Zygosity-specific biomarkers of response to Anti-cancer therapy

Abstract

The present disclosure provides methods for selecting cancer patients for dual anti-CTLA4 and anti-PD-Ll / PD-1 immune checkpoint blockade (ICB) therapy based on biallelic inactivation (mutation plus copy number loss of wild-type allele, "MutLOH") in KEAP1 and STK11.

Description

Atty. Dkt. No.: 115872-3370ZYGOSITY-SPECIFIC BIOMARKERS OF RESPONSE TO ANTI-CANCER THERAPYCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of and priority to U.S. Provisional Patent Application No. 63 / 726,514, filed November 30, 2024, the entire contents of which are incorporated herein by reference.TECHNICAL FIELD

[0002] The present disclosure provides methods for selecting cancer patients for dual anti-CTLA4 and anti-PD-Ll / PD-1 immune checkpoint blockade (ICB) therapy based on biallelic inactivation (via mutation in combination with copy number loss of wild-type allele, “MutLOH”) in KEAP1 and STK11.STATEMENT OF GOVERNMENT SUPPORT

[0003] This invention was made with government support under CA008748 awarded by the National Institutes of Health. The government has certain rights in the invention.BACKGROUND

[0004] The following description of the background of the present technology is provided simply as an aid in understanding the present technology and is not admitted to describe or constitute prior art to the present technology.

[0005] Tumorigenesis is characterized by sequential acquisition of somatic mutations and copy number alterations to one or both alleles of oncogenes and tumor suppressor genes (TSGs)1. The classical, “two-hit” model of TSG inactivation posits that loss of both alleles (biallelic loss) is necessary for inactivation and subsequent tumor initiation2. Many autosomal recessive tumor suppressors have been discovered that exhibit near-ubiquitous biallelic losses in specific cancer types (e.g. RB7 in retinoblastoma3, APC in colorectal cancer4and VHL in clear cell renal cell carcinoma5). However, because the majority of large- scale cancer genomics studies have utilized non-allele-specific copy number analysis methods, neither the extent of biallelic inactivation across TSGs (beyond specific cases suchAtty. Dkt. No.: 115872-3370 as TP53 nor the functional and translational consequences of biallelic inactivation, are well-understood.SUMMARY

[0006] Aspects of the present disclosure are directed to methods of selecting a cancer patient for treatment with dual anti-CTLA4 and anti-PD-Ll / PD-1 immune checkpoint blockade (ICB) therapy. The method may include (a) detecting (i) at least one mutant allele in a tumor suppressor gene and / or (ii) at least one copy number loss in a complementary allele of the tumor suppressor gene in a biological sample obtained from a cancer patient, wherein the tumor suppressor gene is KEAP1 or STK11; and (b) administering to the cancer patient an effective amount of dual anti-CTLA4 and anti-PD- Ll / PD-1 ICB therapy. In some embodiments, the method comprises detecting two mutant alleles (e.g. somatic loss of function mutations, a homozygous deletion, a mutation and a structural rearrangement) in the tumor suppressor gene (e.g., KEAP1 or STK11). In certain embodiments, the method comprises detecting one mutant allele in the tumor suppressor gene and one copy number loss in the complementary allele (e.g., MutLOH) of the tumor suppressor gene (e.g., KEAP1 or STK11).

[0007] Aspects of the present disclosure are directed to systems, devices, apparatuses or non-transitory computer readable media for selecting a cancer patient for treatment with dual anti-CTLA4 and anti-PD-Ll / PD-1 immune checkpoint blockade (ICB) therapy. One or more processors coupled with memory may detect (i) at least one mutant allele in a tumor suppressor gene and / or (ii) at least one copy number loss in a complementary allele of the tumor suppressor gene in a biological sample obtained from a cancer patient, wherein the tumor suppressor gene is KEAP1 or STK11. The one or more processors may provide an instruction to administer to the cancer patient an effective amount of dual anti-CTLA4 and anti-PD-Ll / PD-1 ICB therapy.

[0008] In some embodiments, the at least one mutant allele in the tumor suppressor gene may include at least one of a variant of unknown significance (VUS), a frameshift mutation, a missense mutation, a deletion, an insertion, a nonsense mutation, an inversion, or a translocation. In some embodiments, the at least one mutant allele may include a somatic alteration including at least one of a substitution, an insertion, a deletion, a focalAtty. Dkt. No.: 115872-3370 amplification, a homozygous deletion, or a translocation. In some embodiments, the at least one copy number loss may include at least one of: a copy number segment with a minor copy number of 0 corresponding to a loss of heterozygosity (LOH), or a focal deletion corresponding to a copy number segment smaller than 10 megabases with a total copy number of 1 and harboring 10 or fewer genes.

[0009] In some embodiments, the cancer patient may suffer from or may be diagnosed with lung cancer, pancreatic cancer, or breast cancer. In some embodiments, the dual anti-CTLA4 and anti-PD-Ll / PD-1 ICB therapy may include an anti-PD-Ll inhibitor selected from among Atezolizumab, Avelumab, and Durvalumab or a PD-1 inhibitor selected from among Pembrolizumab, Cemiplimab, and Nivolumab. In some embodiments, the dual anti-CTLA4 and anti-PD-Ll / PD-1 ICB therapy may include an anti-CTLA4 inhibitor selected from among Ipilimumab and Tremelimumab.

[0010] In some embodiments, the biological sample may include at least one of a fresh tissue sample, a frozen tissue sample, or a fixed-formalin paraffin-embedded tissue sample. In some embodiments, the biological sample may include at least one of genomic DNA, cDNA, RNA, and / or mRNA. In some embodiments, the mutation or copy number loss may be detected via next-generation sequencing, PCR, real-time quantitative PCR (qPCR), digital PCR (dPCR), Southern blotting, Reverse transcriptase-PCR (RT-PCR), Northern blotting, microarray, dot or slot blots, in situ hybridization, or fluorescent in situ hybridization (FISH).

[0011] In some embodiments, the detection may include at least one of : detecting two mutant alleles corresponding to a homozygous deletion in the tumor suppressor gene; detecting two mutant alleles corresponding to a point mutation and a translocation in the tumor suppressor gene; detecting two copy number losses in the alleles of the tumor suppressor gene; or detecting a single mutant allele in the tumor suppressor gene and a single copy number loss in the complementary allele of the tumor suppressor gene. In some embodiments, the one or more processors may retrieve: a dataset comprising genomic data of the biological sample obtained from the cancer patient; and parse the genetic profile in the dataset to detect the at least one mutant allele and / or the at least one copy number loss.Atty. Dkt. No.: 115872-3370

[0012] Aspects of the present disclosure are directed to methods of selecting a cancer patient for treatment with anti-PD-Ll / PD-1 immune checkpoint blockade (ICB) monotherapy. The method may include: (a) detecting (i) a single first mutant allele in a tumor suppressor gene in a biological sample obtained from a cancer patient, wherein the cancer patient does not harbor a copy number loss in a complementary allele of the tumor suppressor gene or (ii) a single copy number loss in an allele of the tumor suppressor gene obtained from the cancer patient, wherein the cancer patient does not comprise a second mutant allele in a complementary allele of the tumor suppressor gene, and wherein the tumor suppressor gene is KEAP1 or STK11; and (b) administering to the cancer patient an effective amount of anti-PD-Ll / PD-1 ICB monotherapy.

[0013] Other aspects of the present disclosure are directed to systems, devices, apparatuses or non-transitory computer readable media for selecting a cancer patient for treatment with anti-PD-Ll / PD-1 immune checkpoint blockade (ICB) monotherapy. One or more processors coupled with memory may detect (i) a single first mutant allele in a tumor suppressor gene in a biological sample obtained from a cancer patient, wherein the cancer patient does not harbor a copy number loss in a complementary allele of the tumor suppressor gene or (ii) a single copy number loss in an allele of the tumor suppressor gene obtained from the cancer patient, wherein the cancer patient does not comprise a second mutant allele in a complementary allele of the tumor suppressor gene, and wherein the tumor suppressor gene is KEAP1 or STK11. The one or more processors may provide an instruction to administer to the cancer patient an effective amount of anti-PD-Ll / PD-1 ICB monotherapy.

[0014] In some embodiments, the single first mutant allele in the tumor suppressor gene may include at least one a frameshift mutation, a missense mutation, a deletion, an insertion, a nonsense mutation, an inversion, or a translocation. In some embodiments, the single first mutant allele may include a somatic alteration including at least one of a substitution, an insertion, a deletion, a focal amplification, a homozygous deletion, or a translocation. In some embodiments, the single copy number loss comprises at least one of: a copy number segment with a minor copy number of 0 corresponding to a loss of heterozygosity (LOH), or a focal deletion corresponding to a copy number segment smaller than 10 megabases with a total copy number of 1 and harboring 10 or fewer genes.Atty. Dkt. No.: 115872-3370

[0015] In some embodiments, the cancer patient may suffer from or may be diagnosed with lung cancer, pancreatic cancer, or breast cancer. In some embodiments, the anti-PD-Ll ICB monotherapy may include Atezolizumab, Avelumab, or Durvalumab. In certain embodiments, the anti-PD-1 ICB monotherapy may include Pembrolizumab, Cemiplimab, or Nivolumab. In some embodiments, the biological sample may include at least one of a fresh tissue sample, a frozen tissue sample, or a fixed-formalin paraffin- embedded tissue sample. In some embodiments, the biological sample may include at least one of genomic DNA, cDNA, RNA, and / or mRNA.

