sequences; and iii) identifying hit genes corresponding to guide sequences whose ranking is above a predetermined threshold level . In some embodiments, the method is a positive screen. In some embodiments, the method is negative screening. In some embodiments, steps b) and c) comprise contacting the library of cancer cells with the anticancer drug at a concentration of about IC50 to about IC70 for about 9 to about 10 doubling times while allowing viable cancer cells to The cancer cells are grown, optionally passaged every about 3 doubling times. In some embodiments, steps b) and c) comprise contacting the library of cancer cells with the anticancer drug at a concentration of about IC50 to about IC70 for about 15 to about 16 doubling times while allowing viable cancer cells to The cancer cells are grown, optionally passaged every about 3 doubling times. In some embodiments, the coverage of each hit gene (or sgRNA ^iBAR ) against a cancer cell library remains the same or similar (e.g., within about 10% difference) after passaging for serial anticancer drug treatment . In some embodiments, the sgRNA ^iBAR sequence counts undergo median ratio normalization followed by mean-variance modeling. In some embodiments, the variance of each guide sequence is adjusted based on the profile identity between the iBAR sequences in the sgRNA ^iBAR sequences corresponding to the guide sequences. In some embodiments, the profile identity between iBAR sequences in the sgRNA ^iBAR sequences corresponding to each guide sequence is determined based on the direction of the fold change of each iBAR sequence, wherein if the fold change of the iBAR sequences is relatively The variance of the guide sequences increases relative to each other in different directions (eg, increasing versus decreasing, increasing versus unchanged, or decreasing versus unchanged).

Accordingly, in some embodiments, there is provided a method of identifying a target gene in a cancer cell whose mutation renders the cancer cell sensitized or resistant to an anticancer drug, the method comprising: a) providing a gene comprising targeting one or more hits A cancer cell library of a sgRNA ^iBAR library described herein for genes; b) contacting the cancer cell library with an anticancer drug (e.g., about 9 to about 10 doubling times, or about 15 to about 16 doubling times, with or without c) growing the cancer cell library to obtain a treated cancer cell population (e.g., viable and resistant to an anticancer drug); and d) based on the treated cancer cell population and the control cancer cell population The difference between the profile of sgRNA ^iBAR identifies the target gene, wherein the control cancer cell population is obtained from a cancer cell library cultured under the same conditions and has not been exposed to the anticancer drug, wherein the treated cancer cell population and the control cancer cell The profile of sgRNA ^iBAR in the population is identified by next generation sequencing, wherein step d) comprises comparing the sgRNA ^iBAR sequence counts obtained from the treated cancer cell population to the sgRNA sequence counts obtained from the control cancer cell population, and wherein i ) whose corresponding sgRNA ^iBAR guide sequence is identified as enriched in a post-treatment cancer cell population (e.g., viable and resistant to an anticancer drug) compared to a control cancer cell population with an FDR ≤ 0.1 (and/or with At least about 2-fold enrichment) hit genes identified as target genes whose mutations render the cancer cells resistant to the anticancer drug; and/or ii) their corresponding sgRNA ^iBAR guide sequences compared to the control cancer cell population in Hit genes identified as depleted and having a FDR ≤ 0.1 (and/or having at least about 2-fold depletion) in a cancer cell population after treatment (e.g., viable and resistant to an anticancer drug) are identified as mutations that render The cancer cell is sensitive to a target gene of an anticancer drug. In some embodiments, steps b) and c) comprise contacting the library of cancer cells with the anticancer drug at a concentration of about IC50 to about IC70 for about 9 to about 10 doubling times while allowing viable cancer cells to The cancer cells are grown, optionally passaged every about 3 doubling times. In some embodiments, steps b) and c) comprise contacting the library of cancer cells with the anticancer drug at a concentration of about IC50 to about IC70 for about 15 to about 16 doubling times while allowing viable cancer cells to The cancer cells are grown, optionally passaged every about 3 doubling times. In some embodiments, the coverage of each hit gene (or sgRNA ^iBAR ) against a cancer cell library remains the same or similar (e.g., within about 10% difference) after passaging for serial anticancer drug treatment . In some embodiments, the cancer cell library has about 100-fold to about 1000-fold coverage for each sgRNA ^iBAR , such as about 1000-fold coverage for each sgRNA ^iBAR . In some embodiments, the cancer cell library has at least about 400-fold (eg, about 1200-fold to about 12,000-fold) coverage for each hit gene. In some embodiments, the sgRNA ^iBAR sequence counts undergo median ratio normalization followed by mean-variance modeling. In some embodiments, the variance of each guide sequence is adjusted based on the profile identity between the iBAR sequences in the sgRNA ^iBAR sequences corresponding to the guide sequences. In some embodiments, the profile identity between iBAR sequences in the sgRNA ^iBAR sequences corresponding to each guide sequence is determined based on the direction of the fold change of each iBAR sequence, wherein if the fold change of the iBAR sequences is relatively The variance of the guide sequences increases relative to each other in different directions (eg, increasing versus decreasing, increasing versus unchanged, or decreasing versus unchanged).

In some embodiments, there is provided a method of identifying a target gene in a cancer cell whose mutation renders the cancer cell sensitized or resistant to an anticancer drug, the method comprising: a) providing a gene comprising targeting one or more hit genes A cancer cell library of the sgRNA ^iBAR library described herein; b) subjecting the cancer cell library to at least two separately different anticancer drug treatments (e.g., the treatments described herein); c) growing the cancer cell library to obtain Post-treatment cancer cell populations (e.g., all viable and resistant to anticancer drugs) for each treatment; d1) Based on the comparison of profiles of sgRNA ^iBAR in post-treatment cancer cell populations from each treatment and corresponding control cancer cell populations , identifying one or more hit genes in the post-treatment cancer cell population obtained from each treatment whose mutation confers anticancer drug sensitivity or resistance to the cancer cells, and d2) combining all treatments obtained from One or more hit genes identified, thereby identifying target genes in said cancer cells whose mutations render said cancer cells sensitive or resistant to anticancer drugs; wherein the one or more hit genes in identifying step d1) include For each treatment, the sgRNA ^iBAR (or its guide sequence) sequence counts obtained from the treated cancer cell population were compared to the sgRNA ^iBAR (or its guide sequence) sequence counts obtained from the control cancer cell population, where i) Their corresponding sgRNA ^iBAR guide sequences were identified as enriched in post-treatment cancer cell populations (e.g., viable and resistant to anticancer drugs) compared to control cancer cell populations and had FDR ≤ 0.1 for the corresponding treatments (and /or a hit gene with at least about 2-fold enrichment), identified as a hit gene whose mutation renders the cancer cell resistant to the anticancer drug against the corresponding treatment; and/or ii) its corresponding sgRNA ^iBAR guide sequence Identified as depleted in a post-treatment cancer cell population (e.g., viable and resistant to an anticancer drug) compared to a control cancer cell population and having an FDR ≤ 0.1 (and/or having at least about 2-fold depletion for the corresponding treatment) ) is identified as a hit gene whose mutation sensitizes the cancer cell to the anticancer drug for the corresponding treatment; and wherein step d2) comprises combining all treatments whose mutation sensitizes the cancer cell to the anticancer drug one or more hit genes for drug resistance, thereby identifying target genes in said cancer cells whose mutations render said cancer cells resistant to an anticancer drug; and/or a combination of all treatments whose mutations render said cancer cells One or more hit genes that are sensitive to the anticancer drug, thereby identifying target genes in the cancer cells whose mutations render the cancer cells sensitive to the anticancer drug. In some embodiments, there is provided a method of identifying a target gene in a cancer cell whose mutation renders the cancer cell sensitized or resistant to an anticancer drug, the method comprising: a) providing a gene comprising targeting one or more hit genes A cancer cell library of the sgRNA ^iBAR library described herein; b) subjecting the cancer cell library to at least two separately different anticancer drug treatments (e.g., the treatments described herein); c) growing the cancer cell library to obtain Post-treatment cancer cell populations (e.g., all viable and resistant to anticancer drugs) for each treatment; d1) Based on the comparison of profiles of sgRNA ^iBAR in post-treatment cancer cell populations from each treatment and corresponding control cancer cell populations , identifying one or more hit genes in the post-treatment cancer cell population obtained from each treatment whose mutations render said cancer cells sensitive or resistant to anticancer drugs, and d2) combining the identified genes from all treatments One or more hit genes, thereby identifying target genes in said cancer cells whose mutations render said cancer cells sensitive or resistant to an anticancer drug; wherein i) their corresponding sgRNA ^iBAR guide sequences compared to a control cancer cell population Hits identified as enriched in post-treatment cancer cell populations (e.g., viable and resistant to anticancer drugs) and having a FDR ≤ 0.1 (and/or having at least about 2-fold enrichment) in at least one treatment Genes identified as target genes whose mutations render the cancer cells resistant to the anticancer drug; and/or ii) their corresponding sgRNA ^iBAR guide sequences in post-treatment cancer cell populations (e.g., viable and resistant to anticancer drugs) that were identified as depleted and had FDR ≤ 0.1 (and/or had at least about 2-fold depletion) in at least one treatment, identified as mutations that rendered the cancer cells Anticancer drug sensitive target genes. In some embodiments, there is provided a method of identifying a target gene in a cancer cell whose mutation renders the cancer cell sensitized or resistant to an anticancer drug, the method comprising: a) providing a gene comprising targeting one or more hit genes A cancer cell library of the sgRNA ^iBAR library described herein; b) subjecting the cancer cell library to at least two separately different anticancer drug treatments (e.g., the treatments described herein); c) growing the cancer cell library to obtain Post-treatment cancer cell populations (e.g., all viable and resistant to anticancer drugs) for each treatment, and d) based on post-treatment cancer cell populations and control cancer cell populations on at least two separate treatment basis Differences between profiles of sgRNA ^iBARs in the identified target genes: wherein identifying target genes comprises: i) for each treatment, obtaining the sgRNA ^iBAR (or its guide sequence) sequences in the cancer cell population after treatment; ii) for each treatment A process of sorting the corresponding guide sequences of the sgRNA ^iBAR sequences based on sequence counts, wherein the ranking comprises adjusting each guide sequence based on the data identity between the iBAR sequences in the sgRNA ^iBAR sequences corresponding to the guide sequences and iii) for each treatment, identifying target genes corresponding to guide sequences whose ranking is above a predetermined threshold level; wherein (1) in post-treatment cancer cell populations (live) resistant to anticancer drugs Hit genes identified as depleted and having FDR ≤ 0.1 (and/or having at least about 2-fold depletion) in at least one treatment whose mutations (e.g., inactivation) are identified to render the cancer cells anticancer drugs Sensitive target genes; and/or (2) identified as enriched in post-treatment cancer cell populations (live) resistant to anticancer drugs and having FDR ≤ 0.1 in at least one treatment (and/or having Hit genes that are at least about 2-fold enriched) are identified as target genes whose mutations (eg, inactivation) render the cancer cells resistant to the anticancer drug. In some embodiments, sequence counts obtained for each treatment from a population of cancer cells after treatment are compared to corresponding sequence counts obtained from a population of control cancer cells to provide a fold change (e.g., an actual fold change, or a fraction of a fold change). Derivatives such as log2 or log10 fold change). In some embodiments, the cancer cell library has about 100-fold to about 1000-fold coverage for each sgRNA ^iBAR , such as about 1000-fold coverage for each sgRNA ^iBAR . In some embodiments, the cancer cell library has at least about 400-fold coverage for each hit gene, eg, about 1200-fold to about 12,000-fold coverage for each hit gene. In some embodiments, the method is a positive screen. In some embodiments, the method is negative screening. In some embodiments, the control cancer cell population is obtained from the same cancer cell library cultured under the same conditions without exposure to the anticancer drug, optionally undergoing the same method of obtaining in step c). In some embodiments, the method further comprises performing next generation sequencing on the treated cancer cell population and the control cancer cell population from each treatment. In some embodiments, a treatment comprises contacting the library of cancer cells with the anticancer drug at a concentration of about IC50 to about IC70 for about 9 to about 10 doubling times while allowing viable cancer cells to grow, optionally The cancer cells were passaged approximately every 3 doubling times. In some embodiments, another treatment comprises contacting the library of cancer cells with the anticancer drug at a concentration of about IC50 to about IC70 for about 15 to about 16 doubling times while allowing viable cancer cells to grow, either The cancer cells are optionally passaged every about 3 doubling times. In some embodiments, the coverage of each hit gene (or sgRNA ^iBAR ) against a cancer cell library remains the same or similar (e.g., within about 10% difference) after passaging for serial anticancer drug treatment . In some embodiments, the sgRNA ^iBAR sequence counts undergo median ratio normalization followed by mean-variance modeling. In some embodiments, the variance of each guide sequence is adjusted based on the profile identity between the iBAR sequences in the sgRNA ^iBAR sequences corresponding to the guide sequences. In some embodiments, the profile identity between the iBAR sequences in the sgRNA ^iBAR sequences corresponding to each guide sequence is determined based on the direction of the fold change of each iBAR sequence, wherein if the fold change of the iBAR sequences is relatively The variance of the guide sequences increases relative to each other in different directions (eg, increasing versus decreasing, increasing versus unchanged, or decreasing versus unchanged).

In some embodiments, there is provided a method of identifying a target gene in a cancer cell whose mutation renders the cancer cell sensitized or resistant to an anticancer drug, the method comprising: a) providing a gene comprising targeting one or more hit genes Cancer cell library of the sgRNA ^iBAR library described herein; two separate treatments b1) and b2) of the cancer cell library from step a): b1) combining the cancer cell library from step a) with the anticancer drug contacting at a concentration of about IC50 to about IC70 for about 9 to about 10 doubling times; b2) contacting the library of cancer cells from step a) with the anticancer drug at a concentration of about IC50 to about IC70 for about 15 to about 16 doubling times; c1) growing (eg, passaged every about 3 doubling times in the presence of an anticancer drug) the cancer cell library from treatment b1) to obtain a post-treatment cancer cell population (eg, viable and anticancer drug resistance); c2) growing (e.g., passage every about 3 doubling times in the presence of anticancer drug) the cancer cell library from treatment b2) to obtain a post-treatment cancer cell population (e.g. , viable and resistant to anticancer drugs); d1) identification of treated cancer cells from treatment b1) based on the difference between the profile of sgRNA ^iBAR in the treated cancer cell population from c1) and the corresponding control cancer cell population One or more hit genes in the population, d2) identified in the treated cancer cell population from treatment b2) based on the difference between the profile of sgRNA ^iBAR in the treated cancer cell population from c2) and the corresponding control cancer cell population. and d3) combining the one or more hit genes (sensitive or drug-resistant) identified from all treatments b1) and treatments b2), thereby identifying the cancer cells whose mutations make the Target genes that are sensitive or resistant to anticancer drugs in cancer cells. In some embodiments, the hit genes in the treated cancer cell populations from treatment b1) or b2) in step d1) or d2), respectively, comprise: i) identifying the treated cancer cell populations from each treatment (e.g. , live and anticancer drug resistant) sgRNA ^iBAR sequence; and ii) identifying a hit gene corresponding to the guide sequence of the sgRNA ^iBAR . In some embodiments, identifying the one or more hit genes in steps d1) and/or d2) comprises: for each treatment, a population of cancer cells obtained from the treatment (e.g., alive and resistant to an anticancer drug). ) sgRNA ^iBAR (or its guide sequence) sequence counts are compared with the sgRNA ^iBAR (or its guide sequences) sequence counts obtained from the control cancer cell population, wherein i) its corresponding sgRNA ^iBAR guide sequence compared to the control cancer cell A population is identified as enriched in the post-treatment cancer cell population and has a hit gene with FDR ≤ 0.1 (and/or with at least about 2-fold enrichment) for the corresponding treatment identified as having a mutation that renders the corresponding treatment and/or ii) its corresponding sgRNA ^iBAR guide sequence is identified as depleted in the treated cancer cell population compared to the control cancer cell population and has FDR for the corresponding treatment Hit genes < 0.1 (and/or having at least about 2-fold depletion) were identified as hit genes whose mutations sensitized the cancer cells to the anticancer drug against the corresponding treatment. In some embodiments, step d3) comprises: combining one or more hit genes from all treatments whose mutation renders the cancer cell resistant to an anticancer drug, thereby identifying in the cancer cell whose mutation renders the Target genes for which cancer cells are resistant to anticancer drugs; and/or combining one or more hit genes from all treatments whose mutations render said cancer cells sensitive to anticancer drugs, thereby identifying mutations in said cancer cells Target genes that sensitize the cancer cells to anticancer drugs. In some embodiments, identifying the target gene comprises identifying one or more hit genes in post-treatment cancer cell populations obtained from two separate treatments b1) and b2), wherein: i) its corresponding sgRNA ^iBAR guide Sequences are identified as enriched in post-treatment cancer cell populations (live) that are resistant to anticancer drugs compared to control cancer cell populations and have an FDR ≤ 0.1 in treatment b1) or b2) (and/or have at least approximately 2-fold enrichment) hit genes identified as targets whose mutations render the cancer cells resistant to the anticancer drug; and/or ii) their corresponding sgRNA ^iBAR guide sequences compared to the control cancer cell population in response to Hit genes identified as depleted and having FDR ≤ 0.1 (and/or having at least about 2-fold depletion) in treatment b1) or b2) in anticancer drug-resistant post-treatment cancer cell populations (live) were identified by Target genes whose mutations sensitize the cancer cells to anticancer drugs are identified. In some embodiments, for each treatment, sequence counts obtained from a population of cancer cells after treatment are compared to corresponding sequence counts obtained from a population of control cancer cells to provide a fold change (e.g., an actual fold change, or a fold change derivatives such as log2 or log10 fold change). In some embodiments, the cancer cell library has about 100-fold to about 1000-fold coverage for each sgRNA ^iBAR , such as about 1000-fold coverage for each sgRNA ^iBAR . In some embodiments, the cancer cell library has at least about 400-fold coverage for each hit gene, eg, about 1200-fold to about 12,000-fold coverage for each hit gene. In some embodiments, the method is a positive screen. In some embodiments, the method is negative screening. In some embodiments, the control cancer cell population is obtained from the same cancer cell library cultured under the same conditions without exposure to the anticancer drug, optionally undergoing the same method of obtaining in step c). In some embodiments, the method further comprises performing next generation sequencing on the treated cancer cell population and the control cancer cell population from each treatment. In some embodiments, the coverage of each hit gene (or sgRNA ^iBAR ) against a cancer cell library remains the same or similar (e.g., within about 10% difference) after passaging for serial anticancer drug treatment . In some embodiments, the sgRNA ^iBAR sequence counts undergo median ratio normalization followed by mean-variance modeling. In some embodiments, the variance of each guide sequence is adjusted based on the profile identity between the iBAR sequences in the sgRNA ^iBAR sequences corresponding to the guide sequences. In some embodiments, the profile identity between iBAR sequences in the sgRNA ^iBAR sequences corresponding to each guide sequence is determined based on the direction of the fold change of each iBAR sequence, wherein if the fold change of the iBAR sequences is relatively The variance of the guide sequences increases relative to each other in different directions (eg, increasing versus decreasing, increasing versus unchanged, or decreasing versus unchanged).

In some embodiments, one or more target genes identified using the methods described herein are directed against two or more (e.g., 2, 3, 4, 5 or more) cancer cell lines (eg, HCT116, SW480) for testing. In some embodiments, there is provided a method of identifying a target gene in a cancer cell whose mutation renders the cancer cell sensitized or resistant to an anticancer drug, the method comprising: a) providing two or more cancer cells each comprising a plurality of cancer cells A plurality (e.g., 2, 3, 4, 5, or more) of cancer cell libraries (e.g., a Cas9 ⁺ sgRNA ^iBAR cancer cell library), wherein each of the plurality of cancer cells has a hit gene mutation, wherein Hit genes in at least two of the plurality of cancer cells within the same cancer cell library differ from each other, and wherein the two or more cancer cell libraries arise from different genes of the same cancer type (e.g., colorectal cancer) (e.g., HCT116 or SW480); b) separately contacting each cancer cell library with an anticancer drug (e.g., at a concentration of about IC50 to about IC70 for about 9 to about 10 doubling times, or for about 15 to about 16 doubling times); c) growing each cancer cell library separately to obtain a population of treated cancer cells (e.g., viable and resistant to an anticancer drug); d1) based on the treated cancer cell difference between the profiles of hit gene mutations (or sgRNA or sgRNA ^iBAR ) in the population of cells and the corresponding control cancer cell population, identifying one or more hit genes in the treated cancer cell population obtained from each cancer cell library, respectively; and d2) combining the one or more hit genes identified from all cancer cell libraries, thereby identifying target genes in said cancer cells whose mutations render said cancer cells sensitive or resistant to an anticancer drug. In some embodiments, the processing step b) and the cancer cell acquisition step c) are the same for different cancer cell libraries. In some embodiments, the processing step b) and/or the cancer cell acquisition step c) are different for different cancer cell libraries. In some embodiments, the two or more cancer cell libraries are Cas9 ⁺ sgRNA ^iBAR cancer cell libraries. In some embodiments, the control population of cancer cells is obtained from a corresponding library of identical cancer cells cultured under the same conditions without exposure to the anticancer drug. In some embodiments, the profiles of hit gene mutations or sgRNAs or sgRNA ^iBARs in post-treatment and control cancer cell populations are identified by next generation sequencing. In some embodiments, identifying ^one or more hit genes for each cancer cell library in step d1) comprises: sequence counting and Sequence counts of hit gene mutations (or sgRNA or sgRNA ^iBAR ) obtained from corresponding control cancer cell populations were compared, where: i) their corresponding sgRNA or sgRNA ^iBAR guide sequences or hit gene mutations were compared to corresponding control cancer cell populations in the treated Hit genes identified as enriched and having a FDR ≤ 0.1 (and/or having at least about 2-fold enrichment) in a cancer cell population (e.g., viable and resistant to an anticancer drug) are identified as mutated A hit gene that renders the cancer cells resistant to an anticancer drug; and/or ii) its corresponding sgRNA or sgRNA ^iBAR guide sequence or hit gene mutation compared to a corresponding control cancer cell population after treatment in a cancer cell population (e.g., live and resistant to anticancer drugs) that are identified as depleted and have FDR ≤ 0.1 (and/or have at least about 2-fold depletion) hit genes whose mutations are identified to render the cancer cells anticancer drugs Sensitive hit genes. In some embodiments, step d2) comprises combining one or more gene hits from each of the libraries of all cancer cells whose mutation renders said cancer cells resistant to an anticancer drug, thereby identifying which of said cancer cells mutating target genes that render said cancer cells resistant to an anticancer drug; and/or combining one or more hit genes from each of all cancer cell libraries whose mutations render said cancer cells sensitive to an anticancer drug, by This identifies target genes in the cancer cells whose mutations sensitize the cancer cells to anticancer drugs.

In some embodiments, methods of identifying target genes whose mutations render cancer cells sensitive or resistant to two or more (e.g., 2, 3, 4, 5 or more) different anticancer drugs are provided, The method comprises: a) providing a cancer cell library (e.g., a Cas9 ⁺ sgRNA ^iBAR cancer cell library) comprising a plurality of cancer cells, wherein each of the plurality of cancer cells has a hit gene mutation, wherein in the plurality of cancer cells The hit genes in at least two of the cancer cells are different from each other; b) separately contacting the library of cancer cells with two or more different anticancer drugs (for example, at a concentration of about IC50 to about IC70 for about 9 to about 10 doubling times, or for about 15 to about 16 doubling times); c) growing the cancer cell library separately for each anticancer drug to obtain a population of treated cancer cells (e.g., viable and anticancer drug resistance); d1) separately identifying a set of one or more target genes whose mutations render said cancer cells sensitive to said anticancer drug against two or more different anticancer drugs when treated individually (e.g., using any of the identification methods described herein); and d2) obtaining one or more target genes present in each set of target genes identified for each anticancer drug, thereby identifying mutations thereof that render the cancer Target genes for which cells are sensitive to combined treatment of two or more different anticancer drugs; and/or d1) identification of mutations thereof which make said cancer cells, respectively, directed against two or more different anticancer drugs when treated alone a set of one or more target genes that are resistant to the anticancer drug (e.g., using any of the methods of identification described herein); and d2) obtaining one that is present in the set of target genes identified for all anticancer drugs or a plurality of target genes, thereby identifying target genes whose mutations render the cancer cells resistant to a combination treatment of two or more different anticancer drugs. In some embodiments, the two or more different anticancer drugs target the same cancer target. In some embodiments, the two or more different anticancer drugs target different cancer targets.

Accordingly, in some embodiments, there is provided a method of identifying a target gene in a cancer cell whose mutation renders said cancer cell susceptible to a combination therapy comprising a first anticancer drug and a second anticancer drug, comprising: i) according to herein identifying a first set of one or more target genes in cancer cells whose mutations sensitize said cancer cells to a first anti-cancer drug according to any of the methods described herein; ii) identifying mutations in cancer cells according to any of the methods described herein one or more target genes of a second set that sensitizes said cancer cells to a second anticancer drug; and iii) obtaining one or more target genes present in both the first set of target genes and the second set of target genes genes, thereby identifying target genes whose mutations render said cancer cells sensitive to said combination therapy. In some embodiments, the two anticancer drugs target the same cancer target. In some embodiments, the two anticancer drugs target different cancer targets.

In some embodiments, there is provided a method of identifying a target gene in a cancer cell whose mutation renders the cancer cell sensitive or resistant to a combination therapy comprising a first anticancer drug and a second anticancer drug, the method comprising: a) providing a cancer cell library (eg, sgRNA or sgRNA ^iBAR cancer cell library) comprising a plurality of cancer cells, wherein each of the plurality of cancer cells has a hit gene mutation, wherein at least one of the plurality of cancer cells The hit genes in the two are different from each other; b) contacting the cancer cell library with a first anticancer drug and a second anticancer drug; c) culturing the cancer cell library to obtain a treated cancer cell population (e.g. , alive and resistant to anticancer drugs); and d) identifying the target gene based on the difference between the profile of the hit gene mutation (either sgRNA or sgRNA ^iBAR ) in the treated and control cancer cell populations . In some embodiments, the first anticancer drug and the second anticancer drug are simultaneously contacted with the library of cancer cells. In some embodiments, the first anticancer drug and the second anticancer drug are contacted with the library of cancer cells for overlapping periods of time. In some embodiments, the first anticancer drug and the second anticancer drug are sequentially contacted with the library of cancer cells. In some embodiments, a drug-treated population of cancer cells (e.g., viable, enriched/sorted for viable cells or non-enriched/sorted, with or without reverting to growth phase) is obtained and then Contact with another anticancer drug to obtain the final treated cancer cell population. In some embodiments, the control cancer cell population is obtained from the same cancer cell library cultured under the same conditions without exposure to any anticancer drug, optionally subjected to the same cancer cell harvesting method in step c). In some embodiments, the control cancer cell population is obtained from the same cancer cell library cultured under the same conditions and exposed to only one anticancer drug, optionally subjected to the same cancer cell harvesting method in step c). Accordingly, in some embodiments, there is provided a method of identifying a target gene in a cancer cell whose mutation sensitizes the cancer cell to a combination therapy comprising a first anticancer drug and a second anticancer drug, the method comprising: a ) providing a cancer cell library (e.g., sgRNA or sgRNA ^iBAR cancer cell library) comprising a plurality of cancer cells, wherein each of the plurality of cancer cells has a hit gene mutation, wherein at least two of the plurality of cancer cells wherein said hit genes are different from each other; b) contacting said library of cancer cells with a first anticancer drug and a second anticancer drug; c) growing said library of cancer cells to obtain a population of treated cancer cells (e.g. , alive and resistant to anticancer drugs); d1) Identify the first panel based on the difference between the profile of the hit gene mutation (either sgRNA or sgRNA ^iBAR ) in the treated cancer cell population and the first control cancer cell population one or more hit genes; d2) based on the difference between the profile of the hit gene mutation (or sgRNA or sgRNA ^iBAR ) in the treated cancer cell population and the second control cancer cell population, identify one or more hit genes of the second group hit genes; and d3) combining the one or more hit genes of the first set and the second set identified from d1) and d2), thereby identifying cancer cells whose mutations make said cancer cells contain the first anticancer Combination therapy of the drug and the second anticancer drug to treat sensitive or drug-resistant target genes; wherein the first control cancer cell population is obtained from a cancer cell library cultured under the same conditions and in contact with the first anticancer drug alone, and the steps of Obtained by the same cancer cell acquisition method in c); and wherein the second control cancer cell group is obtained from a cancer cell library cultured under the same conditions and exposed to the second anticancer drug alone, and the same method as in step c) is used. Obtained by cancer cell acquisition method. In some embodiments, identifying a first set of one or more gene hits in d1), and/or identifying a second set of one or more gene hits in d2), may include any of the hits described herein Gene/Target Gene Identification Methods. For example, in some embodiments, identifying the first (or second) set of one or more hit genes comprises: counting sgRNA or sgRNA ^iBAR sequences (or sgRNAs comprising mutations in the hit genes) obtained from a cancer cell population after treatment. sequence count) and the sgRNA or sgRNA ^iBAR sequence count (or the sequence count of the sequence containing the mutation of the hit gene) obtained from the first (or second) control cancer cell population is compared, wherein: i) its corresponding The sgRNA or sgRNA ^iBAR guide sequence (or hit gene mutation) is identified in a post-treatment cancer cell population (e.g., alive and resistant to an anticancer drug) compared to a first (and/or second) control cancer cell population Hit genes that are enriched and have FDR ≤ 0.1 (and/or have at least about 2-fold enrichment), identified as mutations that render the cancer cell as compared to treatment with the first (and/or second) anticancer drug alone treatment of hit genes that are more resistant to the combination therapy; and/or ii) their corresponding sgRNA or sgRNA ^iBAR guide sequence (or hit gene mutation) compared to the first (and/or second) control cancer cell population after treatment cancer A hit gene identified as depleted and having a FDR ≤ 0.1 (and/or having at least about 2-fold depletion) in a cell population (e.g., viable and resistant to an anticancer drug) whose mutations are identified to render the cancer Hit genes for which cells are more sensitive to combination therapy than to treatment with the first (and/or second) anticancer drug alone. In some embodiments, identifying the first (or second) set of one or more hit genes in step d1) (or d2)) further comprises: sgRNA or sgRNA ^iBAR sequence counts obtained from a cancer cell population after treatment (or the sequence count of the sequence containing the mutation in the hit gene) was compared with the sgRNA or sgRNA ^iBAR sequence count (or the sequence count of the sequence containing the mutation in the hit gene) obtained from a control cancer cell population, the control cancer cell Populations were obtained from the same cancer cell library cultured under the same conditions without exposure to any anticancer drugs, where i) its corresponding sgRNA or sgRNA ^iBAR guide sequence (or hit gene mutation) compared to the control cancer cell population and the first (and/or or second) a control cancer cell population identified as enriched in a post-treatment cancer cell population (e.g., viable and resistant to an anticancer drug) and having a FDR ≤ 0.1 (and/or having at least about 2-fold enrichment ) hit genes whose mutations are identified to render the cancer cells more resistant to the combination therapy than treatment with the first (and/or second) anticancer drug alone; and/or ii) its corresponding sgRNA or sgRNA ^iBAR guide sequence (or hit gene mutation) compared to the control cancer cell population and the first (and/or second) control cancer cell population after the treatment of the cancer cell population (e.g., alive and resistant to anticancer drugs) ) that are identified as depleted and have FDR ≤ 0.1 (and/or have at least about 2-fold depletion) hit genes identified as mutations that make the cancer cell compared to the first (and/or second) alone Anticancer drugs address hit genes that are more sensitive to combination therapy. In some embodiments, the method further comprises next generation sequencing to obtain the sgRNA or sgRNA ^iBAR sequence or a sequence comprising the mutation of the hit gene. In some embodiments, the two anticancer drugs target the same cancer target. In some embodiments, the two anticancer drugs target different cancer targets.

In some embodiments, any of the methods of identification described herein further comprises verifying the target gene by: a) modifying the cancer cell by generating a mutation (e.g., an inactivating mutation) in the target gene of the cancer cell cells; b) determining the sensitivity or resistance of said modified cancer cells to said anticancer drug.

Also provided are modified cancer cells obtained by inactivating one or more target genes identified by any of the methods described herein.

Single-stranded guide RNA (sgRNA) library and sgRNA ^iBAR library

In some embodiments, the invention uses a CRISPR/Cas guide RNA (e.g., a single-stranded guide RNA) and a construct encoding the CRISPR/Cas guide RNA to generate mutations (e.g., inactivation) in one or more hit genes. mutation). In some embodiments, the mutation is generated by cleaving the hit gene (eg, using CRISPR/Cas9). In some embodiments, the mutation is produced by modulating (eg, suppressing or reducing) the expression of the hit gene (eg, using CRISPR/dCas fused to a repression domain).

In some embodiments, sgRNA libraries comprising one or more (e.g., 1, 2, 3, 4, 5, 10, 100, 1,000, 10,000, 20,000, or more) sgRNA constructs are provided, wherein each Each sgRNA construct (e.g., lentivirus or lentiviral vector encoding said sgRNA) comprises or encodes sgRNA, and wherein each sgRNA comprises a target site complementary (e.g., at least about 50%, 60% , 70%, 80%, 90%, 95%, 96%, 97%, 98%, 99% or 100% complementary) guide sequence. In some embodiments, the sgRNA library comprises a plurality (e.g., 2, 3, 4, 5, 10, 100, 1,000, 10,000, 20,000, or more) of sgRNA constructs, at least two of which are aligned with guide sequences Complementary hits differ from each other. In some embodiments, the sgRNA construct comprises (or consists of) an sgRNA. In some embodiments, the sgRNA construct encodes a sgRNA. In some embodiments, the sgRNA construct is a plasmid encoding the sgRNA. In some embodiments, the sgRNA construct is a viral vector (eg, a lentiviral vector) encoding the sgRNA. In some embodiments, the sgRNA construct is a virus (eg, a lentivirus) encoding the sgRNA. In some embodiments, each sgRNA comprises a guide sequence fused to a second sequence comprising a repeat-inverter stem-loop that interacts with a Cas protein (eg, Cas9). In some embodiments, the second sequence of each sgRNA further comprises stem-loop 1, stem-loop 2, and/or stem-loop 3. In some embodiments, each guide sequence comprises about 17 to about 23 nucleotides. In some embodiments, the sgRNA library comprises at least about 100 sgRNA constructs, such as at least about 200, 300, 400, 1,000, 1,600, 4,000, 10,000, 15,000, 16,000, 19,000, 20,000, 38,000, 50,000, 100,000, Any of 150,000, 155,000, 200,000 or more sgRNA constructs. In some embodiments, the sgRNA library comprises about 6000 to about 16,000 sgRNA constructs. In some embodiments, the sgRNA library comprises about 10,000 to about 18,000 sgRNA constructs. In some embodiments, the sgRNA library containing multiple sgRNA constructs comprises or encodes sgRNAs with guide sequences complementary to the target site of each annotated gene in the genome (hereinafter also referred to as "genome-wide sgRNA library"). In some embodiments, a sgRNA library comprising a plurality of sgRNA constructs comprises or encodes sgRNAs having guide sequences complementary to target sites of hit genes having a DNA mutation frequency of at least about 5% (e.g., at least about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or more), and in cancer patients (e.g., based on literature or databases) its RNA expression levels are up-regulated or down-regulated by greater than about 2-fold (eg, greater than any of about 2.5, 3, 4, 5, 6, 7, 8, 9, 10, 50, 100-fold or more). In some embodiments, the hit gene encodes a protein that is expressed inside or on the cell surface, in healthy cells or cancer cells. In some embodiments, the sgRNA library comprises at least two (eg, 2, 3, 4, 5 or more, such as 3) sgRNA constructs comprising or encoding genes with the same hit as sgRNAs with guide sequences complementary to at least two (eg, 2, 3, 4, 5 or more, such as 3) different target sites, that is, for the hit gene, the sgRNA library has at least 2- double coverage. In some embodiments, for each hit gene, the sgRNA library comprises at least 3 (eg, about 6 to about 12) sgRNA constructs comprising or encoding at least 3 sgRNA constructs with the same hit gene (eg, about 6 to about 12) sgRNAs of guide sequences complementary to different target sites. In some embodiments, the sgRNA library comprises at least two (eg, 2, 3, 4, 5 or more, such as 3) sgRNA constructs for each annotated gene within the genome sgRNA comprising or encoding a guide sequence complementary to at least two (e.g., 2, 3, 4, 5 or more, such as 3) different target sites within the same hit gene, i.e. for the entire genome The sgRNA library has at least 2-fold coverage. In some embodiments, the sgRNA library further comprises one or more (e.g., 1, 2, 3, 4, 5, 10, 100, 1,000, 2,000, 10,000, or more) "negative control sgRNA constructs ", wherein each negative control sgRNA construct (e.g., lentivirus or lentiviral vector encoding said negative control sgRNA) comprises or encodes a negative control sgRNA, and wherein each negative control sgRNA comprises a guide sequence that: An unrelated sequence that is not within the genome is complementary, is complementary to a control gene (eg, known to respond identically or similarly between the test combination control group after gene inactivation), or is complementary to a sequence that is not related to any annotated gene within the genome. In some embodiments, the sgRNA library further comprises a negative control sgRNA construct in an amount of about 3% to about 30% of the number of hit gene sgRNA constructs in the sgRNA library. In some embodiments, the sgRNA library further comprises about 500 to about 4000 (eg, about 500) negative control sgRNA constructs.

