WO2016061495A1

WO2016061495A1 - Atavarsitic systems and methods for biomarker discovery

Info

Publication number: WO2016061495A1
Application number: PCT/US2015/056001
Authority: WO
Inventors: Asher Nathan
Original assignee: Predictive Therapeutics Ltd.
Priority date: 2014-10-16
Filing date: 2015-10-16
Publication date: 2016-04-21

Abstract

The present disclosure provides methods of identifying and validating biomarkers that are predictive of drug efficacy in a patient. The disclosed methods use avatars (e.g. animal or in vitro models) to identify biomarkers associated with a particular treatment status (e.g. responder or non-responder to drug). These biomarkers are then validated by using previously obtained clinic data and clinical samples from patients who were previously treated with the drug The biomarkers can be used to identify other patient samples which are predicted to respond to given treatments, and the presence or absence of the biomarker can guide patient treatment.

Description

ATAVARSITIC SYSTEMS AND METHODS FOR BIOMARKER

DISCOVERY

CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority under 35 U.S.C. § 119(e) to U.S. Provisional Application Serial No. 62/064,778, filed October 16, 2014 and U.S. Provisional Application Serial No. 62/075,382 filed November 5, 2014, the contents of each of which are herein incorporated by reference in their entireties.

BACKGROUND [0002] Many disease therapies, especially those developed in unselected patient populations, have only limited clinical benefits, with many patients not responding to a particular drug or therapy. Predictive biomarkers that define patient populations who are most likely to benefit from a given therapy, are crucial tools in the field of personalized medicine.

[0003] In cancers, some predictive cancer biomarkers have been identified. For example, in colorectal cancer, KRAS is a predictive biomarker where somatic mutations in KRAS are associated with poor response to anti-EGFR directed therapies (Allegra et al. 2009). Similarly, overexpression of the HER2 gene in breast and gastric cancers predicts response to anti-HER2 agents such as trastuzumab (Bang, et al.2010; Piccart-Gebhart et al., 2005; Romond et al, 2005). However, many biomarker candidates that appear promising in retrospective analyses of clinical trial data do not accurately predict therapy response in unrelated tumors. SUMMARY OF THE INVENTION

[0004] The present disclosure provides systems and methods for identifying and validating biomarkers that will predict which patients are likely to respond to a particular therapy. These methods use patient samples that have available corresponding clinical data. Predictive biomarkers have several advantages, including improving patient health and outcome by not administering treatments that are unlikely to provide a benefit to the patient. Further, accurate predictive biomarkers can be used to select patient subgroups that are likely to respond to treatment for clinical trials. Testing only likely responsive patients can decrease the cost of clinical trials since the trial would need fewer patients. Additionally, clinical trials including only patients likely to respond to a given therapeutic may allow therapeutics that previously failed clinical trials to show efficacy.

[0005] New predictive biomarkers are needed. Many putative biomarkers in the art are identified during retrospective studies where patient samples from a clinical trial are analyzed for the presence or absence of biomarkers that correlate with the drug response observed during the clinical trial. However, discovering biomarkers in retrospective analyses is problematic. For example, the sample set cannot be used to confirm that the biomarker identified is predictive, and these studies often yield a high number of false correlations. Further, confirming putative biomarkers in subsequent clinical trials to rule out random, chance associations and the overfitting of data is costly and time-intensive. Typically in order to validate the biomarker discovered from retrospective analysis (e.g. analyzing samples from a clinical trial), a new prospective clinical trial will need to done. Drugs that failed late stage clinical trials often offer many data points with which to discover potential biomarkers because of the uniform nature of the trial and the large sample sets. However because these trials will typically require a prospective validating trial they are often not done.

[0006] The present disclosure provides methods of identifying and validating disease biomarkers that are predictive of drug response without relying on retrospective analysis of clinical trial data and do not require a subsequent new validating trial. These methods are particularly useful for drugs that have failed late stage clinical trials where there are samples available from the trial. These methods use new patient samples (e.g. tumor or tumor samples) from patients that have not been exposed to the drug under investigation that are implanted into avatars (e.g. in vivo animal or in vitro three-dimensional models) to identify biomarkers associated with response to the therapeutic or treatment tested. The clinical samples that had been previously collected and for which there is clinical response data, can now be used to validate the biomarkers discovered in the avatar since these samples are unrealted to the samples in which the biomarkers were discovered. The samples and data, are analyzed in a prospective“synthetic trial” where the biomarkers can be validated prospectively by using data from the trial that had already completed.

[0007] To increase confidence that the association of the biomarker with therapeutic response is not due to chance, and tracks therapeutic response, or to decrease the number of potential biomarkers, the biological significance of the biomarkers may be tested by silencing or adding the biomarker to cells and observing if this change in the cell leads to a corresponding change in therapeutic response. The gene of the biomarker in question can be silenced, and the avatars or the cells in vitro are tested for therapeutic or treatment responsiveness or non-responsiveness. If alteration of the biomarker causes a phenotypic switch (e.g. from responsive to non-responsive or visa versa) in the response to the therapeutic or treatment, the presence or absence of this biomarker is related to response in the avatar. Because the biomarkers are discovered by using new samples, their validation can be performed in the previously collected patient sample sets in which there is human clinical data of therapeutic response, (e.g. found in a clinical trials). The biomarker is correlated with data from the clinical trial regarding drug response. A positive correlation would be one in which the treatment arm biomarker positive patients fare better than the total treatment arm in general yet no effect is seen in the control arm. This provides prospective data for the biomarker that circumvents the need to perform an expensive and lengthy new clinical trial to confirm the predictive efficacy of the biomarker identified using the methods disclosed herein.

[0008] In some aspects, the present disclosure provides methods of identifying one or more biomarkers that predict a patient’s treatment response comprising: (a) implanting a first patient sample set in an avatar; (b) analyzing a biological component of the patient sample set; (c) exposing the avatar to a therapeutic or treatment; (d) determining the treatment response of the avatar; (e) associating the presence or absence of one or more differentially expressed biological components with the treatment response to identify one or more biomarkers; and (f) detecting the biomarker in a second patient sample set from patients previously exposed to the therapeutic or treatment, and where there is known clinical therapeutic or treatment response data, wherein association of the biomarker with patient treatment response in the second patient sample and no association in a control group validates the biomarker is predictive of patient treatment response.

[0009] In some aspects, the present disclosure provides methods of identifying one or more tumor biomarkers that predict a patient’s treatment response comprising: (a) silencing a gene or genes at random or silencing a plurality of potential candidate genes in tumor cells; (b) screening each transformed cell individually with a therapeutic or treatment for a change in response to therapeutic compared to unmodified tumor cells either in vivo or in vitro; (c) determining the silenced gene or genes from cells that show a change in therapeutic response; and (d) detecting the biomarker in a patient sample set from patients previously exposed to the therapeutic or treatment, and where there is known clinical therapeutic or treatment response data, wherein association of the biomarker with patient treatment response in the patient sample indicates the biomarker is predictive of patient treatment response.

[0010] In some aspects, the present disclosure provides methods of identifying one or more tumor biomarkers that predict a patient’s treatment response comprising: (a) implanting a tumor or tumor sample from the patient in an avatar; (b) analyzing a biological component of the tumor or tumor sample; (c) exposing the avatar to a therapeutic or treatment; (d) determining the treatment response of the avatar; (e) associating the presence or absence of one or more differentially expressed biological components with the treatment response to identify one or more biomarkers; and (f) detecting the biomarker in a second patient sample set from patients previously exposed to the therapeutic or treatment, and where there is known clinical therapeutic or treatment response data, wherein association of the biomarker with patient treatment response in the second patient sample and no association in a control group validates the biomarker is predictive of patient treatment response.

[0011] In some aspects, the present disclosure provides methods of predicting a patient’s response to a cancer therapy comprising identifying one or more tumor biomarkers that predict a patient’s treatment response comprising: (a) implanting a tumor or tumor sample from the patient in an avatar; (b) analyzing a biological component of the tumor or tumor sample; (c) exposing the avatar to a therapeutic or treatment; (d) determining the treatment response of the avatar; (e) associating the presence or absence of one or more differentially expressed biological components with the treatment response to identify one or more biomarkers; and (f) detecting the biomarker in a second patient sample set from patients previously exposed to the therapeutic or treatment, and where there is known clinical therapeutic or treatment response data, wherein association of the biomarker with patient treatment response in the second patient sample and no association in a control group validates the biomarker is predictive of patient treatment response.

[0012] In some embodiments, the present disclosure provides methods of identifying one or more biomarkers that predict a patient’s treatment response comprising: (a) analyzing a biological component of a cell line or animal model; (b) exposing the cell line or animal model to a therapeutic or treatment; (c) determining the treatment response of the cell line or animal model; (d) associating the presence or absence of one or more differentially expressed biological components of the sample saved from step (a) with the treatment response to identify one or more biomarkers; and (e) detecting the biomarker in a second patient sample (validating samples) in which the samples were collected after the patient was exposed to the therapy and to which patient response is known, wherein association of the biomarker with patient treatment response in the validating sample indicates the biomarker has a high probability of predicting patient response.

[0013] In some embodiments, the present disclosure provides methods of predicting therapeutic response in a patient comprising detecting the presence or absence of one or more tumor biomarkers identified by the process of: (a) implanting a first tumor or tumor sample from a patient in an avatar; (b) analyzing a biological component of the tumor or tumor sample; (c) exposing the avatar to a therapeutic or treatment; (d) determining the treatment response of the avatar; (e) associating the presence or absence of one or more differentially expressed biological components with the treatment response to identify one or more biomarkers; and (f) detecting the biomarker in a second patient sample set from patients previously exposed to the therapeutic or treatment, and where there is known clinical therapeutic or treatment response data, wherein association of the biomarker with patient treatment response in the second patient sample and no association in a control group validates the biomarker is predictive of patient treatment response.

[0014] In some embodiments, the present disclosure provides methods of predicting therapeutic response in a patient comprising detecting the presence or absence of one or more tumor biomarkers identified by the process of: (a) analyzing a biological component of a cell line or animal model; (b) exposing the cell line or animal model to a therapeutic or treatment; (c) determining the treatment response of the cell line or animal model; (d) associating the presence or absence of one or more differentially expressed biological components of the sample saved from step (a) with the treatment response to identify one or more biomarkers; and (e) detecting the biomarker in a second patient sample (validating samples) in which the samples were collected after the patient was exposed to the therapy and to which patient response is known, wherein association of the biomarker with patient treatment response in the validating sample indicates the biomarker has a high probability of predicting patient response. BRIEF DESCRIPTION OF THE FIGURES

[0015] Figure 1 illustrates a step of an exemplary in vitro method of identifying biomarkers predictive of patient response to a therapeutic. Cell lines are experimentally interrogated with drug to determine their response (e.g. resistant (green) or responsive (black)).

[0016] Figure 2 illustrates another step of an exemplary in vitro method of identifying biomarkers predictive of patient response to a therapeutic. A biological component of the cells is analyzed before exposure to the therapeutic. After exposure to the therapeutic, the cells are classified as either resistant (green) or responsive (black), and the protein expression in both classifications is analyzed to determine any differences in protein expression (indicated here by the circled lines marked red and blue) between the two classifications.

[0017] Figure 3 illustrates another step of an exemplary in vitro method of identifying biomarkers predictive of patient response to a therapeutic. The presence or absence of a differentially expressed protein (indicated here as red and blue lines) is analyzed for significance to therapeutic response.