[0016] In some embodiments, the single first mutation or the first copy number loss may be detected via next-generation sequencing, PCR, real-time quantitative PCR (qPCR), digital PCR (dPCR), Southern blotting, Reverse transcriptase-PCR (RT-PCR), Northern blotting, microarray, dot or slot blots, in situ hybridization, or fluorescent in situ hybridization (FISH). In some embodiments, the one or more processors may: retrieve a dataset comprising a genomic data of the biological sample obtained from the cancer patient; parse the genetic profile in the dataset to detect the single first mutant allele or the single copy number.BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The foregoing and other objects, aspects, features, and advantages of the disclosure will become more apparent and better understood by referring to the following description taken in conjunction with the accompanying drawings, in which:

[0018] FIGS. 1A-1C: Somatic biallelic inactivation of tumor suppressors in prospectively sequenced patients. FIG. 1A: Study schema describing the MSK-IMPACT cohort. Rigorous sample filtering criteria were adopted to identify high quality samples for robust zygosity assessment (see Methods). The different types of zygosity changes associated with each somatic mutation are shown. Mutations are classified ‘Oncogenic’ based on OncoKB criteria for oncogenic effect. LOH: loss of heterozygosity. Heterozygous copy number losses (LOH), including focal or broad (arm or chromosome level) losses are considered in this study as events of unknown oncogenic significance as their oncogenic effect cannot be reliably determined, and therefore, considered as WT (also see FIG. 7). FIG. IB: Zygosity changes associated with mutations in oncogenes (n=186 genes) are significantlyAtty. Dkt. No.: 115872-3370 different from those observed in tumor suppressors (n=224 genes). FIG. 1C: Types of zygosity changes adopted for biallelic inactivation varied by cancer type for some genes when compared to their pan-cancer patterns. For each of the significant genes (q < 0.05), their “Pan-Cancer” and specific cancer-type patterns are shown.

[0019] FIGs. 2A-2D: Landscape of biallelic alterations by tumor suppressor gene and cancer type. Biallelic inactivation rates for 55 TSGs in 72 cancer types are shown. Tumors with fusion events in the corresponding genes were excluded in this figure as their biallelic status cannot be robustly ascertained. Heterozygous oncogenic TSG mutations with a concomitant VUS mutation in the same gene in the same tumor sample (comprising only 1.6% of all oncogenic alterations in TSGs) were also excluded from this figure as they may be putatively enriched for truly oncogenic biallelic losses. Heterozygous copy number losses (LOH), including focal or broad (arm or chromosome level) losses are considered as WT when calculating biallelic inactivation rates for somatic oncogenic alterations. The top bar chart shows the pan-cancer alteration rate, followed by the overall biallelic rate and prevalence of each mechanism of inactivation. The color in the boxes corresponds to biallelic inactivation rate of each gene within the cancer type. The bar chart below shows the total number of cancer types in which the gene was found altered in at least 10 tumors and an alteration rate of at least 1% within the corresponding cancer type. Of these, the number of cancer types in which the gene presented with a biallelic rate >80% is shown in dark green.

[0020] FIGs. 3A-3D: Selection for biallelic inactivation across genes and cancer types. Signal for selection for MutLOH is determined for each gene in each cancer type by evaluating enrichment of LOH at the gene locus in mutated compared to LOH at the corresponding gene locus in wild-type tumors. All tumors with fusions, homozygous deletions and composite mutations were excluded from consideration when determining each gene’s signal for selection within a cancer type. The circles with black outlines denote those genes and cancer types with statistically significant (adjusted p-value < 0.05) enrichment for biallelic inactivation while blue outlines denotes significant depletion. The circle with red outline denotes genes and cancer type pairs with at least 20 tumors with any oncogenic alteration and an overall biallelic inactivation rate of >80% (as in FIG. 2) but statistical significance for selection for MutLOH was not reached. The two bar charts to the side summarize the selection patterns observed among cancer types with at least 20 oncogenicAtty. Dkt. No.: 115872-3370 mutations in the given gene among cancer types in which selection for MutLOH was evaluated. See Methods for TSG classification criteria.

[0021] FIGs. 4A-4H: Biallelic inactivation among rare drivers identifies late arising APC mutations in several cancers. FIG. 4A: Scatter plot of enrichment for MutLOH (as calculated in FIG. 3) as a function of alteration rate of the TSGs shown in FIG. 3 in each labeled cancer type. FIG. 4B: Fraction of APC mutated and wild-type patients with LOH at the APC locus for MSK-IMPACT, TCGA, and TRACERx cohorts. *** - p-value < 0.001, ** - p-value < 0.01, ns: not significant. LUAD: Lung adenocarcinoma, PRAD: prostate adenocarcinoma, LOH: loss of heterozygosity. FIG. 4C: CTNNB1 gene and protein expression for LUAD patients in TCGA for different APC zygosity and CTNNB1 mutation groups. FIGS. 4D-4E: Oncoprint showing alterations in key (FIG. 4D) LUAD and (FIG. 4E) PRAD cancer genes are shown across different WNT pathway mutation groups. Alterations that are enriched or depleted \nAPCMUT, CTNNB1MUTor OtherWNTl / / / compared to WNT wild-type patients are indicated by (adjusted p-values < 0.05). Other WNT genes include AMER1, AXIN1, AXIN2, GSK3B, LZTR1, RNF43, TCF7L2 and ZNRF3. FIG. 4F: Schematic depicting approach to identify late arising mutations in tumor lineages. In routine clinical sequencing early driver mutations (blue diamond) such as EGFR drivers in lung cancers are expected to be detected in every biopsy sequenced. However, resistance mutations (red diamond) such as EGFR ‘gatekeeper’ mutations in lung cancers are acquired on treatment and are absent in biopsies taken before the tumors acquire resistance. FIG. 4G: APC and CTNNB1 alterations are evaluated corresponding to FIG. 4F to determine if they arise late in given tumor lineages. APC, EGFR and SPOP are known early drivers in colorectal, lung and prostate cancers, respectively. PIK3CA, EGFR-resistance (gatekeeper) and AR mutations are known acquired mutations in the indicated diseases. FIG. 4H: Select lung cancer patients with late-arising / acquired APC and CTNNB1 mutations.

