CN114512184A - Method for predicting cancer curative effect and prognosis, device and application thereof - Google Patents

Abstract

The invention discloses a method for predicting the curative effect and prognosis of cancer, a device and application thereof, relating to the technical field of biomedicine.

Description

Method for predicting cancer curative effect and prognosis, device and application thereof

Technical Field

The invention relates to the technical field of biomedicine, in particular to a method for predicting the curative effect and prognosis of cancer, a device and application thereof.

Background

Traditional clinical treatment modalities for cancer include surgery, radiation therapy, chemotherapy, and their targeted therapies, but have limited overall survival rates. More and more studies have continuously confirmed that cancer is a disease caused by genetic variation, and traditional treatment methods mainly including surgery, chemotherapy and radiotherapy have entered a bottleneck for prolonging the survival of patients. With the continuous and deep research and the continuous and deepened understanding of diseases, new treatment modes and medicines are emerging, and the treatment of the diseases is gradually developed into the precision medicine (precision medicine) from the prior empirical medicine and the evidence-based medicine.

Currently, the precise medical treatments in lung cancer mainly include targeted therapies and immunotherapy. In the latest NCCN guidelines 2021V 5, EGFR (19del, L858R, Exon 20insertion, T790M), alk (Fusion), ROS1(Fusion), BRAF (V600E), ntrk (Fusion), KRAS (code 12mutation), RET (Fusion), ERBB2(mutation), RET (amplification and ion 14 skiping) and other genetic variations have been specifically recommended for non-small cell lung cancer (NSCLC) patients, and the use of corresponding targeted therapeutics has been recommended for patients containing these genetic variations. Taking the EGFR mutation as an example, although most NSCLC patients carrying EGFR-sensitive mutations respond well to EGFR Tyrosine Kinase Inhibitors (TKIs), 20-30% of patients are primarily resistant to EGFR-TKIs, suggesting that clinical researchers, when using EGFR-TKIs, considering only the EGFR-sensitive mutation may not be sufficient for lung cancer patients with primary resistance.

In recent decades, with the rapid development of tumor immunotherapy, Immune Checkpoint Inhibitors (ICIs) represented by programmed cell death receptor 1 (PD-1) and its ligand 1 (PD-L1) PD-1/PD-L1 monoclonal antibody have brought breakthrough progress for the treatment of advanced NSCLC, and have become hot spots for the treatment of NSCLC at present. Monoclonal antibodies against PD-1/PD-L1 and T lymphocyte-associated antigen 4 (cytoxic T-lymphocyte associated protein 4, CTLA-4) have been approved for NSCLC treatment in a number of countries. Four different PD-1/PD-L1 mabs have been approved by the FDA for the treatment of NSCLC. PD-L1 has been demonstrated in a number of immunotherapy clinical trials as a predictive marker for immunotherapy and is currently the only diagnostic marker that has been demonstrated to guide immunotherapy. The KEYNOTE-001 research shows that the 5-year survival rates of the patients treated initially and treated again are 29.6% and 25% respectively in advanced NSCLC patients with the PD-L1 expression being more than or equal to 50%, while the 5-year survival rate of the population negative to PD-L1 is only 3.5%, thus the PD-L1 detection can efficiently assist in clinical screening of the population benefiting from immunotherapy. However, the clinical Objective Remission Rate (ORR) of single drugs in advanced NSCLC of ICIs represented by anti-PD-1 antibody is only about 20%, and 80% of lung cancer patients still have primary (defined as no imaging objective response and treatment duration less than 6 months) or acquired (objective response or treatment duration more than or equal to 6 months) drug resistance to ICIs. It can be seen that PD-L1 expresses a far from ideal companion diagnostic marker, and primary and acquired resistance remain one of the major factors affecting the efficacy of immunotherapy.

Tumor Mutation Burden (TMB) has received increasing attention as an emerging predictive marker of efficacy in a number of clinical trials. Exploratory analysis of the CheckMate026 study for PD-L1>In 1% of patients, patients were classified into three groups of low, medium and high TMB based on TMB tertile, and the clinical response rate of Nivolumab (Nivolumab) of high TMB patients reached 46.8%. Based on the exploratory analysis result of CheckMate026, TMB has become a molecular marker with the same importance as the expression level of PD-L1 in the research of CheckMate227 and is in PD-L1>In 1% of patients, the ORR reached 60.5% in the Nivolumab combination chemotherapy group. Therefore, in 2018, TMB officially incorporated the latest version of NCCN guide for non-small cell lung cancer. In 2021, the MD Anderson cancer center classified solid tumors as increased tumor antigens and CD8⁺The results show that there is clinical benefit in the treatment of patients with TMB-high (TMB-H) NSCLC receiving immunotherapy, and that objective remission rates of TMB-H tumors to immune checkpoint inhibitors are achieved in the data of type I as a whole39.8％。

With the intensive research of more and more clinical research exploratory biological indexes, a plurality of biomarkers capable of predicting the curative effect of immunotherapy appear. Existing biomarkers, however, have limitations in guiding and predicting clinical efficacy and are not effective, even effective, to select from more effective treatment regimens based on the predicted outcome.

In view of this, the invention is particularly proposed.

Disclosure of Invention

The invention aims to provide a method for predicting the curative effect and prognosis of cancer, a device and application thereof.

The invention is realized by the following steps:

in a first aspect, an embodiment of the present invention provides an apparatus for predicting cancer efficacy and prognosis, which includes an obtaining module and a calculating module. The acquisition module is used for acquiring detection data of the molecular marker of the sample to be detected; the molecular markers comprise a first marker and a second marker; the first markers comprise tumor mutation load, HLA genotype, PD-L1 protein expression; the second marker is obtained in the following manner: screening genome characteristics according to objective remission rate and progression-free survival time aiming at different treatment modes, and marking the genome characteristics which are obviously related to the curative effect and prognosis of the treatment modes as second markers; the genomic features include: somatic mutations, copy number variations, tumor neoantigen load, heterozygous deletions, intratumoral heterogeneity, and genomic instability. The calculation module is used for calculating the T-index, wherein the T-index is the number of the molecular markers with the molecular marker states beneficial to the curative effect and the prognosis/the total number of the molecular markers according to the detection data of the molecular markers of the sample to be detected.

In a second aspect, the embodiments of the present invention provide a kit for predicting cancer therapeutic effect and prognosis, which includes a reagent for detecting a molecular marker, wherein the molecular marker is the molecular marker described in the previous embodiments.

In a third aspect, the embodiments of the present invention provide a use of a reagent for detecting a molecular marker in the preparation of a kit for predicting cancer therapeutic effect and prognosis, wherein the molecular marker is the molecular marker described in the previous embodiments.

In a fourth aspect, the embodiments of the present invention provide a method for calculating a therapeutic index of cancer treatment effect and prognosis, which includes: calculating the T-index based on the detection data of the sample molecular marker; t-index ═ molecular marker status is the number of molecular markers beneficial for therapy and prognosis/total number of molecular markers;

the molecular markers comprise a first marker and a second marker; the first markers comprise tumor mutation load, HLA genotype, PD-L1 protein expression; the second marker is obtained in the following manner: screening genome characteristics according to objective remission rate and progression-free survival time aiming at a specific treatment mode, and marking the genome characteristics which are obviously related to the curative effect and prognosis of the treatment mode as a second marker; the genomic features include: somatic mutations, copy number variations, tumor neoantigen load, heterozygous deletions, intratumoral heterogeneity, and genomic instability.

