Screening method to identify mechanisms of cancer resistance and synthetic lethality in resistant cancer cells

Abstract

Embodiments disclosed herein use forward genetics tools (e.g., ORF libraries, perturbation libraries) to study fitness advantages under immune pressure, including resistance mechanisms. Once the resistance mechanisms are identified, vulnerabilities (i.e., dependencies) in resistant cells can be identified (e.g., synthetic lethality screens in resistant cancer cells). For example, drug or CRISPR screening can be performed in cells with an identified resistant state. Embodiments disclosed herein also provide targets for resistance to IFN-y treatment.

Description

SCREENING METHOD TO IDENTIFY MECHANISMS OF CANCER RESISTANCE AND SYNTHETIC LETHALITY IN RESISTANT CANCER CELLSCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Application No. 63 / 650,207 filed May 21, 2024. The content of the above-identified application is hereby incorporated herein by reference in its entirety.TECHNICAL FIELD

[0002] The subject matter disclosed herein is generally directed to screening methods to identify resistance mechanisms in cancer cells to cell growth pressures and dependencies in resistant cells.BACKGROUND

[0003] There is an unmet need for cancer immunotherapy. Immunotherapy has limited efficacy in certain types of cancer, such as pancreatic cancer. Patient selection is crucial to finding potential responders to immunotherapies. New and effective combinational therapies must be determined to enhance the response rate. Resistance mechanisms need to be clarified. Mutations or tumor mutation burden (TMB) cannot fully explain immunotherapy resistance. Thus, there is a need for new methods that can better interrogate and reveal novel resistance mechanisms that can be therapeutically targeted.

[0004] Citation or identification of any document in this application is not an admission that such a document is available as prior art to the present invention.SUMMARY

[0005] In an embodiment, the techniques described herein relate to a method of identifying genetic elements driving tumor fitness under cell growth pressure, including (a) introducing a forward genetics library to a cancer cell line or cancer organoids, wherein each member of the forward genetics library introduces or modifies a genetic element; (b) applying one or more cellgrowth pressures to the cancer cell line or cancer organoids; and (c) identifying members of the forward genetics library in surviving cells or organoids after the one or more cell growth pressures, thereby identifying one or more mechanisms of resistance driving tumor fitness.

[0006] In an embodiment, the techniques described herein relate to a method in which the forward genetics library is an open reading frame (ORF) library.

[0007] In an embodiment, the techniques described herein relate to a method wherein the ORF library is a transcription factor (TF) ORF library.

[0008] In an embodiment, the techniques described herein relate to a method in which the TF ORF expression library includes about 3500 TF isoform ORFs.

[0009] In an embodiment, the techniques described herein relate to a method wherein the forward genetics library is a genetic perturbation library.

[0010] In an embodiment, the techniques described herein relate to a method wherein the genetic perturbation library includes CRISPR guide sequences and a CRISPR enzyme.

[0011] In an embodiment, the techniques described herein relate to a method wherein the genetic perturbation library includes RNAi.

[0012] In an embodiment, the techniques described herein relate to a method wherein the genetic perturbation library includes genetic suppressor elements (GSEs).

[0013] In an embodiment, the techniques described herein relate to a method wherein the forward genetics library is a Validation-Based Insertional Mutagenesis (VBIM) library.

[0014] In an embodiment, the techniques described herein relate to a method wherein the cell growth pressure is an immune pressure.

[0015] In an embodiment, the techniques described herein relate to a method in which the immune pressure includes one or more inflammatory cytokines.

[0016] In an embodiment, the techniques described herein relate to a method in which one or more inflammatory cytokines include interferon-gamma (IFN-y), tumor necrosis factor alpha (TNF-a), and / or interleukin-1 beta (IL-ip).

[0017] In an embodiment, the techniques described herein relate to a method wherein the immune pressure includes one or more immune cells.

[0018] In an embodiment, the techniques described herein relate to a method wherein the one or more immune cells include T cells, macrophages, NK cells, fibroblasts, or a combination thereof.

[0019] In an embodiment, the techniques described herein relate to a method wherein the one or more immune cells include dendritic cells, T cells, macrophages, NK cells, fibroblasts, or a combination thereof.

[0020] In an embodiment, the techniques described herein relate to a method in which the cell growth pressure includes targeted therapy, general cytotoxic therapy, hypoxia, or low nutrients.

[0021] In an embodiment, the techniques described herein relate to a method wherein the cell growth pressure includes an immune pressure in combination with another growth pressure.

[0022] In an embodiment, the techniques described herein relate to a method in which the cancer cell line is a human cancer cell line.

[0023] In an embodiment, the techniques described herein relate to a method wherein the cancer cell line or cancer organoids are derived from cancer selected from the group consisting of pancreatic cancer, skin cancer, bladder cancer, lung cancer, breast cancer, prostate cancer, brain cancer, bone cancer, blood cancer, kidney cancer, liver cancer, stomach cancer, colon cancer, head and neck cancer, ovarian cancer, cervical cancer, uterine cancer testicular cancer, rectal cancer, and thyroid cancer.

[0024] In an embodiment, the techniques described herein relate to a method performed in more than one cancer cell line or cancer organoids derived from different cancers, whereby consensus genetic elements driving tumor fitness across different cancers are identified.

[0025] In an embodiment, the techniques described herein relate to a method wherein the members of the forward genetics library in surviving cells are identified by sequencing.

[0026] In one aspect, the techniques described herein relate to a single-cell sequencing method, which identifies cell states driving tumor fitness.

[0027] In an embodiment, the techniques described herein relate to a method wherein the single-cell sequencing method is selected from the group consisting of scRNA-seq, CITE-seq, and scATAC-seq.

[0028] In an embodiment, the techniques described herein relate to a method wherein genetic elements are further selected from the identified genetic elements and enriched in one or more cancer patients resistant to a treatment, such as immunotherapy.

[0029] In an embodiment, the techniques described herein relate to a method in which the genetic elements are enriched in patients with resistant tumors from more than one cancer type.

[0030] In an embodiment, the techniques described herein relate to a method, further including validating the genetic elements identified by perturbing or overexpressing individual genetic elements in one or more cancer cell lines, in vitro cell models, ex vivo organoid models, or in vivo tumor models; and detecting resistance or a resistance gene signature.

[0031] In an embodiment, the techniques described herein relate to a method in which a singlecell sequencing method is performed on one or more cancer cell lines, in vitro cell models, ex vivo organoid models, or in vivo tumor models, thereby identifying cell states that drive tumor fitness for individual genetic elements.

[0032] In an embodiment, the techniques described herein relate to a method wherein the single-cell sequencing method is selected from the scRNA-seq, CITE-seq, and scATAC-seq.

[0033] In an embodiment, the techniques described herein relate to a method of identifying dependencies or synthetic lethalities in cancer cell lines or cancer organoids resistant to a cell growth pressure, wherein the method includes (a) perturbing or overexpressing individual genetic elements in a cancer cell line or cancer organoids to generate a resistant cancer cell line or cancer organoids, wherein the genetic element and cancer cell line or cancer organoid are identified according to any embodiment herein; (b) contacting the cancer cell line or cancer organoids with one or more agents capable of targeting one or more target genes in the cells of the resistant cancer cell line or cancer organoids; and (c) identifying one or more agents or target genes that reduce survival in the cancer cell line or cancer organoids resistant to a cell growth pressure.

[0034] In an embodiment, the techniques described herein relate to a method in which one or more agents are selected from the group consisting of drug candidates, small molecules, biologies, and programmable nucleases targeting one or more target genes.

[0035] In an embodiment, the techniques described herein relate to a method wherein the cell growth pressure is immune.

[0036] In an embodiment, the techniques described herein relate to a method in which the immune pressure includes one or more inflammatory cytokines.

[0037] In an embodiment, the techniques described herein relate to a method in which one or more inflammatory cytokines include interferon-gamma (IFN-y), tumor necrosis factor alpha (TNF-a), and / or interleukin- 1 beta (IL-ip).

[0038] In an embodiment, the techniques described herein relate to a method wherein the immune pressure includes one or more immune cells.

[0039] In an embodiment, the techniques described herein relate to a method wherein one or more immune cells include T cells, macrophages, NK cells, and / or fibroblasts.

[0040] In an embodiment, the techniques described herein relate to a method wherein the cell growth pressure includes targeted therapy, general cytotoxic therapy, hypoxia, or low nutrients.

[0041] In an embodiment, the techniques described herein relate to a method wherein the cell growth pressure includes an immune pressure in combination with another growth pressure.

[0042] In an embodiment, the techniques described herein relate to a method, wherein the cell growth pressure is an immune pressure including interferon-gamma (IFN-y) and the individual genetic element is an overexpressed ORF selected from the group of TFs consisting of: TP63, TP73, PDX1, and FOXPl; or TP73, TP63, FOXP1, PDX1, JUN, HAND2, POU3F1, IRF2, GLI1, TCF21, FOXN4, EOMES, JUNB, HANOI, IKZF3, TBX6, TBX4, GFI1B, CREM, POU2F1, PPARG, MSC, NKX3, ZNF415, FOXP4, ZNF396, IRF2, HNF1B, FIGLA, NFKBIZ, POU5F1, PBX1, IKZF1, SUPT4H1, ASCL1, PBX3, ZNF7, MEIS2, UBP1, BCL6, FOXF2, NR1I3, CREBL2, GRHL1, RUNX2, FLU, GLIS1, POU2F2, ERG, IRF6, HSF5, MEF2C, EGR2, ALX3, TBX5, TBPL1, TBXT, HOXB2, BHLHE40, ZNF771, FOXD4L5, ZNF256, SMAD2, HESX1, POU4F3, ZNF680, and RELB, whereby dependencies or synthetic lethalities in cells that overexpress the TF and are resistant to IFN-y are identified.

[0043] In an embodiment, the techniques described herein relate to a method of identifying subjects resistant to immunotherapy including detecting in malignant cells obtained from the subject overexpression of one or more transcription factors (TF) and / or gene signatures that result from the TFs being overexpressed, wherein the one or more TFs is selected from the group consisting of: TP63, TP73, PDX1, and FOXP1; or TP73, TP63, FOXP1, PDX1, JUN, HAND2, POU3F1, IRF2, GLI1, TCF21, FOXN4, EOMES, JUNB, HANOI, IKZF3, TBX6, TBX4, GFI1B,CREM, P0U2F1, PPARG, MSC, NKX3, ZNF415, F0XP4, ZNF396, IRF2, HNF1B, FIGLA, NFKBIZ, P0U5F1, PBX1, IKZF1, SUPT4H1, ASCL1, PBX3, ZNF7, MEIS2, UBP1, BCL6, F0XF2, NR1I3, CREBL2, GRHL1, RUNX2, FLU, GLIS1, POU2F2, ERG, IRF6, HSF5, MEF2C, EGR2, ALX3, TBX5, TBPL1, TBXT, H0XB2, BHLHE40, ZNF771, FOXD4L5, ZNF256, SMAD2, HESX1, POU4F3, ZNF680, and RELB.

[0044] In an embodiment, the techniques described herein relate to a method in which the immunotherapy includes interferon-gamma (IFN-y).

[0045] In an embodiment, the techniques described herein relate to a method, including treating a subject resistant to IFN-y with a treatment that does not include IFN-y.

[0046] In an embodiment, the techniques described herein relate to a method, further including treating a subject overexpressing a TF and / or gene signature that results from the TF being overexpressed with one or more inhibitors of the TF or one or more agents capable of reducing the gene signature that results from the TF being overexpressed.

[0047] In an embodiment, the techniques described herein relate to a method, wherein the one or more inhibitors of the TF includes: (a) one or more small molecules; (b) one or more antibodies, antibody fragments, or antibody-like protein scaffolds; (c) one or more PROTACs including a small molecule binder of the TF; (d) a bi-functional molecule including a post-translation modification enzyme linked to a small molecule binder of the TF, wherein the post-translation modification enzyme makes one or more post-translation modifications to the TF that inhibits or reduces TF activity; (e) one or more recombinant gene therapy vectors for reducing expression of the TF; (f) one or more RNAi agents for decreasing expression of the TF; (g) one or more antisense RNA agents for decreasing expression of the TF; (h) a gene editing system that modifies expression of the TF via introduction of one or more modifications including insertions, deletions or replacements that result in reduced expression or activity of the TF; (i) a gene editing system that introduces one or more modifications to a regulatory element controlling expression of the TF such that expression of the TF is reduced; (j) a gene editing system that modifies expression of the TF by binding a transcriptional repressor to an enhancer region that controls expression of the TF such that that expression of the genes is reduced; (k) a gene editing system capable of editing one or more CTCF motifs in a cell genome such that one or more chromosomal loops including the TFare removed; or (1) a gene editing system capable of making one or more epigenetic modifications of a cell genome such that expression of the TF is reduced.

[0048] In an embodiment, the techniques described herein relate to a method, wherein the modifications introduced by the gene editing system of h) are: (i) introduction of one or more missense mutations; (ii) introduction of a pre-mature stop codon; (iii) removal of one or more splice sites resulting in a non-functional gene product; (iv) introduction of one or more splice-sites resulting in a non-functional gene product; (v) introduction of one or more post-translational modification sites that results in one or more post-translational modifications that result in a gene product with reduced activity; (vi) removal of one or more post-translational modification sites that results in a gene product with reduced activity; (vii) deletion of a portion of a coding sequence resulting in a non-functional gene or gene product; (viii) introduction of one or more nonfunctional sequences into an otherwise functional gene that results in a non-functional gene product; (ix) replacement of a portion of a functional sequence of a gene with a non-functional sequence resulting in a non-functional gene or gene produce; (x) introduction of one or more RNAi target sites into a gene or gene product; (xi) introduction of a degron or degradation signal; (xii) deletion of one or more copies of a gene; (xiii) or a combination thereof.

[0049] In an embodiment, the techniques described herein relate to a method wherein the gene editing system used for (i)-(vi) is a programmable nuclease configured to introduce one or more modifications via NHEJ-mediated indels.

[0050] In an embodiment, the techniques described herein relate to a method wherein the gene editing system used for (i)-(xi) is a programmable nuclease configured to introduce the one or more modifications using a donor template and HDR-mediated repair.

[0051] In an embodiment, the techniques described herein relate to a method wherein the gene editing system used for (i)-(vi) is a DNA base editing system.

[0052] In an embodiment, the techniques described herein relate to a method wherein the gene editing system used for (v) or (vi) is an RNA base editing system.

[0053] In an embodiment, the techniques described herein relate to a method wherein the gene editing system used for (i)-(xi) is a prime editing system.

[0054] In an embodiment, the techniques described herein relate to a method wherein the gene editing system used for (i)-(xii) is a twin prime editing system, optionally further including an integrase.

[0055] In an embodiment, the techniques described herein relate to a method wherein the gene editing system used for (i)-(xii) is a CRISPR-associated transposase (CAST) system.

[0056] In an embodiment, the techniques described herein relate to a method wherein the gene editing system used for (i)-(xii) includes a non-LTR retrotransposon system.

[0057] In an embodiment, the techniques described herein relate to a method wherein the modifications introduced by the gene editing system of i) are: (i) removing a portion of the regulatory element of the TF such that expression of the TF is reduced; (ii) introducing a nonfunctional sequence into the regulatory element such that binding of transcription machinery to the regulatory element is reduced; (iii) introducing one or more single nucleotide edits such that binding of the transcription machinery to the regulatory element is reduced; (iv) or a combination thereof.

[0058] In an embodiment, the techniques described herein relate to a method wherein the gene editing system used for (i)-(iii) is a programmable nuclease configured to introduce the one or more modifications via NHEJ-mediated indels, a programmable nuclease configured to introduce the one or more modifications using a donor template and HDR-mediated repair, a prime editing system, a double prime editing system, a CAST system, or a non-LTR retrotransposon system.