[0022] FIGS. 5A-5L: Enrichment of biallelic inactivation among VUSs. FIG. 5A: TSGs that demonstrated significant enrichment for biallelic losses among VUSs are shown. Dashed lines indicate the adjusted p-value=0.05. FIG. 5B: Lollipop plot showing sites of oncogenic and VUS missense mutations in KEAP1 in all patients with lung adenocarcinoma. LUAD tumors with TMB higher than 90th percentile in the disease were excluded here. FIG. 5C: LOH rates of KEAP1 mutated and wild-type tumors in LUADAtty. Dkt. No.: 115872-3370 tumors in MSK-IMPACT and TCGA cohorts. *** - p-value < 0.001. FIG. 5D: Gene and protein expression of NPF2 and NQO1 in LUAD tumors in TCGA with either KEAP1 oncogenic mutations, VUSs or wild-type tumors. *** - p-value < 0.001. Gene expression data shown for 485 LU AD tumors for KEAP1 oncogenic (n=27), VUS (n=37) and wild-type (n=421) patients. Protein expression data shown for 333 LU AD tumors for KEAP1 oncogenic (n=24), VUS (n=27) and wild-type (n=282) patients. FIG. 5E: KEAP1 VUSs show similar co-mutation patterns with genes known to be co-occurring or mutually exclusive withXE4P7 oncogenic mutations. ** - p-value < 0.01, *** - p-value < 0.001, ns - not significant. FIG. 5F: Kaplan-Meier curves showing overall survival (OS, in months) for patients with lung adenocarcinoma in the MSK-IMPACT cohort (n=3895) with tumors harboring KEAP1 oncogenic mutations or VUSs compared to KEAP1WT. p-values are computed from a Cox proportional hazards model accounting for disease status, sex, age at diagnosis, targetable alterations, FGA, TMB, and MSI score (see FIG. 11B for full model). FIG. 5G: Kaplan- Meier curves showing progression free survival (PFS) of advanced NSCLC patients receiving first line chemoimmunotherapy (n=421) by KEAP1 mutation class (see FIG. 11C for full model), p-values are computed from a Cox proportional hazards model accounting for sex, ECOG, PD-L1 status, TMB, derived neutrophil-to-lymphocyte ratio (dNLR), and smoking history in pack years. FIG. 5H: NCI-H1299 cells were transduced with a functional reporter construct to quantify NRF2 activity. Eight copies of the antioxidant response element (8x ARE) were coupled to a GFP reporter, such that in a KEAP1 loss-of-function setting, NRF2 would activate transcription of GFP. These cells were also transduced with the prime editor gene PE7 for constitutive expression. A library of pegRNA-sensor constructs co-expressing a RFP reporter and blasticidin S selection marker was transduced into NCI-H1299 cells stably expressing PE7 and the NRF2 reporter. After 14 days, cells were sorted into four bins (Ql- Q4) on the basis of their GFP expression level followed by genomic DNA isolation and sequencing of pegRNA-sensor cassettes. MinP = minimal promoter, EFla = Elongation factor 1 -alpha promoter, NeoR = neomycin selection marker, P2A = peptide 2ART, EFS = Elongation factor 1 -alpha short promoter, Puro = puromycin selection marker, BlastR = blasticidin S selection marker. FIG. 51: Maximum pegRNA correct editing percentage for missense mutation-inducing pegRNAs in the library. FIG. 5J: The log2 fold-change (LFC) of the highest GFP-expressing bin (Q4) relative to pre-sort populations for different KEAP1 variant or control classes. Sensor editing measurements were used to focus this analysis onAtty. Dkt. No.: 115872-3370 samples showing >40% and >20% sensor editing in the pre-sort population for missense and silent pegRNAs, respectively. Statistics shown for t-test of independent samples with Bonferroni correction. ** - p-value < .01, **** - p-value < .0001, ns - not significant (p- value > .05). FIG. 5K: The LFC for low (QI and Q2) and high (Q4) GFP-expressing cells relative to the pre-sort population for missense pegRNAs with >40% editing, and selected silent pegRNAs with >20% editing. FIG. 5L: Flow cytometry -based validation of the ARE- reporter activity (GFP+ %) of individual pegRNA-expressing NCI-H1299 cells 10 days after pegRNA transduction (ST = safe-targeting, NT = non-targeting). Statistics shown for t-test of independent samples with Bonferroni correction. * - p-value < .05, **** - p-value < .0001, ns - not significant (p-value > .05).

[0023] FIGS. 6A-6H: Zygosity as a prognostic and predictive biomarker in MSK-IMPACT cohort. FIG. 6A: Forest plot of OS hazard ratio when comparing patients comprising different mutation groups to those that are wild-type for any oncogenic alteration within the gene. Biallelic group includes patients with homozygous deletions and composite mutations. “Altered” group includes patients with both monoallelic and biallelic alterations. Only genes that showed statistically significant difference in outcomes in either biallelic or monoallelic groups compared to wild-type are shown (data not shown). Hazard ratios are from multivariate Cox proportional hazards models of OS by zygosity and alteration, adjusting for disease status, age, sex, FGA, MSI score, and TMB within each gene / subtype pair (see Methods). FIG. 6B: Comparison of OS for patients with tumors with biallelic inactivation relative to those with monoallelic inactivation among all gene / subtype pairs with sufficient sample size (see Methods). Pairs in which biallelic inactivation was associated with significantly shorter OS compared to WT are highlighted and labeled. FIG. 6C: Kaplan- Meier curve of OS by KEAP1 zygosity status among patients with LUAD. FIG. 6D: Forest plot of multivariate Cox regression model of OS by KEAP1 zygosity as in FIG. 6C, accounting for disease status, sex, age, FGA, TMB, MSI score, and presence of an OncoKB Level 1 actionable alteration. FIG. 6E: Kaplan-Meier curve of PFS on first-line chemoimmunotherapy among patients with advanced NSCLC. FIG. 6F: Forest plot of multivariate Cox regression model of PFS on first-line chemoimmunotherapy by KEAP1 zygosity as in FIG. 6E, accounting for sex, ECOG, PD-L1 level, TMB, dNLR, NSCLC histology, and smoking pack years. FIG. 6G: Kaplan-Meier curve of PFS on first-line immunotherapy alone by KEAP1 zygosity among patients with advanced NSCLC. FIG. 6H:Atty. Dkt. No.: 115872-3370Forest plot of multivariate Cox regression model of PFS on first-line immunotherapy alone by KEAP1 zygosity as in FIG. 6G, accounting for sex, ECOG, PD-L1 level, TMB, dNLR, NSCLC histology, and smoking pack years.

[0024] FIG. 7: Zygosity changes associated with somatic alterations in oncogenes and tumor suppressors, related to FIG. 1. Focal - LOH events are determined as described in Methods. Of note, Focal - LOH events include only those without any other somatic oncogenic alterations in that gene. Any Focal - LOH events in tumors that also harbor a concomitant loss of function mutation are considered as Mut. + LOH.

[0025] FIGS. 8A-8B: Pathway level patterns of biallelic inactivation among tumor suppressor genes across cancers, related to FIG. 2 and FIG. 3. FIG. 8A: Overall alteration rate of each pathway across cancer types is shown. A pathway is considered altered if any one of the genes in the pathway in that tumor type harbors a loss of function mutation. Tumors with fusion events in the corresponding genes were excluded in this figure as their biallelic status cannot be robustly ascertained. Pathway alteration rates less than 1% are not shown. FIG. 8B: Evidence for selection for MutLOH among mutations within a given pathway in each cancer type is shown. Similar to the analysis in FIG. 3, all tumors with fusions, homozygous deletions and composite mutations were excluded from consideration when determining enrichment of selection for MutLOH among mutations in a given pathway in a given disease. The circles with black outlines denote those pathways and cancer types with statistically significant (q < 0.05) enrichment for MutLOH while blue outlines denote depletion.

[0026] FIGS. 9A-9G: Tumor suppressor gene classification by biallelic patterns across cancer types. FIG. 9A: Schematic describing the TSG classification approach (see Methods). “Altered” tumors and biallelic rate includes homozygous deletions and composite mutations, as in FIG. 2. FIG. 9B: Biallelic rate (<80% or >=80%), and selection for MutLOH for the 181 gene and cancer type pairs used in TSG classification that exhibited positive selection. FIG. 9C: Distribution of biallelic rate for all genes and cancer types evaluated for TSG classification, colored by whether significant selection was observed (adjusted p-value < 0.05) or not. FIG. 9D: Variation in distribution of biallelic inactivation rates by gene across cancer types. FIG. 9E: Confusion matrix of TSG class assignments (in FIG. 3) when varying the biallelic rate threshold to either 70% or 90%. FIG. 9F: Changes to TSG class assignmentsAtty. Dkt. No.: 115872-3370(in FIG. 3) when varying the minimum mutation or alteration count threshold to either 10 or 30. FIG. 9G: Changes to TSG class assignments (in FIG. 3) when varying the threshold for the fraction of cancer types exhibiting positive selection for a gene to be considered as Class 1. For FIGS. 9E-9G, concordant class assignments are highlighted in green and discordant in red.

[0027] FIGS. 10A-10B: Differences in selection for MutLOH between primary and metastatic tumors. FIG. 10A: log-odds ratio from the logistic regression model evaluating selection for MutLOH after adjusting for fraction of genome altered and tumor mutational burden in primary (n=9,789) and metastatic (n=13,337) disease cohorts (see Methods). Data points are labeled with gene and cancer type if the difference in log-odds ratio between primary and metastatic tumors is greater than 2, or, if the differences in rates of MutLOH between the disease states differs by more than 20%, or, finally, if the gene is APC (related to FIG. 5). FIG. 10B: Scatter plot showing MutLOH rates (i.e., proportion of mutated tumors that also harbor an LOH at that locus) in primary and metastatic tumors. All gene and cancer type pairs between the two slanted lines have differences in MutLOH rates less than 20%.

[0028] FIGS. 11A-11C: Overall and progression-free survival differences by TSG mutation class, related to FIG. 5. FIG. 11 A: Volcano plot displaying comparisons of OS in patients with tumors harboring VUS vs. WT in all gene / subtype pairs with sufficient sample size (see Methods). Each point represents the log2 of the adjusted hazard ratio vs. - loglO p-value from a multivariate Cox proportional hazards models of OS by mutation class, adjusting for disease status, age, sex, FGA, MSI score, and TMB within each gene / subtype pair. The 15 pairs where significant selection for biallelic inactivation in VUS was observed are highlighted (FIG. 5A). P-values were adjusted for multiple testing using the FDR method with n=290 comparisons. NS: not significant. FIG. 11B: Forest plot of OS by KEAP1 mutation class corresponding to FIG. 5F. FIG. 11C: Forest plot of PFS on chemoimmunotherapy by KEAP1 mutation class corresponding to FIG. 5G.