In a fifth aspect, an embodiment of the present invention provides an electronic device, which includes: a processor and a memory for storing one or more programs which, when executed by the processor, cause the processor to implement the method of calculating a therapeutic index for cancer treatment efficacy and prognosis as described in the previous embodiments.

In a sixth aspect, the present invention provides a computer readable medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the method for calculating the therapeutic index of cancer treatment effect and prognosis as described in the foregoing embodiments.

The invention has the following beneficial effects:

according to the invention, the genomic characteristics of the samples are analyzed and sorted, the molecular markers with remarkable prediction effects on clinical curative effect and prognosis are screened, and the matching degree of the states (height, existence and the like) of the molecular markers in each sample and the treatment modes is calculated, so that the curative effect and prognosis of the treatment modes are effectively predicted, and thus, a patient can select a more effective treatment mode in time, the cancer treatment rate is improved and the life span of the patient is prolonged under the condition of reducing the treatment cost.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.

FIG. 1 is a schematic diagram showing the construction process of T-index in example 1;

FIG. 2 shows the sequencing result of 654panel in example 1;

FIG. 3 shows the results of classifying samples into T-index high and low classes according to ORR using the minimum P value method in example 1;

FIG. 4 is a box plot of AUC and c-index in example 2;

FIG. 5 is a graph showing the predicted efficacy of each index in example 3 on the clinical efficacy and prognosis of immunotherapy.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. The examples, in which specific conditions are not specified, were conducted under conventional conditions or conditions recommended by the manufacturer. The reagents or instruments used are not indicated by the manufacturer, and are all conventional products available commercially.

An embodiment of the present invention provides an apparatus for predicting cancer efficacy and prognosis, which includes:

the acquisition module is used for acquiring detection data of the molecular marker of the sample to be detected; the molecular markers comprise a first marker and a second marker; the first markers comprise tumor mutation load, HLA genotype, PD-L1 protein expression; the second marker is obtained in the following manner: screening genome characteristics according to objective remission rate and progression-free survival time aiming at different treatment modes, and marking the genome characteristics which are obviously related to the curative effect and prognosis of the treatment modes as second markers; the genomic features include: at least one of somatic mutation, copy number variation, tumor neoantigen load, loss of heterozygosity, intratumoral heterogeneity, and genomic instability;

and the calculating module is used for calculating the T-index, wherein the T-index is the number of the molecular markers with the molecular marker states beneficial to the curative effect and the prognosis/the total number of the molecular markers according to the detection data of the molecular markers of the sample to be detected.

It should be noted that the "second marker" herein is a feature that is screened from genome features based on objective remission rate and progression-free survival and significantly related to the efficacy and prognosis of a treatment modality by detecting a molecular marker of a known sample (known treatment modality, efficacy and prognosis).

Significance in "significantly related" herein may refer to the case where P < 0.05, i.e., of statistical significance.

The T-index (therapeutic index) is a therapeutic index with the range of [0-1], is an index obtained by calculating the matching degree of the states of a plurality of molecular markers in a patient and a treatment mode, and can be used for predicting the clinical curative effect and prognosis of different treatment modes on the patient, so that the cancer cure rate of the patient is improved, and the life span is prolonged.

Herein, "detection data of a molecular marker of a sample to be tested" can be obtained by WES (whole exome sequencing) or gene detection panel. In some embodiments, the detection range of the genetic test panel may not include all molecular markers, and when one or more molecular markers cannot be detected in the genetic test panel, the T-index is calculated, and the molecular markers which cannot be detected are omitted and not included in the calculation.

In the data for WES, tumor mutation burden TMB is the number of missense mutations/sequencing range length of WES. In the gene detection panel, the calculation formula of the tumor mutation load is selected from any one of TMBa, TMBb, TMBc and TMBd.

TMBa-the number of missense mutations/length of sequencing range;

TMBb ═ (number of missense mutations + number of synonymous mutations)/sequencing range length;

TMBc ═ (number of missense mutations-number of synonymous mutations)/sequencing range length;

TMBd ═ number of missense mutations + number of synonymous mutations-number of hot spot mutations)/sequencing range length.

Preferably, the formula for calculating the tumor mutation load is shown as TMBd. The results show that TMBd is more consistent with the TMB results in WES, and therefore, TMBd is used as the TMB result for gene testing panel.

The tumor neoantigen is an antigen which is not expressed in normal tissues and is only expressed in tumor tissues. Tumor neoantigen load is an indicator of immunogenicity that directly reflects tumor surface antigens. The present invention predicts a neoantigen based on the affinity of a polypeptide produced by somatic mutation to HLA, and the tumor neoantigen load TNB is the number of tumor neoantigens per length of the sequencing range.

Loss of heterozygosity (LOH): one allele of an allele pair is mutated or deleted, leaving only one allele, and losing the possibility of becoming heterozygous, called heterozygous deletion.

Preferably, the detection indicators of loss of heterozygosity include: the proportion of the region where LOH occurs to the whole Genome (% Genome of LOH region); and/or, the Number of gene mutations that occur in the LOH region (Number of mutations in the LOH region).

Intratumoral heterogeneity (ITH), which refers to: the difference in molecular biology or genes in the tumor can make the growth rate, invasion ability, sensitivity to drugs, prognosis, etc. of each part of the tumor different. The lower the intratumoral heterogeneity, the better the prognosis of clinical treatment.

Preferably, the detection index of intratumoral heterogeneity includes at least one of (a) to (d):

(a) tumor Heterogeneity (MATH) of allelic mutations, the degree to which the Minimum Allelic Frequency (MAF) of each gene mutation deviates from the MAF overall distribution of the sample is evaluated, the higher the deviation, the higher the MATH, the higher the intratumoral heterogeneity;

the invention evaluates the degree of deviation of the minimum allele frequency MAF of each gene mutation from the MAF overall distribution of the sample, the degree of deviation of the Cancer Cell Fraction (CCF) of each gene mutation from the CCF overall distribution of the sample, MATH is 100 x (mean (CCFi-mean (CCF))/mean (CCF));

where mean is a computer function that returns the median of a given value.

(b) Late mutation ratio (pelm), pLM ═ number of subcloning mutations/number of all mutations;

(c) number of Clones (No. of Clones); the method uses Pyclone to cluster the mutation in each sample, each class is classified as a clone, and the more the number of clones is, the higher the heterogeneity is;

(d) shannon-verner Diversity Index (SI), SI ═ Σ (Pi) (lnPi) in the samples, Pi being the CCF of each clone, the higher the SI, the higher the intratumoral heterogeneity.

More preferably, the present invention uses 4 indices to assess the level of intratumoral heterogeneity in each sample.

Preferably, the detection indicators of genomic instability include: the number of chromosomes contained in the tumor cells (also known as Ploidy of chromosomes) and/or genome wide duplication (WGD). Ploidy and WGD replicate all genes within the genome.

Preferably, the molecular markers further comprise a third marker, wherein the third marker is a molecular marker which is reported to be related to the treatment effect and prognosis of the cancer.

Preferably, the treatment modality is selected from: at least one of immunotherapy, targeted therapy, and chemotherapy.

Preferably, the treatment is primary treatment or secondary treatment.

Preferably, the cancer is lung cancer, more preferably non-small cell lung cancer.

The embodiment of the invention also provides a kit for predicting the curative effect and prognosis of cancer, which comprises a reagent for detecting the molecular marker, wherein the molecular marker is the molecular marker in any embodiment.