[0059] In an embodiment, the techniques described herein relate to a method wherein the gene editing system used for (iii) is a DNA base editing system.

[0060] In an embodiment, a method for treating cancer in a subject in need is disclosed, comprising administering an immunotherapy regimen to the subject in an effective amount to prevent or reduce cancer progression, only if a normalized immunotherapy -resistant gene expression signature of a biological sample containing the subject’s tumor cells is not overexpressed compared to the normalized gene expression signature in a biological sample from a subject known to respond to immunotherapy, wherein the immunotherapy-resistant gene signature includes one or more transcription factors selected from TP63, TP73, PDX1, and FOXP1; or from TP73, TP63, FOXP1, PDX1, JUN, HAND2, POU3F1, IRF2, GLI1, TCF21, FOXN4, EOMES, JUNB, HANOI, IKZF3, TBX6, TBX4, GFI1B, CREM, POU2F1, PPARG,MSC, NKX3, ZNF415, F0XP4, ZNF396, IRF2, HNF1B, FIGLA, NFKBIZ, P0U5F1, PBX1, IKZF1, SUPT4H1, ASCL1, PBX3, ZNF7, MEIS2, UBP1, BCL6, F0XF2, NR1I3, CREBL2, GRHL1, RUNX2, FLU, GLIS1, POU2F2, ERG, IRF6, HSF5, MEF2C, EGR2, ALX3, TBX5, TBPL1, TBXT, H0XB2, BHLHE40, ZNF771, FOXD4L5, ZNF256, SMAD2, HESX1, POU4F3, ZNF680, and RELB.

[0061] In an embodiment, a method for treating cancer in a subject in need is disclosed, comprising administering an immunotherapy regimen to the subject in an effective amount to prevent or reduce cancer progression, only if a normalized immunotherapy-resistant gene expression signature of a biological sample containing the subject’s tumor cells is not overexpressed compared to the normalized gene expression signature in a biological sample from a subject known to respond to immunotherapy, wherein the immunotherapy -resistant gene signature includes two or more transcription factors selected from TP63, TP73, PDX1, and FOXP1; or from TP73, TP63, FOXP1, PDX1, JUN, HAND2, POU3F1, IRF2, GLI1, TCF21, FOXN4, EOMES, JUNB, HAND!, IKZF3, TBX6, TBX4, GFUB, CREM, POU2F1, PPARG, MSC, NKX3, ZNF415, FOXP4, ZNF396, IRF2, HNF1B, FIGLA, NFKBIZ, POU5F1, PBX1, IKZF1, SUPT4H1, ASCL1, PBX3, ZNF7, MEIS2, UBP1, BCL6, FOXF2, NR1I3, CREBL2, GRHL1, RUNX2, FLU, GLIS1, POU2F2, ERG, IRF6, HSF5, MEF2C, EGR2, ALX3, TBX5, TBPL1, TBXT, HOXB2, BHLHE40, ZNF771, FOXD4L5, ZNF256, SMAD2, HESX1, POU4F3, ZNF680, and RELB.

[0062] In an embodiment, a method for treating cancer in a subject in need is disclosed, comprising administering an immunotherapy regimen to the subject in an effective amount to prevent or reduce cancer progression, only if a normalized immunotherapy -resistant gene expression signature of a biological sample containing the subject’s tumor cells is not overexpressed compared to the normalized gene expression signature in a biological sample from a subject known to respond to immunotherapy, wherein the immunotherapy-resistant gene signature includes three or more transcription factors selected from TP63, TP73, PDX1, and FOXP1; or from TP73, TP63, FOXP1, PDX1, JUN, HAND2, POU3F1, IRF2, GLI1, TCF21, FOXN4, EOMES, JUNB, HANOI, IKZF3, TBX6, TBX4, GFI1B, CREM, POU2F1, PPARG, MSC, NKX3, ZNF415, FOXP4, ZNF396, IRF2, HNF1B, FIGLA, NFKBIZ, POU5F1, PBX1, IKZF1, SUPT4H1, ASCL1, PBX3, ZNF7, MEIS2, UBP1, BCL6, FOXF2, NR1I3, CREBL2,GRHL1, RUNX2, FLU, GLIS1, POU2F2, ERG, IRF6, HSF5, MEF2C, EGR2, ALX3, TBX5, TBPL1, TBXT, HOXB2, BHLHE40, ZNF771, FOXD4L5, ZNF256, SMAD2, HESX1, POU4F3, ZNF680, and RELB.

[0063] In an embodiment, the immunotherapy -resistant gene signature is normalized to the expression of a housekeeping gene.

[0064] In an embodiment, the immunotherapy regimen comprises a checkpoint inhibitor blockade.

[0065] In an embodiment, the checkpoint inhibitor blockade comprises a CTLA-4 inhibitor (e g., ipilimumab (Yervoy™), a PD-1 inhibitor (e g., pembrolizumab (Keytruda™), nivolumab (Opdivo™) and / or a PD-Ll inhibitor (e.g., atezolizumab (Tecentriq™).

[0066] These and other aspects, objects, features, and advantages of the example embodiments will become apparent to those with ordinary skill in the art upon considering the following detailed description of example embodiments.BRIEF DESCRIPTION OF THE DRAWINGS

[0067] An understanding of the features and advantages of the present disclosure will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the disclosure may be utilized, and the accompanying drawings of which:

[0068] FIG. 1 - Diagram showing a summary of a high-throughput positive selection screen on cancer cells under immune pressure.

[0069] FIG. 2 - Transcription factors that can drive fitness advantage in pancreatic cancer cell lines under IFNy stress. Heat map showing the log fold change (LFC) of transcription factors overexpressed in pancreatic cancer cell lines across six replicates.

[0070] FIG. 3 - Transcription factors that can drive resistance in cancer patients. Heat maps illustrating the enrichment of transcription factors in 31 melanoma patients and one pancreatic cancer patient, both before and after immunotherapy.

[0071] FIG. 4 - Validation of individual transcription factors that can drive fitness advantage in pancreatic cancer cell lines under IFNy stress. Graph showing cell growth in pancreatic cancer cells overexpressing individual transcription factors under IFNy pressure.

[0072] FIG. 5 - scRNA-seq in cells overexpressing individual transcription. Heat map showing differentially expressed genes in single cells overexpressing the indicated individual transcription factors or control (GFP).

[0073] FIG. 6 - IFNy positive selection screen in multiple cancer cell lines across different cancer lineages. Schematic of a high-throughput positive selection screen on cells under IFNy treatment. Four different cell lines spanning three different cancer lineages were used. Pooled overexpression of all human transcription factor (TF) isoforms was performed on four well- selected cancer cell lines across three cancer lineages.

[0074] FIG. 7 - Consensus hits across different cancer cell lines and different cancer lineages. Venn diagram demonstrating the number of consensus hits from the IFNy screen outlined in FIG. 6.

[0075] FIG. 8 - Heat map showing differentially expressed transcription factors in the different cancer cell lines and different cancer lineages.

[0076] FIG. 9 - Therapeutic agent positive selection screen in cancer cells. Schematic of a high-throughput positive selection screen on cells, e.g., cancer cells, under different disease-related (e.g., cancer-related) treatments.

[0077] FIG. 10 - Meta-analysis of transcription factors responsive to therapeutic cancer treatment and / or immune pressure.

[0078] FIG. 11 - Model of intrinsic and extrinsic components influencing RNA state heterogeneity.

[0079] FIG. 12 - Transcription factor expression can influence RNA state heterogeneity.

[0080] FIG. 13 - Pipeline for engineering cancer cells with specific cell states using transcription factors. Specific transcription factors (TFs) may drive distinct cell states (e.g., classical or basal cell states) in pancreatic ductal adenocarcinoma (PDAC) cells. All human transcription factors can be overexpressed in cancer cells (e.g., PDAC cells). Cells are then sorted based on state-specific surface markers. Isogenic models of different cell states can be generated via this pipeline.

[0081] The figures herein are for illustrative purposes only and are not necessarily drawn to scale.DETAILED DESCRIPTION OF THE EXAMPLE EMBODIMENTSGeneral Definitions

[0082] Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. Definitions of standard terms and techniques in molecular biology may be found in Molecular Cloning: A Laboratory Manual, 2ndedition (1989) (Sambrook, Fritsch, and Maniatis); Molecular Cloning: A Laboratory Manual, 4thedition (2012) (Green and Sambrook); Current Protocols in Molecular Biology (1987) (F.M. Ausubel et al. eds.); the series Methods in Enzymology (Academic Press, Inc.): PCR2: A Practical Approach (1995) (M.J. MacPherson, B.D. Hames, and G.R. Taylor eds.): Antibodies, A Laboratory Manual (1988) (Harlow and Lane, eds.): Antibodies A Laboratory Manual, 2ndedition 2013 (E.A. Greenfield ed.); Animal Cell Culture (1987) (R.I. Freshney, ed.); Benjamin Lewin, Genes IX, published by Jones and Bartlet, 2008 (ISBN 0763752223); Kendrew etal. (eds.), The Encyclopedia of Molecular Biology, published by Blackwell Science Ltd., 1994 (ISBN 0632021829); Robert A. Meyers (ed.), Molecular Biology and Biotechnology: a Comprehensive Desk Reference, published by VCH Publishers, Inc., 1995 (ISBN 9780471185710); Singleton etal., Dictionary of Microbiology and Molecular Biology 2nd ed., J. Wiley & Sons (New York, N.Y. 1994), March, Advanced Organic Chemistry Reactions, Mechanisms and Structure 4th ed., John Wiley & Sons (New York, N.Y. 1992); Marten H. Hofker and Jan van Deursen, Transgenic Mouse Methods and Protocols, 2ndedition (2011) and CRISPR Gene Editing: Methods and Protocols, Editor: Yonglun Luo, Publisher: Springer New York.

[0083] As used herein, the singular forms “a,” “an,” and “the” include both singular and plural referents unless the context dictates otherwise.

[0084] The term “optional” or “optionally” means that the subsequent described event, circumstance, or substituent may or may not occur and that the description includes instances where the event or circumstance occurs and instances where it does not.

[0085] The recitation of numerical ranges by endpoints includes all numbers and fractions subsumed within the respective ranges and the recited endpoints.

[0086] The terms “about” or “approximately,” as used herein when referring to a measurable value such as a parameter, an amount, a temporal duration, and the like, are meant to encompassvariations of and from the specified value, such as variations of + / -10% or less, + / -5% or less, + / - 1% or less, and + / -0.1% or less of and from the specified value, insofar such variations are appropriate to perform in the disclosure. It is understood that the value to which the modifier “about” or “approximately” refers is also specifically and preferably disclosed.

[0087] As used herein, a “biological sample” may contain whole cells and / or live cells and / or cell debris. The biological sample may include (or be derived from) a “bodily fluid.” The present disclosure encompasses embodiments wherein the bodily fluid is selected from amniotic fluid, aqueous humor, vitreous humor, bile, blood serum, breast milk, cerebrospinal fluid, cerumen (earwax), chyle, chyme, endolymph, perilymph, exudates, feces, female ejaculate, gastric acid, gastric juice, lymph, mucus (including nasal drainage and phlegm), pericardial fluid, peritoneal fluid, pleural fluid, pus, rheum, saliva, sebum (skin oil), semen, sputum, synovial fluid, sweat, tears, urine, vaginal secretion, vomit and mixtures of one or more thereof. Biological samples include cell cultures, bodily fluids, and cell cultures from bodily fluids. Bodily fluids may be obtained from a mammal organism, for example, by puncture or other collecting or sampling procedures.

[0088] The terms “subject,” “individual,” and “patient” are used interchangeably herein to refer to a vertebrate, preferably a mammal, more preferably a human. Mammals include, but are not limited to, murines, simians, humans, farm animals, sport animals, and pets. Tissues, cells, and the progeny of a biological entity obtained in vivo or cultured in vitro are also encompassed.

[0089] In certain embodiments, the term “treatment” or “treating” refers to an intervention (e.g. the administration of an agent to a subject) that cures, ameliorates, or lessens the symptoms of cancer or removes (or lessens the impact of) drug resistance.

[0090] As used herein, the term 'overexpression' refers to an increased level of expression of a gene, RNA, or protein relative to a reference expression level. Overexpression may be achieved through various methods, including but not limited to the introduction of exogenous gene copies, the use of strong promoters, enhancers, or other regulatory elements, the removal of negative regulatory elements, the genetic modification of endogenous regulatory regions, the modification of mRNA stability, the alteration of protein stability, or combinations thereof. The reference expression level may be the endogenous or basal expression level in the same cell type, expression in a control or parental cell line, expression in normal tissue corresponding to the cell type, anarbitrary threshold, or any other suitable comparative baseline. Overexpression may be transient or stable, inducible or constitutive, and may range from a slight increase above the reference level to several orders of magnitude higher than the reference level. Overexpression may be detected by various methods including, but not limited to, quantitative PCR, RNA sequencing, microarray analysis, western blotting, immunohistochemistry, flow cytometry, or reporter gene assays

[0091] Various embodiments are described hereinafter. It should be noted that the specific embodiments are not intended as an exhaustive description or as a limitation to the broader aspects discussed herein. One aspect described in conjunction with a particular embodiment is not necessarily limited to that embodiment and can be practiced with any other embodiment(s). Reference throughout this specification to “one embodiment,” “an embodiment,” and “an example embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, appearances of the phrases “in one embodiment,” “in an embodiment,” or “an example embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment but may.

[0092] Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner, as would be apparent to a person skilled in the art from this disclosure in one or more embodiments. Furthermore, while some embodiments described herein include some but not other features included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the disclosure. For example, in the appended claims, any of the claimed embodiments can be used in any combination.

[0093] All publications, published patent documents, and patent applications cited herein are hereby incorporated by reference to the same extent as though each publication, published patent document, or patent application was specifically and individually indicated as being incorporated by reference.OVERVIEW

[0094] Immunotherapy has limited efficacy in certain cancers, like pancreatic cancer. The resistance to immunotherapy cannot be explained solely by mutations or tumor mutation burden.It is crucial to interrogate more thoroughly and identify resistance mechanisms, as well as screenfor novel combination immunotherapies and responders to immunotherapies, to improve treatment outcomes. The embodiments disclosed herein provide methods that match cell states to specific functions, such as resistance to immunotherapy. The methods disclosed herein can identify resistance mechanisms that are independent of mutation or tumor mutation burden, select appropriate patients for appropriate therapies, and identify novel combinations of immunotherapies that are more effective in a given population. The methods may be applied in a way that is agnostic to an initial immunotherapy by leveraging how cancer cells respond to specific cell growth pressures, which is an early step in immune escape.