[0029] FIGS. 12A-12I: A prime editing sensor screen coupled to quantitative assessment of NRF2 activity validates KEAP1 VUSs. FIG. 12A: Activation of the 8x ARE-GFP NRF2 activity reporter in NCLH1299 cells treated with increasing doses of tBHQ. Plots show the GFP positive cell fraction of cells harboring the reporter construct. FIG. 12B:Atty. Dkt. No.: 115872-3370Quantification of prime editing activity (GFP positive cell percentage) in H1299-PE7 and H1299-WT cells at 4- and 7-days post-transduction with the Lenti-PEAR-mCherry reporter. In this system, cells turn on GFP in the event of successful prime editing. FIG. 12C: Plot of codon locations io KEAPl missense variants included in the library, colored by variant class. FIG. 12D: Summary of the variants included in the prime editing sensor library (left) and the number of pegRNAs for each variant or control class in the library (right). Silent = silent substitution control, NT control = non-targeting control, ST control = safe-targeting control. FIG. 12E: The average correct sensor editing percentage for oncogenic KEAP1 variants, VUS, and silent substitution variants for each sorted population (Q1-Q4), as well as the presorted populations and plasmid pool. Statistics shown for t-test of independent samples with Bonferroni correction. FIG. 12F: Scatter plot of correct editing percentage (pre-sort) and LFC of Q4 relative to the pre-sort populations of missense-inducing pegRNAs (left) and silent substitution-inducing pegRNAs (right) for pegRNAs with >5 % editing. FIG. 12G: The log2 fold-change (LFC) of the highest GFP-expressing bin (Q4) relative to pre-sort populations for different KEAP1 variant or control classes. Sensor editing measurements were used to focus this analysis on samples showing >30% and >20% sensor editing in the pre-sort population for missense and silent pegRNAs, respectively (left), and expanded to show individual variants (right). Statistics shown for t-test of independent samples with Bonferroni correction. FIG. 12H: Representative Sanger sequencing results of the endogenous KEAP1 locus for the A184G and G186C pegRNAs. FIG. 121: Validation flow cytometry analyses for ARE-reporter activity (GFP+ %) of individual pegRNA-expressing NCI-H1299 cells (ST = safe-targeting, NT = non-targeting). Here, ARE-reporter activity is normalized to the expected number of homozygous variants by dividing by the square of the sensor editing rate for missense-inducing pegRNAs. Statistics shown for t-test of independent samples with Bonferroni correction. * - p-value < .05, ** - p-value < .01, **** - p-value < .0001, ns - not significant (p-value > .05)

[0030] FIGs. 13A and 13B Clinical outcomes in KEAP1 mutated non-small cell lung cancer patients treated with different immunotherapy regimens. Included here are metastatic NSCLC patients with KEAP1 mutations who received immune checkpoint inhibitors. Progression-free survival (PFS) was calculated from the time of ICI start until radiographic progression or death using Cox proportional hazards model. “chemoIO” cohort comprises patients receiving Pembrolizumab or Cemiplimab with chemo regimens such as carboplatin,Atty. Dkt. No.: 115872-3370 cisplatin, pemetrexed or gemcitabine. “duallCB” cohort comprises patients receiving Nivolumab or Tremelimumab together with anti-CTLA4 inhibitors such as ipilimumab or durvalumab. “IO” cohort comprises only anti-PD(L)l or anti-CTLA4 without any chemo regimens. PFS outcomes are shown io KEAPl biallelic (FIG. 13A) and KEAP1 monoallelic (FIG. 13B) patients.

[0031] FIGs. 14A-14D Clinical outcomes in all NSCLC patients (n=l,810) treated with different immunotherapy regimens and stratified by KEAP1 and STK11 mutation zygosity. Cohort inclusion criteria, treatment groups and PFS are calculated as described in FIGs. 13A and 13B. PFS outcomes are evaluated in different groups of patients defined by KEAP1 and STK11 zygosity. FIG. 14A: PFS outcomes in patients with either KEAP1 or STK11 mutations (irrespective of zygosity). FIG. 14B: PFS outcomes in patients with either KEAP1 biallelic or STK11 biallelic mutations. FIG. 14C: PFS outcomes in patients with only monoallelic alterations in KEAP1 or STK11 are included here. Patients with biallelic mutations in either KEAP1 or STK11 were excluded, “mono.” refers to monoallelic. FIG. 14D: PFS outcomes in patients that are wild-type for any oncogenic alterations in KEAP1 or STK11.

[0032] FIG. 15 depicts a block diagram of a system for selecting cancer patients for treatments based on detecting biallelic or monoallelic alterations in gene data of cancer patients, in accordance with an illustrative embodiment.

[0033] FIG. 16 depicts a block diagram of a process for processing gene data of cancer patients to detect alterations in the system for selecting cancer patients for treatments, in accordance with an illustrative embodiment.

[0034] FIG. 17 depicts a flow diagram of a method of selecting cancer patients for treatments based on detecting biallelic or monoallelic alterations in gene data of cancer patients, in accordance with an illustrative embodiment.

[0035] FIG. 18 depicts a block diagram of a server system and a client computer system, in accordance with one or more implementations.Atty. Dkt. No.: 115872-3370DETAILED DESCRIPTION

[0036] It is to be appreciated that certain aspects, modes, embodiments, variations and features of the present methods are described below in various levels of detail in order to provide a substantial understanding of the present technology.

[0037] In practicing the present methods, many conventional techniques in molecular biology, protein biochemistry, cell biology, immunology, microbiology and recombinant DNA are used. See, e.g., Sambrook and Russell eds. (2001) Molecular Cloning: A Laboratory Manual, 3rd edition; the series Ausubel el al. eds. (2007) Current Protocols in Molecular Biology, the series Methods in Enzymology (Academic Press, Inc., N.Y.); MacPherson et al. (1991) PCR 1: A Practical Approach (IRL Press at Oxford University Press); MacPherson etal. (1995) CT? 2 : A Practical Approach,' Harlow and Lane eds. (1999) Antibodies, A Laboratory Manual, Freshney (2005) Culture of Animal Cells: A Manual of Basic Technique, 5th edition; Gait ed. (1984) Oligonucleotide Synthesis,' U.S. Patent No. 4,683,195; Hames and Higgins eds. (1984) Nucleic Acid Hybridization,' Anderson (1999) Nucleic Acid Hybridization,' Hames and Higgins eds. (1984) Transcription and Translation; Immobilized Cells and Enzymes (IRL Press (1986)); Perbal (1984) A Practical Guide to Molecular Cloning; Miller and Calos eds. (1987) Gene Transfer Vectors for Mammalian Cells (Cold Spring Harbor Laboratory); Makrides ed. (2003) Gene Transfer and Expression in Mammalian Cells; Mayer and Walker eds. (1987) Immunochemical Methods in Cell and Molecular Biology (Academic Press, London); and Herzenberg et al. eds (1996) Weir ’s Handbook of Experimental Immunology. Methods to detect and measure levels of polypeptide gene expression products (i.e., gene translation level) are well-known in the art and include the use of polypeptide detection methods such as antibody detection and quantification techniques. (See also, Strachan & Read, Human Molecular Genetics, Second Edition. (John Wiley and Sons, Inc., NY, 1999)).

[0038] It was reasoned that a comprehensive allele-specific analysis of TSG alterations in a large cohort of prospectively sequenced cancer patients could lead to insights into disease etiology and the role TSGs play in mediating therapeutic responses. Therefore the frequency of biallelic inactivation was explored across 224 TSGs in 48,179 cancer patients in nearly a hundred histological subtypes of major cancers. Matched tumor and normal sequencing together with deep sequencing coverage enabled robust inference of allele-Atty. Dkt. No.: 115872-3370 specific copy number and its co-occurrence with somatic mutations. It is shown that the frequency of biallelic inactivation for most major TSGs varies dramatically across cancer types, with most TSGs demonstrating lineage-specific patterns of enrichment for biallelic inactivation.

[0039] By modeling the selective pressure for the most common form of biallelic inactivation (mutation plus copy number loss of wild-type allele, “MutLOH”), rare but highly selected loss of APC were identified in lung and prostate adenocarcinomas. Similarly, by investigating the selective pressure for MutLOH in variants of unknown clinical significance (VUSs), it was discovered that IEZP7 VUSs in lung adenocarcinoma are strongly enriched for biallelic alterations and phenocopy well-established KEAP1 oncogenic alleles. Consequently, it was observed that KEAP1 zygosity, rather than annotated oncogenic status, is correlated to overall survival and predictive of response to multiple standard-of-care therapies.Definitions

[0040] Unless defined otherwise, all technical and scientific terms used herein generally have the same meaning as commonly understood by one of ordinary skill in the art to which this technology belongs. As used in this specification and the appended claims, the singular forms “a”, “an” and “the” include plural referents unless the content clearly dictates otherwise. For example, reference to “a cell” includes a combination of two or more cells, and the like. Generally, the nomenclature used herein and the laboratory procedures in cell culture, molecular genetics, organic chemistry, analytical chemistry and nucleic acid chemistry and hybridization described below are those well-known and commonly employed in the art.

