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WO2025245269A1 - Procédé de criblage pour identifier des mécanismes de résistance au cancer et de létalité synthétique dans des cellules cancéreuses résistantes - Google Patents

Procédé de criblage pour identifier des mécanismes de résistance au cancer et de létalité synthétique dans des cellules cancéreuses résistantes

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Publication number
WO2025245269A1
WO2025245269A1 PCT/US2025/030435 US2025030435W WO2025245269A1 WO 2025245269 A1 WO2025245269 A1 WO 2025245269A1 US 2025030435 W US2025030435 W US 2025030435W WO 2025245269 A1 WO2025245269 A1 WO 2025245269A1
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WIPO (PCT)
Prior art keywords
cancer
cell
cells
resistant
library
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English (en)
Inventor
Alexander K. Shalek
Peter Winter
Yuzhou Tong
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Massachusetts Institute of Technology
Broad Institute Inc
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Massachusetts Institute of Technology
Broad Institute Inc
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12N—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
    • C12N15/00—Mutation or genetic engineering; DNA or RNA concerning genetic engineering, vectors, e.g. plasmids, or their isolation, preparation or purification; Use of hosts therefor
    • C12N15/09—Recombinant DNA-technology
    • C12N15/10—Processes for the isolation, preparation or purification of DNA or RNA
    • C12N15/1034—Isolating an individual clone by screening libraries
    • C12N15/1093—General methods of preparing gene libraries, not provided for in other subgroups
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12N—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
    • C12N15/00—Mutation or genetic engineering; DNA or RNA concerning genetic engineering, vectors, e.g. plasmids, or their isolation, preparation or purification; Use of hosts therefor
    • C12N15/09—Recombinant DNA-technology
    • C12N15/10—Processes for the isolation, preparation or purification of DNA or RNA
    • C12N15/102—Mutagenizing nucleic acids
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
    • G01N33/5008—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells for testing or evaluating the effect of chemical or biological compounds, e.g. drugs, cosmetics
    • G01N33/5011—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells for testing or evaluating the effect of chemical or biological compounds, e.g. drugs, cosmetics for testing antineoplastic activity

Definitions

  • 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.
  • 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 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 mechanisms of resistance driving tumor fitness.
  • the techniques described herein relate to a method wherein the ORF library is a transcription factor (TF) ORF library.
  • the ORF library is a transcription factor (TF) ORF library.
  • the techniques described herein relate to a method in which the TF ORF expression library includes about 3500 TF isoform ORFs.
  • the techniques described herein relate to a method wherein the forward genetics library is a genetic perturbation library.
  • the techniques described herein relate to a method wherein the genetic perturbation library includes CRISPR guide sequences and a CRISPR enzyme.
  • the techniques described herein relate to a method wherein the genetic perturbation library includes RNAi.
  • the techniques described herein relate to a method wherein the forward genetics library is a Validation-Based Insertional Mutagenesis (VBIM) library.
  • VBIM Validation-Based Insertional Mutagenesis
  • the techniques described herein relate to a method wherein the cell growth pressure is an immune pressure.
  • the techniques described herein relate to a method in which the immune pressure includes one or more inflammatory cytokines.
  • 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).
  • IFN-y interferon-gamma
  • TNF-a tumor necrosis factor alpha
  • IL-ip interleukin-1 beta
  • the techniques described herein relate to a method wherein the immune pressure includes one or more immune cells.
  • 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.
  • 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.
  • the techniques described herein relate to a method in which the cell growth pressure includes targeted therapy, general cytotoxic therapy, hypoxia, or low nutrients.
  • the techniques described herein relate to a method wherein the cell growth pressure includes an immune pressure in combination with another growth pressure.
  • the techniques described herein relate to a method in which the cancer cell line is a human cancer cell line.
  • 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.
  • 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.
  • the techniques described herein relate to a method wherein the members of the forward genetics library in surviving cells are identified by sequencing.
  • the techniques described herein relate to a single-cell sequencing method, which identifies cell states driving tumor fitness.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • the techniques described herein relate to a method wherein the cell growth pressure is immune.
  • the techniques described herein relate to a method in which the immune pressure includes one or more inflammatory cytokines.