[0018] Figure 4 illustrates another step of an exemplary in vitro method of identifying biomarkers predictive of patient response to a therapeutic. Here, putative biomarkers (identified in the previous step and indicated as red) are used to create a biomarker panel.

[0019] Figure 5 illustrates another step of an exemplary in vitro method of identifying biomarkers predictive of patient response to a therapeutic. A prospective data analysis is performed in previously collected clinical trial sample sets which are analyzed for the presence or absence of the biomarkers identified in Figures 1-4 (indicated here as blue and red). The biomarkers are confirmed by their association with patient survival data from the clinical trial.

[0020] Figure 6 illustrates exemplary clinical trial results without the use of a predictive biomarker. Here, there is no significant difference in survival between placebo-treated and drug- treated groups.

[0021] Figure 7 illustrates how the clinical trial results from Figure 6 can change with the use of a predictive biomarker. Here, instead of two groups (placebo v drug), the patient population can be separated into 8 different groups based upon treatment, patient response, and the presence of a biomarker (indicated here as red and blue). The addition of the biomarker information allows determination that particular subgroups of patients demonstrated significantly better survival than others.

[0022] Figure 8 illustrates a step of an exemplary in vivo method of identifying biomarkers predictive of patient response to a therapeutic. Here, an animal model is exposed to a therapeutic and the animal response is observed (e.g. live v dead).

[0023] Figure 9 illustrates another step of an exemplary in vivo method of identifying biomarkers predictive of patient response to a therapeutic. Before exposure to the therapeutic, biological samples are taken from the animal and analyzed. After the animal treatment, the biological samples are classified as belonging to either live or dead animals and the samples are classified accordingly. The protein expression in both classifications is analyzed to determine any differences in protein expression (indicated here by the circled line marked red) between the two classifications.

[0024] Figure 10 illustrates another step of an exemplary in vivo method of identifying biomarkers predictive of patient response to a therapeutic. A prospective data analysis is performed in previously collected clinical trial sample sets which are analyzed for the presence or absence of the biomarker identified in Figures 8 and 9. The biomarker is confirmed by its association with patient survival data from the clinical trial.

[0025] Figure 11 illustrates exemplary clinical trial results without the use of a predictive biomarker. Here, there is no significant difference in survival between placebo-treated and drug- treated groups.

[0026] Figure 12 illustrates how the clinical trial results from Figure 11 can change with the use of a predictive biomarker. Here, instead of two groups (placebo v drug), the patient population can be separated into 3 different groups based upon treatment, patient response, and the presence of a biomarker (indicated here as red and blue). The addition of the biomarker information allows determination that particular subgroups of patients demonstrated significantly better survival than others. DETAILED DESCRIPTION

[0027] The present disclosure provides systems and methods for identifying and validating biomarkers associated with therapeutic or treatment response. Because not all patients respond to any given therapeutic or treatment, a key goal in research is to identify ways to select only those patients who will respond to a given therapeutic or treatment. In a clinical setting, this reduces patients being treated with ineffective, and in many cases, dangerous treatments. In clinical trials, enrolling only patients who are likely to respond to a given therapeutic or treatment increases the likelihood of success of the trial, decreases the number of patients needed, and decreases the cost and length of the clinical trial.

[0028] Biomarkers are measurable differences or molecular substances that differ between groups of cells or patients (e.g. subgroups of cancer cells with different responses to therapeutics). The identification of reliable biomarkers that accurately predict whether a particular patient will be sensitive to a given therapeutic is an important avenue in the treatment of disease. Development and validation of predictive biomarkers is difficult; approximately 30%- 50% of biomarkers are coupled to drug development programs, but only 3%-5% actually reach the clinic (de Gramont et al., 2015). There are several major challenges to the identification and validation of predictive biomarkers. For example, in cancer, the complexity of the host and tumor systems interacting with each other causes heterogeneity in cancer response, and requires a high number of patients for multivariate analysis to identify biomarkers. Further, agents used in clinical trials are often combined with other therapies which may mask the association between any one agent used in a treatment regimen and the biomarkers under consideration. Finally, many predictive biomarkers are identified in retrospective studies, where samples are labeled as either responsive or non-responsive to a therapeutic or treatment based on clinical data. Once patients or patient samples are sorted into a drug response group, biological components of the patient samples (e.g. DNA, RNA, proteins, microRNA) are analyzed to identify putative biomarkers that predict patient response. These retrospective analyses are known to yield high rates of false correlations, and the putative biomarkers must be confirmed in subsequent prospective clinical trials to rule out random, chance associations and the overfitting of data. These clinical trials to confirm the putative biomarkers are expensive, time-consuming, and due to the high false-positive rate, are often deemed as too-risky to perform. Moreover, in cancer, it is not always possible to correctly assign a response label to a patient, since outcomes such as survival, or progression free survival are not necessarily dependent on the drug itself (e.g. survival may be impacted by events such as accidents unrelated to the cancer diagnosis). Finally, retrospective studies use an inherently circular process of discovery, where finding of a biomarker in the sample will be confirmed as true when re-tested in that same sample set. Often, biomarkers discovered in this way do not have clinical significance when tested in a different sample set. Many putative biomarkers are identified, but because of the high false positive rate, few are ever validated.

[0029] The present disclosure solves these problems by using biomarker discovery coupled with synthetic trials. Here, the biomarkers are discovered in a first patient sample set (e.g. tumors or tumor samples from newly diagnosed cancer patients), and optionally, may be partially validated by observing a change in treatment response phenotype after altering the biomarker (e.g. through gene silencing). The biomarkers are then confirmed by analyzing biomarker expression in an independent second sample set from a clinical trial which has corresponding clinical data of patient response to the therapeutic. Moreover, the present methods allow for treatment with only the therapeutic or treatment under study. Further, for discovery of predictive cancer biomarkers, a patient sample (e.g. tumor or tumor sample) is implanted in an avatar (e.g. in vivo animal or in vitro systems) which can decrease the heterogeneity of host-tumor interactions.

[0030] In some aspects, a first sample is obtained from a naïve-treated (e.g. untreated) patient. The first patient sample may then be implanted (e.g. orthotopically or grafted) in an avatar which can be any appropriate animal model, or an appropriate in vitro system (e.g. 3D cancer system which can model tumor response with fidelity). Biological components of the first patient sample (e.g. proteins, peptides, DNA, RNA, epigenetic signatures, etc.) are analyzed, and the avatars are subjected to the therapeutic or treatment under study. The avatars are classified as either responders or nonresponders based on the avatar’s response to the therapeutic or treatment administered. Multivariate analysis is used to identify one or more putative biomarkers associated with drug response. Samples are taken from the treated avatars, and the putative biomarkers may be manipulated (e.g. through gene silencing using CRISPR/Cas9). The altered patient samples are screened for therapeutic responsiveness. If the altered patient sample shows a phenotypic shift (e.g. from responsive to nonresponsive), the putative biomarker is responsible, in some part, for the therapeutic response observed.

[0031] Alternatively, in some embodiments, the present disclosure provides methods using cell lines or animal models of disease that do not contain any tissue or cells derived from a patient. Here, the biological components of a first cell line or animal model (e.g. for a particular disease) is analyzed for biomarkers. The cell line or animal model is exposed to the treatment or therapeutic under study, and assayed for response. The therapeutic response is then correlated with any biological component differences between the different response groups to identify putative biomarkers that predict response to the therapeutic.

[0032] A synthetic trial is then performed where the presence or absence of the biomarker obtained as explained above is validated in a second sample set (e.g. from a clinical trial) that is independent from the first sample set. The clinical data in the second sample set is compared between patients who are biomarker positive and those who are biomarker negative to confirm that the putative biomarker accurately predicts patient response. Since the biomarker is discovered in the first patient samples, cell lines, or animal models independently of the second sample set, the clinical trial data is considered prospective for the biomarker, thereby circumventing the requirement for a long and costly clinical trial to confirm the predictive biomarker identified using the methods disclosed herein. The synthetic trial demonstrates the biomarker is able to show positive separation (e.g. classification) between responders and nonresponders in the treatment group, whereas in a control population, who had not received the therapeutic, no such separation should be seen.

[0033] In other embodiments, the first patient sample, cell line, or animal model is subjected to random or semi-random gene silencing (e.g. using CRISPR/Cas9) before its biological components are analyzed and the therapeutic or treatment is applied to the modified cells. The gene silencing is performed so that each cell contains on average one (or more) silenced gene, and the cells are screened by functional genomic screening with a therapeutic or treatment. The altered patient samples are then scored for drug responsiveness. A change in drug response indicates that the silenced gene is a potential biomarker A correlation between the presence or absence of a particular biomarker and therapeutic or treatment responsiveness indicates putative predictive biomarkers which may be confirmed in a synthetic trial as disclosed above.

[0034] In some embodiments, the present disclosure provides methods of identifying one or more biomarkers that predict a patient’s treatment response comprising: (a) implanting a first patient sample in an avatar; (b) saving some of the sample in order to later analyze a biological component of the sample before it was exposed to the therapeutic agent; (c) exposing the avatar to a therapeutic or treatment; (d) determining the treatment response of the avatar; (e) associating the presence or absence of one or more differentially expressed biological components of the sample saved from step (b) with the treatment response to identify one or more biomarkers; and (f) detecting the biomarker in a second patient sample (validating samples) in which the samples were collected after the patient was exposed to the therapy and to which patient response is known, wherein association of the biomarker with patient treatment response in the validating sample indicates the biomarker has a high probability of predicting patient response.

[0035] In some embodiments, the present disclosure provides methods of identifying one or more biomarkers that predict a patient’s treatment response comprising: (a) creating a library of differentially randomly or semi-randomly silenced genes in cell; (b) screening for differences in response in each cell compared to a parent (e.g. not-altered) upon exposure to therapy; (c) determining which gene or genes were silenced in cells that changed responsiveness as well as other changes that may be present in the cell; (d) identifying the silenced genes as well as other potential changes as potential biomarkers; and (e) detecting the biomarker in a second validating patient sample set wherein association of the biomarker with patient treatment response in the second patient sample indicates the biomarker is predictive of patient treatment response. [0036] In some embodiments, the present disclosure provides methods of identifying one or more biomarkers that predict a patient’s treatment response comprising: (a) analyzing a biological component of a cell line or animal model; (b) exposing the cell line or animal model to a therapeutic or treatment; (c) determining the treatment response of the cell line or animal model; (d) associating the presence or absence of one or more differentially expressed biological components with the treatment response to identify one or more biomarkers; and (e) detecting the biomarker in a second patient sample (validating samples) in which the samples were collected after the patient was exposed to the therapy and to which patient response is known, wherein association of the biomarker with patient treatment response in the validating sample indicates the biomarker has a high probability of predicting patient response.

[0037] In some embodiments, the present disclosure provides methods of identifying one or more tumor biomarkers that predict a patient’s treatment response comprising: (a) silencing a gene or genes at random or from a plurality of potential genes that are more disease specific in tumor cells; (b) Screening each transformed cell individually for change in response to drug compared to unmodified tumor cells either in vivo or in vitro; (c) determining the silenced gene or genes from cells that demonstrate a change in therapeutic response; and (d) detecting the biomarker in a second tumor or tumor sample set where the patient was exposed to the therapeutic and their clinical response can be determined, wherein association of the biomarker with patient treatment response in the second tumor or tumor sample indicates the biomarker is predictive of patient treatment response. Alternatively proteomic or other strategies can be used to determine protein changes when comparing silenced cells to non-silenced cells and associating the presence or absence of one or more differentially expressed biological components with the treatment response to identify one or more biomarkers.