[0041] As used herein, the term “about” in reference to a number is generally taken to include numbers that fall within a range of 1%, 5%, or 10% in either direction (greater than or less than) of the number unless otherwise stated or otherwise evident from the context (except where such number would be less than 0% or exceed 100% of a possible value).

[0042] The term “adapter” refers to a short, chemically synthesized, nucleic acid sequence which can be used to ligate to the end of a nucleic acid sequence in order to facilitateAtty. Dkt. No.: 115872-3370 attachment to another molecule. The adapter can be single-stranded or double-stranded. An adapter can incorporate a short (typically less than 50 base pairs) sequence useful for PCR amplification or sequencing.

[0043] As used herein, an “alteration” of a gene or gene product (e.g., a marker gene or gene product) refers to the presence of a mutation or mutations within the gene or gene product, e.g., a mutation, which affects the quantity or activity of the gene or gene product, as compared to the normal or wild-type gene. The genetic alteration can result in changes in the quantity, structure, and / or activity of the gene or gene product in a cancer tissue or cancer cell, as compared to its quantity, structure, and / or activity, in a normal or healthy tissue or cell (e.g., a control). For example, an alteration which is associated with cancer, or predictive of responsiveness to anti-cancer therapeutics, can have an altered nucleotide sequence (e.g., a mutation), amino acid sequence, chromosomal translocation, intra-chromosomal inversion, copy number, expression level, protein level, protein activity, in a cancer tissue or cancer cell, as compared to a normal, healthy tissue or cell. Exemplary mutations include, but are not limited to, point mutations (e.g., silent, missense, or nonsense), deletions, insertions, inversions, linking mutations, duplications, translocations, inter- and intra-chromosomal structural rearrangements. Mutations can be present in the coding or non-coding region of the gene.

[0044] As used herein, the “administration” of an agent or drug to a subject includes any route of introducing or delivering to a subject a compound to perform its intended function. Administration can be carried out by any suitable route, including orally, intranasally, parenterally (intravenously, intramuscularly, intraperitoneally, or subcutaneously), or topically. Administration includes self-administration and the administration by another.

[0045] The terms “cancer” or “tumor” are used interchangeably and refer to the presence of cells possessing characteristics typical of cancer-causing cells, such as uncontrolled proliferation, immortality, metastatic potential, rapid growth and proliferation rate, and certain characteristic morphological features. Cancer cells are often in the form of a tumor, but such cells can exist alone within an animal, or can be a non-tumorigenic cancer cell. As used herein, the term “cancer” includes premalignant, as well as malignant cancers.Atty. Dkt. No.: 115872-3370

[0046] As used herein, a “control” is an alternative sample used in an experiment for comparison purpose. A control can be “positive” or “negative.” For example, where the purpose of the experiment is to determine a correlation of the efficacy of a therapeutic agent for the treatment for a particular type of disease or condition, a positive control (a compound or composition known to exhibit the desired therapeutic effect) and a negative control (a subject or a sample that does not receive the therapy or receives a placebo) are typically employed.

[0047] “Detecting” as used herein refers to determining the presence of a mutation or alteration in a nucleic acid of interest in a sample. Detection does not require the method to provide 100% sensitivity. Analysis of nucleic acid markers can be performed using techniques known in the art including, but not limited to, sequence analysis, and electrophoretic analysis. Non-limiting examples of sequence analysis include Maxam- Gilbert sequencing, Sanger sequencing, capillary array DNA sequencing, thermal cycle sequencing (Sears et al., Biotechniques, 13:626-633 (1992)), solid-phase sequencing (Zimmerman et al., Methods Mol. Cell Biol, 3:39-42 (1992)), sequencing with mass spectrometry such as matrix-assisted laser desorption / ionization time-of-flight mass spectrometry (MALDI-TOF / MS; Fu et al., Nat. Biotechnol, 16:381-384 (1998)), and sequencing by hybridization. Chee et al., Science, 274:610-614 (1996); Drmanac et al., Science, 260: 1649-1652 (1993); Drmanac et al., Nat. Biotechnol, 16:54-58 (1998). Nonlimiting examples of electrophoretic analysis include slab gel electrophoresis such as agarose or polyacrylamide gel electrophoresis, capillary electrophoresis, and denaturing gradient gel electrophoresis. Additionally, next generation sequencing methods can be performed using commercially available kits and instruments from companies such as the Life Technologies / Ion Torrent PGM or Proton, the Illumina HiSEQ or MiSEQ, and the Roche / 454 next generation sequencing system.

[0048] As used herein, the term “effective amount” refers to a quantity sufficient to achieve a desired therapeutic and / or prophylactic effect, e.g., an amount which results in the prevention of, or a decrease in a disease or condition described herein or one or more signs or symptoms associated with a disease or condition described herein. In the context of therapeutic or prophylactic applications, the amount of a composition administered to the subject will vary depending on the composition, the degree, type, and severity of the diseaseAtty. Dkt. No.: 115872-3370 and on the characteristics of the individual, such as general health, age, sex, body weight and tolerance to drugs. The skilled artisan will be able to determine appropriate dosages depending on these and other factors. The compositions can also be administered in combination with one or more additional therapeutic compounds. In the methods described herein, the therapeutic compositions may be administered to a subject having one or more signs or symptoms of cancer. As used herein, a “therapeutically effective amount” of a composition refers to composition levels in which the physiological effects of a disease or condition are ameliorated or eliminated. A therapeutically effective amount can be given in one or more administrations.

[0049] As used herein, “expression” includes one or more of the following: transcription of the gene into precursor mRNA; splicing and other processing of the precursor mRNA to produce mature mRNA; mRNA stability; translation of the mature mRNA into protein (including codon usage and tRNA availability); and glycosylation and / or other modifications of the translation product, if required for proper expression and function.

[0050] As used herein, the term “gene” means a segment of DNA that contains all the information for the regulated biosynthesis of an RNA product (which may have a non-coding function (e.g., a ribosomal or transfer RNA) or which may include a polypeptide or a polypeptide precursor), including promoters, exons, introns, and other untranslated regions that control expression. The RNA or polypeptide may be encoded by a full length coding sequence or by any portion of the coding sequence so long as the desired activity or function is retained. Although a sequence of the nucleic acids may be shown in the form of DNA, a person of ordinary skill in the art recognizes that the corresponding RNA sequence will have a similar sequence with the thymine being replaced by uracil, i.e., "T" is replaced with "U."

[0051] The term “hybridize” as used herein refers to a process where two substantially complementary nucleic acid strands (at least about 65% complementary over a stretch of at least 14 to 25 nucleotides, at least about 75%, or at least about 90% complementary) anneal to each other under appropriately stringent conditions to form a duplex or heteroduplex through formation of hydrogen bonds between complementary base pairs. Hybridizations are typically and preferably conducted with probe-length nucleic acid molecules, preferably 15- 100 nucleotides in length, more preferably 18-50 nucleotides in length. Nucleic acid hybridization techniques are well known in the art. See, e.g., Sambrook, et al., 1989,Atty. Dkt. No.: 115872-3370Molecular Cloning: A Laboratory Manual, Second Edition, Cold Spring Harbor Press, Plainview, N.Y. Hybridization and the strength of hybridization (z.e., the strength of the association between the nucleic acids) is influenced by such factors as the degree of complementarity between the nucleic acids, stringency of the conditions involved, and the thermal melting point (Tm) of the formed hybrid. Those skilled in the art understand how to estimate and adjust the stringency of hybridization conditions such that sequences having at least a desired level of complementarity will stably hybridize, while those having lower complementarity will not. For examples of hybridization conditions and parameters, see, e.g., Sambrook, et al., 1989, Molecular Cloning: A Laboratory Manual, Second Edition, Cold Spring Harbor Press, Plainview, N.Y.; Ausubel, F. M. et al. 1994, Current Protocols in Molecular Biology, John Wiley & Sons, Secaucus, N.J. In some embodiments, specific hybridization occurs under stringent hybridization conditions. An oligonucleotide or polynucleotide (e.g., a probe or a primer) that is specific for a target nucleic acid will “hybridize” to the target nucleic acid under suitable conditions.

[0052] As used herein, the term “library” refers to a collection of nucleic acid sequences, e.g., a collection of nucleic acids derived from whole genomic, subgenomic fragments, cDNA, cDNA fragments, RNA, RNA fragments, or a combination thereof. In one embodiment, a portion or all of the library nucleic acid sequences comprises an adapter sequence. The adapter sequence can be located at one or both ends. The adapter sequence can be useful, e.g., for a sequencing method (e.g., an NGS method), for amplification, for reverse transcription, or for cloning into a vector.