Preferably, the reagent is a detection panel comprising reagents for detecting at least 300 genes in the target gene, the detection result of the sample molecular marker being obtainable based on the sequencing result of the target gene. Compared with the whole genome sequencing data, the sequencing data for detecting the panel can also obtain an index (molecular marker) for calculating the T-index, and the detection speed is higher and the cost is lower.

Specifically, the target gene includes: ABL, CCND, EP300, GNB, KLF, NOTCH, RAD, STAT, ABRAXAS, CCND, EP400, GPS, KLHL, NOTCH, RAD, STAT5, ACTG, CCNE, EPAS, GREM, KMT2, NPM, RAD, STK, ACVR, CCNQ, EPCAM, GRIN2, KMT2, NRAS, RAD51, SUVR 1, CD160, EPHA, GRM, KMT2, NRG, RAD51, SUZ, ACVR2, CD, EPHA, GSK3, KMT2, NSD, RAD51, SYK, ACVRL, CD244, EPHA, GSTN, KNRN, NSD, RAD, TAF, AGO, CD274, EPHA, GSTP, KRAS, NSD, RAR 54, TAKRS 2R, AJBA, CD, GTF, HDAC2, TARC, TARD, TARB, TARC, TARD, TAK, TAR 2, TAK, TAR, TAK, TARB, TAR 2, TARB, TARG, TARB, TARG, TARB, APC, CD79A, ERCC2, HDAC6, LTK, PAK1, REL, TET2, APEX1, CD79B, ERCC3, HGF, LYN, PAK5, RELN, TGFB1, AR, CD80, ERCC4, HIST1H1B, MAF, PALB2, RET, TGFBR1, ARAF, CD86, ERCC5, HIST1H1C, MAGEA C, PARP C, RHEB, TGFBR C, AR6854, CDC C, ERF, HIST1H1C, MAGEA C, PDG C, CDK C, C, ASXL1, CDKN2C, EZH2, HLA-DPB1, MAP3K13, PDK1, RPL5, TNFSF11, ASXL2, CEBPA, FANCA, HLA-DQA1, MAPK1, PHF6, RPS6KB1, TNFSF14, ATM, CFTR, FANCC, HLA-DQA2, MAPK11, PHOX2B, RPTOR, TNFSF18, ATP11B, CHEK1, FANCD2, HLA-DQB1, MAPK3, MEGA, RRAS2, TNFSF4, ATP6AP1, CHEK2, FACCG, HLA-DQ B2, MAPK 2, SACK 2, FATTF 2, FATTP 2, FATTF 2, SADTH 2, SACK 2, SATSK 2, TSK 6853, 2, SATSK 2, SATSK 6853, 2, SATSK 2, 6853, 2, SATSK 2, 6853, 2, SATSK 2, 6853, 2, SATSK 2, SATSK 2, SATSS-2, SATSS-2, SATSS-2, SATSK 2, SATSK 2, SATSS-2, SATSS-2, SATSK 2, SATSS-2, SATSK 2, SATSS-6853, 2, SATSS-2, SATSS-2, SATSB 2, SATSK 2, SATSS-6853, 2, SATSK 2, SATSS-6853, 2, 6853, SATSB 6853, SATSK 2, SATSK 2, SATSF 2, SATSK 2, SATSS-2, SATSK 2, SATSK 2, 6853, SATSK 2, SATSB 2, SATSK 6853, SATSB 2, SATSK 2, SATSB 2, SATSK 2, SATSB 2, SATSK 685, HRAS, MERK, PIK3R, SDHD, TYK, B2, CSNK2A, FGF, HSD3B, MET, PIM, SESN, TYRO, B4GALT, CTAG, FGF, ICOS, MGA, PLCG, SESN, U2AF, BACH, CTCF, FGF, ICOSLG, MGMT, PLCG, SESN, USP9, BAGE, CTLA, FGF, ID, MITF, PLK, SETBP, VAV, BAP, CTNNA, FGF, IDH, MKNK, PMAIP, SETD, VEGFA, BARD, CTNNB, FGF, IDH, MLH, PMS, SETD, VEGFB, BBC, CUL, FGFR, IFITM, MLH, PMS, SF3B, VEZF, CULL, CUL, CUIL 4, MPL, FGFR, PNP, SGK, VHL, VHF, SHFC, SHCK, MUC, PPP2R2, SLX, ZNF217, BCORL, DDR, FOXA, INPP4, MUTYH, PPP6, SMAD, ZNF703, BCR, DDR, FOXL, INPPL, MYC, PRDM, SMAD, ZRSR, BIRC, DDX3, FOXO, INSR, MYCL, PRF, SMAD, CD, BIRC, DICER, FOXO, IRF, MYCN, PRKAR1, SMARCA, CLTC, BLM, DIS, FOXP, IRF, MYD, PRKCI, SMARCB, EML, BMPR1, DNJB, FRK, IRF, MYO18, PRKN, SMC1, ETV, BRAF, DNMT3, FUBP, IRF, MYOD, PRSS, SMC, DNETV, BRCA, BRA, IRMT, NAV, NAPT, SMTP, GABTNF, SMTP, BCR, DDL, NPRD, ZNTR, ZNO, ZPAPR, ZNO, ZPAPR, ZNO, ZBR, ZNO, ZPTNF, ZPAPR, ZNO, ZPTNF, ZBR, ZNO, ZBR, ZNO, ZPTPN, ZPTNF, ZPTPN, ZNO, ZPTF, ZPTPN, ZPTN, ZPTF, ZPTPN, ZPTF, ZPTPN, ZPTP, ZPTPN, ZPTP, ZPTN, ZPTP, ZPTN, PSP, PSN, PSP, PSN, PSP, PSN, PSP, PSN, PSP, PSN, PSP, PSN, PSP, PSN, PSP, PSN, PS, PTPRK, SPEN, SLC34A2, CARD11, EGR3, GID4, KDR, NFKBIA, PTPRO, SPINK1, TPM3, CARM1, EIF1AX, GLI1, KEAP1, NKX2-1, PTPRS, SPOP, VAMP2, CASP8, EIF4A2, GNA11, KEL, NKX2-8, PTPRT, SPRED 2, PRKACA, CBFB, ELF 2, GNA 2, KIT, NKX2-1, QKI, SRC, TERC, CBL, EMSY, GNAQ, KITLG, NOTCH2, RAB 2, SRSF2, CCND2, EOMES, GNAS, KLF 2, NOTCH2, NORAC 2, and 2.

Preferably, the detecting panel comprises a reagent for detecting at least 457 genes in the target gene; more preferably, the T-index is calculated based on the detected indexes of the sequencing data of 600 target genes, and the treatment and prognosis of the cancer can be more accurately predicted.

The embodiment of the invention also provides application of a reagent for detecting the molecular marker in preparing a kit for predicting the curative effect and prognosis of cancer, wherein the molecular marker is the molecular marker in any embodiment.

The embodiment of the invention also provides a method for calculating the therapeutic index of the cancer curative effect and prognosis, which comprises the following steps: calculating the T-index based on the detection data of the sample molecular marker; t-index is the number of molecular markers whose status is beneficial for therapeutic efficacy and prognosis/total number of molecular markers;

the molecular markers comprise a first marker and a second marker; the first markers comprise tumor mutation load, HLA genotype, PD-L1 protein expression; the second marker is obtained in the following manner: screening genome characteristics according to objective remission rate and progression-free survival time aiming at a specific treatment mode, and marking the genome characteristics which are obviously related to the curative effect and prognosis of the treatment mode as a second marker; the genomic features include: somatic mutations, copy number variations, tumor neoantigen load, heterozygous deletions, intratumoral heterogeneity, and genomic instability. An embodiment of the present invention further provides an electronic device, which includes: a processor and a memory for storing one or more programs which, when executed by the processor, cause the processor to implement the method of calculating a therapeutic index for cancer treatment efficacy and prognosis as described in the previous embodiments.