[0038] In some embodiments, the present disclosure provides methods of identifying one or more biomarkers that predict a patient’s treatment response comprising: (a) analyzing a biological component of a cell line or animal model; (b) exposing the cell line or animal model to a therapeutic or treatment; (c) determining the treatment response of the cell line or animal model; (d) associating the presence or absence of one or more differentially expressed biological components with the treatment response to identify one or more biomarkers; and (e) detecting the biomarker in a second patient sample (validating samples) in which the samples were collected after the patient was exposed to the therapy and to which patient response is known, wherein association of the biomarker with patient treatment response in the validating sample indicates the biomarker has a high probability of predicting patient response.

[0039] As used herein, the term“biological specimen” is any specimen obtained from a patient, cell line, or animal model. In non-limiting embodiments, the biological specimen is a cancer, a biopsy, a tumor, a tumor cell, any tissue, saliva, cerebrospinal fluid, blood, exhaled breath, semen, urine, fecal matter, sweat, cells, circulating stem cells, sputum, breast milk, pus, peripheral blood, tumor microenvironment, cell membrane, cytoplasm, mitochondria, nucleus, nucleoplasm, skin, etc.

[0040] As used herein, the term“biological component” refers to any portion of a biological specimen assayed for the presence of biomarkers. In some embodiments, the biological component is a peptide, a protein, an amino acid sequence, a nucleic acid, a chromosome, a ribosome, a chemical, a chemical modification, or an epigenetic signature.

[0041] As used herein, the term“patient sample” refers to any diseased or non-diseased sample obtained from a patient, cell line, or animal model. In non-limiting embodiments, the patient sample is cancer, a tumor, a tumor sample, any tissue, stem cells, circulating stem cells, etc. This term also refers to patient sample sets, and any material obtained from a patient sample. This term also refers to patient sample sets, and any material obtained from a patient sample.

[0042] As used herein, the term“tumor” is one or more tumor cells capable of forming an invasive mass that can progressively displace or destroy normal tissues.

[0043] As used herein,“tumor cell” refers to a cell which is a component of a tumor in an animal, or a cell which is determined to be destined to become a component of a tumor, i.e., a cell which is a component of a precancerous lesion in an animal, or a cell line established in vitro from a primary tumor.

[0044] As used herein, the term“tumor sample” refers to a portion of a tumor separated from said tumor. In some embodiments, a tumor sample is a biopsy or a clone of a tumor cell. This term also refers to tumor sample sets, and any material obtained from a tumor.

[0045] As used herein, the term“biomarker” refers to any measurable or detectable change between therapeutic response groups (e.g. responder v nonresponder). In non-limited embodiments, a biomarker is a protein, peptide, DNA, RNA, mRNA, microRNA, SNPs, circulating stem cells, immune regulators, metabolites, and epigenetic signatures. In some embodiments, a biomarker is a phenotypic response in a patient, including but not limited to, increased negative side effects from treatment, decreased positive side effects from treatment, rash, change in body fluid color or characterization, change in chemical composition, etc.

[0046] As used herein, the terms“therapeutic” and“treatment” refer to any appropriate drug or therapy for a disease. These terms may be used interchangeably.

[0047] As used herein, the term“differentially expressed” refers to any measurable difference in a biological component or biomarker observed between therapeutic response groups (e.g. responder v nonresponder).

Patient Samples

[0048] The present disclosure provides methods of identifying biomarkers in a biological component of any patient sample. The biological component may be isolated from any appropriate biological specimen.

[0049] In some embodiments, a patient sample is obtained from a patient, and then assayed for biomarkers unique to that patient. In some embodiments, a patient sample is obtained from a patient, and then assayed for biomarkers that are predictive of therapeutic response for that patient.

[0050] In some embodiments, a patient sample is obtained from a patient, and then assayed for biomarkers unique to a particular disease type.

[0051] In some embodiments, the patient sample is a tumor or tumor sample. In some embodiments, the tumor or tumor sample is a biopsy, fresh, preserved, or frozen. In some embodiments, the present disclosure provides methods using tumors or tumor samples obtained from sources such as, but not limited to, cells, tissues, organs, biological fluids, and combinations thereof. In some embodiments, the tumor or tumor sample assayed for therapeutic or treatment response is a benign tumor, a metastatic tumor, a pre-cancerous tumor, or a cancer.

[0052] In some embodiments, the tumor or tumor sample is a biopsy selected from a tissue sample, frozen tumor tissue specimen, cultured cells, circulating tumor cells, and a formalin- fixed paraffin-embedded tumor tissue specimen. In further embodiments, the tumor or tumor sample is a peripheral blood sample, a lymph-node sample, a bone marrow sample, or an organ tissue sample. In other embodiments, the tumor or tumor sample is a cancer stem cell. In other embodiments, the tumor or tumor sample is derived from the biopsy of a non-solid tumor. In further embodiments, the tumor or tumor sample is derived from a circulating tumor cell. In other embodiments, the tumor or tumor sample is derived from the biopsy of a solid tumor cancer.

[0053] In some embodiments, the tumor or tumor sample assayed for therapeutic or treatment response is from a human cancer patient. In some embodiments, the human cancer patient has been treated with one or more cancer therapeutics or treatments. In some embodiments, the human cancer patient has not been treated with one or more cancer therapeutics or treatments. In some embodiments, the tumor or tumor sample from a human patient has been exposed to one or more cancer therapeutics or treatments. In some embodiments, the tumor or tumor sample from a human patient has been exposed to one or more cancer therapeutics or treatments and has been classified as a responder or a nonresponder. In some embodiments, the tumor or tumor sample from a human patient has not been exposed to one or more cancer therapeutics or treatments.

[0054] In some embodiments, the tumor or tumor sample assayed for therapeutic or treatment response comprises cells obtained from one or more cell line banks. In further embodiments, the tumor or tumor sample is a human tumor-derived cell line. In further embodiments, the cell line is a cancer cell line. In further embodiments, the cell line has been exposed to one or more cancer therapeutics or treatments. In further embodiments, the cell line has been exposed to one or more cancer therapeutics or treatments and has been classified as a responder or a nonresponder. In other embodiments, the cell line has not been exposed to one or more cancer therapeutics or treatments.

[0055] In further embodiments, the tumor or tumor sample is obtained from an animal model. In further embodiments, the animal model has been exposed to one or more cancer therapeutics or treatments. In further embodiments, the animal model has been exposed to one or more cancer therapeutics or treatments and has been classified as either a responder or a nonresponder. In other embodiments, the animal model has not been exposed to one or more cancer therapeutics or treatments.

[0056] In some embodiments, the tumor or tumor sample is, but is not limited to, one or more of the following cancers: acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), adrenocortical carcinoma, AIDS-related cancers, anal cancer, appendix cancer, astrocytoma (e.g. childhood cerebellar or cerebral), basal-cell carcinoma, bile duct cancer, bladder cancer, bone tumor (e.g. osteosarcoma, malignant fibrous histiocytoma), brainstem glioma, brain cancer, brain tumors (e.g. cerebellar astrocytoma, cerebral astrocytoma/malignant glioma, ependymoma, medulloblastoma, supratentorial primitive neuroectodermal tumors, visual pathway and hypothalamic glioma), breast cancer, bronchial adenomas/carcinoids, Burkitt's lymphoma, carcinoid tumors, central nervous system lymphomas, cerebellar astrocytoma, cervical cancer, chronic lymphocytic leukemia (CLL), chronic myelogenous leukemia (CML), chronic myeloproliferative disorders, colon cancer, cutaneous t-cell lymphoma, desmoplastic small round cell tumor, endometrial cancer, ependymoma, esophageal cancer, Ewing’s sarcoma, extracranial germ cell tumor, extragonadal germ cell tumor, extrahepatic bile duct cancer, eye cancer, gallbladder cancer, gastric (stomach) cancer, gastrointestinal stromal tumor (GIST), germ cell tumor (e.g. extracranial, extragonadal, ovarian), gestational trophoblastic tumor, gliomas (e.g. brain stem, cerebral astrocytoma, visual pathway and hypothalamic), gastric carcinoid, head and neck cancer, heart cancer, hepatocellular (liver) cancer, hypopharyngeal cancer, hypothalamic and visual pathway glioma, intraocular melanoma, islet cell carcinoma (endocrine pancreas), kidney cancer (renal cell cancer), laryngeal cancer, leukemias (e.g. acute lymphocytic leukemia, acute myelogenous leukemia, chronic lymphocytic leukemia, chronic myeloid leukemia, hairy cell), lip and oral cavity cancer, liposarcoma, liver cancer, lung cancer (e.g. non-small cell, small cell), lymphoma (e.g. AIDS-related, Burkitt, cutaneous T-cell Hodgkin, non-Hodgkin, primary central nervous system), medulloblastoma, melanoma, Merkel cell carcinoma, mesothelioma, metastatic squamous neck cancer, mouth cancer, multiple endocrine neoplasia syndrome, multiple myeloma, mycosis fungoides, myelodysplastic syndromes, myelodysplastic/myeloproliferative diseases, myelogenous leukemia, myeloid leukemia, myeloid leukemia, myeloproliferative disorders, chronic, nasal cavity and paranasal sinus cancer, nasopharyngeal carcinoma, neuroblastoma, non-Hodgkin lymphoma, non-small cell lung cancer, oral cancer, oropharyngeal cancer, osteosarcoma, ovarian cancer, pancreatic cancer, pancreatic cancer, paranasal sinus and nasal cavity cancer, parathyroid cancer, penile cancer, pharyngeal cancer, pheochromocytoma, pineal astrocytoma and/or germinoma, pineoblastoma and supratentorial primitive neuroectodermal tumors, pituitary adenoma, plasma cell neoplasia/multiple myeloma, pleuropulmonary blastoma, primary central nervous system lymphoma, prostate cancer, rectal cancer, renal cell carcinoma (kidney cancer), renal pelvis and ureter, retinoblastoma, rhabdomyosarcoma, salivary gland cancer, sarcoma (e.g. Ewing family, Kaposi, soft tissue, uterine), Sézary syndrome, skin cancer (e.g. nonmelanoma, melanoma, merkel cell), small cell lung cancer, small intestine cancer, soft tissue sarcoma, squamous cell carcinoma, squamous neck cancer, stomach cancer, supratentorial primitive neuroectodermal tumor, t-cell lymphoma, testicular cancer, throat cancer, thymoma and thymic carcinoma, thyroid cancer, trophoblastic tumors, ureter and renal pelvis cancers, urethral cancer, uterine cancer, uterine sarcoma, vaginal cancer, visual pathway and hypothalamic glioma, vulvar cancer, Waldenström macroglobulinemia, and Wilms tumor.