In some embodiments, the sgRNA further comprises an internal barcode (iBAR) sequence (the sgRNA is hereinafter referred to as "sgRNA ^iBAR "). In some embodiments, the iBAR is located within the sgRNA such that the resulting sgRNA ^iBAR can operate with a Cas protein (e.g., Cas9) to modify (e.g., cleave or regulate expression) the guide sequence complementary to the sgRNA ^iBAR hit genes. Accordingly, in some embodiments, the sgRNA library described herein is an sgRNA ^iBAR library. In some embodiments, the sgRNA ^iBAR library comprises one or more (e.g., 1, 2, 3, 4, 5, 10, 100, 1,000, 10,000, 20,000, or more) sgRNA ^iBAR constructs, wherein Each sgRNA ^iBAR construct comprises or encodes a sgRNA ^iBAR , wherein each sgRNA ^iBAR comprises a guide sequence and an iBAR sequence, and wherein each guide sequence is complementary (e.g., at least about 50%, 60% , 70%, 80%, 90%, 95%, 96%, 97%, 98%, 99% or 100% complementary). In some embodiments, the sgRNA ^iBAR library comprises a plurality (e.g., 2, 3, 4, 5, 10, 100, 1,000, 2,000, 10,000, or more) of sgRNA ^iBAR constructs, at least two of which are associated with The hit genes complementary to the guide sequence were different from each other. In some embodiments, each sgRNA ^iBAR comprises a first stem-loop and a second stem-loop in the 5'-to-3' direction, wherein the first stem-loop sequence hybridizes to the second stem-loop sequence to form an interaction with the Cas protein. An acting double-stranded RNA (dsRNA) region, and wherein the iBAR sequence is located between the 3' end of the first stem-loop sequence and the 5' end of the second stem-loop sequence. In some embodiments, each sgRNA ^iBAR comprises a guide sequence fused to a second sequence comprising a repeat-inverter stem-loop that interacts with a Cas9 protein (eg, Cas9). In some embodiments, the second sequence of each sgRNA ^iBAR further comprises stem-loop 1, stem-loop 2, and/or stem-loop 3. In some embodiments, the Cas protein is Cas9, and the iBAR sequence of each sgRNA ^iBAR is inserted into the loop region of the repeat-invert repeat stem-loop. In some embodiments, each sgRNA ^iBAR comprises from 5'-to-3': a guide sequence, a repeat-inverted repeat stem-loop with the iBAR sequence inserted into the loop region, stem-loop 1, stem-loop 2, and stem-loop Ring 3. In some embodiments, a sgRNA ^iBAR library comprising multiple sets of sgRNA ^iBAR constructs is provided, wherein each set of sgRNA ^iBAR constructs comprises 3 or more (e.g., 3, 4, 5 or more, such as 4 a) sgRNA ^iBAR constructs (for example, lentiviruses or lentiviral vectors encoding said sgRNA ^iBARs ), each of which comprises or encodes a sgRNA ^iBAR , wherein each sgRNA ^iBAR comprises a guide sequence and an iBAR sequence, wherein said 3 The guide sequences of the three or more sgRNA ^iBAR constructs are identical, wherein the iBAR sequences of each of the 3 or more sgRNA ^iBAR constructs are different from each other, and wherein the guide sequences of each set of sgRNA ^iBAR constructs are identical to Different target sites (e.g., different hit genes, or different sites within the same hit gene) of corresponding hit genes are complementary (e.g., at least about 50%, 60%, 70%, 80%, 90%, 95%, 96% %, 97%, 98%, 99% or 100% complementary). In some embodiments, each set of sgRNA ^iBAR constructs comprises 4 sgRNA ^iBAR constructs, and wherein the iBAR sequences of each of the 4 sgRNA ^iBAR constructs are different from each other. ^{Accordingly, in some embodiments, there is provided a sgRNA iBAR library} comprising sets of sgRNA iBAR constructs, wherein each set of sgRNA ^iBAR constructs comprises four sgRNA ^iBAR constructs, each comprising or encoding a sgRNA ^iBAR , wherein each Each sgRNA ^iBAR comprises a guide sequence and an iBAR sequence, wherein the guide sequences of the four sgRNA ^iBAR constructs are identical, wherein the iBAR sequences of each of the four sgRNA ^iBAR constructs are different from each other, and wherein each set of sgRNA ^iBAR The guide sequence of the construct is complementary (e.g., at least about 50%, 60%, 70%, 80%, 90%, 95%, 96%, 97%, 98%, 99% or 100% complementary). In some embodiments, the sgRNA ^iBAR library comprises at least about 100 (e.g., at least about 200, 400, 1,000, 1,300, 1,600, 4,000, 10,000, 15,000, 19,000, 20,000, 38,000, 50,000, 100,000, 150,000, 155,000, Any of 200,000 or more) sets of sgRNA ^iBAR constructs, such as about 1000 to about 4000 sets of sgRNA ^iBAR constructs. In some embodiments, the iBAR sequences of at least two sgRNA ^iBAR constructs in different sets of sgRNA ^iBAR constructs are identical (e.g., the first and second set of sgRNA ^iBAR constructs in the two sets of sgRNA ^iBAR constructs have at least 1, 2, 3, 4 or more consensus iBAR sequences). In some embodiments, the iBAR sequences of at least two sets of sgRNA ^iBAR constructs are identical. In some embodiments, a sgRNA ^iBAR library comprising multiple sets of sgRNA ^iBAR constructs comprises or encodes a sgRNA ^iBAR with a guide sequence complementary to the target site of each annotated gene in the genome (hereinafter also referred to as a "genome-wide sgRNA ^iBAR library") "). In some embodiments, a sgRNA ^iBAR library comprising panels of sgRNA ^iBAR constructs comprises or encodes sgRNA ^iBARs with guide sequences that correlate with DNA mutation frequencies in cancer patients (e.g., based on literature or databases) is at least about 5% (e.g., at least about any of 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or more) and its RNA expression level is upregulated or Target sites for hit genes that are downregulated by greater than about 2-fold (eg, greater than any of about 2.5, 3, 4, 5, 6, 7, 8, 9, 10, 50, 100-fold, or more) are complemented. In some embodiments, the hit gene encodes a protein expressed inside or on the cell surface in a healthy cell or a cancer cell. In some embodiments, the sgRNA ^iBAR library comprises at least two sets (e.g., 2, 3, 4, 5 or more, such as 3 sets) of sgRNA ^iBAR constructs comprising or encoding at least two genes with the same hit gene. sgRNA ^iBAR of guide sequences complementary to different target gene loci (for example, 2, 3, 4, 5 or more, such as 3), that is, for the hit gene, the sgRNA ^iBAR library has at least two double the coverage. In some embodiments, for each hit gene, the sgRNA ^iBAR library comprises 3 sets of sgRNA ^iBAR constructs comprising or encoding sgRNA ^iBARs with guide sequences complementary to 3 different target gene sites of the same hit gene. In some embodiments, for each annotated gene in the genome, the sgRNA ^iBAR library comprises at least two sets (e.g., 2, 3, 4, 5 or more, such as 3 sets) of sgRNA ^iBAR constructs comprising or Encoding a guide sequence complementary to at least two (e.g., 2, 3, 4, 5 or more, such as 3) target gene loci that differ from at least two of the same hit genes, i.e., for the entire genome, the The sgRNA ^iBAR library has at least two-fold coverage. In some embodiments, each guide sequence comprises about 17 to about 23 nucleotides. In some embodiments, each iBAR sequence comprises about 1 to about 50 (eg, about 6) nucleotides. In some embodiments, the sgRNA ^iBAR construct comprises (or consists of) a sgRNA ^iBAR . In some embodiments, the sgRNA ^iBAR construct encodes a sgRNA ^iBAR . In some embodiments, the sgRNA ^iBAR construct is a plasmid encoding the sgRNA ^iBAR . In some embodiments, the sgRNA ^iBAR construct is a viral vector (eg, a lentiviral vector) encoding the sgRNA iBAR. In some embodiments, the sgRNA ^iBAR construct is a virus (eg, a lentivirus) encoding the sgRNA iBAR. Panels of different sgRNA ^iBAR constructs with different iBAR sequences can be used in a single gene editing and screening experiment to provide replicate profiles. In some embodiments, the sgRNA ^iBAR library further comprises one or more sets of "negative control sgRNA ^iBAR constructs", wherein each set of negative control sgRNA ^iBAR constructs comprises 3 or more (e.g., 3, 4, 5 or more, such as 4) negative control sgRNA ^iBAR constructs (for example, lentivirus or lentiviral vectors encoding said negative control sgRNA ^iBAR ), each of said constructs comprising or encoding a negative control sgRNA ^iBAR , wherein each negative control sgRNA ^iBAR comprises a guide sequence and an iBAR sequence, wherein the guide sequences of the three or more negative control sgRNA ^iBAR constructs are identical, wherein the three or more negative control sgRNA ^iBAR constructs The iBAR sequences of each of the constructs are different from each other, and wherein the guide sequence of each set of negative control sgRNA ^iBAR constructs: Complementary to a target site unrelated to any annotated gene in the genome, Complementary to a control gene (e.g., known The same or similar response between the test combination control group after gene inactivation), or complementary to an unrelated sequence not in the genome. ^In some embodiments, the sgRNA ^iBAR library further comprises negative control sgRNA iBAR constructs in an amount of about 3% to about 30% of the number of hit gene sgRNA ^iBAR constructs in the sgRNA ^iBAR library. In some embodiments, the sgRNA ^iBAR library further comprises from about 500 to about 4000 negative control sgRNA ^iBAR constructs (eg, 2000) or sets of negative control sgRNA ^iBAR constructs (eg, 500 sets).

In some embodiments, an sgRNA library (e.g., an sgRNA ^iBAR library) comprising one or more sgRNA constructs (e.g., an sgRNA ^iBAR construct) is provided, wherein each sgRNA construct (e.g., a slow Viral or lentiviral vectors) comprise or encode sgRNAs (e.g., sgRNA ^iBAR ), and wherein each sgRNA comprises (e.g., at least about 50%, 60%, 70%, 80%, 90% , 95%, 96%, 97%, 98%, 99% or 100% complementary), the target gene is selected from the group consisting of ARID2, ATM, BIRC6, BRCA1, BRCA2, CCNA2, CCND1 , CDK2, FBXW7, HRAS, KAT2B, NBN, PBRM1, PTEN, SKP2, SMAD7, TGFB2, TSC1, TSC2, ATR, RIF1, POLQ, AXIN1, GSK3A, GSK3B, CHD7, SCAF4, FANCM, NIPBL, ATRX, STAG1, RAD51 , RAD51B, RAD51C, RAD51D, FANCL, EXO1, DIDO1, LRBA, FAM71A, HDAC2, PMS2, MSH6, MSH2, MLH1, and WEE1. In some embodiments, an sgRNA library (e.g., an sgRNA ^iBAR library) comprising one or more sgRNA constructs (e.g., an sgRNA ^iBAR construct) is provided, wherein each sgRNA construct (e.g., a slow Viral or lentiviral vectors) comprise or encode sgRNAs (e.g., sgRNA ^iBAR ), and wherein each sgRNA comprises (e.g., at least about 50%, 60%, 70%, 80%, 90% , 95%, 96%, 97%, 98%, 99% or 100% complementary), the target gene is selected from the group consisting of AKT1, CDKN1A, CKS1B, CKS2, CTNNB1, DLG5, E2F3 , E2F4, HDAC1, MAPK1, MYC, RAC1, RAF1, RICTOR, SMAD4, TP53, BRAF, HSP90B1, PARP2, PARP1, PIK3CA, EIF3A, CCNA1, RBL1, ZMYND8, MED12, GCN1, Kras, TP53BP1, CHD2, DOCK5, IGF1R , ILK, IRS1, RAPGEF1, EP300, TCF7L2, KMT2B, CDKN2A, CHEK1, CHEK2, RHEB, SPTA1, PKMYT1, SIDT2, APC, and SETD2.

In some embodiments, a sgRNA ^iBAR library comprising multiple sets of sgRNA ^iBAR constructs is provided, wherein each set of sgRNA ^iBAR constructs comprises 3 or more (e.g., 3, 4, 5 or more, such as 4 a) sgRNA ^iBAR constructs (for example, lentiviruses or lentiviral vectors encoding said sgRNA ^iBARs ), each of which comprises or encodes a sgRNA ^iBAR , wherein each sgRNA ^iBAR comprises a guide sequence and an iBAR sequence, wherein said 3 The guide sequences of three or more sgRNA ^iBAR constructs are identical, wherein the iBAR sequences of each of the 3 or more sgRNA ^iBAR constructs are different from each other, wherein the guide sequences of each set of sgRNA ^iBAR constructs are identical to the corresponding Different target sites (e.g., different hit genes, or different sites within the same hit gene) of the hit genes are complementary (e.g., at least about 50%, 60%, 70%, 80%, 90%, 95%, 96% , 97%, 98%, 99% or 100% complementary), and wherein each sgRNA ^iBAR is operable with the Cas9 protein to modify the target site. In some embodiments, a sgRNA ^iBAR library comprising multiple sets of sgRNA ^iBAR constructs is provided, wherein each set of sgRNA ^iBAR constructs comprises four sgRNA ^iBAR constructs, each comprising or encoding a sgRNA ^iBAR , wherein each sgRNA ^iBAR comprises a guide sequence and an iBAR sequence, wherein the guide sequences of the four sgRNA ^iBAR constructs are identical, wherein the iBAR sequences of each of the four sgRNA ^iBAR constructs are different from each other, wherein each set of sgRNA ^iBAR constructs The guide sequence is complementary (e.g., at least about 50%, 60%, 70%, 80%, 90%, 95%, 96%, 97%, 98%, 99% or 100% complementary), and wherein each sgRNA ^iBAR is operable with the Cas9 protein to modify the target site. In some embodiments, each sgRNA ^iBAR sequence comprises a guide sequence fused to a second sequence comprising a repeat-inverter stem-loop that interacts with Cas9. In some embodiments, the second sequence of each sgRNA ^iBAR sequence further comprises stem-loop 1, stem-loop 2, and/or stem-loop 3. In some embodiments, the iBAR sequence is inserted into the loop region of the repeat-inverted repeat stem-loop, and/or the loop region of stem-loop 1, stem-loop 2, or stem-loop 3. In some embodiments, each iBAR sequence comprises about 1-50 (eg, about 6) nucleotides. In some embodiments, each sgRNA ^iBAR construct is RNA, plasmid, viral vector (eg, lentiviral vector), or virus (eg, lentivirus). In some embodiments, the sgRNA ^iBAR library comprises at least about 100 (e.g., at least about 200, 400, 1,000, 1,300, 1,600, 4,000, 10,000, 15,000, 19,000, 20,000, 38,000, 50,000, 100,000, 150,000, 155,000, Any of 200,000 or more) sets of sgRNA ^iBAR constructs, such as about 1000 to about 4000 sets of sgRNA ^iBAR constructs. In some embodiments, for at least two sgRNA ^iBAR constructs in different sets of sgRNA ^iBAR constructs, the iBAR sequences are identical (e.g., the first set and the second set of sgRNA ^iBAR constructs in the two sets of sgRNA ^iBAR constructs There are at least 1, 2, 3, 4 or more consensus iBAR sequences in the construct). In some embodiments, the iBAR sequences are identical for at least two sets of sgRNA ^iBAR constructs. In some embodiments, the sgRNA ^iBAR library comprising sets of sgRNA ^iBAR constructs comprises or encodes DNA mutations with a frequency of at least about 5% (e.g., at least about 10%) in cancer patients (e.g., based on a literature or database). , 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or higher) and its RNA expression level is up-regulated or down-regulated by more than about 2-fold (for example, by more than about 2.5, 3, 4, 5, 6, 7, 8, 9, 10, 50, 100 times or higher) sgRNA ^iBAR with complementary guide sequence to the target site of the hit gene. In some embodiments, the sgRNA ^iBAR library comprises at least two sets (e.g., 2, 3, 4, 5 or more, such as 3 sets) of sgRNA ^iBAR constructs comprising or encoding sgRNA ^iBARs with guide sequences , the guide sequence has a DNA mutation frequency of at least about 5% (e.g., at least about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or higher) and its RNA expression level is up-regulated or down-regulated by more than about 2-fold (e.g., more than about 2.5, 3, 4, 5, 6, Any of 7, 8, 9, 10, 50, 100-fold or more) of the same hit genes differ in at least two (e.g., 2, 3, 4, 5 or more, such as 3) The target site is complementary. In some embodiments, the hit gene encodes a protein that is expressed inside or on the cell surface, in healthy cells or cancer cells. In some embodiments, each guide sequence comprises about 17 to about 23 nucleotides.

In some embodiments, a sgRNA ^iBAR library comprising multiple sets of sgRNA ^iBAR constructs is provided, wherein each set of sgRNA ^iBAR constructs comprises 3 or more (e.g., 3, 4, 5 or more, such as 4 a) sgRNA ^iBAR constructs, each of which comprises or encodes a sgRNA ^iBAR , wherein each sgRNA ^iBAR comprises a guide sequence, a second sequence and an iBAR sequence, wherein the guide sequences of the three or more sgRNA ^iBAR constructs are identical, wherein the iBAR sequences of each of the 3 or more sgRNA ^iBAR constructs are different from each other, wherein the guide sequence is fused to a second sequence, wherein the second sequence comprises a Cas9 protein-interacting Repeat-inverse repeat stem-loop, wherein the iBAR sequence is inserted into the loop region of the repeat-invert repeat stem-loop, wherein the guide sequence of each set of sgRNA ^iBAR constructs is aligned with a different target site of the corresponding hit gene (e.g., Different hit genes, or different sites within the same hit gene) are complementary (e.g., at least about 50%, 60%, 70%, 80%, 90%, 95%, 96%, 97%, 98%, 99%, or 100% complementary), and wherein each sgRNA ^iBAR is operable with the Cas9 protein to modify the target site. In some embodiments, a sgRNA ^iBAR library comprising multiple sets of sgRNA ^iBAR constructs is provided, wherein each set of sgRNA ^iBAR constructs comprises four sgRNA ^iBAR constructs, each comprising or encoding a sgRNA ^iBAR , wherein each sgRNA ^iBAR comprises a guide sequence, a second sequence and an iBAR sequence, wherein the guide sequences of the four sgRNA ^iBAR constructs are identical, wherein the iBAR sequences of each of the four sgRNA ^iBAR constructs are different from each other, wherein the guide sequences are identical to The second sequence is fused, wherein the second sequence comprises a repeat-repeat stem-loop interacting with the Cas9 protein, wherein the iBAR sequence is inserted into the loop region of the repeat-repeat stem-loop, wherein each set of sgRNA The guide sequences of ^{the iBAR} constructs are complementary (e.g., at least about 50%, 60%, 70%, 80%, 90%, 95%, 96%, 97%, 98%, 99% or 100% complementary), and wherein each sgRNA ^iBAR is operable with the Cas9 protein to modify the target site. In some embodiments, the second sequence of each sgRNA ^iBAR sequence further comprises stem-loop 1, stem-loop 2, and/or stem-loop 3, e.g., fused to the 3' end of the repeat-invert repeat stem-loop sequence. In some embodiments, each iBAR sequence comprises about 1-50 (eg, 6) nucleotides. In some embodiments, each sgRNA ^iBAR construct is RNA, plasmid, viral vector (eg, lentiviral vector), or virus (eg, lentivirus). In some embodiments, the sgRNA ^iBAR library comprises at least about 100 panels (e.g., at least about any of 200, 400, 1,000, 1,300, 1,600, 4,000, 10,000, 15,000, 19,000, 20,000, 38,000, 50,000, 100,000, 150,000 , 155,000, 200,000 sets or more) sgRNA ^iBAR constructs, such as about 1000 to about 4000 sets of sgRNA ^iBAR constructs. In some embodiments, the iBAR sequences of at least two sgRNA ^iBAR constructs in different sets of sgRNA ^iBAR constructs are identical (for example, the sgRNA ^iBAR constructs of the first set and the second set are identical in the two sets of sgRNA ^iBAR constructs individuals with at least 1, 2, 3, 4 or more consensus iBAR sequences). In some embodiments, the iBAR sequences of at least two sets of sgRNA ^iBAR constructs are identical. In some embodiments, a sgRNA ^iBAR library comprising sets of sgRNA ^iBAR constructs comprising or encoding sgRNA ^iBARs with guide sequences complementary to target sites of hit genes described in cancer patients (e.g., based on literature or databases) The DNA mutation frequency of the hit gene is at least about 5% (e.g., at least about any of 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or higher) and The level of RNA expression is up-regulated or down-regulated by greater than about 2-fold (eg, greater than any of about 2.5, 3, 4, 5, 6, 7, 8, 9, 10, 50, 100-fold or more). In some embodiments, the sgRNA ^iBAR library comprises at least two sets (e.g., 2, 3, 4, 5 or more sets, such as 3 sets) of sgRNA ^iBAR constructs comprising or encoding sgRNA ^iBARs having A guide sequence complementary to at least two (e.g., 2, 3, 4, 5 or more, such as 3) different target gene loci within the same hit gene for each hit gene, in cancer patients ( For example, based on literature or databases), the DNA mutation frequency of the hit gene is at least about 5% (e.g., at least about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or higher) and its RNA expression level is up-regulated or down-regulated by more than about 2-fold (e.g., greater than about 2.5, 3, 4, 5, 6, 7, 8, 9, 10, 50, 100-fold or any higher school). In some embodiments, the hit gene encodes a protein expressed inside or on the cell surface in a healthy cell or a cancer cell. In some embodiments, each guide sequence comprises about 17 to about 23 nucleotides.

In some embodiments, sgRNA ^iBAR constructs are provided comprising a guide sequence complementary to the target site in the corresponding hit gene (e.g. , at least about any of 50%, 60%, 70%, 80%, 90%, 95%, 96%, 97%, 98%, 99% or 100% complementary), wherein the iBAR is embedded into the four in the ring as an internal repeat. In some embodiments, the iBAR comprises 1 nucleotide ("nt")-50nt consisting of A, T, C, and G nucleotides (e.g., 1nt-40nt, 1nt-30nt, 1nt-25nt, 2nt-20nt, 3nt-18nt, 3nt-16nt, 3nt-14nt, 3nt-12nt, 3nt-10nt, 3nt-9nt, 4nt-8nt, 5nt-7nt; preferably, 3nt, 4nt, 5nt, 6nt, 7nt) sequence. In some embodiments, the length of the guide sequence is about 17-23, 18-22, or any one of 19-21 nucleotides, and the hairpin sequence can bind to the Cas nuclease once transcribed ( For example, Cas9). In some embodiments, the sgRNA ^iBAR construct further comprises sequences encoding stem-loop 1, stem-loop 2, and/or stem-loop 3. In some embodiments, each sgRNA ^iBAR construct is RNA, plasmid, viral vector (eg, lentiviral vector), or virus (eg, lentivirus).

Also provided are sgRNA molecules encoded by any of the sgRNA constructs or libraries described herein. Also provided are sgRNA ^iBAR molecules encoded by any of the sgRNA ^iBAR constructs, panels or libraries described herein. Compositions and kits comprising any of the described sgRNA or sgRNA ^iBAR constructs, molecules, panels or libraries are also provided.

In some embodiments, cancer cells comprising a modification of any of the sgRNA or sgRNA ^iBAR constructs, molecules, panels or libraries described herein are provided. In some embodiments, a library of cancer cells is provided, wherein each cancer cell comprises one or more sgRNA constructs from the sgRNA library described herein, or one or more sgRNA ^iBAR constructs from the sgRNA ^iBAR library described herein . In some embodiments, the cancer cell library comprises a sgRNA library or sgRNA ^iBAR library described herein targeting any of the target genes identified herein, or any gene hit, in cancer patients compared to healthy individuals (e.g., based on literature or database), the DNA mutation frequency of the hit gene is at least about 5% (e.g., at least about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or more high school) and its RNA expression level is up-regulated or down-regulated by greater than about 2-fold (e.g., greater than about 2.5, 3, 4, 5, 6, 7, 8, 9, 10, 50, 100-fold or higher) either). In some embodiments, the modified cancer cells or the initial population of cancer cells comprise or express one or more components of a CRISPR/Cas system, such as a Cas protein operable with the sgRNA or sgRNA ^iBAR construct (eg, Cas9).

iBAR sequence

A set of sgRNA ^iBAR constructs comprises 3 or more sgRNA ^iBAR constructs, each of which comprises a different iBAR sequence. In some embodiments, a set of sgRNA ^iBAR constructs comprises 3 sgRNA ^iBAR constructs, each of said constructs comprising a different iBAR sequence. In some embodiments, a set of sgRNA ^iBAR constructs comprises 4 sgRNA ^iBAR constructs, each of said constructs comprising a different iBAR sequence. In some embodiments, a set of sgRNA ^iBAR constructs comprises 5 sgRNA ^iBAR constructs, each of said constructs comprising a different iBAR sequence. In some embodiments, a set of sgRNA ^iBAR constructs comprises 6 or more sgRNA ^iBAR constructs, each of said constructs comprising a different iBAR sequence.

The iBAR sequence can be of any suitable length. In some embodiments, each iBAR sequence is about 1-50 nucleotides ("nt") in length, such as about 1nt-40nt, 1nt-30nt, 1nt-20nt, 2nt-20nt, 3nt-18nt, 3nt- Any one of 16nt, 3nt-14nt, 3nt-12nt, 3nt-10nt, 3nt-9nt, 3nt-8nt, 4nt-8nt, or 5nt-7nt. In some embodiments, each iBAR sequence is about any of 2nt, 3nt, 4nt, 5nt, 6nt, 7nt, or 8nt in length. In some embodiments, the iBAR sequences in each sgRNA ^iBAR construct are the same length. In some embodiments, the iBAR sequences of different sgRNA ^iBAR constructs have different lengths. In some embodiments, the iBAR sequences within a set of sgRNA ^iBAR constructs are the same length. In some embodiments, the iBAR sequences within a set of sgRNA ^iBAR constructs are of different lengths. In some embodiments, the iBAR sequences within one set of sgRNA ^iBAR constructs are of different lengths than the iBAR sequences within another set of sgRNA ^iBAR constructs. In some embodiments, the iBAR sequence is about 6 nt, hereinafter referred to as "iBAR6". In some embodiments, each iBAR sequence within the sgRNA ^iBAR library is about 6 nt.

The iBAR sequence may have any suitable sequence. In some embodiments, the iBAR sequence is a DNA sequence made from any of A, T, C, and/or G nucleotides. In some embodiments, the iBAR sequence is an RNA sequence made from any of A, U, C, and/or G nucleotides. In some embodiments, the iBAR sequence has unconventional or modified nucleotides other than A, T/U, C, and G. In some embodiments, each iBAR sequence consisting of A, T, C, and G nucleotides is 6 nucleotides in length. In some embodiments, the iBAR sequence in the encoded sgRNA ^iBAR is 6 nucleotides in length consisting of A, U, C, and G nucleotides.

In some embodiments, the set of iBAR sequences associated with each set of sgRNA ^iBAR constructs in the sgRNA ^iBAR library are different from each other. In some embodiments, the iBAR sequences for at least two sgRNA ^iBAR constructs in different sets of sgRNA ^iBAR constructs are identical (e.g., the first set and the second set of sgRNA ^iBAR constructs in the two sets of sgRNA ^iBAR constructs are identical) Constructs with at least 1, 2, 3, 4 or more consensus iBAR sequences, but different iBAR sequences for each sgRNA ^iBAR construct within the same set of sgRNA ^iBAR constructs). In some embodiments, the sgRNA ^iBAR library is directed against at least two sets (e.g., at least about any of 2, 3, 4, 5, 10, 50, 100, 1000 sets, or more) of sgRNA ^iBAR constructs The iBAR sequence is the same. In some embodiments, one or more identical iBAR sequences are used for one or more sgRNA ^{iBAR constructs per set of sgRNA iBAR constructs} within the sgRNA ^iBAR library (but for each sgRNA within the same set of sgRNA ^iBAR constructs). The iBAR sequences of the ^iBAR constructs differ from each other). In some embodiments, the same set of iBAR sequences is used for each set of sgRNA ^iBAR constructs in the sgRNA ^iBAR library. In some embodiments, different sets of iBARs need not be designed for different sets of sgRNA ^iBAR constructs. In some embodiments, a fixed set of iBARs is used for all sets of sgRNA ^iBAR constructs in the sgRNA ^iBAR library. In some embodiments, multiple iBAR sequences are randomly assigned to different sets of sgRNA ^iBAR constructs of the sgRNA ^iBAR library. The iBAR strategy described here (MAGeCKiBAR; Zhu et al., Genome Biol. 2019; 20:20) with improved analytical tools could facilitate large-scale CRISPR/Cas screening for biopharmaceutical discovery in various settings.

The iBAR sequence can be inserted (including attached) into any suitable region in a guide RNA (e.g., sgRNA) that does not affect the efficiency of the gRNA to guide a Cas nuclease (e.g., Cas9) to its target site . In some embodiments, the iBAR sequence is located 3' to the sgRNA. In some embodiments, the iBAR sequence is located at the 5' end of the sgRNA. In some embodiments, the iBAR sequence is located internal to the sgRNA. For example, sgRNAs can comprise various stem-loops that interact with Cas nucleases in the CRISPR complex, and the iBAR sequence can be inserted into a loop region in either stem-loop. In some embodiments, each sgRNA ^iBAR sequence comprises a first stem-loop sequence and a second stem-loop sequence in the 5'-to-3' direction, wherein the first stem-loop sequence hybridizes to the second stem-loop sequence to form a A double-stranded RNA (dsRNA) region of Cas protein interaction, and wherein the iBAR sequence is positioned between the 3' end of the first stem-loop sequence and the 5' end of the second stem-loop sequence. In some embodiments, the guide RNA (e.g., sgRNA) further comprises stem-loop 1, stem-loop 2, and/or stem-loop 3, and wherein the iBAR sequence is inserted into the loop region of stem-loop 1, stem-loop 2 and/or stem loop 3.

For example, the guide RNA of the CRISPR/Cas9 system can comprise a guide sequence targeting a genomic locus (eg, a target site in a hit gene), and a guide hairpin sequence encoding a repeat:inverted repeat duplex and a tetraloop. In some embodiments, the iBAR is inserted into the tetraloop as an internal repeat. In the context of the endogenous CRISPR/Cas9 system, the crRNA hybridizes to a trans-promoting crRNA (tracrRNA) to form a crRNA:tracrRNA duplex, which is loaded into Cas9 to direct a pair carrying the appropriate prospacer-adjacent motif (PAM) cleavage of homologous DNA sequences. The endogenous crRNA sequence can be divided into a guide region (20 nt) and a repeat region (12 nt), while the endogenous tracrRNA sequence can be divided into an inverted repeat (14 nt) and three tracrRNA stem-loops. In some embodiments, the sgRNA binds target DNA to form a T-shaped structure comprising guide:target heteroduplex, repeat:inverted repeat duplex, and stem-loops 1-3. In some embodiments, the repeat and anti-repeat portions are linked by the tetraloop, and the repeats and anti-repeats form a repeat:inverter duplex linked to stem-loop 1 by a single nucleotide (A51) , while stem-loops 1 and 2 are joined by a 5 nt single-linker head (nucleotides 63–67). In some embodiments, the guide sequence (nucleotides 1-20) and target DNA (nucleotides 10-200) form a guide:target through 20 pairs of Watson-Crick base pairs The heteroduplex, and the repeat (nucleotides 21–32) and inverted repeat (nucleotides 37–50) are passed through nine Watson-Crick base pairs (U22:A49–A26:U45 and G29 :C40–A32:U37) form a repeat:inverted repeat duplex. In some embodiments, the tracrRNA tails (nucleotides 68-81 and 82-96) are separated by 4 and 6 Watson-Crick base pairs (A69:U80-U72:A77 and G82:C96- G87:C91) form stem-loops 2 and 3. The crystal structure of an exemplary CRISPR/Cas9 system is described by Nishimasu et al. (Nishimasu et al. “Crystal structure of cas9 in complex with guide RNA and target DNA.” Cell. 2014; 156:935–949), the contents of which are incorporated by reference incorporated herein in its entirety.

In some embodiments, the iBAR sequence is inserted into the tetraloop, or loop region of the repeat:inverted repeat stem-loop of the sgRNA. In some embodiments, the iBAR sequence of each sgRNA ^iBAR within the library is inserted into the loop region of the repeat-invert repeat stem-loop. The four loops of the Cas9 sgRNA backbone are located outside the Cas9-sgRNA ribonucleoprotein complex and have been modified for various purposes without affecting the activity of its upstream guide sequence (Gilbert et al. Cell 159, 647-661 (2014 ); Zhu et al. Methods Mol Biol 1656, 175-181 (2017)). The applicant has previously demonstrated in WO2020125762 that a 6-nt iBAR (iBAR6) can be embedded into the four rings of a typical Cas9 sgRNA backbone without affecting the gene editing efficiency of the sgRNA or increasing off-target effects, and the iBAR6 There is no serial bias. The exemplary iBAR6 resulted in 4,096 barcode combinations, which provided sufficient variation for high-throughput screening (see Figure 1A of WO2020125762).

wizard sequence

The guide sequence hybridizes to a target sequence (eg, hits a target site in a gene) and directs sequence-specific binding of the CRISPR complex to the target sequence. In some embodiments, the degree of complementarity between the guide sequence and its corresponding target sequence is about or greater than about 50%, 60%, 70%, 75%, 80% when optimally aligned using a suitable alignment algorithm. %, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or higher (e.g., 100% complementary). A guide sequence that is "complementary" to a target site or gene hit can be fully or partially complementary (e.g., at least about 50%, 60%, 70%, 80%, 90%, 95% to the target site or gene hit). %, 96%, 97%, 98%, 99% or 100% complementary). Sequences may be aligned to determine optimal alignment using any suitable algorithm, non-limiting implementations of which include the Smith-Waterman algorithm, the Needleman-Wimsch algorithm, method, based on Burrows-Wheeler (Burrows-Wheeler) transformation algorithm. In some embodiments, the length of the guide sequence is about or greater than about 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27 , 28, 29, 30 or more nucleotides. In some embodiments, the guide sequence comprises about 17 to about 23 nucleotides. The ability of a guide sequence to direct sequence-specific binding of a CRISPR complex to a target sequence can be assessed by any suitable assay. For example, components of a CRISPR system sufficient to form a CRISRP complex (including the guide sequence to be tested) can be provided to a host cell having the corresponding target sequence by, for example, transfection with a vector encoding the CRISPR sequence component staining and then assess preferential cleavage within the target sequence. Similarly, cleavage of a target polynucleotide sequence can be assessed in a test tube by providing the target sequence, the components of the CRISPR complex, including the guide sequence to be tested and a control guide sequence different from the test guide sequence, and Compare the binding and cleavage ratios at the target sequence between the test guide sequence and the control guide sequence reactions.

In some embodiments, the guide sequence can be as short as about 10 nucleotides and as long as about 30 nucleotides. In some embodiments, the guide sequence is any of about 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, or 25 nucleotides in length. Synthetic guide sequences can be about 20 nucleotides in length, but can be longer or shorter. For example, the guide sequence for the CRISPR/Cas9 system can consist of 20 nucleotides complementary to the target sequence (e.g., the target site in the hit gene), that is, the guide sequence can be 20 nucleotides upstream of the PAM sequence. Same, except for the A/U difference between DNA and RNA. In some embodiments, the guide sequence comprises about 17 to about 23 nucleotides. In some embodiments, the guide sequences of each sgRNA or sgRNA ^iBAR within the library are the same length. In some embodiments, the guide sequences of at least two sgRNAs or sgRNA ^iBARs within the library are of different lengths. In some embodiments, the guide sequences within a set of sgRNA ^iBAR constructs are the same length. In some embodiments, guide sequences within a set of sgRNA ^iBAR constructs are of different lengths. In some embodiments, the guide sequences within one set of sgRNA ^iBAR constructs have a different length than the guide sequences within another set of sgRNA ^iBAR constructs.

In some embodiments, the guide sequences within a set of sgRNA ^iBAR constructs are identical. In some embodiments, the guide sequences within a set of sgRNA ^iBAR constructs are identical, and the guide sequences of each set of sgRNA ^iBAR constructs are associated with different targets (e.g., different hit genes, or different target sites of the same hit gene) complementary. In some embodiments, the guide sequences of at least two sets of sgRNA ^iBAR constructs are complementary to two different target sites of the same hit gene. In some embodiments, the guide sequences of the 3 sets of sgRNA ^iBAR constructs are complementary to 3 different target sites of the same hit gene. In some embodiments, at least two (e.g., 2, 3, 4 or more, such as 3) different target sites, each gene hit is identified by at least two groups (e.g., 2, 3, 4, or More, such as 3 sets) of at least two (eg, 2, 3, 4 or more, such as 3) guide sequences of the sgRNA ^iBAR construct are targeted. In some embodiments, the guide sequences in each set of sgRNA ^iBAR constructs are complementary to different hit genes of the genome.

The guide sequence of the sgRNA construct or sgRNA ^iBAR construct can be designed according to any method known in the art. Guide sequences can target coding regions such as exons or splice site sites, 5' untranslated regions (UTRs) or 3' untranslated regions (UTRs) of a gene of interest. For example, the reading frame of a gene may be disrupted by a double-strand break (DSB)-mediated indel at the target site of the guide RNA. Alternatively, guide RNA targeting the 5' end of the coding sequence can be used to efficiently generate gene knockouts. Guide sequences can be designed and optimized based on specific sequence features to achieve high on-target gene editing activity and low off-target effects. For example, the GC content of the guide sequence can be from about 20% to about 70%, and sequences containing homopolymeric extensions (eg, TTTT, GGGG) can be avoided.

Guide sequences can be designed to target any genomic locus of interest (eg, any target site of any gene hit). In some embodiments, the guide sequence targets a gene encoding a protein. In some embodiments, the guide sequence targets a gene encoding an RNA, such as a small RNA (e.g., microRNA, piRNA, siRNA, snoRNA, tRNA, rRNA, and snRNA), ribosomal RNA, or long noncoding RNA (lincRNA) . In some embodiments, the guide sequence targets a non-coding region of the genome. In some embodiments, the guide sequence targets a chromosomal locus. In some embodiments, the guide sequence targets an extrachromosomal locus. In some embodiments, the guide sequence targets a mitochondrial gene. In some embodiments, the guide sequence is complementary to the target site of any annotated gene within the genome (eg, the human genome). In some embodiments, the guide sequence targets a gene that has a DNA mutation frequency of at least about 5% (e.g., at least about 10%, 20%, etc.) in cancer patients (e.g., based on literature or databases). %, 30%, 40%, 50%, 60%, 70%, 80%, 90% or higher). In some embodiments, the guide sequence targets a gene whose RNA expression level is up-regulated or down-regulated by greater than about 1.2-fold (e.g., greater than about 1.5, 2, 2.5, 3, 4, 5, 6, 7, 8, 9, 10, 50, 100 times or more). In some embodiments, the guide sequence targets a gene that has a DNA mutation frequency of at least about 5% (e.g., at least about 10%, 20%, etc.) in cancer patients (e.g., based on literature or databases). %, 30%, 40%, 50%, 60%, 70%, 80%, 90% or higher) and its RNA expression level is up-regulated or down-regulated by more than about 2-fold (e.g., greater than about 2.5, 3, 4, 5, 6, 7, 8, 9, 10, 50, 100 times or more). In some embodiments, the guide sequence targets a gene encoding a protein that is expressed in the cell or on the cell surface (in healthy or cancer cells). In some embodiments, the guide sequence targets a region of the genome that does not have any gene annotation ("nongenic region"). An sgRNA or sgRNA ^iBAR construct containing or encoding a guide sequence complementary to a nongenic region can be used as a negative control.

In some embodiments, the guide sequence is designed to inhibit or inactivate the expression of any hit or target gene of interest. The hit gene or target gene can be an endogenous gene or a transgene. In some embodiments, the hit or target gene may be known to be associated with a particular phenotype. In some embodiments, the hit gene or target gene is a gene that is not associated with a particular phenotype, such as a known gene that is not known to be associated with a particular phenotype or an unknown gene that has not yet been characterized. In some embodiments, the region targeted by the guide sequence is on a different chromosome than the hit or target gene.

Other sgRNA or sgRNA ^iBAR components

In some embodiments, the sgRNA or sgRNA ^iBAR comprises additional sequence elements that facilitate formation of a CRISPR complex with a Cas protein. In some embodiments, the sgRNA or sgRNA ^iBAR comprises a second sequence comprising a repeat-invert repeat stem-loop. The repeat-inverted repeat stem-loop comprises a tracr mate sequence fused to a tracr sequence that is complementary to the tracr mate sequence through the loop region.

Typically, in the context of an endogenous CRISPR/Cas9 system, formation of the CRISPR complex (including a guide sequence that hybridizes to the target sequence and complexes with one or more Cas proteins) results in , 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 50 or more alkyl pairs) of one or both strands. The tracr sequence, which may comprise all or part of the wild-type tracr sequence (e.g., about or greater than about 20, 26, 32, 45, 48, 54, 63, 67, 85 or more of the wild-type tracr sequence or consisting of any of the nucleotides) may also form part of the CRISPR complex, such as by hybridizing along at least a portion of the tracr sequence to all or part of the tracr mate sequence operably linked to the guide sequence. In some embodiments, the tracr sequence is sufficiently complementary to the tracr mate sequence to hybridize and participate in the formation of the CRISPR complex. It is not believed to be necessary for the target sequence to be completely complementary, but only sufficient to be effective. In some embodiments, when optimally aligned, the tracr sequence has at least about 50%, 60%, 70%, 80%, 90%, 95%, or 99% of the tracr sequence along the length of the tracr mate sequence. Either in % sequence complementarity. Determining the optimal alignment is within the ability of those skilled in the art. For example, there are public and commercial alignment algorithms and programs such as, but not limited to, ClustalW, Smith-Waterman in Matlab, Bowtie, Geneious, Biopython, and SeqMan. In some embodiments, the tracr sequence is about or greater than about 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, Any of 30, 40, 50 or more nucleotides. Any of the known tracr mate sequences and tracr sequences from naturally occurring CRISPR systems can be used, such as from the S. pyogenes CRISPR/Cas9 system described in US8697359 and the tracr mate sequences and tracr sequences described herein. sequence.

In some embodiments, the tracr sequence and the tracr mate sequence are contained within a single transcript such that hybridization between the two produces a transcript with a secondary structure, such as a stem-loop (also known as a hairpin), referred to as "Repeat-repeat stem-loops".

In some embodiments, the loop region of the stem-loop in the sgRNA construct without an iBAR sequence is 4 nucleotides in length, and the loop region is also referred to as a "tetraloop". In some embodiments, the loop region has the sequence GAAA. However, longer or shorter loop sequences may be used, or alternative sequences may be used, such as sequences comprising nucleotide triplets (eg, AAA), as well as other nucleotides (eg, C or G). In some embodiments, the sequence of the loop region is CAAA or AAAG. In some embodiments, the iBAR is inserted into a loop region, such as a tetraloop. For example, the iBAR sequence can be inserted before the first nucleotide, between the first and second nucleotide, between the second and third nucleotide, between the third and third nucleotides in the tetraloop. Between a nucleotide and the fourth nucleotide, or after the fourth nucleotide. In some embodiments, the iBAR sequence replaces one or more nucleotides in the loop region.

In some embodiments, the sgRNA ^iBAR comprises at least two or more stem-loops. In some embodiments, the sgRNA ^iBAR has two, 3, 4 or 5 stem-loops. In some embodiments, the sgRNA ^iBAR has at most 5 hairpins. In some embodiments, the sgRNA or sgRNA ^iBAR construct further includes a transcription termination sequence, such as a polyT sequence, eg, 6 T nucleotides.

In some embodiments, wherein the Cas protein is Cas9, each sgRNA or sgRNA ^iBAR comprises a guide sequence fused to a second sequence comprising a repeat-inverter stem-loop that interacts with Cas9. In some embodiments, the iBAR sequence is inserted into the loop region of the repeat-invert repeat stem-loop. In some embodiments, the iBAR sequence replaces one or more nucleotides in the loop region of the repeat-inverted repeat stem-loop. In some embodiments, the second sequence of each sgRNA or sgRNA ^iBAR further comprises stem-loop 1, stem-loop 2, and/or stem-loop 3. In some embodiments, the iBAR sequence is inserted into the loop region of stem-loop 1. In some embodiments, the iBAR sequence replaces one or more nucleotides in the loop region of stem-loop 1. In some embodiments, the iBAR sequence is inserted into the loop region of stem-loop 2. In some embodiments, the iBAR sequence replaces one or more nucleotides in the loop region of stem-loop 2. In some embodiments, the iBAR sequence is inserted into the loop region of stem-loop 3. In some embodiments, the iBAR sequence replaces one or more nucleotides in the loop region of stem-loop 3.

In some embodiments, each sgRNA ^iBAR comprises a first stem-loop sequence and a second stem-loop sequence in the 5'-to-3' direction, wherein the first stem-loop sequence hybridizes with the second stem-loop sequence to form a Cas A protein-interacting double-stranded RNA (dsRNA) region, and wherein the iBAR sequence is located between the 3' end of the first stem-loop sequence and the 5' end of the second stem-loop sequence.

In the CRISPR/Cas9 system, guide RNA can be used to guide the cleavage of genomic DNA by the Cas9 nuclease. For example, the guide RNA may consist of a nucleotide spacer sequence of variable sequence (guide sequence) and an invariant hairpin sequence, which allows the CRISPR/Cas system nuclease to target the genomic location in a sequence-specific manner, And the invariant hairpin sequence remains unchanged in different guide RNAs and allows the guide RNAs to bind to the Cas nuclease. In some embodiments, a CRISPR/Cas guide RNA comprising a CRISPR/Cas variable guide sequence homologous or complementary to a target genomic sequence (e.g., a target site of a hit gene) in a host cell and capable of binding upon transcription The invariant hairpin sequence of a Cas nuclease (eg, Cas9), wherein the hairpin sequence encodes a repeat:inverted repeat duplex and a tetraloop, and the iBAR is embedded in the tetraloop region.