  • 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).
  • IFN-y interferon-gamma
  • TNF-a tumor necrosis factor alpha
  • IL-ip interleukin- 1 beta
  • the techniques described herein relate to a method wherein the immune pressure includes one or more immune cells.
  • the techniques described herein relate to a method wherein one or more immune cells include T cells, macrophages, NK cells, and/or fibroblasts.
  • the techniques described herein relate to a method wherein the cell growth pressure includes targeted therapy, general cytotoxic therapy, hypoxia, or low nutrients.
  • the techniques described herein relate to a method wherein the cell growth pressure includes an immune pressure in combination with another growth pressure.
  • 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, C
  • 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
  • TF transcription factors
  • the techniques described herein relate to a method in which the immunotherapy includes interferon-gamma (IFN-y).
  • IFN-y interferon-gamma
  • 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.
  • 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.
  • 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 a
  • 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
  • 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.
  • 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.
  • the techniques described herein relate to a method wherein the gene editing system used for (i)-(vi) is a DNA base editing system.
  • the techniques described herein relate to a method wherein the gene editing system used for (v) or (vi) is an RNA base editing system.
  • 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.
  • the techniques described herein relate to a method wherein the gene editing system used for (i)-(xii) is a CRISPR-associated transposase (CAST) system.
  • CAST CRISPR-associated transposase
  • the techniques described herein relate to a method wherein the gene editing system used for (i)-(xii) includes a non-LTR retrotransposon system.
  • 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.
  • 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.
  • the techniques described herein relate to a method wherein the gene editing system used for (iii) is a DNA base editing system.
  • a method for treating cancer in a subject in need 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
  • a method for treating cancer in a subject in need 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, NF
  • a method for treating cancer in a subject in need 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,
  • the immunotherapy -resistant gene signature is normalized to the expression of a housekeeping gene.
  • the immunotherapy regimen comprises a checkpoint inhibitor blockade.
  • the checkpoint inhibitor blockade comprises a CTLA-4 inhibitor (e g., ipilimumab (YervoyTM), a PD-1 inhibitor (e g., pembrolizumab (KeytrudaTM), nivolumab (OpdivoTM) and/or a PD-Ll inhibitor (e.g., atezolizumab (TecentriqTM).
  • CTLA-4 inhibitor e ipilimumab (YervoyTM
  • a PD-1 inhibitor e g., pembrolizumab (KeytrudaTM)
  • nivolumab nivolumab (OpdivoTM)
  • a PD-Ll inhibitor e.g., atezolizumab (TecentriqTM.
  • FIG. 1 Diagram showing a summary of a high-throughput positive selection screen on cancer cells under immune pressure.
  • 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.
  • 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.
  • 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).
  • 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.
  • TF human transcription factor
  • 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.
  • FIG. 8 Heat map showing differentially expressed transcription factors in the different cancer cell lines and different cancer lineages.
  • 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.
  • FIG. 10 Meta-analysis of transcription factors responsive to therapeutic cancer treatment and/or immune pressure.
  • FIG. 11 Model of intrinsic and extrinsic components influencing RNA state heterogeneity.
  • FIG. 13 - Pipeline for engineering cancer cells with specific cell states using transcription factors 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.
  • TFs Specific transcription factors
  • PDAC pancreatic ductal adenocarcinoma
  • 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 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
  • subject refers 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.
  • treatment 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.
  • 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, an arbitrary 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).
  • Immunotherapy has limited efficacy in certain cancers, like pancreatic cancer.
  • the resistance to immunotherapy cannot be explained solely by mutations or tumor mutation burden.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • vulnerabilities i.e., dependencies
  • resistant cells can be identified (e.g., synthetic lethality screens in resistant cancer cells).
  • 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.
  • 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.
  • the methods disclosed herein can be used to match cell states to functions and combination therapies.
  • 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.
  • the methods disclosed herein can be utilized to extend immunotherapies to cancer types with low response rates, such as pancreatic cancer.
  • the methods disclosed herein can be used to propose new combinational therapies that can overcome resistance mechanisms.
  • 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.
  • 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).
  • TMB tumor mutation burden
  • 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.
  • 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 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.