[0095] In one aspect, embodiments disclosed herein are directed to methods for identifying resistance mechanisms in cancer cell populations. A forward genetics library is introduced into a population of cancer cells. As used herein, a “forward genetics library” refers to the introduction of random genetic elements (perturbations) into a population of cancer cells to induce heterogeneity in cell states among the cancer cells. Cell growth pressure is then applied to the cancer cell population. As used herein, a “cell growth pressure” refers to any pressure applied to cells that challenges cell growth or viability. The forward genetic elements (perturbations) in surviving cells from the cancer cell population screened are then identified to determine one or more resistance mechanisms. As used herein, the term "resistance mechanisms" refers to cellular, molecular, or genetic processes, alterations, or adaptations that enable cancer cells to survive, evade, or overcome cell growth pressures, including therapeutic interventions and immune responses. Resistance mechanisms may be intrinsic (pre-existing) or acquired (developed in response to pressure), and may involve genetic mutations, epigenetic changes, transcriptional reprogramming, altered signaling pathways, metabolic adaptations, and / or changes in cell state or phenotype. Resistance mechanisms may be specific to particular pressures or may confer crossresistance to multiple different pressures. The resistance mechanisms may be optionally compared to patient data from treatment responders and non -responders. For example, determining whether patients have overexpression of identified transcription factors or gene programs regulated by these transcription factors, or whether they exhibit identified cell states indicative of resistance (e g., gene signatures, chromatin accessibility), or whether they display identified perturbations of genes. Once the resistance mechanisms are identified, vulnerabilities (i.e., dependencies) in resistant cells can be identified (e.g., synthetic lethality screens in resistant cancer cells). Forexample, drug or CRISPR screening can be performed in cells with an identified resistant state to determine vulnerabilities (i.e., dependencies) in these resistant cells, allowing for treatments specific to resistant cells or cells expressing a resistant gene signature. As used herein, the term 'dependencies' refers to genes, proteins, pathways, or cellular processes that cancer cells rely on for survival, growth, or maintenance of their phenotype. Dependencies may be general (common to most cells) or specific to particular cancer cells, cancer types, or cell states. A dependency in cancer cells may represent a potential therapeutic vulnerability that can be targeted to affect the viability of those cancer cells selectively. Dependencies may be constitutive or context-dependent, emerging only under specific conditions such as nutrient deprivation, hypoxia, or treatment with therapeutic agents.

[0096] In one aspect, the methods disclosed herein can be used to match cell states to functions and combination therapies. In another aspect, the methods disclosed herein are agnostic to initial immune checkpoint blockade (ICB) targets, they leverage how cancer cells respond to specific inflammatory cytokines, which is an initial step in overcoming immune escape. In another aspect, the methods disclosed herein can be utilized to extend immunotherapies to cancer types with low response rates, such as pancreatic cancer. In another aspect, the methods disclosed herein can be used to propose new combinational therapies that can overcome resistance mechanisms. In another aspect, the methods disclosed herein can be used to identify common molecular mechanisms in different cancer types that drive cells to be intrinsically resistant to immunotherapies. In another aspect, the methods disclosed herein can be used to understand why patients do not respond to immunotherapies, except in cases of specific mutations or high tumor mutation burden (TMB).Methods of identifying cancer resistance mechanisms

[0097] In an embodiment, tumor fitness mechanisms are identified by applying cell growth pressure to cancer cells and determining the mechanisms that enable cancer cells to become resistant to this pressure. As used herein, the term 'tumor fitness' refers to the ability of cancer cells to survive, proliferate, and / or maintain their phenotype under various conditions, challenges, or selective pressures. Tumor fitness may encompass resistance to treatments (e.g., chemotherapy, targeted therapy, immunotherapy), adaptation to microenvironmental conditions (e.g., hypoxia, nutrient deprivation), evasion of immune surveillance, and / or maintenance of cancer-specificproperties. Tumor fitness may be assessed through various measures, including, but not limited to, cell viability, proliferation rate, colony formation, tumor growth, metastatic capability, and resistance to cell death. A library comprising different identifiable genetic elements is introduced into a cancer cell system (e.g., a cancer cell line or cancer organoids), and the cell growth pressure is applied to the cancer cell system. Genetic elements in cancer cells with increased tumor fitness are then identified. The cell state (e.g., transcriptome profile) of single cells positively selected for increased tumor fitness may be additionally determined. In an embodiment, tumor fitness mechanisms (e.g., resistance of tumor cells to a treatment) are identified by introducing a library comprising different identifiable genetic elements into a cancer cell system (e.g., a cancer cell line or cancer organoids), and genetic elements in cancer cells with increased tumor fitness are identified. In some embodiments, cells expressing different identifiable genetic elements can be sorted based on one or more surface markers. In some embodiments, one or more cell surface markers can be cell-state-specific.

[0098] In an embodiment, the methods may be performed in a pooled format. Pooled screens involve introducing a “pool” or mixture of genetic elements (e.g., a forward genetic library) into a single population of cells (or organoids) as a whole. For example, the entire “pool” is assayed in a single plate of cells. In an embodiment, a positive or negative selective pressure is applied to select cells with the desired viability phenotype. For example, in an unpooled format, each genetic element is assayed individually in a different population of unpooled cells. In an embodiment, the methods may be performed in individual wells of a plate (e g., 96 or 384 well plates). In an embodiment, genetic elements are identified in a pooled format, and the genetic elements are validated in a plate or unpooled format. In an embodiment, genetic elements and cell states are compared to patient data (e.g., patient data associated with treatment response).Forward Genetics Libraries

[0099] A forward genetic library is comprised of genetic elements. As used herein, “genetic elements” may comprise polynucleotides that encode for a gene or gene fragment. For example, suppose the goal is to study the effects of cancer cell phenotype under over-expression of certain genes. In that case, the genetic elements are polynucleotides encoding the set of genes. In an embodiment, the genetic element may be a genetic suppressor element (GSE), a gene fragment, ora whole gene that can suppress a phenotype associated with another gene when introduced into a cell. The genetic element may be a non-coding regulatory sequence. Examples of non-coding regulatory sequences include promoters, enhancers, insulators, silences, microRNAs (miRNAs), long non-coding RNAs (IncRNAs), small interfering RNAs (siRNAs), introns, transposable elements, and chromatin boundaries (see, e.g., Sanborn AL, Rao SS, Huang SC, et al. Chromatin extrusion explains key loop and domain formation features in wild-type and engineered genomes. Proc Natl Acad Set U S A. 2015;l 12(47):E6456-E6465: and Rao SS, Huntley MH, Durand NC, et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping [published correction appears in Cell. 2015 Jul 30; 162(3):687-8], Cell. 2014;159(7):1665-1680), and others that do not code for protein but regulate gene expression at various levels, including transcription, RNA splicing, and translation. In an embodiment, the genetic element may be a polynucleotide encoding an antisense oligonucleotide (ASO) or RNA interference (RNAi) polynucleotide. In an embodiment, the genetic element may be a polynucleotide encoding a programmable nuclease configured to make insertions / deletions / substitutions in one or more targeted loci in a cell genome. Example programmable nucleases include zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), meganucleases, CRISPR-Cas, and OMEGA systems. CRISPR-Cas and OMEGA systems, in particular, are reprogrammable via a guide nucleotide. Each genetic element may comprise a polynucleotide that may encode a set of programmable nucleases, each nuclease configured to make an edit at a different genetic locus. Accordingly, in an embodiment, the genetic element may comprise a polynucleotide encoding a Cas enzyme and a set of polynucleotides encoding guide molecules, each guide molecule directing the Cas enzyme to make an edit at different loci. CRISPR-Cas systems, and similarly OMEGA systems, have evolved beyond the canonical CRISPR-Cas9 system, which can make edits via non-homologous end joining or homology-directed repair, to include, for example, base editors, prime editors, CRISPR-associated transposases (CAST), Non-LTR retrotransposon systems, and epigenetic editors, and all such systems are contemplated within the meaning of CRISPR-Cas and OMEGA systems.

[0100] In an embodiment, a genetic suppressor element (GSE) is a short, biologically active gene fragments that inhibits specific gene functions by encoding dominantly acting peptides or antisense RNAs. These elements work by interfering with the activity of their target genes, eitherby blocking protein domains, e.g., by interfering with native protein-protein interactions, preventing mRNA translation or promoting mRNA degradation (see, for example, Roninson IB, et al. ok np

[0101] Genetic suppressor elements: new tools for molecular oncology— thirteenth Cornelius P. Rhoads Memorial Award Lecture. Cancer Res. 1995 Sep 15;55(18):4023-8. PMID: 7664275).

[0102] In an embodiment, the forward genetic library is a transcription factor open reading frame (ORF) library. As used herein, the term 'transcription factor (TF) ORF library' refers to a collection of nucleic acids, vectors, or constructs encoding the open reading frames (ORFs) of multiple transcription factor proteins. Transcription factors are proteins that bind to specific DNA sequences to regulate the rate of transcription, a process that converts genetic information from DNA to RNA. The library may include open reading frames (ORFs) encoding various transcription factor families, classes, isoforms, splice variants, and mutants. A transcription factor ORF library may comprise naturally occurring sequences, modified sequences, synthetic sequences, or combinations thereof. The library may encode up to about 100, up to about 1,000, up to about 3,500, up to about 5,000, up to about 10,000, or more distinct transcription factor ORFs. The transcription factor ORF library may be configured for expression in eukaryotic cells, such as cancer cells, and may include regulatory elements to drive expression of the transcription factors. The library may be delivered to cells through viral vectors, non-viral vectors, or other delivery methods to facilitate expression of the transcription factors in target cells. Representative transcription factor ORF libraries are known in the art (see, e.g., Yang et al., 2011, A public genome-scale lentiviral expression library of human ORFs, Nature Methods 8, 659-66; and Broad Genomic Perturbation Platform (Broad GPP) found at portals.broadinstitute.org / gpp / public / ). Other example transcription factor libraries applicable to the present disclosure have been used to differentiate stem cells (e.g., International Patent application publication No. WO2023283631A2). Transcription factor libraries, as used herein, include all variants and isoforms. As used herein, “variant” is an alteration in the most common DNA nucleotide sequence. The term variant can describe an alteration that may be benign, pathogenic, or of unknown significance. As used herein, “isoform” refers to any of two or more functionally similar proteins with a similar but not an identical amino acid sequence. In an embodiment, an isoform is a member of a set of highly similar proteins originating from a single gene or gene family. For example, a gene may have splicingvariants generating different isoforms, and the transcription library would encode each isoform separately. In an example embodiment, isoforms include expressed transcripts of any transcription factor comprising at least 25% of the expressed transcript. In an embodiment, the native sequences of transcription factors may differ between or even within individuals of the same species due to somatic mutations, post-transcriptional modifications, or post-translational modifications. Any such variants or isoforms of transcription factors are intended herein. Accordingly, all sequences of transcription factors found in or derived from nature are considered “native.”

[0103] Libraries can include a varying number of members (e.g., genetic elements or perturbations), such as up to about 100 members, up to about 1,000 members, up to about 3500 members up to about 5,000 members, up to about 10,000 members, up to about 100,000 members, up to about 500,000 members, or even more than 500,000 members. In an embodiment, one or more perturbations are genome-wide perturbations. In an embodiment, one or more perturbations target specific genes of interest (e.g., transcription factors).Delivery of Forward Genetic Elements

[0104] A library of vectors may introduce the forward genetic elements disclosed herein. In an embodiment, the genetic elements are encoded by a vector. In an embodiment, the vector encodes for an effector enzyme (e.g., a CRISPR enzyme) along with a genetic component or perturbation (e.g., CRISPR guide sequence). For example, the vector encodes an effector enzyme when the cancer cell line or organoid does not express an effector enzyme. In general, and throughout this specification, the term “vector” refers to a nucleic acid molecule capable of transporting another nucleic acid to which it has been linked. Vectors include, but are not limited to, nucleic acid molecules that are single- stranded, double-stranded, or partially double-stranded; nucleic acid molecules that comprise one or more free ends, no free ends (e.g., circular); nucleic acid molecules that comprise DNA, RNA, or both; and other varieties of polynucleotides known in the art. One vector type is a “plasmid,” which refers to a circular double-stranded DNA loop into which additional DNA segments can be inserted, such as by standard molecular cloning techniques. Another type of vector is a viral vector, wherein virally-derived DNA or RNA sequences are present in the vector for packaging into a virus (e.g., retroviruses, replication defective retroviruses, adenoviruses, replication defective adenoviruses, and adeno-associatedviruses). Viral vectors also include polynucleotides, which a virus carries for transfection into a host cell. Certain vectors are capable of autonomous replication in a host cell into which they are introduced (e.g., episomal mammalian vectors). Other vectors (e.g., non-episomal mammalian vectors) are integrated into the host cell's genome upon introduction into the host cell and, thereby, are replicated along with the host genome (e.g., lentivirus).

[0105] Moreover, specific vectors can direct the expression of genes to which they are operatively linked (i.e., operably linked to a regulatory element). Such vectors are referred to herein as “expression vectors.” Vectors for and that result in expression in a eukaryotic cell can be referred to herein as “eukaryotic expression vectors.” Common expression vectors of utility in recombinant DNA techniques are often in the form of plasmids. The term “regulatory element” is intended to include promoters, enhancers, internal ribosomal entry sites (IRES), and other expression control elements (e.g., transcription termination signals, such as polyadenylation signals and poly-U sequences). In some embodiments, a vector comprises one or more pol III promoters (e.g., 1, 2, 3, 4, 5, or more pol III promoters), one or more pol II promoters (e.g., 1, 2, 3, 4, 5, or more pol II promoters), one or more pol I promoters (e.g., 1, 2, 3, 4, 5, or more pol I promoters), or combinations thereof. Examples of pol III promoters include, but are not limited to, U6 and Hl promoters. Examples of pol II promoters include, but are not limited to, the retroviral Rous sarcoma virus (RSV) LTR promoter (optionally with the RSV enhancer), the cytomegalovirus (CMV) promoter (optionally with the CMV enhancer) (see, e.g., Boshart et al., Cell, 41 :521-530 (1985)), the SV40 promoter, the dihydrofolate reductase promoter, the -actin promoter, the phosphoglycerol kinase (PGK) promoter, and the EFla promoter.

[0106] Also encompassed by the term “regulatory element” are enhancer elements, such as WPRE; CMV enhancers; the R-U5’ segment in LTR of HTLV-I (Mol. Cell. Biol., Vol. 8(1), p. 466-472, 1988); SV40 enhancer; and the intron sequence between exons 2 and 3 of rabbit -globin (Proc. Natl. Acad. Sci. USA., Vol. 78(3), p. 1527-31, 1981). It will be appreciated by those skilled in the art that the design of the expression vector can depend on such factors as the choice of the host cell to be transformed, the level of expression desired, etc. A vector can be introduced into host cells to thereby produce transcripts, proteins, or peptides, including fusion proteins or peptides, encoded by nucleic acids as described herein (e.g., clustered regularly interspersed shortpalindromic repeats (CRISPR) transcripts, proteins, enzymes, mutant forms thereof, fusion proteins thereof, etc.).

[0107] In an embodiment, the vector is a lentivirus vector. The term “lentivirus vector,” as used herein, refers to a viral vector derived from complex retroviruses such as the human immunodeficiency virus (HIV). In the present disclosure, lentiviral vectors derived from any strain and subtype can be used. The lentiviral vector may be based on a human or primate lentivirus, such as HIV, or a non-non-human lentivirus, such as Feline immunodeficiency virus, simian immunodeficiency virus, and equine infectious anemia virus (EIAV). In an embodiment, the lentiviral vector is an HIV-based vector, especially an HIV-l-based vector (see, e g., Dull T, Zufferey R, Kelly M, et al.) A third-generation lentivirus vector with a conditional packaging system. J Virol. 1998;72(11):8463-8471; and Zufferey R, Dull T, Mandel RJ, et al. Selfinactivating lentivirus vector for safe and efficient m vivo gene delivery. J Virol. 1998;72(12):9873-9880). The HIV 5’ LTR comprises the viral promoter for transcribing the viral genome RNA. In an embodiment, the LTR viral promoter is partially deleted and fused to a heterologous enhancer / promoter such as CMV or RSV.