[0053] The library can comprise a collection of nucleic acid sequences, e.g., a target nucleic acid sequence (e.g., a tumor nucleic acid sequence), a reference nucleic acid sequence, or a combination thereof. In some embodiments, the nucleic acid sequences of the library can be derived from a single subject. In other embodiments, a library can comprise nucleic acid sequences from more than one subject (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30 or more subjects). In some embodiments, two or more libraries from different subjects can be combined to form a library having nucleic acid sequences from more than one subject.

[0054] A “library nucleic acid sequence” refers to a nucleic acid molecule, e.g., a DNA, RNA, or a combination thereof, that is a member of a library. Typically, a library nucleic acid sequence is a DNA molecule, e.g., genomic DNA or cDNA. In someAtty. Dkt. No.: 115872-3370 embodiments, a library nucleic acid sequence is fragmented, e.g., sheared or enzymatically prepared, genomic DNA. In certain embodiments, the library nucleic acid sequences comprise sequence from a subject and sequence not derived from the subject, e.g., adapter sequence, a primer sequence, or other sequences that allow for identification, e.g., “barcode” sequences.

[0055] As used herein, “LOH” or “loss of heterozygosity” refers to a type of alteration that results in the loss of one copy of a segment of DNA (typically containing a gene or group of genes). For most parts of the genome, human cells have two copies of any genomic segment — one from each parent — so in the case of LOH, only one copy would still be present.

[0056] “Next-generation sequencing or NGS” as used herein, refers to any sequencing method that determines the nucleotide sequence of either individual nucleic acid molecules (e.g., in single molecule sequencing) or clonally expanded proxies for individual nucleic acid molecules in a high throughput parallel fashion (e.g., greater than 103, 104, 105or more molecules are sequenced simultaneously). In one embodiment, the relative abundance of the nucleic acid species in the library can be estimated by counting the relative number of occurrences of their cognate sequences in the data generated by the sequencing experiment. Next generation sequencing methods are known in the art, and are described, e.g., in Metzker, M. Nature Biotechnology Reviews 11 :31-46 (2010).

[0057] As used herein, “oligonucleotide” refers to a molecule that has a sequence of nucleic acid bases on a backbone comprised mainly of identical monomer units at defined intervals. The bases are arranged on the backbone in such a way that they can bind with a nucleic acid having a sequence of bases that are complementary to the bases of the oligonucleotide. The most common oligonucleotides have a backbone of sugar phosphate units. A distinction may be made between oligodeoxyribonucleotides that do not have a hydroxyl group at the 2' position and oligoribonucleotides that have a hydroxyl group at the 2' position. Oligonucleotides may also include derivatives, in which the hydrogen of the hydroxyl group is replaced with organic groups, e.g., an allyl group. One or more bases of the oligonucleotide may also be modified to include a phosphorothioate bond (e.g., one of the two oxygen atoms in the phosphate backbone which is not involved in the intemucleotide bridge, is replaced by a sulfur atom) to increase resistance to nuclease degradation. The exactAtty. Dkt. No.: 115872-3370 size of the oligonucleotide will depend on many factors, which in turn depend on the ultimate function or use of the oligonucleotide. The oligonucleotide may be generated in any manner, including, for example, chemical synthesis, DNA replication, restriction endonuclease digestion of plasmids or phage DNA, reverse transcription, PCR, or a combination thereof. The oligonucleotide may be modified e.g., by addition of a methyl group, a biotin or digoxigenin moiety, a fluorescent tag or by using radioactive nucleotides.

[0058] As used herein, the term “pharmaceutically-acceptable carrier” is intended to include any and all solvents, dispersion media, coatings, antibacterial and antifungal compounds, isotonic and absorption delaying compounds, and the like, compatible with pharmaceutical administration. Pharmaceutically-acceptable carriers and their formulations are known to one skilled in the art and are described, for example, in Remington’s Pharmaceutical Sciences (20th edition, ed. A. Gennaro, 2000, Lippincott, Williams & Wilkins, Philadelphia, Pa.).

[0059] As used herein, the term “polynucleotide” or “nucleic acid” means any RNA or DNA, which may be unmodified or modified RNA or DNA. Polynucleotides include, without limitation, single- and double-stranded DNA, DNA that is a mixture of single- and double-stranded regions, single- and double-stranded RNA, RNA that is mixture of single- and double-stranded regions, and hybrid molecules comprising DNA and RNA that may be single-stranded or, more typically, double-stranded or a mixture of single- and doublestranded regions. In addition, polynucleotide refers to triple-stranded regions comprising RNA or DNA or both RNA and DNA. The term polynucleotide also includes DNAs or RNAs containing one or more modified bases and DNAs or RNAs with backbones modified for stability or for other reasons.

[0060] As used herein, the term “sample” refers to clinical samples obtained from a subject. Biological samples may include tissues, cells, protein or membrane extracts of cells, mucus, sputum, bone marrow, bronchial alveolar lavage (BAL), bronchial wash (BW), and biological fluids (e.g., ascites fluid or cerebrospinal fluid (CSF)) isolated from a subject, as well as tissues, cells and fluids (blood, plasma, saliva, urine, serum etc.) present within a subject.Atty. Dkt. No.: 115872-3370

[0061] As used herein, the term “separate” therapeutic use refers to an administration of at least two active ingredients at the same time or at substantially the same time by different routes.

[0062] As used herein, the term “sequential” therapeutic use refers to administration of at least two active ingredients at different times, the administration route being identical or different. More particularly, sequential use refers to the whole administration of one of the active ingredients before administration of the other or others commences. It is thus possible to administer one of the active ingredients over several minutes, hours, or days before administering the other active ingredient or ingredients. There is no simultaneous treatment in this case.

[0063] As used herein, the term “simultaneous” therapeutic use refers to the administration of at least two active ingredients by the same route and at the same time or at substantially the same time.

[0064] As used herein, the terms “subject,” “individual,” or “patient” are used interchangeably and refer to an individual organism, a vertebrate, a mammal, or a human. In certain embodiments, the individual, patient or subject is a human.

[0065] “Treating”, “treat”, or “treatment” as used herein covers the treatment of a disease or disorder described herein, in a subject, such as a human, and includes: (i) inhibiting a disease or disorder, i.e., arresting its development; (ii) relieving a disease or disorder, i.e., causing regression of the disorder; (iii) slowing progression of the disorder; and / or (iv) inhibiting, relieving, or slowing progression of one or more symptoms of the disease or disorder. In some embodiments, treatment means that the symptoms associated with the disease are, e.g., alleviated, reduced, cured, or placed in a state of remission.

[0066] It is also to be appreciated that the various modes of treatment or prevention of medical diseases and conditions as described are intended to mean “substantial,” which includes total but also less than total treatment or prevention, and wherein some biologically or medically relevant result is achieved. The treatment may be a continuous prolonged treatment for a chronic disease or a single, or few time administrations for the treatment of an acute condition.Atty. Dkt. No.: 115872-3370Methods for Detecting Polynucleotides Associated with Responsiveness to Dual Anti-CTLA4 and anti-PD-Ll / PD-1 Immune Checkpoint Blockade (ICB) Therapy

[0067] Polynucleotides associated with responsiveness to dual anti-CTLA4 and anti- PD-L1 / PD-1 ICB may be detected by a variety of methods known in the art. Non-limiting examples of detection methods are described below. The detection assays in the methods of the present technology may include purified or isolated DNA (genomic or cDNA), RNA or protein or the detection step may be performed directly from a biological sample without the need for further DNA, RNA or protein purification / isolation.Nucleic Acid Amplification and / or Detection

[0068] Polynucleotides associated with responsiveness to dual anti-CTLA4 and anti- PD-L1 / PD-1 ICB can be detected by the use of nucleic acid amplification techniques that are well known in the art. The starting material may be genomic DNA, cDNA, RNA, ctDNA, cfDNA, or mRNA. Nucleic acid amplification can be linear or exponential. Specific variants or mutations may be detected by the use of amplification methods with the aid of oligonucleotide primers or probes designed to interact with or hybridize to a particular target sequence in a specific manner, thus amplifying only the target variant.

[0069] Non-limiting examples of nucleic acid amplification techniques include polymerase chain reaction (PCR), real-time quantitative PCR (qPCR), digital PCR (dPCR), reverse transcriptase polymerase chain reaction (RT-PCR), nested PCR, ligase chain reaction (see Abravaya, K. et al., Nucleic Acids Res. (1995), 23:675-682), branched DNA signal amplification (see Urdea, M. S. et al., AIDS (1993), 7(suppl 2):S11- S14), amplifiable RNA reporters, Q-beta replication, transcription-based amplification, boomerang DNA amplification, strand displacement activation, cycling probe technology, isothermal nucleic acid sequence based amplification (NASBA) (see Kievits, T. et al., J Virological Methods (1991), 35:273-286), Invader Technology, next-generation sequencing technology or other sequence replication assays or signal amplification assays.