The Memory may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read Only Memory (PROM), an Erasable Read Only Memory (EPROM), an electrically Erasable Read Only Memory (EEPROM), and the like.

The processor may be an integrated circuit chip having signal processing capabilities. The Processor may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), etc.; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components.

In practical applications, the electronic device may be a server, a cloud platform, a mobile phone, a tablet computer, a notebook computer, an ultra-mobile personal computer (UMPC), a handheld computer, a netbook, a Personal Digital Assistant (PDA), a wearable electronic device, a virtual reality device, and the like, and therefore, the embodiment of the present application does not limit the type of the electronic device.

Embodiments of the present invention also provide a computer readable medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the method for calculating the therapeutic index of cancer treatment effect and prognosis as described in the foregoing embodiments.

The computer readable medium includes: u disk, removable hard disk, read only memory, random access memory, magnetic or optical disk, etc. for storing program codes.

It is understood that the cancer in the kit, the use of the reagent in the preparation of the kit, the computing method, the electronic device and the computer storage medium is preferably lung cancer, and particularly non-small cell lung cancer.

The features and properties of the present invention are described in further detail below with reference to examples.

Example 1

A method for calculating therapeutic index T-index of cancer curative effect and prognosis is disclosed, wherein the cancer is lung cancer in this embodiment, the process is shown in figure 1, and the method specifically comprises the following steps.

1. The indices used to calculate the T-index were derived from whole exome sequencing data and 457Panel sequencing data as a control, as well as PD-L1 immunohistochemical detection data.

Patients with non-surgical stage IV non-small cell lung cancer NSCLC were enrolled in two groups.

The first 175 patients, 15 of which were treated with the best support therapy, underwent WES and 457gene panel based on tissue samples collected and leukocyte controls; the number of patients with clinical efficacy and prognostic analysis among the participants was 160.

The second set of 296 patients, 10 of which were treated in the best support therapy, were sequenced with 654gene panel (fig. 2) also based on the collected tissue samples and leukocyte controls. The number of patients with clinical efficacy and prognostic analysis among the participants was 286.

And then, performing quality control and comparison on the sequencing data, and analyzing and calculating a prediction index. 16 of the two groups of patients were enrolled for comparative analysis of the goodness of fit of the two data sets.

2. A prediction index (molecular marker) based on WES,457gene panel, 654gene panel data was used for the construction and validation of T-indexes.

2.1 extraction, capture and sequencing of DNA.

(1) Extraction of DNA from tumor tissue: for formalin-fixed paraffin-embedded (FFPE) samples, the samples need to be processed in advance (slide: 2ul DS buffer is added at the sample, DS is evenly smeared and scraped by a blade and put into a 1.5ml centrifuge tube with 320ul DS buffer for extraction; wax roll: a part of the sample is picked by a 200 or 100 ul pipette tip and put into a 1.5ml EP tube, and then extracted after centrifugation for 3min at 20000 g; wax block is scraped by a blade and put into a 1.5ml EP tube and centrifuged for 3min at 20000 g); AllPrep DNA/RNA FFPE Kit (50) was used, following stringent product instructions. The volume of the extracted final product DNA solution was controlled to 30. mu.L. (2) And (3) extracting leucocyte DNA: centrifuging the whole blood sample for 10 minutes at 1600g, sucking the supernatant, and sucking about 1ml of white membrane layer for later use; the supernatant sample is centrifuged at 16000g for 10 min to obtain the upper plasma, and the precipitate is discarded. DNA was extracted using 100. mu.L of the sample and DNeasy Blood was used&Tissue Kit, operating strictly according to product instructions, extracted end productThe volume of the DNA solution was 200. mu.L. (3) Constructing a pre-library: the library construction requires 17.5. mu.L of the extracted final product DNA solution for end repair, and linker ligation using the end-repaired PCR product to construct a pre-library. After ligation, AMPure XP beads were used for purification and the library was collected using 80% ethanol elution. (4) Capturing: a500 ng volume of the pre-library was taken into a 1.5mL low adsorption centrifuge tube and concentrated to dryness by adding Human Cot DNA. Use of

Hybridization and Wash Kit corresponding components were used to pretreat probes from WES and 457gene panel using DynaBeads^TMM-270Streptavidin was amplified after capture, purified using Ampure XP Beads, and eluted with 80% ethanol to collect the final library. (5) Quality control of the library: performing real-time fluorescent quantitative PCR detection by using KAPA Library quantitative reagents kit, wherein the effective concentration of the Library is more than or equal to 5nM, and judging the Library to be qualified; and (3) judging the size of the library by using capillary electrophoresis or common agarose electrophoresis, wherein the main peak of the library is between 230-450bp, and no obvious Dimer peak exists, so that the judgment is qualified. (6) And (3) machine sequencing: paired end 150bp sequencing using NextSeq CN500 sequencing platform; the average sample size for a single sample was WES 500X, 457gene panel 2000X, 654gene panel 2000X.

And (3) performing quality control on sequencing data, and comparing: using Fastq software to perform data filtration on the sequencing off-line data, wherein the data filtration comprises subtracting a sequencing joint sequence, removing a DNA fragment with a sequencing read length less than 50bp, and removing a DNA fragment with lower sequencing quality; the filtered data were aligned to the Hg19 reference genome using BWA.

Based on the comparative data, the following data of molecular markers were obtained.

(1) HLA genotype. Patients who are fully heterozygous for the HLA genotype receive immunotherapy with a better prognosis.

(2) Tumor Mutational Burden (TMB): in the data for WES, TMB ═ number of missense mutations/sequencing range length of WES; in 457gene panel and 654gene panel, the present invention uses TMBd as the TMB result of 457/654gene panel.

TMBd ═ the length of the sequencing range (number of missense mutations + number of synonymous mutations-number of hot spot mutations)/457 (654) gene panel.

(3) PD-L1 protein expression: the detection result of the PD-L1 immunohistochemistry is screened for related molecular markers of the curative effect and prognosis of different treatment modes.

(4) Somatic mutations (somatic mutations) and Copy Number Variations (CNV): somatic mutations in WES,457gene panel and 654gene panel were mined using Mutect2 and mutloc, respectively, and copy number variations were mined using CNVkit. And (3) annotating the somatic mutation and the copy number variation in the sample according to the obtained somatic mutation and copy number variation result file and databases such as FDA (food and drug administration) and NCCN (national center for China), and keeping the annotation Level at Level1-4, namely the obtained somatic mutation and copy number variation result file are considered to be the somatic variation with stronger evidence to indicate clinical significance and are used for constructing the T-Index.

(5) Tumor Neoantigen Burden (TNB): the new antigen is predicted according to the affinity of polypeptide generated by somatic mutation and HLA, and TNB is the number of tumor new antigens/WES sequencing range length.

(6) Loss of heterozygosity (LOH): the patient's LOH is evaluated from two levels (two indices), one of which is the proportion of the region where LOH occurs to the entire Genome (% Genome of LOH region); second, the Number of gene mutations occurring in the LOH region (Number of mutations in the LOH region). It should be noted that both indicators participate in prognostic analysis, and that which indicator is significant, and which indicator is used, as well as the subsequent intratumoral heterogeneity and genomic instability.