[0108] In an embodiment, genetic elements are introduced using a perturb-seq vector (see e.g., Dixit etal., “Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens” 2016, Cell 167, 1853-1866; Adamson etal., “A Multiplexed SingleCell CRISPR Screening Platform Enables Systematic Dissection of the Unfolded Protein Response” 2016, Cell 167, 1867-1882; Jaitin DA, Weiner A, Yofe I, et al. Dissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq. Cell. 2016;167(7):1883- 1896. el 5; Feldman et al., Lentiviral co-packaging mitigates the effects of intermolecular recombination and multiple integrations in pooled genetic screens, bioRxiv 262121, doi: doi.org / 10.1101 / 262121; Datlinger, et al., 2017, Pooled CRISPR screening with single-cell transcriptome readout. Nature Methods. Vol.14 No.3 DOI: 10.1038 / nmeth.4177; Hill et al., On the design of CRISPR-based single cell molecular screens, Nat Methods. 2018 Apr; 15(4): 271- 274; Replogle et al., “Combinatorial single-cell CRISPR screens by direct guide RNA capture and targeted sequencing” Nat Biotechnol (2020). doi.org / 10.1038 / s41587-020-0470-y; Schraivogel D, Gschwind AR, Milbank JH, et al. "Targeted Perturb-seq enables genome-scale genetic screens in single cells.” Nat Methods. 2020;17(6):629-635; Frangieh CJ, Melms JC, Thakore PI, et al.Multimodal pooled Perturb-CITE-seq screens in patient models define mechanisms of cancer immune evasion. Nat Genet. 2021;53(3):332-341; US patent application publication number US20200283843A1; and US Patent number US11214797B2). In an embodiment, using a perturb- seq vector allows for performing single-cell RNA sequencing (scRNA-seq) of positively selected cells to determine the identity of the genetic element and cell state of single cells with a particular genetic element simultaneously.

[0109] In an embodiment, genetic elements can be inserted randomly throughout the genome by using chemical or insertional mutagens, such as in the Validation-Based Insertional Mutagenesis (VBIM) strategy, where modified lentiviruses act as insertional mutagens, placing strong promoters throughout the genome (see, e.g., De S, Tamagno I, Stark GR, Jackson MW. Validation-Based Insertional Mutagenesis (VBIM), A Powerful Forward Genetic Screening Strategy. Curr Protoc . 2022;2(3):e394).Barcodes

[0110] In an embodiment, genetic elements introduced by a vector of the present disclosure are identified by a barcode sequence unique to each genetic element. Being associated with the genetic element means the barcode sequence is linked to the genetic element. For example, the barcode is incorporated into the sequence encoding the genetic element or is a sequence that is only encoded on a vector encoding the genetic element. In an embodiment, the barcode identifying a genetic element can be the genetic element, such as a guide sequence targeting a specific sequence.

[0111] The term “barcode,” as used herein, refers to a short sequence of nucleotides (for example, DNA or RNA) that is used as an identifier for an associated molecule, such as a target molecule and target nucleic acid, or as an identifier of the source of an associated molecule, such as a cell-of-origin, sample of origin, or individual transcript. A barcode may also refer to any unique, non-naturally occurring nucleic acid sequence that may be used to identify the originating source of a nucleic acid fragment. Although it is not necessary to understand the mechanism of an invention, it is believed that the barcode sequence provides a high-quality individual read of the barcode associated with a perturbation, single cell, single nuclei, a viral vector, labeling ligand (e.g., antibody or aptamer), protein, shRNA, sgRNA or cDNA such that multiple species can besequenced together. Barcoding may be performed based on any of the compositions or methods disclosed in patent publication WO 2014047561 Al, Compositions and methods for labeling of agents, incorporated herein in its entirety. In an embodiment, barcoding uses an error-correcting scheme (T. K. Moon, Error Correction Coding: Mathematical Methods and Algorithms (Wiley, New York, ed. 1, 2005)).

[0112] A nucleic acid barcode can have a length of at least, for example, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 45, 50, 60, 70, 80, 90, or 100 nucleotides, and can be in single- or double-stranded form. Target molecules and / or target nucleic acids can be labeled with multiple nucleic acid barcodes in a combinatorial fashion, such as a nucleic acid barcode concatemer. Typically, a nucleic acid barcode is used to identify a target molecule and / or target nucleic acid, or as being from a particular discrete volume, having a specific physical property (for example, affinity, length, sequence, etc.), or having been subject to certain treatment conditions. Target molecules and / or target nucleic acids can be associated with multiple nucleic acid barcodes to provide information about these features (and more).Cancer Cells

[0113] In an embodiment, cancer cells or organoids are used to identify genetic elements driving tumor fitness. In an embodiment, the cancer cell line or cancer organoids are derived from a cancer selected from the group consisting of pancreatic cancer, skin cancer, bladder cancer, lung cancer, breast cancer, prostate cancer, brain cancer, bone cancer, blood cancer, kidney cancer, liver cancer, stomach cancer, colon cancer, head and neck cancer, ovarian cancer, cervical cancer, uterine cancer testicular cancer, rectal cancer, and thyroid cancer. In an embodiment, more than one cancer cell line or cancer organoids derived from different cancers are used, whereby consensus genetic elements driving tumor fitness across different cancers are identified.Cancer cell lines

[0114] In an embodiment, cancer cell lines are used in the methods described herein, for example, to study cancer biology, drug resistance, drug efficacy, and to validate cancer targets.

[0115] As used herein, the term "cancer cell line" refers to a population of cancer cells derived from human or animal tumor tissue that has been adapted to grow in laboratory conditions and canbe maintained in culture through multiple passages. Cancer cell lines may be derived from primary tumors, metastatic lesions, or recurrent tumors, and may retain some or many characteristics of the original tumor. Cancer cell lines can be established or commercially available, or they may be newly derived from patient samples. In one embodiment, cancer cells may be obtained from a collection of cancer cell lines. For example, the Cancer Cell Line Encyclopedia (CCLE) is an effort to generate large-scale profiling data sets across nearly 1,000 cell lines from diverse tissue lineages. Its original release included gene expression, DNA copy numbers, and hybrid capture sequencing (Barretina J, Caponigro G, Stransky N, et al. The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity [published correction appears in Nature. 2012 Dec 13;492(7428):290] [published correction appears in Nature. 2019 Jan;565(7738):E5-E6], Nature. 2012;483(7391):603-607). In an embodiment, different cancer cell lines can be compared.Organoids

[0116] In an embodiment, cancer organoids are used in the methods described herein. As used herein, the term “organoid” or “epithelial organoid” refers to a three-dimensional ex vivo tissue culture, cell cluster, or aggregate grown from embryonic stem cells, induced pluripotent stem cells, or tissue-resident progenitor cells that resembles an organ, or part of an organ, and possesses cell types relevant to that particular organ. Organoid systems have been described previously, for example, for brain, retinal, stomach, lung, thyroid, small intestine, colon, liver, kidney, pancreas, prostate, mammary gland, fallopian tube, taste buds, salivary glands, and esophagus (see, e.g., Clevers, Modeling Development and Disease with Organoids, Cell. 2016 Jun 16; 165(7): 1586- 1597). Tumor organoid systems have also been described (see, e.g., Porter, R.J., Murray, G.I. & McLean, M.H. Current concepts in tumor-derived organoids. Br J Cancer 123, 1209-1218 (2020). doi.org / 10.1038 / s41416-020-0993-5). Organoids develop by self-organization and can accurately represent cancer's diverse genetic, cellular, and pathophysiological hallmarks. Id. In addition, coculture methods and the ability to genetically manipulate these organoids have widened their utility in cancer research (e.g., co-culture of epithelial cancer organoids with immune cells). Id.

[0117] Organoids are grown within a flexible extracellular matrix. In preferred embodiments, the matrix for use in generating organoid fragments is Matrigel (a gelatinous protein matrix that provides the structural architecture to support 3D growth). Matrigel, which is currently widelyused in the synthesis of organoids, is a basement membrane matrix with biological activity derived from Engelbreth-Holm-Swarm murine sarcomas (see, e.g., Kibbey, M. C. Maintenance of the EHS sarcoma and Matrigel preparation. J. Tissue Cult. Meth 16, 227-230 (1994)). In other example embodiments, self-generating hydrogels comprising extracellular matrix derived from human tissue are used instead of Matrigel (see, e.g., Mollica, P. A., Booth-Creech, E. N., Reid, J. A., Zamponi, M., Sullivan, S. M., Palmer, X. L. et al. 3D bioprinted mammary organoids and tumoroids in human mammary derived ECM hydrogels. Acta Biomater. 95, 201-213 (2019)). These hydrogels retain biological signaling responses that differentiate between cancer and normal epithelial organoid cultures. In an example embodiment, animal-free alternatives, such as hydrogels made from alginates, can be used for organoid fragments (see, e.g., Chaji, S., Al-Saleh, J. & Gomillion, C. T. Bioprinted three-dimensional cell-laden hydrogels to evaluate adipocytebreast cancer cell interactions. Gels 6, pii E10 (2020)) or matrices utilizing hyaluronic acid and collagen (see, e.g., Maloney, E., Clark, C., Sivakumar, H., Yoo, K., Aleman, J., Rajan, S. A. P. et al. Immersion bioprinting of tumor organoids in multi-well plates for increasing chemotherapy screening throughput. Micromachines 11, pii: E208 (2020)). In an embodiment, the method includes preparing small organoid fragments by mechanical disruption. Mechanical disruption can include shearing, sonication, homogenizing, chopping, scissors, or cutting.Applying Cell Growth Pressure

[0118] Following the introduction of the forward genetic elements, a cell growth pressure is applied to the cancer cell line or cancer organoids, which have forward genetics library members expressed or introduced. As used herein, “cell growth pressure” refers to any pressure applied to cells that challenges or provides resistance to cell growth or viability of cells. In an embodiment, a combination of cell growth pressures is used. For example, an immune and another growth pressure are applied to the cancer cell line or organoids, with forward genetics library members expressed or introduced.Immune pressures

[0119] In an embodiment, immune pressure is applied to the cancer cells, which express different library members. As used herein, the term 'immune pressure' refers to any condition,factor, or agent that challenges the growth, survival, or proliferation of cancer cells through mechanisms associated with immune system function. Immune pressure may include, but is not limited to, exposure to inflammatory cytokines, interaction with immune cells, activation of immune-related signaling pathways, and / or treatment with immunotherapeutic agents. Immune pressure may be applied in vitro, ex vivo, or in vivo. In an embodiment, the immune pressure can be exposure to one or more inflammatory cytokines (see, e.g., Zhang JM, An J. Cytokines, inflammation, and pain. Int Anesthesiol Clin. 2007;45(2):27-37).

[0120] In an embodiment, the term "inflammatory cytokines" refers to soluble proteins, peptides, or glycoproteins that can modulate immune responses and / or inflammatory processes. Inflammatory cytokines include, but are not limited to, interferons (e.g., IFN-a, IFN-0, IFN-y), interleukins (e.g., IL-la, IL-ip, IL-2, IL-6, IL-12, IL-17), tumor necrosis factors (e.g., TNF-a, TNF-P), chemokines, and colony-stimulating factors. Inflammatory cytokines may be naturally occurring, recombinant, or synthetic and may be used individually or in combination.

[0121] In an embodiment, the immune pressure is applied to the cancer cells by co-culture with immune cells (e.g., T cells, macrophages, NK cells, and / or fibroblasts) to identify genetic elements associated with specific immune cell resistance mechanisms (e.g., T cell killing, macrophage phagocytosis) (see, e.g., Gardner M, Turner JE, Youssef OA, Cheshier S. In vitro Macrophage-Mediated Phagocytosis Assay of Brain Tumors. Cureus. 2020;12(10):el0964). For example, the forward genetics library can be introduced to cancer cells, followed by adding T cells specific for the tumor cells, such that the immune pressures are the T cells and tumor cells with a fitness advantage are identified. In an embodiment, immune cell-mediated tumor-killing assays are used with resistant cells in secondary assays.Other cell growth pressures

[0122] In an embodiment, the cell growth pressure applied to the cancer cells expressing different library members includes targeted therapy, general cytotoxic therapy, hypoxia (low oxygen levels), or low nutrients. In an embodiment, the cell growth pressure applied to the cancer cells expressing different members of the library includes environmental stress, such as but not limited to heat shock, osmolarity, hypoxia, cold, oxidative stress, radiation, starvation, a chemical (for example a therapeutic agent or potential therapeutic agent) and the like.