[0070] Primers'. Oligonucleotide primers for use in amplification methods can be designed according to general guidance well known in the art as described herein, as well as with specific requirements as described herein for each step of the particular methods described. In some embodiments, oligonucleotide primers for cDNA synthesis and PCR areAtty. Dkt. No.: 115872-337010 to 100 nucleotides in length, preferably between about 15 and about 60 nucleotides in length, more preferably 25 and about 50 nucleotides in length, and most preferably between about 25 and about 40 nucleotides in length.

[0071] Tm of a polynucleotide affects its hybridization to another polynucleotide (e.g., the annealing of an oligonucleotide primer to a template polynucleotide). In certain embodiments of the disclosed methods, the oligonucleotide primer used in various steps selectively hybridizes to a target template or polynucleotides derived from the target template (z.e., first and second strand cDNAs and amplified products). Typically, selective hybridization occurs when two polynucleotide sequences are substantially complementary (at least about 65% complementary over a stretch of at least 14 to 25 nucleotides, preferably at least about 75%, more preferably at least about 90% complementary). See Kanehisa, M., Polynucleotides Res. (1984), 12:203, incorporated herein by reference. As a result, it is expected that a certain degree of mismatch at the priming site is tolerated. Such mismatch may be small, such as a mono-, di- or tri -nucleotide. In certain embodiments, 100% complementarity exists.

[0072] Probes'. Probes are capable of hybridizing to at least a portion of the nucleic acid of interest or a reference nucleic acid ( / .< ., wild-type sequence). Probes may be an oligonucleotide, artificial chromosome, fragmented artificial chromosome, genomic nucleic acid, fragmented genomic nucleic acid, RNA, recombinant nucleic acid, fragmented recombinant nucleic acid, peptide nucleic acid (PNA), locked nucleic acid, oligomer of cyclic heterocycles, or conjugates of nucleic acid. Probes may be used for detecting and / or capturing / purifying a nucleic acid of interest.

[0073] Typically, probes can be about 10 nucleotides, about 20 nucleotides, about 25 nucleotides, about 30 nucleotides, about 35 nucleotides, about 40 nucleotides, about 50 nucleotides, about 60 nucleotides, about 75 nucleotides, or about 100 nucleotides long. However, longer probes are possible. Longer probes can be about 200 nucleotides, about 300 nucleotides, about 400 nucleotides, about 500 nucleotides, about 750 nucleotides, about 1,000 nucleotides, about 1,500 nucleotides, about 2,000 nucleotides, about 2,500 nucleotides, about 3,000 nucleotides, about 3,500 nucleotides, about 4,000 nucleotides, about 5,000 nucleotides, about 7,500 nucleotides, or about 10,000 nucleotides long.Atty. Dkt. No.: 115872-3370

[0074] Probes may also include a detectable label or a plurality of detectable labels. The detectable label associated with the probe can generate a detectable signal directly. Additionally, the detectable label associated with the probe can be detected indirectly using a reagent, wherein the reagent includes a detectable label, and binds to the label associated with the probe.

[0075] In some embodiments, detectably labeled probes can be used in hybridization assays including, but not limited to Northern blots, Southern blots, microarray, dot or slot blots, and in situ hybridization assays such as fluorescent in situ hybridization (FISH) to detect a target nucleic acid sequence within a biological sample. Certain embodiments may employ hybridization methods for measuring expression of a polynucleotide gene product, such as mRNA. Methods for conducting polynucleotide hybridization assays have been well developed in the art. Hybridization assay procedures and conditions will vary depending on the application and are selected in accordance with the general binding methods known including those referred to in: Maniatis et al. Molecular Cloning: A Laboratory Manual (2nd Ed. Cold Spring Harbor, N.Y., 1989); Berger and Kimmel Methods in Enzymology, Vol. 152, Guide to Molecular Cloning Techniques (Academic Press, Inc., San Diego, Calif, 1987); Young and Davis, PNAS. 80: 1194 (1983).

[0076] Detectably labeled probes can also be used to monitor the amplification of a target nucleic acid sequence. In some embodiments, detectably labeled probes present in an amplification reaction are suitable for monitoring the amount of amplicon(s) produced as a function of time. Examples of such probes include, but are not limited to, the 5'- exonuclease assay (TAQMAN® probes described herein (see also U.S. Pat. No. 5,538,848) various stemloop molecular beacons (see for example, U.S. Pat. Nos. 6,103,476 and 5,925,517 and Tyagi and Kramer, 1996, Nature Biotechnology 14:303- 308), stemless or linear beacons (see, e.g., WO 99 / 21881), PNA Molecular Beacons™ (see, e.g., U.S. Pat. Nos. 6,355,421 and 6,593,091), linear PNA beacons (see, for example, Kubista et al., 2001, SPIE 4264:53-58), non-FRET probes (see, for example, U.S. Pat. No. 6,150,097), Sunrise® / Amplifluor™ probes (U.S. Pat. No. 6,548,250), stem-loop and duplex Scorpion probes (Solinas etal., 2001, Nucleic Acids Research 29:E96 and U.S. Pat. No. 6,589,743), bulge loop probes (U.S. Pat. No. 6,590,091), pseudo knot probes (U.S. Pat. No. 6,589,250), cyclicons (U.S. Pat. No. 6,383,752), MGB Eclipse™ probe (Epoch Biosciences), hairpin probes (U.S. Pat. No.Atty. Dkt. No.: 115872-33706,596,490), peptide nucleic acid (PNA) light-up probes, self-assembled nanoparticle probes, and ferrocene-modified probes described, for example, in U.S. Pat. No. 6,485,901 ; Mhlanga et al., 2001, Methods 25:463-471 Whitcombe et al., 1999, Nature Biotechnology. 17:804- 807; Isacsson et al., 2000, Molecular Cell Probes. 14:321-328; Svanvik et al., 2000, Anal Biochem. 281 :26-35; Wolffs etal., 2001, Biotechniques 766.769-77 Tsourkas etal., 2002, Nucleic Acids Research. 30:4208-4215; Riccelli et al., 2002, Nucleic Acids Research 30:4088-4093; Zhang et al., 2002 Shanghai. 34:329-332; Maxwell et al., 2002, J. Am. Chem. Soc. 124:9606-9612; Broude et al., 2002, Trends Biotechnol. 20:249-56; Huang et al., 2002, Chem. Res. Toxicol. 15:118- 126; and Yu et al., 2001, J. Am. Chem. Soc 14: 11155-11161.

[0077] In some embodiments, the detectable label is a fluorophore. Suitable fluorescent moieties include but are not limited to the following fluorophores working individually or in combination: 4-acetamido-4'-isothiocyanatostilbene- 2,2'disulfonic acid; acridine and derivatives: acridine, acridine isothiocyanate; Alexa Fluors: Alexa Fluor® 350, Alexa Fluor® 488, Alexa Fluor® 546, Alexa Fluor® 555, Alexa Fluor® 568, Alexa Fluor® 594, Alexa Fluor® 647 (Molecular Probes); 5-(2- aminoethyl)aminonaphthalene-l -sulfonic acid (EDANS); 4-amino-N-[3- vinylsulfonyl)phenyl]naphthalimide-3,5 disulfonate (Lucifer Yellow VS); N-(4-anilino-l- naphthyl)mal eimide; anthranilamide; Black Hole Quencher™ (BHQ™) dyes (biosearch Technologies); BODIPY dyes: BODIPY® R-6G, BOPIPY® 530 / 550, BODIPY® FL; Brilliant Yellow; coumarin and derivatives: coumarin, 7-amino-4- methylcoumarin (AMC, Coumarin 120),7-amino-4-trifluoromethylcouluarin (Coumarin 151); Cy2®, Cy3®, Cy3.5®, Cy5®, Cy5.5®; cyanosine; 4',6-diaminidino-2-phenylindole (DAPI); 5', 5" -dibrom opyrogallol- sulfonephthalein (Bromopyrogallol Red); 7-diethylamino-3-(4'-isothiocyanatophenyl)-4- methylcoumarin; diethylenetriamine pentaacetate; 4,4'- diisothiocyanatodihydro-stilbene-2,2'- disulfonic acid; 4,4'-diisothiocyanatostilbene-2,2'- disulfonic acid; 5- [dimethylamino]naphthalene-l -sulfonyl chloride (DNS, dansyl chloride);4-(4'- dimethylaminophenylazo)benzoic acid (DABCYL); 4- dimethylaminophenylazophenyl-4'- isothiocyanate (DABITC); Eclipse™ (Epoch Biosciences Inc.); eosin and derivatives: eosin, eosin isothiocyanate; erythrosin and derivatives: erythrosin B, erythrosin isothiocyanate; ethidium; fluorescein and derivatives: 5- carboxyfluorescein (FAM), 5-(4,6-dichlorotriazin-2- yl)amino fluorescein (DTAF), 2', 7'- dimethoxy-4'5'-dichloro-6-carboxyfluorescein (JOE), fluorescein, fluorescein isothiocyanate (FITC), hexachloro-6-carboxyfluorescein (HEX), QFITC (XRITC), tetrachlorofluorescemAtty. Dkt. No.: 115872-3370(TET); fiuorescamine; IR144; IR1446; lanthamide phosphors; Malachite Green isothiocyanate; 4-methylumbelliferone; ortho cresolphthalein; nitrotyrosine; pararosaniline; Phenol Red; B-phycoerythrin, R-phycoerythrin; allophycocyanin; o-phthaldialdehyde; Oregon Green®; propidium iodide; pyrene and derivatives: pyrene, pyrene butyrate, succinimidyl 1 -pyrene butyrate; QSY® 7; QSY® 9; QSY® 21; QSY® 35 (Molecular Probes); Reactive Red 4 (Cibacron®Brilliant Red 3B-A); rhodamine and derivatives: 6- carboxy-X-rhodamine (ROX), 6-carboxyrhodamine (R6G), lissamine rhodamine B sulfonyl chloride, rhodamine (Rhod), rhodamine B, rhodamine 123, rhodamine green, rhodamine X isothiocyanate, riboflavin, rosolic acid, sulforhodamine B, sulforhodamine 101, sulfonyl chloride derivative of sulforhodamine 101 (Texas Red); terbium chelate derivatives; N,N,N',N'-tetramethyl-6-carboxyrhodamine (TAMRA); tetramethyl rhodamine; tetramethyl rhodamine isothiocyanate (TRITC); and VIC®. Detector probes can also comprise sulfonate derivatives of fluorescenin dyes with S03 instead of the carboxylate group, phosphoramidite forms of fluorescein, phosphoramidite forms of CY 5 (commercially available for example from Amersham).