[0057] Cancer cell lines that may be used include, but are not limited to, SH-SY5Y( Human neuroblastoma), Hep G2 (Human Caucasian hepatocyte carcinoma), 293/HEK 293 (Human Embryo Kidney), RAW 264.7 (Mouse monocyte macrophage), HeLa (Human Negroid cervix epitheloid carcinoma), MRC-5 (PD 19) (Human fetal lung), A2780 (Human ovarian carcinoma), CACO-2 (Human Caucasian colon adenocarcinoma), THP 1 (Human monocytic leukemia), A549 (Human Caucasian lung carcinoma), MRC-5/PD 30 (Human fetal lung), MCF7 (Human Caucasian breast adenocarcinoma), SNL 76/7 (Mouse SIM strain embryonic fibroblast), C2C12 (Mouse C3H muscle myoblast), Jurkat E6.1 (Human leukemic T cell lymphoblast), U937 (Human Caucasian histiocytic lymphoma), L929 (Mouse C3H/An connective tissue), 3T3 L1 (Mouse Embryo), HL60 (Human Caucasian promyelocytic leukemia), PC-12 (Rat adrenal phaeochromocytoma), HT29 (Human Caucasian colon adenocarcinoma), OE33 (Human Caucasian esophageal carcinoma), OE19 (Human Caucasian esophageal carcinoma), NIH 3T3 (Mouse Swiss NIH embryo), MDA-MB-231 (Human Caucasian breast adenocarcinoma), K562 (Human Caucasian chronic myelogenous leukemia), U-87 MG (Human glioblastoma astrocytoma), MRC-5/PD 25 (Human fetal lung), A2780cis (Human ovarian carcinoma), B9 (Mouse B cell hybridoma), CHO-K1 (Hamster Chinese ovary), MDCK (Canine Cocker Spaniel kidney), 1321N1 (Human brain astrocytoma), A431 (Human squamous carcinoma), ATDC5 (Mouse 129 teratocarcinoma AT805 derived), RCC4 PLUS VECTOR ALONE (Renal cell carcinoma cell line RCC4 stably transfected with an empty expression vector, pcDNA3, conferring neomycin resistance), HUVEC (S200-05n) (Human Pre-screened Umbilical Vein Endothelial Cells (HUVEC); neonatal), Vero (Monkey African Green kidney), RCC4 PLUS VHL (Renal cell carcinoma cell line RCC4 stably transfected with pcDNA3-VHL), Fao (Rat hepatoma), J774A.1 (Mouse BALB/c monocyte macrophage), MC3T3-E1 (Mouse C57BL/6 calvaria), J774.2 (Mouse BALB/c monocyte macrophage), PNT1A (Human post pubertal prostate normal, immortalized with SV40), U-2 OS (Human Osteosarcoma), HCT 116 (Human colon carcinoma), MA104 (Monkey African Green kidney), BEAS-2B (Human bronchial epithelium, normal), NB2-11 (Rat lymphoma), BHK 21 (clone 13) (Hamster Syrian kidney ), NS0 (Mouse myeloma), Neuro 2a (Mouse Albino neuroblastoma), SP2/0-Ag14 (Mouse x Mouse myeloma, non-producing), T47D (Human breast tumor), 1301 (Human T-cell leukemia), MDCK-II (Canine Cocker Spaniel Kidney), PNT2 (Human prostate normal, immortalized with SV40), PC-3 (Human Caucasian prostate adenocarcinoma), TF1 (Human erythroleukaemia), COS-7 (Monkey African green kidney, SV40 transformed), MDCK (Canine Cocker Spaniel kidney), HUVEC (200-05n) (Human Umbilical Vein Endothelial Cells (HUVEC); neonatal), NCI-H322 (Human Caucasian bronchioalveolar carcinoma), SK.N.SH (Human Caucasian neuroblastoma), LNCaP.FGC (Human Caucasian prostate carcinoma), OE21 (Human Caucasian esophageal squamous cell carcinoma), PSN1 (Human pancreatic adenocarcinoma ), ISHIKAWA (Human Asian endometrial adenocarcinoma), MFE- 280 (Human Caucasian endometrial adenocarcinoma), MG-63 (Human osteosarcoma), RK 13 (Rabbit kidney, BVDV negative), EoL-1 cell (Human eosinophilic leukemia), VCaP (Human Prostate Cancer Metastasis), tsA201 (Human embryonal kidney, SV40 transformed), CHO (Hamster Chinese ovary), HT 1080 (Human fibrosarcoma), PANC-1 (Human Caucasian pancreas), Saos-2 (Human primary osteogenic sarcoma), ND7/23 (Mouse neuroblastoma x Rat neurone hybrid), SK-OV-3 (Human Caucasian ovary adenocarcinoma), COV434 (Human ovarian granulosa tumor), Hep 3B (Human Negroid hepatocyte carcinoma), Vero (WHO) (Monkey African Green kidney), Nthy-ori 3-1 (Human thyroid follicular epithelial), U373 MG (Uppsala) (Human glioblastoma astrocytoma), A375 (Human malignant melanoma), AGS (Human Caucasian gastric adenocarcinoma), CAKI 2 (Human Caucasian kidney carcinoma), COLO 205 (Human Caucasian colon adenocarcinoma), COR-L23 (Human Caucasian lung large cell carcinoma), IMR 32 (Human Caucasian neuroblastoma), QT 35 (Quail Japanese fibrosarcoma), WI 38 (Human Caucasian fetal lung), HMVII (Human vaginal malignant melanoma), HT55 (Human colon carcinoma), TK6 (Human lymphoblast, thymidine kinase heterozygote), SP2/0-AG14 (AC-FREE) (Mouse x mouse hybridoma non-secreting, serum-free, animal component (AC) free), and AR42J RAT PANCREATIC TUMOR (Rat exocrine pancreatic tumor).

[0058] In some embodiments, the patient sample is obtained from a patient diagnosed with a disease or disorder selected from, but not limited to, Alzheimer’s disease, Parkinson’s disease, autoimmune disease, Systemic Lupus Erythematosus, Lupus Nephritis, cardiovascular disease, nephropathy, inflammation, kidney disease, kidney failure, kidney injury, HIV-associated neurocognitive disorders, prostate inflammation, atheromatous renovascular disease, vascular disease, non-alcoholic fatty liver disease, inflammatory bowel disease, peripheral arterial disease, chronic obstructive pulmonary disease, type 2 diabetes, type 1 diabetes, neuropsychiatric disease, inflammatory bowel disease, preeclampsia, periodontal disease, rheumatoid arthritis, bacterial or viral infection, parasitic infection, joint disease, and transplant rejection.

[0059] In some embodiments, the patient sample is from a cell line established as a model or an animal model for any disease, including, but not limited to those disclosed herein. In some embodiments, the patient sample is obtained from an established cell line. In some embodiments, the patient sample is obtained from an established cell line that models a disease. In some embodiments, the patient sample is obtained from an animal model. In some embodiments, the patient sample is obtained from an animal model of disease. Avatars

[0060] In some aspects, the present disclosure provides methods of identifying predictive biomarkers in tumor or tumor samples using avatars (e.g. patient-derived xenografts) which preserve the original characteristics of a patient’s cancer and mimic the disease more effectively than deriving samples from cell lines. The use of avatars allows for each patient (or patient sample) to have their own cancer growing in an in vivo or in vitro system to ascertain in a model system if the patient would respond to a therapeutic. Further, the use of avatars allows the tumor to be grown and harvested at different time points, which allows for understanding the molecular changes driving metastasis and resistance to drug therapy. (Malaney, et al., 2014).

[0061] In some embodiments, the avatar is an in vivo avatar containing a tumor or tumor sample. In some embodiments, the in vivo avatar is an animal model. Any appropriate animal model may be used. In some embodiments, the animal model is selected from the group including, but not limited to, mice, rats, hamsters, gerbils, rabbits, dogs, cats, livestock, and pigs. In some embodiments, the animal model avatar is immunocompromised. In some embodiments, the patient sample is implanted into the animal model avatar. In some embodiments, the patient sample is implanted subcutaneously. In some embodiments, the patient sample is grafted orthotopically.

[0062] In some embodiments, the avatar is an in vitro avatar containing the patient sample. In some embodiments, the avatar is a tumor in vitro avatar containing a tumor or tumor sample. In some embodiments, the in vitro avatar is a three-dimensional bioengineered avatar. In some embodiments, the in vitro avatar is a three-dimensional bioengineered tumor avatar which is formed when tumor cells are cultured within a three-dimensional in vitro environment, and thereby acquire phenotypes and respond to stimuli analogous to in vivo biological systems. (Szot et al., 2011). Three-dimensional bioengineered avatars may be engineered using any appropriate means in the art. Three-dimensional bioengineered avatars may be engineered using any appropriate patient sample. In some embodiments, the three-dimensional bioengineered avatar is engineered using a human tumor or tumor sample. In some embodiments, the three-dimensional bioengineered avatar is engineered using a tumor or tumor sample obtained from a cancer patient. In some embodiments, the three-dimensional bioengineered avatar is engineered using a cell line. In some embodiments, the three-dimensional bioengineered avatar is engineered using a tumor cell line. In some embodiments, the three-dimensional bioengineered avatar is engineered using a cell or portion of an organ capable of growth and/or replication.

[0063] In some embodiments, the present disclosure provides methods employing a panel of one or more avatars. In some embodiments, each avatar in the panel includes a patient sample from the same patient. In some embodiments, each avatar in the panel includes a patient sample that has not been exposed to a therapeutic or treatment. In some embodiments, each avatar in the panel includes a patient sample that has been exposed to a therapeutic or treatment. In some embodiments, some avatars in the panel include a patient sample that has not been exposed to a therapeutic or treatment and some avatars in the panel include a patient sample that has been exposed to a therapeutic or treatment. In some embodiments, each avatar in the panel is exposed to a different therapeutic or treatment. In some embodiments, each avatar in the panel is exposed to different concentrations or time-courses of the same therapeutic or treatment. In some embodiments, each avatar in the panel contains a different patient sample. In some embodiments, the different patient samples are obtained from different patients. In some embodiments, the patient sample is a tumor or tumor sample. In some embodiments, each avatar in the panel contains a tumor or tumor sample, and has a different stage of the tumor or tumor sample (e.g. benign, metastatic, pre-metastatic, etc.). In some embodiments, the different tumors or tumor samples are of the same type of cancer obtained from different patients. In some embodiments, the different tumors or tumor samples are obtained from the same patient. Identification of Biomarkers

[0064] In some aspects, the present disclosure provides methods of analyzing biological components of a patient sample to identify one or more biomarkers. In some embodiments, a sample of the patient sample is obtained before the patient sample is exposed to the therapeutic, and the biological components are analyzed later. [0065] In some embodiments, the one or more biomarkers are identified in sets of patient samples from the same patient. In some embodiments, the one or more biomarkers are identified in sets of patient samples that have not been exposed to a therapeutic or treatment. In some embodiments, the one or more biomarkers are identified in sets of patient samples that have been exposed to a therapeutic or treatment. In some embodiments, the one or more biomarkers are identified in sets of patient samples exposed to different therapeutics or treatments. In some embodiments, the one or more biomarkers are identified in sets of patient samples exposed to different concentrations or time-courses of the same therapeutic or treatment. In some embodiments, the one or more biomarkers are identified in sets of patient samples from different patient samples. In some embodiments, the different tumors or tumor samples are obtained from different patients. In some embodiments, the one or more biomarkers are identified in sets of tumors or tumor samples from different stages of the tumor or tumor sample (e.g. benign, metastatic, pre-metastatic, etc.). In some embodiments, different tumors or tumor samples are of the same type of cancer obtained from different patients. In some embodiments, the different tumors or tumor samples are obtained from the same patient.