[0123] Targeted therapy is a type of cancer treatment that targets proteins that control how cancer cells grow, divide, and spread. Most targeted therapies are either small-molecule drugs or monoclonal antibodies. Non-limiting examples of targeted therapies include: Targeted therapy approved for bladder cancer: atezolizumab (Tecentriq), avelumab (Bavencio), enfortumab vedotin-ejfv (Padcev), erdafitinib (Bal versa), nivolumab (Opdivo), pembrolizumab (Keytruda), sacituzumab govitecan-hziy (Trodelvy); Targeted therapy approved for brain cancer: belzutifan (Welireg), bevacizumab (Avastin), dabrafenib (Tafinlar), everolimus (Afinitor), trametinib (Mekinist); Targeted therapy approved for breast cancer: abemaciclib (Verzenio), ado-trastuzumab emtansine (Kadcyla), alpelisib (Piqray), anastrozole (Arimidex), capivasertib (Truqap), elacestrant dihydrochloride (Orserdu), everolimus (Afinitor), exemestane (Aromasin), fam-trastuzumab deruxtecan-nxki (Enhertu), fulvestrant (Faslodex), lapatinib ditosylate (Tykerb), letrozole (Femara), margetuximab-cmkb (Margenza), neratinib maleate (Nerlynx), olaparib (Lynparza), palbociclib (Ibrance), pembrolizumab (Keytruda), pertuzumab (Perjeta), pertuzumab, trastuzumab, and hyaluronidase-zzxf (Phesgo), ribociclib (Kisqali), sacituzumab govitecan-hziy (Trodelvy), talazoparib tosylate (Talzenna), tamoxifen citrate (Soltamox), toremifene (Fareston), trastuzumab (Herceptin), tucatinib (Tukysa); Targeted therapy approved for cervical cancer: bevacizumab (Avastin), pembrolizumab (Keytruda), tisotumab vedotin-tftv (Tivdak); Targeted therapy approved for colorectal cancer: bevacizumab (Avastin), cetuximab (Erbitux), encorafenib (Braftovi), fruquintinib (Fruzalqa), ipilimumab (Yervoy), nivolumab (Opdivo), panitumumab (Vectibix), pembrolizumab (Keytruda), ramucirumab (Cyramza), regorafenib (Stivarga), tucatinib (Tukysa), ziv-aflibercept (Zaltrap); Targeted therapy approved for dermatofibrosarcoma protuberans: imatinib mesylate (Gleevec); Targeted therapy approved for endocrine and neuroendocrine tumors: avelumab (Bavencio), iobenguane I 131 (Azedra), lanreotide acetate (Somatuline Depot), lutetium Lu 177-dotatate (Lutathera); Targeted therapy approved for endometrial cancer: dostarlimab-gxly (Jemperli), lenvatinib mesylate (Lenvima), pembrolizumab (Keytruda); Targeted therapy approved for esophageal cancer: fam -trastuzumab deruxtecan-nxki (Enhertu), ipilimumab (Yervoy), nivolumab (Opdivo), pembrolizumab (Keytruda), ramucirumab (Cyramza), trastuzumab (Herceptin); Targeted therapy approved for head and neck cancer: cetuximab (Erbitux), nivolumab (Opdivo), pembrolizumab (Keytruda), toripalimab-tpzi (Loqtorzi); Targeted therapy approved for gastrointestinal stromal tumor: avapritinib (Ayvakit),imatinib mesylate (Gleevec), regorafenib (Stivarga), ripretinib (Qinlock), sunitinib malate (Sutent); Targeted therapy approved for giant cell tumor: denosumab (Xgeva), pexidartinib hydrochloride (Turalio); Targeted therapy approved for kidney cancer: avelumab (Bavencio), axitinib (Inlyta), belzutifan (Welireg), bevacizumab (Avastin), cabozantinib-s-malate (Cabometyx), everolimus (Afinitor), ipilimumab (Yervoy), lenvatinib mesylate (Lenvima), nivolumab (Opdivo), pazopanib hydrochloride (Votrient), pembrolizumab (Keytruda), sorafenib tosylate (Nexavar), sunitinib malate (Sutent), temsirolimus (Torisel), tivozanib hydrochloride (Fotivda); Targeted therapy approved for leukemia: acalabrutinib (Calquence), alemtuzumab (Campath), asciminib hydrochloride (Scemblix), avapritinib (Ayvakit), blinatumomab (Blincyto), bosutinib (Bosulif), brexucabtagene autoleucel (Tecartus), dasatinib (Sprycel), duvelisib (Copiktra), enasidenib mesylate (Idhifa), gemtuzumab ozogamicin (Mylotarg), gilteritinib fumarate (Xospata), glasdegib maleate (Daurismo), ibrutinib (Imbruvica), idelalisib (Zydelig), imatinib mesylate (Gleevec), inotuzumab ozogamicin (Besponsa), ivosidenib (Tibsovo), midostaurin (Rydapt), moxetumomab pasudotox-tdfk (Lumoxiti), nilotinib (Tasigna), obinutuzumab (Gazyva), ofatumumab (Arzerra), olutasidenib (Rezlidhia), pemigatinib (Pemazyre), pirtobrutinib (Jaypirca), ponatinib hydrochloride (Iclusig), quizartinib dihydrochloride (Vanflyta), rituximab (Rituxan), rituximab and hyaluronidase human (Rituxan Hycela), tagraxofusp-erzs (Elzonris), tisagenlecleucel (Kymriah), tretinoin (Vesanoid) , venetoclax (Venclexta), zanubrutinib (Brukinsa); Targeted therapy approved for liver and bile duct cancer: atezolizumab (Tecentriq), bevacizumab (Avastin), cabozantinib-s-malate (Cabometyx), durvalumab (Imfinzi), futibatinib (Lytgobi), infigratinib phosphate (Truseltiq), ipilimumab (Yervoy), ivosidenib (Tibsovo), lenvatinib mesylate (Lenvima), nivolumab (Opdivo), pembrolizumab (Keytruda), pemigatinib (Pemazyre), ramucirumab (Cyramza), regorafenib (Stivarga), sorafenib tosylate (Nexavar), tremelimumab-actl (Imjudo); Targeted therapy approved for lung cancer: adagrasib (Krazati), afatinib dimaleate (Gilotrif), alectinib (Alecensa), amivantamab-vmjw (Rybrevant), atezolizumab (Tecentriq), bevacizumab (Avastin), binimetinib (Mektovi), brigatinib (Alunbrig), capmatinib hydrochloride (Tabrecta), cemiplimab-rwlc (Libtayo), ceritinib (Zykadia), crizotinib (Xalkori), dabrafenib mesylate (Tafinlar), dacomitinib (Vizimpro), durvalumab (Imfinzi), encorafenib (Braftovi), entrectinib (Rozlytrek), erlotinib hydrochloride (Tarceva), fam-trastuzumab deruxtecan-nxki (Enhertu) , gefitinib (Iressa),ipilimumab (Yervoy), lorlatinib (Lorbrena), necitumumab (Portrazza), nivolumab (Opdivo), osimertinib mesylate (Tagrisso), pembrolizumab (Keytruda), pralsetinib (Gavreto), ramucirumab (Cyramza), repotrectinib (Augtyro), selpercatinib (Retevmo), sotorasib (Lumakras), tepotinib hydrochloride (Tepmetko), trametinib dimethyl sulfoxide (Mekinist), tremelimumab-actl (Imjudo); Targeted therapy approved for lymphoma: acalabrutinib (Calquence), axicabtagene ciloleucel (Yescarta), belinostat (Beleodaq), bexarotene (Targretin), bortezomib (Velcade), brentuximab vedotin (Adcetris), brexucabtagene autoleucel (Tecartus), crizotinib (Xalkori), denileukin diftitox (Ontak), duvelisib (Copiktra), epcoritamab-bysp (Epkinly), glofitamab-gxbm (Columvi), ibritumomab tiuxetan (Zevalin), ibrutinib (Imbruvica), lisocabtagene maraleucel (Breyanzi), loncastuximab tesirine-lpyl (Zynlonta), mogamulizumab-kpkc (Poteligeo), mosunetuzumab-axgb (Lunsumio), nivolumab (Opdivo), obinutuzumab (Gazyva), pembrolizumab (Keytruda), pemigatinib (Pemazyre), pirtobrutinib (Jaypirca), polatuzumab vedotin-piiq (Polivy), pralatrexate (Folotyn), rituximab (Rituxan), rituximab and hyaluronidase human (Rituxan Hycela), romidepsin (Istodax), selinexor (Xpovio), siltuximab (Sylvant), tafasitamab-cxix (Monjuvi), tazemetostat hydrobromide (Tazverik), tisagenlecleucel (Kymriah), venetoclax (Venclexta), vorinostat (Zolinza), zanubrutinib (Brukinsa); Targeted therapy approved for malignant mesothelioma: ipilimumab (Yervoy), nivolumab (Opdivo); Targeted therapy approved for multiple myeloma: bortezomib (Velcade), carfdzomib (Kyprolis), ciltacabtagene autoleucel (Carvykti), daratumumab (Darzalex), daratumumab and hyaluronidase-fihj (Darzalex Faspro), elranatamab-bcmm (Elrexfio), elotuzumab (Empliciti), idecabtagene vicleucel (Abecma), isatuximab-irfc (Sarclisa), ixazomib citrate (Ninlaro), talquetamab-tgvs (Talvey), selinexor (Xpovio), teclistamab-cqyv (Tecvayli); Targeted therapy approved for myelodysplastic and myeloproliferative disorders: fedratinib hydrochloride (Inrebic), imatinib mesylate (Gleevec), ivosidenib (Tibsovo), momelotinib dihydrochloride monohydrate (Ojjaara), pacritinib citrate (Vonjo), pemigatinib (Pemazyre), ruxolitinib phosphate (Jakafi); Targeted therapy approved for neuroblastoma: dinutuximab (Unituxin), naxitamab-gqgk (Danyelza); Targeted therapy approved for ovarian epithelial, fallopian tube, and primary peritoneal cancers: bevacizumab (Avastin), mirvetuximab soravtansine-gynx (Elahere), niraparib tosylate monohydrate (Zejula), olaparib (Lynparza), rucaparib camsylate (Rubraca); Targeted therapy approved for pancreatic cancer: belzutifan (Welireg), erlotinib hydrochloride (Tarceva), everolimus (Afinitor), olaparib(Lynparza), sunitinib malate (Sutent); Targeted therapy approved for plexiform neurofibroma: selumetinib sulfate (Koselugo); Targeted therapy approved for prostate cancer: abiraterone acetate (Zytiga), apalutamide (Erleada), cabazitaxel (Jevtana), darolutamide (Nubeqa), enzalutamide (Xtandi), lutetium Lu 177 vipivotide tetraxetan (Pluvicto), niraparib tosylate monohydrate and abiraterone acetate (Akeega), olaparib (Lynparza), talazoparib tosylate (Talzenna), radium 223 dichloride (Xofigo), rucaparib camsylate (Rubraca); Targeted therapy approved for skin cancer: alitretinoin (Panretin), atezolizumab (Tecentriq), avelumab (Bavencio), binimetinib (Mektovi), cemiplimab-rwlc (Libtayo), cobimetinib fumarate (Cotellic), dabrafenib mesylate (Tafinlar), encorafenib (Braftovi), ipilimumab (Yervoy), nivolumab (Opdivo), nivolumab and relatlimab- rmbw (Opdualag), pembrolizumab (Keytruda), retifanlimab-dlwr (Zynyz), sonidegib (Odomzo), tebentafusp-tebn (Kimmtrak), trametinib dimethyl sulfoxide (Mekinist), vismodegib (Erivedge), vemurafenib (Zelboraf); Targeted therapy approved for soft tissue sarcoma: alitretinoin (Panretin), atezolizumab (Tecentriq) , crizotinib (Xalkori), nirogacestat hydrobromide (Ogsiveo), pazopanib hydrochloride (Votrient), sirolimus protein-bound particles (Fyarro), tazemetostat hydrobromide (Tazverik); Targeted therapy approved for solid tumors anywhere in the body: dabrafenib mesylate (Tafinlar), dostarlimab-gxly (Jemperli), entrectinib (Rozlytrek), larotrectinib sulfate (Vitrakvi), pembrolizumab (Keytruda), selpercatinib (Retevmo), trametinib dimethyl sulfoxide (Mekinist); Targeted therapy approved for stomach (gastric) cancer: fam -trastuzumab deruxtecan-nxki (Enhertu), nivolumab (Opdivo), pembrolizumab (Keytruda), ramucirumab (Cyramza), trastuzumab (Herceptin); Targeted therapy approved for systemic mastocytosis: avapritinib (Ayvakit), imatinib mesylate (Gleevec), midostaurin (Rydapt); Targeted therapy approved for thyroid cancer: cabozantinib-s-malate (Cometriq), dabrafenib mesylate (Tafinlar), lenvatinib mesylate (Lenvima), pralsetinib (Gavreto), selpercatinib (Retevmo), sorafenib tosylate (Nexavar), trametinib dimethyl sulfoxide (Mekinist), vandetanib (Caprelsa).

[0124] Cytotoxic therapies are treatment approaches that can kill cancer cells or slow their growth. Non-limiting cytotoxic therapies include chemotherapy, low-dose chemotherapy, and metronomic chemotherapy. Non-limiting examples of chemotherapy include Nucleoside analogues: Azacitidine, Capecitabine, Carmofur, Cladribine, Clofarabine, Cytarabine, Decitabine, Floxuridine, Fludarabine, Fluorouracil, Gemcitabine, Mercaptopurine, Nelarabine, Pentostatin, Tegafur, Tioguanine; Antifolates: Methotrexate, Pemetrexed, Raltitrexed; Other antimetabolites:Hydroxycarbamide; Topoisomerase I inhibitor: Irinotecan, Topotecan; Anthracyclines: Daunorubicin, Doxorubicin, Epirubicin, Idarubicin, Mitoxantrone, Valrubicin; Podophyllotoxins: Etoposide, Teniposide; Taxanes: Cabazitaxel, Docetaxel, Paclitaxel; Vinca alkaloids: Vinblastine, Vincristine, Vindesine, Vinflunine, Vinorelbine; Alkylating agents: Bendamustine, Busulfan, Carmustine, Chlorambucil, Chlormethine, Cyclophosphamide, Dacarbazine, Fotemustine, Ifosfamide, Lomustine, Melphalan, Streptozotocin, Temozolomide; Platinum compounds: Carboplatin, Cisplatin, Nedaplatin, Oxaliplatin; Miscellaneous others: Altretamine, Bleomycin, Bortezomib, Dactinomycin, Estramustine, Ixabepilone, Mitomycin, Procarbazine.Identifying Genetic Elements Driving Tumor Fitness

[0125] In an embodiment, the members of the forward genetics library are identified in surviving cells or organoids after one or more cell growth pressures are applied. The genetic elements or perturbations can be identified by sequencing. In an embodiment, at least one barcode sequence can identify each genetic element or perturbation. In an embodiment, barcodes in a population of positively selected cells are amplified (e.g., dial-out PCR) and sequenced.

[0126] In an embodiment, sequencing comprises high-throughput (formerly "nextgeneration") technologies to generate sequencing reads. In DNA sequencing, a read is an inferred sequence of base pairs (or base pair probabilities) corresponding to all or part of a single DNA fragment. A typical sequencing experiment involves the fragmentation of the genome into millions of molecules or the generation of complementary DNA (cDNA) fragments, which are size-selected and ligated to adapters. The set of fragments is referred to as a sequencing library, which is sequenced to produce a set of reads. Methods for constructing sequencing libraries are known in the art (see, e.g., Head et al., Library construction for next-generation sequencing: Overviews and challenges. Biotechniques. 2014; 56(2): 61-77; and Trombetta, J. J., Gennert, D., Lu, D., Satija, R., Shalek, A. K. & Regev, A. Preparation of Single-Cell RNA-Seq Libraries for Next Generation Sequencing. Curr Protoc Mol Biol. 107, 422 21-2422 17, doi: 10.1002 / 0471142727.mb0422sl07 (2014). PMCID:4338574). A “library” or “fragment library” may be a collection of nucleic acid molecules derived from one or more nucleic acid samples in which fragments of nucleic acid have been modified, generally by incorporating terminal adapter sequences comprising one or more primer binding sites and identifiable sequence tags. In an embodiment, the library members (e.g.,genomic DNA, cDNA) may include sequencing adaptors that are compatible with use in, e.g., Illumina's reversible terminator method, long read nanopore sequencing, Roche's pyrosequencing method (454), Life Technologies' sequencing by ligation (the SOLiD platform), PacBio long read sequencing, or Life Technologies Ion Torrent platform. Recent advances in long-read sequencing have enabled sequencing full-length transcripts; Pacific Biosciences (PacBio) single-molecule real-time (SMRT) sequencing and Oxford Nanopore Technologies (ONT) nanopore sequencing can generate reads >10 Kb (see, e.g., Amarasinghe SL, Su S, Dong X, Zappia L, Ritchie ME, Gouil Q. Opportunities and challenges in long-read sequencing data analysis. Genome Biol. 2020;21(l):30). Examples of such methods are described in the following references: Margulies etal. (Nature 2005 437: 376-80); Schneider and Dekker (Nat Biotechnol. 2012 Apr 10;30(4):326- 8); Ronaghi et al. (Analytical Biochemistry 1996 242: 84-9); Shendure et al. (Science 2005 309: 1728-32); Imelfort et al (Brief Bioinform. 2009 10:609-18); Fox et al (Methods Mol. Biol. 2009; 553:79-108); Appleby et al (Methods Mol. Biol. 2009; 513: 19-39); Wenger, A. M., et al. (2019) Accurate circular consensus long-read sequencing improves variant detection and assembly of a human genome. Nature Biotechnology, 37, 1155-1162, Leung SK, Jeffries AR, Castanho I, et al. Full-length transcript sequencing of human and mouse cerebral cortex identifies widespread isoform diversity and alternative splicing. Cell Rep. 2021;37(7): l 10022, Gordon SP, Tseng E, Salamov A, et al. Widespread Polycistronic Transcripts in Fungi Revealed by Single-Molecule mRNA Sequencing. PLoS One. 2015;10(7):e0132628, and Morozova et al. (Genomics. 2008 92:255-64), which are incorporated by reference for the general descriptions of the methods and the particular steps of the methods, including all starting products, reagents, and final products for each of the steps.

[0127] In an embodiment, single-cell states are determined for genetic elements by a singlecell sequencing method, whereby cell states driving tumor fitness are identified. As used herein, the term 'tumor fitness' refers to the ability of cancer cells to survive, proliferate, and / or maintain their phenotype under various conditions, challenges, or selective pressures. Tumor fitness may encompass resistance to treatments (e.g., chemotherapy, targeted therapy, immunotherapy), adaptation to microenvironmental conditions (e.g., hypoxia, nutrient deprivation), evasion of immune surveillance, and / or maintenance of cancer-specific properties. Tumor fitness may beassessed through various measures, including, but not limited to, cell viability, proliferation rate, colony formation, tumor growth, metastatic capability, and resistance to cell death.