  • tumor fitness mechanisms e.g., resistance of tumor cells to a treatment
  • cells expressing different identifiable genetic elements can be sorted based on one or more surface markers.
  • one or more cell surface markers can be cell-state-specific.
  • 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.
  • a positive or negative selective pressure is applied to select cells with the desired viability phenotype.
  • each genetic element is assayed individually in a different population of unpooled cells.
  • the methods may be performed in individual wells of a plate (e g., 96 or 384 well plates).
  • genetic elements are identified in a pooled format, and the genetic elements are validated in a plate or unpooled format.
  • genetic elements and cell states are compared to patient data (e.g., patient data associated with treatment response).
  • a forward genetic library is comprised of genetic elements.
  • 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.
  • the genetic element may be a genetic suppressor element (GSE), a gene fragment, or a whole gene that can suppress a phenotype associated with another gene when introduced into a cell.
  • GSE genetic suppressor element
  • the genetic element may be a non-coding regulatory sequence.
  • 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.
  • the genetic element may be a polynucleotide encoding an antisense oligonucleotide (ASO) or RNA interference (RNAi) polynucleotide.
  • ASO antisense oligonucleotide
  • RNAi RNA interference
  • 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.
  • a genetic suppressor element 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, either by 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
  • the forward genetic library is a transcription factor open reading frame (ORF) library.
  • ORF transcription factor open reading frame
  • TF transcription factor open reading frame
  • 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.
  • 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.
  • 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.
  • isoform refers to any of two or more functionally similar proteins with a similar but not an identical amino acid sequence.
  • an isoform is a member of a set of highly similar proteins originating from a single gene or gene family.
  • a gene may have splicing variants generating different isoforms, and the transcription library would encode each isoform separately.
  • isoforms include expressed transcripts of any transcription factor comprising at least 25% of the expressed transcript.
  • 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.”
  • 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.
  • members e.g., genetic elements or perturbations
  • one or more perturbations are genome-wide perturbations.
  • one or more perturbations target specific genes of interest (e.g., transcription factors).
  • a library of vectors may introduce the forward genetic elements disclosed herein.
  • the genetic elements are encoded by a vector.
  • the vector encodes for an effector enzyme (e.g., a CRISPR enzyme) along with a genetic component or perturbation (e.g., CRISPR guide sequence).
  • the vector encodes an effector enzyme when the cancer cell line or organoid does not express an effector enzyme.
  • 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.
  • viral vector 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-associated viruses).
  • 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
  • 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).
  • 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.
  • pol III promoters include, but are not limited to, U6 and Hl promoters.
  • 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.
  • RSV Rous sarcoma virus
  • CMV cytomegalovirus
  • PGK phosphoglycerol kinase
  • 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).
  • WPRE WPRE
  • CMV enhancers the R-U5’ segment in LTR of HTLV-I
  • SV40 enhancer SV40 enhancer
  • the intron sequence between exons 2 and 3 of rabbit -globin Proc. Natl. Acad. Sci. USA., Vol. 78(3), p. 1527-31, 1981.
  • 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 short palindromic repeats (CRISPR) transcripts, proteins, enzymes, mutant forms thereof, fusion proteins thereof, etc.).
  • CRISPR clustered regularly interspersed short palindromic repeats
  • the vector is a lentivirus vector.
  • lentivirus vector refers to a viral vector derived from complex retroviruses such as the human immunodeficiency virus (HIV).
  • HIV human immunodeficiency virus
  • 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).
  • EIAV equine infectious anemia virus
  • 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.
  • the LTR viral promoter is partially deleted and fused to a heterologous enhancer/promoter such as CMV or RSV.
  • 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.
  • 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., “
  • 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.
  • scRNA-seq single-cell RNA sequencing
  • 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).
  • VBIM Validation-Based Insertional Mutagenesis
  • 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.
  • 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.
  • the barcode identifying a genetic element can be the genetic element, such as a guide sequence targeting a specific sequence.
  • barcode 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.
  • 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 be sequenced 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.
  • barcoding uses an error-correcting scheme (T. K. Moon, Error Correction Coding: Mathematical Methods and Algorithms (Wiley, New York, ed. 1, 2005)).
  • 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.