The guide sequence length of the CRISPR/Cas9 guide RNA can be any of about 17-23, 18-22, or 19-21 nucleotides in length. The guide sequence can target the Cas nuclease to the genomic locus in a sequence-specific manner and can be designed according to general principles known in the art. An invariant guide RNA hairpin sequence can be provided according to common knowledge in the art, for example, as disclosed by Nishimasu et al. (Nishimasu H, et al. Crystal structure of Cas9 in complex with guide RNA and target DNA. Cell. 2014; 156:935–949). Any invariant hairpin sequence can be used as long as it can bind Cas nuclease after transcription.

Previous studies have shown that, although sgRNAs with 48 nt tracrRNA tails (termed sgRNA ( ⁺⁴⁸ )) are the smallest regions, for in vitro Cas9-catalyzed DNA cleavage (Jinek et al., 2012), sgRNAs with extended tracrRNA tails , sgRNA( ⁺⁶⁷ ) and sgRNA( ⁺⁸⁵ ) can improve the cleavage activity of Cas9 in vivo (Hsu et al., 2013). In some embodiments, the sgRNA or sgRNA ^iBAR comprises stem-loop 1, stem-loop 2, and/or stem-loop 3. The stem-loop 1, stem-loop 2 and/or stem-loop 3 regions can improve editing efficiency in the CRISPR/Cas9 system.

In some embodiments, from 5' to 3' the sgRNA comprises: a guide sequence, a repeat-repeat stem-loop, stem-loop 1, stem-loop 2, and stem-loop 3. In some embodiments, the sgRNA ^iBAR comprises from 5' to 3': a guide sequence, a repeat-inverted repeat stem-loop and iBAR sequences are inserted into the loop region, stem-loop 1, stem-loop 2, and stem-loop 3.

Carriers (vectors/vehicles)

In some embodiments, the sgRNA construct comprises one or more regulatory elements operably linked to a guide RNA sequence. In some embodiments, the sgRNA ^iBAR construct comprises one or more regulatory elements operably linked to a guide RNA sequence and the iBAR sequence. Exemplary regulatory elements include, but are not limited to, promoters, enhancers, internal ribosomal entry sites (IRES), and other expression control elements (eg, transcription termination signals, such as polyadenylation signals and poly-U sequences). Such regulatory elements are described, for example, in Goeddel, GENE EXPRESSION TECHNOLOGY: METHODS IN ENZYMOLOGY 185, Academic Press, San Diego, Calif. (1990). Regulatory elements include those that direct the constitutive expression of a nucleotide sequence in many types of host cells, as well as those that direct the expression of a nucleotide sequence only in certain host cells (eg, tissue-specific regulatory sequences).

The sgRNA or sgRNA ^iBAR construct can be present in a vector. In some embodiments, the vector is suitable for replication and integration in eukaryotic cells, such as mammalian cells (eg, cancer cells). In some embodiments, the sgRNA or sgRNA ^iBAR construct is an expression vector, such as a viral vector or plasmid. Examples of viral vectors include, but are not limited to, adenoviral vectors, adeno-associated viral vectors, lentiviral vectors, retroviral vectors, herpes simplex virus vectors, and derivatives thereof. Viral vector technology is well known in the art and is described, for example, in Sambrook et al. 2001, Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Laboratory, New York, and other handbooks of virology and molecular biology. Those skilled in the art will appreciate that the design of the expression vector may depend on factors such as the choice of host cell to be transformed, the level of expression desired, and the like. In some embodiments, the sgRNA or sgRNA ^iBAR construct is a lentiviral vector. In some embodiments, the sgRNA or sgRNA ^iBAR construct is a virus. In some embodiments, the sgRNA or sgRNA ^iBAR construct is an adenovirus or an adeno-associated virus. In some embodiments, the sgRNA or sgRNA ^iBAR construct is a lentivirus. In some embodiments, the vector further comprises a selectable marker. In some embodiments, the vector further comprises one or more nucleotide sequences encoding one or more elements of the CRISPR/Cas system, such as a nucleotide sequence encoding a Cas nuclease (eg, Cas9). In some embodiments, a vector system is provided comprising one or more vectors encoding nucleotide sequences encoding one or more elements of the CRISPR/Cas system, and comprising any one of the Vector for sgRNA or sgRNA ^iBAR constructs. A vector may include one or more of the following elements: an origin of replication, one or more regulatory sequences (e.g., a promoter and/or enhancer) that regulate expression of a polypeptide of interest, and/or one or more selectable marker genes (e.g., an antibiotic resistance gene or fluorescent protein-encoding gene).

A number of virus-based systems have been developed for gene transfer into mammalian cells. For example, retroviruses provide a convenient platform for gene delivery systems. The heterologous nucleic acid can be inserted into a vector and packaged in retroviral particles using techniques known in the art. The recombinant virus can then be isolated in vitro or ex vivo and delivered to engineered mammalian cells. Many retroviral systems are known in the art. In some embodiments, adenoviral vectors are used. Many adenoviral vectors are known in the art. In some embodiments, lentiviral vectors are used. In some embodiments, a self-inactivating lentiviral vector is used. Self-inactivating lentiviral vectors can be packaged into lentiviruses using protocols known in the art. The resulting lentiviruses can be used to transduce mammalian cells (eg, cancer cells) using methods known in the art. Vectors derived from retroviruses such as lentiviruses are suitable tools to achieve long-term gene transfer because they allow long-term, stable integration of the transgene and its propagation in progeny cells. Lentiviral vectors are also low immunogenic and can transduce non-proliferating cells.

In some embodiments, the vector is a non-viral vector. In some embodiments, the vector is a transposon, such as the Sleeping Beauty transposon system, or the PiggyBac transposon system. In some embodiments, the vector is a polymer-based non-viral vector including, for example, poly(lactic-co-glycolic acid) (PLGA) and polylactic acid (PLA), poly(ethyleneimine) (PEI), and dendritic macromolecule. In some embodiments, the vector is a cationic lipid-based non-viral vector, such as cationic liposomes, lipid nanoemulsions, and solid lipid nanoparticles (SLN). In some embodiments, the vector is a peptide-based genetic non-viral vector, such as poly-L-lysine. Any known non-viral vector suitable for gene editing can be used to introduce nucleic acid encoding sgRNA or sgRNA ^iBAR into cancer cells. See, for example, Yin H. et al. Nature Rev. Genetics (2014) 15:521-555; Aronovich EL et al. “The Sleeping Beauty transposon system: a non-viral vector for gene therapy.” Hum. Mol. Genet. (2011) R1: R14-20; and Zhao S. et al. “PiggyBac transposon vectors: the tools of the human gene editing.” Transl. Lung Cancer Res. (2016) 5(1): 120-125, via incorporated herein by reference. In some embodiments, any one or more nucleic acids encoding sgRNAs or sgRNA ^iBARs described herein are introduced into cancer cells by physical means, including but not limited to: electroporation, sonication, photoporation, magnetofection, hydroporation.

In some embodiments, the nucleic acid encoding the sgRNA or sgRNA ^iBAR and the nucleic acid or nucleic acids encoding one or more elements of the CRISPR/Cas system (e.g., a Cas nuclease, such as Cas9) are located on different vectors (e.g., a viral vector such as a lentigo viral vector). In some embodiments, the nucleic acid encoding the sgRNA or sgRNA ^iBAR and the nucleic acid or nucleic acids encoding one or more elements of the CRISPR/Cas system are on the same vector. In some embodiments, the nucleic acid encoding the sgRNA or sgRNA ^iBAR and the nucleic acid or nucleic acids encoding one or more elements of the CRISPR/Cas system are operably controlled by separate promoters. In some embodiments, the nucleic acid encoding the sgRNA or sgRNA ^iBAR and the nucleic acid or nucleic acids encoding one or more elements of the CRISPR/Cas system are operably controlled by the same promoter. In some embodiments, nucleic acid encoding a sgRNA or sgRNA ^iBAR and one or more nucleic acids encoding one or more elements of a CRISPR/Cas system are linked by one or more linker sequences (eg, IRES).

Nucleic acids can be cloned into vectors using any molecular cloning method known in the art, including, for example, the use of restriction endonuclease sites and one or more selectable markers. In some embodiments, the nucleic acid is operably linked to a promoter. A variety of promoters have been explored for gene expression in mammalian cells, and any promoter known in the art may be used in the present invention. Promoters can be broadly classified as constitutive promoters or regulated promoters, such as inducible promoters.

In some embodiments, a nucleic acid encoding a sgRNA or sgRNA ^iBAR and/or one or more nucleic acids encoding one or more elements of a CRISPR/Cas system (eg, Cas9) is operably linked to a constitutive promoter. Constitutive promoters allow the constitutive expression of heterologous genes (also known as transgenes) in host cells. Exemplary promoters contemplated herein include, but are not limited to: cytomegalovirus early transient promoter (CMVIE), human elongation factor-1α (hEF1α), ubiquitin C promoter (UbiC), phosphoglycerol kinase promoter (PGK) , simian virus 40 early promoter (SV40), chicken β-actin promoter coupled to CMV early enhancer (CAGG), Rous sarcoma virus (RSV) promoter, polyoma enhancer/herpes simplex breast Glycoside kinase (MC1) promoter, β-actin (β-ACT) promoter, "myeloproliferative sarcoma virus enhancer, negative control region deletion, d1587rev primer binding site substitution (MND)" promoter. The efficiency of this constitutive promoter in driving transgene expression has been extensively compared in numerous studies.

In some embodiments, a nucleic acid encoding a sgRNA or sgRNA ^iBAR and/or one or more nucleic acids encoding one or more elements of a CRISPR/Cas system (eg, Cas9) is operably linked to an inducible promoter. Inducible promoters are regulated promoters. An inducible promoter can be induced by one or more conditions, such as physical conditions, the microenvironment of a cancer cell (e.g., an engineered cancer cell), or the physiological state of a cancer cell, an inducing agent (i.e., an agent for induction), or a combination thereof. In some embodiments, the inducing conditions do not induce expression of an endogenous gene in the engineered cancer cell and/or in the subject receiving cancer cell therapy. In some embodiments, the inducing condition is selected from the group consisting of inducing agent, radiation (eg, ionizing radiation, light), temperature (eg, heat), redox state, tumor environment, and activation state of cells of the engineered cancer. In some embodiments, the inducible promoter can be a NFAT promoter, a TETON® promoter, or an NFκB promoter.

library

The sgRNA libraries described herein comprise one or more sgRNA constructs, wherein each sgRNA construct comprises or encodes a sgRNA, and wherein each sgRNA comprises a target site complementary (e.g., at least about 50%, 60%, %, 70%, 80%, 90%, 95%, 96%, 97%, 98%, 99% or 100% complementary) guide sequence. According to the needs of genetic screening, the sgRNA library described herein can be designed to target one or more genomic loci (eg, multiple target sites in one or more hit genes in the genome). In some embodiments, a single sgRNA construct is designed to target each hit gene. In some embodiments, multiple (eg, at least about 2, 3, 4, 5, 10, 20, 100, or more) sgRNA constructs can be designed with different guide sequences targeting a single hit gene. For example, such multiple sgRNA constructs may comprise or encode guide sequences targeting different target sites of a single hit gene, such as 3 (or about 6 to about 12) different target sites of a single hit gene.

A sgRNA library comprising one or more sgRNA ^iBAR constructs is also referred to herein as a sgRNA ^iBAR library, wherein each sgRNA construct comprises or encodes an iBAR sequence. The sgRNA ^iBAR library described herein comprises one or more sgRNA ^iBAR constructs, wherein each sgRNA ^iBAR construct comprises or encodes a sgRNA ^iBAR , and wherein each sgRNA ^iBAR comprises a guide sequence corresponding to the target in the hit gene The sites are complementary (eg, at least about any of 50%, 60%, 70%, 80%, 90%, 95%, 96%, 97%, 98%, 99%, or 100% complementary). According to the needs of genetic screening, the sgRNA ^iBAR library described herein can be designed to target one or more genomic loci (eg, multiple target sites in one or more hit genes in the genome). In some embodiments, a single sgRNA ^iBAR construct is designed to target each hit gene. In some embodiments, multiple (eg, at least about 2, 3, 4, 5, 10, 20, or more) sgRNA ^iBAR constructs can be designed with different guide sequences targeting a single hit gene. For example, such multiple sgRNA ^iBAR constructs may contain or encode guide sequences targeting different target sites of a single hit gene, such as 3 different target sites of a single hit gene.

In some embodiments, the sgRNA ^iBAR library described herein comprises one or more sets of sgRNA ^iBAR constructs, wherein each set of sgRNA ^iBAR constructs comprises 3 or more (e.g., 3, 4, 5 or more , such as 4) sgRNA ^iBAR constructs, each of which comprises or encodes a sgRNA ^iBAR , wherein each sgRNA ^iBAR comprises a guide sequence and an iBAR sequence, wherein the guide sequences of the three or more sgRNA ^iBAR constructs are Identical, wherein the iBAR sequences of each of the 3 or more sgRNA ^iBAR constructs are different from each other, and wherein the guide sequences of each set of sgRNA ^iBAR constructs correspond to different target sites of the hit genes (e.g., different hit genes , or different sites within the same hit gene) complementary (e.g., at least about 50%, 60%, 70%, 80%, 90%, 95%, 96%, 97%, 98%, 99%, or 100% complementary any of them). In some embodiments, each set of sgRNA ^iBAR constructs comprises 4 sgRNA ^iBAR constructs, and wherein the iBAR sequences for each of the 4 sgRNA ^iBAR constructs are different from each other. In some embodiments, a single set of sgRNA ^iBAR constructs is designed to target each hit gene. In some embodiments, the sgRNA ^iBAR library comprises multiple sets (e.g., at least about 2, 3, 4, 5, 10, 20, or more sets) of sgRNA ^iBAR constructs with distinct genes targeting a single hit gene. wizard sequence. In some embodiments, the sgRNA ^iBAR library comprises at least 3 sets of sgRNA ^iBAR constructs designed to target 3 different target sites for each hit gene, wherein each set of sgRNA ^iBAR constructs comprises 4 sgRNA ^iBAR constructs. In some embodiments, the sgRNA ^iBAR library comprises at least about 100 sets of sgRNA ^iBAR constructs, such as at least about 200, 300, 400, 800, 1,000, 2,000, 3,000, 5,000, 10,000, 15,000, 19,000, 20,000, 40,000, Any of 50,000, 100,000, 150,000, 200,000 or more sets of sgRNA ^iBAR constructs. In some embodiments, the sgRNA ^iBAR library comprises about 100 to about 30,000 sets of sgRNA ^iBAR constructs, such as about 1000 to about 4000, about 1000 to about 6000, or about 3000 to about 5000 sets of sgRNA ^iBAR constructs.

In some embodiments, the sgRNA library or sgRNA ^iBAR library comprises at least about 1, 2, 3, 4, 5, 10, 20, 50, 100, 200, 400, 500, 1,000, 2,000, 3,000, 4,000, 5,000 , 10,000, 15,000, 19,000, 20,000, 38,000, 39,000, 40,000, 50,000, 100,000, 150,000, 155,000, 200,000 or more sgRNA constructs or sgRNA ^iBAR constructs. In some embodiments, the sgRNA library or sgRNA ^iBAR library comprises at least about 100 (e.g., at least about 200, 300, 400, 600, 1000, 1200, 3000, 6000, 10,000, 20,000 or more of any of a) sgRNA constructs or sgRNA ^iBAR constructs, such as at least about 300 or about 400 sgRNA constructs or sgRNA ^iBAR constructs. In some embodiments, the sgRNA library comprises about 1000 to about 300,000 sgRNA constructs, such as about 6000 to about 14,000, about 1000 to about 20,000, about 1000 to about 5000, about 10,000 to about 200,000, about 15,000 to about 20,000, about 100,000 to about 300,000, or about 150,000 to about 180,000 sgRNA constructs. In some embodiments, the sgRNA ^iBAR library comprises about 1000 to about 1,200,000 sgRNA ^iBAR constructs, such as about 1000 to about 20,000, about 10,000 to about 18,000, about 1000 to about 5000, about 10,000 to about 200,000, about 15,000 to about 20,000, about 100,000 to about 300,000, about 300,000 to about 1,200,000, or about 150,000 to about 180,000 sgRNA ^iBAR constructs. In some embodiments, the sgRNA ^iBAR library comprises at least about 1, 2, 3, 4, 5, 10, 20, 50, 100, 200, 400, 500, 1,000, 2,000, 3,000, 5,000, 10,000, 15,000, Any of 19,000, 20,000, 38,000, 50,000, 100,000, 150,000, 200,000 or more sets of sgRNA ^iBAR constructs, such as about 1000 to about 4000 sets of sgRNA ^iBAR constructs. In some embodiments, the sgRNA library or the sgRNA ^iBAR library targets at least about 1, 2, 3, 4, 5, 10, 20, 50, 100, 200, 500, 1,000, 2,000 , 5,000, 10,000, 15,000, 19,000, 20,000, 38,000, 50,000 or more genes. In some embodiments, the organism is a human. In some embodiments, the sgRNA library or the sgRNA ^iBAR library is a genome-wide library for protein-coding genes and/or non-coding RNAs. In some embodiments, the sgRNA library or the sgRNA ^iBAR library is a genome-wide library for each annotated gene. In some embodiments, the sgRNA library or the sgRNA ^iBAR library targets at least about 0.1%, 0.5%, 1%, 2%, 3%, 4%, 5%, 6%, Any of 7%, 8%, 9%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 95%. In some embodiments, the sgRNA library or the sgRNA ^iBAR library is a targeted library that targets selected genes in signaling pathways or genes related to cellular processes, such as susceptibility to anticancer drug-mediated killing Sex or resistance, cell proliferation, cell cycle, transcriptional regulation, ubiquitination, apoptosis, immune responses such as autoimmunity, tumor metastasis, malignant transformation of tumors, etc. In some embodiments, sgRNA libraries or sgRNA ^iBAR libraries are used for genome-wide screens associated with specific regulatory phenotypes, such as sensitivity or resistance to anticancer drug-mediated killing. In some embodiments, the sgRNA library or sgRNA ^iBAR library is used in a genome-wide screen to identify at least one target gene associated with a particular regulatory phenotype, such as a target gene in a cancer cell that modulates the activity of the cancer cell in response to an anticancer drug. In some embodiments, the sgRNA library or the sgRNA ^iBAR library targets "cancer-associated genes," e.g., genes with a DNA mutation frequency of at least about 5% (e.g., at least about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or more), and/or the RNA expression level of the gene in cancer patients is up-regulated or down-regulated by at least About 1.2-fold (e.g., at least about any of 1.5, 2, 2.5, 3, 4, 5, 6, 7, 8, 9, 10, 50, 100-fold or higher, such as about 2-fold), such as Based on literature or databases. In some embodiments, the sgRNA library or the sgRNA ^iBAR library targets genes whose encoded mRNAs and/or proteins are expressed intracellularly (in healthy cells or cancer cells). In some embodiments, the sgRNA library or sgRNA ^iBAR library targets genes whose encoded proteins are expressed on the cell surface (in healthy cells or cancer cells). In some embodiments, the sgRNA library or the sgRNA ^iBAR library targets genes that i) have a DNA mutation frequency of at least about 5% (e.g., at least Any of about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or more), ii) in cancer patients (eg, based on literature or databases) its RNA expression level is up-regulated or down-regulated by more than about 2-fold (e.g., by any of greater than about 2.5, 3, 4, 5, 6, 7, 8, 9, 10, 50, 100-fold or more), and iii ) in healthy cells or cancer cells, the mRNA or protein it encodes is expressed inside the cell, or the protein it encodes is expressed on the cell surface. Thus, in some embodiments, a sgRNA library comprising a plurality of sgRNA constructs comprises or encodes sgRNAs with guide sequences complementary to target sites of cancer-associated genes, such as the targets of approximately 1323 colorectal cancer-associated genes in the human genome. Loci, whose DNA mutation frequency is ≥ 5%, and whose RNA expression levels are up-regulated or down-regulated more than 2 times that of stage III and stage IV colorectal cancer patients, have gene products expressed on cells or on the cell surface. In some embodiments, a sgRNA ^iBAR library comprising multiple sgRNA ^iBAR constructs comprises or encodes sgRNA ^iBARs with guide sequences complementary to target sites of cancer-associated genes, such as about 1323 colorectal cancer-associated genes in the human genome The target site, whose DNA mutation frequency is ≥5%, and whose RNA expression level is up-regulated or down-regulated more than 2 times that of stage III and stage IV colorectal cancer patients, has a gene product expressed on the cell or cell surface. In some embodiments, the sgRNA library or sgRNA ^iBAR library is designed to target a eukaryotic genome, such as a mammalian genome. Exemplary genomes of interest include those of rodents (mouse, rat, hamster, guinea pig), domestic animals (e.g., cattle, sheep, cats, dogs, horses, or rabbits), non-human primates (e.g., monkeys), fish (eg zebrafish), invertebrates (eg Drosophila melanogaster and Caenorhabditis elegans) and humans.

Guide sequences for the sgRNA library or sgRNA ^iBAR library can be designed using any known algorithm that recognizes CRISPR/Cas target sites in a user-defined list with high target specificity in the human genome, Such as genome target scanning (GT-Scan) (see O'Brien et al., Bioinformatics (2014) 30:2673-2675), DeepCRISPR, CasFinder, CHOPCHOP, CRISPRscan, etc. In some embodiments, at least about 100, 400, 500, 1,000, 3,000, 5,000, 10,000, 15,000, 19,000, 20,000, 50,000, 100,000, 150,000, 155,000, 200,000 or more sgRNA constructs can be generated on a single array Either of the body or the sgRNA ^iBAR construct. This method can also be scaled up to enable genome-wide screening by synthesizing multiple sgRNA libraries or sgRNA ^iBAR libraries in parallel. The exact number of sgRNA constructs in an sgRNA library, or the exact number of sgRNA ^iBAR constructs (or sets of sgRNA ^iBAR constructs) in a sgRNA ^iBAR library can depend on whether the screen is: 1) targeting a gene or regulatory element, 2) targeting the entire genome or subsets of genomic genes.

In some embodiments, the sgRNA library or the sgRNA ^iBAR library is designed for every PAM sequence overlapping with a gene in the genome, wherein the PAM sequence corresponds to the Cas protein. In some embodiments, the sgRNA library or sgRNA ^iBAR library is designed to target a subset of PAM sequences found in the genome, where the PAM sequences correspond to Cas proteins.

In some embodiments, the sgRNA library comprises one or more control sgRNA constructs that do not target any genomic locus in the genome. In some embodiments, sgRNA constructs that do not target putative genomic genes can be included in the sgRNA library as negative controls. In some embodiments, the sgRNA ^iBAR library comprises one or more control sgRNA ^iBAR constructs that do not target any genomic locus in the genome. In some embodiments, sgRNA ^iBAR constructs that do not target putative genomic genes can be included in sgRNA ^iBAR libraries as negative controls. In some embodiments, the sgRNA library (or sgRNA ^iBAR library) comprises one or more control sgRNA constructs (or control sgRNA ^iBAR constructs) targeting non-cancer-associated genes, e.g., genes in cancer patients and There will be no at least 1.2-fold (e.g., at least about any of 1.5, 2, 2.5, 3, 4, 5-fold or more) difference in its expression (RNA level or protein level) between healthy individuals; e.g., all The expression level of the gene differs less than 2-fold between cancer patients and healthy individuals; and/or the gene has a mutation frequency of less than about 5% (e.g., less than about 4%, 3%, 2%, or any one of 1%).

The sgRNA constructs and libraries described herein can be prepared using any nucleic acid synthesis and/or molecular cloning method known in the art. In some embodiments, the sgRNA library is synthesized electrochemically on an array (e.g., CustomArray, Twist, Gen9), DNA printing (e.g., Agilent), or solid-phase synthesis of individual oligos (e.g., IDT) . The sgRNA constructs can be amplified by PCR and cloned into expression vectors (eg, lentiviral vectors). In some embodiments, the lentiviral vector also encodes one or more components of a CRISPR/Cas-based genetic editing system, such as a Cas protein, such as Cas9.

In some embodiments, the invention provides an isolated nucleic acid encoding any one of the sgRNA constructs, sgRNA ^iBAR constructs, sets of sgRNA ^iBAR constructs, sgRNA libraries, or sgRNA ^iBAR libraries described herein. Also provided are vectors comprising any nucleic acid encoding any of the sgRNA constructs, sgRNA ^iBAR constructs, sets of sgRNA ^iBAR constructs, sgRNA libraries, or sgRNA ^iBAR libraries described herein (e.g., non-viral vectors, or viral vectors such as slow viral vectors) and viruses (eg, lentiviruses).

Cas protein

The sgRNA constructs or sgRNA ^iBAR constructs described herein are designed to operate with any naturally occurring or engineered CRISPR/Cas system known in the art. In some embodiments, the sgRNA construct or the sgRNA ^iBAR construct is operable with a Type I CRISPR/Cas system. In some embodiments, the sgRNA construct or the sgRNA ^iBAR construct is operable with a Type II CRISPR/Cas system. In some embodiments, the sgRNA construct or the sgRNA ^iBAR construct is operable with a Type III CRISPR/Cas system. Exemplary CRISPR/Cas systems can be found in WO2013176772, WO2014065596, WO2014018423, WO2016011080, US8697359, US8932814, US10113167B2, the disclosures of which are incorporated herein by reference in their entireties for all purposes.

In some embodiments, the sgRNA construct or the sgRNA ^iBAR construct is operable with a Cas protein from a CRISPR/Cas Type I, Type II, or Type III system with an RNA-guided Polynucleotide binding and/or nuclease activity. Examples of such Cas proteins are described, eg, in WO2014144761, WO2014144592, WO2013176772, US20140273226 and US20140273233, which are incorporated herein by reference in their entirety.

In some embodiments, the Cas protein is from a Type II CRISPR-Cas system. In some embodiments, the Cas protein is or is derived from a Cas9 protein. In some embodiments, the Cas protein is or is derived from a bacterial Cas9 protein, including those identified in WO2014144761.

In some embodiments, the sgRNA construct or the sgRNA ^iBAR construct is operable with Cas9 (also known as Csn1 and Csx12), a homolog thereof, or a modified form thereof. In some embodiments, the sgRNA construct or the sgRNA ^iBAR construct can operate with two or more (eg, 2, 3, 4, 5 or more) Cas proteins. In some embodiments, the sgRNA construct or the sgRNA ^iBAR construct operates with a Cas9 protein from S. pyogenes or S. pneumoniae. Cas enzymes are known in the art; for example, the amino acid sequence of the Streptococcus pyogenes Cas9 protein can be found in the SwissProt database with accession number Q99ZW2.

Cas proteins (also referred to herein as "Cas nucleases") provide the desired activity, such as target binding, target nicking, or cleavage activity. In certain embodiments, the desired activity is target binding. In certain embodiments, the desired activity is target nicking or target cleavage. In certain embodiments, the desired activity also includes a function provided by a polypeptide covalently fused to a Cas protein or a nuclease-deficient Cas protein. Examples of such desired activities include transcriptional regulatory activity (initiation or repression), epigenetic modification activity, or target visualization/recognition activity.

In some embodiments, the sgRNA construct or the sgRNA ^iBAR construct is operable with a Cas nuclease that cleaves a target sequence, including double-strand cleavage and single-strand cleavage. In some embodiments, the sgRNA construct or the sgRNA ^iBAR construct is operable with a catalytically inactive Cas ("dCas"). In some embodiments, the sgRNA construct or the sgRNA ^iBAR construct is operable with a dCas of a CRISPR initiation ("CRISPRa") system, wherein the dCas is fused to a transcriptional promoter. In some embodiments, the sgRNA construct or the sgRNA ^iBAR construct is operable with the dCas of the CRISPR perturbation (CRISPRi) system. In some embodiments, the dCas is fused to a repression domain, such as a KRAB domain. Such CRISPR/Cas systems can be used to regulate (eg, induce, inhibit, increase or decrease) gene expression.

In certain embodiments, the Cas protein is a mutant or fragment thereof of a wild-type Cas protein (such as Cas9). Cas9 proteins typically have at least two nuclease (eg, DNase) domains. For example, a Cas9 protein can have a RuvC-like nuclease domain and an HNH-like nuclease domain. The RuvC and HNH domains work together to cleave both strands of the target site to cause a double-strand break in the target polynucleotide (Jinek et al., Science 337: 816-21). In certain embodiments, the mutant Cas9 protein is modified to comprise only one functional nuclease domain (RuvC-like or HNH-like nuclease domain). For example, in certain embodiments, a mutant Cas9 protein is modified such that one nuclease domain is deleted or mutated so that it is no longer functional (ie, has no nuclease activity). In some embodiments in which one of the nuclease domains is inactivated, the mutant is capable of introducing a nick into a double-stranded polynucleotide (the protein is termed a "nickase") but is unable to cleave the double-stranded polynucleotide. In certain embodiments, a Cas protein is modified to increase nucleic acid binding affinity and/or specificity, to alter enzymatic activity, and/or to alter another property of the protein. In certain embodiments, the Cas protein is truncated or modified to optimize the activity of the effector domain. In certain embodiments, both the RuvC-like nuclease domain and the HNH-like nuclease domain are modified or removed such that the mutant Cas9 protein is unable to nick or cleave the target polynucleotide. In certain embodiments, however, a Cas9 protein lacking some or all nuclease activity maintains target recognition activity to a greater or lesser extent relative to a wild-type counterpart.

In certain embodiments, the Cas protein is a fusion protein comprising naturally occurring Cas or a variant thereof fused to another polypeptide or effector domain. Another polypeptide or effector domain can be, for example, a cleavage domain, a transcription initiation domain, a transcription repression domain, or an epigenetic modification domain. In certain embodiments, the fusion protein comprises a modified or mutated Cas protein in which all nuclease domains have been inactivated or deleted. In certain embodiments, the RuvC and/or HNH domains of the Cas protein are modified or mutated such that they no longer have nuclease activity.

In certain embodiments, the effector domain of the fusion protein is a cleavage domain having the desired properties obtained from any endonuclease or exonuclease.

In certain embodiments, the effector domain of the fusion protein is a transcription initiation domain. Generally, a transcription initiation domain interacts with transcriptional control elements and/or transcriptional regulatory proteins (ie, transcription factors, RNA polymerases, etc.) to increase and/or initiate transcription of a gene. In certain embodiments, the transcription initiation domain is a herpes simplex virus VP16 initiation domain, VP64 (which is a tetrameric derivative of VP16), NFκB p65 initiation domain, p53 initiation domains 1 and 2, CREB ( cAMP response element binding protein) promoter domain, E2A promoter domain, or NFAT (nuclear factor of activated T cell) promoter domain. In certain embodiments, the transcription initiation domain is Gal4, Gcn4, MLL, Rtg3, Gln3, Oaf1, Pip2, Pdrl, Pdr3, Pho4, or Leu3. The transcription initiation domain may be wild type, or a modified or truncated form of the original transcription initiation domain.

In certain embodiments, the effector domain of the fusion protein is a transcriptional repression domain, such as an inducible cAMP early repression (ICER) domain, a Kruppel-associated box A (KRAB-A) repression domain, a YY1 glycine-rich Repression domain, Sp1-like repressor, E(spI) repressor, I. kappa (kappa). B repressor or MeCP2.

In certain embodiments, the effector domain of the fusion protein is an epigenetic modification domain that alters gene expression by altering histone structure and/or chromosomal structure, such as histone acetyltransferase domain, histone decapitation An acetylase domain, a histone methyltransferase domain, a histone demethylase domain, a DNA methyltransferase domain or a DNA demethylase domain.

In certain embodiments, the Cas protein also includes at least one other domain, such as a nuclear localizer signal (NLS), a cell penetration or translocation domain, and a marker domain (eg, a fluorescent protein marker).

The Cas protein can be introduced into cancer cells as: (i) Cas protein, or (ii) mRNA encoding Cas protein, or (iii) linear or circular DNA encoding the protein. The Cas protein or the construct encoding the Cas protein may be purified or non-purified in the composition. Methods for introducing proteins or nucleic acid constructs into host cells are well known in the art and are applicable to all methods described herein that require introducing a Cas protein or a construct thereof into a cancer cell. In some embodiments, the Cas protein is delivered to cancer cells as a protein. In some embodiments, the Cas protein is constitutively expressed from mRNA or DNA of a host cancer cell (eg, engineered cancer cell). In some embodiments, expression of the Cas protein from mRNA or DNA is inducible or induced in a host cancer cell. In some embodiments, the Cas protein can be introduced into host cancer cells in the form of a Cas protein:sgRNA complex using recombinant techniques known in the art. Exemplary methods of introducing Cas proteins or constructs thereof are described, for example, in WO2014144761, WO2014144592 and WO2013176772, which are incorporated herein by reference in their entirety.

In some embodiments, the method uses the CRISPR/Cas9 system. Cas9 is a nuclease from a microbial type II CRISPR (clustered regularly interspaced short palindromic repeats) system that has been shown to cleave DNA when paired complementary to a single-stranded guide RNA (sgRNA). The sgRNA guides Cas9 to a complementary region in the target genomic gene, which can lead to site-specific double-strand breaks (DSBs) that can be repaired in an error-prone manner by the cellular non-homologous end-joining (NHEJ) mechanism . Wild-type Cas9 primarily cleaves genomic sites where the gRNA sequence is followed by the PAM sequence (-NGG). NHEJ-mediated Cas9-induced DSB repair can induce extensive mutations initiated at the cleavage site, which are typically small (<10 bp) insertions/deletions (indels) but can include large (>100 bp) insertions/deletions missing.

cancer cell library

Cancer cell libraries described herein comprise a plurality (e.g., at least about , 1×108 or more) cancer cells, wherein each of the plurality of cancer cells has a mutation (e.g., an inactivating mutation) in a hit gene in a genome (e.g., a human genome), and wherein the hit genes in at least two of the plurality of cancer cells are different from each other.

In some embodiments, the cancer cell library comprises a plurality of cancer cells at least about 2, 3, 4, 5, 10, 20, 50, 100, 200, 500, 1,000, 2,000, Any of the 5,000, 10,000, 20,000, 50,000 or more hit genes have a mutation (eg, an inactivating mutation). In some embodiments, the organism is a human. In some embodiments, the cancer cell library comprises a plurality of cancer cells having mutations (e.g., inactivating mutations) in about 100 to about 30,000 hit genes, such as about 500 to about 5000, or about 1000 to about 1500 hit genes ). In some embodiments, the cancer cell library comprises at least about 2, 3, 4, 5, 10, 20, 50, 100, 200, 500, 1,000, 2,000, 5,000, 1×104, 2×104, 5× Any of 104, 1×105, 2×105, 1×106, 5×106, 1×107, 1.5×107, 2×107, 1×108, 1×109, 1×1010 or more of cancer cells. In some embodiments, at least two cancer cells in the cancer cell library have mutations (eg, inactivating mutations) at different target sites (eg, different hit genes, or different sites within the same hit gene). In some embodiments, each cancer cell of the cancer cell library has mutations (eg, inactivating mutations) at different hit genes. In some embodiments, each cancer cell of the cancer cell library has a mutation (eg, an inactivating mutation) at a different target site (eg, can be within the same hit gene, or within a different hit gene). In some embodiments, the cancer cell library does not comprise cancer cells with mutations (e.g., inactivating mutations) in the same hit gene, such as inactivating mutations at the same target site of the same hit gene, or different mutations in the same hit gene. Inactivating mutations at target gene loci. In some embodiments, the library of cancer cells does not comprise cancer cells with mutations (eg, inactivating mutations) at the same target site. In some embodiments, a plurality (e.g., at least about 2, 3, 4, 5, 10, 100, 500, 1000, 2000, 5000, 10000, 2×107, or more) of the cancer cell library ) Cancer cells have mutations (eg, inactivating mutations) in the same hit gene, such as inactivating mutations at the same target site of the same hit gene, or inactivating mutations at different target gene sites of the same hit gene. In some embodiments, the cancer cell library comprises a plurality of cancer cells comprising at least about 0.1%, 0.5%, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 20%, 30%, 40%, 60%, 70%, 80%, 90%, 95% or more of the hit genes contained mutations (eg, inactivating mutations). In some embodiments, the cancer cell library comprises a plurality of cancer cells comprising mutations in all genes (eg, all annotated genes of the human genome) of a genome (also referred to herein as a "genome-wide cancer cell library") (eg, , inactivating mutation). In some embodiments, the cancer cell library has at least two (eg, 2, 3, 4, 5 or more, such as 3) cancer cells for each annotated gene of the genome or for each hit gene. The cells each contain a mutation (e.g., an inactivating mutation) at a different target site of the same hit gene, e.g., cancer cell A contains a mutation (e.g., an inactivating mutation) at the target site A' of gene X, and cancer cell B contains a mutation (e.g., an inactivating mutation) at gene X's target site Target site B' of X contains a mutation (eg, an inactivating mutation), and cancer cell C contains a mutation (eg, an inactivating mutation) at target site C' of gene X. In some embodiments, the cancer cell library is a targeted library comprising mutations (e.g., inactivating mutations) in selected genes in signal transduction pathways or associated with cellular processes, such as against Cancer drug-mediated killing with sensitivity or resistance, cell proliferation, cell cycle, transcriptional regulation, ubiquitination, apoptosis, immune responses such as autoimmunity, tumor metastasis, malignant transformation of tumors, etc. In some embodiments, the cancer cell library is used for genome-wide screens associated with a particular regulated phenotype, such as sensitivity or resistance to anticancer drug-mediated killing. In some embodiments, the library of cancer cells is used in a genome-wide screen to identify at least one target gene associated with a particular regulated phenotype, such as a gene in a cancer cell that regulates the activity of the cancer cell in response to anticancer drug treatment. target gene. In some embodiments, the cancer cell library is a mammalian cancer cell library. Exemplary target genes encompassed by the cancer cell library include rodents (mouse, rat, hamster, guinea pig), domesticated animals (e.g., cows, sheep, cats, dogs, horses, or rabbits), non-human primate Animal (eg, monkey), fish (eg, zebrafish), invertebrate (eg, Drosophila melanogaster and Caenorhabditis elegans) and human genomes. In some embodiments, the cancer cell library is a human cancer cell library, such as a human colorectal cancer cell library.

In some embodiments, the cancer cell library comprises mutations in "cancer-associated genes," e.g., genes with a DNA mutation frequency of at least about 5% (e.g., at least about 10%, 20%, 30% in cancer patients). %, 40%, 50%, 60%, 70%, 80%, 90% or higher), and/or at least about 1.2-fold up-regulation or down-regulation of the RNA expression level of the gene in the cancer patient (e.g., at least about any of 1.5, 2, 2.5, 3, 4, 5, 6, 7, 8, 9, 10, 50, 100-fold or higher, such as about 2-fold), as based on literature or sources library. In some embodiments, the cancer cell library comprises mutations in genes encoding mRNAs and/or proteins expressed in cells (either healthy cells or cancer cells). In some embodiments, the cancer cell library comprises mutations in genes encoding proteins expressed on the cell surface (either in healthy cells or in cancer cells). In some embodiments, the library of cancer cells comprises mutations in genes that: i) have a DNA mutation frequency of at least about 5% (e.g., at least Any of about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or more), ii) in cancer patients (eg, based on literature or databases) , whose RNA expression levels are up-regulated or down-regulated by more than about 2-fold (e.g., by any of greater than about 2.5, 3, 4, 5, 6, 7, 8, 9, 10, 50, 100-fold or more), and iii) In healthy cells or cancer cells, the encoded mRNA and/or protein is expressed in the cell, or the encoded protein is expressed on the cell surface. In some embodiments, the cancer cell library contains mutations in about 1323 colorectal cancer-related genes in the human genome, and the DNA mutation frequency of stage III and stage IV colorectal cancer patients is ≥ 5% and the RNA expression level is up-regulated or down-regulated Greater than 2-fold, the gene product is expressed on the cell or on the cell surface.

In some embodiments, a plurality (e.g., about 2, 3, 4, 5, 10, 100, 500, 1000, 2000, 5000, 10000 or more) of cancer cells in the cancer cell library have in the same hit gene Mutations (e.g., inactivating mutations), the cancer cell library is also referred to as "X-fold coverage for hit genes", where "X" is cancer cells with mutations (e.g., inactivating mutations) in the same hit genes number. For example, for a cancer cell library targeting about 1000 hit genes and comprising about 2 x 107 cancer cells, the cancer cell library has about 20,000-fold coverage for each hit gene. In some embodiments, the cancer cell libraries described herein have at least about 2-fold, 3-fold, 4-fold, 5-fold, 10-fold, 100-fold , 200-fold, 500-fold, 1,000-fold, 2,000-fold, 5,000-fold, 10,000-fold or more coverage, such as an average of about 600-fold to about 12000-fold for each hit gene, an average of about 600-fold to about 1200-fold, or an average of about 1200-fold to about 12000-fold coverage. In some embodiments, the Cas9 ⁺ sgRNA cancer cell library has an average of about 600-fold to about 1200-fold coverage for each sgRNA. In some embodiments, the Cas9 ⁺ sgRNA (or mutagen-induced mutation) cancer cell library described herein has an average of about 600-fold to about 1200-fold coverage for each hit gene (eg, a cancer-associated gene). In some embodiments, the Cas9 ⁺ sgRNA ^iBAR cancer cell library has an average of about 100-fold to about 1,000-fold, such as about 1000-fold coverage for each sgRNA ^iBAR . In some embodiments, the Cas9 ⁺ sgRNA ^iBAR cancer cell library has an average of about 400-fold to about 4000-fold, such as about 4000-fold coverage for each set of sgRNA ^iBARs . In some embodiments, the Cas9 ⁺ sgRNA ^iBAR cancer cell library described herein has an average of about 1200-fold to about 12,000-fold, such as about 12,000-fold coverage for each hit gene (eg, a cancer-related gene).