[0066] In some embodiments, the one or more biomarkers are identified in a cell line, or sets of cell lines. In some embodiments, the one or more biomarkers are identified in a tumor cell line or sets of cell lines. In some embodiments, the one or more biomarkers are identified in a cell line or sets of cell lines that are an established model of a disease. In some embodiments, the one or more biomarkers are identified in a cell line or sets of cell lines that are isolated from human disease. In some embodiments, the one or more biomarkers are identified in an animal model. In some embodiments, the one or more biomarkers are identified in an animal model of disease.

[0067] Biomarkers can be identified at any appropriate level, including but not limited to, genomic, proteomic, and metabolomic levels. Biomarkers can be identified using any suitable technique including, but not limited to, northern blot, gene expression, DNA Microarray, serial analysis of gene expression (SAGE), 2D- polyacrylamide gel electrophoresis (PAGE), mass spectrometry-based approaches (e.g., liquid chromatography(LC)–MS, surface-enhanced laser desorption/ionization-time-of-flight(SELDI-TOF)-MS, matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF)-MS, etc.), an antibody array, a tissue microarray, metabolomics studies, lipidomics studies, imaging approaches (e.g., using magnetic resonance imaging), and/or combinations thereof. [0068] In some aspects, the one or more biomarkers are differentially expressed between a first set of patient samples (or a subset thereof) and a second set of patient samples (or a subset thereof). In some embodiments, a first set of patient samples are those that are classified as responsive (e.g. sensitive) to the therapeutic or treatment tested, and a second set of patient samples are those that are classified as nonresponsive (e.g. resistant) to the therapeutic or treatment tested.

[0069] In some embodiments, the one or more biomarkers are present in a therapeutic or treatment responsive patient sample set. In some embodiments, the one or more biomarkers are present in greater amounts in a therapeutic or treatment responsive patient sample set compared to the amount observed in a therapeutic or treatment non-responsive (resistant) patient sample set. In some embodiments, the one or more biomarkers are present at levels about 10% to about 100% greater or more in a therapeutic or treatment responsive patient sample set compared to the amount of the one or more biomarkers observed in a therapeutic or treatment non-responsive patient sample set. In some embodiments, the one or more biomarkers are present in a therapeutic or treatment responsive patient sample set at levels about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, about 95%, about 98%, about 99%, about 100% greater or more compared to the amount of the one or more biomarkers observed in a therapeutic or treatment non-responsive patient sample set.

[0070] In some embodiments, the one or more biomarkers are absent in a therapeutic or treatment responsive patient sample set. In some embodiments, the one or more biomarkers are present in decreased amounts in a therapeutic or treatment responsive patient sample set compared to the amount observed in a therapeutic or treatment non-responsive (resistant) patient sample set. In some embodiments, the one or more biomarkers are present at levels in a therapeutic or treatment responsive patient sample set that are decreased by about 10% to about 100% compared to the amount of the one or more biomarkers observed in a therapeutic or treatment non-responsive patient sample set. In some embodiments, the one or more biomarkers are present in a therapeutic or treatment responsive patient sample set at levels decreased about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, about 95%, about 98%, or about 99% or more compared to the amount of the one or more biomarkers observed in a therapeutic or treatment non-responsive patient sample set.

[0071] In some embodiments, the one or more biomarkers are present in a therapeutic or treatment non-responsive patient sample set. In some embodiments, the one or more biomarkers are present in greater amounts in a therapeutic or treatment non-responsive patient sample set compared to the amount observed in a therapeutic or treatment responsive (sensitive) patient sample set. In some embodiments, the one or more biomarkers are present at levels about 10% to about 100% greater or more in a therapeutic or treatment non-responsive patient sample set compared to the amount of the one or more biomarkers observed in a therapeutic or treatment responsive patient sample set. In some embodiments, the one or more biomarkers are present in a therapeutic or treatment non-responsive patient sample set at levels about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, about 95%, about 98%, about 99%, about 100% greater or more compared to the amount of the one or more biomarkers observed in a therapeutic or treatment responsive patient sample set.

[0072] In some embodiments, the one or more biomarkers are absent in a therapeutic or treatment non-responsive patient sample set. In some embodiments, the one or more biomarkers are present in decreased amounts in a therapeutic or treatment non-responsive patient sample set compared to the amount observed in a therapeutic or treatment responsive (sensitive) patient sample set. In some embodiments, the one or more biomarkers are present at levels in a therapeutic or treatment non-responsive patient sample set that are decreased by about 10% to about 100% compared to the amount of the one or more biomarkers observed in a therapeutic or treatment responsive patient sample set. In some embodiments, the one or more biomarkers are present in a therapeutic or treatment non-responsive patient sample set at levels decreased about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, about 95%, about 98%, or about 99% or more compared to the amount of the one or more biomarkers observed in a therapeutic or treatment responsive patient sample set.

[0073] In some embodiments, the methods include determining the extent of association between the expression of the one or more biomarkers in one subgroup of patient samples (e.g. responders) and the expression of the one or more biomarkers in a second subgroup of patient samples (e.g. nonresponders). For example, this approach can be employed to determine if differences in expression of a specific biomarker (e.g. protein,) between therapeutic or treatment resistant/non-responsive cells and therapeutic or treatment susceptible/responsive cells can be responsible for the different therapeutic or treatment response between the two subgroups.

[0074] In some aspects of the present disclosure, the correlation between the presence or absence of one or more biomarkers to patient sample responsiveness is made by any appropriate means known in the art. In some embodiments, the correlation is made by multivariate analysis. In some embodiments, the correlation is made using a coefficient which measures the strength of the association between two variables (e.g. the Pearson’s correlation).

[0075] In some embodiments, aspects of the methods disclosed herein can be performed by a system (not shown) that includes and/or interfaces with the various components disclosed herein, such as, for example, a MALDI-TOF apparatus. In some embodiments, the system can include a computer device configured to execute aspects of the methods disclosed herein. In some embodiments, the compute device includes at least a processor configured to execute computer- readable instructions for implementing the methods disclosed herein, and/or a memory (e.g., anon-transitory processor-readable medium) storing the computer-readable instructions.

[0076] Some embodiments described herein relate to a computer storage product with a non- transitory computer-readable medium (also referred to as a non-transitory processor-readable medium) having instructions or computer code thereon for performing various computer- implemented operations. The computer-readable medium (or processor-readable medium) is non-transitory in the sense that it does not include transitory propagating signals (e.g., a propagating electromagnetic wave carrying information on a transmission medium such as space or a cable). The media and computer code (also referred to herein as code) may be those designed and constructed for the specific purpose or purposes. Examples of non-transitory computer-readable media include, but are not limited to: flash memory, magnetic storage media such as hard disks, optical storage media such as Compact Disc/Digital Video Discs (CD/DVDs), Compact Disc-Read Only Memories (CD-ROMs), magneto-optical storage media such as optical disks, carrier wave signal processing modules, and hardware devices that are specially configured to store and execute program code, such as Application-Specific Integrated Circuits (ASICs), Programmable Logic Devices (PLDs), Read-Only Memory (ROM) and Random-Access Memory (RAM) devices.

[0077] Examples of computer code include, but are not limited to, micro-code or micro- instructions, machine instructions, such as produced by a compiler, code used to produce a web service, and files containing higher-level instructions that are executed by a computer using an interpreter. For example, embodiments may be implemented using Java, C++, or other programming languages and/or other development tools. Therapeutics and Treatments [0078] In some aspects, the present disclosure provides methods of identifying biomarkers associated with patient sample response to any therapeutic or treatment. In some embodiments, the therapeutic or treatments include, but are not limited to, chemotherapy, cancer therapeutics, radiation, immunosuppressants, immunomodulators, monoclonal antibodies, NSAIDs, statins, ACE inhibitors, angiotensin II receptor blockers, antiarrhythmic, antiplatelet drugs, aspirin, beta- blocker therapy, calcium channel blocker drugs, clot buster drugs digoxin, warfarin, blood thinners, steroids, cholinesterase inhibitors, or mematine.

[0079] In some aspects, the present disclosure provides methods of identifying biomarkers associated with tumor or tumor sample response to cancer therapeutics or treatments. In the present methods, avatars (either in vivo or in vitro) are implanted with a tumor or tumor sample. After implantation, the avatar is exposed to one or more cancer therapeutics or treatments. Examples of such therapeutics or treatments include, but are not limited to, one or more of anti- cancer drugs, chemotherapy, surgery, adjuvant therapy, and neoadjuvant therapy.

[0080] The avatars may be screened for therapeutic or treatment response using any appropriate method known in the art. In some embodiments, responsiveness is scored by measuring avatar survival. In some embodiments, responsiveness is scored by measuring avatar survival after a given time point. In some embodiments, an avatar is scored as responsive if its condition does not worsen compared to other avatars tested. In some embodiments, an avatar is scored as responsive if it exhibits no, or minimal, tumor growth or tumor cell proliferation compared to other avatars tested. In some embodiments, responsiveness is measured as the lack of generation of tumor metastasis. In some embodiments, responsiveness is measured as a decrease in the number of cancer lesions generated. In some embodiments, responsiveness is measured as the number of cancer lesions generated remaining about the same. Tumor growth, tumor cell proliferation, or tumor viability can be measured using any appropriate means. In some embodiments an avatar is scored as non-responsive to a therapeutic or treatment tested if it does not meet the threshold criteria for responsiveness. In other embodiments, responsiveness is scored by measuring a decrease in tumor cell number or proliferation. In other embodiments, responsiveness is scored by measuring a decrease in tumor cell number or proliferation after a given time point. In some embodiments, an avatar is scored as responsive to a therapeutic or treatment tested if the tumor cell number, proliferation or viability decreases by about 10% to about 100% compared to tumor cell number, proliferation, or viability earlier in the study (e.g. time 0). In some embodiments, an avatar is scored as responsive to a therapeutic or treatment tested if the tumor cell number, proliferation, or viability decreases by about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, about 95%, about 98%, about 99%, or about 100% compared to tumor cell number, proliferation, or viability earlier in the study (e.g. time 0).

[0081] In some embodiments, when the avatar is three-dimensional avatar, responsiveness is measured by measuring the size of the spheroids. A decrease in the size of spheroid indicates the avatar is responsive to the therapeutic (e.g. there are fewer cancer cells). In some embodiments, an avatar is scored as responsive to a therapeutic or treatment tested if the size of the spheroids decreases by about 10% to about 100% compared to spheroid size earlier in the study (e.g. time 0). In some embodiments, an avatar is scored as responsive to a therapeutic or treatment tested if size of the spheroid decreases by about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, about 95%, about 98%, about 99%, or about 100% compared to the spheroid size earlier in the study (e.g. time 0). Spheroid size may be measured using any appropriate means in the art.

[0082] In some embodiments, when the avatar is three-dimensional avatar, responsiveness is measured by measuring the level of cell metabolism markers in the medium. A decrease in the cell’s metabolism indicates the avatar is responsive to the therapeutic (e.g. there are fewer cells). In some embodiments, an avatar is scored as responsive to a therapeutic or treatment tested if the metabolism decreases by about 10% to about 100% compared to levels of metabolism earlier in the study (e.g. time 0). In some embodiments, an avatar is scored as responsive to a therapeutic or treatment tested if metabolism decreases by about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, about 95%, about 98%, about 99%, or about 100% compared to levels of metabolism earlier in the study (e.g. time 0). Metabolism levels may be measured using any appropriate means in the art.