  • 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 or organoids are used to identify genetic elements driving tumor fitness.
  • 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.
  • 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 are used in the methods described herein, for example, to study cancer biology, drug resistance, drug efficacy, and to validate cancer targets.
  • 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 can be 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.
  • CCLE Cancer Cell Line Encyclopedia
  • cancer organoids are used in the methods described herein.
  • 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.
  • 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 widely used 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)).
  • 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.
  • animal-free alternatives such as hydrogels made from alginates
  • 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.
  • Micromachines 11, pii: E208 (2020) the method includes preparing small organoid fragments by mechanical disruption. Mechanical disruption can include shearing, sonication, homogenizing, chopping, scissors, or cutting.
  • a cell growth pressure is applied to the cancer cell line or cancer organoids, which have forward genetics library members expressed or introduced.
  • cell growth pressure refers to any pressure applied to cells that challenges or provides resistance to cell growth or viability of cells.
  • a combination of cell growth pressures is used.
  • an immune and another growth pressure are applied to the cancer cell line or organoids, with forward genetics library members expressed or introduced.
  • immune pressure is applied to the cancer cells, which express different library members.
  • 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).
  • 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.
  • 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).
  • immune cells e.g., T cells, macrophages, NK cells, and/or fibroblasts
  • 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.
  • immune cell-mediated tumor-killing assays are used with resistant cells in secondary assays.
  • 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.
  • 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.
  • 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), anastr
  • 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
  • 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.
  • at least one barcode sequence can identify each genetic element or perturbation.
  • barcodes in a population of positively selected cells are amplified (e.g., dial-out PCR) and sequenced.
  • sequencing comprises high-throughput (formerly "nextgeneration") technologies to generate sequencing reads.
  • a read is an inferred sequence of base pairs (or base pair probabilities) corresponding to all or part of a single DNA fragment.
  • cDNA complementary DNA
  • 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.
  • 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.
  • the library members 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.
  • 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.
  • single-cell states are determined for genetic elements by a singlecell sequencing method, whereby cell states driving tumor fitness are identified.
  • 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 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.
  • 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.
  • 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).
  • 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).
  • the disclosure involves high-throughput single-cell RNA-seq.
  • 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.
  • the disclosure involves single-nucleus RNA sequencing.
  • 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.
  • 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-binding proteins, 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.
  • sequencing 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; Bu
  • 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).
  • 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.
  • 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).
  • 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).
  • 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.
  • 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.
  • chromatin accessibility may be accessed using scATAC-seq. Buenrostro et al. Nature 523, 486-490 (2015).
  • genetic elements identified in pooled screens, as described herein are validated individually in validation assays.
  • 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.
  • 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.
  • genetic elements are validated in a tumor model.
  • 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.
  • cell states are identified using single -cell methods for one or more tumor samples obtained from the mouse tumor model.
  • tumor mouse models include the CT26 colon carcinoma, MC38-Ova colon carcinoma, and B16F10 melanoma models (see, e.g., Singer, M. et al.
  • 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.
  • 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.
  • mechanisms of resistance are confirmed in patient samples.
  • validated genetic elements are screened in cancer cells to identify dependencies or synthetic lethality(ies).
  • 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.
  • 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.
  • 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.
  • the candidate agents can be selected based on targeting particular gene signatures identified in the resistant cancer cells.
  • signatures associated with cell growth pressure can be targeted to make the cancer cells sensitive to cell growth pressure.
  • 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.
  • 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.
  • one or more agents can be drug candidates, small molecules, biologies, and programmable nucleases targeting one or more target genes.
  • 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.
  • the candidate agents can be selected based on targeting specific gene signatures identified in the resistant cancer cells.
  • signatures associated with cell growth pressure can be targeted to make the cancer organoids sensitive to the cell growth pressure.
  • validated genetic elements are screened in a tumor model to identify dependencies or synthetic lethalities in cancers resistant to cell growth pressure.
  • 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.
  • the candidate agents can be selected based on targeting specific gene signatures identified in the resistant cancer cells.
  • signatures associated with a cell growth pressure can be targeted to allow the cancer to be then sensitive to the cell growth pressure.
  • 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.
  • 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.