Mutations in hit genes

In some embodiments, all annotated genes of a genome (eg, a human genome) are selected as hit genes. In some embodiments, the frequency of DNA mutations in cancer patients (e.g., based on literature or databases) is at least about 5% (e.g., at least about 10%, 20%, 30%, 40%, 50%, 60% %, 70%, 80%, 90% or higher) were selected as hit genes. In some embodiments, RNA expression levels are up-regulated or down-regulated by at least about 1.2-fold (e.g., at least about 1.5, 2, 3, 4, 5, 6, Any of 7, 8, 9, 10, 50, 100-fold or more, such as about 2-fold) genes were selected as hit genes. In some embodiments, the DNA mutation frequency is at least about 5% (e.g., at least about 10%) among cancer patients (e.g., based on literature or databases), such as stage III and/or stage IV colorectal cancer patients , 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or higher) and its RNA expression level is up-regulated or down-regulated by more than about 2-fold (for example, by more than about 2.5, 3, 4, 5, 6, 7, 8, 9, 10, 50, 100 times or higher) genes were selected as hit genes. In some embodiments, hit genes are further selected based on the expression of the encoded mRNA or protein within the cell, or the expression of the encoded protein on the cell surface, in healthy cells or cancer cells.

In some embodiments, the mutation in the hit gene is a pathogenic or inactivating mutation. The inactivating mutations described herein can be any mutations, such as insertions, deletions (indels), substitutions, frameshifts, chromosomal rearrangements, or combinations thereof, which result in complete gene expression (transcription and/or translation) and/or function repeal or eliminate. In some embodiments, an inactivating mutation can completely abolish transcription, translation, post-translational modification, association with other molecules (e.g., other molecules in a protein complex), and/or function (e.g., signal transduction or body start). In some embodiments, the mutation in the hit gene is a mutation that reduces (e.g., reduces by at least about 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%) , 90%, 95%, 96%, 97%, 98%, 99% or more) or affect (e.g., disrupt) one or more of the following: hit gene transcription, hit gene translation, hit gene Gene mRNA processing, hit mRNA stability, hit mRNA function, hit protein function, association with other molecules (eg, other molecules in protein complexes), and post-translational modification of hits. Mutations (e.g., inactivating mutations) of a hit gene can be within one or more regulatory regions of the hit gene such as enhancers, promoters, 5' untranslated regions (UTRs), 3' UTRs, or within coding regions such as extronic sub or splice site. A hit gene described herein can be any genomic sequence, such as protein-coding genes, RNA-coding genes such as small RNAs (e.g., microRNAs, piRNAs, siRNAs, snoRNAs, tRNAs, rRNAs, and snRNAs), ribosomal RNAs, long non-coding RNAs (lincRNA) or mitochondrial genes. The hit gene may be known to be associated with a particular phenotype (eg, a cancer phenotype), or not, such as a known gene not known to be associated with a particular phenotype, or an unknown gene that has not yet been characterized. In some embodiments, the hit gene is a genomic sequence that does not encode anything or is not yet known to encode what it encodes.

Pathogenic inactivating mutations (loss of function) in certain genes can be identified by reviewing the published scientific literature for experimental evidence and critical regions that may be disrupted, including but not limited to: frameshifts, missense mutations, truncating mutations , deletions, copy number variations, nonsense mutations, and loss or deletion of genes. Pathogenic or inactivating mutations include, but are not limited to: homozygous deletions with defined effects, biallelic (double hit gene) mutations, splice site mutations (eg, second or additional splice site mutations) , frameshift mutations, nonsense mutations, and missense mutations in coding regions.

In some embodiments, the library of cancer cells is generated by subjecting (eg, contacting) an initial population of cancer cells to a mutagen. Mutagens can be divided into three classes: physical (e.g., gamma rays, ultraviolet radiation), chemical (e.g., ethyl methanesulfonate or EMS), and translatable elements (e.g., transposons, retrotransposons, T-DNA, retroviruses).

In some embodiments, the library of cancer cells is generated by subjecting an initial population of cancer cells to gene editing. Any known gene editing method can be used to generate the cancer cell library described herein, such as zinc finger nucleases (ZFNs), transcriptional promoter-like effector nucleases (TALENs), and CRISPR/based gene editing or genome engineering methods. Cas method. See eg, Gaj et al. (Trends Biotechnol. 2013; 31(7): 397–405). In some embodiments, the library of cancer cells is generated by subjecting an initial population of cancer cells to gene editing by a CRISPR/Cas-based approach.

In some embodiments, the cancer cell library is made from an initial sgRNA construct or sgRNA ^iBAR construct and the Cas module under conditions that allow introduction of the initial cancer cell population and mutations in hit genes. A cancer cell population is produced by contacting i) an sgRNA library or an sgRNA ^iBAR library as described herein; and ii) a Cas element (eg, Cas9) comprising a Cas protein or a nucleic acid encoding a Cas protein. Accordingly, in some embodiments, the cancer cell library is prepared by combining an initial cancer cell population with Generated by contacting: i) an sgRNA library comprising a plurality of sgRNA constructs, wherein each sgRNA construct comprises or encodes an sgRNA, and wherein each sgRNA comprises a target site complementary (e.g., at least about 50 %, 60%, 70%, 80%, 90%, 95%, 96%, 97%, 98%, 99% or 100% complementary) guide sequence; and ii) a Cas element comprising a Cas protein (for example, Cas9) or the nucleic acid encoding described Cas protein. In some embodiments, the cancer cell library is obtained by contacting the initial cancer cell population with Generated: i) a sgRNA ^iBAR library comprising multiple sets of sgRNA ^iBAR constructs, wherein each set of sgRNA ^iBAR constructs comprises 3 or more (e.g., 3, 4, 5 or more, such as 4) sgRNAs ^iBAR constructs, each of which comprises or encodes a sgRNA ^iBAR , wherein the guide sequences of the three or more sgRNA ^iBAR constructs are identical, wherein each of the three or more sgRNA ^iBAR constructs The iBAR sequences of one are different from each other, and wherein the guide sequences of each set of sgRNA ^iBAR constructs are complementary (e.g., at least about 50% to target sites of different hit genes (e.g., different hit genes, or different sites within the same hit gene). %, 60%, 70%, 80%, 90%, 95%, 96%, 97%, 98%, 99% or 100% complementary); and ii) a nucleic acid comprising or encoding a Cas protein The Cas component. In some embodiments, each set of sgRNA ^iBAR constructs comprises 4 sgRNA ^iBAR constructs, and wherein the iBAR sequences for each of the 4 sgRNA ^iBAR constructs are different from each other. In some embodiments, the sgRNA library or the sgRNA ^iBAR library and the Cas module are introduced into the naive cancer cell population via a separate vector (eg, a lentiviral vector) or a separate virus. In some embodiments, the sgRNA library or the sgRNA ^iBAR library and the Cas module are introduced into the naive cancer cell population via the same vector or the same virus. In some embodiments, the sgRNA library or the sgRNA ^iBAR library is introduced into the naive cancer cell population via a lentiviral vector or lentivirus, and a Cas element is introduced into the naive cancer cell population as an encoding Cas element (e.g., Cas9) mRNA. In some embodiments, the initial population of cancer cells has each carried a Cas element (for example, transgenic Cas9, or ^Cas9 introduced as mRNA; lentiviral vector) or virus (eg, lentivirus) to introduce the sgRNA library or the sgRNA ^iBAR library into each cell.

In some embodiments, the cancer cell library only comprises the sgRNA library or the sgRNA ^iBAR library described herein without a Cas element (e.g., Cas9), that is, the sgRNA library or the sgRNA ^iBAR in the cancer cell library Hit genes targeted by the library remain inactivated until Cas elements (e.g., Cas9) are further introduced. A cancer cell library comprising only the sgRNA library or sgRNA ^iBAR library described herein is hereinafter referred to as "sgRNA cancer cell library" or "sgRNA ^iBAR cancer cell library". In some embodiments, the cancer cell library comprises the sgRNA library or the sgRNA ^iBAR library and a Cas element (eg, Cas9), ie, the cancer cell library comprises an inactivated hit gene. In some embodiments, the initial population of cancer cells expresses a Cas protein. In some embodiments, the cancer cell library is generated by contacting an initial population of cancer cells expressing a Cas protein with the sgRNA library or sgRNA ^iBAR library described herein, which will generate a cancer cell library comprising an inactivated hit gene . A cancer cell library comprising a sgRNA library or sgRNA ^iBAR library described herein and a Cas9 element (e.g., a Cas9 protein, or a nucleic acid encoding the same) is hereinafter referred to as a "Cas9 ⁺ sgRNA cancer cell library" or a "Cas9 ⁺ sgRNA ^iBAR cancer cell library" .

In some embodiments, a Cas element (eg, Cas9) is introduced into the cancer cell prior to the introduction of the sgRNA library or the sgRNA ^iBAR library. In some embodiments, the cancer cells are sorted to obtain Cas ⁺ cancer cells before introducing the sgRNA library or the sgRNA ^iBAR library. In some embodiments, the sgRNA library or the sgRNA ^iBAR library is introduced into the cancer cell followed by the introduction of a Cas element (eg, Cas9). In some embodiments, the cancer cells are sorted to obtain sgRNA ⁺ or sgRNA ^iBAR+ cancer cells prior to the introduction of a Cas element (eg, Cas9). In some embodiments, a Cas element (eg, Cas9) and the sgRNA library or the sgRNA ^iBAR library are introduced into the cancer cell simultaneously. In some embodiments, the cancer cells are sorted to obtain Cas ⁺ sgRNA ⁺ cancer cells (Cas ⁺ sgRNA ⁺ cancer cell library) or Cas ⁺ sgRNA ^{iBAR +} cancer cells (Cas ⁺ sgRNA ^{iBAR +} cancer cell library) prior to drug administration. deal with.

In some embodiments, at least about 50% (such as at least about any of 60%, 70%, 80%, 90%, 95%, 96%, 97%, 98%, 99% or more) of The sgRNA constructs of the sgRNA library, or the sgRNA ^iBAR constructs of the sgRNA ^iBAR library, or the set of sgRNA ^iBAR constructs of the sgRNA ^iBAR library are introduced into the initial cancer cell population or Cas9 ⁺ cancer cells described herein. In some embodiments, at least about 95% (e.g., at least about any of 96%, 97%, 98%, 99%, or more) of the sgRNA constructs of the sgRNA library, or the sgRNA ^iBAR A library of sgRNA ^iBAR constructs, or a set of sgRNA ^iBAR constructs of the sgRNA ^iBAR library, is introduced into the initial cancer cell population or Cas9 ⁺ cancer cells. In some embodiments, the hit gene inactivation efficiency of the sgRNA library or the sgRNA ^iBAR library is at least about 80%, such as at least about 81%, 82%, 83%, 84%, 85%, 86%, 87% %, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or more. In some embodiments, the hit gene inactivation efficiency of the sgRNA library or the sgRNA ^iBAR library is at least about 90%.

In some embodiments, the cancer cell library comprises one or more (e.g., about 2, 3, 4, 5, 8, 10, 100, 250, 400, 500, 1,000, 2,000, 5,000, 10,000, or more Multiple) cancer cells comprising the same sgRNA construct or the same sgRNA ^iBAR construct targeting the target site of the same hit gene. Such a cancer cell library is also referred to as "X-fold coverage for the sgRNA/sgRNA ^iBAR " or "X-fold coverage for each sgRNA/sgRNA ^iBAR ", where "X" is expression of the same sgRNA or sgRNA Number of cancer cells in ^iBAR . In some embodiments, the cancer cell library has about 1 to about 12,000 fold coverage for each sgRNA or sgRNA ^iBAR , or each set of sgRNA ^iBARs , such as about 1 ^{to about 12,000 fold coverage for each sgRNA or sgRNA iBAR ,} or about 1,000 to about 5,000, about 1 to about 1,000, about 10 to about 100, about 50 to about 500, about 80 to about 200, about 100 to about 400, about 100 to about 800, about 100 to about 1,000, or about 300 to any of about 600x coverage. In some embodiments ^, the cancer cell library has at least about 1-fold, 2-fold, 3-fold, ⁴ -fold, 5-fold, 10-fold , 100-fold, 400-fold, 500-fold, 1,000-fold, 2,000-fold, 5,000-fold, 10,000-fold or more coverage.

In some embodiments, the cancer cell library has at least about 100-fold (eg, at least about 200-, 400-, 500-, 1,000-, 5,000-fold or more) coverage. In some embodiments, each gene hit is targeted by about 6 to about 12 different sgRNAs, or has mutations at at least 2 (eg, about 6 to about 12) different target gene sites. In some embodiments, the library of cancer cells has at least about 100-fold (e.g., at least about 200-, 300-, 400-, 500-, 1,000-, 5,000-fold, or more) for each hit gene. any) coverage, such as about 600-fold to about 1200-fold coverage for each hit gene.

In some embodiments, the cancer cell library has at least about 100-fold (e.g., at least about any of 200-, 400-, 500-, 1,000-, 5,000-fold, or more) ^iBAR per sgRNA ) coverage, such as about 100-fold to about 1000-fold, or about 1000-fold coverage, for each sgRNA ^iBAR . In some embodiments, the cancer cell library has at least about 400-fold (e.g., at least about any of 800-, 1000-, 2000-, 4000-, 16,000-fold, or more ^{) iBAR} for each set of sgRNAs. ) coverage, such as about 400-fold to about 4000-fold, or about 4000-fold coverage for each set of sgRNA ^iBARs . In some embodiments, the library of cancer cells is at least about 100-fold (e.g., at least about any of 200-, 400-, 500-, 1,000-, 5,000-fold, or more) against the sgRNA ^iBAR library. a) coverage, such as about 100-fold to about 1000-fold, or about 1000-fold coverage for the sgRNA ^iBAR library. In some embodiments, the cancer cell library has at least about 400-fold (e.g., at least about 800-, 1000-, 2000-, 4000-, 10,000, 16,000-fold, or more) for each hit gene Either) coverage, such as about 1200-fold to about 12,000-fold coverage for each hit gene, or about 12,000-fold coverage for each hit gene. In some embodiments, the sgRNA ^iBAR library targets every annotated gene in the genome (ie, the sgRNA ^iBAR library is a genome-wide sgRNA ^iBAR library). In some embodiments, the cancer cell library has at least about 100-fold (e.g., at least about any of 400-fold, 800-fold, 1000-fold, or 1200-fold) coverage against a genome-wide sgRNA ^iBAR library Rate.

endogenous mutation

In some embodiments, the cancer cells in the initial cancer cell population or in the final cancer cell library may contain endogenous mutations, such as naturally occurring mutations, not produced by the CRISPR/Cas system or mutagens (e.g., EMS) , or mutations in cancer cells that do not meet the hit gene selection criteria (e.g., a DNA mutation frequency of at least about 5% in cancer patients, and/or RNA expression levels that are up- or down-regulated by greater than about 2-fold, and/or encoding RNA /protein is expressed in the cell or the encoded protein is expressed on the cell surface). Endogenous mutations should not affect the target gene identification method described here, since the profile of sgRNAs or hit gene mutations in treated cancer cell populations was compared to control cancer cell populations containing the same endogenous mutations.

cancer cell

In some embodiments, there is provided a method of editing a genomic locus in a cancer cell comprising introducing into a host cancer cell (e.g., a naive cancer cell, an unmodified cancer cell) a guide RNA construct comprising a targeting genome Guide sequences and coding repeats for loci (e.g., target sites of hit genes): inverted repeat double-stranded and four-loop guide hairpin sequences, where iBAR is embedded into the four-loop as an internal repeat, expression targeting host cancer cells guide RNA at the genomic locus, and thereby edit the targeted genomic locus (eg, hit gene) in the presence of a Cas nuclease (eg, Cas9).

In some embodiments, cancer cells prepared by transfecting any of the sgRNA libraries described herein or the sgRNA ^iBAR libraries described herein into a plurality of host cancer cells (e.g., an initial population of cancer cells, with or without Cas elements) are provided. A library of cells, wherein the sgRNA construct or the sgRNA ^iBAR construct is present in a viral vector (eg, a lentiviral vector) or a virus (eg, a lentivirus). In some embodiments, the method further comprises introducing into the initial population of cancer cells a Cas element comprising a Cas protein or a nucleic acid encoding a Cas protein, e.g., as Cas9 mRNA. In some embodiments, the multiplicity of infection (MOI) between the viral vector or virus and the host cancer cells (eg, the initial population of cancer cells) during transfection is at least about 1. In some embodiments, the MOI is at least about any of 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5, 10 or more one. In some embodiments, the MOI is about 1, about 1.5, about 2, about 2.5, about 3, about 3.5, about 4, about 4.5, about 5, about 5.5, about 6, about 6.5, about 7, about 7.5, About 8, about 8.5, about 9, about 9.5 or about 10. In some embodiments, the MOI is about any of 1-10, 1-3, 3-5, 5-10, 2-9, 3-8, 4-6, or 2-5. In some embodiments, the MOI between the viral vector or virus and the host cancer cells (eg, the initial population of cancer cells) during transfection is less than 1, such as less than about any of 0.8, 0.5, 0.3 or less. In some embodiments, the MOI is from about 0.3 to about 1. In some embodiments, the viral sgRNA library or the viral sgRNA ^iBAR library is contacted with the initial population of cancer cells at an MOI of at least about 2, such as at least about 3.

In some embodiments, one or more vectors that drive expression of one or more elements of the CRISPR/Cas system are introduced into host cancer cells (e.g., the naive cancer cell population) such that expression of the CRISPR system elements directs expression in a One or more target sites of one or more hit genes form a CRISPR complex with the sgRNA molecule or sgRNA ^iBAR molecule described herein. In some embodiments, the host cancer cells (eg, the initial cancer cell population) have introduced a Cas nuclease (eg, Cas9 mRNA) or are engineered to stably express a CRISPR/Cas nuclease.

In some embodiments, the host cancer cells (eg, the initial population of cancer cells) are cancer cell lines, such as pre-established cancer cell lines. Host cancer cells and cancer cell lines can be human cancer cells or cancer cell lines, or they can be non-human mammalian cancer cells or cancer cell lines. In some embodiments, host cancer cells are difficult to transfect with a viral vector, such as a lentiviral vector, at a low MOI (eg, less than 1, 0.5, or 0.3). In some embodiments, the host cancer cells are difficult to edit with a CRISPR/Cas system at a low MOI (eg, less than 1, 0.5, or 0.3). In some embodiments, host cancer cells are provided in limited numbers. In some embodiments, host cancer cells are obtained from a tumor sample from an individual (eg, a human cancer patient).

The methods described herein are applicable to the identification of sensitive or drug-resistant target genes in a wide variety of cancer cells, including solid and hematological cancers, and in all stages of cancer, including early-stage cancers, non-metastatic cancers, primary cancers, advanced cancers, locally advanced cancers Cancer, metastatic cancer, or cancer in remission. In some embodiments, the solid or blood cancer can be any of stages I, II, III, and IV according to the American Joint Committee on Cancer (AJCC) staging groups.

In some embodiments, the cancer is a solid cancer selected from the group consisting of colon cancer, rectal cancer, renal cell carcinoma, liver cancer, non-small cell lung cancer, small bowel cancer, esophageal cancer, melanoma, bone cancer, pancreatic cancer, skin cancer , head and neck cancer, skin or intraocular malignant melanoma, uterine cancer, breast cancer, ovarian cancer, rectal cancer, cancer of the anal region, stomach cancer, testicular cancer, uterine cancer, fallopian tube cancer, endometrial cancer, cervical cancer, vaginal cancer , vulvar cancer, Hodgkin's disease, non-Hodgkin's lymphoma (NHL), cutaneous T-cell lymphoma (CTCL), endocrine system cancer, thyroid cancer, parathyroid cancer, adrenal cancer, soft tissue sarcoma, urethral cancer, Penile cancer, childhood solid tumors, bladder cancer, renal or ureteral cancer, renal pelvis cancer, central nervous system tumor, primary central nervous system lymphoma, tumor angiogenesis, spinal tumor, brainstem glioma, pituitary adenoma, card Percy's sarcoma, epidermoid carcinoma, squamous cell carcinoma, T-cell lymphoma, environmentally induced cancer, combinations of the above cancers, and metastatic lesions of the above cancers.

In some embodiments, the cancer is a hematological cancer selected from one or more of the following: acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), acute leukemia, acute lymphoblastic leukemia (ALL), B-cell acute lymphoblastic leukemia B-cell leukemia (B-ALL), T-cell acute lymphoblastic leukemia (T-ALL), chronic myelogenous leukemia (CML), B-cell prolymphocytic leukemia, blastic plasmacytoid dendritic cell neoplasm (BPDCN), primary Kitt Lymphoma, Diffuse Large B-Cell Lymphoma, Follicular Lymphoma, Hairy Cell Leukemia, Small or Large Cell Follicular Lymphoma, Malignant Lymphoproliferative Disorder, MALT Lymphoma, Mantle Cell Lymphoma, Marginal Lymphoma, multiple myeloma, myelodysplasia and myelodysplastic syndrome, non-Hodgkin's lymphoma, Hodgkin's lymphoma, plasmablastic lymphoma, plasmacytoid dendritic cell neoplasm, Waldenstrom ) macroglobulinemia or preleukemia.

In some embodiments, the cancer cells are from a cancer cell line. In some embodiments, the cancer cells are from xenogeneic sources, eg, from mice, rats, non-human primates, and pigs. In some embodiments, the cancer cells are human cancer cells. In some aspects, cancer cells are primary cells, such as those isolated directly from a subject and/or isolated from a subject and frozen. In some embodiments, the initial population of cancer cells is homogeneous. In some embodiments, the initial population of cancer cells is heterogeneous, such as primary cancer cells, or cell lines comprising mixed stages of the same cancer cells, or a mix of the same cancer cell types (eg, colorectal cancer). In some embodiments, after the cancer cells are collected from the subject, the cancer cells are sorted to obtain subpopulations of cancer cells, eg, using immunomagnetic beads. In some embodiments, cancer cells are obtained directly from a patient following cancer treatment (eg, with an anticancer agent). It is contemplated in the context of the present invention that cancer cells are harvested as host cancer cells during their recovery period, or tested for changes in expression levels of hit genes.

In some embodiments, the cancer cells are stage III or IV colorectal cancer cells. In some embodiments, the initial cancer cell population is HCT116 (a human colon cancer cell line). In some embodiments, the initial cancer cell population is SW480 (a human colorectal adenocarcinoma cell line). In some embodiments, the colorectal cancer is any of the following: advanced colon cancer, malignant colon cancer, metastatic colon cancer, stage I, II, III or IV colon cancer, colon cancer characterized by genomic instability , colon cancer characterized by a path change, colon cancer classified as CCS1, CCS2, or CCS3 by the Colon Cancer Subtype (CCS) system, and classified by the Colorectal Cancer Classifier (CRCA system) as stem-shaped, cup-shaped, inflammatory, Colon cancer of the transit-amplified or enterocytic subtype, classified by the Colon Cancer Molecular Subtype (CCMS) system as C1, C2, C3, C4, C5, or C6 colon cancer, by the CRC Inherent Subtype (CRCIS) system Colon cancer classified as subtype A, B, or C, or colon cancer classified as CMS1, CMS2, CMS3, or CMS4 by the Colorectal Cancer Subtype Consortium (CRCSC) classification system. In some embodiments, the colon cancer has a microsatellite instability (MSI) status of MSI high or MSI low. In some embodiments, cancer cells are obtained from individuals (eg, humans) who have previously undergone treatment (eg, chemotherapy, radiation, surgery, or immunomodulatory therapy). In some embodiments, the individual has not responded to previous therapy (eg, chemotherapy, radiation, surgery, or immunomodulatory therapy).

Cancer cells, such as the starting population of cancer cells, or the library of cancer cells described herein can be cultured using any suitable method or medium known in the art. See, eg, Cree, Ian A. (Ed.), "Cancer Cell Culture. Methods and Protocols, 2nd Edition," 2011, Springer Science ⁺ Business Media, New York, NY, USA.

Anticancer drug treatment and acquisition of anticancer drug resistant cancer cells

The methods described herein include treating a library of cancer cells described herein (e.g., a library of cancer cells generated by a mutagen, a library of Cas9 ⁺ sgRNA cancer cells, or a library of Cas9 ⁺ sgRNA ^iBAR cancer cells) with an anticancer drug, and extracting a library of cancer cells from an anticancer drug. A cancer cell library is obtained by treating cancer cells with drug-killing drug resistance. In some embodiments, the method comprises contacting the library of cancer cells described herein with an anticancer drug, and growing the library of cancer cells to obtain a population of treated cancer cells. See also Example 1 and Figure 2 for exemplary methods.

Anti-cancer drugs

Any agent used to treat cancer can be used herein as an anticancer drug. Anticancer drugs include, but are not limited to: anticancer substances for all types and stages of cancer and cancer therapy (chemotherapy, proliferative, acute, genetic, idiopathic, etc.), antiproliferative agents, chemosensitizers, anti-inflammatory agents (including steroids anti-inflammatory and non-steroidal anti-inflammatory agents and antipyretics), antioxidants, hormones, immunosuppressants, enzyme inhibitors, cytostatic and anti-adhesion molecules, inhibitors of DNA, RNA or protein synthesis, anti-angiogenic Factors, antisecretory factors, radioactive agents. In some embodiments, the anticancer drug is a small molecule drug. In some embodiments, the anticancer drug is an antibody. In some embodiments, the anticancer drug is an antibody-drug conjugate (ADC).

In some embodiments, the anticancer drug is a PARP inhibitor. In some embodiments, the PARP inhibitor is any of the following: talazoparib, veliparib, pamiparib, olaparib, rucapar Rucaparib, veliparib, CEP 9722, E7016, iniparib, or 3-aminobenzamide.

Steps for exposing a library of cancer cells to an anticancer drug

In some embodiments, anticancer agent treatment (hereinafter also referred to as "anticancer drug treatment step", "anticancer drug treatment step b)", or "step b)") comprises contacting the library of cancer cells with an anticancer drug single step. In some embodiments, step b) comprises combining the library of cancer cells with an anticancer drug at least about IC5 (e.g., at least about IC10, IC20, IC30, IC40, IC50, IC60, IC70, IC80, IC90, IC95 or higher) Any one, or about IC20 to about IC95) concentration exposure for at least about 1 (e.g., at least about 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 , 16, 17, 18, 19, 20, 30, 40, 50 or more) doubling times. "IC50", or half-maximal inhibitory concentration (IC), refers to the concentration of an inhibitory substance (such as an anticancer drug) required to inhibit 50% of a given biological process (such as cancer cell proliferation) or biological component (such as cancer cells) in vitro concentration. Likewise, IC70 or 70% inhibitory concentration herein refers to the inhibitory concentration of an anticancer drug required to inhibit 70% of cancer cell proliferation (or kill 70% of cancer cells). In some embodiments, a drug toxicity profile is measured to determine the anticancer drug concentration prior to treatment step b). Briefly, a population of cancer cells (e.g., an unmodified initial cancer cell population) is tested for a series of anticancer drug concentrations, cells are grown in the presence of the anticancer drug for several (e.g., 3) doubling times, The percentage of cell survival or the rate of cell killing is then plotted against the concentration of the anticancer drug to obtain an IC (eg, IC50, IC70, etc.). In some embodiments, an ATP assay (eg, CellTiter Glo® Luminescent Cell Viability Assay) can be performed to measure drug toxicity profiles. Cell killing or mortality can also be tested using any other known method, such as propidium iodide (PI) staining. In some embodiments, the cell culture medium containing the anticancer drug is changed once, twice, 3 times, 4 times per day or every 2, 3, 4, 5, 6, 7, 8, 9, 10 or longer , 5 times, 6 times or more times, and provide anticancer drugs continuously. In some embodiments, the cell culture medium is changed after each doubling time, for example, the cell culture medium is changed twice a day with a doubling time of 12 h. In some embodiments, the cell culture medium is changed after at least about 2 doubling times, eg, at least about any of 3, 4, 5, 6, 7 or more doubling times. In some embodiments, the cell culture medium containing the anticancer drug is changed every 3 days. For example, the cell culture medium containing the anticancer drug is changed every 3 days, and the doubling time is about 20 to 40 h, such as about 21 h, or about 38 h.

In some embodiments, the anticancer drug treatment step b) comprises subjecting the library of cancer cells to the anticancer drug at a concentration of about IC50 to about IC70 (e.g., about IC50, IC55, IC60, IC65, IC70, or any value therebetween. any of ) for about 9 to about 10 (eg, any of about 9, 9.5, 10, or any value therebetween) doubling times. In some embodiments, the anticancer drug treatment step b) comprises subjecting the cancer cell library to the anticancer drug at a concentration of about IC50 to about IC70 (e.g., about IC50, IC55, IC60, IC65, IC70, or any value in between). any of values) for about 15 to about 16 (eg, any of about 15, 15.5, 16, or any value therebetween) doubling times. In some embodiments, the anticancer drug treatment step b) comprises subjecting the cancer cell library to the anticancer drug at a concentration of about IC50 to about IC70 (e.g., about IC50, IC55, IC60, IC65, IC70, or any value in between). any of values) for about 18 to about 19 (eg, any of about 18, 18.5, 19, or any value therebetween) doubling times.

In some embodiments, the anticancer drug treatment step comprises (or consists essentially of, or consists of): contacting the library of cancer cells with an anticancer agent for at least about 24 h, such as at least about 30 h, 36 h h, 48 h, 50 h, 52 h, 54 h, 56 h, 58 h, 60 h, 62 h, 64 h, 66 h, 68 h, 70 h, 72 h, 74 h, 76 h, 78 h, 80 h, 84 h, 96 h, 5 days, 6 days, 7 days, 8 days, 9 days, 10 days, 12 days, 14 days, 16 days, 18 days, 20 days, 24 days, 30 days or longer any of the In some embodiments, the anticancer drug treating step comprises (or consists essentially of, or consists of) contacting the library of cancer cells with the anticancer drug for about 6 to about 10 days, about 12 to about 14 days days, from about 14 to about 16 days, or from about 22 to about 26 days.

The longer the anticancer drug exposure time, and/or the higher the anticancer drug concentration, the harsher the treatment conditions.

In some embodiments, cancer cells (eg, those that are insensitive or less sensitive to killing by the anticancer drug) continue to grow during the anticancer drug contacting step. In some embodiments, cancer cells are passaged every 1, 2, 3, 4, 5, or more (eg, 3) doubling times and targeted for each hit gene (or for each mutation, or sgRNA , or sgRNA ^iBAR ) maintain the same or similar (eg, within ~10% difference) library fold coverage for sequential anticancer drug treatments. In some embodiments, cancer cells are passaged when they reach about 90% confluency.

Step of growing a library of cancer cells treated with an anticancer drug to obtain a population of treated cancer cells

In some embodiments, cancer cells are obtained from an anticancer drug-treated cancer cell library that is resistant to an anticancer drug (hereinafter also referred to as "post-treatment cancer cell population acquisition step", "cancer cell acquisition step c)" or "Step c)"), comprising a single step of growing said anticancer drug-treated cancer cell library to obtain a treated cancer cell population. In some embodiments, the resulting treated cancer cell population is a viable cell population, ie, resistant to killing by an anticancer drug.

In some embodiments, the cells grown in step b) and step c) may occur simultaneously or overlap, eg drug treatment may overlap with a cell growth period, hereinafter also referred to as a "treatment/growth step". See, eg, Example 1. For example, in some embodiments, the library of cancer cells is contacted with the anticancer drug by providing the anticancer drug in the medium (step b)), allowing the cancer cells to grow (step c)) while passing through the medium containing the anticancer drug. Ongoing treatment (step b)), the medium containing the anticancer drug may be changed every few hours or days, such as every 3 days (step c)), and at several doubling times (e.g., about 9 to about 10 doublings) time, or about 15 to about 16 doubling times), the cancer cells are harvested to obtain a treated cancer cell population (step c)). In some embodiments, cancer cells are passaged every 1, 2, 3, 4, 5 or more (eg, 3) doubling times while targeting each hit gene (or targeting each mutation, or sgRNA, or sgRNA ^iBAR ) maintain the same or similar (eg, within ~10% difference) library fold coverage for sequential anticancer drug treatments. In some embodiments, cancer cells are passaged when they reach about 90% confluency.

In some embodiments, growing the library of anticancer drug-treated cancer cells to obtain a population of treated cancer cells includes a "recovery step" following anticancer drug treatment, where the treated cancer cells are grown in the absence of any anticancer drug. Drugs in fresh medium. Accordingly, in some embodiments, step c) comprises a reinstatement step comprising growing the treated cancer cells in the absence of the anticancer drug after the anticancer drug contacting step b). In some embodiments, the recovering step comprises growing the cancer cells prior to contacting the library of cancer cells with an anticancer drug for at least about 24 h, such as at least about 26 h, 28 h, 30 h, 32 h, 34 h , 36 h, 38 h, 40 h, 48 h, 52 h, 56 h, 60 h, 64 h, 68 h, 72 h, 78 h, 84 h, 96 h, 5 days, 6 days, 7 days, 8 Days, 9 days, 10 days, 12 days, 14 days, 16 days, 18 days, 20 days, 24 days, 30 days or longer.

The culture conditions during the "recovery step" should be suitable for cancer cell growth and/or proliferation. In some embodiments, the culture conditions do not induce cancer cells to develop a particular phenotype during expansion/growth. Such culture conditions are well known in the art. For example, in a 37°C, 5% CO2 incubator. See also Cree, Ian A. id. In some embodiments, the medium is complete cancer cell medium. In some embodiments, the culture conditions are the same as the culture conditions of the cancer cell library prior to anticancer drug treatment. The type of medium for successful culture depends on the type of cancer cell. In some embodiments, the medium is further supplemented with reagents for selectable markers, eg, to select for cancer cells that do not lose transgenes or mutations during proliferation.

In some embodiments, "obtaining a population of treated cancer cells" comprises (or consists essentially of, or consists of, a single "harvesting step"): i.e. removing the medium (which may contain dead or floating cells), and Harvest the remaining cancer cells after the anticancer drug treatment/growth step, or collect the remaining cancer cells after the recovery step. In some embodiments, the cancer cell harvesting step includes collecting the treated/grown or recovered cancer cells into containers (eg, Falcon centrifuge tubes, EP tubes, or centrifuge tubes) for storage or for subsequent experiments. In some embodiments, the harvesting step includes washing the harvested cancer cells so that the cancer cells are subjected to suitable storage conditions (e.g., 4°C, -20°C, or -80°C storage) or subsequent experiments (e.g., cell lysis, PCR or sequencing). For example, for adherent cancer cells, after removing the culture medium (containing dead or floating cells), trypsinize the remaining cancer cells in the cell culture vessel (e.g., a cell culture dish) and collect (e.g., transfer to a fresh in the container). The resulting post-treatment cancer cell population will be live cancer cells, or those cells that are resistant to killing by anticancer drugs.

optional enrichment step

If it is desired to obtain a surviving (or drug-resistant) population of cancer cells after treatment/growth or recovery from non-adherent cancer cells (e.g., hematopoietic cancers), or if desired (e.g., from cells) to obtain an enriched (or more pure) surviving population of cancer cells following treatment/growth or recovery, the method of "obtaining a population of treated cancer cells" may include an "enrichment step" comprising sorting the cancer cells to obtain a pure live cancer cell population. In some embodiments, "obtaining a population of treated cancer cells" comprises sorting a population of treated/growing or recovered cancer cells to obtain a population of viable cancer cells, i.e. treated cancer cells resistant to an anticancer drug cluster (hereinafter also referred to as "live enrichment").

In some embodiments, the enriching step further comprises staining the cancer cells after treatment/growth or recovery with a cell viability marker (eg, a dye) prior to sorting. Methods and reagents for assessing cellular activity are well known in the art, such as fluorometric or colorimetric (enzyme) based. For example, membrane permeability-based assays, such as staining with DAPI, propidium iodide (PI), 7-AAD, or amine reactive dyes, indicate cell death; whereas acridine orange stains live cells more efficiently. Carboxyfluorescein diacetate (CFDA) is a non-fluorescent, cell-permeable dye that is hydrolyzed by nonspecific intracellular esterases present only in living cells to form the fluorescent molecule carboxyfluorescein. CFDA-SE is a derivative of CFDA that is better retained in living cells after hydrolysis. Tetramethylrhodamine ethyl ester (TMRE) and tetramethylrhodamine methyl ester (TMRM) localize to the mitochondria of healthy cells and the cytoplasm of dead cells. JC-1 is a commonly used potentiometric dye. In healthy cells, JC-1 localizes to mitochondria, where it forms red fluorescent aggregates. After the mitochondrial membrane potential collapses, JC-1 diffuses throughout the cell and exists as a green fluorescent monomer. The incorporation of BrdU into newly synthesized DNA indicates that the cell is alive.

In some embodiments, the enriching step further comprises staining the treated/growing or recovered cancer cells with propidium iodide (PI) prior to sorting, wherein PI staining is indicative of cell death. Thus, in some embodiments, the enrichment step comprises sorting PI-negative (no PI staining) post-treatment/growth or recovery cancer cells, thereby obtaining anticancer drug-resistant (live) post-treatment cancer cell population. Any cell sorting method can be used herein, such as fluorescence activated cell sorting (FACS), magnetic activated cell sorting (MACS), microfluidic cell sorting, buoyancy activated cell sorting, etc.

Thus, in some embodiments, the anticancer drug treatment step b) and cancer cell harvesting step c) comprise: contacting the cancer cell library with an anticancer drug while allowing viable cancer cells to grow (eg, for about 9 to about 10 doubling times, or about 15 to about 16 doubling times), and harvesting the cancer cells by removing the cell culture medium (and dead floating cells) containing the anticancer drug and collecting the remaining adherent cancer cells (for example, by pancreatic protease digestion) to obtain the treated cancer cell population. For adherent cancer cells, most or all of these cells survive, or are resistant to anticancer drugs.

In some embodiments, the anticancer drug treatment step b) and cancer cell harvesting step c) comprise: contacting the cancer cell library with the anticancer drug while allowing viable cancer cells to grow (e.g., for about 9 to about 10 doubling time, or about 15 to about 16 doubling times), and removing the cell culture medium (and dead floating cells) containing the anticancer drug and growing the remaining adherent cancer cells in cell culture medium without the anticancer drug (recovery step), and harvesting the cancer cells by removing the cell culture medium and collecting the remaining adherent cancer cells (eg, by trypsinization) to obtain a post-treatment cancer cell population. For adherent cancer cells, most or all of these cells survived, or were resistant to anticancer drugs.

In some embodiments, the anticancer drug treatment step b) and cancer cell harvesting step c) comprise: contacting the cancer cell library with the anticancer drug while allowing viable cancer cells to grow (e.g., for about 9 to about 10 doubling time, or about 15 to about 16 doubling times), optionally remove the cell culture medium (and dead floating cells) containing the anticancer drug, stain the remaining cancer cells with a cell viability marker (such as PI), and have a positive effect on survival The cancer cells are sorted (PI negative, eg by FACS) to obtain a treated cancer cell population. The resulting population of treated cancer cells is enriched in live cancer cells, or resistant to anticancer drugs. For non-adherent cancer cells (such as hematopoietic cancer cells), do not remove the cell culture medium before staining and sorting, or add a centrifugation step when removing the cell culture medium to collect all cancer cells (a mixture of live and dead cells).