[0128] In an embodiment, the disclosure involves single-cell RNA sequencing (see, e.g., Qi Z, Barrett T, Parikh AS, Tirosh I, Puram SV. Single-cell sequencing and its applications in head and neck cancer. Oral Oncol. 2019;99: 104441; Kalisky, T., Blainey, P. & Quake, S. R. Genomic Analysis at the Single-Cell Level. Annual review of genetics 45, 431-445, (2011); Kalisky, T. & Quake, S. R. Single-cell genomics. Nature Methods 8, 311-314 (2011); Islam, S. et al. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Research, (2011); Tang, F. et al. RNA-Seq analysis to capture the transcriptome landscape of a single cell. Nature Protocols 5, 516-535, (2010); Tang, F. et al. mRNA-Seq whole- transcriptome analysis of a single cell. Nature Methods 6, 377-382, (2009); Ramskold, D. et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nature Biotechnology 30, 777-782, (2012); and Hashimshony, T., Wagner, F., Sher, N. & Yanai, I. CEL-Seq: Single-Cell RNA-Seq by Multiplexed Linear Amplification. Cell Reports, Volume 2, Issue 3, p666-673, 2012).

[0129] In an embodiment, the disclosure involves plate-based single-cell RNA sequencing (see, e.g., Picelli, S. etal., 2014, “Full-length RNA-seq from single cells using Smart-seq2” Nature protocols 9, 171-181, doi:10.1038 / nprot.2014.006).

[0130] In an embodiment, the disclosure involves high-throughput single-cell RNA-seq. In this regard, reference is made to Macosko et al., 2015, “Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets” Cell 161, 1202-1214; International patent application number PCT / US2015 / 049178, published as WO2016 / 040476 on March 17, 2016; Klein et al., 2015, “Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells” Cell 161, 1187-1201; International patent application number PCT / US2016 / 027734, published as WO2016168584A1 on October 20, 2016; Zheng, etal., 2016, “Haplotyping germline and cancer genomes with high-throughput linked-read sequencing” Nature Biotechnology 34, 303-311; Zheng, et al., 2017, “Massively parallel digital transcriptional profiling of single cells” Nat. Commun. 8, 14049 doi: 10.1038 / ncommsl4049; International patent publication number WO2014210353A2; Zilionis et al., 2017, “Single-cell barcoding and sequencing using droplet microfluidics” Nat Protoc. Jan;12(l):44-73; Cao et al., 2017, “Comprehensive single-celltranscriptional profiling of a multicellular organism by combinatorial indexing” bioRxiv preprint first posted online Feb. 2, 2017, doi: dx.doi.org / 10.1101 / 104844; Rosenberg et al., 2017, “Scaling single cell transcriptomics through split pool barcoding” bioRxiv preprint first posted online Feb. 2, 2017, doi: dx.doi.org / 10.1101 / 105163; Rosenberg etal., “Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding” Science 15 Mar 2018; Vitak et al., “Sequencing thousands of single-cell genomes with combinatorial indexing” Nature Methods, 14(3):302-308, 2017; Cao et al., Comprehensive single-cell transcriptional profiling of a multicellular organism. Science, 357(6352):661-667, 2017; Gierahn et al., “Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput” Nature Methods 14, 395-398 (2017); and Hughes et al., “Highly Efficient, Massively-Parallel Single-Cell RNA-Seq Reveals Cellular States and Molecular Features of Human Skin Pathology” bioRxiv 689273; doi: doi.org / 10.1101 / 689273, all the contents and disclosure of each of which are herein incorporated by reference in their entirety.

[0131] In an embodiment, the disclosure involves single-nucleus RNA sequencing. In this regard reference is made to Swiech et al., 2014, "In vivo interrogation of gene function in the mammalian brain using CRISPR-Cas9” Nature Biotechnology Vol. 33, pp. 102-106; Habib etal., 2016, “Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons” Science, Vol. 353, Issue 6302, pp. 925-928; Habib etal., 2017, “Massively parallel single-nucleus RNA-seq with DroNc-seq” Nat Methods. 2017 Oct;14(10):955-958; International Patent Application No. PCT / US2016 / 059239, published as WO2017164936 on September 28, 2017; International Patent Application No.PCT / US2018 / 060860, published as WO / 2019 / 094984 on May 16, 2019; International Patent Application No. PCT / US2019 / 055894, published as WO / 2020 / 077236 on April 16, 2020; Drokhlyansky et al., “The enteric nervous system of the human and mouse colon at a single-cell resolution,” bioRxiv 746743; doi: doi.org / 10.1101 / 746743; and Drokhlyansky E, Smillie CS, Van Wittenberghe N, etal. The Human and Mouse Enteric Nervous System at Single-Cell Resolution. Cell. 2020; 182(6): 1606-1622. e23, which are herein incorporated by reference in their entirety.

[0132] In an embodiment, the disclosure involves the Assay for Transposase Accessible Chromatin using sequencing (ATAC-seq) as described, (e.g., Buenrostro et al., Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-bindingproteins, and nucleosome position. Nature Methods 2013; 10 (12): 1213-1218; Buenrostro et al., Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486-490 (2015); Cusanovich, D. A., Daza, R., Adey, A., Pliner, H., Christiansen, L., Gunderson, K. L., Steemers, F. J., Trapnell, C. & Shendure, J. Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing. Science. 2015 May 22;348(6237):910-4. doi: 10.1126 / science.aabl601. Epub 2015 May 7; US20160208323A1; US20160060691A1; and WO2017156336A1). In an embodiment, the single-cell genomics sequencing library is a singlecell Assay for Transposase Accessible Chromatin using sequencing (ATAC-seq) sequencing library. ATAC-seq can identify accessible chromatin in a cell (see, e.g., Buenrostro et al., Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins, and nucleosome position. Nature Methods 2013; 10 (12): 1213-1218). Using plate-, droplet-, or combinatorial indexing-based methods, thousands to hundreds of thousands of individual cells / nuclei can be analyzed in a single sample (see, e g., Buenrostro etal., Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486-490 (2015); Cusanovich, D. A., Daza, R., Adey, A., Pliner, H., Christiansen, L., Gunderson, K. L., Steemers, F. J., Trapnell, C. & Shendure, J. Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing. Science. 2015 May 22;348(6237):910-4. doi: 10.1126 / science.aabl601. Epub 2015 May 7; Cusanovich DA, Hill AJ, Aghamirzaie D, et al. A Single-Cell atlas of in vivo mammalian chromatin accessibility. Cell. 2018;174: 1309-24; Lake BB, Chen S, Sos BC, et al. Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain. Nat Biotechnol. 2018;36:70-80; Preissl S, Fang R, Huang H, et al. Single-nucleus analysis of accessible chromatin in developing mouse forebrain reveals cell-type- specific transcriptional regulation. Nat Neurosci. 2018;21 :432-9; Satpathy AT, Granja JM, Yost KE, etal. Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion. Nat Biotechnol. 2019;37:925-36; Xu W, Wen Y, Liang Y, et al. A plate-based single-cell ATAC-seq workflow for fast and robust profiling of chromatin accessibility. Nat Protoc. 2021;16:4084-107; US20160208323A1; US20160060691A1; and WO2017156336A1). Single nuclei ATAC-seq can also be performed by partitioning nuclei in droplets and subsequent snATAC-Seq library construction using the Chromium Next GEM Single Cell ATAC Reagent Kit vl.l (10 x Genomics, Pleasanton, CA, USA) (see, e.g., Briel N, Ruf VC,Pratsch K, et al. Single-nucleus chromatin accessibility profiling highlights distinct astrocyte signatures in progressive supranuclear palsy and corticobasal degeneration. Acta Neuropathol. 2022;144(4):615-635).

[0133] In an embodiment, the single-cell states are determined by multimodal methods (see, e.g., Lee J, Hyeon DY, Hwang D. Single-cell multi-omics: technologies and data analysis methods. Exp Mol Med. 2020;52(9): 1428-1442. doi: 10.1038 / sl2276-020-0420-2). In an embodiment, SHARE-Seq (Ma, S. et al. Chromatin potential identified by shared single-cell profiling of RNA and chromatin. bioRxiv 2020.06.17.156943 (2020) doi: 10.1101 / 2020.06.17.156943) is used to generate single-cell RNA-seq and chromatin accessibility data. In an embodiment, CITE-seq (Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865-868 (2017)) (cellular proteins) is used to generate single-cell RNA-seq and proteomics data. In an embodiment, chromatin accessibility may be accessed using scATAC-seq. Buenrostro et al. Nature 523, 486-490 (2015).Validation assays

[0134] In an embodiment, genetic elements identified in pooled screens, as described herein, are validated individually in validation assays. For example, the identified genetic elements can be individually perturbed or expressed in one or more cancer cell lines, in vitro cell models, ex vivo organoid models, or in vivo tumor models, followed by detecting resistance to the cell growth pressure or a resistance gene signature. In an embodiment, a single-cell sequencing method is performed on one or more cancer cell lines, in vitro cell models, ex vivo organoid models, or in vivo tumor models, whereby cell states driving tumor fitness are identified for individual genetic elements.

[0135] In an embodiment, genetic elements are validated in a tumor model. In an embodiment, genetic elements are introduced to tumor cells implanted into a mouse model. The mouse model can be treated with the cell growth pressure or a specific treatment (e.g., IFN-y), and resistance can be compared to wild-type tumor cells. In an embodiment, in vivo, cell states are identified using single -cell methods for one or more tumor samples obtained from the mouse tumor model. Non-limiting examples of tumor mouse models include the CT26 colon carcinoma, MC38-Ova colon carcinoma, and B16F10 melanoma models (see, e.g., Singer, M. et al. A Distinct GeneModule for Dysfunction Uncoupled from Activation in Tumor-Infdtrating T Cells. Cell 171, 1221 - 1223 (2017); and Kurtulus, S. et al. Checkpoint Blockade Immunotherapy Induces Dynamic Changes in PD-1(-)CD8(+) Tumor-Infiltrating T Cells. Immunity 50, 181-194 el86 (2019)).

[0136] In an embodiment, the tumor sample can be obtained over a time course to capture interactions occurring at specific time points during tumor progression (see, e.g., International Patent Application Nos. PCT / US2018 / 053791, PCT / US2018 / 061812). The time course can be from 0 to 365 days after implantation of a tumor in the mouse model. Time points can be taken on any day within the time course. In preferred embodiments, the time course lasts about 20 days and includes samples taken at about 5, 10, 15, and 20 days.

[0137] In an embodiment, tumor samples can be obtained from one or more subjects suffering from cancer and being treated (e.g., immunotherapy, chemotherapy, or targeted agents). The sample may be fresh or frozen. Samples can be obtained from a subject throughout the cancer treatment process, both before and after treatment. In an embodiment, mechanisms of resistance are confirmed in patient samples.Secondary screening

[0138] In an embodiment, validated genetic elements are screened in cancer cells to identify dependencies or synthetic lethality(ies). As used herein, the term 'synthetic lethality' refers to relationships between two or more genes, proteins, or pathways where the simultaneous disruption or inhibition of these elements results in cell death. In contrast, disruption of any single element alone does not. In the context of cancer, synthetic lethality may occur between a genetic alteration present in cancer cells (e.g., a mutation, deletion, or altered gene expression) and a therapeutic intervention targeting a different gene or pathway. Synthetic lethality can be leveraged to develop therapeutic strategies that selectively affect cancer cells while sparing normal cells. Synthetic lethality may be identified through genetic or pharmacological screening approaches in cancers resistant to cell growth pressure. In an embodiment, one or more genetic elements are introduced into cancer cells (e g., a cancer cell line) to render them resistant (e.g., to a transcription factor described herein, such as TP63, TP73, PDX1, and FOXP1). The cancer cells can be treated with the cell growth pressure or a specific treatment (e.g., IFN-y) and one or more candidate agents. In an embodiment, the candidate agents can be selected based on targeting particular gene signaturesidentified in the resistant cancer cells. Thus, in an embodiment, signatures associated with cell growth pressure can be targeted to make the cancer cells sensitive to cell growth pressure.

[0139] The term “agent” broadly encompasses any condition, substance, or agent capable of modulating one or more phenotypic aspects of a cell or cell population as disclosed herein. Such conditions, substances, or agents may be physical, chemical, biochemical, and / or biological. The term “candidate agent” refers to any condition, substance, or agent that is being examined for the ability to modulate one or more phenotypic aspects of a cell or cell population as disclosed herein in a method comprising applying the candidate agent to the cell or cell population (e.g., exposing the cell or cell population to the candidate agent or contacting the cell or cell population with the candidate agent) and observing whether the desired modulation takes place. Agents may include any potential class of biologically active conditions, substances, or agents, such as antibodies, proteins, peptides, nucleic acids, oligonucleotides, small molecules, genetic modifying agents (described further herein), or combinations thereof, as described herein. In an embodiment, one or more agents can be drug candidates, small molecules, biologies, and programmable nucleases targeting one or more target genes.

[0140] In an embodiment, validated genetic elements are screened in cancer organoids to identify dependencies or synthetic lethalities in cancers resistant to cell growth pressure. Genetic elements are introduced to cancer stem cells in an embodiment and expanded in a cellular matrix to obtain resistant organoids. The cancer organoids can be treated with the cell growth pressure or a specific treatment (e.g., IFN-y) and one or more candidate agents. In an embodiment, the candidate agents can be selected based on targeting specific gene signatures identified in the resistant cancer cells. Thus, in an embodiment, signatures associated with cell growth pressure can be targeted to make the cancer organoids sensitive to the cell growth pressure.

[0141] In an embodiment, validated genetic elements are screened in a tumor model to identify dependencies or synthetic lethalities in cancers resistant to cell growth pressure. In an embodiment, genetic elements are introduced to tumor cells to make them resistant, and the tumor cells are implanted into a mouse model to obtain a resistant tumor mouse model. The mouse model can be treated with the cell growth pressure or a specific treatment (e.g., IFN-y) and one or more candidate agents. In an embodiment, the candidate agents can be selected based on targeting specific gene signatures identified in the resistant cancer cells. Thus, in an embodiment, signatures associatedwith a cell growth pressure can be targeted to allow the cancer to be then sensitive to the cell growth pressure.

[0142] In an embodiment, the screening methods described herein may be optimized through adjustment of various parameters to enhance sensitivity, specificity, and robustness. The intensity of the cell growth pressure, such as concentration of cytokines or ratio of immune cells to cancer cells, may be titrated to achieve appropriate selection stringency. Generally, the pressure should induce approximately 70-90% cell death in the control population to allow for robust detection of resistance mechanisms.

[0143] The time period over which cell growth pressure is applied can be optimized based on the specific pressure and cell type. Typically, cytokine treatments require 48-96 hours, while coculture with immune cells requires 24-72 hours. The representation of each library member, referring to the number of cells expressing each transcription factor, may be adjusted to ensure adequate statistical power, with a minimum of 500-1000 cells per library member recommended at the beginning of the screen. The depth of sequencing used to identify enriched genetic elements may be adjusted based on library complexity and expected effect sizes, with a minimum of 100- 200 reads per library member generally recommended. The number of biological replicates may be adjusted based on expected variability, with a minimum of 3-6 replicates generally recommended to ensure robust identification of enriched genetic elements.

[0144] Various technical challenges may arise in implementing the screening methods. If the transduction efficiency of the forward genetics library is low, optimization of viral titer, adjustment of multiplicity of infection, or use of alternative delivery methods such as electroporation may be employed, along with the selection of more amenable cell lines. If a high background of resistant cells is observed in the control population, increasing the intensity of cell growth pressure, applying cell growth pressure sequentially, using a combination of cell growth pressures, or selecting more sensitive cell lines may be considered. If resistant cells cannot be clearly distinguished from non-resistant cells, the addition of reporter systems, flow cytometry-based sorting, temporal analysis, or single-cell analysis may be employed. If high variability is observed between biological replicates, standardizing experimental conditions, increasing the number of biological replicates, or refining statistical analysis methods may be used.