(7) Intratumoral heterogeneity (ITH): the intratumoral heterogeneity of each sample was evaluated using four indices, one of which, tumor heterogeneity (MATH) of allelic mutations, the degree to which the Minimum Allelic Frequency (MAF) of each gene mutation deviates from the MAF overall distribution of the sample was evaluated, the higher the deviation, the higher the MATH, and the higher the intratumoral heterogeneity. On the basis, the invention evaluates the degree of deviation of Cancer Cell Fraction (CCF) of each gene mutation from the overall distribution of sample CCF, MATH is 100 [ (| CCFi-mean (CCF)) | ]/mean (CCF)); second, Late mutation ratio (pLM), pLM ═ number of subclone mutations/number of all mutations; thirdly, the number of Clones (No. of Clones), Pyclone was used to cluster the mutations in each sample, each class was classified as a clone, the more the number of Clones, the higher the heterogeneity; fourthly, on the basis of No. of Clones, Shannon's Diversity Index (SI), SI ═ Σ (Pi) (lnPi), Pi being CCF of each clone, was calculated in one sample, the higher the SI, the higher the intratumoral heterogeneity.

(8) Genomic instability: instability at the genomic level was assessed using two indicators, one, the number of chromosome sets contained in the tumor cells, called chromosome Ploidy (Ploidy); second, the Whole Genome Doubles (WGD).

(9) According to previous reports, immune positively-related genes (KRAS, TP53, BRCA1, ATM, BRCA2, MLH1, MSH6, MSH2, PBRM1, POLE, CHEK2, POLD1, PALB2, PMS2, ATR, FANCA, RAD50), immune negatively-related genes (EGFR, ALK, PTEN, CTNNB1, STK11, JAK2, JAK1, MAPK1, APC, B2M) and immune hyper-progression genes (DNMT3A, MDM2, MDM4) are screened for each sample as third molecular markers and participate in the calculation of T-index.

Further, detection information of 457gene panel and 654gene panel is as follows.

The panel size of 457gene panel was 1.2M, detection range: 457 genes (7 fusion genes, 97 genetic genes, detection of 22 CNV genes), wherein the 457 genes specifically comprise: TNFRSF18, FH, CD86, FGFR4, GATA4, WT1, DIS3, CDK12, POLD1, TNFRSF4, AKT3, GATA2, NSD1, EGR1, MAPK8IP1, FGF1, ERBB 1, PPP2R 11, TNFRSF1, MYCN, NEK1, FLT1, PPP2R1, VEGFB, ERCC1, RARA, PCNA, RPL 1, DNMT3 1, EPHB1, IRF1, ZNF703, MEN1, IRS 1, STAT 1, FOXA1, ERRFIL 1, ALK 1, PTA 1, 1-1, 1, ERCC3, MAP3K13, NT5E, CD274, CBL, TSHR, PRKAR1A, PTK6, CSF3 6, CXCR 6, EIF4A 6, PRDM 6, PDCD1LG 6, ARHGEF 6, DICER 6, SOX 6, ARFRP 6, MYCL, ACVR 26, BCL6, FOXO 6, PTPRD, CHEK 6, AKT 6, RPTOR, BAGE, MPL, PDK 6, FGF6, HDAC6, MTAP, KDM5 6, GREM 6, GATA6, RUNX 6, MUTYH, NFE2L 6, RADPAK, 6, CDK6, GALNT12, KRAS, IGF1R, INSR, MAPK11, HSD3B1, PDCD1, FBXW 1, RPA1, TLR 1, H3F 1, AXIN1, KEAP1, P2RY 1, NOTCH1, VHL, IRF1, ETV1, ABL1, LRRK 1, TSC1, SMARCA 1, BCOR, CD160, PPARG, SDHA, NFE2L 1, TSC1, ARID1, CREBP, CALRRK 1, TSC1, MDMS, 1, AKT, MED, ELF, MITF, HDAC, TAS2R, CYP17A, POLE, AURKB, PRX, TAF, BTG, FOXP, CSF1, EPHB, NT5C, CDK, MAP2K, AXL, ATRX, PIK3C2, EPHA, PDGFRB, KEL, SMC, FLT, NCOR, TGFB, BTK, MDM, BTLA, HAVCR, EPHA, FGFR, FLT, FLCN, CD79, STAG, IKBKE, CD, RADK, EZH, MGMT, BRCA, GID, CIC, SH2D1, H3F3, GSK3, GABRA, KMT2, HRAS, TNFSF, NF, ERCC, BCORL, PARP, POLQ, PANPM, XRCC, IGF, RB, HG 51, ARAP, MAPHF, GEMAPHA, GENRA, GENGA, GENRA, GEZH, and TGFB.