[0083] In some embodiments, non-responsiveness is scored by measuring avatar survival. In some embodiments, non-responsiveness is scored by measuring avatar survival after a given time point. In some embodiments, an avatar is scored as non-responsive if it exhibits tumor growth or tumor cell proliferation, compared to other avatars tested. In some embodiments, nonresponsiveness is measured as the generation of tumor metastasis. In some embodiments, nonresponsiveness is measured as an increase in the number of cancer lesions generated. Tumor growth or tumor cell proliferation can be measured using any appropriate means. In some embodiments an avatar is scored as responsive to a therapeutic or treatment tested if it does not meet the threshold criteria for non-responsiveness.

[0084] In other embodiments, non-responsiveness is scored by measuring an increase in tumor cell number or proliferation. In some embodiments, non-responsiveness is scored by measuring an increase in tumor cell number, proliferation, or viability after a given time point. In some embodiments, an avatar is scored as non-responsive to a therapeutic or treatment tested if the tumor cell number, proliferation, or viability increases by about 10% to about 100% or more compared to tumor cell number, proliferation, or viability earlier in the study (e.g. time 0). In some embodiments, an avatar is scored as non-responsive to a therapeutic or treatment tested if the tumor cell number, proliferation, or viability increases by about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, about 95%, about 98%, about 99%, or about 100% or more compared to tumor cell number, proliferation, or viability earlier in the study (e.g. time 0). In some embodiments, an avatar is scored as non-responsive if its condition worsens compared to other avatars tested.

[0085] In some embodiments, when the avatar is three-dimensional avatar, non-responsiveness is measured by measuring the size of the spheroids. An increase in the size of spheroid indicates the avatar is nonresponsive to the therapeutic (e.g. there are more cancer cells). In some embodiments, an avatar is scored as nonresponsive to a therapeutic or treatment tested if the size of the spheroids increases by about 10% to about 100% or more compared to spheroid size earlier in the study (e.g. time 0). In some embodiments, an avatar is scored as nonresponsive to a therapeutic or treatment tested if size of the spheroid increases by about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, about 95%, about 98%, about 99%, or about 100%, or more compared to the spheroid size earlier in the study (e.g. time 0). Spheroid size may be measured using any appropriate means in the art.

[0086] In some embodiments, when the avatar is three-dimensional avatar, nonresponsiveness is measured by measuring the level of cell metabolism markers in the medium. An increase in the cell’s metabolism indicates the avatar is nonresponsive to the therapeutic (e.g. there are more cells). In some embodiments, an avatar is scored as nonresponsive to a therapeutic or treatment tested if the metabolism increases by about 10% to about 100% compared to levels of metabolism earlier in the study (e.g. time 0). In some embodiments, an avatar is scored as nonresponsive to a therapeutic or treatment tested if metabolism increases by about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, about 95%, about 98%, about 99%, or about 100% compared to levels of metabolism earlier in the study (e.g. time 0). Metabolism levels may be measured using any appropriate means in the art.

[0087] Cancer therapeutics or treatments used in the methods disclosed herein include, but are not limited to, one or more of immune system modulators, Opdivo^® (nivoluman), Keytruda^® (pembrolizumab), ipilimumab, alkylating agents such as thiotepa and CYTOXAN cyclosphosphamide; kinesin-spindle protein stabilizing agent; proteasome inhibitor; modulator of cell cycle regulation (by way of non-limiting example, a cyclin dependent kinase inhibitor); a modulator of cellular epigenetic mechanistic (by way of non-limiting example, one or more of a histone deacetylase (HDAC) (e.g. one or more of vorinostat or entinostat), azacytidine, decitabine); a glucocorticoid; a steroid; a monoclonal antibody; an antibody-drug conjugate; a thalidomide derivative; an inhibitor of MCL1; alkyl sulfonates such as busulfan, improsulfan and piposulfan; aziridines such as benzodopa, carboquone, meturedopa, and uredopa; ethylenimines and methylamelamines including altretamine, triethylenemelamine, trietylenephosphoramide, triethiylenethiophosphoramide and trimethylolomelamine; acetogenins (e.g., bullatacin and bullatacinone); a camptothecin (including the synthetic analogue topotecan); bryostatin; cally statin; CC-1065 (including its adozelesin, carzelesin and bizelesin synthetic analogues); cryptophycins (e.g., cryptophycin 1 and cryptophycin 8); dolastatin; duocarmycin (including the synthetic analogues, KW-2189 and CB 1-TM1); eleutherobin; pancratistatin; a sarcodictyin; spongistatin; nitrogen mustards such as chlorambucil, chlornaphazine, cholophosphamide, estramustine, ifosfamide, mechlorethamine, mechlorethamine oxide hydrochloride, melphalan, novembichin, phenesterine, prednimustine, trofosfamide, uracil mustard; nitrosureas such as carmustine, chlorozotocin, fotemustine, lomustine, nimustine, and ranimnustine; antibiotics such as the enediyne antibiotics (e.g., calicheamicin, especially calicheamicin gammall and calicheamicin omegall (see, e.g., Agnew, Chem. Intl. Ed. Engl., 33: 183-186 (1994)); dynemicin, including dynemicin A; bisphosphonates, such as clodronate; an esperamicin; as well as neocarzinostatin chromophore and related chromoprotein enediyne antibiotic chromophores), aclacinomysins, actinomycin, authramycin, azaserine, bleomycins, cactinomycin, carabicin, caminomycin, carzinophilin, chromomycinis, dactinomycin, daunorubicin, detorubicin, 6-diazo- 5-oxo-L-norleucine, ADRIAMYCIN doxorubicin (including morpholino- doxorubicin, cyanomorpholino-doxorubicin, 2-pyrrolino-doxorubicin and deoxy doxorubicin), epirubicin, esorubicin, idarubicin, marcellomycin, mitomycins such as mitomycin C, mycophenolic acid, nogalamycin, olivomycins, peplomycin, potfiromycin, puromycin, quelamycin, rodorubicin, streptonigrin, streptozocin, tubercidin, ubenimex, zinostatin, zorubicin; an anthracycline or anthracenedione; anti-metabolites such as methotrexate and 5-fluorouracil (5-FU); folic acid analogues such as denopterin, methotrexate, Palifosfamide, pteropterin, trimetrexate; purine analogs such as fludarabine, 6-mercaptopurine, thiamiprine, thioguanine; pyrimidine analogs such as ancitabine, Anyara, azacitidine, 6-azauridine, carmofur, cytarabine, a cytarabine-based chemotherapy, dideoxyuridine, doxifluridine, enocitabine, floxuridine; androgens such as calusterone, dromostanolone propionate, epitiostanol, mepitiostane, testolactone; anti-adrenals such as minoglutethimide, mitotane, trilostane; folic acid replenisher such as frolinic acid; aceglatone; aldophosphamide glycoside; aminolevulinic acid; eniluracil; amsacrine; bestrabucil; bisantrene; edatraxate; demecolcine; diaziquone; elformithine; elliptinium acetate; an epothilone; etoglucid; gallium nitrate; hydroxyurea; lentinan; lonidainine; maytansinoids such as maytansine and ansamitocins; mitoguazone; mitoxantrone; mopidanmol; nitraerine; pentostatin; phenamet; pirarubicin; losoxantrone; podophyllinic acid; 2-ethylhydrazide; procarbazine; PSK polysaccharide complex (JHS Natural Products, Eugene, Oreg.); razoxane; rhizoxin; sizofuran; spirogermanium; tenuazonic acid; triaziquone; 2,2',2"-trichlorotriethylamine; trichothecenes (e.g., T-2 toxin, verracurin A, roridin A and anguidine); urethan; vindesine; dacarbazine; mannomustine; mitobronitol; mitolactol; pipobroman; gacytosine; arabinoside (“Ara-C”); cyclophosphamide; thiotepa; taxoids, e.g., TAXOL paclitaxel (Bristol-Myers Squibb Oncology, Princeton, N.J.), ABRAXANE Cremophor-free, albumin-engineered nanoparticle formulation of paclitaxel (American Pharmaceutical Partners, Schaumberg, 111.), and TAXOTERE doxetaxel (Rhone-Poulenc Rorer, Antony, France); chloranbucil; GEMZAR gemcitabine; 6-thioguanine; mercaptopurine; methotrexate; platinum analogs such as cisplatin, oxaliplatin and carboplatin; vinblastine; platinum; etoposide (VP-16); ifosfamide; mitoxantrone; vincristine; NAVELBINE. vinorelbine; novantrone; teniposide; edatrexate; daunomycin; aminopterin; xeloda; ibandronate; irinotecan (Camptosar, CPT-11) (including the treatment regimen of irinotecan with 5-FU and leucovorin); topoisomerase inhibitor e.g. RFS 2000; difluoromethylornithine (DMFO); retinoids such as retinoic acid; capecitabine; combretastatin; leucovorin (LV); oxaliplatin, including the oxaliplatin treatment regimen (FOLFOX); lapatinib (Tykerb); inhibitors of PKC-Į^^5DI^^+-Ras, EGFR (e.g., erlotinib (Tarceva)) and VEGF-A that reduce cell proliferation, dacogen, velcade, and pharmaceutically acceptable salts, acids or derivatives of any of the above. [0088] In some aspects, the present disclosure provides methods of identifying biomarkers associated with patient sample response to therapeutics or treatments.

[0089] The cell lines or animal models may be screened for therapeutic or treatment response using any appropriate method known in the art. In some embodiments, responsiveness is scored by measuring cell line or animal model survival. In some embodiments, responsiveness is scored by measuring cell line or animal model survival after a given time point. In some embodiments, a cell line or animal model is scored as responsive if its condition does not worsen compared to other cell lines or animal models tested. In some embodiments a cell line or animal model is scored as non-responsive to a therapeutic or treatment tested if it does not meet the threshold criteria for responsiveness.

[0090] In some embodiments, nonresponsiveness is scored by measuring cell line or animal model survival. In some embodiments, nonresponsiveness is scored by measuring cell line or animal model survival after a given time point. In some embodiments, a cell line or animal model is scored as nonresponsive if its condition worsens compared to other cell lines or animal models tested. In some embodiments a cell line or animal model is scored as responsive to a therapeutic or treatment tested if it does not meet the threshold criteria for responsiveness.

Biomarker Manipulation

[0091] In some aspects, the present disclosure provides methods of validating one or more putative biomarkers identified by comparing biological component patterns in two sets of patient samples having different responses to a therapeutic or treatment (e.g. responders/nonresponders). Validation may demonstrate the response of the avatar is due to the presence or absence of the one or more putative biomarkers in the patient sample. Phenotypic changes (e.g. tumor cells switching from responsive to non-responsive) after alteration of one or more biomarkers indicates these biomarkers may play a role, in any part, in the response to a given therapeutic or treatment. This validation lessens the likelihood that the presence or absence of the biomarker is a chance association not connected to the responder status of the patient sample. However, because of the complexity of disease and patient samples, a biomarker may be strongly associated with therapeutic response, but not in itself cause any significant change in the therapeutic response when manipulated (e.g. response is multi-allelic, and alteration of one marker does not significantly affect the phenotype). [0092] In some embodiments, after one or more putative biomarkers are identified, they are then altered in a cell which is subsequently tested for response to a therapeutic or treatment. In some embodiments, the altered patient sample cells are retested with the same therapeutic or treatment used to identify the one or more biomarkers. In some embodiments, the patient sample is a tumor or tumor sample. In some embodiments, the patient sample is a cell line. In some embodiments, the patient sample is an animal model.