  • candidate agents are selected by signature screening.
  • 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.
  • pharmacological screens may be used to identify selectively toxic drugs to cells having a signature.
  • the Connectivity Map 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).
  • Cmap can be used to screen for small molecules capable of modulating a signature of the present disclosure in silico.
  • 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.
  • 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.
  • 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 of randomized oligonucleotides such as antisense oligonucleotides and oligopeptides, is also possible.
  • libraries of natural compounds in the form of bacterial, fungal, plant, and animal extracts are available or can be readily produced.
  • 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.
  • biomarkers e.g., phenotype-specific or cell type
  • Biomarkers 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.
  • biomarkers include the signature genes, signature gene products, and/or cells as described herein.
  • the detection of expression of a transcription factor indicates that a tumor is resistant to immunotherapy, such as IFN-y.
  • a transcription factor e.g., TP63, TP73, PDX1, and FOXP1
  • a gene signature regulated by a transcription factor indicates that a tumor is resistant to immunotherapy, such as IFN-y.
  • 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, FOX
  • a transcription factor e.g., TP63, TP73, PDX1, and FOXP1
  • a gene signature regulated by a transcription factor e.g., TP63, TP73, PDX1, and FOXP1
  • the subject is treated according to any embodiment herein.
  • 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 and pathways. 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.
  • TP63 or TP73 may repress STAT1 activation or induce expression of negative regulators of the pathway, such as SOCS proteins.
  • the identified transcription factors may also drive the transition to a cell state with intrinsic resistance to immune pressure.
  • 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.
  • 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).
  • 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.
  • 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.
  • 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.
  • IHC immunohistochemistry
  • FACS fluorescence-activated cell sorting
  • MS mass spectrometry
  • CDT mass cytometry
  • RNA-seq single-cell RNA- seq
  • quantitative RT-PCR single-cell qPCR
  • FISH single-cell qPCR
  • RNA-FISH RNA-FISH
  • MERFISH multiplex (in situ) RNA FISH
  • 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).
  • 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.
  • PDAC pancreatic ductal adenocarcinoma
  • 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.
  • cell state is not used to direct treatment.
  • 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).
  • the Applicant hypothesizes that transcription factors can drive cell state heterogeneity and plasticity (e.g., FIG. 12).
  • the first step includes selecting a cancer cell line that faithfully recapitulates an in vivo tumor for cancer of interest.
  • 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.
  • 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.
  • IFN-y interferongamma
  • 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.
  • 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.
  • 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).
  • 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).
  • 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.
  • 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.
  • 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).
  • 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).
  • Different pressures e.g., hypoxia, low nutrients, TNFa, IL1B, drugs, etc.
  • combinatorial pressures can be applied to cells.
  • 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
  • T cell killing, macrophage phagocytosis 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.
  • 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.
  • 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 chemotherapy and 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
  • RNA state e.g., cell state
  • plasticity within tumors
  • 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.
  • 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.
  • all transcription factors e.g., all human transcription factors
  • 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
  • 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.
  • 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.
  • transcription factors conferring resistance specific to a cancer type 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.
  • Example 5 Method for Identifying and Treating Patients Resistant to Immunotherapy
  • 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.
  • 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 .
  • the expression levels of the identified transcription factors 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.
  • 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 and gene signatures will be monitored to detect the development of resistance during treatment. Treatment will be adjusted based on molecular changes observed during monitoring.
  • 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.
  • 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.
  • 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.

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Abstract

Des modes de réalisation divulgués dans les présentes utilisent des outils de génétique avancés (par exemple, des bibliothèques d'ORF, des bibliothèques de perturbation) pour étudier des avantages de forme physique sous pression immunitaire, y compris des mécanismes de résistance. Une fois que les mécanismes de résistance sont identifiés, des vulnérabilités (c'est-à-dire, des dépendances) dans des cellules résistantes peuvent être identifiées (par exemple, des écrans de létalité synthétique dans des cellules cancéreuses résistantes). Par exemple, un médicament ou un criblage CRISPR peut être effectué dans des cellules présentant un état résistant identifié. Des modes de réalisation divulgués dans les présentes concernent également des cibles pour la résistance au traitement de l'IFN-y.
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