654gene panel has a panel size of 3M; detection range: 638 full coverage of the coding region of the tumorigenic-associated gene (1.6Mb + coding region coverage); 500kb + gene fusion high-incidence region coverage (42 gene fusion tumorigenic genes); c.102 tumor genetic risk associated genes; HLA region coverage (HLA typing, tumor neoantigen); e.200 microsatellite unstable MSI information correlation areas (200 short reset points assist MSI detection); f. coverage of evenly distributed SNP sites on 3900 genomes. The genes to be detected specifically include: ABL, CCND, EP300, GNB, KLF, NOTCH, RAD, STAT, ABRAXAS, CCND, EP400, GPS, KLHL, NOTCH, RAD, STAT5, ACTG, CCNE, EPAS, GREM, KMT2, NPM, RAD, STK, ACVR, CCNQ, EPCAM, GRIN2, KMT2, NRAS, RAD51, SUVR 1, CD160, EPHA, GRM, KMT2, NRG, RAD51, SUZ, ACVR2, CD, EPHA, GSK3, KMT2, NSD, RAD51, SYK, ACVRL, CD244, EPHA, GSTN, KNRN, NSD, RAD, TAF, AGO, CD274, EPHA, GSTP, KRAS, NSD, RAR 54, TAKRS 2R, AJHA, CD, GTH, HDAC, TARC, TARD, TARB, TARC, TARD, TARB, TAR, TARD, TAK, TARB, TARC, TARB, TARG, TARB, TARG, TARB, TAC, TARB, TAR, TARB, TAC, TARB, TAR, TAC, TARB, TAR, TAC, TAR, CD79, ERCC, HDAC, LTK, PAK, REL, TET, APEX, CD79, ERCC, HGF, LYN, PAK, RELN, TGFB, AR, CD, ERCC, HIST1H1, MAF, PALB, RET, TGFBR, ARAF, CD, ERCC, HIST1H1, MAGEA, PARP, RHEB, TGFBR, ARFRP, CDC, ERF, HIST1H1, MAGEA, PARP, RHOA, TIAF, ARHGAP, CDH, ERG, HIST1H2, MAGEA, PARP, RICTOR, PARP, ARHGEF, CDK, ERRFI, HLA-, PAX, RIT, TLR, ARID1, CDK, ESCO, HLA-, PBRM, RNF, TMDMA, TMCDK 127, CDK, ESR, HLA-C, MAGGOH, PRBO, PCBBO 1, PCBBO, PDCP, TMARID, CDK, CDRAD, CDRP, TARD-1, CDK, TARD-1, CDK, TARD-2, TAD, TARD-1, TAD, TAG, TAD, TAG, TAD, TAR, TA, ASXL1, CDKN2C, EZH2, HLA-DPB1, MAP3K13, PDK1, RPL5, TNFSF11, ASXL2, CEBPA, FANCA, HLA-DQA1, MAPK1, PHF6, RPS6KB1, TNFSF14, ATM, CFTR, FANCC, HLA-DQA2, MAPK11, PHOX2B, RPTOR, TNFSF18, ATP11B, CHEK1, FANCD2, HLA-DQB1, MAPK3, MEGA, RRAS2, TNFSF4, ATP6AP1, CHEK2, FACCG, HLA-DQ B2, MAPK 2, SACK 2, FATTF 2, FATTP 2, FATTF 2, SADTH 2, SACK 2, SATSK 2, TSK 6853, 2, SATSK 2, SATSK 6853, 2, SATSK 2, 6853, 2, SATSK 2, 6853, 2, SATSK 2, 6853, 2, SATSK 2, SATSK 2, SATSS-2, SATSS-2, SATSS-2, SATSK 2, SATSK 2, SATSS-2, SATSS-2, SATSK 2, SATSS-2, SATSK 2, SATSS-6853, 2, SATSS-2, SATSS-2, SATSB 2, SATSK 2, SATSS-6853, 2, SATSK 2, SATSS-6853, 2, 6853, SATSB 6853, SATSK 2, SATSK 2, SATSF 2, SATSK 2, SATSS-2, SATSK 2, SATSK 2, 6853, SATSK 2, SATSB 2, SATSK 6853, SATSB 2, SATSK 2, SATSB 2, SATSK 2, SATSB 2, SATSK 685, HRAS, MERK, PIK3R, SDHD, TYK, B2, CSNK2A, FGF, HSD3B, MET, PIM, SESN, TYRO, B4GALT, CTAG, FGF, ICOS, MGA, PLCG, SESN, U2AF, BACH, CTCF, FGF, ICOSLG, MGMT, PLCG, SESN, USP9, BAGE, CTLA, FGF, ID, MITF, PLK, SETBP, VAV, BAP, CTNNA, FGF, IDH, MKNK, PMAIP, SETD, VEGFA, BARD, CTNNB, FGF, IDH, MLH, PMS, SETD, VEGFB, BBC, CUL, FGFR, IFITM, MLH, PMS, SF3B, VEZF, CULL, CUL, CUIL 4, MPL, FGFR, PNP, SGK, VHL, VHF, SHFC, SHCK, MUC, PPP2R2, SLX, ZNF217, BCORL, DDR, FOXA, INPP4, MUTYH, PPP6, SMAD, ZNF703, BCR, DDR, FOXL, INPPL, MYC, PRDM, SMAD, ZRSR, BIRC, DDX3, FOXO, INSR, MYCL, PRF, SMAD, CD, BIRC, DICER, FOXO, IRF, MYCN, PRKAR1, SMARCA, CLTC, BLM, DIS, FOXP, IRF, MYD, PRKCI, SMARCB, EML, BMPR1, DNJB, FRK, IRF, MYO18, PRKN, SMC1, ETV, BRAF, DNMT3, FUBP, IRF, MYOD, PRSS, SMC, DNETV, BRCA, BRA, IRMT, NAV, NAPT, SMTP, GABTNF, SMTP, BCR, DDL, NPRD, ZNTR, ZNO, ZPAPR, ZNO, ZPAPR, ZNO, ZBR, ZNO, ZPTNF, ZPAPR, ZNO, ZPTNF, ZBR, ZNO, ZBR, ZNO, ZPTPN, ZPTNF, ZPTPN, ZNO, ZPTF, ZPTPN, ZPTN, ZPTF, ZPTPN, ZPTF, ZPTPN, ZPTP, ZPTPN, ZPTP, ZPTN, ZPTP, ZPTN, PSP, PSN, PSP, PSN, PSP, PSN, PSP, PSN, PSP, PSN, PSP, PSN, PSP, PSN, PSP, PSN, PS, PTPRK, SPEN, SLC34A2, CARD11, EGR3, GID4, KDR, NFKBIA, PTPRO, SPINK1, TPM3, CARM1, EIF1AX, GLI1, KEAP1, NKX2-1, PTPRS, SPOP, VAMP2, CASP8, EIF4A2, GNA11, KEL, NKX2-8, PTPRT, SPRED 2, PRKACA, CBFB, ELF 2, GNA 2, KIT, NKX2-1, QKI, SRC, TERC, CBL, EMSY, GNAQ, KITLG, NOTCH2, RAB 2, SRSF2, CCND2, EOMES, GNAS, KLF 2, NOTCH2, NORAC 2, and 2.

Since the 457gene panel sequencing range does not cover HLA sites and is severely unevenly distributed on the genome, TNB index, HLA genotype, LOH index and genome instability index are missing. While the sequencing range of 654gene panel covers the data required for constructing the T-index.

The molecular markers used to construct the T-index include a first marker, a second marker, and a third marker. The first marker includes the markers described in (1) to (3) above: tumor mutation burden, HLA genotype and PD-L1 protein expression. The third marker comprises the molecular marker of (9).

The second marker was obtained in the following manner: dividing the first batch of samples into an immunotherapy group, a targeted therapy group and a chemotherapy group according to the treatment modes, screening molecular markers which are respectively and obviously related to the clinical curative effect and the prognosis of the three treatment modes in the genomic characteristics according to Objective Remission Rate (ORR) and progression-free survival (PFS), and marking the genomic characteristics which are obviously related to the curative effect and the prognosis of the treatment modes as second markers.

In this example, molecular markers mined from whole exome sequencing data were screened for therapeutic efficacy and prognosis-related molecular markers by dividing the first sample into immunotherapy (32 cases), targeted therapy (83 cases), and chemotherapy (45 cases) according to the treatment regimen, and the molecular markers related to therapeutic efficacy and prognosis were selected. And (3) carrying out comprehensive matching score on the screened features and the features reported in the article, calculating the T-index of each sample in the whole exome sequencing data and 457gene panel sequencing data, and predicting the curative effect and the prognosis of the sample. And the second batch was divided into immunotherapy (64 cases), targeted therapy (129 cases) and chemotherapy (94 cases) at the same time, and the T-index was calculated based on the T-index calculation rules established in the first batch, confirming its predictive efficacy for clinical prognosis.

In clinical treatment, whether other treatments are received before or not has a great influence on ORR and PFS of patients, therefore, the embodiment further divides patients in different treatment modes into two groups according to primary treatment/secondary treatment and respectively screens the molecular markers related to the curative effect and the prognosis.

For different treatment modalities (immunotherapy, targeted therapy or chemotherapy), molecular markers (first, second and third markers) associated with this treatment modality are listed for participation in the calculation of T-index.

In each patient, the number of molecular markers with molecular marker states (high/low, presence/absence) beneficial to the curative effect and prognosis is counted as a numerator, all molecular markers participating in the calculation of the clinical treatment (immunity/targeting/chemotherapy) T-index are counted as denominators, and the ratio is the T-index; in the range of 0-1. The embodiment of the invention uses a minimum P value method to define a T-index threshold according to ORR, and samples are divided into a high group and a low group (figure 3).

In the first sample, there were molecular markers mined from the sequencing data of 457gene panel in addition to the individual molecular markers mined from the sequencing data of WES. However, the sequencing range and the imbalance in genome distribution of 457gene panel were not calculated for the TNB, LOH, ITH and HLA indicators. According to the same calculation, ignoring the molecular markers that could not be calculated in 457gene panel, the T-index of each patient was calculated as well, and the samples were classified into T-index high and low classes using the minimum P-value method according to ORR (FIG. 3).