[0093] In some embodiments, one or more biomarkers are altered to change expression in the tumor or tumor sample. In some embodiments, expression of one or more biomarkers is increased after alteration. In some embodiments, expression of one or more biomarkers is decreased after alteration. In some embodiments, expression of one or more biomarkers is silenced after alteration. In some embodiments, expression of one or more biomarkers is unchanged after alteration.

[0094] In some embodiments, one or more biomarkers are altered in the same patient sample previously exposed to the therapeutic or treatment. In some embodiments, one or more biomarkers are altered in a different patient sample than the one previously exposed to the therapeutic or treatment. In some embodiments, one or more biomarkers are altered in a patient sample derived from the first patient sample before said first patient sample is exposed to the therapeutic or treatment. In some embodiments, one or more biomarkers are altered in a clone of the first patient sample before said first patient sample is exposed to the therapeutic or treatment. In some embodiments, one or more biomarkers are altered in an avatar. In some embodiments, one or more biomarkers are altered in an in vivo avatar. In some embodiments, one or more biomarkers are altered in an in vitro avatar. In some embodiments, one or more biomarkers are altered in tumor cells taken from an avatar. In some embodiments, one or more biomarkers are altered in tumor cells not implanted in an avatar. In some embodiments, one or more biomarkers are altered in cells taken from an avatar to create a library of differentially randomly altered genes in cells. In some embodiments, one or more biomarkers are altered in cells taken from a treated avatar to create a library of differentially randomly altered genes in cells. In some embodiments, the one or more biomarkers are silenced.

[0095] In some embodiments, one or more biomarkers are altered in a cell line. In some embodiments, the cell line is an established model for human disease. In some embodiments, one or more biomarkers are altered in a cell line to create a library of differentially randomly altered genes in cells. In some embodiments, one or more biomarkers are altered in an animal model. In some embodiments, one or more biomarkers are altered in an animal model of disease. In some embodiments, the one or more biomarkers are silenced

[0096] In some embodiments, the patient samples are subjected to random or semi-random genomic alteration before being exposed to a therapeutic or treatment. The genome alteration may occur in vivo (e.g. after implantation of the patient sample in an animal avatar, or in an animal model), or in vitro (e.g. before implantation of the patient sample in an avatar, or in a cell line). In some embodiments, the altered cells are not implanted in an avatar after alteration. Therapeutics or treatments are administered to the altered cells, and biomarkers are identified as disclosed herein.

[0097] Any method of altering gene expression or creating genomic modifications may be used in the present methods. In some embodiments, one or more biomarkers are altered using RNAi. In some embodiments, one or more biomarkers are altered to modify an epigenetic signature. In some embodiments, one or more biomarkers are altered using genome modifying methods. Any construct that modifies genomic DNA can be used in the methods disclosed herein. In some embodiments, the DNA-modifying construct is targeted to modify one or more particular biomarkers. In general, the DNA-modifying construct includes a DNA-binding sequence (e.g. polynucleotide or amino acid sequence) associated with or conjugated to a cleavage domain (e.g. a nuclease or an effector domain of a nuclease) that creates double-strand breaks in genomic DNA. In some embodiments, the genome-modifying construct is a CRISPR, TALEN, or zinc finger nuclease.

[0098] In one aspect, the present disclosure provides methods for introducing genomic modifications using a CRISPR complex. The CRISPR complex is a commonly used RNA- guided nuclease that includes a guide RNA (gRNA). The CRISPR complex is recruited to a genomic DNA sequence by base-pairing between the gRNA sequence and its complement in the genomic DNA. In some embodiments, the CRISPR complex is a gRNA/cas9 CRISPR.

[0099] In some embodiments, the present disclosure provides methods for introducing genomic modifications using a TALEN, a targeted construct containing a nuclease fused to a TAL effector DNA binding domain. TALENS are described in greater detail in US Patent Application No. 2011/0145940. In some embodiments, the TALEN is a fusion polypeptide of the Fok I nuclease and a TAL effector DNA binding domain.

[00100] In some embodiments, the present disclosure provides methods for introducing genomic modifications using a zinc finger nuclease. Zinc finger nucleases (ZFNs) are targeted constructs that include a nuclease fused to a zinc finger DNA binding domain which binds DNA in a sequence-specific manner through one or more zinc fingers. ZFNs are described in greater detail in U.S. Pat. No.7,888,121 and U.S. Pat. No.7,972,854. In some embodiments, the ZFN is a fusion of the Fok I nuclease and a zinc finger DNA binding domain. Synthetic Trials

[00101] To identify biomarkers that effectively predict a patient’s likelihood of responding to a given treatment, it is necessary to show that the presence or absence of the putative biomarkers impacts clinical outcome. This often requires analysis of a series of randomized clinical trials, showing that the biomarker differences between responder groups is concordant with the differences in clinical outcome. The present disclosure provides methods of performing synthetic trials on previously collected patient samples. Since the biomarker is identified and validated using a first set of patient samples that are independent of a second set of patient samples collected for a clinical trial, these methods allow for confirmation of the biomarker’s predictive power.

[00102] In some embodiments, the predictive biomarker is detected in the patient samples obtained in and collected for a clinical trial. Using the data from the clinical trial, the patient samples are classified as responsive or nonresponsive based upon established clinical trial data (e.g. patient outcome). The patient samples are sorted based upon this classification. The presence or absence of the predictive biomarker in the patient samples is correlated using standard means in the art to demonstrate the association of the predictive biomarker with patient outcome. If there is a strong correlation between patient outcome and the presence or absence of the predictive biomarker, this biomarker will be useful to identify other patient populations that are likely to respond to the therapeutic or treatment tested. In some embodiments, the predictive biomarker is used to identify other patient populations that are likely to respond to therapeutics or treatments similar to the therapeutic or treatment tested. Exemplary Clinical Decisions

[00103] In some aspects, the present disclosure provides methods of predicting whether a patient will respond to a therapeutic or treatment. Response to a therapeutic or treatment is a prediction of a patient’s medical outcome when receiving this therapeutic or treatment. Responses to a therapeutic or treatment can be, by way of non-limiting examples, pathological complete response, survival, and progression free survival, time to progression, probability of recurrence.

[00104] In some embodiments, a comparison of the data generated in the identification of one or more biomarkers performed at various time points during treatment shows a change in biomarker presence or expression indicating a change in the disease’s sensitivity to a particular treatment. In other embodiments, the determination of a disease’s change in sensitivity to a particular treatment is used to re-classify the patient and to guide the course of future treatment.

[00105] In various embodiments, a therapeutic or treatment is administered or withheld based on the methods described herein. In some embodiments, the therapeutic or treatment is a cancer therapeutic or treatment. Exemplary therapeutics or treatments include surgical resection, radiation therapy (including the use of the compounds as described herein as, or in combination with, radiosensitizing agents), chemotherapy, pharmacodynamic therapy, targeted therapy, immunotherapy, and supportive therapy (e.g., painkillers, diuretics, antidiuretics, antivirals, antibiotics, nutritional supplements, anemia therapeutics, blood clotting therapeutics, bone therapeutics, and psychiatric and psychological therapeutics). In other embodiments, the present methods are useful in predicting a cancer patient’s response to any of the therapeutics or treatments described herein. In some embodiments, the present disclosure predicts a cancer patient’s likelihood of response to chemotherapy.

[00106] In various embodiments, the present methods direct a clinical decision regarding whether a patient is to receive a specific treatment. In some embodiments, the present methods are predictive of a positive response to neoadjuvant and/or adjuvant chemotherapy or a non- responsiveness to neoadjuvant and/or adjuvant chemotherapy. In various embodiments, the present disclosure directs the treatment of a cancer patient, including, for example, what type of treatment should be administered or withheld.

[00107] As used herein, the term“neoadjuvant therapy” refers to treatment given as a first step to shrink a tumor before the main treatment, which is usually surgery, is given. Examples of neoadjuvant therapy include chemotherapy, radiation therapy, and hormone therapy. In some embodiments, the present methods direct a patient’s treatment to include neoadjuvant therapy. For example, a patient that is scored to be responsive to a specific treatment may receive such treatment as neoadjuvant therapy. In some embodiments, neoadjuvant therapy means chemotherapy administered to cancer patients prior to surgery. In some embodiments, neoadjuvant therapy means a therapeutic or treatment, including those described herein, administered to cancer patients prior to surgery. Further, the present methods may direct the identity of a neoadjuvant therapy. In some embodiments, the present methods may indicate that a patient will be less responsive to a specific treatment, and therefore such a patient may not receive such treatment as neoadjuvant therapy. Accordingly, in some embodiments, the present methods provide for providing or withholding neoadjuvant therapy according to a patient’s likely response. In this way, a patient’s quality of life, and the cost of care, may be improved.

[00108] As used herein, the term“adjuvant therapy” refers to additional cancer treatment given after the primary treatment to lower the risk that the cancer will come back. Adjuvant therapy may include chemotherapy, radiation therapy, hormone therapy, targeted therapy, or biological therapy. In some embodiments, the present methods direct a patient’s treatment to include adjuvant therapy. For example, a patient that is scored to be responsive to a specific treatment may receive such treatment as adjuvant therapy. Further, the present methods may direct the identity of an adjuvant therapy. In some embodiments, the present methods may indicate that a patient will be less responsive to a specific treatment, and therefore such a patient may not receive such treatment as adjuvant therapy. Accordingly, in some embodiments, the present methods provide for providing or withholding adjuvant therapy according to a patient’s likely response. In this way, a patient’s quality of life, and the cost of care, may be improved.

Methods of Predicting Patient Response to Therapeutic

[00109] In some aspects, the present disclosure provides methods of predicting a therapeutic response in a patient by detecting the presence or absence of the predictive biomarkers identified using the methods disclosed herein. In some embodiments, the presence of one or more biomarkers in the patient’s sample predicts the patient’s response to the therapeutic or treatment. In some embodiments, the absence of one or more biomarkers in the patient’s sample predicts the patient’s response to the therapeutic or treatment. In some embodiments, the patient is a cancer patient.

[00110] In some embodiments, the present disclosure provides methods of methods of predicting a therapeutic response in a patient to a cancer therapy comprising detecting the presence or absence of one or more tumor biomarkers identified by the process of: (a) implanting a tumor or tumor sample from the patient in an avatar; (b) analyzing a biological component of the tumor or tumor sample; (c) exposing the avatar to a therapeutic or treatment; (d) determining the treatment response of the avatar; (e) associating the presence or absence of one or more differentially expressed biological components with the treatment response to identify one or more biomarkers; and (f) detecting the biomarker in a second patient sample set from patients previously exposed to the therapeutic or treatment, and where there is known clinical therapeutic or treatment response data, wherein association of the biomarker with patient treatment response in the second patient sample and no association in a control group validates the biomarker is predictive of patient treatment response.