In some embodiments, the anticancer drug treatment step b) and cancer cell harvesting step c) comprise: contacting the cancer cell library with the anticancer drug while allowing viable cancer cells to grow (e.g., for about 9 to about 10 doubling time, or about 15 to about 16 doubling times), the cell culture medium (and dead floating cells) containing the anticancer drug was removed, and the remaining adherent cancer cells were grown in cell culture medium without the anticancer drug (recovery step), remove the cell culture medium, stain the remaining cancer cells with cell viability markers (such as PI), and sort the live cancer cells (PI negative, such as by FACS), so as to obtain enriched live cancer cells and anticancer Drug-resistant post-treatment cancer cell populations.

optional second processing step

In some embodiments, the library of cancer cells undergoes two processing steps. In some embodiments, the methods described herein include a second processing step comprising substituting the initially treated cancer cells (with or without further culturing during the recovery step, or with or without sorting viable cancer cells during the enrichment step). cells) in contact with anticancer drugs. In some embodiments, the treatment conditions of the two treatment steps are the same, that is, the concentration of the anticancer drug is the same, and the treatment time is the same. In some embodiments, the processing conditions of the two processing steps are different. In some embodiments, the second treatment step is harsher than the first treatment step, i.e., the cancer cells are exposed to a higher concentration of the anticancer drug in the second treatment step than in the first treatment step. The concentration in the step is higher such as at least about 1.1-fold, 1.2-fold, 1.3-fold, 1.4-fold, 1.5-fold, 2-fold, 3-fold, 4-fold, 5-fold, 6-fold, 7-fold Any one of -fold, 8-fold, 9-fold, 10-fold, 15-fold, or 20-fold; and/or the cancer cells are in contact with the anticancer drug for a longer period of time than the first treatment step The contact time in is longer such as about 10 min, 20 min, 30 min, 40 min, 50 min, 1 h, 2 h, 4 h, 6 h, 8 h, 10 h, 12 h, 24 h, 36 h, Any one of 48 h, 60 h, 72 h, 84 h, 96 h, 5 days, 6 days, 7 days, 8 days, 9 days or 10 days. In some embodiments, the second treatment step is milder than the first treatment step, i.e., the cancer cells are contacted with a lower concentration of the anticancer drug in the second treatment step than in the first treatment step. The concentration in the step is lower such as at least about 1.1-fold, 1.2-fold, 1.3-fold, 1.4-fold, 1.5-fold, 2-fold, 3-fold, 4-fold, 5-fold, 6-fold, 7-fold Any one of -fold, 8-fold, 9-fold, 10-fold, 15-fold or 20-fold; and/or the cancer cells are in contact with the anticancer drug for a shorter time than in the first treatment Shorter contact times in steps such as about 10 min, 20 min, 30 min, 40 min, 50 min, 1 h, 2 h, 4 h, 6 h, 8 h, 10 h, 12 h, 24 h, 36 h , 48 h, 60 h, 72 h, 84 h, 96 h, 5 days, 6 days, 7 days, 8 days, 9 days or 10 days.

optional second recovery step

In some embodiments, after the second processing step (or after the second processing step, after the enrichment step for live cancer cells), the method further comprises an additional recovery step comprising the absence of any Cancer cells after the second treatment were grown in fresh medium with anticancer drugs. In some embodiments, the second recovery step has the same culture conditions, eg, the same culture period, as in the first recovery step. In some embodiments, the second recovery step has different culture conditions than in the first recovery step. In some embodiments, the second recovery step is longer than the first recovery step, such as at least about 10 min, 20 min, 30 min, 40 min, 50 min, 1 min longer than the first recovery step. h, 2 h, 4 h, 6 h, 8 h, 10 h, 12 h, 24 h, 36 h, 48 h, 60 h, 72 h, 84 h, 96 h, 5 days, 6 days, 7 days, Either 8 days, 9 days or 10 days. In some embodiments, the second recovery step is shorter than the first recovery step, such as at least about 10 min, 20 min, 30 min, 40 min, 50 min, 1 h, 2 h, 4 h, 6 h, 8 h, 10 h, 12 h, 24 h, 36 h, 48 h, 60 h, 72 h, 84 h, 96 h, 5 days, 6 days, 7 days, Either 8 days, 9 days, or 10 days.

optional second enrichment step

In some embodiments, the library of cancer cells undergoes two enrichment steps. In some embodiments, the method of obtaining a population of treated cancer cells described herein further comprises sorting the cancer cells after a second treatment/growth or after a second recovery to obtain a pure population of viable cancer cells. In some embodiments, the method comprises sorting a second treatment/growth or second recovery cancer cell population to obtain a pure viable cancer cell population, i.e., a treated cancer cell population resistant to an anticancer drug ( Hereinafter also referred to as "second live enrichment"). In some embodiments, the second enrichment method is the same as the first enrichment method, e.g., the cells are labeled with the same cell viability marker (e.g., both stained with PI), and the cells are sorted by the same method Sorting was performed (eg, both using FACS). In some embodiments, the second enrichment method is different from the first enrichment method, e.g., cells are labeled with different cell viability markers (e.g., PI and DAPI staining in two enrichment steps, or Cells were sorted in a second enrichment step based on morphology under the microscope), and/or using a different sorting method (e.g., FACS vs. manual sorting, or by washing away dead floating cells).

Thus, in some embodiments, the anticancer drug treatment step b) and the cancer cell acquisition step c) comprise: contacting the cancer cell library with an anticancer drug while allowing viable cancer cells to grow (first treatment step, e.g., for about 9 to about 10 doubling times), the cell culture medium (and dead floating cells) containing the anticancer drug was removed, and the remaining adherent cancer cells were grown in cell culture medium without the anticancer drug (first recovery step ), remove the cell culture medium without the anticancer drug, and expose the remaining cancer cells (adherent cancer cells, mostly or all alive) to the anticancer drug (a second treatment step, e.g., for about 15 to about 16 doubling time), removal of cell culture medium (and dead floating cells) containing anticancer drug, growth of remaining adherent cancer cells in cell culture medium without anticancer drug (second recovery step), and (and dead floating cells, if any) to harvest the cancer cells and collect the remaining adherent cancer cells (eg, by trypsinization) to obtain a processed anticancer drug-resistant cancer cell population. In some embodiments, between said first treatment step and said first recovery step, between said first recovery step and said second treatment step, between said second treatment step and said second recovery Between the steps, and/or between the second recovery step and the harvesting step, the method may include one or more enrichment steps, such as by staining cancer cells with a cell viability marker (e.g., PI) and analyzing the viability Cancer cells were sorted (PI negative, first enrichment step, eg by FACS).

In some embodiments, the anticancer drug treatment step b) and cancer cell acquisition step c) comprise: contacting the cancer cell library with an anticancer drug while allowing viable cancer cells to grow (the first treatment step, for example, for about 9 to about 10 doubling times), the cell culture medium (and dead floating cells) containing the anticancer drug was removed, and the remaining adherent cancer cells were grown in cell culture medium without the anticancer drug (first recovery step), The cell culture medium without the anticancer drug is removed and the remaining cancer cells (adherent cancer cells, most or all viable) are exposed to the anticancer drug (second treatment step, e.g., for about 15 to about 16 doubling times ), as well as harvesting the cancer cells by removing the cell culture medium (and dead floating cells) containing the anticancer drug and collecting the remaining adherent cancer cells (e.g., by trypsinization), resulting in processed anticancer drug-resistant Drug-based cancer cell populations. In some embodiments, between said first processing step and said first recovery step, between said first recovery step and said second processing step, and/or between said second recovery step and harvesting step In between, the method may include one or more enrichment steps, such as by staining cancer cells with a cell viability marker (eg PI) and sorting live cancer cells (PI negative, first enrichment step, eg by FACS).

In some embodiments, the anticancer drug treatment step b) and cancer cell acquisition step c) comprise: contacting the cancer cell library with an anticancer drug while allowing viable cancer cells to grow (the first treatment step, for example, for about 9 to about 10 doubling times), optionally remove the cell culture medium containing the anticancer drug, stain the remaining cancer cells with a marker of cell viability (such as PI), and sort the viable cancer cells (PI negative, first Enrichment step, e.g. by FACS), optionally growing sorted live cancer cells in cell culture medium without anticancer drug (optional first recovery step), combining sorted live cancer cells with anticancer Drug exposure (a second treatment step, e.g., for about 15 to about 16 doubling times), and allowing viable cancer cells to grow, optionally removing the cell culture medium containing the anticancer drug, optionally using markers of cell viability (e.g., , PI) to stain the remaining cancer cells and sort the live cancer cells (PI negative, second enrichment step, eg by FACS) to obtain the treated cancer cell population resistant to anticancer drugs. In some embodiments, the method further comprises growing the sorted live cancer cells in cell culture medium without the anticancer drug after the second enrichment step (optional second recovery step), followed by Cell culture medium (and dead floating cells, if any) are removed to harvest cancer cells and remaining adherent cancer cells are collected (eg, by trypsinization).

Hit gene identification

The methods described herein include identifying hit genes in a population of treated cancer cells resistant to an anticancer drug ("hit gene identification step"). In some embodiments, hit genes identified from a population of treated cancer cells resistant to an anticancer drug are considered target genes whose mutations render said cancer cells sensitized or resistant to the anticancer drug, respectively.

In some embodiments, the step of identifying the hit gene comprises: i) identifying the sequence comprising the mutation of the hit gene (for example, an inactivating mutation) from the treated cancer cell population obtained in the "cancer cell obtaining step c)" and ii) identifying a hit gene corresponding to a sequence comprising the hit gene mutation (eg, an inactivating mutation). In some embodiments, mutations (e.g., inactivating mutations) comprising the hit gene are identified by sequencing, e.g., PCR sequencing (e.g., Sanger sequencing) or genome sequencing (or DNA-seq, such as next-generation sequencing or "NGS") the sequence of. For example, in some embodiments, post-treatment cancer cell populations that are resistant to anticancer drugs are identified by sequencing, by comparison to wild-type (or healthy individual) genome sequence, or by comparison to the genome sequence of the naive cancer cell population Sequences (nucleic acid fragments, PCR fragments, or whole genomes), and sequences containing hit gene mutations (eg, inactivating mutations) can be identified and mapped to hit genes. In some embodiments, the step of identifying hit genes further comprises isolating genomic DNA or RNA from the treated cancer cell population in step c). In some embodiments, the hit gene identifying step further comprises PCR amplification of nucleic acid sequences comprising hit gene mutations (eg, inactivating mutations).

In some embodiments, the cancer cell library described herein comprises said sgRNA construct or said sgRNA ^iBAR construct directed against a hit gene described herein. Therefore, in some embodiments, the step of identifying hit genes includes: i) identifying the sgRNA sequence or sgRNA ^iBAR sequence from the treated cancer cell population obtained in "cancer cell obtaining step c)"; and ii) identifying The hit gene corresponding to the guide sequence (targeted) of the sgRNA sequence or sgRNA ^iBAR sequence. In some embodiments, the sgRNA sequence or sgRNA ^iBAR sequence is identified by RNA sequencing (RNA-seq), eg, RNA NGS. In some embodiments, the step of identifying hit genes includes: i) identifying the nucleic acid sequence encoding the sgRNA or sgRNA ^iBAR from the treated cancer cell population obtained in "cancer cell obtaining step c)"; and ii) identifying A hit gene corresponding to the guide sequence encoded by the nucleic acid sequence. In some embodiments, the nucleic acid sequence encoding the sgRNA or sgRNA ^iBAR is identified by sequencing, eg, PCR sequencing (eg, Sanger sequencing) or genome sequencing (DNA-seq), eg, NGS. In some embodiments, the iBAR sequence can be used to identify the sgRNA ^iBAR sequence or the nucleic acid sequence encoding the sgRNA ^iBAR . In some embodiments, the step of identifying hit genes further includes isolating genomic DNA or RNA from the treated cancer cell population obtained in "cancer cell obtaining step c)". In some embodiments, the hit gene identifying step further comprises PCR amplification of the nucleic acid sequence encoding the sgRNA or sgRNA ^iBAR .

Methods of DNA-seq, RNA-seq, PCR sequencing (e.g., Sanger sequencing), DNA/RNA extraction, cDNA preparation, and data analysis are well known in the art and can be used herein to identify post-treatment cancer cell populations as appropriate Hit genes in anticancer drug resistance. Sequencing data can be analyzed and aligned to the genome using any method known in the art.

Target gene identification

In some embodiments, hit genes identified from a population of treated cancer cells resistant to an anticancer drug are considered targets in said cancer cells whose mutations render said cancer cells sensitive or resistant to an anticancer drug, respectively. Gene. In some embodiments, a hit gene identified from a population of treated cancer cells resistant to an anticancer drug (i.e., a viable population of treated cancer cells) is one whose mutation (eg, inactivation) renders the cancer cells resistant to Target genes of anticancer drug resistance.

In some embodiments, the hit genes identified from the treated cancer cell population resistant to an anticancer drug are further compared to a control, and/or further sorted and/or filtered according to a predetermined threshold level.

In some embodiments, identifying the target gene comprises: i) obtaining sequences comprising said hit gene mutations (eg, inactivating mutations) in the treated cancer cell population obtained from step c); ii) counting sequences comprising ranking the sequences of the hit gene mutations (eg, inactivating mutations); and iii) identifying hit genes that are ranked above a predetermined threshold level corresponding to sequences comprising the hit gene mutations (eg, inactivating mutations). In some embodiments, the ordering step includes adjusting the sequence based on the identity of the data in all sequences containing mutations (e.g., inactivating mutations) of the same hit gene (or the same target site of the same hit gene) corresponding to the same hit gene. Ranking of each sequence containing mutations in hit genes (eg, inactivating mutations). For example, data inconsistencies (eg, different directions of fold change relative to controls) can increase the variance of sequences corresponding to the same hit gene containing mutations in the hit gene (e.g., inactivating mutations) and decrease the ranking of the hit gene. In some embodiments, under the null hypothesis based on the RRA or α-RRA algorithm, hit genes are identified as corresponding to sequences comprising mutations (e.g., inactivating mutations) of the hit genes that are present in the aligned sequences The ordering of is always better than expected. In some embodiments, the predetermined threshold level is an FDR of value "X" (e.g., 0.1), and hits corresponding to sequences comprising said hit gene mutations (e.g., inactivating mutations) having FDR ≤ "X" Genes were identified as the target genes. In some embodiments, the predetermined threshold level is a value "X"-fold (eg, about 2-fold) enrichment or depletion, and corresponds to sequences comprising said hit gene mutation (eg, an inactivating mutation) and Hit genes with enrichment or depletion > "X"-fold were identified as the target genes. In some embodiments, the sequence comprising the hit gene mutation (eg, an inactivating mutation) is identified by sequencing, eg, Sanger sequencing or genome sequencing (or DNA-seq, such as NGS).

In some embodiments, the cancer cell library described herein comprises said sgRNA construct or said sgRNA ^iBAR construct directed against a hit gene described herein. Accordingly, in some embodiments, identifying the target gene comprises: i) obtaining the sgRNA sequence or sgRNA ^iBAR sequence in the treated cancer cell population obtained from step c); ii) corresponding sequence counting of the sgRNA sequence or sgRNA ^iBAR sequence ranking the guide sequences; and iii) identifying hit genes corresponding to guide sequences ranking above a predetermined threshold level. In some embodiments, the ranking includes aligning each guide for the sgRNA sequence or sgRNA ^iBAR sequence based on the identity of the data among all guide sequences corresponding to the same hit gene (or the same target site for the same hit gene). sequence ordering. For example, data inconsistencies (such as different directions of fold change relative to controls) can increase the variance of guide sequences corresponding to the same hit gene and decrease the ranking of that hit gene. In some embodiments, under the null hypothesis based on the RRA or α-RRA algorithm, hit genes are identified as corresponding to guide sequences that consistently rank better than expected among the aligned guide sequences. In some embodiments, the predetermined threshold level is an FDR of value "X" (eg, 0.1), and hit genes corresponding to guide sequences with FDR≦"X" are identified as the target genes. In some embodiments, the predetermined threshold level is an enrichment or depletion of "X"-fold (e.g., about 2-fold) in value, and hits corresponding to guide sequences with an enrichment or depletion ≥ "X"-fold Genes were identified as the target genes. In some embodiments, the sgRNA sequence or sgRNA ^iBAR sequence is identified by RNA-seq, eg, RNA NGS. In some embodiments, the nucleic acid sequence encoding the sgRNA or sgRNA ^iBAR is identified by genome sequencing (DNA-seq), such as NGS.

In some embodiments, a cancer cell library described herein comprises said sgRNA ^iBAR construct directed against a hit gene described herein. In some embodiments, identifying the target gene comprises: i) obtaining the sgRNA ^iBAR sequences in the treated cancer cell population obtained from step c); ii) ranking the corresponding guide sequences of the sgRNA ^iBAR sequences based on sequence counts, wherein the Ranking includes adjusting the rank of each guide sequence based on data concordance between iBAR sequences in the sgRNA ^iBAR sequences corresponding to guide sequences; and iii) identifying hit genes corresponding to guide sequences ranked above a predetermined threshold level. In some embodiments, under the null hypothesis based on the RRA or α-RRA algorithm, hit genes are identified as corresponding to guide sequences that consistently rank better than expected among the aligned guide sequences. In some embodiments, the predetermined threshold level is an FDR of value "X" (eg, 0.1), and hit genes corresponding to guide sequences with FDR≦"X" are identified as the target genes. In some embodiments, the predetermined threshold level is at least about 2-fold enrichment or depletion.

In some embodiments, sequence counts of sequences or guide RNAs comprising the hit mutations (eg, inactivating mutations) are determined from statistical analysis. In some embodiments, sequence counts for guide RNA and corresponding iBAR sequences are determined from statistical analysis. See Figure 3 for an exemplary target gene identification workflow. Statistical methods can be used to determine the identity of sequences comprising the hit gene mutation (eg, inactivating mutation), the sgRNA molecule, or the sequence of the sgRNA ^iBAR molecule that are enriched or depleted in the cancer cell population after treatment. In some embodiments, greater than 1 (eg, 2, 3, or more) biological or technical replicates are performed against the anticancer drug-treated cancer cell library. In some embodiments, greater than 1 (eg, 2, 3 or more) biological or technical replicates are performed against a control cancer cell population. In some embodiments, 2 or more (eg, 2, 3 or more) replicates from the anticancer drug treatment group (or control group) comprising the hit gene mutation (eg, an inactivating mutation ) sequences or guide RNAs are combined for calculating the mean and variance among the repetitions of the anticancer drug treatment group (or control group). Example statistical methods include, but are not limited to: linear regression, generalized linear regression, and hierarchical regression. In some embodiments, sequence counts follow a normalization method, such as total count normalization or median ratio normalization. In some embodiments, eg, for positive screening, median ratio normalization is preferred. In some embodiments, for example, for sequence counts following a normal distribution, the sequence counts are normalized by the median ratio and then subjected to mean-variance modeling. In some embodiments, MAGeCK (Li, W. et al. MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biol 15, 554 (2014)) is used to identify genes comprising the hit. Sequences of mutations (eg, inactivating mutations) or guide RNA sequences are sequenced, and/or target genes are identified. In some embodiments, MAGeCKiBAR (Zhu et al., Genome Biol. 2019; 20:20) is used to rank sequences or guide RNA sequences comprising said hit gene mutations (e.g., inactivating mutations), and/or or identify target genes.

In some embodiments, based on the difference between the profiles of sgRNA (or sgRNA ^iBAR ) or hit gene mutations in a treated cancer cell population and a control cancer cell population, mutations thereof that render the cancer cells sensitive or resistant to an anticancer drug are identified drug target gene. In some embodiments, the target gene is identified based on the difference between the profile of the hit gene mutation in a treated cancer cell population and a control cancer cell population. In some embodiments, the target gene is identified based on the difference between the profile of the sgRNA (or sgRNA ^iBAR ) in the treated cancer cell population and the control cancer cell population. In some embodiments, the control population of cancer cells is obtained from a library of cancer cells cultured under the same conditions and not exposed to the anticancer drug. In some embodiments, profiles of sgRNA (or sgRNA ^iBAR ) or hit gene mutations in treated and control cancer cell populations are identified by next generation sequencing (NGS), such as DNA-seq or RNA-seq . In some embodiments, the profile of the sgRNA (or sgRNA ^iBAR ) comprises: a sequence count of the sgRNA (or sgRNA ^iBAR ), or a sequence count of the corresponding guide sequence of the sgRNA (or sgRNA ^iBAR ). In some embodiments, the profile of a sgRNA (or sgRNA ^iBAR ) comprises: a sequence count of nucleic acids encoding said sgRNA (or sgRNA ^iBAR ), or a sequence count of nucleic acids encoding a guide sequence of the corresponding sgRNA (or sgRNA ^iBAR ). In some embodiments, the profile of hit gene mutations comprises: sequence counts of sequences comprising the hit gene mutations. In some embodiments, the methods described herein further comprise: culturing the same library of cancer cells under the same conditions without contacting the anticancer drug.

In some embodiments, the sequence count of the treated cancer cell population obtained from step c) (for example, the sequence count of the sgRNA or sgRNA ^iBAR or its guide sequence, the sequence count of the nucleic acid encoding the sgRNA or sgRNA ^iBAR or its guide sequence The sequence count, or the sequence count of the sequence containing the mutation in the hit gene) is compared to the corresponding sequence count obtained from a control cancer cell population or a control cancer cell library, for example, to provide a fold change (e.g., an actual fold change, or a fold Derivatives of change such as log2 or log10 fold change), for significance testing (e.g. FDR, p-value), for distribution statistics, and/or to provide gene or sequence ranking by scoring and/or derivation. In some embodiments, the control cancer cell population is obtained from a cancer cell library cultured under the same conditions without exposure to the anticancer drug, e.g., continuously cultured under the same culture conditions with Test group (anticancer drug treatment group) at the same time. In some embodiments, the control cancer cell population is an entire identical library of cancer cells cultured under the same conditions without being subjected to anticancer drug treatment, nor to any selection, recovery or acquisition method in step b) and step c , hereinafter also referred to as "control cancer cell library". In some embodiments, the control cancer cell population is obtained from the same cancer cell library cultured under the same conditions without undergoing anticancer drug treatment and the same obtaining method in step c).

In some embodiments, the methods described herein further comprise culturing the same cancer cell library under the same conditions without contacting the anticancer drug, and optionally undergoing the same acquisition method in "cancer cell acquisition step c)" Obtaining a control cancer cell population, wherein identifying a sequence from the control cancer cell population or a control cancer cell library corresponding to the sequence comprising the hit gene mutation (for example, an inactivating mutation) or the guide sequence of the sgRNA or sgRNA ^iBAR The presence of the hit gene, but its absence in the population of cancer cells after the treatment from step c) is identified, the hit gene is identified as the target gene. For example, for a cancer cell library comprising mutations A, B, and C in separate cancer cells, if only mutation A is identified from a population of treated cancer cells, the difference between mutations B and C is identified from the population of treated cancer cells. exists, indicating that hit genes B and C are target genes that, for example, confer susceptibility to anticancer drug killing when mutated.

In some embodiments, the obtained population of treated cancer cells is viable cancer cells that are resistant to an anticancer drug. In some embodiments, identifying the target gene comprises comparing the sequence count of the sgRNA (or sgRNA ^iBAR or guide sequence thereof, or nucleic acid encoding the sgRNA or sgRNA ^iBAR or guide sequence thereof) obtained from a cancer cell population after treatment with Sequence counts of sgRNA (or sgRNA ^iBAR or its guide sequence, or nucleic acid encoding sgRNA or sgRNA ^iBAR or its guide sequence) obtained from said control cancer cell population, wherein: i) its corresponding sgRNA (or sgRNA ^iBAR ) guide Sequences are identified as enriched in post-treatment cancer cell populations (e.g., viable and resistant to anticancer drugs) compared to control cancer cell populations and have FDR ≤ 0.1 (e.g., FDR ≤ 0.09, 0.08, 0.07, 0.06 , 0.05, 0.04, 0.03, 0.02, 0.01, 0.005, 0.001 or less) (and/or have at least about 2-fold enrichment, such as about 3-, 4-, 5-, 10-, 20- , 50-, 100-fold or more enrichment in any one) hit genes identified as targets whose mutations make said cancer cells resistant to anticancer drugs; and/or ii) their corresponding sgRNAs ( or sgRNA ^iBAR ) guide sequences were identified as depleted in post-treatment cancer cell populations (e.g., viable and resistant to anticancer drugs) with FDR ≤ 0.1 (e.g., FDR ≤ 0.09, 0.08) compared to control cancer cell populations , 0.07, 0.06, 0.05, 0.04, 0.03, 0.02, 0.01, 0.005, 0.001 or less any one) (and/or have at least about 2-fold depletion, such as about 3-, 4-, 5-, 10- , 20-, 50-, 100-fold or more depletion) were identified as target genes whose mutations sensitized the cancer cells to anticancer drugs. In some embodiments, the sgRNA (or sgRNA ^iBAR or guide sequence thereof, or nucleic acid encoding the sgRNA or sgRNA ^iBAR or guide sequence thereof) sequence counts are subjected to median ratio normalization followed by mean-variance modeling. In some embodiments, identifying the target gene comprises comparing the sequence count of the hit gene mutation obtained from the treated cancer cell population to the sequence count of the hit gene mutation obtained from the control cancer cell population, wherein: i) Their corresponding hit mutation sequences are identified as enriched in a post-treatment cancer cell population (e.g., viable and resistant to an anticancer drug) with an FDR ≤ 0.1 (e.g., FDR ≤ 0.09) compared to a control cancer cell population , 0.08, 0.07, 0.06, 0.05, 0.04, 0.03, 0.02, 0.01, 0.005, 0.001 or less in any one) (and/or have at least about 2-fold enrichment, such as about 3-, 4-, 5- , 10-, 20-, 50-, 100-fold or more enrichment), identified as target genes whose mutations make said cancer cells resistant to anticancer drugs; and/or ii ) whose corresponding hit gene mutation sequence is identified as depleted in a post-treatment cancer cell population (e.g., viable and resistant to an anticancer drug) with an FDR ≤ 0.1 (e.g., FDR ≤ 0.09) compared to a control cancer cell population , 0.08, 0.07, 0.06, 0.05, 0.04, 0.03, 0.02, 0.01, 0.005, 0.001 or less any one) (and/or have at least about 2-fold depletion, such as about 3-, 4-, 5-, 10-, 20-, 50-, 100-fold or more depletion) were identified as target genes whose mutations sensitized the cancer cells to anticancer drugs. In some embodiments, the hit mutation sequence counts are subjected to median ratio normalization followed by mean-variance modeling.

In some embodiments, the sgRNA library is an sgRNA ^iBAR library. In some embodiments, the variance of each guide sequence is adjusted based on the profile identity between the iBAR sequences in the sgRNA ^iBAR sequences corresponding to the guide sequences. In some embodiments, the variance of each guide sequence or sequence comprising a mutation (eg, an inactivating mutation) of the hit gene is adjusted based on the identity of the data in the same gene. As used herein, "data consistency" refers to the consistency of the sequencing results of the same guide sequence (e.g., sequence count, normalized sequence count, ordering or fold change) corresponding to different iBAR sequences in a screening experiment; or Concordance of sequencing results corresponding to different hit mutations of the same gene such as inactivating mutations (eg, different target loci of the same hit gene) or different sgRNA sequences. Theoretically true hits from the screen should have biologically relevant performance similarities, such as similar normalized sequence counts, ranks, and/or fold changes corresponding to sgRNA ^iBAR constructs with identical guide sequences, but different iBARs and/or similar normalized sequence counts, ranks, and/or fold changes corresponding to the same gene, but different hit gene mutation sequences such as inactivation mutation sequences (e.g., different target loci of the hit gene ) or different sgRNA sequences. See also WO2020125762 how to perform mean-variance modeling and how to adjust the variance of each guide sequence based on the data consistency between the iBAR sequences in the sgRNA ^iBAR sequences corresponding to the guide sequences.

In some embodiments, the profile identity between iBAR sequences in the sgRNA ^iBAR sequences corresponding to each guide sequence is determined based on the direction of the fold change of each iBAR sequence, wherein if the fold change of the iBAR sequences is relatively The variance of the guide sequence increases relative to each other in different directions (for example, increasing versus decreasing, increasing versus unchanged, or decreasing versus unchanged, all considered different directions). In some embodiments, the identity of data among different hit gene mutation (e.g., inactivating mutation) sequences or different sgRNA sequences corresponding to the same gene is based on each hit gene mutation (e.g., inactivating mutation) sequence or each The direction of the fold change of each sgRNA sequence is determined, wherein if the sequence of different hit mutations (for example, inactivation mutations) or the fold changes of different sgRNA sequences are in different directions relative to each other, then the hit mutation (for example, inactivation mutation) Live mutation) sequence or guide sequence variance increased. The increased variance caused by such data inconsistencies helps to exclude rare but significantly altered hit gene mutations (such as inactivating mutations)/sgRNA/sgRNA ^iBAR sequences in positive screening under high MOI conditions. For example, for the iBAR system, due to the high MOI during library construction, there can be a "free rider" of false positive sgRNAs associated with sgRNAs for true positive hit genes. As used herein, "free riders" refer to sgRNAs that enter the same cancer cell targeting unrelated sequences (eg, irrelevant hit genes) that are not related to sgRNAs that target true positive hit genes. In some embodiments, the variance of sgRNA ^iBARs is modified based on the direction of enrichment for different iBARs for each guide sequence within a set of sgRNA ^iBAR constructs. If all iBARs (i.e., all iBARs corresponding to the same guide sequence) of a group of sgRNA ^iBAR constructs exhibit the same fold-change direction, that is, they are all larger or smaller than the control group, then the variance of the group of sgRNA ^iBAR constructs (or guide sequence The variance of ) will be constant. If the iBARs of a set of sgRNA ^iBAR constructs (or iBARs corresponding to the same guide sequences) showed inconsistent fold change directions compared to controls, the corresponding guide sequences were penalized for increasing their variance. In some embodiments, the final adjusted variance for discordant sgRNA ^iBARs is the model estimated variance (eg, by mean-variance modeling) plus the experimental variance calculated from anticancer drug treated samples and controls. In some embodiments, the hit gene comprises 2 or more (eg, 2, 3, 4, 5 or more, such as 3) hit gene mutations (eg, inactivating mutations), or the hit gene consists of 2 or more (e.g., 2, 3, 4, 5 or more, such as 3) different guide sequences targeting (e.g., 2 or more different sgRNAs, or 2 or more sets of sgRNA ^iBAR constructs, each comprising a guide sequence targeting a different target site). In some embodiments, the data identity between the iBAR sequences in the sgRNA ^iBAR sequences corresponding to each guide sequence and the same hit gene is determined based on the fold change direction of each iBAR sequence, wherein if the corresponding iBAR If the fold changes of the sequences are in different directions relative to each other, the variance of the guide sequences increases, and if two or more (e.g., 2, 3, 4, 5 or more, e.g., 3) targeting the same hit gene (1) the fold changes of different guide sequences relative to each other are in different directions, then the variance of the guide sequences (or the variance of the hit genes) is further increased. For example, for sgRNA A and sgRNA B targeting different target sites X of the same hit gene, if the guide sequences of both sgRNA A and sgRNA B are enriched or depleted compared to the control, then each guide sequence or hit gene The variance was unchanged; the variance per guide or hit gene increased if the guide was enriched for sgRNA A and depleted for guide for sgRNA B compared to the control. In some embodiments, data identity between iBAR sequences corresponding to sgRNA ^iBAR sequences of the same hit gene is determined based on the direction of the fold change of each iBAR sequence, wherein if the fold change of the iBAR sequence corresponding to the same hit gene In different orientations relative to each other, the variance of each guide sequence targeting the same hit gene increases, and the variance of each guide sequence targeting the same hit gene (or the variance of the hit genes) increases. For example, if 3 sets of sgRNA ^iBARs (4 sgRNA ^iBARs per set) target 3 different target loci of the same hit gene, if all 12 iBAR sequences are identified as enriched compared to the control, then all 3 guide sequences The variance of α remained constant; if some iBAR sequences were identified as enriched, while others were identified as unchanged or depleted compared to controls, the variance of all 3 guide sequences increased.

In some embodiments, different target gene loci of the same hit gene comprise sequences of hit gene mutations (eg, inactivating mutations), and the fold change of the same hit gene in the corresponding target loci exhibits different directions; targeting The sgRNA or sgRNA ^iBAR of different target gene sites of the same hit gene, the fold change of the same hit gene in the corresponding target site presents different directions; or the fold change of the sgRNA in the corresponding iBAR presents different directions, Scores can be penalized by increasing variance, resulting in lower scores and rankings for certain hits. For example, if 3 sets of sgRNA ^iBARs (4 sgRNA ^iBARs per set) target 3 different target loci of the same hit gene, the hit gene has low variance if all 12 iBAR sequences are identified as enriched compared to the control , and thus rank and/or score high (e.g., highly ranked drug-sensitivity genes, with higher sensitivity scores); if some iBAR sequences are identified as enriched, while others are identified as not compared to controls variable or depleted, then the hit genes have high variance and are therefore ranked and/or scored low (eg, a low ranked resistance gene has a low resistance score).

Within a set of sgRNA ^iBAR constructs, the ranking for guide sequences can be adjusted based on the concordance of enrichment directions for a predetermined threshold number x of different iBAR sequences in the set, where x is an integer from 1 to y. For example, if at least x iBAR sequences of the sgRNA ^iBAR group exhibit the same fold change direction, that is, they are all larger or smaller than the fold change direction of the control cancer cell population, then the ranking (or variance) of the guide sequences does not change . However, if more than yx different iBAR sequences show inconsistencies in the direction of fold change, then the sgRNA ^iBAR set is penalized by lowering its rank, eg, by increasing its variance. In some embodiments, based on a predetermined threshold number x of mutations (e.g., inactivating mutations) of different hit genes or the agreement of the enrichment directions of different guide sequences corresponding to the same hit gene, the target for inclusion can be adjusted (or further adjusted). Ranking of sequences or guide sequences that hit genetic mutations (eg, inactivating mutations), where x is an integer from 1 to y. For example, if at least x hit gene mutations (e.g., inactivating mutations) or x guide sequences corresponding to the same hit gene exhibit the same fold change direction, that is, they are all larger or smaller than the fold change direction of the control cancer cell population, The ordering (or variance) is then unchanged. However, if more than yx different hit gene mutations (e.g., inactivating mutations) or more than yx different guide sequences show inconsistencies in the direction of fold change, then the sequence comprising the hit gene mutation (e.g., inactivating mutation) or all If the guide sequence described above is penalized by reducing its rank, for example, by increasing its variance.

In some embodiments, the P-value for each sequence comprising a hit gene mutation (e.g., an inactivating mutation), or the P-value for each guide sequence of an sgRNA or sgRNA ^iBAR , is calculated using the treatment group compared to the control group. The mean and variance (for example, experimental variance, model estimated variance, or corrected variance based on data inconsistency) were calculated.

Robust Rank Aggregation (RRA; Kolde R et al. Bioinformatics. 2012;28:573–580) or modified RRA (eg, α-RRA in MAGeCK; Li W et al. Genome Biol. 2014;15 :554) is one of the tools available in the field for statistics and ranking, which detects genes that rank consistently better than expected under the null hypothesis of uncorrelated inputs, assigns a significance score to each gene, and ranks Lists are merged into a single sort. It assumes that all information-normalized ranks come from a distribution strongly skewed towards zero, and computes binomial probabilities from the assumed uniform distribution of ranks to detect these distributions. And, assign a P-value to each element in the aggregated list that ranks the genes and describes how much better they rank than expected, making randomly ordered genes less significant. The underlying probabilistic model makes the RRA algorithm parameter-free and robust to outliers, noise, and errors. Significance scoring also provides a rigorous way to keep only statistically relevant genes in the final list. These properties make this method powerful and attractive in many settings. Briefly, in RRA and α-RRA, for each sequence containing a hit gene mutation (e.g., an inactivating mutation), each sgRNA guide sequence corresponding to the hit gene or each sgRNA ^iBAR guide sequence (hereinafter also Called "hit gene mutation (e.g., inactivating mutation)/sgRNA guide sequence/sgRNA ^iBAR guide sequence") (e.g., when there are 3 sgRNAs targeting the same hit gene), the algorithm looks at how the sequence localizes in A normalized ranked list of all hit gene mutations (such as inactivating mutations)/sgRNA guide sequences/sgRNA ^iBAR guide sequences obtained from treated cancer cell populations or control cancer cell populations/control cancer cell libraries and combined with all Baseline comparisons were made to hit gene mutations (eg, inactivating mutations)/sgRNA-guide sequences/sgRNA ^iBAR -guide sequence random rearrangements (“aligned sequences”). As a result, P-values are assigned to all hit gene mutations (e.g., inactivating mutations)/sgRNA guides/sgRNA ^iBAR guides corresponding to their hit genes, showing how much better their position in the ranked list is than would be expected by random . This P-value was used to rerank the hit gene mutation (eg, inactivating mutation)/sgRNA guide sequence/sgRNA ^iBAR guide sequence corresponding to the hit gene and determine its significance. Those skilled in the art can understand that other tools can also be used for statistics and sorting. In some embodiments, RRA or α-RRA is used to calculate a final score for each hit to obtain a ranking of the hits based on the mean and variance (eg, modified variance) of each hit.

In some embodiments, the hit gene mutations (e.g., inactivating Mutation), sgRNA guide sequence or sgRNA ^iBAR guide sequence (hit gene mutation (eg, inactivating mutation)/sgRNA guide sequence/sgRNA ^iBAR guide sequence), the distribution model is used to estimate biological/experimental replicates and treatment groups The probability of each hit gene mutation (such as an inactivating mutation)/sgRNA guide sequence/sgRNA ^iBAR guide sequence compared with the control group, and then apply the RRA or α-RRA algorithm to identify the corresponding top-ranked (for example, top-ranked α%, such as top 5%) of hit gene mutations (eg, inactivating mutations)/sgRNA guides/sgRNA ^iBAR guides for positive or negative selection of hit genes. Lower RRA scores correspond to stronger enrichment of hit genes. In some embodiments, the top hit gene mutations (e.g., inactivating mutations)/sgRNA guide sequences/sgRNA ^iBAR guide sequences with P values below a threshold value (e.g., P value<0.25) are selected, and the corresponding hit genes identified as target genes. In some embodiments, the top hit mutations (e.g., inactivating mutations)/sgRNA guide sequences/sgRNA ^iBAR guide sequences with FDR below a threshold (e.g., FDR≦0.1) are selected and the corresponding hit genes are identified as target gene. In some embodiments, when multiple hit gene mutations (e.g., inactivating mutations)/sgRNA guides/sgRNA ^iBAR guides are designed for the same hit gene, only one gene rank by Previous hit gene mutation (eg, inactivating mutation)/sgRNA guide sequence/sgRNA ^iBAR guide sequence. RRA or α-RRA assumes that if a hit gene has no effect on cancer cell sensitivity/resistance to anticancer drug treatment, then the hit gene mutation (e.g., inactivating mutation)/sgRNA guide sequence corresponding to such a hit gene /sgRNA ^iBAR guide sequences should be evenly distributed in the sorted list of all hit gene mutations (such as inactivating mutations)/sgRNA guide sequences/sgRNA ^iBAR guide sequences obtained from the cancer cell library. In some embodiments, all hit gene mutations (e.g., inactivating mutations)/sgRNA guide sequences/sgRNA ^iBAR guide sequences are sorted and compared between the treatment group and the control group according to their relative ranking in each group and the differences in each group. distribution, compared with RRA or α-RRA. Hit genes covered by all cancer cell libraries were ranked by comparing the slope of the beta distribution of hit gene mutations (such as inactivating mutations)/sgRNA guides/sgRNA ^iBAR guides to the uniform null hypothesis model, and their corresponding Hit gene mutations (such as inactivating mutations)/sgRNA guides/sgRNA ^iBAR guides rank consistently higher than expected hits by permutation test for statistical significance (P-value) and/or acceptable FDR by Benjamini-Hochberg procedure Genes, are prioritized in RRA or α-RRA (lower RRA score). This RRA or α-RRA analysis can significantly reduce or eliminate false positives due to perturbations in experiments or sampling. In some embodiments, hits are scored based on ranking scores of corresponding hit gene mutations (e.g., inactivating mutations)/sgRNA guide sequences/sgRNA ^iBAR guide sequences obtained by mean-variance modeling after normalization by median ratio Gene sequencing. In some embodiments, the hit genes are further ranked by RRA or α-RRA considering multiple hit mutations (eg, inactivating mutations)/sgRNA guide sequences/sgRNA ^iBAR guide sequences for the same hit gene.

In some embodiments, the predetermined threshold level is the FDR value of the permutation test of all hit gene mutations (eg, inactivating mutations)/sgRNA guide sequences/sgRNA ^iBAR guide sequences obtained from the experiment (treatment or control). In some embodiments, FDR values are determined by considering the largest potential true target genes (eg, involved in specific pathways in response to anticancer drug treatment) in a particular screen. In some embodiments, the threshold is the top β% (normalized or not) of sequence counts obtained from a cancer cell library, and corresponding hit genes are identified as target genes.

Any target identification method known in the art can be used herein. For example, Empirical Bayesian (Target Identification by Likelihood) or algorithms based thereon, such as casTLE (cas9 High Throughput maximum Likelihood Estimator, cas9 High Throughput Maximum Likelihood Estimator), which uses Empirical Bayesian Yess framework to account for multiple sources of variability, including reagent efficacy and off-target effects for analysis of large-scale genomic perturbation screens, and providing casTLE scores for ranking and threshold cutoffs (Morgens, DW et al. (2016) Nat Biotechnol 34, 634-636). In some embodiments, log2 ratio differences and p-values from t-tests can be used to identify target genes. For example, RIGER (Luo, J. et al. (2009). Cell 137, 835-848), which ranks shRNAs according to their differential effects between two types of samples, then identifies shRNAs targeting The genes that are critical to the difference between the classes are thus identified. LFC and P-values can be used for ranking and threshold cutoffs. In some embodiments, the probability mass function of the binomial distribution (or an algorithm based on the binomial distribution) can be used for target gene identification. For example, STARS (Doench, JG, et al. (2016) Nat Biotechnol 34, 184-191), where STAR scores can be used for ranking and threshold cutoffs. In some embodiments, based on negative binomial model and α-RRA algorithm can be used for target gene identification, such as MAGeCK (Li, W. et al. (2014) Genome Biol 15, 554), and RRA score for ranking and threshold cutoff. In some embodiments, algorithms based on β-binomial modeling can be used for target gene identification, such as CRISPR-β-binomial (CB2) (Jeong, HH et al. (2019). Genome Res 29, 999 -1008), P-value or FDR can be used for ranking and threshold cutoff. In some embodiments, sgRNA or sgRNA ^iBAR raw read count ranking, normalized read count ranking, and/or log2 fold change between treatment and control groups can be used for target gene identification, such as during stringent positive screening , for example, hit genes corresponding to the top X% of read counts are identified as target genes.