In the embodiment of the invention, 654gene panel sequencing is performed on the second batch of samples, and the related DNA extraction, capture and sequencing are as above. The invention excavates the molecular marker needed for calculating the T-index from the sequencing data of 654gene panel, calculates the T-index of each sample, and classifies the samples into two types of T-index high and low by using a minimum P value method according to ORR (figure 3).

The invention draws the box-plot (box-plot) of the predicted result evaluation index AUC and c-index of clinical curative effect and prognosis of T-index calculated by WES and T-index calculated by 457gene panel to the same coordinate, and draws the predicted result evaluation index AUC and c-index of clinical curative effect and prognosis of T-index calculated by 654gene panel in the second batch of samples to the same coordinate, so as to visually identify the difference of prediction efficiency. Meanwhile, in a sample receiving immunotherapy, a box plot of the T-index and a single molecular marker calculated in WES on the evaluation indexes of clinical curative effect and prognosis, namely AUC and c-index, is drawn in the same coordinate, and the highest indexes of AUC and c-index have the highest prediction effectiveness.

Currently, most of the existing researches use a single index to predict clinical curative effect and prognosis, for example, TMB is used for immunotherapy, PD-L1 is used for immunotherapy, and the driver gene EGFR/ALK/ROS1 is used for targeted therapy, so that a plurality of indexes are rarely comprehensively and comprehensively considered for combined prediction. The invention mines data from multiple aspects, screens molecular markers related to curative effect/prognosis, establishes a new method for predicting clinical curative effect and prognosis by using next generation sequencing data, and points out methods for comparing prediction performances (AUC and c-index). For different treatment modes, aiming at the molecular markers related to various aspects of genome change, screening whether the clinical curative effect and the prognosis are related or not, in WES sequencing data and 457gene panel sequencing data, calculating the matching degree of the states (including height, existence and the like) of multiple indexes in each patient and the treatment modes of the multiple indexes, establishing a T-index, and finally verifying the prediction performance of the T-index on the clinical curative effect and the prognosis by using 654gene panels which are specially designed and have wide sequencing range and are uniformly distributed on a genome.

Example 2

WES and 457gene panel targeted sequencing was performed on the first batch of samples, and 654gene panel targeted sequencing was performed on the second batch of samples. 457gene panel and 654gene panel are the same as in example 1.

Corresponding T-indices were obtained by calculating the data of WES and 457gene panel (same as example 1), and the predicted results were plotted as a box plot of AUC and c-Index for comparison, while the T-Index calculated from the data of 654gene panel in the second sample was plotted on the same coordinate system for comparison.

The results are shown in FIG. 4, samples are classified into chemotherapy groups (CCT) and non-chemotherapy groups (targeted therapy, TT; and immunotherapy) according to index mode, and the AUC and c-index of WES T-index are significantly higher than those of 457gene panel T-index (AUC: P ═ 1.78 e-12; c-index: P ═ 1.00e-24), which shows that T-index comprehensively evaluated by using more molecular markers in WES has better prediction effect on clinical efficacy and prognosis. Similarly, in the validation set data sequenced using the redesigned and optimized 654gene panel, the AUC was also significantly higher than 457gene panel (P ═ 4.80e-34), but the c-index difference was not significant (P ═ 0.152), probably because the follow-up time for the second sample was insufficient and some samples did not reach the clinical endpoint. The result proves that clinically, the T-index calculated by using 654gene panel sequencing data with lower cost and deeper sequencing depth can also achieve the predicted effect of WES (P is 1.78e-12), thereby effectively reducing the clinical cost.

Example 3

This example plots box plots of AUC and c-index for the index T-index of the comprehensive match score for multiple molecular markers calculated using WES data and the predicted results of multiple reports of molecular markers (TMB, TNB, PD-L1 and HLA genotype) associated with the therapeutic efficacy and prognosis of immunotherapy in samples receiving immunotherapy, comparing the predicted efficacy of each index for the clinical efficacy and prognosis of immunotherapy.

As shown in FIG. 5, the AUC and c-index of the T-index are both significantly higher than those of each single-molecule index, and the results show that the curative effect and the prognostic prediction effect of the T-index after comprehensive matching of the multi-molecule marker are significantly better than those of each single-molecule, thereby further highlighting the improvement of the curative effect and the prognostic prediction effect of the method disclosed by the invention.

The invention provides a new idea of integrating and predicting curative effect and prognosis by multiple molecular indexes, the prediction effect of the curative effect and prognosis by the method of the invention is superior to the prediction effect of single molecule, the effectiveness of the invention is verified in the sequencing data of 654gene panel of a second batch of samples, and the sequencing cost in clinical application is greatly reduced.

The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. An apparatus for predicting cancer efficacy and prognosis, comprising:

the acquisition module is used for acquiring detection data of the molecular marker of the sample to be detected; the molecular markers comprise a first marker and a second marker; the first markers include tumor mutation load, HLA genotype, and PD-L1 protein expression; the second marker is obtained in the following manner: screening genome characteristics according to objective remission rate and progression-free survival time aiming at different treatment modes, and marking the genome characteristics which are obviously related to the curative effect and prognosis of the treatment modes as second markers; the genomic features include: at least one of somatic mutation, copy number variation, tumor neoantigen load, loss of heterozygosity, intratumoral heterogeneity, and genomic instability;

and the calculating module is used for calculating the T-index, wherein the T-index is the number of the molecular markers with the molecular marker states beneficial to the curative effect and the prognosis/the total number of the molecular markers according to the detection data of the molecular markers of the sample to be detected.

2. The apparatus for predicting the therapeutic effect and prognosis of cancer according to claim 1, wherein the calculation formula of the tumor mutation load is selected from any one of TMBa, TMBb, TMBc and TMBd:

TMBa-the number of missense mutations/length of sequencing range;

TMBb ═ (number of missense mutations + number of synonymous mutations)/sequencing range length;

TMBc ═ (number of missense mutations-number of synonymous mutations)/sequencing range length;

TMBd ═ number of missense mutations + number of synonymous mutations-number of hot spot mutations)/sequencing range length.

3. The device for predicting cancer efficacy and prognosis as claimed in claim 1, wherein said tumor neoantigen burden is tumor neoantigen number/sequencing range length;

preferably, the detection indicators of loss of heterozygosity include: the proportion of the region where LOH occurs to the entire genome; and/or, the number of gene mutations that occur in the LOH region;

preferably, the detection index of intratumoral heterogeneity includes at least one of (a) to (d):

(a) tumor heterogeneity of allelic mutations, MATH;

(b) late mutation ratio pLM;

(c) the number of clones; the cloning is as follows: clustering the mutations in each sample, each class being classified as a clone;

(d) shannon-verner Diversity Index (SI), SI ═ Σ (Pi) (lnPi) in the sample, Pi being the CCF of each clone;

preferably, the detection indicators of genomic instability include: the number of chromosomes and/or the whole genome contained in the tumor cell is doubled.

4. The device for predicting the treatment effect and prognosis of cancer according to any one of claims 1 to 3, wherein the molecular markers further comprise a third marker, and the third marker is a molecular marker related to the treatment effect and prognosis of cancer reported now.

5. The device for predicting the treatment effect and prognosis of cancer according to any one of claims 1 to 3, wherein the treatment is selected from the group consisting of: at least one of immunotherapy, targeted therapy, and chemotherapy;

preferably, the treatment mode is primary treatment or secondary treatment;

preferably, the cancer is lung cancer, more preferably non-small cell lung cancer.