[00111] In some embodiments, the present disclosure provides methods of methods of predicting a therapeutic response in a patient to a cancer therapy comprising detecting the presence or absence of one or more tumor biomarkers identified by the process of: (a) collecting some of a cell line or a sample from an animal model sample in order to later analyze a biological component of the sample before it was exposed to the therapeutic agent; (b) exposing the cell line or animal model to a therapeutic or treatment; (c) determining the treatment response of the cell line or animal model; (d) associating the presence or absence of one or more differentially expressed biological components of the sample saved from step (a) with the treatment response to identify one or more biomarkers; and (e) detecting the biomarker in a second patient sample (validating samples) in which the samples were collected after the patient was exposed to the therapy and to which patient response is known, wherein association of the biomarker with patient treatment response in the validating sample indicates the biomarker has a high probability of predicting patient response.

[00112] Where methods and/or schematics described above indicate certain events and/or flow patterns occurring in certain order, the ordering of certain events and/or flow patterns may be modified. Additionally certain events may be performed concurrently in parallel processes when possible, as well as performed sequentially.

[00113] It should be understood that singular forms such as“a,”“an,” and“the” are used throughout this application for convenience, however, except where context or an explicit statement indicates otherwise, the singular forms are intended to include the plural. All numerical ranges should be understood to include each and every numerical point within the numerical range, and should be interpreted as reciting each and every numerical point individually. The endpoints of all ranges directed to the same component or property are inclusive, and intended to be independently combinable.

[00114] The term“about” when used in connection with a referenced numeric indication means the referenced numeric indication plus or minus up to 10% of that referenced numeric indication. For example, the language“about 50” covers the range of 45 to 55.

[00115] As used herein, the word“include,” and its variants, is intended to be non-limiting, such that recitation of items in a list is not to the exclusion of other like items that may also be useful in the materials, compositions, devices, and methods of this technology. Similarly, the terms“can” and“may” and their variants are intended to be non-limiting, such that recitation that an embodiment can or may comprise certain elements or features does not exclude other embodiments of the present technology that do not contain those elements or features. Although the open-ended term“comprising,” as a synonym of terms such as including, containing, or having, is used herein to describe and claim the disclosure, the present technology, or embodiments thereof, may alternatively be described using more limiting terms such as “consisting of” or“consisting essentially of” the recited ingredients.

INCORPORATION BY REFERENCE

[00116] All publications, patents, and patent publications cited are incorporated by reference herein in their entirety for all purposes. EXAMPLES

Example 1– Biomarker Discovery in vitro

[00117] The methods described herein can be used to discover both genes and expressed proteins that determine the patient response to the drug. Briefly, a bank of cancer cell lines or primary cancer cells from patients is assayed for susceptibility to the drug being tested. The cancer cells are assayed to find at least one cell line or primary cancer cell from one patient that responds to the drug (e.g. at a particular concentration) and at least one cell line or primary cancer cell from one patient that does not respond to the drug (e.g. at the same concentration). The proteins expressed in each cell line are analyzed to identify any expression pattern differences between“responders” and“nonresponders”. Using standard molecular biology techniques (e.g. RNA interference silencing or CRISPR/Cas9 constructs) whether the drug response is due to the differentially expressed protein or proteins is demonstrated. This biomarker’s predictive ability is validated by detecting the biomarker in patients’ samples (e.g. from a clinical trial) where the patient was actually exposed to the drug as opposed to the avatars in which it wasn’t and to which there is clinical data available on that patient and correlating the presence of the differentially expressed protein or proteins with patient survival or other therapeutic outcomes after receiving the drug. Biomarkers that are differentially expressed in cells that respond to the treatment, and correlate with a positive outcome in patient samples are predictive of patient response to the drug tested. Example 2– Biomarker Discovery in Animal Models

[00118] Animal models are implanted with different tumors (e.g. A and B; Fig.8) and are experimentally interrogated with a drug to determine their response. The animals are classified as responders or nonresponders based on their reaction to the drug. As in Example 1, the genes or proteins can be assayed to determine which biomarker plays a role in drug response. For example, the proteins from responder mice and nonresponder mice are assayed to identify any that are differentially expressed (Fig. 9), and the presence of absence of these proteins is correlated with the animal’s classification. Patient samples are assayed for the presence of absence of the biomarkers identified in the animal model screen that correlate with drug response, and a correlation is made between the expression/absence of a particular biomarker and a patient’s clinical outcome (e.g. survival or other therapeutic endpoint) (Fig. 10). Biomarkers that are differentially expressed in animals that respond to treatment, and correlate with an outcome in patient samples are predictive of patient response to the drug tested (Figs.11 and 12). Example 3– Avataristic Discovery of Biomarkers

[00119] This method uses an integrative approach to the discovery and validation of biomarkers using new clinical samples and independent clinical data sets. In cancer, since only a percentage of patients will typically respond to any drug, it is preferable to predict those patients who will respond before treatment starts.

[00120] Standard biomarker discovery mechanisms have several problems, including chance associations that cannot be formally ruled out without expensive and time consuming clinical trials, a high likelihood of data overfitting, and the interaction of host biology and the tumor. On the other hand avataristic data is not sufficient without corresponding human clinical data in order to rule out biomarkers that are due to artefacts of the model system. The present methods solve these problems.

[00121] Here, new tumors or tumor samples that are completely unrelated to any existing sample set (e.g. clinical trial samples) are collected and obtained. The tumors are implanted in an avatar such as an appropriate animal model, or an appropriate three-dimensional in vitro model system. The biological components of the tumor are analyzed, and the avatars are exposed to one or more therapeutics or treatments. After treatment, the avatars are scored for their responsiveness to the cancer treatment (e.g. responsive/nonresponsive). The biological components of the two groups of tumors are compared, with any differences being potential biomarkers for tumor response. The presence or absence of particular biomarkers is correlated with the avatar’s treatment response. The presence of any biomarkers found to be associated with treatment response is validated by gene silencing. The avatars are exposed to a gene silencing construct (e.g. CRISPR/Cas9) to silence the particular biomarker, and the avatars are exposed to the therapeutic or treatment again. A phenotypic conversion from responder to non- responder after gene silencing of the putative biomarker indicates the biomarker causes therapeutic response.

[00122] Once biomarkers associated with responsiveness or non-responsiveness to treatment are identified and validated using the avatar method, other cancer sample sets (e.g. original sample sets from a clinical trial) are analyzed for the presence or absence of these biomarkers. The clinical data (e.g. patient survival) is compared between patients who are biomarker positive and those who are biomarker negative in the clinical trial sample set to confirm the predictive power of the biomarker. Since the biomarker is discovered in new sample sets that are completely independent from the clinical trial sample set, the analysis of the trial data is prospective data for that biomarker. Demonstrating the predictive efficacy of the biomarker in this prospective analysis circumvents the need for a confirmatory clinical trial to show this biomarker accurately predicts patient response to a therapeutic or treatment.

Claims

CLAIMS 1. A method of identifying one or more biomarkers that predict a patient’s treatment response comprising: (a) implanting a first patient sample set in an avatar; (b) analyzing a biological component of the patient sample set; (c) exposing the avatar to a therapeutic or treatment; (d) determining the treatment response of the avatar; (e) associating the presence or absence of one or more differentially expressed biological components with the treatment response to identify one or more biomarkers; and (f) detecting the biomarker in a second patient sample set from patients previously exposed to the therapeutic or treatment, and where there is known clinical therapeutic or treatment response data, wherein association of the biomarker with patient treatment response in the second patient sample and no association in a control group validates the biomarker is predictive of patient treatment response.

2. A method of identifying one or more tumor biomarkers that predict a patient’s treatment response comprising: (a) silencing a gene or genes at random or from a plurality of potential genes in tumor cells; (b) screening each transformed cell individually with a therapeutic or treatment for a change in response to drug compared to unmodified tumor cells either in vivo or in vitro; (c) determining the silenced gene or genes from cells that show a change in therapeutic response; and (d) detecting the biomarker in a patient sample set from patients previously exposed to the therapeutic or treatment, and where there is known clinical therapeutic or treatment response data, wherein association of the biomarker with patient treatment response in the patient sample indicates the biomarker is predictive of patient treatment response.

3. A method of identifying one or more tumor biomarkers that predict a patient’s treatment response comprising: (a) implanting a tumor or tumor sample from the patient in an avatar; (b) analyzing a biological component of the tumor or tumor sample; (c) exposing the avatar to a therapeutic or treatment; (d) determining the treatment response of the avatar; (e) associating the presence or absence of one or more differentially expressed biological components with the treatment response to identify one or more biomarkers; and (f) detecting the biomarker in a second patient sample set from patients previously exposed to the therapeutic or treatment, and where there is known clinical therapeutic or treatment response data, wherein association of the biomarker with patient treatment response in the second patient sample and no association in a control group validates the biomarker is predictive of patient treatment response.

4. A method of predicting a patient’s response to a cancer therapy comprising identifying one or more tumor biomarkers that predict a patient’s treatment response comprising: (a) implanting a tumor or tumor sample from the patient in an avatar; (b) analyzing a biological component of the tumor or tumor sample; (c) exposing the avatar to a therapeutic or treatment; (d) determining the treatment response of the avatar; (e) associating the presence or absence of one or more differentially expressed biological components with the treatment response to identify one or more biomarkers; and (f) detecting the biomarker in a second patient sample set from patients previously exposed to the therapeutic or treatment, and where there is known clinical therapeutic or treatment response data, wherein association of the biomarker with patient treatment response in the second patient sample and no association in a control group validates the biomarker is predictive of patient treatment response.

5. A method of identifying one or more biomarkers that predict a patient’s treatment response comprising: (a) analyzing a biological component of a cell line or animal model; (b) exposing the cell line or animal model to a therapeutic or treatment; (c) determining the treatment response of the cell line or animal model; (d) associating the presence or absence of one or more differentially expressed biological components with the treatment response to identify one or more biomarkers; and (e) detecting the biomarker in a second patient sample (validating samples) in which the samples were collected after the patient was exposed to the therapy and to which patient response is known, wherein association of the biomarker with patient treatment response in the validating sample indicates the biomarker has a high probability of predicting patient response.

6. A method of predicting therapeutic response in a patient to a therapeutic comprising detecting the presence or absence of one or more tumor biomarkers identified by the process of any one of claims 1-5.

7. The method of claim 1, wherein the patient sample is a tumor or tumor sample.

8. The method of any of claims 1 and 3-6, further comprising altering the biomarker gene in the sample after step (e), and then testing the avatar or modified cells directly for treatment response and selecting those biomarkers where gene alteration causes change in response.

9. The method of any of claims 1-6, wherein the treatment response is either responsive or non- responsive.

10. The method of any of claims 1 and 3-6, wherein the first sample is obtained from a cancer patient, and the second sample is obtained from a different cancer patient.

11. The method of any of claims 1 and 3-6, wherein the first tumor sample is a tumor cell line.

12. The method of any of claims 1-6, wherein detection of the presence or absence of the biomarker in a patient sample guides patient treatment.

13. The method of any of claims 1 and 3-6, wherein the avatar is an animal model.

14. The method of claim 13, wherein the animal model is a murine model.

15. The method of any of claims 1 and 3-6, wherein the avatar is an in vitro model.

16. The method of claim 15, wherein the in vitro model is a 3D tumor.

17. The method of any of claims 1-6, wherein the biological component analyzed is selected from the group consisting of, peptides, proteins, DNA, RNA, epigenetic signatures, circulating stem cells, micro RNA, and immune regulators.

18. The method of claim 17, wherein the biological component analyzed is proteins.

19. The method of claim 8, wherein the biomarker is altered by gene silencing.

20. The method of claim 19, wherein the biomarker is silenced using CRISPR-Cas9 gene silencing.