In some embodiments, target genes are identified as a positive screen by identifying hit gene mutation (eg, inactivating mutation) sequences or guide sequences that are enriched in cancer cell populations following treatment. In some embodiments, target gene identification is a negative screen by identifying hit gene mutation (eg, inactivating mutation) sequences or guide sequences that are depleted in the cancer cell population following treatment. Hit gene mutations (e.g., inactivating mutations) sequences or guide sequences that are enriched in the post-treatment cancer cell population based on sequence count or fold change are highly ranked, while hits that are depleted in the post-treatment cancer cell population based on sequence count or fold change Gene mutation (eg, inactivating mutation) sequences or guide sequences are ranked low. In some embodiments, the enrichment or depletion is relative to total sequence counts obtained from the population of cancer cells after treatment. In some embodiments, the enrichment or depletion is counted relative to corresponding sequences in a control cancer cell population or a control cancer cell library, such as a control cancer cell population from the same cancer cell library that has not been treated with an anticancer drug. In some embodiments, the enrichment or depletion is calculated based on the RRA or α-RRA algorithm.

In some embodiments, the method comprises subjecting the cancer cell library from step a) to at least two (e.g., at least 3, 4, 5, 6, 7, 7, 8) of the anticancer drugs in step b). , 10 or more) separate different treatments, and growing the cancer cell library in step c) to obtain a post-treatment cancer cell population from each treatment (e.g., all viable and anticancer drug drug resistance), identifying one or more hit genes in the post-treatment cancer cell population obtained from each treatment; and obtaining one or more hit genes identified from all treatments, thereby identifying mutations thereof in the cancer cells Target genes that make the cancer cells sensitized or resistant to anticancer drugs. In some embodiments, identifying the target gene comprises identifying one or more hit genes in a treated cancer cell population obtained from at least two (e.g., at least 3, 4, 5, 6, 7, 7, 8, 10 or more) individual different treatments, wherein: i) its corresponding sgRNA or sgRNA ^iBAR guide sequence or hit gene mutation compared to the control cancer cell population in the anticancer drug Drug-resistant post-treatment cancer cell populations (live) were identified as enriched and had FDR ≤ 0.1 (e.g., FDR ≤ 0.09, 0.08, 0.07, 0.06, 0.05, 0.04, 0.03, Any of 0.02, 0.01, 0.005, 0.001 or less) (and/or having at least about 2-fold enrichment, such as about 3-, 4-, 5-, 10-, 20-, 50-, 100-fold or more enriched) hit genes identified as target genes whose mutations make said cancer cells resistant to anticancer drugs; and/or ii) their corresponding sgRNA or sgRNA ^iBAR guide sequences or hit genes Mutations were identified as depleted in anticancer drug resistant post-treatment cancer cell populations (live) compared to control cancer cell populations and had FDR ≤ 0.1 (e.g., FDR ≤ 0.09, 0.08 , 0.07, 0.06, 0.05, 0.04, 0.03, 0.02, 0.01, 0.005, 0.001 or less any one) (and/or have at least about 2-fold depletion, such as about 3-, 4-, 5-, 10- , 20-, 50-, 100-fold or more depletion) were identified as target genes whose mutations sensitized the cancer cells to anticancer drugs.

In some embodiments, the method comprises subjecting the cancer cell library from step a) to at least two (e.g., at least 3, 4, 5, 6, 7, 7, 8) of the anticancer drugs in step b). , 10 or more) separate different treatments, and culturing said cancer cell library in step c) to obtain a post-treatment cancer cell population from each treatment (e.g., all viable and resistant to an anticancer drug drug), identifying one or more hit genes in the post-treatment cancer cell population obtained from each treatment; and combining the one or more hit genes identified from all treatments, thereby identifying in the cancer cells whose mutations make the Target genes that are sensitive or resistant to anticancer drugs in cancer cells. In some embodiments, identifying the target gene comprises identifying one or more hit genes in a treated cancer cell population from at least two (e.g., at least 3, 4, 5 , 6, 7, 7, 8, 10 or more) individual different treatments in which: i) its corresponding sgRNA or sgRNA ^iBAR guide sequence or hit gene mutations are more effective in anticancer drug resistance than control cancer cell populations Drug-treated cancer cell populations (live) identified as enriched and having a FDR ≤ 0.1 in at least one treatment (e.g., FDR ≤ 0.09, 0.08, 0.07, 0.06, 0.05, 0.04, 0.03, 0.02, 0.01 , 0.005, 0.001 or less) (and/or having at least about 2-fold enrichment, such as about 3-, 4-, 5-, 10-, 20-, 50-, 100-fold or more Any one of the hit genes in the enrichment) identified as target genes whose mutations render the cancer cells resistant to anticancer drugs; and/or ii) their corresponding sgRNA or sgRNA ^iBAR guide sequence or hit gene mutations compared Control cancer cell populations are identified as depleted in post-treatment cancer cell populations (viable) that are resistant to anticancer drugs and have a FDR ≤ 0.1 (e.g., FDR ≤ 0.09, 0.08, 0.07, 0.06, 0.05, 0.04, 0.03, 0.02, 0.01, 0.005, 0.001 or less) (and/or have at least about 2-fold depletion, such as about 3-, 4-, 5-, 10-, 20-, 50 -, 100-fold or more depletion) were identified as target genes whose mutations sensitized the cancer cells to anticancer drugs.

In some embodiments, the methods described herein comprise subjecting the cancer cell library from step a) to two separate treatments b1) and b2): b1) combining the cancer cell library from step a) with the anticancer drug contacting at a concentration of about IC50 to about IC70 for about 9 to about 10 doubling times; b2) contacting the cancer cell library from step a) with the anticancer drug at a concentration of about IC50 to about IC70 for about 15 to about 16 doubling times; c1) growing the cancer cell library from treatment b1) to obtain a post-treatment cancer cell population (e.g., viable and resistant to an anticancer drug); c2) growing the cancer cell library from treatment b2) Obtaining a post-treatment cancer cell population (e.g., viable and resistant to an anticancer drug); d1) identifying one or more hit genes in the post-treatment cancer cell population from treatment b1), d2) identification from treatment b2) one or more hit genes in the treated cancer cell population of , and d3) obtaining one or more hit genes identified from treatments b1) and b2), thereby identifying mutations in said cancer cells that render said cancer cells Target genes for sensitivity or resistance to anticancer drugs. In some embodiments, identifying said target gene comprises identifying one or more hit genes in post-treatment cancer cell populations obtained from two separate treatments b1) and b2), wherein: i) its corresponding sgRNA or sgRNA ^iBAR guide sequence or hit gene mutations were identified as enriched in anticancer drug resistant post-treatment cancer cell populations (live) compared to control cancer cell populations and had FDR in both treatments b1) and b2) ≤ 0.1 (e.g., any of FDR ≤ 0.09, 0.08, 0.07, 0.06, 0.05, 0.04, 0.03, 0.02, 0.01, 0.005, 0.001 or less) (and/or have at least about 2-fold enrichment, such as about 3-, 4-, 5-, 10-, 20-, 50-, 100-fold or more enrichment) hit genes identified as mutations that render the cancer cells resistant to the anticancer drug and/or ii) its corresponding sgRNA or sgRNA ^iBAR guide sequence or hit gene mutations are identified as depleted in post-treatment cancer cell populations (live) resistant to anticancer drugs compared to control cancer cell populations and have FDR ≤ 0.1 in both treatments b1) and b2) (eg, FDR ≤ any of 0.09, 0.08, 0.07, 0.06, 0.05, 0.04, 0.03, 0.02, 0.01, 0.005, 0.001 or less) (and/or have at least about 2-fold depletion, such as any of about 3-, 4-, 5-, 10-, 20-, 50-, 100-fold or more depletion), are identified as A target gene whose mutation renders the cancer cell sensitive to anticancer drugs.

In some embodiments, the methods described herein comprise subjecting the cancer cell library from step a) to two separate treatments b1) and b2): b1) combining the cancer cell library from step a) with the anticancer drug contacting at a concentration of about IC50 to about IC70 for about 9 to about 10 doubling times; b2) contacting the cancer cell library from step a) with the anticancer drug at a concentration of about IC50 to about IC70 for about 15 to about 16 doubling times; c1) growing the cancer cell library from treatment b1) to obtain a post-treatment cancer cell population (e.g., viable and resistant to an anticancer drug); c2) growing the cancer cell library from treatment b2) Obtaining a post-treatment cancer cell population (eg, viable and resistant to an anticancer drug); d1) identifying one or more hit genes in the post-treatment cancer cell population from treatment b1), d2) identification from treatment b2) One or more hit genes in the treated cancer cell population of , and d3) combining the one or more hit genes identified from treatment b1) and treatment b2), thereby identifying said cancer cells whose mutations make said cancer cells Target genes of cells that are sensitive or resistant to anticancer drugs. In some embodiments, identifying said target gene comprises identifying one or more hit genes in post-treatment cancer cell populations obtained from two separate treatments b1) and b2), wherein: i) its corresponding sgRNA or sgRNA ^iBAR guide sequence or hit gene mutations are identified as enriched in post-treatment cancer cell populations (live) resistant to anticancer drugs compared to control cancer cell populations and have FDR ≤ 0.1 in treatment b1) or b2) (e.g., any of FDR≤0.09, 0.08, 0.07, 0.06, 0.05, 0.04, 0.03, 0.02, 0.01, 0.005, 0.001 or less) (and/or have at least about 2-fold enrichment, such as about 3- , 4-, 5-, 10-, 20-, 50-, 100-fold or more enrichment) hit genes identified as targets whose mutation renders the cancer cell resistant to the anticancer drug gene; and/or ii) its corresponding sgRNA or sgRNA ^iBAR guide sequence or hit gene mutation is identified as depleted in a post-treatment cancer cell population (live) resistant to an anticancer drug compared to a control cancer cell population and Have FDR ≤ 0.1 (e.g., any of FDR ≤ 0.09, 0.08, 0.07, 0.06, 0.05, 0.04, 0.03, 0.02, 0.01, 0.005, 0.001 or less) in treatment b1) or b2) (and/or Hit genes with at least about 2-fold depletion, such as about any of 3-, 4-, 5-, 10-, 20-, 50-, 100-fold or more depletion, are identified as mutations that render the Target genes sensitive to anticancer drugs in cancer cells.

In some embodiments, the methods include identifying targets in cancer cells whose mutations render the cancer cells sensitive or resistant to two or more (e.g., 2, 3, 4, 5, or more) anticancer drugs. Gene. In some embodiments, the two or more different anticancer drugs target the same cancer target (eg, PARP). In some embodiments, the two or more different anticancer drugs target different cancer targets (eg, one targets PARP and the other targets a non-PARP target). In some embodiments, the method comprises: i) using any of the methods described herein for two or more (e.g., 2, 3, 4, 5 or more) different anticancer drugs when treated separately. method (for example, may comprise one or more separate different treatments), each identifying a set of one or more target genes whose mutations sensitize the cancer cells to an anticancer drug; and ii) obtaining the presence of One or more target genes in each set of target genes identified for each anticancer drug, thereby identifying target genes whose mutations render the cancer cell sensitive to combined treatment of two or more different anticancer drugs . In some embodiments, the method comprises: i) using any of the methods described herein for two or more (e.g., 2, 3, 4, 5 or more) different anticancer drugs when treated separately. A method (eg, which may comprise one or more separate distinct treatments) of respectively identifying a set of one or more target genes whose mutations render said cancer cells resistant to an anticancer drug; and ii) obtaining One or more target genes present in combination in the set of target genes identified for all anticancer drugs, thereby identifying targets whose mutations render said cancer cells resistant to the combined treatment of two or more different anticancer drugs Gene. In some embodiments, the method comprises: a) providing a cancer cell library described herein; b) combining the cancer cell library with two or more (eg, 2, 3, 4, 5 or more) Combinatorial exposure (e.g., simultaneous exposure, overlapping time exposure, or sequential exposure) of different anticancer drugs; c) growing the cancer cell library to obtain a treated cancer cell population (e.g., viable and anticancer drug drug resistance); and using any of the target gene identification methods described herein, identifying the target gene based on the difference between the profile of the sgRNA or sgRNA ^iBAR or hit gene mutation in the treated cancer cell population and the control cancer cell population.

In some embodiments, the method further comprises ranking the identified target genes based on the enrichment or depletion of sgRNA or sgRNA ^iBAR guide sequence or hit gene mutations in the treated cancer cell population compared to the control cancer cell population. Target gene ranking was performed according to degree (eg, fold enriched, fold depleted, enriched FDR, or depleted FDR). In some embodiments, the ranking of the target genes is further adjusted based on the identity of the data among all sequences comprising the mutation of the hit gene (eg, an inactivating mutation) corresponding to the same target gene. In some embodiments, the sgRNA library is a sgRNA ^iBAR library and is based on data identity in the iBAR sequence of the sgRNA ^iBAR sequence corresponding to the guide sequence of the target gene, and/or based on the identity of the iBAR sequence corresponding to the same target gene (e.g. , the same or different target sites), the data consistency among all guide sequences further adjusts the target gene ranking. In some embodiments, the RRA or α-RRA algorithm is used to rank the identified target genes. In some embodiments, the ranking of the identified target genes is: i) based on the identity of the data in all sequences containing the mutations in the hit gene (e.g., inactivating mutations) corresponding to the same target gene; or ii) based on Data identity in the iBAR sequence of said sgRNA ^iBAR sequence corresponding to the guide sequence of the target gene; and/or iii) based on sgRNA or sgRNA ^iBAR corresponding to the same target gene (e.g., same or different target site) Data identity across all guide sequences; where the identified target genes are ranked from highest to lowest based on the degree of data identity from highest to lowest. In some embodiments, the treated cancer cell population is a viable cell population, ie resistant to an anticancer drug. In some embodiments, the method further comprises assigning a sensitivity score or a drug resistance score to the identified target gene, wherein the post-treatment cancer cell population (e.g., viable and Anticancer drug resistance) The multiple of enrichment of the sgRNA or sgRNA ^iBAR guide sequence or hit gene mutation (or based on enrichment FDR - the smaller the FDR, the higher the ranking; or based on the degree of data consistency - data consistency The higher the degree, the higher the ranking), the target genes whose mutations make the cancer cells resistant to anticancer drugs are ranked from high to low, and a drug resistance score is assigned to each target gene accordingly and/or wherein the sgRNA or sgRNA iBAR guide sequence or hit gene mutations are depleted based on the depletion of the sgRNA or sgRNA ^iBAR guide sequence or hit gene mutation in the treated cancer cell population (e.g., viable and resistant to an anticancer drug) as compared to the control cancer cell population Fold (either based on depleted FDR - the smaller the FDR, the higher the ranking; or based on the degree of data agreement - the higher the degree of data agreement, the higher the ranking), target genes whose mutations sensitize said cancer cells to anticancer drugs Ranked from highest to lowest, and each target gene is assigned a sensitivity score accordingly, from highest to lowest.

In some embodiments, the method further comprises validating the identified target gene by: a) modifying the cancer cell by creating a mutation (e.g., an inactivating mutation) in the target gene of the cancer cell; and b ) determining the sensitivity or resistance of the modified cancer cells to the anticancer drug. In some embodiments, the method comprises subjecting the modified cancer cells to any of the anticancer drug treatment steps b) described herein, and optionally any cancer cell harvesting steps. Any cell viability assay known in the art and described herein can be used to determine the sensitivity or resistance of the modified cancer cells to anticancer drugs. When the modified cancer cells are a homogeneous population (i.e. contain the same mutation such as an inactivating mutation), more cell viability assays can be used, such as those based on metabolic activity, e.g. diazole (redox indicator) , tetrazolium salt MTT and XTT, dihydrorhodamine, calcein or luciferin, luminescent ATP determination. Mutations in target genes (e.g., inactivating mutations) can be generated by any method known in the art and described herein, such as by mutagens or TALEN-, ZFN-, or CRISPR/Cas-mediated gene editing (e.g., For target genes using Cas, sgRNA). In some embodiments, the cancer cell prior to the development of a mutation (eg, an inactivating mutation) in the target gene contains an endogenous mutation, as endogenous mutations often occur in cancer cells.

III. Methods of Treating Cancer and/or Selecting Patients

Another aspect of the invention provides a method of treating cancer in an individual based on any of the target genes described herein, or based on one or more target genes identified using any of the target gene identification methods described herein, selecting an individual with cancer Methods of administering anticancer drug therapy, and methods of excluding individuals with cancer from anticancer drug therapy.

"Aberrations" of genes (e.g., target genes, drug-sensitivity genes, drug-resistance genes) refer to genetic and/or epigenetic aberrations, abnormal expression levels, and/or abnormal activity levels of genes, and/or genes (or gene product , such as RNA or protein), which may lead to abnormal loss of function or reduced function and/or abnormal expression (such as reduction or deletion) of RNA and/or protein encoded by the gene. In some embodiments, genetic aberrations include alterations (i.e., mutations) in nucleic acid (eg, DNA or RNA) or protein sequence or abnormal epigenetic features associated with genes, including but not limited to: coding, non-coding, regulatory , enhancers, silencers, promoters, introns, exons and untranslated regions. In some embodiments, genetic aberrations include genetic mutations, including but not limited to: deletions, frameshifts, insertions, indels, missense mutations, nonsense mutations, point mutations, silent mutations, splice site mutations, splice mutations body and translocation. In some embodiments, the mutation may be a loss or deletion of a gene. In some embodiments, the mutation is a deleterious mutation. In some embodiments, an aberration of a gene comprises an aberrant expression (eg, reduction or deletion) of a gene (eg, mRNA or protein) compared to a control level. In some embodiments, an aberration of a gene comprises an aberrant (eg, reduced or eliminated) activity of a gene product (eg, RNA or protein) compared to a control level, such as activation or inhibition of a downstream target. In some embodiments, genetic aberrations include aberrant modifications (e.g., increases, decreases, or mismodifications) of genes (e.g., at the DNA level or histone levels) or gene products (e.g., RNA or proteins) compared to control levels. ), such as post-translational modifications (eg, phosphorylation, ubiquitination). In some embodiments, an aberration of a gene comprises a copy number change of the gene. In some embodiments, the copy number change of a gene results from a structural rearrangement of the genome, including deletions, duplications, inversions, and translocations. In some embodiments, aberrations in a gene include abnormal epigenetic features of the gene, including but not limited to: DNA methylation, hydroxymethylation, increased or decreased histone binding, histone methylation, histone acetylation chromatin remodeling, etc. In some embodiments, aberrations are determined by comparison to a control or reference, such as a reference sequence (e.g., nucleic acid sequence or protein sequence), control expression (e.g., RNA or protein expression) levels, control activity (e.g., activation or inhibition of downstream targets). ) level, or a control modification (eg, post-translational modification or epigenetic modification) level. In some embodiments, the abnormal expression level or abnormal activity level in the gene can be lower than the control level (such as about 10%, 20%, 30%, 40%, 60%, 70%, 80%, lower than the control level). 90% or more). In some embodiments, the level of aberrant modification (e.g., modification of DNA, nucleosomes, RNA, or protein) in a gene may be lower than a control level (e.g., about 10%, 20%, 30%, 40% lower than a control level , 60%, 70%, 80%, 90% or more), or higher than the control level (such as about 10%, 20%, 30%, 40%, 60%, 70% higher than the control level , 80%, 90% or more). In some embodiments, the abnormal modification in the gene is a wrong modification, such as ubiquitination instead of phosphorylation. In some embodiments, the control level (eg, expression level, or activity level, or modification level) is the median level (eg, expression level, or activity level, or modification level) of a control population. In some embodiments, the control population is a population having the same cancer as the individual being/to be treated. In some embodiments, the control population is a healthy population without cancer, and optionally has comparable demographic characteristics (eg, gender, age, race, etc.) as the individuals being/to be treated. In some embodiments, the control level (eg, expression level, or activity level, or modified level) is a level (eg, expression level, or activity level, or modified level) from healthy tissue of the same individual. Genetic aberrations can be identified by comparison to a reference sequence, including the epigenetic pattern of the reference sequence in a control sample. In some embodiments, the reference sequence is a sequence (DNA, RNA, or protein sequence) corresponding to a fully functional allele of the corresponding gene, such as an allele of the corresponding gene present in healthy population individuals without cancer (e.g., prevalent alleles), but may also optionally have similar demographic characteristics (eg, gender, age, race, etc.) to the individual being/to be treated.

Herein, the aberration of the target gene is also referred to as "target gene aberration", including but not limited to target gene mutation. Aberrations in drug-sensitive genes, also referred to herein as "drug-sensitive aberrations," include, but are not limited to, drug-sensitive mutations that sensitize cancer cells to anticancer drugs. Aberrations in drug resistance genes, also referred to herein as "drug resistance aberrations," include, but are not limited to, drug resistance mutations that render cancer cells resistant to anticancer drugs. An aberration of a patient's gene is also referred to herein as a "patient's genetic aberration", including but not limited to a patient's gene mutation. An aberration of a patient target gene, also referred to herein as a "patient target gene aberration," includes, but is not limited to, a patient target gene mutation.

The "status" of a genetic aberration may refer to the presence or absence of the aberration, or the level of aberration (level of expression, activity, or modification) of the gene. In some embodiments, the presence of an aberration (e.g., a mutation) in one or more drug-sensitive genes, compared to a control, indicates that: (a) the individual is more likely to respond to anticancer drug treatment, or (b) the individual is selected For anticancer drug treatment. In some embodiments, the absence of aberrations (eg, mutations) in one or more drug-sensitive genes indicates that (a) the individual is less responsive to anticancer drug treatment, or (b) the individual is not being treated with an anticancer drug, compared to a control group. Selected for anticancer drug therapy. In some embodiments, abnormal levels (eg, expression levels, or activity levels, or modification levels) of one or more drug-sensitivity genes and/or one or more drug-resistance genes correlate with the individual's likelihood of responding to treatment. For example, a greater deviation in the level (eg, expression, activity, or modification level) of one or more drug-sensitive genes toward reduced or eliminated gene function indicates that an individual is more likely to respond to anticancer drug treatment. In some embodiments, based on a predictive model (e.g., composite score) of the level (e.g., expression level, or activity level, or modification level) of one or more drug-sensitive genes and/or one or more drug-resistant genes, Used to predict: (a) the likelihood that an individual will respond to anticancer drug treatment, and (b) whether an individual will be selected for anticancer drug treatment. In some embodiments, a predictive model (including, for example, coefficients for each level) can be obtained by statistical analysis (eg, regression analysis) using clinical trial data.

In some embodiments, there is provided a method of treating cancer in an individual, such as a human, comprising administering to the individual an effective amount of an anti-cancer drug, wherein the individual is selected for treatment based on: (“Drug Sensitivity Gene”) has an aberration (e.g., carries a mutation) that sensitizes cancer cells to an anticancer drug (a “drug sensitivity aberration” (eg, a “drug sensitivity mutation”), and wherein the drug sensitivity Genes (or drug-sensitive mutations) are identified using any of the target gene identification methods described herein. In some embodiments, methods for treating colorectal cancer in an individual (e.g., a human) are provided, comprising administering to the individual an effective amount of PARPi, wherein the individual is selected for treatment on the basis that the individual has a drug sensitivity aberration in a drug sensitivity gene (e.g., carries a drug sensitivity mutation), and wherein the drug sensitivity gene is selected from the group consisting of: ARID2 , ATM, BIRC6, BRCA1, BRCA2, CCNA2, CCND1, CDK2, FBXW7, HRAS, KAT2B, NBN, PBRM1, PTEN, SKP2, SMAD7, TGFB2, TSC1, TSC2, ATR, RIF1, POLQ, AXIN1, GSK3A, GSK3B, CHD7 , SCAF4, FANCM, NIPBL, ATRX, STAG1, RAD51, RAD51B, RAD51C, RAD51D, FANCL, EXO1, DIDO1, LRBA, FAM71A, HDAC2, PMS2, MSH6, MSH2, MLH1h, and WEE1.

In some embodiments, provided herein are methods of identifying an individual (e.g., a human) with cancer who would benefit from treatment comprising administering an anticancer drug, the method comprising detecting in a sample from the individual One or more drug sensitive aberrations (for example, drug sensitive mutations) in one or more drug sensitive genes identified by any of the target gene identification methods, wherein one or more drug sensitive aberrations (for example, drug sensitive mutations) are present in the sample mutation), the individual is identified as one who may benefit from the treatment. In some embodiments, provided herein is a method of identifying an individual (e.g., a human) with colorectal cancer who may benefit from treatment comprising administering PARPi, the method comprising detecting in a sample from the individual one or more One or more drug-sensitive aberrations (e.g., drug-sensitive mutations) in a drug-sensitive gene selected from the group consisting of ARID2, ATM, BIRC6, BRCA1, BRCA2, CCNA2, CCND1, CDK2 , FBXW7, HRAS, KAT2B, NBN, PBRM1, PTEN, SKP2, SMAD7, TGFB2, TSC1, TSC2, ATR, RIF1, POLQ, AXIN1, GSK3A, GSK3B, CHD7, SCAF4, FANCM, NIPBL, ATRX, STAG1, RAD51, RAD51B , RAD51C, RAD51D, FANCL, EXO1, DIDO1, LRBA, FAM71A, HDAC2, PMS2, MSH6, MSH2, MLH1, and WEE1, where one or more drug-sensitive aberrations (eg, drug-sensitive mutations) are present in the sample, the individual is identified For individuals who may benefit from treatment.

In some embodiments, provided herein are methods of selecting treatment for an individual (e.g., a human) with cancer, the method comprising detecting in a sample from the individual one or more genes identified using any of the target gene identification methods described herein. One or more drug-susceptibility aberrations (e.g., drug-sensitivity mutations) in a drug-sensitivity gene, where one or more drug-susceptibility aberrations (e.g., drug-sensitivity mutations) are present in a sample, would include treatment identification for administration of anticancer drugs suitable treatment for such individuals. In some embodiments, provided herein are methods of selecting treatment for an individual (e.g., a human) with colorectal cancer, the method comprising detecting one or more of one or more drug-sensitivity genes in a sample from the individual Drug susceptibility aberrations (eg, drug susceptibility mutations), the one or more drug susceptibility genes selected from the group consisting of ARID2, ATM, BIRC6, BRCA1, BRCA2, CCNA2, CCND1, CDK2, FBXW7, HRAS, KAT2B, NBN, PBRM1 , PTEN, SKP2, SMAD7, TGFB2, TSC1, TSC2, ATR, RIF1, POLQ, AXIN1, GSK3A, GSK3B, CHD7, SCAF4, FANCM, NIPBL, ATRX, STAG1, RAD51, RAD51B, RAD51C, RAD51D, FANCL, EXO1, DIDO1 , LRBA, FAM71A, HDAC2, PMS2, MSH6, MSH2, MLH1, and WEE1, where one or more drug-sensitive aberrations (e.g., drug-sensitive mutations) are present in the sample, then treatment comprising administration of PARPi is identified as appropriate for the individual treat.

In some embodiments, there is provided a method of excluding an individual (e.g., a human) with cancer from treatment comprising administering to the individual an effective amount of an anticancer drug, wherein if the individual has a target gene ("drug resistance") gene") that has an aberration (e.g., carries a mutation) that renders cancer cells resistant to anticancer drugs ("drug resistance aberration" (such as "drug resistance mutation")), and wherein The drug resistance gene is identified using any of the target gene identification methods described herein. In some embodiments, there is provided a method of excluding an individual (e.g., a human) with colorectal cancer from treatment comprising administering to the individual an effective amount of PARPi, wherein if the individual has resistance in a drug resistance gene Drug aberrations (for example, carrying drug-resistant mutations) exclude the individual, and wherein the drug-resistant genes are selected from the group consisting of AKT1, CDKN1A, CKS1B, CKS2, CTNNB1, DLG5, E2F3, E2F4, HDAC1, MAPK1, MYC, RAC1, RAF1, RICTOR, SMAD4, TP53, BRAF, HSP90B1, PARP2, PARP1, PIK3CA, EIF3A, CCNA1, RBL1, ZMYND8, MED12, GCN1, Kras, TP53BP1, CHD2, DOCK5, IGF1R, ILK, IRS1, RAPGEF1, EP300, TCF7L2, KMT2B, CDKN2A, CHEK1, CHEK2, RHEB, SPTA1, PKMYT1, SIDT2, APC, and SETD2.

In some embodiments, provided herein are methods of identifying an individual (e.g., a human) with cancer who would not benefit from treatment comprising administration of an anticancer drug, the method comprising detecting in a sample from the individual the use of One or more drug resistance aberrations (e.g., drug resistance mutations) in one or more drug resistance genes identified by any of the target gene identification methods described herein, wherein one or more drug resistance aberrations (e.g., resistance mutations) are present in the sample drug mutation), the individual is identified as one who cannot benefit from the treatment. In some embodiments, there is provided a method of identifying an individual (e.g., a human) with colorectal cancer who would not benefit from treatment comprising administering PARPi, the method comprising detecting in a sample from the individual one or One or more drug resistance aberrations (eg, drug resistance mutations) in a plurality of drug resistance genes selected from the group consisting of AKT1, CDKN1A, CKS1B, CKS2, CTNNB1, DLG5, E2F3, E2F4, HDAC1, MAPK1, MYC, RAC1, RAF1, RICTOR, SMAD4, TP53, BRAF, HSP90B1, PARP2, PARP1, PIK3CA, EIF3A, CCNA1, RBL1, ZMYND8, MED12, GCN1, Kras, TP53BP1, CHD2, DOCK5, IGF1R, ILK, IRS1, RAPGEF1, EP300, TCF7L2, KMT2B, CDKN2A, CHEK1, CHEK2, RHEB, SPTA1, PKMYT1, SIDT2, APC, and SETD2, where one or more drug-resistant aberrations (e.g., drug-resistant mutations) are present in the sample, then Individuals are identified as those who would not benefit from the treatment.

In some embodiments, provided herein are methods of excluding therapy from an individual (e.g., a human) with cancer, the method comprising detecting in a sample from the individual a target gene identified using any of the target gene identification methods described herein. One or more resistance aberrations (e.g., drug resistance mutations) in one or more drug resistance genes, where one or more drug resistance aberrations (e.g., drug resistance mutations) are present in the sample, exclusions involving the administration of anticancer drugs Treatment is an appropriate treatment for said individual. In some embodiments, provided herein is a method of precluding treatment in an individual (e.g., a human) with colorectal cancer, the method comprising detecting one or more of one or more drug resistance genes in a sample from the individual A drug-resistant aberration (for example, a drug-resistant mutation), the one or more drug-resistant genes are selected from the group consisting of AKT1, CDKN1A, CKS1B, CKS2, CTNNB1, DLG5, E2F3, E2F4, HDAC1, MAPK1, MYC, RAC1, RAF1, RICTOR, SMAD4, TP53, BRAF, HSP90B1, PARP2, PARP1, PIK3CA, EIF3A, CCNA1, RBL1, ZMYND8, MED12, GCN1, Kras, TP53BP1, CHD2, DOCK5, IGF1R, ILK, IRS1, RAPGEF1, EP300, TCF7L2, KMT2B, CDKN2A, CHEK1, CHEK2, RHEB, SPTA1, PKMYT1, SIDT2, APC, and SETD2, wherein one or more drug-resistant aberrations (e.g., drug-resistant mutations) are present in the sample, then exclude the treatment that includes the administration of PARPi as the individual appropriate treatment.

In some embodiments, there is provided a method of treating cancer in an individual, such as a human, comprising administering to the individual an effective amount of an anticancer drug, wherein the individual is selected based on: i) one or more target genes (“ Aberrations (e.g., mutations) in a drug-sensitive gene”) that sensitize cancer cells to anticancer drugs (“drug-sensitive aberrations”), and ii) one or more target genes (“drug-resistant genes”) Aberrations (e.g., mutations) that render the cancer cells resistant to anticancer drugs ("drug resistance aberrations"), wherein the drug-sensitive and drug-resistant genes are identified using any of the target genes described herein method, and wherein if the combined score of the drug-sensitivity aberration and the drug-resistant aberration is higher than the combined score threshold level, the individual is selected for treatment.

In some embodiments, the method of treating cancer or selecting or excluding cancer therapy in a patient further comprises detecting one or more drug-sensitive aberrations (e.g., drug-sensitive mutations) and/or one or more Resistance aberrations (eg, resistance mutations) (eg, by NGS). In some embodiments, the method further comprises identifying the one or more drug sensitivity genes and/or the one or more drug resistance genes. In some embodiments, the method further comprises detecting aberrant (e.g., reduced or absent) expression (e.g., RNA or protein), such as by qPCR, RNA-seq, mass spectrometry, Western blot, or any other method for detecting RNA or protein expression levels. In some embodiments, the method further comprises detecting abnormal modifications, such as epigenetic modifications (e.g., DNA methylation, , histone methylation, histone acetylation) or post-translational modifications (such as phosphorylation, glycosylation, ubiquitination, nitrosation, methylation, acetylation, lipidation, and proteolysis). Herein, any known method can be used to detect the modification of DNA, nucleosome, RNA or protein, such as ChIP-seq, ChIP-qPCR, DNase-seq, MNase-seq, mass spectrometry, western blotting, etc. In some embodiments, the method further comprises detecting an abnormality (eg, a decrease in expression product (eg, RNA or protein)) of one or more drug-sensitive genes and/or one or more drug-resistant genes compared to a control level. or lack thereof) activity. Any suitable assay for gene function/activity may be used herein, such as detection of signal transduction, initiation status (e.g., phosphorylation state) of downstream pathway molecules, protein-protein binding affinity and/or specificity, metabolism, cellular behavior (e.g., cell proliferation, death, cell cycle), cytokine release, etc. In some embodiments, the method further comprises obtaining a composite score for the individual. In some embodiments, the composite score is based on one or more drug sensitivity and/or resistance aberrations, such as one or more of the following: drug sensitivity mutations, drug resistance mutations, aberrant expression of drug sensitivity genes, resistance Abnormal expression of drug-sensitive genes, abnormal activity of drug-sensitive gene expression products, abnormal activity of drug-resistant gene expression products, abnormal modification of drug-sensitive genes (or gene products), abnormal modification of drug-resistant genes (or gene products), etc. In some embodiments, the composite score is obtained by subtracting (number of drug-susceptible genes with drug-susceptibility aberrations carried by the patient) minus (number of drug-resistant genes with drug-resistant aberrations carried by the patient), wherein if If the composite score is above zero, the individual is selected for treatment. In some embodiments, the severity of a drug-susceptibility mutation or a drug-resistance mutation in a patient adds weight to the composite score, e.g., compared to another drug-susceptibility mutation that affects expression and/or activity of the same drug-sensitivity gene to a lesser extent, Drug-sensitivity mutations that decrease the expression and/or activity of drug-sensitivity genes add more weight to the composite score. In some embodiments, the degree of aberrant expression of a drug-sensitivity or resistance gene in a patient adds weight to the composite score, as compared to control levels (e.g., healthy individuals), e.g., compared to reduced Expression, loss of expression of drug-sensitive genes adds more weight to the composite score. In some embodiments, the degree of aberrant activity (e.g., RNA or protein activity) of a patient's drug-sensitivity or resistance gene compared to control levels (e.g., healthy individuals) adds weight to the composite score, e.g., compared to the same Reduced protein activity (eg, reduced binding) of drug sensitive genes, loss of protein activity of drug sensitive genes (eg, abolished binding) adds more weight to the composite score. In some embodiments, the degree of aberrant modification (e.g., DNA, nucleosome, RNA, or protein modification) of a patient's drug-sensitivity or resistance gene compared to a control level (e.g., a healthy individual) contributes to an increase in the composite score For example, loss of protein phosphorylation (eg, abrogation of signal transduction) of a drug-sensitive gene adds more weight to the composite score than a decrease in protein phosphorylation of the same drug-sensitive gene. In some embodiments, the composite score is obtained by subtracting (the absolute value of the total number of sensitivity scores for the drug-sensitive genes) minus (the absolute value of the total number of drug resistance scores for the drug-resistant genes), Wherein if the composite score is higher than zero, the individual is selected for treatment. In some embodiments, the method further comprises ranking the drug-sensitive genes and drug-resistant genes identified by any of the target gene identification methods described herein, wherein the ranking of the drug-resistant genes or drug-sensitive genes is based on the comparison of the The control cancer cell population, the degree of enrichment or depletion ( ^for example, the enriched fold, fold depleted, enriched FDR, or depleted FDR). In some embodiments, the ordering of drug-resistant or drug-sensitive genes is further adjusted: i) based on data concordance in the iBAR sequence of the sgRNA ^iBAR sequence corresponding to the guide sequence of the same target gene, or ii) based on the corresponding data identity in all guide sequences for the same target gene (or the same target site for the same target gene), or iii) based on all guide sequences corresponding to the same target gene (or the same target site for the same target gene) containing the Data identity among sequences of hit gene mutations (eg, inactivating mutations). In some embodiments, the RRA or α-RRA algorithm is used to rank drug resistance genes and/or drug sensitivity genes. In some embodiments, the method further comprises assigning a sensitivity score to the identified drug-susceptible gene, and/or assigning a drug-resistance score to the identified drug-resistant gene, i) wherein based on the comparison of the control cancer cells Population, the fold enrichment (or FDR based on enrichment) of the sgRNA or sgRNA ^iBAR guide sequence (or sequence containing the mutation of the hit gene) in the cancer cell population (e.g., live) after treatment - the smaller the FDR, the ranking or based on the degree of data consistency – the higher the data consistency, the higher the ranking), rank the resistance genes from high to low, and assign resistance to each resistance gene accordingly from high to low scoring; and/or ii) wherein the sgRNA or sgRNA iBAR guide sequence (or sequence comprising the hit gene mutation) is based on the sgRNA or sgRNA ^iBAR guide sequence (or sequence comprising the hit gene mutation) in a cancer cell population (e.g., live) after treatment compared to the control cancer cell population The multiple of depletion (either based on depletion FDR - the smaller the FDR, the higher the ranking; or based on the degree of data agreement - the higher the data consistency, the higher the ranking), the drug susceptibility genes are ranked from high to low, and accordingly Each drug susceptibility gene is assigned a sensitivity score from high to low.

In addition to the methods described above, any method known in the art may be used to calculate the composite score, and/or select a composite score threshold level. For example, see Response Score or Recombined Proficiency Score (RPS) in US20160369353, see also US20200254259, US20180068083, the contents of each of which are hereby incorporated by reference in their entirety.

In some embodiments, for a particular cancer type (e.g., colorectal cancer) and/or a particular anticancer drug (e.g., PARPi), the parameter "m" is the drug resistance and drug sensitivity genes identified using any of the methods described herein or the total number of genes in the combination of the following drug resistance gene panels (i) AKT1, CDKN1A, CKS1B, CKS2, CTNNB1, DLG5, E2F3, E2F4, HDAC1, MAPK1, MYC, RAC1 , RAF1, RICTOR, SMAD4, TP53, BRAF, HSP90B1, PARP2, PARP1, PIK3CA, EIF3A, CCNA1, RBL1, ZMYND8, MED12, GCN1, Kras, TP53BP1, CHD2, DOCK5, IGF1R, ILK, IRS1, RAPGEF1, EP300, TCF7L2 , KMT2B, CDKN2A, CHEK1, CHEK2, RHEB, SPTA1, PKMYT1, SIDT2, APC, and SETD2, and a panel of drug-sensitive genes (ii) ARID2, ATM, BIRC6, BRCA1, BRCA2, CCNA2, CCND1, CDK2, FBXW7, HRAS, KAT2B , NBN, PBRM1, PTEN, SKP2, SMAD7, TGFB2, TSC1, TSC2, ATR, RIF1, POLQ, AXIN1, GSK3A, GSK3B, CHD7, SCAF4, FANCM, NIPBL, ATRX, STAG1, RAD51, RAD51B, RAD51C, RAD51D, FANCL , EXO1, DIDO1, LRBA, FAM71A, HDAC2, PMS2, MSH6, MSH2, MLH1, and WEE1. In some embodiments, one or more patient aberrations (e.g., mutations or aberrant expression/activity/modifications) are identified in one or more patient genes from a sample from an individual, such as one or more patient mutations ( For example, non-synonymous, nonsense, missense, frameshift, insertion, deletion, stop loss, stop gain, mutations leading to missplicing, gene fusion, etc.) that are gene resistant in patients identified using any of the methods described herein A combination of genes and drug susceptibility genes, or a combination belonging to groups (i) and (ii) above. In some embodiments, from an individual sample, among patient genes belonging to the combination of drug resistance genes and drug susceptibility genes identified using any of the methods described herein, or belonging to the combination of groups (i) and (ii) above In the patient's genes, no patient aberrations (eg, patient mutations) were identified. Patient genes or patient aberrations (e.g., mutations or aberrant expression/activity/modifications) identified from patients (e.g., patient samples, such as by NGS) that are drug resistance genes or drug susceptibility identified using any of the methods described herein Genes, or drug resistance genes or drug susceptibility genes identified in the above (i) and (ii) target gene panels, are also referred to below as "patient target genes" or "patient target aberrations" respectively (e.g. "patient target mutations" ). The parameter "m" is an integer of at least 1, which is a constant for a particular cancer type and a particular anticancer drug.