6. A kit for predicting the efficacy and prognosis of cancer, comprising reagents for detecting a molecular marker according to any one of claims 1 to 5;

preferably, the reagent is a detection panel comprising reagents for detecting at least 300 genes in a target gene comprising: ABL, CCND, EP300, GNB, KLF, NOTCH, RAD, STAT, ABRAXAS, CCND, EP400, GPS, KLHL, NOTCH, RAD, STAT5, ACTG, CCNE, EPAS, GREM, KMT2, NPM, RAD, STK, ACVR, CCNQ, EPCAM, GRIN2, KMT2, NRAS, RAD51, SUVR 1, CD160, EPHA, GRM, KMT2, NRG, RAD51, SUZ, ACVR2, CD, EPHA, GSK3, KMT2, NSD, RAD51, SYK, ACVRL, CD244, EPHA, GSTN, KNRN, NSD, RAD, TAF, AGO, CD274, EPHA, GSTP, KRAS, NSD, RAR 54, TAKRS 2R, AJBA, CD, GTF, TARC, TAFH, TARB, TARD, TARF 5, TARD, TARB, TARC, TARB, TACK, TARB, APC, CD79A, ERCC2, HDAC6, LTK, PAK1, REL, TET2, APEX1, CD79B, ERCC3, HGF, LYN, PAK5, RELN, TGFB1, AR, CD80, ERCC4, HIST1H1B, MAF, PALB2, RET, TGFBR1, ARAF, CD86, ERCC5, HIST1H1C, MAGEA C, PARP C, RHEB, TGFBR C, AR6854, CDC C, ERF, HIST1H1C, MAGEA C, PDG C, CDK C, C, ASXL1, CDKN2C, EZH2, HLA-DPB1, MAP3K13, PDK1, RPL5, TNFSF11, ASXL2, CEBPA, FANCA, HLA-DQA1, MAPK1, PHF6, RPS6KB1, TNFSF14, ATM, CFTR, FANCC, HLA-DQA2, MAPK11, PHOX2B, RPTOR, TNFSF18, ATP11B, CHEK1, FANCD2, HLA-DQB1, MAPK3, MEGA, RRAS2, TNFSF4, ATP6AP1, CHEK2, FACCG, HLA-DQ B2, MAPK 2, SACK 2, FATTF 2, FATTP 2, FATTF 2, SADTH 2, SACK 2, SATSK 2, TSK 6853, 2, SATSK 2, SATSK 6853, 2, SATSK 2, 6853, 2, SATSK 2, 6853, 2, SATSK 2, 6853, 2, SATSK 2, SATSK 2, SATSS-2, SATSS-2, SATSS-2, SATSK 2, SATSK 2, SATSS-2, SATSS-2, SATSK 2, SATSS-2, SATSK 2, SATSS-6853, 2, SATSS-2, SATSS-2, SATSB 2, SATSK 2, SATSS-6853, 2, SATSK 2, SATSS-6853, 2, 6853, SATSB 6853, SATSK 2, SATSK 2, SATSF 2, SATSK 2, SATSS-2, SATSK 2, SATSK 2, 6853, SATSK 2, SATSB 2, SATSK 6853, SATSB 2, SATSK 2, SATSB 2, SATSK 2, SATSB 2, SATSK 685, HRAS, MERK, PIK3R, SDHD, TYK, B2, CSNK2A, FGF, HSD3B, MET, PIM, SESN, TYRO, B4GALT, CTAG, FGF, ICOS, MGA, PLCG, SESN, U2AF, BACH, CTCF, FGF, ICOSLG, MGMT, PLCG, SESN, USP9, BAGE, CTLA, FGF, ID, MITF, PLK, SETBP, VAV, BAP, CTNNA, FGF, IDH, MKNK, PMAIP, SETD, VEGFA, BARD, CTNNB, FGF, IDH, MLH, PMS, SETD, VEGFB, BBC, CUL, FGFR, IFITM, MLH, PMS, SF3B, VEZF, CULL, CUL, CUIL 4, MPL, FGFR, PNP, SGK, VHL, VHF, SHFC, SHCK, MUC, PPP2R2, SLX, ZNF217, BCORL, DDR, FOXA, INPP4, MUTYH, PPP6, SMAD, ZNF703, BCR, DDR, FOXL, INPPL, MYC, PRDM, SMAD, ZRSR, BIRC, DDX3, FOXO, INSR, MYCL, PRF, SMAD, CD, BIRC, DICER, FOXO, IRF, MYCN, PRKAR1, SMARCA, CLTC, BLM, DIS, FOXP, IRF, MYD, PRKCI, SMARCB, EML, BMPR1, DNJB, FRK, IRF, MYO18, PRKN, SMC1, ETV, BRAF, DNMT3, FUBP, IRF, MYOD, PRSS, SMC, DNETV, BRCA, BRA, IRMT, NAV, NAPT, SMTP, GABTNF, SMTP, BCR, DDL, NPRD, ZNTR, ZNO, ZPAPR, ZNO, ZPAPR, ZNO, ZBR, ZNO, ZPTNF, ZPAPR, ZNO, ZPTNF, ZBR, ZNO, ZBR, ZNO, ZPTPN, ZPTNF, ZPTPN, ZNO, ZPTF, ZPTPN, ZPTN, ZPTF, ZPTPN, ZPTF, ZPTPN, ZPTP, ZPTPN, ZPTP, ZPTN, ZPTP, ZPTN, PSP, PSN, PSP, PSN, PSP, PSN, PSP, PSN, PSP, PSN, PSP, PSN, PSP, PSN, PSP, PSN, PS, PTPRK, SPEN, SLC34A2, CARD11, EGR3, GID4, KDR, NFKBIA, PTPRO, SPINK1, TPM3, CARM1, EIF1AX, GLI1, KEAP1, NKX2-1, PTPRS, SPOP, VAMP2, CASP8, EIF4A2, GNA11, KEL, NKX2-8, PTPRT, SPRED 2, PRKACA, CBFB, ELF 2, GNA 2, KIT, NKX2-1, QKI, SRC, TERC, CBL, EMSY, GNAQ, KITLG, NOTCH2, RAB 2, SRSF2, CCND2, EOMES, GNAS, KLF 2, NOTCH2, NORAC 2 and 2;

preferably, the detecting panel comprises a reagent for detecting at least 300 of the target genes;

preferably, the detecting panel comprises reagents for detecting at least 457 genes of the target genes.

7. Use of a reagent for detecting a molecular marker for the preparation of a kit for predicting the efficacy and prognosis of cancer, wherein the molecular marker is according to any one of claims 1 to 5.

8. A method for calculating a therapeutic index for the treatment and prognosis of cancer, comprising: calculating the T-index based on the detection data of the sample molecular marker; t-index ═ molecular marker status is the number of molecular markers beneficial for therapy and prognosis/total number of molecular markers;

the molecular markers comprise a first marker and a second marker; the first markers comprise tumor mutation load, HLA genotype and PD-L1 protein expression; the second marker is obtained in the following manner: screening genome characteristics according to objective remission rate and progression-free survival time aiming at a specific treatment mode, and marking the genome characteristics which are obviously related to the curative effect and prognosis of the treatment mode as a second marker; the genomic features include: somatic mutations, copy number variations, tumor neoantigen load, heterozygous deletions, intratumoral heterogeneity, and genomic instability.

9. An electronic device, comprising: a processor and a memory for storing one or more programs that, when executed by the processor, cause the processor to implement the method of calculating a therapeutic index for cancer treatment efficacy and prognosis of claim 8.

10. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out a method of calculating a therapeutic index for cancer treatment and prognosis according to claim 8.

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