[0145] In an embodiment, candidate agents are selected by signature screening. The concept of signature screening was introduced by Stegmaier et al. (Gene expression-based high-throughput screening (GE-HTS) and application to leukemia differentiation. Nature Genet. 36, 257-263 (2004)), who realized that if a gene-expression signature was the proxy for a phenotype of interest, it could be used to find small molecules that effect that phenotype without knowledge of a validated drug target. The signatures of the present disclosure associated with resistance mechanisms may be used to screen for drugs that reduce the signature in cells, as described herein. The signature may be used for GE-HTS. In an embodiment, pharmacological screens may be used to identify selectively toxic drugs to cells having a signature.

[0146] The Connectivity Map (cmap) is a collection of genome-wide transcriptional expression data from cultured human cells treated with bioactive small molecules and simple pattern-matching algorithms that together enable the discovery of functional connections between drugs, genes, and diseases through the transitory feature of common gene expression changes (see, Lamb et al., The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease. Science 29 Sep 2006: Vol. 313, Issue 5795, pp. 1929-1935, DOI: 10.1126 / science.1132939; and Lamb, J., The Connectivity Map: a new tool for biomedical research. Nature Reviews Cancer January 2007: Vol. 7, pp. 54-60). In an embodiment, Cmap can be used to screen for small molecules capable of modulating a signature of the present disclosure in silico.

[0147] In an embodiment, small molecules are derived from a combinatorial library containing many potential therapeutic compounds. A combinatorial chemical library may be a collection of diverse chemical compounds generated by chemical or biological synthesis by combining several chemical "building blocks" such as reagents. For example, a linear combinatorial chemical library, such as a polypeptide library, is formed by combining a set of chemical building blocks (amino acids) in every possible way for a given compound length (for example, the number of amino acids in a polypeptide compound). Millions of chemical compounds can be synthesized through such combinatorial mixing of chemical building blocks. Appropriate agents can be contained in libraries, such as synthetic or natural compounds in a combinatorial library. Numerous libraries are commercially available or can be readily produced, which means that random and directed synthesis of a wide variety of organic compounds and biomolecules, including the expression ofrandomized oligonucleotides such as antisense oligonucleotides and oligopeptides, is also possible. Alternatively, libraries of natural compounds in the form of bacterial, fungal, plant, and animal extracts are available or can be readily produced. Additionally, natural or synthetically produced libraries and compounds can be readily modified through conventional chemical, physical, and biochemical means, and may be used to produce combinatorial libraries. Such libraries are useful for the screening of a large number of different compounds.Diagnostic Methods

[0148] The disclosure provides biomarkers (e.g., phenotype-specific or cell type) for the identification, diagnosis, prognosis, and manipulation of cell properties for various diagnostic and / or therapeutic indications. Biomarkers, as used in the context of the present disclosure, encompass, without limitation, nucleic acids, proteins, reaction products, metabolites, and their corresponding polymorphisms, mutations, variants, modifications, subunits, fragments, and other analytes or sample-derived measures. In certain embodiments, biomarkers include the signature genes, signature gene products, and / or cells as described herein.

[0149] In an embodiment, the detection of expression of a transcription factor (e.g., TP63, TP73, PDX1, and FOXP1) or a gene signature regulated by a transcription factor indicates that a tumor is resistant to immunotherapy, such as IFN-y. In an embodiment, the transcription factor for resistance to IFN-y may be TP73, TP63, FOXP1, PDX1, JUN, HAND2, POU3F1, IRF2, GLI1, TCF21, FOXN4, HOMES, JUNB, HANOI, IKZF3, TBX6, TBX4, GFI1B, CREM, POU2F1, PPARG, MSC, NKX3, ZNF415, FOXP4, ZNF396, IRF2, HNF1B, FIGLA, NFKBIZ, POU5F1, PBX1, IKZF1, SUPT4H1, ASCL1, PBX3, ZNF7, MEIS2, UBP1, BCL6, FOXF2, NR1I3, CREBL2, GRHL1, RUNX2, FLU, GLIS1, POU2F2, ERG, IRF6, HSF5, MEF2C, EGR2, ALX3, TBX5, TBPL1, TBXT, H0XB2, BHLHE40, ZNF771, FOXD4L5, ZNF256, SMAD2, HESX1, POU4F3, ZNF680, and / or RELB. In an embodiment, if the expression of a transcription factor (e.g., TP63, TP73, PDX1, and FOXP1) or a gene signature regulated by a transcription factor is detected in a subject, the subject is treated according to any embodiment herein.

[0150] In an embodiment, the transcription factors identified herein may confer resistance to cell growth pressures through multiple potential mechanisms. The transcription factors such as TP63, TP73, PDX1, and FOXP1 may modulate resistance through various cellular processes andpathways. These transcription factors may directly regulate the expression of components of the IFN-y signaling pathway, including but not limited to IFNGR1, IFNGR2, JAK1, JAK2, STAT1, and IRF1. In some embodiments, TP63 or TP73 may repress STAT1 activation or induce expression of negative regulators of the pathway, such as SOCS proteins.

[0151] The identified transcription factors may also drive the transition to a cell state with intrinsic resistance to immune pressure. For instance, TP63 and TP73 may induce a basal-like or mesenchymal-like state characterized by reduced antigen presentation, altered cytokine secretion, and modified immune cell attraction or activation. Furthermore, these transcription factors may downregulate components of the antigen processing and presentation machinery, including MHC class I molecules (HLA-A, HLA-B, HLA-C), p2-microglobulin, TAP1 / 2, or components of the immunoproteasome (PSMB8, PSMB9, PSMB10).

[0152] In certain embodiments, the transcription factors may upregulate anti-apoptotic factors (e.g., BCL2, BCL-XL, MCL1) or downregulate pro-apoptotic factors (e.g., BAX, BAK, BIM), thereby enhancing cell survival under immune attack. The transcription factors may also reprogram cellular metabolism to allow survival under immune pressure, including modulation of glycolysis, oxidative phosphorylation, or amino acid metabolism. Additionally, they may alter the profile of secreted cytokines, chemokines, and other factors to create an immunosuppressive microenvironment.

[0153] In specific embodiments, the mechanism of resistance may be identified through further analyses, such as RNA-seq, ChlP-seq, or ATAC-seq, to identify the direct targets of the transcription factors, or through pathway analysis to identify affected signaling pathways.

[0154] In one embodiment, the signature genes, biomarkers, and / or cells may be detected or isolated by immunofluorescence, immunohistochemistry (IHC), fluorescence-activated cell sorting (FACS), mass spectrometry (MS), mass cytometry (CyTOF), RNA-seq, single-cell RNA- seq (described further herein), quantitative RT-PCR, single-cell qPCR, FISH, RNA-FISH, MERFISH (multiplex (in situ) RNA FISH) and / or by in situ hybridization. Other methods, including absorbance and colorimetric assays, are known in the art and may be used herein. Detection may comprise primers and / or probes or fluorescently bar-coded oligonucleotide probes for hybridization to RNA (see, e.g., Geiss GK et al., Direct multiplexed measurement of gene expression with color-coded probe pairs. Nat Biotechnol. 2008 Mar;26(3):317-25).

[0155] The following Examples illustrate further embodiments. They are given for illustrative purposes only and are not intended to limit the scope of the disclosure.EXAMPLESExample 1 - Transcriptional Factor over-expression screen under IFNy pressure

[0156] Historically, mutations have guided cancer therapy, but many cancers lack tractable genetic targets for effective treatment. Pancreatic ductal adenocarcinoma (PDAC) is an example of such a cancer. As such, there is a need for different approaches to evaluating and treating such cancers. Using PDAC as an example, PDAC tumors exhibit diverse transcriptional programs or “cell states”, which have prognostic implications. PDAC tumors in the “classical state” generally have better outcomes and are believed to be more responsive to chemotherapy, while those in the “basal state” are more aggressive. The same is observed in other cancers with different states being associated with different outcomes and / or resistance to conventional therapies. Currently, cell state is not used to direct treatment.

[0157] Cell state can be characterized by the transcriptional program expressed by the cell. Cell state integrates intrinsic (e.g., mutations, epigenetics, etc.) and extrinsic (e.g., cytokines, other environmental factors) factors. See, e.g., FIG. 11. Single-cell analysis of PDAC tumor samples revealed heterogeneity of cell state and cell state plasticity (see e.g., Moffitt et al., Nature Genet. 2015, 47(10), 1168-1178, and Raghavan el al., Cell, 2021, 184, 6119-6137). However, the drivers of these cell states and their therapeutic vulnerabilities remain unknown. Without being bound by theory, the Applicant hypothesizes that transcription factors can drive cell state heterogeneity and plasticity (e.g., FIG. 12).

[0158] To efficiently identify transcription factors (TFs) that drive cell states, Applicants generated a novel screen utilizing pooled over-expression of all human TF isoforms on well- selected cancer cell lines across different cancer types (FIG. 1). In an embodiment, the first step includes selecting a cancer cell line that faithfully recapitulates an in vivo tumor for cancer of interest. For example, the cancer cell line represents one or more patient features, such as similar tumor mutations, gene expression programs, or metastatic or migration properties. An exemplary graph shows the degree of patient features one and two in different cancer cell lines. In anembodiment, the second step includes overexpressing a transcription factor open reading frame library in a selected cell line and applying an immune pressure to the cells, such as interferongamma (IFN-y) treatment. In an embodiment, the third step includes a positive selection of cells that survive the immune pressure. The TFs overexpressed in the surviving cells can be identified by sequencing. Sequencing can consist of a dial-out PCR of a sequence identifying each transcription factor. Enrichment of each transcription factor can be determined. The method can be repeated with additional cell lines for a particular cancer or different cancer types. In an embodiment, the fourth step includes comparing overexpressed transcription factor hits identified for multiple conditions, such as different immune pressures or cancer cell lines. For example, the graph shows hits enriched at similar levels in conditions one and two. In an embodiment, step 4 also compares consensus hits with patient data to determine whether the overexpression of the transcription factors corresponds to responders and non-responders to immune pressures (e.g., immunotherapy). In an embodiment, gene signatures associated with each transcription factor are also compared. In an embodiment, the fifth step includes validating individual hits in the cancer cell lines by overexpressing each hit individually in a cancer cell line and determining viability and / or gene expression in single cells overexpressing each hit. A viability graph and clustering of individual cells by gene expression are shown using a dimension reduction method (e.g., UMAP, tSNE). In an embodiment, the sixth step includes identifying common transcriptional states that provide a fitness advantage in the cancer cells. For example, cells overexpressing different transcription factors with a fitness advantage may induce common transcriptional states. In an embodiment, the seventh step includes secondary screens on resistant cells either by overexpressing an identified transcription factor or cell lines that express an identified resistant signature to identify therapeutic vulnerabilities in the resistant cancer cells. Shown are performing a drug screen of resistant cells or a CRISPR KO screen in resistant cells.

[0159] Applicants over-expressed a transcription factor ORF library (Joung el al., 2023, Cell 186, 209-229. e26) in 6 pancreatic cancer cell lines and determined the enrichment of each transcription factor construct in cells having a fitness advantage (FIG. 2). Applicants identified a list of TFs that can drive fitness advantage in pancreatic cancer cell lines under IFN-y stress. Applicants confirmed that some TF hits are enriched in patients post immunotherapies, adding further confidence in clinical relevance (FIG. 3). Applicants also validated four hits (TP63, TP73,PDX1, and F0XP1) from the screen for their fitness advantage in vitro (FIG. 4). Applicants performed scRNA-seq in cells overexpressing individual transcription and identified differentially expressed genes in the single cells overexpressing each transcription factor (FIG. 5). Moreover, Applicants demonstrated that some TF hits (TP63A and TP73) converge to a common state (basal signature).

[0160] The same workflow can be applied to other cancer types (e.g., melanoma, bladder cancer, NSCLC, triple-negative breast cancer, etc.). Applicant used the immune pressure positive selection screen on four cancer cell lines that spanned three cancer lineages (FIG. 6). Results are shown in FIGS. 7-8 and Tables 1 and 2 show cell line specific and consensus hits, respectively. Exemplary cell lines for evaluating the Basal or EMT state include BXCP3 (PDAC), WM983B / PRMI7951 (Melanoma), A549 (NSCLC), UMUC6 (bladder cancer) ONCODG1 (ovarian cancer), and HCC1954 (breast cancer). Exemplary cell lines for evaluating non-basal or non-EMT state include HPAFII (PDAC), K029AX (melanoma), NCIH2122 (NSCLC), UMUC7 / UMUC5 (bladder cancer), OAW42 (ovarian cancer), MCF7 and (breast cancer).

[0161] Different pressures (e.g., hypoxia, low nutrients, TNFa, IL1B, drugs, etc.) or combinatorial pressures can be applied to cells.

[0162] The methods allow for validating consensus hits across multiple cancer types to find common resistance mechanisms. Applying the same approach across numerous cancer cell lines can lead to identifying common resistance mechanisms across cancer types. An immune-cancer co-culture system (e.g., T cells-cancer cells, macrophages-cancer cells) can be applied to study specific resistance mechanisms (e.g., T cell killing, macrophage phagocytosis). Secondary CRISPR and drug screens can also be performed in identified IFNy resistant or other selection pressure models to identify selective dependencies.

[0163] The screening platform can allow for a systematic and unbiased identification of transcription factors and associated cell states that can mediate tolerance to the selection pressure in cell types and lineages.

[0164] Applicants established a workflow to complete the screening method within 4 weeks. Promising results can be validated in vitro. Identifying novel resistance mechanisms can identify new therapeutic targets, stratify patients, and / or improve current treatment regimens.Example 2 - Transcriptional factor over-expression screen under therapeutic pressure

[0165] As previously discussed, the transcriptional factor over-expression screen can be performed using different and combinatorial selection pressures. Applicant used the same approach used in Example 1 but utilized different cancer therapeutics as the selection pressure for positive selection in a population of PDACs that overexpress the pooled library of human transcriptional factor isoforms (FIG. 9). Different modalities of therapeutics were utilized (e.g., chemotherapeutics, targeted therapeutics, immunotherapy, and a combination of chemotherapyand immunotherapy) using representative therapeutics (FIG. 9). Results are shown in FIG. 10 and Table 3.Example 3 - Pipeline for generating isogenic cell populations with different cell states

[0166] As previously discussed, single-cell molecular subtyping of PDAC tumors identified RNA state (e.g., cell state) heterogeneity and plasticity within tumors (see e.g., Moffitt et al., Nature Genet. 2015, 47(10), 1168-1178, and Raghavan et al., Cell, 2021, 184, 6119-6137). Without being bound by theory, the RNA / cell state heterogeneity can be useful in prognosis and therapy. There are no good ex vivo or in vitro models that are similar to PDAC tumors. The same deficiency exists with other cancers as well. Applicant has demonstrated a high-throughput positive selection screening platform that can identify cell-state-specific transcription factors responses to selection pressures (e.g., Examples 1 and 2). Building on this, Applicant has generated an efficient platform to generate cell populations with different cell states. An advantage of this platform is that isogeneic cell populations of different cell states can be developed.

[0167] FIG. 13 shows a schematic of this platform. A cell population (such as one or more cancer cell lines) is engineered to overexpress all transcription factors (e.g., all human transcription factors), similar to Examples 1 and 2. After the TFs are overexpressed, cells can be sorted based on the surface expression of surface markers specific to a cell state. The cell state of the sorted cells can be validated using an appropriate method, such as scRNAseq.Example 4 - Application of the Screening Method to Diverse Cancer Types

[0168] In one embodiment, the screening methodology described in Examples 1-3 is applied to identify tumor fitness mechanisms across different cancer types. A panel of cancer cell lines representing diverse cancer types is selected, including but not limited to melanoma (e.g., SK- MEL-28, A375, WM-115), non-small cell lung cancer (e.g., A549, NCI-H1975, NCI-H460), triple-negative breast cancer (e.g., MDA-MB-231, BT-549, Hs578T), colorectal cancer (e.g., HCT116, HT-29, DLD-1), ovarian cancer (e.g., SKOV3, OVCAR-3, A2780), and bladder cancer (e.g., T24, UMUC3, RT4). Each cell line is engineered to express a transcription factor ORF library as described in Example 1.