”)，ii)從患者(例如，通過NGS)鑒定的特定患者靶基因中攜帶特定有害突變的細胞的估計分數(參數“

”)，iii)相對于正常組織，患者疾病組織中患者靶基因表達水準的對數標度(例如，log2)倍變化(參數“

”)等。在一些實施方案中，一個或多個患者相關參數是根據患者樣本的資料/資訊得出的，如測序讀取計數。參數“

”表示從患者身上鑒定的第i個患者靶基因中攜帶第j個突變的細胞的估計分數。0 ＜

≤ 1。“

”是至少為1的整數，並且是檢測到的相應已鑒定患者靶基因的有害患者靶突變的總數。“

”是整數，且1 ≤

≤

。“

”是整數，且0 ≤

≤

。當

= 0，這表明從個體樣本中，在屬於使用本文所述的任何方法鑒定的耐藥基因和藥物敏感基因的組合的任何患者基因中，或屬於上述基因小組(i)和(ii)的組合的任何患者基因中，未發現有害突變。在一些實施方案中，第i個患者靶基因中攜帶第j個突變的細胞的分數，是基於從患者樣本中鑒定的在第i個患者靶基因中包含突變的所有序列中第j個突變的序列的分數來估計的。參數“

”表示相對于正常組織，患者疾病組織中第i個患者靶基因表達水準的對數標度(例如log2)倍數變化。患者靶基因的表達水準可以用任何已知的方法測量，如RNA-seq、qPCR、質譜、蛋白質印跡、FISH、免疫螢光染色等。 In some embodiments, a composite score is calculated based on one or more patient-related parameters, such as i) deleterious mutations (e.g., nonsynonymous, nonsense, missense) carried on each patient's target gene identified from the patient (e.g., by NGS). number of senses, frameshifts, insertions, deletions, stop losses, stop gains, mutations leading to missplicing, gene fusions, etc.) (parameter "

”), ii) the estimated fraction of cells carrying a specific deleterious mutation in a specific patient target gene identified (e.g., by NGS) from the patient (parameter “

"), iii) the logarithmic scale (eg, log2) fold change in the expression level of the patient's target gene in the patient's disease tissue relative to normal tissue (parameter "

"), etc. In some embodiments, one or more patient-related parameters are derived from data/information from the patient sample, such as sequencing read counts. Parameters"

” represents the estimated fraction of cells carrying the jth mutation in the ith patient target gene identified from the patient. 0 <

≤ 1. "

"is an integer of at least 1 and is the total number of detected deleterious patient target mutations corresponding to the identified patient target gene."

” is an integer, and 1 ≤

≤

. "

” is an integer, and 0 ≤

≤

. when

= 0, which indicates that from the individual sample, in any patient gene that belongs to the combination of drug-resistant and drug-susceptible genes identified using any of the methods described herein, or belongs to the combination of gene groups (i) and (ii) above No deleterious mutations were found in any of the patient's genes. In some embodiments, the fraction of cells carrying the jth mutation in the ith patient target gene is based on the jth mutation among all sequences comprising a mutation in the ith patient target gene identified from the patient sample Sequence scores are estimated. parameter"

"Represents the logarithmic scale (eg log2) fold change of the i-th patient's target gene expression level in the patient's disease tissue relative to the normal tissue. The expression level of the patient's target gene can be measured by any known method, such as RNA-seq, qPCR, mass spectrometry, western blot, FISH, immunofluorescent staining, etc.

”)，其源於機器學習(例如，基於來自關於細胞系的公共資料的訓練模型)，ii)回應抗癌藥物治療的患者靶基因的歸一化權重(參數“

”)，其源於機器學習(例如，基於來自關於細胞系的公共資料的訓練模型)，iii)預測的患者靶基因的有害突變的影響(參數“

”；例如，基於採用公共資料庫的有害預測，如異常的基因或基因產物活性), iv)根據卡普蘭-梅爾(Kaplan-Meier)生存曲線，在給定時間點，患者靶基因的淨存活率與總存活率的比值(參數“

”；例如，基於癌基因圖譜(TCGA)資料庫和/或cBioPortal資料庫)，v)相對于正常組織，患者疾病組織中患者靶基因表達水準的對數標度(例如log2)倍數變化(參數“

”; 例如，基於患者資料庫，即從患有相同癌症的患者收集的資訊)等。在一些實施方案中，一個或多個基因相關的參數來自基於公共的或患者的資料庫的資料，用於訓練綜合評分模型。參數“

”表示從患者鑒定的第i個患者靶基因與抗癌藥物治療之間的相關性(正相關或負相關)(例如，在IC50)，其來自機器學習。參數“

”表示回應抗癌藥物治療的第i個患者靶基因的歸一化權重(即，對於抗癌藥物治療，第i個患者靶基因的功能喪失的貢獻)，其源自機器學習。參數“

”表示預測的第i個患者靶基因的第j個有害突變的影響(例如，基於採用公共資料庫的有害預測，或手工分配的常數)。參數“

”表示根據卡普蘭-梅爾(Kaplan-Meier)生存曲線，在給定時間點，第i個患者靶基因的淨存活率與總存活率的比值(例如，基於TCGA和/或cBioPortal資料庫)。參數“

”表示相對于正常組織，患者疾病組織中第i個患者靶基因表達水準的對數標度(例如log2)倍數變化(例如，基於患者資料庫，即從患有相同癌症的患者收集的資訊)。“

”是整數，0 ≤

≤

. “

”是整數，且1 ≤

≤

。 In some embodiments, the composite score is calculated based on one or more gene-related parameters, such as i) the correlation (positive or negative) between the patient's target gene and the anticancer drug treatment (eg, at IC50 ) (parameter "

”) derived from machine learning (e.g., a trained model based on public data on cell lines), ii) normalized weights of patient target genes in response to anticancer drug treatment (parameter “

”) derived from machine learning (e.g., a trained model based on public data on cell lines), iii) the predicted impact of deleterious mutations in patient target genes (parameters “

”; e.g., based on predictions of deleteriousness using public databases, such as abnormal gene or gene product activity), iv) according to Kaplan-Meier (Kaplan-Meier) survival curve, at a given time point, the patient's net The ratio of survival rate to overall survival rate (parameter "

”; for example, based on The Cancer Gene Atlas (TCGA) database and/or the cBioPortal database), v) the logarithmic scale (eg log2) fold change in the expression level of the patient's target gene in the patient's disease tissue relative to normal tissue (parameter "

"; for example, based on patient databases, i.e., information collected from patients with the same cancer), etc. In some embodiments, one or more gene-related parameters are derived from public or patient-based database data, using for training the comprehensive scoring model. Parameters "

"Denotes the correlation (positive or negative) between the i-th patient target gene identified from a patient and anticancer drug treatment (eg, at IC50), which is derived from machine learning. Parameters"

"Represents the normalized weight of the i-th patient's target gene in response to anticancer drug treatment (i.e., the contribution of the loss-of-function of the i-th patient's target gene to anticancer drug treatment), derived from machine learning. Parameters"

"Represents the predicted effect of the jth deleterious mutation in the i'th patient's target gene (e.g., based on deleterious predictions using public databases, or a manually assigned constant). Parameter"

"Indicates the ratio of the net survival rate to the overall survival rate of the i-th patient's target gene at a given time point according to the Kaplan-Meier survival curve (for example, based on TCGA and/or cBioPortal database) .parameter"

" denotes the logarithmic scale (eg log2) fold change in the i-th patient's target gene expression level in the patient's disease tissue relative to normal tissue (eg, based on a patient database, ie, information collected from patients with the same cancer). "

" is an integer, 0 ≤

≤

. "

” is an integer, and 1 ≤

≤

.

”；例如，基於公共資料庫如KEGG和InterProScan)，ii)在抗癌藥物相關的路徑中患者靶基因的歸一化權重(參數“

”; 例如，基於公共資料庫)等。在一些實施方案中，一個或多個路徑相關的參數來自基於公共資料庫的資料，用於訓練綜合評分模型。參數“

”表示在涉及第i個患者靶基因的路徑和/或調控網路中第i個患者靶基因的估計權重(例如，基於公共資料庫 如KEGG和InterProScan)。參數“

”表示在抗癌藥物相關的路徑中第i個患者靶基因的歸一化權重，例如，基於公共資料庫。“

”是整數，以及0 ≤

≤

。 In some embodiments, the composite score is calculated based on one or more pathway-related parameters, such as i) the estimated weight of the patient's target gene in the pathway and/or regulatory network involving the patient's target gene (parameter "

”; for example, based on public databases such as KEGG and InterProScan), ii) normalized weights of patient target genes in anticancer drug-related pathways (parameters “

"; for example, based on a public database), etc. In some embodiments, one or more path-related parameters are derived from data based on a public database for training the composite scoring model. Parameters"

"Denotes the estimated weight of the ith patient target gene in the pathway and/or regulatory network involving the ith patient target gene (eg, based on public databases such as KEGG and InterProScan). Parameter"

"Represents the normalized weights of the i-th patient's target gene in a pathway associated with an anticancer drug, e.g., based on a public database."

” is an integer, and 0 ≤

≤

.

In some embodiments, the composite score is calculated based on one or more parameters selected from one or more of the following: patient-related parameters, gene-related parameters, and pathway-related parameters described herein.

In some embodiments, the composite score is calculated using Formula I below:

in

、

和

是模型調整常數(例如，來自相應抗癌藥物的訓練模型的常數)，其中-1 ≤

≤ 1, -1 ≤

≤ 1，且-1 ≤

≤ 1；

,

and

is the model tuning constant (e.g., the constant from the trained model for the corresponding anticancer drug), where -1 ≤

≤ 1, -1 ≤

≤ 1, and -1 ≤

≤ 1;

是用本文所述的任何靶基因鑒定方法鑒定的耐藥基因和藥物敏感基因的總數或上述小組(i)和(ii)的組合中靶基因的總數；

is the total number of drug-resistant genes and drug-susceptible genes identified by any of the target gene identification methods described herein or the total number of target genes in the combination of the above panels (i) and (ii);

是針對患者的第i個患者靶基因檢測的有害突變數目；

is the number of deleterious mutations detected for the i-th patient target gene of the patient;

”是第i個患者靶基因中攜帶第j個有害突變的患者細胞的估計分數，其中0 ＜

≤ 1； "

” is the estimated fraction of patient cells carrying the jth deleterious mutation in the target gene of the ith patient, where 0 <

≤ 1;

是第i個患者靶基因和抗癌藥物治療(例如，在IC50)之間的相關性(正相關性或負相關性)；

is the correlation (positive or negative) between the i-th patient's target gene and anticancer drug treatment (eg, at IC50);

是響應抗癌藥物治療的第i個患者靶基因的歸一化權重；

is the normalized weight of the i-th patient's target gene in response to anticancer drug treatment;

是第i個患者靶基因的第j個有害突變或的預測的影響；

is the predicted impact of the jth deleterious mutation or the target gene of the ith patient;

是根據卡普蘭-梅爾(Kaplan-Meier)生存曲線，在給定時間點，第i個患者靶基因的淨存活率與總存活率的比值；

is the ratio of the net survival rate of the i-th patient's target gene to the overall survival rate at a given time point according to the Kaplan-Meier survival curve;

是相對于正常組織，疾病組織中第i個患者靶基因表達水準的對數標度(例如log2)倍數變化；

is the logarithmic scale (eg log2) fold change of the i-th patient's target gene expression level in the disease tissue relative to the normal tissue;

是在涉及第i個患者靶基因的路徑和/或調控網路中第i個患者靶基因的估計的權重；

is the estimated weight of the ith patient target gene in the pathway and/or regulatory network involving the ith patient target gene;

是在抗癌藥物相關的路徑中第i個患者靶基因的歸一化權重；

is the normalized weight of the i-th patient's target gene in the anticancer drug-related pathway;

和 均為整數，0 ≤

≤

，且1 ≤

≤

；以及 in

and are integers, 0 ≤

≤

, and 1 ≤

≤

;as well as

是

的標準評分(“Z-評分”)：

yes

Standardized scoring ("Z-score") for :

是相對于正常組織，疾病組織中第i個患者靶基因表達水準的中值的對數標度(例如log2)倍數變化(例如，基於患者資料庫，即從患有相同癌症的患者收集的資訊)；以及 in

is the logarithmic scale (e.g. log2) fold change in the median value of the target gene expression level of the ith patient in disease tissue relative to normal tissue (e.g. based on a patient database, i.e. information collected from patients with the same cancer) ;as well as

是相對于正常組織，疾病組織中第i個患者靶基因表達水準的標準差的對數標度(例如log2)倍數變化(例如，基於患者資料庫，即從患有相同癌症的患者收集的資訊)。 in

is the logarithmic scale (e.g. log2) fold change in the standard deviation of the i-th patient's target gene expression level in disease tissue relative to normal tissue (e.g. based on a patient database, i.e. information collected from patients with the same cancer) .

In some embodiments, the composite score threshold level is zero. In some embodiments, a patient is suitable for (ie likely to benefit from) anticancer drug treatment if the patient's composite score according to Formula I is above 0. In some embodiments, a patient is selected or recommended for anticancer drug treatment if the patient's composite score according to Formula I is greater than or equal to at least 0.1 (eg, 0.3). In some embodiments, if the patient's composite score according to Formula I is greater than 0 but less than 0.1, the patient is suitable for anticancer drug therapy, but should be further evaluated using other methods (e.g., drug dosage testing, cancer genetic testing (e.g., Find other synergistic mutations that may contribute to anticancer drug treatment, or verify the primary cancer type), etc.) or based on other information (for example, the patient's clinical record or known drug resistance, etc.) to determine whether it should be selected or recommended The patient is treated with anticancer drugs. In some embodiments, if the patient's composite score according to Formula I is less than or equal to 0, the patient is not suitable for (ie, likely not to benefit from) or excluded from anticancer drug treatment. In some embodiments, other methods (e.g., drug dose testing, cancer genetic testing (e.g., looking for other synergistic mutations that might facilitate anticancer drug therapy, or verifying primary cancer type), etc.) or based on other information ( Such as the patient's clinical records or known drug resistance, etc.) for further evaluation, if the patient's composite score according to formula I is equal to 0 or very close to 0 (such as -0.1 to 0), the patient should be completely excluded from receiving anticancer drugs before treatment.

Accordingly, in some embodiments, there is provided a method of treating cancer in an individual (eg, a human) comprising administering to the individual an effective amount of an anticancer drug, wherein the individual is selected based on: i) one or more drug sensitive One or more drug-sensitive aberrations (e.g., drug-sensitive mutations) in the gene, and ii) one or more drug-resistant aberrations (e.g., drug-resistant mutations) in one or more drug-resistant genes, wherein using Any target gene identification method to identify drug-sensitive genes and drug-resistant genes, and wherein if the combined score of the drug-sensitive aberration (eg, drug-sensitive mutation) and drug-resistant aberration (eg, drug-resistant mutation) is higher than the combined score threshold level, then select the individual for treatment; wherein the comprehensive score is (the absolute value of the total number of sensitivity scores of the drug-sensitive gene) minus (the absolute value of the total number of drug resistance scores of the drug-resistant gene) to obtain , and the comprehensive score threshold level is zero. In some embodiments, there is provided a method of treating cancer in an individual (e.g., a human) comprising administering to the individual an effective amount of an anticancer drug, wherein the individual is selected based on: i) one or more of the drug sensitivity genes and ii) one or more drug-resistant aberrations (e.g., drug-resistant mutations) in one or more drug-resistant genes, wherein using any of the herein described target gene identification method to identify drug-sensitive genes and drug-resistant genes, and wherein if the comprehensive score of the drug-sensitive aberration (for example, drug-sensitive mutation) and drug-resistant aberration (for example, drug-resistant mutation) according to formula I is higher than zero ( For example, greater than or equal to at least 0.1 (eg, 0.3)), the individual is selected for treatment. In some embodiments, the method further comprises detecting one or more drug susceptibility aberrations (e.g., mutation, aberrant expression, aberrant activity, aberrant modification) and one or more drug resistance aberrations (e.g., mutation, abnormal expression, abnormal activity, abnormal modification).

In some embodiments, there is provided a method of treating colorectal cancer in an individual (e.g., a human) comprising administering to the individual an effective PARPi, wherein the individual is selected based on: i) one of one or more drug sensitive genes or multiple drug sensitive aberrations (eg, drug sensitive mutations) selected from the group consisting of ARID2, ATM, BIRC6, BRCA1, BRCA2, CCNA2, CCND1, CDK2, FBXW7, HRAS, KAT2B, NBN, PBRM1, PTEN, SKP2, SMAD7, TGFB2, TSC1, TSC2, ATR, RIF1, POLQ, AXIN1, GSK3A, GSK3B, CHD7, SCAF4, FANCM, NIPBL, ATRX, STAG1, RAD51, RAD51B, RAD51C, RAD51D, FANCL, EXO1, DIDO1, LRBA, FAM71A, HDAC2, PMS2, MSH6, MSH2, MLH1, and WEE1, and ii) one or more drug resistance aberrations (eg, drug resistance mutations) in one or more drug resistance genes selected from Lower group: AKT1, CDKN1A, CKS1B, CKS2, CTNNB1, DLG5, E2F3, E2F4, HDAC1, MAPK1, MYC, RAC1, RAF1, RICTOR, SMAD4, TP53, BRAF, HSP90B1, PARP2, PARP1, PIK3CA, EIF3A, CCNA1, RBL1 , ZMYND8, MED12, GCN1, Kras, TP53BP1, CHD2, DOCK5, IGF1R, ILK, IRS1, RAPGEF1, EP300, TCF7L2, KMT2B, CDKN2A, CHEK1, CHEK2, RHEB, SPTA1, PKMYT1, SIDT2, APC, and SETD2, and if If the composite score of the drug-sensitive aberration (for example, drug-sensitive mutation) and drug-resistant aberration (for example, drug-resistant mutation) is higher than the threshold level of the composite score, the individual is selected for treatment. In some embodiments, the method further comprises detecting one or more drug-sensitizing aberrations (eg, drug-sensitizing mutations) and one or more drug-resistant aberrations (eg, drug-resistance mutations) in the sample from the individual. In some embodiments, the method further comprises obtaining a composite score for the individual. In some embodiments, the composite score is obtained by subtracting (the absolute value of the total number of sensitivity scores for the drug-sensitive genes) minus (the absolute value of the total number of drug resistance scores for the drug-resistant genes), Wherein if the composite score is higher than zero, the individual is selected for treatment. In some embodiments, a composite score is obtained according to Formula I, wherein the individual is selected for treatment if the composite score is above zero (eg, greater than or equal to at least 0.1 (eg, 0.3)).

In some embodiments, provided herein are methods of identifying an individual (e.g., a human) with cancer who would benefit from treatment comprising administering an anticancer drug, the method comprising: detecting in a sample from the individual a method described herein One or more drug-sensitivity aberrations (e.g., drug-sensitivity mutations) in one or more drug-sensitivity genes identified by any of the target gene identification methods, and one or more drug resistance identified by any of the target gene identification methods described herein One or more drug-resistant aberrations (eg, drug-resistant mutations) in a gene, wherein the combined score of the drug-sensitive aberrations (eg, drug-sensitive mutations) and drug-resistant aberrations (eg, drug-resistant mutations) is above the combined score threshold level, the individual is identified as benefiting from the treatment. In some embodiments, provided herein is a method of identifying an individual (e.g., a human) with colorectal cancer who may benefit from treatment comprising administering a PARPi, the method comprising: detecting in a sample from the individual one or more One or more drug-sensitive aberrations (e.g., drug-sensitive mutations) in a drug-sensitive gene selected from the group consisting of ARID2, ATM, BIRC6, BRCA1, BRCA2, CCNA2, CCND1, CDK2, FBXW7, HRAS, KAT2B, NBN, PBRM1, PTEN, SKP2, SMAD7, TGFB2, TSC1, TSC2, ATR, RIF1, POLQ, AXIN1, GSK3A, GSK3B, CHD7, SCAF4, FANCM, NIPBL, ATRX, STAG1, RAD51, RAD51B, RAD51C, RAD51D, FANCL, EXO1, DIDO1, LRBA, FAM71A, HDAC2, PMS2, MSH6, MSH2, MLH1, and WEE1; and one or more resistance aberrations (e.g., resistance mutations) of one or more resistance genes that The gene is selected from the group consisting of AKT1, CDKN1A, CKS1B, CKS2, CTNNB1, DLG5, E2F3, E2F4, HDAC1, MAPK1, MYC, RAC1, RAF1, RICTOR, SMAD4, TP53, BRAF, HSP90B1, PARP2, PARP1, PIK3CA, EIF3A, CCNA1, RBL1, ZMYND8, MED12, GCN1, Kras, TP53BP1, CHD2, DOCK5, IGF1R, ILK, IRS1, RAPGEF1, EP300, TCF7L2, KMT2B, CDKN2A, CHEK1, CHEK2, RHEB, SPTA1, PKMYT1, SIDT2, APC, and SETD2, Where the composite score of the drug-susceptibility aberration (eg, drug-susceptibility mutation) and drug-resistance aberration (eg, drug-resistance mutation) is above a composite score threshold level, the individual is identified as benefiting from the treatment. In some embodiments, the method further comprises detecting one or more drug susceptibility aberrations (e.g., mutation, aberrant expression, aberrant activity, aberrant modification) and one or more drug resistance aberrations (e.g., mutation , abnormal expression, abnormal activity, abnormal modification). In some embodiments, the method further comprises obtaining a composite score for the individual. In some embodiments, the composite score is obtained by subtracting (the absolute value of the total number of sensitivity scores for the drug-sensitive genes) minus (the absolute value of the total number of drug resistance scores for the drug-resistant genes), Where the composite score is above zero, the individual is identified as benefiting from the treatment. In some embodiments, a composite score is obtained according to Formula I, wherein the composite score above zero (eg, greater than or equal to at least 0.1 (eg, 0.3)) identifies the individual as benefiting from the treatment.

In some embodiments, provided herein are methods of selecting treatment for an individual (e.g., a human) with cancer, the method comprising detecting in a sample from the individual one or more genes identified using any of the target gene identification methods described herein. One or more drug-sensitivity aberrations (e.g., drug-sensitivity mutations) in a drug-sensitivity gene, and one or more drug-resistance aberrations in one or more drug-resistant genes identified using any of the target gene identification methods described herein (e.g., drug-resistant mutations), where the combined score of drug-sensitive aberrations (e.g., drug-sensitive mutations) and drug-resistant aberrations (e.g., drug-resistant mutations) in the sample is above the combined score threshold level, identification includes administration of anticancer drugs treatment as an appropriate treatment for the individual. In some embodiments, provided herein are methods of selecting treatment for an individual (e.g., a human) with colorectal cancer, the method comprising detecting one or more of one or more drug-sensitivity genes in a sample from the individual Drug-sensitive aberrations (eg, drug-sensitive mutations) selected from the group consisting of ARID2, ATM, BIRC6, BRCA1, BRCA2, CCNA2, CCND1, CDK2, FBXW7, HRAS, KAT2B, NBN, PBRM1, PTEN, SKP2 , SMAD7, TGFB2, TSC1, TSC2, ATR, RIF1, POLQ, AXIN1, GSK3A, GSK3B, CHD7, SCAF4, FANCM, NIPBL, ATRX, STAG1, RAD51, RAD51B, RAD51C, RAD51D, FANCL, EXO1, DIDO1, LRBA, FAM71A , HDAC2, PMS2, MSH6, MSH2, MLH1 and WEE1, and one or more drug resistance aberrations (eg, drug resistance mutations) in one or more drug resistance genes selected from the group consisting of AKT1, CDKN1A, CKS1B, CKS2, CTNNB1, DLG5, E2F3, E2F4, HDAC1, MAPK1, MYC, RAC1, RAF1, RICTOR, SMAD4, TP53, BRAF, HSP90B1, PARP2, PARP1, PIK3CA, EIF3A, CCNA1, RBL1, ZMYND8, MED12, GCN1, Kras, TP53BP1, CHD2, DOCK5, IGF1R, ILK, IRS1, RAPGEF1, EP300, TCF7L2, KMT2B, CDKN2A, CHEK1, CHEK2, RHEB, SPTA1, PKMYT1, SIDT2, APC, and SETD2, among which drug-sensitive aberrations in samples ( For example, a combined score for a drug-sensitizing mutation) and a drug-resistant aberration (eg, a drug-resistant mutation) is above a combined score threshold level, identifying a treatment comprising administration of PARPi as an appropriate treatment for the individual. In some embodiments, the method further comprises detecting one or more drug-susceptibility aberrations (e.g., mutation, aberrant expression, aberrant activity, aberrant modification) and one or more drug-resistant aberrations (e.g., mutation , abnormal expression, abnormal activity, abnormal modification). In some embodiments, the method further comprises obtaining a composite score for the individual. In some embodiments, the composite score is obtained by subtracting (the absolute value of the total number of sensitivity scores for the drug-sensitive genes) minus (the absolute value of the total number of drug resistance scores for the drug-resistant genes), Where said composite score is above zero, treatment comprising administration of PARPi is identified as an appropriate treatment for said individual. In some embodiments, a composite score is obtained according to Formula I, wherein a composite score greater than zero (eg, greater than or equal to at least 0.1 (eg, 0.3)) identifies treatment comprising administration of PARPi as an appropriate treatment for the individual.

IV. Modified Cancer Cells and Methods of Production

One aspect of the present invention provides methods for generating modified cancer cells, such as modified cancer cells that are resistant to or sensitive to anticancer drugs. In some embodiments, the method of generating a modified cancer cell comprises inactivating in the cancer cell one or more target genes identified by any of the target gene identification methods described herein. Also provided are modified cancer cells produced by any of the methods described herein.

In some embodiments, the method of generating a modified cancer cell comprises one or more mutations (eg, inactivating mutations) in one or more target genes identified by any of the target gene identification methods described herein. In some embodiments, the methods comprise contacting an initial population of cancer cells with a mutagen, and selecting modified cells comprising one or more mutations (e.g., inactivating mutations) in one or more target genes identified herein. cancer cell. Methods for detecting such mutations are well known in the art, such as by PCR. In some embodiments, the methods comprise producing one or more of the target genes identified herein in cancer cells by gene editing, such as any gene editing method known in the art or described herein. Mutations (eg, inactivating mutations). For example, non-homologous end joining (NHEJ)- or homologous recombination-mediated gene disruption or ZFN-, TALEN-, or CRISPR/Cas-mediated gene disruption. In some embodiments, the method of producing a modified cancer cell comprises introducing a sgRNA construct into a host cancer cell, wherein the sgRNA construct comprises or encodes an sgRNA (e.g., an sgRNA, or a vector carrying a nucleic acid encoding the sgRNA (e.g., , viral vectors such as lentiviral vectors)), wherein the sgRNA comprises a target site complementary to a target gene identified herein (e.g., at least about 60%, 70%, 80%, 90%, 95%, 96%, 97%, 98%, 99% or 100% complementary) guide sequence. In some embodiments, the method further comprises introducing a vector (e.g., a viral vector such as a lentiviral vector) carrying a nucleic acid encoding a Cas protein (e.g., Cas9) or Cas (e.g., Cas9) mRNA into a host cancer cell or comprising the Host cancer cells for the sgRNA constructs. In some embodiments, the host cancer cell comprises a Cas module. In some embodiments, the sgRNA construct for the target gene and/or a Cas element (eg, vector, or mRNA) comprising a Cas protein or a nucleic acid encoding a Cas protein is simultaneously introduced into a host cancer cell. In some embodiments, the nucleic acid encoding the target gene sgRNA, and/or the nucleic acid encoding the Cas protein is on the same vector, under the control of the same promoter, or under the control of separate promoters. In some embodiments, the nucleic acid encoding the target gene sgRNA, and/or the nucleic acid encoding the Cas protein is connected by one or more IRES linking sequences and is under the control of the same promoter. In some embodiments, the nucleic acid encoding the target gene sgRNA, and/or the nucleic acid encoding the Cas protein are on different vectors. In some embodiments, the sgRNA construct for the target gene, and/or a Cas element (eg, vector, or mRNA) comprising a Cas protein or a nucleic acid encoding a Cas protein is sequentially introduced into the host cancer cell.

In some embodiments, when a population of host cancer cells (or an initial population of cancer cells) is used to generate the modified cancer cells described herein, the method includes one or more isolation and/or enrichment steps, e.g., from The population of cancer cells contacted with the modifying reagent isolates and/or enriches cancer cells comprising one or more mutations (eg, inactivating mutations) in the target gene, target gene sgRNA construct, or Cas element. Such separation and/or enrichment steps can be performed using any technique known in the art and described herein, such as magnetically activated cell sorting (MACS). See also the method described in the "Optional Enrichment Step" subsection above.

In some embodiments, by transduction/transfection of nucleic acid (DNA or RNA) or its encoding vector (eg, non-viral vector, or viral vector such as lentiviral vector), or virus (eg, lentiviral vector) containing its encoding nucleic acid ), introducing target gene sgRNA constructs and/or Cas components into host cancer cells. In some embodiments, a Cas element (eg, Cas9 protein) is introduced into host cancer cells by inserting the protein into the cell membrane while delivering the cells through a microfluidic system such as CELL SQUEEZE® (see, eg, US Patent Application Publication US20140287509).

Methods for introducing vectors (eg, viral vectors) or isolated nucleic acids into mammalian cells are known in the art. The nucleic acids or vectors described herein can be transferred into cancer cells by physical, chemical or biological means.

Physical methods for introducing vectors (eg, viral vectors) into cancer cells include calcium phosphate precipitation, lipid infection, particle bombardment, microinjection, electroporation, and the like. Methods for producing cells comprising vectors and/or exogenous nucleic acids are well known in the art. See, eg, Sambrook et al. (2001) Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Laboratory, New York. In some embodiments, vectors (eg, viral vectors) are introduced into cancer cells by electroporation.

Biological methods for introducing vectors into cancer cells include the use of DNA and RNA vectors. Viral vectors have become the most widely used method for inserting genes into mammalian (such as human) cells.

Chemical methods for introducing vectors, such as viral vectors, into cancer cells include colloidal dispersion systems such as macromolecular complexes, nanocapsules, microspheres, beads, and lipid-based systems including oil-in-water emulsions, micelles, mixed micelles, and Liposomes. An exemplary colloidal system for use as a delivery vehicle in vitro is a liposome (eg, an artificial membrane vesicle).

In some embodiments, RNA molecules (eg, sgRNA or mRNA encoding Cas9) can be prepared by conventional methods (eg, in vitro transcription) and then introduced into cancer cells by known methods (eg, mRNA electroporation). See, eg, Rabinovich et al., Human Gene Therapy 17:1027-1035.

In some embodiments, a viral vector (lentiviral vector) or virus (e.g., lentivirus) comprising a nucleic acid encoding any of the target gene sgRNA described herein, the target gene sgRNA ^iBAR , and/or the Cas protein is combined with a host cancer cell (or initial cancer cell population), for example, in at least about 1, such as at least about 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, 6, 6.5, 7, 8, 9 or 10 MOI of either contact. In some embodiments, a viral vector (lentiviral vector) or virus (e.g., lentivirus) comprising a nucleic acid encoding any of the target gene sgRNA described herein, the target gene sgRNA ^iBAR , and/or the Cas protein is combined with a host cancer cell (or the initial cancer cell population) were contacted at an MOI of about 3.

In some embodiments, transduced/transfected cancer cells are propagated ex vivo following introduction of the vector or isolated nucleic acid. In some embodiments, the transduced/transfected cancer cells are cultured to proliferate for at least about 1 day, 2 days, 3 days, 4 days, 5 days, 6 days, 7 days, 10 days, 12 days, or 14 days either one. In some embodiments, the transduced/transfected cancer cells are further evaluated or screened to select the desired modified cancer cells described herein.

Reporter genes can be used to identify potentially transfected/transduced cells and to evaluate the function of regulatory sequences. In general, a reporter gene is a gene that is absent or not expressed in the recipient organism or tissue and that encodes a polypeptide whose expression exhibits some readily detectable property, such as enzymatic activity. Expression of the reporter gene is measured at an appropriate time after the DNA/RNA has been introduced into the recipient cells. Suitable reporter genes may include: genes encoding luciferase, β-galactosidase, chloramphenicol acetyltransferase, secreted alkaline phosphatase, or the green fluorescent protein (GFP) gene (e.g., Ui- Tei et al. FEBS Letters 479: 79-82 (2000)). Suitable expression systems are known and can be prepared using known techniques or obtained commercially. Antibiotic selection markers can also be used to identify potentially transfected/transduced cells.

Other methods for confirming the presence of any of the nucleic acids described herein (e.g., sgRNA constructs) or the presence of mutations (e.g., inactivating mutations) in target genes of modified cancer cells include, for example, molecular biology methods known to those skilled in the art Assays, such as Southern and Northern blots, RT-PCR, PCR, DNA-seq or RNA-seq; biochemical assays, such as detection of the presence or absence of specific peptides, for example, by immunological methods (such as ELISA and Western blot), fluorescence priming Cell sorting (FACS) or magnetically activated cell sorting (MACS).

In some embodiments, provided are modified colorectal cancer cells comprising one or more mutations (e.g., inactivating mutations such as knockouts) in one or more target genes, wherein the target genes are selected from the group consisting of: Groups: ARID2, ATM, BIRC6, BRCA1, BRCA2, CCNA2, CCND1, CDK2, FBXW7, HRAS, KAT2B, NBN, PBRM1, PTEN, SKP2, SMAD7, TGFB2, TSC1, TSC2, ATR, RIF1, POLQ, AXIN1, GSK3A, GSK3B, CHD7, SCAF4, FANCM, NIPBL, ATRX, STAG1, RAD51, RAD51B, RAD51C, RAD51D, FANCL, EXO1, DIDO1, LRBA, FAM71A, HDAC2, PMS2, MSH6, MSH2, MLH1, and WEE1. In some embodiments, provided are modified colorectal cancer cells comprising one or more mutations (e.g., inactivating mutations such as knockouts) in one or more target genes, wherein the target genes are selected from the group consisting of: Groups: AKT1, CDKN1A, CKS1B, CKS2, CTNNB1, DLG5, E2F3, E2F4, HDAC1, MAPK1, MYC, RAC1, RAF1, RICTOR, SMAD4, TP53, BRAF, HSP90B1, PARP2, PARP1, PIK3CA, EIF3A, CCNA1, RBL1, ZMYND8, MED12, GCN1, Kras, TP53BP1, CHD2, DOCK5, IGF1R, ILK, IRS1, RAPGEF1, EP300, TCF7L2, KMT2B, CDKN2A, CHEK1, CHEK2, RHEB, SPTA1, PKMYT1, SIDT2, APC, and SETD2.

In some embodiments, there is provided a method of screening for an anticancer drug capable of treating a cancer (e.g., colorectal cancer) in an individual (e.g., a human), wherein the cancer comprises a gene identified using any of the target gene identification methods described herein. One or more drug resistance mutations in one or more drug resistance genes, the method comprising: a) providing a library of cancer cells comprising the one or more drug resistance mutations in the one or more drug resistance genes, b) Individually contacting the library of cancer cells with one or more candidate anticancer drugs, wherein growth of the library of cancer cells is inhibited above a certain threshold (e.g., inhibited by at least about 10%, 20%, 30%, 40%, 50% or more growth) are identified as anticancer drugs capable of treating an individual's cancer.

V. Kits and Articles

The present application also provides kits and articles of manufacture for use in any of the embodiments of the methods for identifying target genes in cancer cells described herein, such as using the sgRNA library or the sgRNA ^iBAR library described herein. Also provided are kits and articles of manufacture for generating modified cancer cells that are sensitive and resistant to anticancer drugs.

In some embodiments, kits are provided for identifying target genes in cancer cells whose mutations render the cancer cells sensitive or resistant to anticancer drugs, comprising any of the sgRNA libraries or sgRNA ^iBAR libraries described herein. In some embodiments, the kit further comprises a Cas protein or a nucleic acid encoding a Cas protein (eg, Cas9). In some embodiments, the kit further comprises one or more positive and/or negative control sgRNA ^iBAR constructs, or one or more positive and/or negative control sgRNA constructs. In some embodiments, the kit further comprises an anticancer drug and/or the initial population of cancer cells, or cancer cells comprising the Cas component. In some embodiments, the kit further comprises data analysis software. In some embodiments, the kit comprises instructions for performing any of the methods described herein.

In some embodiments, there is provided a kit for identifying a target gene in a cancer cell whose mutation renders the cancer cell sensitive or resistant to an anticancer drug, comprising any of the cancer cell libraries described herein, such as in the genome ( or cancer-associated genes), or a cancer cell library comprising any of the sgRNA libraries or sgRNA ^iBAR libraries described herein. In some embodiments, the kit further comprises a Cas protein or a nucleic acid encoding the Cas protein. In some embodiments, the kit further comprises an anticancer drug. In some embodiments, the kit further comprises a library of control cancer cells, such as having one or more mutations (e.g., inactivating mutations) in a nongenic region of the genome, or comprising one or more endogenous cancer mutations, Or sgRNA constructs comprising one or more positive and/or negative controls or one or more positive and/or negative control sgRNA ^iBAR constructs. In some embodiments, the kit further comprises data analysis software. In some embodiments, the kit comprises instructions for performing any of the methods described herein.

The kit may comprise other components, such as containers, reagents, media, primers, buffers, enzymes, etc., to facilitate performance of any of the screening methods described herein. In some embodiments, the kit comprises reagents, buffers, and vectors for introducing the sgRNA library or sgRNA ^iBAR library and the Cas protein or nucleic acid encoding the Cas protein into cancer cells. In some embodiments, the kit comprises primers, reagents, and enzymes (e.g., polymerases) for preparing a sequencing library of sequences comprising hit gene mutations (e.g., inactivating mutation), sgRNA sequence or sgRNA ^iBAR sequence.

The kits of the present application are provided in suitable packaging. Suitable packaging includes, but is not limited to, vials, bottles, jars, flexible packaging (eg, sealed mylar or plastic bags), and the like. Kits may optionally provide other elements such as buffers and explanatory information. Accordingly, the present application also provides articles of manufacture, including vials (eg, sealed vials), bottles, jars, flexible packaging, and the like.

An article of manufacture may include a container and a label or package insert on or associated with the container. Suitable containers include, for example, bottles, vials, syringes, and the like. Containers can be made from a variety of materials such as glass or plastic. Typically, the container contains a component (eg, modified cancer cells that are sensitive or resistant to an anticancer drug) and may have a sterile inlet. A package insert is an instruction usually included in commercial packaging that contains information about the directions and/or warnings for use of such products. It can also include other materials, including other buffers, diluents, screening procedures, as desired from a commercial and user standpoint.

Example

The following examples and exemplary embodiments are intended purely as illustrations of the invention and therefore should not be construed as limiting the invention in any way. The following examples and detailed description are offered by way of illustration and not limitation.

Example 1: Identification of drug-sensitive and drug-resistant genes in cancer cells

This example provides exemplary methods for identifying drug susceptibility genes and/or drug resistance genes. In brief, a cancer cell library carrying sgRNA ^iBARs targeting cancer-associated genes was constructed for Cas9-mediated gene knockout (KO). By detecting the anticancer drug (such as PARPi) killing efficacy of the constructed Cas9 ⁺ sgRNA ^iBAR cancer cell library, genes that endow anticancer drug-killing drug-resistant or sensitive phenotypes after gene knockout (KO) can be identified. Figure 1-2 shows an example workflow.

1. Design and construction of sgRNA ^iBAR library

Based on public databases, genes with a DNA mutation frequency ≥5% and RNA expression levels up- or down-regulated >2-fold (expressed in cells or on the cell surface) from patients with stage III and IV colorectal cancer were selected for use Library genes for further sgRNA ^iBAR design (1323 genes in total).

The design and construction of the sgRNA ^iBAR library is similar to that described in WO2020125762 and Zhu et al. "Guide RNAs with embedded barcodes boost CRISPR-pooled screens," Genome Biol. 2019; 20:20, the contents of each of which are incorporated by reference in their entirety. into this article. In brief, more than 1323 selected genes were retrieved from the UCSC human genome. The sgRNA targeting each gene was designed using the DeepRank algorithm (see Zhu et al.), with three different targeting sgRNAs per gene, and four 6-bp iBARs (iBAR6) were randomly assigned to each sgRNA (“sgRNA ^iBAR "). The internal barcode sequence is designed to be located in the tetraloop of the outer gRNA backbone of the Cas9 sgRNA ribonucleoprotein complex, which will not affect the activity of its upstream guide sequence. In addition, 500 control sgRNAs that did not target any human genes were designed as negative controls, and four iBAR6s were randomly assigned to each control sgRNA (“control sgRNA ^iBAR ”). Therefore, the designed CRISPR sgRNA ^iBAR library included a total of 17876 sgRNA ^iBARs (targets and controls).

DNA oligonucleotides encoding sgRNA ^iBARs were designed and synthesized (from Twist Bioscience), followed by PCR amplification. PCR products were purified with a PCR purification kit and then cloned by Golden Gate cloning into an in-lab modified lentiviral sgRNA ^iBAR -expression backbone based on pLenti-sgRNA-Lib (Addgene #53121) to yield sgRNA ^iBAR plasmids , which encode 15876 sgRNA ^iBARs covering 1323 human genes (3 groups of sgRNA ^iBARs per gene, each gene targeting 3 different target sites; each group of sgRNA ^iBARs contains 4 sgRNA ^iBARs ), and 500 2000 control sgRNA ^iBARs for each non-genic region (one group of sgRNA ^iBARs for each non-genic region, and each group of sgRNA ^iBARs contains 4 sgRNA ^iBARs ).

To ensure the abundance of sgRNA ^iBARs in cancer cell libraries (at least 1000-fold coverage for each sgRNA ^iBAR ), 10 electroporation reactions were performed using the sgRNA ^iBAR plasmids obtained above. For each electroporation reaction, 1 μL of sgRNA ^iBAR plasmid was added to a sterile 1.5 mL Eppendorf centrifuge tube, and 50 μL of competent cells (E. coli) were added to the tube and spun before electroporation. Immediately add 950 μL of SOC (Super Optimal Broth) medium without antibiotics to each reaction tube, mix gently with a pipette, and then incubate in a shaker at 37°C and 225 rpm for 1 h. The resulting bacteria were transferred to 1 L of LB liquid medium supplemented with ampicillin and cultured overnight at 37°C and 225 rpm in a shaker. The next day, the obtained bacteria were subjected to plasmid extraction using the EndoFree® Plasmid Purification Kit (QIAGEN, #12391).

The sgRNA ^iBAR library lentiviruses were obtained using standard protocols. Briefly, 1×107 293T cells were placed in a 150mm cell culture dish, 20 mL of cell culture medium was added, and then the 293T cells were cultured overnight in a 37°C, 5% CO2 incubator. The next day, discard the medium and add 10 mL of fresh serum-free medium to the 293T cells. Prepare the transfection complex with serum-free medium (4 mL), sgRNA ^iBAR library plasmid (20 μg) obtained above, pCMVR8.74 plasmid (20 μg) and pCMV-VSV-G plasmid (2 μg); after mixing, add 105 μL PEI; mix Afterwards, the transfection complex was allowed to stand at room temperature for 15 min. The transfection complex was then added to 293T cells in 10 mL of fresh serum-free medium and incubated at 37°C, 5% CO2 in an incubator for 6 h. Cell culture medium was discarded. Add 20 mL of fresh complete medium to the 293T cells, then culture in an incubator at 37 °C, 5% CO2. After 3 days, the medium was collected and centrifuged at 1000 rpm at 4°C. Supernatants containing lentiviruses from the sgRNA ^iBAR library were collected, titered and aliquoted for future use.

2. Construction of Cas9 ⁺ sgRNA ^iBAR cancer cell library

HCT116 (human colon cancer cell line) and SW480 (human colorectal cancer cell line) were selected for Cas9 ⁺ sgRNA ^iBAR cancer cell library construction and drug treatment.

2 × 105 cancer cells of each cell line were seeded in 6-well plates and cultured in a 37°C, 5% CO2 incubator. After 24 h, 100 μL of Cas9-packaged lentivirus was added to the cell culture medium, and the cancer cells were cultured in a 37°C, 5% CO2 incubator. After 24 h, the medium was discarded and fresh complete medium was added to the cancer cells. Cancer cells were grown in a 37°C, 5% CO2 incubator for 7 days and then sorted by FACS using the mCherry marker (on a Cas9 lentiviral vector). The sorted cancer cells with mCherry fluorescence were Cas9-expressing (Cas9 ⁺ ) cells, which were amplified to construct the Cas9 ⁺ sgRNA ^iBAR library.

In order to ensure that the coverage of sgRNA ^iBAR in the Cas9 ⁺ sgRNA ^iBAR cancer cell library is at least 1000-fold, the sgRNA ^iBAR library lentivirus obtained above was added to 2×107 Cas9 ⁺ cancer cell culture medium (without antibiotics) at an MOI of 3 and mix gently. Cas9 ⁺ cancer cells were cultured for 24 h in a 37°C, 5% CO2 incubator for infection. The next day, discard the medium and add fresh complete medium to the Cas9 ⁺ cancer cells, then culture in a 37°C, 5% CO2 incubator. Cas9 ⁺ cancer cells were passaged every 3 days in fresh complete medium supplemented with puromycin. Cas9 ⁺ cancer cells unsuccessfully transfected with sgRNA ^iBAR plasmids die. After two consecutive passages, the sgRNA ^iBAR cancer cell library (hereinafter respectively referred to as "Cas9 ⁺ sgRNA ^iBAR HCT116 library" and "Cas9 ⁺ sgRNA ^iBAR SW480 library") was obtained.

3. Screening of Cas9 ⁺ sgRNA ^iBAR Cancer Cell Library Treated with Anticancer Drugs

The drug toxicity profile of each cancer cell line was measured first, and then the Cas9 ⁺ sgRNA ^iBAR cancer cell library was treated with anticancer drugs (e.g., PARP inhibitor; PARPi).

Add 2000 HCT116 or SW480 cells to each well in a 96-well plate, add 100 μL of medium to each well, and then culture in a 37°C, 5% CO2 cell incubator. The next day, various concentrations of anticancer drugs (eg, PARPi) were added to each well in triplicate for each concentration. Final drug concentrations were 33 μM, 11 μM, 3.7 μM, 1.23 μM, 0.41 μM, 0.14 μM, 0.05 μM and 0.02 μM. After three doubling times in the presence of anticancer drugs, the CellTiter Glo® Luminescent Cell Viability Assay (ATP Assay) was performed to obtain drug toxicity curves.

Based on the obtained drug toxicity curves for each cancer cell line, drug concentrations corresponding to cell growth inhibition with IC50-IC70 were selected for the Cas9 ⁺ sgRNA ^iBAR cancer cell library screening. For example, the PARPi concentrations of HCT116 and SW480 were 5 μM and 10 μM, respectively. 1×106 Cas9 ⁺ sgRNA ^iBAR cancer cells were placed in a 150 mm cell culture dish and cultured in a 37°C, 5% CO2 cell incubator. The next day, Cas9 ⁺ sgRNA ^iBAR cancer cells were treated with anticancer drugs (e.g., PARPi; test group) or DMSO (control group). Two biological replicates were set up for each group. Fresh cell culture medium (addition of drug or DMSO) was changed every 3 days. Drug or control treatment was continued and cells were harvested after 9-10 doubling times or 15-16 doubling times of treatment (see Figure 2). For adherent cells, dead cells will be floating in the medium, so the adherent cells harvested by trypsinization are all viable (or mostly viable) cells. Throughout the screening process and cell collection, the number of cells was consistently at least about 1000-fold the size of the sgRNA ^iBAR library for each replicate, ie at least about 1000 cells per sgRNA ^iBAR .

4. Identification and Analysis of Target Genes

Genomic DNA was extracted from the treated cancer cells collected above (mostly live Cas9 ⁺ sgRNA ^iBAR cancer cells). For each cancer cell type, there are "9-10 PDT test group", "15-16 PDT test group", "9-10 PD control group" and "15-16 PDT control group"; each group has two biological Learn to repeat. For each anticancer drug (eg, PARPi), two different cell line libraries (eg, Cas9 ⁺ sgRNA ^iBAR HCT116 library and Cas9 ⁺ sgRNA ^iBAR SW480 library) were tested. The sgRNA ^iBAR coding fragment was PCR amplified from the extracted genome, purified and prepared for NGS sequencing. Sequencing data analysis was performed using the MAGeCKiBAR algorithm (see Zhu et al., "Guide RNAs with embedded barcodes boost CRISPR-pooled screens," Genome Biol. 2019; 20:20; the contents of which are incorporated herein by reference in their entirety), which Consists of three main sections: Analysis Preparation, Statistical Tests, and Ranked Aggregation. In summary, the genes targeted by each sgRNA ^iBAR are scored and ranked based on the degree of enrichment or depletion of each gene between the test and control groups in order to determine whether the gene is a candidate gene with high confidence . See Figure 3 for the workflow for target gene identification. For candidate genes whose inactivation leads to anticancer drug-killing sensitive phenotypes, the sgRNA ^iBAR coding fragment will be depleted compared with the control group (negative screening); sgRNA ^iBAR coding fragments will be enriched compared with the control group (positive screening). These top candidate genes were found to be involved in cell proliferation, cell death, or cell cycle regulation.

5. Results

Candidate genes whose sgRNA ^iBAR coding fragments were depleted in harvested live cells and had FDR ≤ 0.1 in each cell line library in the "9-10 PDT test group" or "15-16 PDT test group" compared to the control group , are classified as drug-sensitive genes whose inactivation sensitizes cancer cells to anticancer drugs. Exemplary drug sensitive genes (e.g., for PARPi) include, but are not limited to: ARID2, ATM, BIRC6, BRCA1, BRCA2, CCNA2, CCND1, CDK2, FBXW7, HRAS, KAT2B, NBN, PBRM1, PTEN, SKP2, SMAD7, TGFB2, TSC1, TSC2, ATR, RIF1, POLQ, AXIN1, GSK3A, GSK3B, CHD7, SCAF4, FANCM, NIPBL, ATRX, STAG1, RAD51, RAD51B, RAD51C, RAD51D, FANCL, EXO1, DIDO1, LRBA, FAM71A, HDAC2, PMS2, MSH6, MSH2, MLH1 and WEE1.

Candidates whose sgRNA ^iBAR- encoding fragments were enriched in harvested live cells in the "9-10 PDT test group" or "15-16 PDT test group" compared to the control group and had FDR ≤ 0.1 in each cell line library Genes, classified as resistance genes whose inactivation renders cancer cells resistant to anticancer drugs. Exemplary drug resistance genes (e.g., for PARPi) include, but are not limited to: AKT1, CDKN1A, CKS1B, CKS2, CTNNB1, DLG5, E2F3, E2F4, HDAC1, MAPK1, MYC, RAC1, RAF1, RICTOR, SMAD4, TP53, BRAF, HSP90B1, PARP2, PARP1, PIK3CA, EIF3A, CCNA1, RBL1, ZMYND8, MED12, GCN1, Kras, TP53BP1, CHD2, DOCK5, IGF1R, ILK, IRS1, RAPGEF1, EP300, TCF7L2, KMT2B, CDKN2A, CHEK1, CHEK2, RHEB, SPTA1, PKMYT1, SIDT2, APC, and SETD2.

Screening scores (reflecting the significance of enrichment/depletion) against PARPi for a small set of PARPi-sensitive and resistant genes identified in the "15-16 PDT test set" and the Cas9 ⁺ sgRNA ^iBAR HCT116 library or the Cas9 ⁺ sgRNA ^iBAR SW480 library and degree) and FDR (reflection significance) are shown in Table 1.

Table 1. Drug-sensitive or resistant genes against PARPi PARPi drug sensitive gene PARPi resistance gene Gene Screening score FDR Gene Screening score FDR PTEN 27.72 0.00 PARP1 34.15 0.00 ATMs 26.01 0.00 MED12 33.39 0.00 RIF1 24.53 0.00 MAPK1 30.19 0.00 BRCA1 23.82 0.00 TCF7L2 27.27 0.00 TSC1 20.37 0.00 CKS2 20.86 0.00 STAG1 19.29 0.00 TP53 20.70 0.00 FBXW7 19.25 0.00 HSP90B1 20.20 0.00 NBN 19.23 0.00 CDKN1A 20.17 0.00 CHD7 16.84 0.00 TP53BP1 18.56 0.00 SKP2 16.56 0.00 RHEB 18.34 0.00 GSK3B 15.55 0.00 CTNNB1 18.09 0.00 TSC2 14.79 0.00 ILK 17.88 0.00 POLQ 13.56 0.00 MYC 17.75 0.00 CDK2 11.13 0.00 PARP2 17.62 0.00 FANCM 9.87 0.00 RICTOR 17.47 0.00 ATR 8.98 0.00 ZMYND8 17.46 0.00 SCAF4 7.55 0.00 PIK3CA 14.84 0.00 BIRC6 7.48 0.00 RAC1 14.76 0.00 PBRM1 7.31 0.00 RAPGEF1 14.22 0.00 KAT2B 7.12 0.00 GCN1 13.96 0.00 GSK3A 7.07 0.00 EIF3A 13.11 0.00 NIPBL 6.90 0.00 SMAD4 12.46 0.00 HRAS 6.80 0.00 DLG5 11.37 0.00 TGFB2 6.78 0.00 CHD2 11.23 0.00 WEE1 6.66 0.00 BRAF 10.87 0.00 CCNA2 6.20 0.00 AKT1 10.76 0.00 HDAC2 5.91 0.00 IGF1R 10.50 0.00 AXIN1 5.72 0.00 CKS1B 10.07 0.00 SMAD7 4.77 0.01 IRS1 9.38 0.00 ARID2 4.10 0.03 RBL1 8.59 0.00 CCND1 3.52 0.09 DOCK5 7.75 0.00 DIDO1 3.42 0.08 KMT2B 6.56 0.00 BRCA2 3.36 0.11 EP300 5.90 0.00 SETD2 5.15 0.00 PKMYT1 5.08 0.00 CCNA1 4.72 0.01 E2F3 4.68 0.01 SPTA1 4.57 0.02 HDAC1 4.18 0.02 E2F4 3.43 0.15

The results obtained here, particularly those genes whose inactivation was found to render cancer cells sensitive to killing by anticancer drugs (e.g., PARPi), demonstrate their value as targets in cancer therapy and biological candidates for patient selection. landmark. The inactivation of resistance genes that render cancer cells resistant to anticancer drugs would serve as a biomarker for not selecting such patients, and/or that alternative cancer therapeutics should be used.

6. Target gene validation

In order to verify the identified drug-sensitive genes and drug-resistant genes, a gene subset (see Table 2) was selected from PARPi-sensitive genes and PARPi-resistant genes for experimental testing.

Briefly, nucleic acids encoding sgRNAs targeting these genes were designed and synthesized. The forward and reverse strands are allowed to anneal to form a double-stranded nucleic acid with overhangs at both ends. Based on pLenti sgRNA-Lib (Addgene #53121), the lentiviral sgRNA-expressed backbone modified in-house is digested, and the double-stranded nucleic acid is ligated to the cutting site to obtain the sgRNA plasmid carrying purines Mycin and ampicillin antibiotic genes.

To amplify the sgRNA plasmid, add 2 µL of the sgRNA plasmid to 20 µL of competent cells (E. coli) in a 1.5 mL Eppendorf centrifuge tube, followed by a standard ice/heat shock transformation protocol in liquid LB in a 37 °C shaker Grow for 1 h in , then spread onto LBAmp+ plates and grow overnight at 37°C. The next day, 5-10 single colonies were picked and grown overnight in LBAmp+ liquid medium in a shaker at 37°C. The next day, the sgRNA plasmid was extracted with a kit and then sequenced to verify the sequence.

sgRNA lentiviruses were then obtained using standard protocols. Briefly, 5 × 106 293T cells were placed in a 10 cm cell culture dish and cultured overnight in a 37°C, 5% CO2 incubator. The next day, discard the medium and add fresh serum-free medium to the 293T cells. Prepare a transfection complex with serum-free medium (1 mL), the above-purified sgRNA plasmid (10 μg), pCMVR8.74 plasmid (10 μg), and pCMV-VSV-G plasmid (1 μg); after mixing, add 52.5 μL of PEI. After mixing, the transfection complex was allowed to stand at room temperature for 15 min. The transfection complex was then added to 293T cells in fresh serum-free medium and cultured in an incubator at 37°C with 5% CO2 for 6-8h. Discard the cell culture medium, add fresh complete medium to the 293T cells, and culture in a 37°C, 5% CO2 incubator. After 72 hours, the cell cultures were collected and centrifuged at 200 g for 5 minutes. The supernatant containing sgRNA lentivirus was collected, filtered with a 0.45 μm screening program, and then stored at -80°C for future use.

To construct cancer cell lines with target gene knockout (KO), 2×105 SW620 cancer cells were seeded into 6-well plates and cultured in a 37°C, 5% CO2 incubator. After 24 h, 100 μL of Cas9-packaged lentivirus was added to the cell culture medium, and the cancer cells were cultured in a 37°C, 5% CO2 incubator. After 24 h, the medium was discarded and fresh complete medium was added to the cancer cells. The cancer cells were allowed to grow for 7 days in a 37°C, 5% CO2 incubator, and then sorted by FACS using the mCherry marker (on a Cas9-lentiviral vector). Cancer cells with mCherry fluorescence were sorted as Cas9-expressing (Cas9+) cells and expanded for construction of Cas9+ sgRNA. Add 500 μL of the non-condensed sgRNA lentivirus obtained above to 2×107 Cas9+ cancer cells in medium at an MOI of 3 (without antibiotics) and mix gently. Cas9+ cancer cells were cultured overnight in a 37°C, 5% CO2 incubator for infection. The next day, the medium was discarded, and fresh complete medium was added to the Cas9+ cancer cells, followed by culturing in a 37°C, 5% CO2 incubator for 48h. Then add 1 μL of puromycin to the medium for selection. Cas9+ cancer cells that are not successfully transfected with the sgRNA plasmid will die.

To test target gene knockout (KO) efficiency (%), a subpopulation of cancer cells treated with puromycin as described above was collected. Genomic DNA is extracted, and the target gene sequence is amplified and sequenced. Knockout (KO) efficiencies were calculated using the Decomposition Tracing Indels (TIDE) web tool, which accurately reconstructs indel profiles from sequence traces and reports detected indels and their frequencies as knockout Divide (KO) efficiency. The results are summarized in Table 2.

In order to measure the response of target gene knockout (KO) cancer cells to PARPi treatment, 1000 cancer cells were put into a 96-well plate for each target gene knockout (KO), and medium was added to each well, and then culture medium was added at 37 °C, 5% CO2 in a cell incubator overnight. The next day, different concentrations of PARPi were prepared at a 1:3 dilution and added to each well in triplicate for each concentration. Final PARPi concentrations were 33.3 μM, 11.1 μM, 3.70 μM, 1.27 μM, 0.41 μM, 0.13 μM and 0.05 μM and 0.015 μM. Control experiments were performed with cancer cells that did not carry any target gene knockout (KO) ("WT cancer cells"), cultured under the same conditions and treated with PARPi. After 2-3 doubling times in the presence of PARPi, the CellTiter Glo® Luminescent Cell Viability Assay (ATP Assay) was performed to obtain drug toxicity curves (see Figure 4). IC50 results are shown in Table 2.

Table 2. Validation of PARPi's drug-sensitive genes and drug-resistant genes PARPi drug sensitive gene PARPi resistance gene Gene Knockout (KO) efficiency (%) IC50 fold change (KO vs. WT) Gene Knockout (KO) efficiency (%) IC50 fold change (KO vs. WT) ATMs 80 -56.45 MYC 57 6.43 NBN 51 -15.96 GCN1 62 5.50 FANCM 69 -13.27 ZMYND8 40 5.50 BRCA1 90 -9.86 PARP1 75 3.48 WEE1 63 -8.69 SETD2 57 2.50 ARID2 87 -5.55 RICTOR 60 2.42 RIF1 68 -3.55 CTNNB1 47 2.00 CHD7 76 -3.06 TP53 79 1.94 POLQ 58 -1.88 EIF3A 29 1.77 DIDO1 58 -1.88 MED12 70 1.50 PTEN 51 -1.78 ATR 32 -1.67 STAG1 70 -1.63 BIRC6 88 -1.50

As shown in Figure 4 and Table 2, drug-sensitive genes identified by screening after knockout (KO) did confer susceptibility to PARPi killing in cancer cells (see, for example, ATM, BRCA1, WEE1, etc.), and in knockout (KO) (KO) Resistance genes identified by post-screening indeed confer resistance to PARPi killing in cancer cells (eg, see PARP1, MYC). Furthermore, the IC50 fold change between target gene knockout (KO) and WT cancer cells mainly follows the following screening results: highly enriched or depleted target genes from the screen (e.g., with higher screen score, see Table 1 for example). ) also showed a large difference in IC50.

The verification results of these target genes show that the screening method described in this paper effectively obtains drug sensitive genes and/or drug resistant genes, and the drug sensitive genes and drug resistant genes provided herein have higher accuracy and will be used in cancer diagnosis and treatment. of great value.

7. Discussion

The above method can be used for any anti-cancer drugs (such as drugs targeting different pathways or the same pathway) and drug-sensitive and/or drug-resistant genes of any cancer type. The obtained drug-sensitive genes and/or drug-resistant genes are of great significance in cancer treatment, patient selection, and new drug screening or design.

For example, if a cancer patient's diagnosis shows that, for a single pathway (e.g., targeted by PARPi, etc.): 1) the patient has only such an inactivating mutation in the target gene, the inactivation of which results in the development of a pathway-targeted drug sensitivity, then the patient is a perfect candidate for treatment with this type of drug; 2) the patient has only this inactivating mutation in the target gene that inactivates resistance to pathway-targeted drugs, then the patient Patients may not be suitable for treatment with drugs targeting this pathway, and alternative treatments should be sought; 3) Patients have both target genes whose inactivation leads to resistance to pathway-targeted drugs and whose inactivation leads to resistance to pathway-targeted drugs Two inactivating mutations in a target gene that confers sensitivity to a drug would require more analysis, for example, if the drug is sensitive enough to help kill cancer cells before resistance emerges, and if it is related to the gene that confers drug sensitivity Genes that confer drug resistance are less important in cancer development than genes that confer drug resistance, so should a drug that targets one pathway be chosen over another, should one drug be used before the other or used together, whether there are alternative treatments, etc. A composite score of drug-susceptibility and drug-resistance aberrations described herein (eg, Formula I) may also be helpful in treatment decisions.

Target genes obtained for multiple anticancer drugs, such as those targeting the same or different pathways involved in cancer development, can be combined or overlapped to find common target genes. Gene function and/or mechanism of action can be further analyzed for therapeutic decisions and/or drug design/development. For example, if a patient carries an inactivating mutation in a gene whose inactivation confers sensitivity to drugs X, Y, and Z, combination therapy with drugs X, Y, and Z may result in synergistic anticancer activity. For example, if a patient carries inactivating mutations in different genes (same pathway or different pathways) whose inactivation confers sensitivity to drugs X, Y, and Z, combination therapy with drugs X, Y, and Z can produce synergy Anticancer activity. Another example is that if a new drug can be designed to target multiple pathways involving target genes whose loss results in sensitivity to known drugs, the resulting new drug can be more effective than the known drug. have a better therapeutic effect.

For example, for multiple pathways targeted by, for example, PARPi, other drugs to treat colorectal cancer, or other drugs to treat other cancer types but not yet tested/developed for colorectal cancer treatment, if a patient is diagnosed as : 1) There are inactivating mutations in a common target gene (or different genes with a shared pathway) whose inactivation results in sensitivity to drugs targeting multiple pathways, then use a single or preferred combination of such drugs Therapies can be used to treat cancer; 2) have inactivating mutations in a common target gene (or different genes with a shared pathway) whose inactivation results in resistance to drugs targeting multiple pathways, so alternatives should be sought Alternatively, gene function and/or mechanism of action should be further analyzed to determine whether combination therapy with drugs targeting such pathways (eg, one prior to the other) can alleviate the resistance phenotype. For example, drug X, which would experience resistance from target gene mutations later in the course of treatment, could be given first, while drug Y, which would experience resistance from target gene mutations earlier but might be sufficiently effective, could only be given at the beginning or throughout the course In combination with Drug X.

Example 2: Comprehensive Score Correctly Reflects the Anticancer Efficacy of Anticancer Drugs

This example provides evidence that based on the identification of drug susceptibility and resistance genes to anticancer agents (e.g., DNA damaging agents such as PARPi or ATRi) using the screening methods described herein, using the methods described herein (e.g., Formula I) The calculated composite score correctly reflects/can predict the cancer-killing efficacy of the corresponding anti-cancer agent.

1. Randomly select colorectal cancer samples for composite score calculation

Based on public databases, genes with a DNA mutation frequency ≥5% and RNA expression levels up- or down-regulated >2-fold (expressed in cells or on the cell surface) from patients with stage III and IV colorectal cancer were selected for use Library genes for further sgRNA ^iBAR design (1323 genes in total).

Harvested colorectal cancer cell lines and patient-derived xenografts (PDX) were tested for PARPi treatment response by measuring cell viability (reflected as IC50) or PDX growth inhibition (following standard methods) (see also Example 1 ). Based on multiple responses to PARPi treatment, 16 cancer samples (10 PDX and 6 cancer cell lines; see Figure 5A) were selected for composite score calculation. Its corresponding cellular viability response or PDX growth inhibition response is reflected as "drug response" in Figure 5C.

2. Detection, filtering and annotation of mutations in selected cancer samples

The 16 cancer samples selected above were individually sequenced by NGS. For each sample, mutations were detected from the sequencing data. The original mutation sites were further screened according to the mutation quality to remove low confidence mutation sites. The remaining high-quality mutation sites were mapped to the corresponding genes, and the effects of the mutations on the corresponding gene functions were annotated based on the database. Only mutation sites with deleterious effects on the corresponding genes were retained for subsequent analysis.

3. Gene-level functional annotation and integration of series database information for further filtering

The remaining mutation sites above were then annotated based on prevalence, clinical significance, cure impact, Gene Ontology and pathway information from external and internal database sources. Low clinical impact mutations were further filtered out. The overall loss-of-function (LOF) probability for each gene mapped to the remaining mutations in each sample was then calculated.

4. Composite Score Calculation

部分。通過整合相應的權重(

)、每個基因的相關係數(

)，以及預先計算的PARPi反應中相關通路的權重(

)，量化“測試基因組”中檢測到的突變對PARPi治療的基因水準貢獻和通路水準貢獻，以計算原始綜合評分。根據樣本類型(即細胞系、PDX、患者)進一步調整和縮放原始綜合評分，以生成每個癌症樣本的最終綜合評分(參見圖5B和5C“綜合評分”行)。 In order to calculate the composite score for each cancer sample and test its accuracy in predicting response to PARPi therapy, a total of 51 PARPi-sensitive genes and PARPi-resistant genes were selected, which were obtained and/or validated from Example 1, and at least one deleterious mutation with high confidence in any of the filtered 16 selected cancer samples ("Test Genome Panel"). Their corresponding LOF probabilities are shown in Fig. 5A. For each cancer sample, the gene-level LOF probability of 51 PARPi-sensitive/resistant genes was used to calculate the

part. By integrating the corresponding weights (

), the correlation coefficient of each gene (

), and the weights of the relevant pathways in the precomputed PARPi responses (

), quantify the gene-level contribution and the pathway-level contribution of mutations detected in the "test genome" to PARPi treatment to calculate the raw composite score. The raw composite score was further adjusted and scaled by sample type (i.e., cell line, PDX, patient) to generate the final composite score for each cancer sample (see Figure 5B and 5C row "Composite score").

5. Results and conclusions

As shown in Figures 5B and 5C, cancer samples did show susceptibility to PARPi killing when their composite score according to Formula I was above 0; whereas cancer samples according to Formula I had a composite score below 0 , the cancer samples did show resistance to PARPi killing. Furthermore, the higher the absolute value of the composite score, the better the predictive power of the actual PARPi response for that cancer sample (see row "Prediction" based on composite score in Figure 5C). For example, for cancer samples with a composite score above 0.1, the sample's actual sensitivity to PARPi killing was predicted to be "true" (see PDX3, PDX6, PDX10, Line 1, Line 2, and Line 6). For cancer samples with a smaller negative composite score (larger absolute value of the composite score), the prediction of the sample's actual resistance to PARPi killing was "true" (see PDX8, Cell Line 4, Cell Line 4). According to Formula I, the composite score of sensitive cancer samples without actual testing was lower than 0, which indicates the strong predictive power of "true positive" of the method described here. A practically tested sensitive cancer sample cell line 3 with a composite score of 0. According to formula I, all the drug-resistant cancer samples actually tested had composite scores lower than or equal to 0 (indicating the strong "true positive" predictive power of the method described herein), except for PDX9 which had a composite score of 0.011. Therefore, for cancer samples with a composite score close to or equal to 0 (e.g., -0.1 to +0.1), the prediction of response to PARPi therapy based on the composite score is 'uncertain'. Additional evaluations may be required to identify false positives and false negatives based on composite score predictions.

These findings suggest that drug-sensitivity and resistance genes for anticancer agents (e.g., DNA damaging agents such as PARPi or ATRi) identified using the screening methods described herein, and based on their composite scores obtained using the methods described herein, correctly reflect It has/can predict the cancer-killing efficacy of corresponding anticancer agents, and can be used as a tool for cancer diagnosis, treatment selection and/or patient selection. For example, when the patient's composite score according to Formula I is higher than 0, the patient may be suitable for (ie, likely to benefit from) treatment with an anticancer drug (eg, a DNA damaging agent, such as PARPi or ATRi). If the patient's composite score according to Formula I is greater than or equal to at least 0.1 (eg, 0.3), the patient may be selected or recommended for anticancer drug treatment. If the patient's composite score according to formula I is greater than 0 but less than 0.1, the patient may be suitable for anticancer drug therapy, but should be further evaluated using other methods (eg, drug dosing tests, cancer genetic testing (eg, looking for other synergistic mutations for anticancer drug treatment, or verify the primary cancer type), etc.), or based on other information (such as the patient's clinical records or known drug resistance, etc.) to determine whether the patient should be selected or recommended for anticancer drugs treat. If the patient's composite score according to Formula I is less than or equal to 0, the patient may not be suitable (ie, may not benefit) or should be excluded from anticancer drug treatment. If a patient's composite score according to Formula I is equal to 0 or very close to 0 (eg, -0.1 to 0) before the patient is completely excluded from anticancer drug therapy, further evaluation using other methods or based on other information described herein may be required .

none

Figure 1 shows an exemplary process for screening drug sensitivity and/or resistance genes against anticancer drugs. Figure 2 shows an exemplary screening workflow against a Cas9+ sgRNA ^iBAR cancer cell library. Figure 3 shows an exemplary target gene identification workflow for the Cas9+ sgRNA ^iBAR cancer cell library. Figure 4 shows the PARPi response curves of cancer cells with drug-sensitive or drug-resistant gene knockout (KO). Cancer cells without this KO treated with PARPi were used as controls (WT). Figure 5A shows the loss-of-function (LOF) mutation probability of drug-sensitive and resistant genes (y-axis, 51 genes in total) of PARPi in 16 cancer samples. Figure 5B shows the composite score calculated using Formula I based on 51 genes for each cancer sample. Figure 5C shows the composite score, response to PARPi treatment, and prediction of treatment efficacy based on the composite score for each cancer sample.

Claims (43)

Methods of identifying target genes in cancer cells whose mutations render the cancer cells sensitive or resistant to anticancer drugs, comprising: a) providing a cancer cell library comprising a plurality of cancer cells, wherein each of the plurality of cancer cells has a mutation in a hit gene ("hit gene mutation"), wherein in at least two of the plurality of cancer cells The hit genes of are different from each other; wherein the cancer cell library is generated by contacting the initial cancer cell population with the : i) a single-stranded guide RNA (“sgRNA”) library comprising a plurality of sgRNA constructs, wherein each sgRNA construct comprises or encodes a sgRNA, and wherein each sgRNA comprises a guide complementary to a target site in a corresponding hit gene sequence; and ii) a Cas component comprising a Cas protein or a nucleic acid encoding the Cas protein; b) contacting said library of cancer cells with said anticancer drug; c) growing the library of cancer cells to obtain a population of treated cancer cells; and d) identifying said target gene based on the difference between the profiles of sgRNA or hit gene mutations in said treated cancer cell population and a control cancer cell population. The method according to claim 1, wherein the control cancer cell population is obtained from a cancer cell library cultured under the same conditions and not exposed to the anticancer drug. The method according to claim 1 or 2, wherein the identification of the target gene is performed based on the difference between the sgRNA profiles in the treated cancer cell population and the control cancer cell population. The method of claim 3, wherein the sgRNA profiles in the treated cancer cell population and the control cancer cell population are identified by next generation sequencing. The method of claim 4, wherein the method comprises comparing sgRNA sequence counts obtained from the treated cancer cell population with sgRNA sequence counts obtained from the control cancer cell population, wherein: i) Hit genes whose corresponding sgRNA guide sequences are identified as enriched in the treated cancer cell population compared to the control cancer cell population and have a false discovery rate (FDR) ≤ 0.1, are identified as mutations a target gene that renders the cancer cell resistant to the anticancer drug; and/or ii) a hit gene whose corresponding sgRNA guide sequence is identified as depleted in the treated cancer cell population compared to the control cancer cell population and has an FDR ≤ 0.1, is identified as having a mutation that renders the cancer cell resistant to The target gene sensitive to the anticancer drug. The method of any one of claims 1-5, wherein the sgRNA library and the Cas element are sequentially introduced into the initial cancer cell population. The method according to any one of claims 1-6, wherein the Cas protein is Cas9. The method of claim 7, wherein each sgRNA comprises a guide sequence fused to a second sequence, wherein the second sequence comprises a repeat-inverse repeat stem-loop interacting with the Cas9. The method according to claim 8, wherein the second sequence of each sgRNA further comprises stem-loop 1, stem-loop 2 and/or stem-loop 3. The method of any one of claims 1-9, wherein each sgRNA further comprises an internal barcode (iBAR) sequence ("sgRNA ^iBAR "), wherein each sgRNA ^iBAR is operable with a Cas protein to modify the hit Gene. The method according to claim 10, wherein each sgRNA ^iBAR comprises a first stem-loop sequence and a second stem-loop sequence in the 5'-to-3' direction, wherein the first stem-loop sequence and the second The stem-loop sequence is hybridized to form a double-stranded RNA (dsRNA) region interacting with the Cas protein, and wherein the iBAR sequence is positioned at the 3' end of the first stem-loop sequence and the 5' end of the second stem-loop sequence ' between the ends. The method of claim 10 or 11, wherein the Cas protein is Cas9, and wherein the iBAR sequence of each sgRNA ^iBAR is inserted into the loop region of the repeat-repeat stem-loop. The method of any one of claims 1-12, wherein each guide sequence comprises about 17 to about 23 nucleotides. The method according to any one of claims 10-13, wherein the sgRNA library is a sgRNA ^iBAR library, wherein the sgRNA ^iBAR library comprises multiple sets of sgRNA ^iBAR constructs, wherein each set of sgRNA ^iBAR constructs comprises 4 sgRNAs ^iBAR constructs each comprising or encoding a sgRNA ^iBAR , wherein the guide sequences of the four sgRNA ^iBAR constructs are identical, wherein the iBAR sequences of each of the four sgRNA ^iBAR constructs are different from each other, and The guide sequences of each group of sgRNA ^iBAR constructs are complementary to different targets in the hit genes. The method of any one of claims 1-14, wherein at least about 95% of the sgRNA constructs in the sgRNA library are introduced into the initial cancer cell population. The method of any one of claims 10-15, wherein the cancer cell library has at least about 100-fold coverage for each sgRNA ^iBAR . The method of any one of claims 1-16, wherein the cancer cell library has at least about 400-fold coverage for each sgRNA. The method of any one of claims 1-17, wherein the sgRNA library comprises at least about 400 sgRNA constructs. The method of any one of claims 1-18, wherein each sgRNA construct in the sgRNA library is a plasmid. The method according to any one of claims 1-18, wherein each sgRNA construct in the sgRNA library is a viral vector. The method of claim 20, wherein the viral vector is a lentiviral vector. The method of claim 20 or 21, wherein the sgRNA library is contacted with the initial population of cancer cells at a multiplicity of infection (MOI) of at least about 2. The method of any one of claims 1-22, wherein step b) comprises contacting the cancer cell library with the anticancer drug at a concentration of about IC50 to about IC70 for about 9 to about 10 doubling times . The method of any one of claims 1-22, wherein step b) comprises contacting the cancer cell library with the anticancer drug at a concentration of about IC50 to about IC70 for about 15 to about 16 doubling times . The method of any one of claims 5-24, wherein the sgRNA sequence counts undergo median ratio normalization followed by mean-variance modeling. The method of claim 25, wherein the sgRNA library is a sgRNA ^iBAR library, and wherein each guide sequence is adjusted based on data identity between the iBAR sequences in the sgRNA ^iBAR sequences corresponding to the guide sequences Variance. The method of claim 26, wherein the data consistency between the iBAR sequences in the sgRNA ^iBAR sequences corresponding to each guide sequence is determined based on the direction of the fold change of each iBAR sequence, wherein if the iBAR The variance of the guide sequences is increased if the fold changes of the sequences are in different directions relative to each other. The method according to any one of claims 1-27, wherein said method comprises: The cancer cell library from step a) is subjected to at least two separate treatments with the anticancer drug in step b): growing the library of cancer cells to obtain a population of post-treatment cancer cells from each treatment; identifying one or more hit genes in the post-treatment cancer cell populations obtained from each treatment; and The one or more hit genes identified from all treatments are combined, thereby identifying target genes in the cancer cells whose mutations render the cancer cells sensitive or resistant to the anticancer drug. The method according to any one of claims 1-28, wherein said method comprises Two separate treatments b1) and b2) are performed on the cancer cell library from step a): b1) contacting the cancer cell library from step a) with the anticancer drug at a concentration of about IC50 to about IC70 for about 9 to about 10 doubling times; b2) contacting the cancer cell library from step a) with the anticancer drug at a concentration of about IC50 to about IC70 for about 15 to about 16 doubling times; c1) growing the cancer cell library from treatment b1) to obtain a post-treatment cancer cell population; c2) growing the cancer cell library from treatment b2) to obtain a post-treatment cancer cell population; d1) identifying one or more hit genes in the post-treatment cancer cell population from treatment b1), d2) identifying one or more hit genes in the post-treatment cancer cell population from treatment b2), and d3) combining the one or more hit genes identified from treatment b1) and treatment b2), thereby identifying target genes in said cancer cells whose mutations render said cancer cells sensitive or resistant to said anticancer drug. The method of claim 28 or 29, wherein i) Hit genes whose corresponding sgRNA guide sequences are identified as enriched in the post-treatment cancer cell population compared to the control cancer cell population and have an FDR ≤ 0.1 in at least one treatment, are identified as mutated a target gene that renders the cancer cell resistant to the anticancer drug; and/or ii) Hit genes whose corresponding sgRNA guide sequences are identified as depleted in the post-treatment cancer cell population compared to the control cancer cell population and have an FDR ≤ 0.1 in at least one treatment, are identified as mutations that cause The target gene that the cancer cell is sensitive to the anticancer drug. The method according to any one of claims 1-30, comprising: i) respectively identifying a set of one or more target genes whose mutations sensitize said cancer cells to one anticancer drug, and when treated alone to two or more different anticancer drugs; ii) obtaining one or more target genes present in each set of target genes identified for each anticancer drug, thereby identifying mutations thereof that render the cancer cell responsive to the two or more different anticancer drugs Combined treatment of sensitive target genes; and/or i) respectively identifying a set of one or more target genes whose mutations render said cancer cells resistant to one anticancer drug, or when treated alone, to two or more different anticancer drugs; ii) Obtaining one or more target genes present in the combination of target genes identified for all anticancer drugs, thereby identifying mutations thereof that make said cancer cells responsive to said combination of two or more different anticancer drugs Target genes for drug resistance. The method of claim 31, wherein the two or more different anticancer drugs target the same cancer target. The method of claim 31, wherein the two or more different anticancer drugs target different cancer targets. The method of any one of claims 5-33, further comprising ranking the identified target genes based on the sgRNA guide sequence in the treated cancer cell population compared to the control cancer cell population According to the degree of enrichment or depletion, target gene sequencing was performed. The method of claim 34, wherein the sgRNA library is a sgRNA ^iBAR library, and wherein based on the data consistency between the iBAR sequences in the sgRNA ^iBAR sequences corresponding to the guide sequences of the target gene The sequence of the target genes is further adjusted. The method of claim 34 or 35, further comprising assigning a sensitivity score or a drug resistance score to the identified target gene, wherein based on the enrichment factor of the sgRNA guide sequence in the treated cancer cell population compared to the control cancer cell population, the target gene whose mutation makes the cancer cells resistant to the anticancer drug is changed from Ranked from highest to lowest, with a corresponding resistance score assigned to each target gene from highest to lowest; and/or wherein the target genes whose mutations sensitize the cancer cells to the anticancer drug are ranged from high to Rank low and assign a sensitivity score to each target gene accordingly, from high to low. The method of any one of claims 1-36, wherein the anticancer drug is a PARP inhibitor. The method of any one of claims 1-37, wherein the cancer cells are colorectal cancer cells. A method of identifying a target gene in a cancer cell whose mutation sensitizes the cancer cell to a combination therapy comprising a first anticancer drug and a second anticancer drug, the method comprising: i) identifying a first set of one or more target genes in cancer cells whose mutation renders the cancer cells sensitive to the first anticancer drug according to the method of any one of claims 1-38 ; ii) identifying a second set of one or more target genes in cancer cells whose mutation renders the cancer cells sensitive to the second anticancer drug according to the method of any one of claims 1-38 ;as well as iii) obtaining one or more target genes present in both said first set of target genes and said second set of target genes, thereby identifying target genes whose mutations render said cancer cells sensitive to said combination therapy . A method of treating cancer in an individual comprising administering to said individual an effective amount of an anticancer drug based on said individual possessing a target gene ("drug sensitive gene") that sensitizes said cancer cells to said anticancer drug The individual is selected for treatment by an aberration in , and wherein the drug sensitivity gene is identified using the method of any one of claims 1-39. A method of excluding from treatment an individual with cancer comprising administering to the individual an effective amount of an anticancer drug, wherein if the individual has a target gene that renders the cancer cells resistant to the anticancer drug ( An aberration in a "drug resistance gene") excludes said individual, and wherein said drug resistance gene is identified using the method of any one of claims 1-38. A method of treating cancer in an individual comprising administering to the individual an effective amount of an anticancer drug, wherein the individual is selected based on: i) an aberration in one or more target genes ("drug sensitivity genes") that sensitizes said cancer cells to said anticancer drug ("drug sensitivity aberration"), and ii) an aberration in one or more target genes ("resistance genes") that renders said cancer cells resistant to said anticancer drug ("drug resistance aberration"), wherein the drug susceptibility gene and the drug resistance gene are identified using the method of any one of claims 1-39, and Wherein if the comprehensive score of the drug-sensitive aberration and drug-resistant aberration is higher than the comprehensive score threshold level, the individual is selected for treatment. The method of claim 42, wherein the composite score is obtained by: (i) (the absolute value of the total number of sensitivity scores for the drug-sensitive genes) minus (the absolute value of the total number of drug resistance scores for the drug-resistant genes), or (ii) Formula I, Wherein said individual is selected for treatment if said composite score is higher than zero.

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