[0169] The engineered cells are subjected to immune pressure using three independent conditions: (1) treatment with IFN-y (100 ng / ml for 72 hours); (2) treatment with TNF-a (50 ng / ml for 72 hours); and (3) co-culture with activated T cells at an effector-to-target (E:T) ratio of 5: 1 for 24 hours. Following treatment, viable cells are collected and subjected to high-throughput sequencing to identify enriched transcription factors conferring resistance.

[0170] This approach revealed (i) transcription factors conferring resistance specific to a cancer type, (ii) transcription factors conferring resistance across multiple cancer types, and (iii) transcription factors conferring resistance specific to a given immune pressure condition. Hierarchical clustering was performed to identify enrichment patterns, and pathway analysis identified commonly regulated biological processes. Integration of transcription factor profiles with patient data demonstrated clinically relevant resistance mechanisms.

[0171] This example demonstrates the broad applicability of the screening method across cancer types and enables therapeutic strategies designed to overcome resistance to immunotherapy.Example 5 - Method for Identifying and Treating Patients Resistant to Immunotherapy

[0172] This example describes a method for using the transcription factors and gene signatures identified in Examples 1-4 to stratify cancer patients and guide treatment decisions.Materials and MethodsPatient Sample Collection and Processing

[0173] Tumor samples will be obtained from patients with advanced cancer prior to the initiation of immunotherapy. These samples will undergo several processing steps, including RNA extraction and gene expression analysis through bulk RNA-seq or NanoString methods. Additionally, single-cell RNA-seq will be employed to assess cellular heterogeneity. Immunohistochemistry will also be performed for selected transcription factors, such as TP63, TP73, FOXP1, and PDXl .Patient Stratification Based on Resistance Signatures

[0174] The expression levels of the identified transcription factors, including TP63, TP73, FOXP1, and PDX1, will be quantified. Additionally, gene signatures associated with each transcription factor will be assessed. Based on these evaluations, patients will be stratified into potential "responder" and "non-responder" groups. This stratification will consider factors such as high expression of resistance-associated transcription factors, the presence of resistance-associated gene signatures, and the proportion of cells exhibiting resistant states, which will be determined through single-cell analysis.Treatment Decision Algorithm

[0175] Patients predicted to be responders to standard immunotherapy will receive checkpoint inhibitors, such as anti-PD-l / PD-Ll. In contrast, those predicted to be non-responders will be directed towards a combination of therapies that target both checkpoint inhibition and the identified resistance mechanism. Additionally, they may be considered for alternative treatment modalities or clinical trials involving agents that specifically target the identified resistance mechanism.Monitoring Treatment Response

[0176] Serial liquid biopsies will be collected to monitor changes in circulating tumor cells and cell-free DNA. Additionally, the expression of resistance-associated transcription factors andgene signatures will be monitored to detect the development of resistance during treatment. Treatment will be adjusted based on molecular changes observed during monitoring.Expected Results

[0177] Improved stratification of patients for immunotherapy, leading to enhanced response rates in the selected population. Early detection of developing resistance will allow for timely intervention with alternative treatment strategies. Development of a companion diagnostic test for immunotherapy response prediction may then be used.Example 6 - Systematic Identification of Vulnerabilities in Resistant Cancer Cells

[0178] In one embodiment, a systematic approach is used to identify targetable dependencies in cancer cells resistant to immune pressure. Stable cell lines are established to overexpress resistance-associated transcription factors including TP63, TP73, FOXP1, and PDX1. Control cell lines expressing GFP are generated in parallel. Resistance to IFN-y is confirmed by performing viability assays in both resistant and control cell lines.

[0179] The resistant and control cell lines are subjected to genome-wide CRISPR-Cas9 knockout screening using a library targeting approximately 20,000 human genes. Following transduction, the cells are cultured under standard conditions for 14 days. Guide RNA frequencies are quantified by deep sequencing. Genes whose loss selectively impairs viability in the resistant cell lines but not the control lines are identified as candidate synthetic lethal targets.

[0180] In parallel, the resistant and control lines are screened with a library of approximately 1,500 small molecules, including FDA-approved agents and investigational compounds. Compounds that demonstrate selective toxicity in resistant lines are identified, and dose-response curves are generated to confirm selective activity.

[0181] Top hits from the CRISPR and small molecule screens are further validated in multiple resistant cell models. The mechanistic basis of synthetic lethality is explored using pathway enrichment analysis, transcriptomic profiling, and protein interaction assays.

[0182] This approach enabled identification of genes and pathways essential for the survival of resistant cells, and discovery of compounds with selective activity. Mechanistic studies revealed that resistant cells exhibit dependencies in metabolic regulation, DNA repair, epigenetic control,cell cycle progression, and stress response pathways. This example supports the development of combination strategies designed to overcome resistance to immune pressure in cancer therapy.* * *

[0183] Various modifications and variations of the described methods, pharmaceutical compositions, and kits of the invention will be apparent to those skilled in the art without departing from the scope and spirit of the invention. Although the invention has been described in connection with specific embodiments, it will be understood that it is capable of further modifications and that the invention, as claimed, should not be unduly limited to such particular embodiments. Indeed, various modifications of the described modes for carrying out the invention that are obvious to those skilled in the art are intended to be within the scope of the invention. This application is designed to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure that come within known customary practice within the art to which the invention pertains and may be applied to the essential features herein before set forth.

Claims

CLAIMSWhat is claimed:

1. A method of identifying genetic elements driving tumor fitness under a cell growth pressure comprising: a) introducing a forward genetics library to a cancer cell line or cancer organoids, wherein each member of the forward genetics library introduces or modifies a genetic element; b) applying one or more cell growth pressures to the cancer cell line or cancer organoids; and c) identifying members of the forward genetics library in surviving cells or organoids after the one or more cell growth pressures, thereby identifying one or more resistance mechanisms driving tumor fitness.

2. The method of claim 1, wherein the forward genetics library is an open reading frame (ORF) library.

3. The method of claim 2, wherein the ORF library is a transcription factor (TF) ORF library.

4. The method of claim 3, wherein the ORF library comprises about 3500 TF isoform ORFs.

5. The method of claim 4, wherein the forward genetic library comprises CRISPR guide sequences and a CRISPR enzyme.

6. The method of claim 4, wherein the forward genetic library comprises RNAi.

7. The method of claim 4, wherein the forward genetic library comprises genetic suppressor elements (GSEs).

8. The method of claim 1, wherein the forward genetics library is a Validation-Based Insertional Mutagenesis (VBIM) library.

9. The method of any of claims 1 to 8, wherein the cell growth pressure is an immune pressure.

10. The method of claim 9, wherein the immune pressure comprises one or more inflammatory cytokines.

11. The method of claim 10, wherein the one or more inflammatory cytokines comprise interferon-gamma (IFN-y), tumor necrosis factor alpha (TNF-a), and / or interleukin-1 beta (1L-1P).

12. The method of claim 10, wherein the immune pressure comprises one or more immune cells.

13. The method of claim 12, wherein the one or more immune cells comprise T cells, macrophages, NK cells, and / or fibroblasts.

14. The method of any of claims 1 to 8, wherein the cell growth pressure comprises a targeted therapy, general cytotoxic therapy, hypoxia, or low nutrients.

15. The method of any of claims 1 to 14, wherein the cell growth pressure comprises an immune pressure in combination with another growth pressure.

16. The method of any of claims 1 to 15, wherein the cancer cell line is a human cancer cell line.

17. The method of any of claims 1 to 16, wherein the cancer cell line or cancer organoids are derived from a cancer selected from the group consisting of pancreatic cancer, skin cancer, bladder cancer, lung cancer, breast cancer, prostate cancer, brain cancer, bone cancer, blood cancer, kidney cancer, liver cancer, stomach cancer, colon cancer, head and neck cancer, ovarian cancer, cervical cancer, uterine cancer testicular cancer, rectal cancer, and thyroid cancer.

18. The method of any of claims 1 to 17, wherein the method is performed in more than one cancer cell line or cancer organoids derived from different cancers, thereby identifying consensus genetic elements that drive tumor fitness across different cancers.

19. The method of any of claims 1 to 18, wherein the members of the forward genetics library in surviving cells are identified by sequencing.

20. The method of claim 19, wherein the sequencing is a single-cell sequencing method, whereby cell states driving tumor fitness are identified.

21. The method of claim 20, wherein the single-cell sequencing method is selected from the group consisting of scRNA-seq, CITE-seq, and scATAC-seq.

22. The method of any of claims 1 to 21, wherein genetic elements are further selected from the identified genetic elements, wherein the selected genetic elements are enriched in one or more cancer patients resistant to a treatment, such as immunotherapy.

23. The method of claim 22, wherein the genetic elements are enriched in patients with resistant disease from more than one cancer type.

24. The method of any of claims 1 to 23, further comprising validating the genetic elements identified by perturbing or overexpressing individual genetic elements in one or more cancer cell lines, in vitro cell models, ex vivo organoid models, or in vivo tumor models, and detecting resistance or a resistance gene signature.

25. The method of claim 24, wherein a single-cell sequencing method is performed on the one or more cancer cell lines, in vitro cell models, ex vivo organoid models, or in vivo tumor models, whereby cell states driving tumor fitness are identified for individual genetic elements.

26. The method of claim 25, wherein the single cell sequencing method is selected from the group consisting of scRNA-seq, CITE-seq, and scATAC-seq.

27. The method of any of claims 1 to 26, further comprising identifying dependencies or synthetic lethality in cancer cell lines or cancer organoids resistant to a cell growth pressure, said method comprising:a) perturbing or overexpressing one or more individual genetic elements in a cancer cell line or cancer organoids to generate a resistant cancer cell line or cancer organoids; b) contacting the cancer cell line or cancer organoids with one or more agents capable of targeting one or more target genes in the cells of the resistant cancer cell line or cancer organoids; and c) identifying one or more agents or target genes that reduce survival in the cancer cell line or cancer organoids resistant to a cell growth pressure.

28. The method of claim 27, wherein the one or more agents are selected from the group consisting of drug candidates, small molecules, biologies, and programmable nucleases targeting one or more target genes.

29. The method of claim 27 or 28, wherein the cell growth pressure is an immune pressure.

30. The method of claim 29, wherein the immune pressure comprises one or more inflammatory cytokines.

31. The method of claim 30, wherein the one or more inflammatory cytokines comprise interferon-gamma (IFN-y), tumor necrosis factor alpha (TNF-a), and / or interleukin-1 beta (IL-1P).

32. The method of claim 29, wherein the immune pressure comprises one or more immune cells.

33. The method of claim 32, wherein the one or more immune cells comprise T cells, macrophages, NK cells, and / or fibroblasts.

34. The method of claim 27 or 28, wherein the cell growth pressure comprises a targeted therapy, general cytotoxic therapy, hypoxia, or low nutrients.

35. The method of any of claims 27 to 34, wherein the cell growth pressure comprises an immune pressure in combination with another growth pressure.

36. The method of claim 27 or 28, wherein the cell growth pressure is an immune pressure comprising interferon-gamma (IFN-y), and the one or more individual genetic elements, include an overexpressed ORF selected from the group of TFs consisting of:TP63, TP73, PDX1, and FOXP1; orTP73, TP63, FOXP1, PDX1, JUN, HAND2, POU3F1, IRF2, GLI1, TCF21, FOXN4, EOMES, JUNB, HAND1, IKZF3, TBX6, TBX4, GF11B, CREM, POU2F1, PPARG, MSC, NKX3, ZNF415, FOXP4, ZNF396, IRF2, HNF1B, FIGLA, NFKBIZ, POU5F1, PBX1, IKZF1, SUPT4H1, ASCL1, PBX3, ZNF7, MEIS2, UBP1, BCL6, FOXF2, NR1I3, CREBL2, GRHL1, RUNX2, FLU, GLIS1, POU2F2, ERG, IRF6, HSF5, MEF2C, EGR2, ALX3, TBX5, TBPL1, TBXT, H0XB2, BHLHE40, ZNF771, FOXD4L5, ZNF256, SMAD2, HESX1, POU4F3, ZNF680, and RELB, and wherein dependencies or synthetic lethality are identified in cells that overexpress the TFs and are resistant to IFN-y.

37. A method for identifying subjects resistant to immunotherapy comprising detecting malignant cells from a subject that exhibit overexpression of one or more transcription factors (TF) and / or gene signatures resulting from the overexpression of the one or more transcription factors, wherein the transcription factors are selected from the group:TP63, TP73, PDX1, and FOXP1; orTP73, TP63, FOXP1, PDX1, JUN, HAND2, POU3F1, IRF2, GLI1, TCF21, F0XN4, EOMES, JUNB, HANOI, IKZF3, TBX6, TBX4, GFI1B, CREM, POU2F1, PPARG, MSC, NKX3, ZNF415, FOXP4, ZNF396, IRF2, HNF1B, FIGLA, NFKBIZ, POU5F1, PBX1, IKZF1, SUPT4H1, ASCL1, PBX3, ZNF7, MEIS2, UBP1, BCL6, FOXF2, NR1I3, CREBL2, GRHL1, RUNX2, FLU, GLIS1, POU2F2, ERG, IRF6, HSF5, MEF2C, EGR2, ALX3, TBX5, TBPL1, TBXT, H0XB2, BHLHE40, ZNF771, FOXD4L5, ZNF256, SMAD2, HESX1, POU4F3, ZNF680, and RELB.

38. The method of claim 37, wherein the immunotherapy comprises interferon-gamma (IFN- Y)-39. The method of claim 38, further comprising treating the subject resistant to IFN-y with a treatment not comprising IFN-y.

40. A method of identifying dependencies or synthetic lethality in cancer cell lines or cancer organoids resistant to a cell growth pressure comprising: a) overexpressing one or more transcription factors selected from the group consisting of: TP73, TP63, FOXP1, PDX1, JUN, HAND2, POU3F1, IRF2, GLI1, TCF21, FOXN4, EOMES, JUNB, HANOI, IKZF3, TBX6, TBX4, GFI1B, CREM, POU2F1, PPARG, MSC, NKX3, ZNF415, FOXP4, ZNF396, IRF2, HNF1B, FIGLA, NFKBIZ, POU5F1 , PBX1, IKZF1, SUPT4H1, ASCL1, PBX3, ZNF7, MEIS2, UBP1, BCL6, FOXF2, NR1I3, CREBL2, GRHL1, RUNX2, FLU, GLIS1, POU2F2, ERG, IRF6, HSF5, MEF2C, EGR2, ALX3, TBX5, TBPL1, TBXT, HOXB2, BHLHE40, ZNF771, FOXD4L5, ZNF256, SMAD2, HESX1, POU4F3, ZNF680, and RELB in a cancer cell line or cancer organoids to generate a resistant cancer cell line or cancer organoids; b) contacting the cancer cell line or cancer organoids with one or more agents capable of targeting one or more target genes in the cells of the resistant cancer cell line or cancer organoids; and c) identifying the one or more agents or target genes that reduce survival in the cancer cell line or cancer organoids resistant to a cell growth pressure.

41. The method of claim 40, wherein the one or more agents are selected from the group consisting of drug candidates, small molecules, biologies, and programmable nucleases targeting one or more target genes.

42. The method of claim 40 or 41, wherein the cell growth pressure is an immune pressure.

43. The method of claim 42, wherein the immune pressure comprises one or more inflammatory cytokines.

44. The method of claim 43, wherein the one or more inflammatory cytokines comprises interferon-gamma (IFN-y).

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