[0078] Detectably labeled probes can also include quenchers, including without limitation black hole quenchers (Biosearch), Iowa Black (IDT), QSY quencher (Molecular Probes), and Dabsyl and Dabcel sulfonate / carboxylate Quenchers (Epoch).

[0079] Detectably labeled probes can also include two probes, wherein for example a fluorophore is on one probe, and a quencher is on the other probe, wherein hybridization of the two probes together on a target quenches the signal, or wherein hybridization on the target alters the signal signature via a change in fluorescence.

[0080] In some embodiments, interchelating labels such as ethidium bromide, SYBR® Green I (Molecular Probes), and PicoGreen® (Molecular Probes) are used, thereby allowing visualization in real-time, or at the end point, of an amplification product in the absence of a detector probe. In some embodiments, real-time visualization may involve the use of both an intercalating detector probe and a sequence-based detector probe. In some embodiments, the detector probe is at least partially quenched when not hybridized to a complementary sequence in the amplification reaction, and is at least partially unquenched when hybridized to a complementary sequence in the amplification reaction.Atty. Dkt. No.: 115872-3370

[0081] In some embodiments, the amount of probe that gives a fluorescent signal in response to an excited light typically relates to the amount of nucleic acid produced in the amplification reaction. Thus, in some embodiments, the amount of fluorescent signal is related to the amount of product created in the amplification reaction. In such embodiments, one can therefore measure the amount of amplification product by measuring the intensity of the fluorescent signal from the fluorescent indicator.

[0082] Primers or probes may be designed to selectively hybridize to any portion of a nucleic acid sequence encoding a polypeptide that is over-expressed in lung cancer patients. Exemplary nucleic acid sequences include, but are not limited to, KEAP1 and STK11. Primers or probes can be designed so that they hybridize under stringent conditions to mutant nucleotide sequences of a polypeptide that is expressed in cancer patients, but not to the respective wild-type nucleotide sequences. Primers or probes can also be prepared that are complementary and specific for the wild-type nucleotide sequences of a polypeptide that is expressed in cancer patients, but not to any of the corresponding mutant nucleotide sequences. In some embodiments, the mutant nucleotide sequences of a polypeptide that is expressed in cancer patients may be a frameshift mutation, a missense mutation, a deletion, an insertion, a nonsense mutation, an inversion, a translocation, a duplication, or a CNV that results in the altered gene expression and / or activity.

[0083] In some embodiments, detection can occur through any of a variety of mobility dependent analytical techniques based on the differential rates of migration between different nucleic acid sequences. Exemplary mobility-dependent analysis techniques include electrophoresis, chromatography, mass spectroscopy, sedimentation, gradient centrifugation, field-flow fractionation, multi-stage extraction techniques, and the like. In some embodiments, mobility probes can be hybridized to amplification products, and the identity of the target nucleic acid sequence determined via a mobility dependent analysis technique of the eluted mobility probes, as described in Published PCT Applications WO04 / 46344 and WOO 1 / 92579. In some embodiments, detection can be achieved by various microarrays and related software such as the Applied Biosystems Array System with the Applied Biosystems 1700 Chemiluminescent Microarray Analyzer and other commercially available array systems available from Affymetrix, Agilent, Illumina, and Amersham Biosciences, amongAtty. Dkt. No.: 115872-3370 others (see also Gerry et al., J. Mol. Biol. 292:251-62, 1999; De Bellis et al., Minerva Biotec 14:247-52, 2002; and Stears et al., Nat. Med. 9: 14045, including supplements, 2003).

[0084] It is also understood that detection can comprise reporter groups that are incorporated into the reaction products, either as part of labeled primers or due to the incorporation of labeled dNTPs during an amplification, or attached to reaction products, for example but not limited to, via hybridization tag complements comprising reporter groups or via linker arms that are integral or attached to reaction products. In some embodiments, unlabeled reaction products may be detected using mass spectrometry.NGS Platforms

[0085] In some embodiments, high throughput, massively parallel sequencing employs sequencing-by-synthesis with reversible dye terminators. In other embodiments, sequencing is performed via sequencing-by-ligation. In yet other embodiments, sequencing is single molecule sequencing. Examples of Next Generation Sequencing techniques include, but are not limited to pyrosequencing, Reversible dye-terminator sequencing, SOLiD sequencing, Ion semiconductor sequencing, Helioscope single molecule sequencing etc.

[0086] The Ion Torrent™ (Life Technologies, Carlsbad, CA) amplicon sequencing system employs a flow-based approach that detects pH changes caused by the release of hydrogen ions during incorporation of unmodified nucleotides in DNA replication. For use with this system, a sequencing library is initially produced by generating DNA fragments flanked by sequencing adapters. In some embodiments, these fragments can be clonally amplified on particles by emulsion PCR. The particles with the amplified template are then placed in a silicon semiconductor sequencing chip. During replication, the chip is flooded with one nucleotide after another, and if a nucleotide complements the DNA molecule in a particular microwell of the chip, then it will be incorporated. A proton is naturally released when a nucleotide is incorporated by the polymerase in the DNA molecule, resulting in a detectable local change of pH. The pH of the solution then changes in that well and is detected by the ion sensor. If homopolymer repeats are present in the template sequence, multiple nucleotides will be incorporated in a single cycle. This leads to a corresponding number of released hydrogens and a proportionally higher electronic signal.Atty. Dkt. No.: 115872-3370

[0087] The 454TM GS FLX ™ sequencing system (Roche, Germany), employs a light-based detection methodology in a large-scale parallel pyrosequencing system. Pyrosequencing uses DNA polymerization, adding one nucleotide species at a time and detecting and quantifying the number of nucleotides added to a given location through the light emitted by the release of attached pyrophosphates. For use with the 454™ system, adapter-ligated DNA fragments are fixed to small DNA-capture beads in a water-in-oil emulsion and amplified by PCR (emulsion PCR). Each DNA-bound bead is placed into a well on a picotiter plate and sequencing reagents are delivered across the wells of the plate. The four DNA nucleotides are added sequentially in a fixed order across the picotiter plate device during a sequencing run. During the nucleotide flow, millions of copies of DNA bound to each of the beads are sequenced in parallel. When a nucleotide complementary to the template strand is added to a well, the nucleotide is incorporated onto the existing DNA strand, generating a light signal that is recorded by a CCD camera in the instrument.

[0088] Sequencing technology based on reversible dye-terminators: DNA molecules are first attached to primers on a slide and amplified so that local clonal colonies are formed. Four types of reversible terminator bases (RT-bases) are added, and non-incorporated nucleotides are washed away. Unlike pyrosequencing, the DNA can only be extended one nucleotide at a time. A camera takes images of the fluorescently labeled nucleotides, then the dye along with the terminal 3' blocker is chemically removed from the DNA, allowing the next cycle.

[0089] Helicos's single-molecule sequencing uses DNA fragments with added polyA tail adapters, which are attached to the flow cell surface. At each cycle, DNA polymerase and a single species of fluorescently labeled nucleotide are added, resulting in templatedependent extension of the surface-immobilized primer-template duplexes. The reads are performed by the Helioscope sequencer. After acquisition of images tiling the full array, chemical cleavage and release of the fluorescent label permits the subsequent cycle of extension and imaging.

[0090] Sequencing by synthesis (SBS), like the "old style" dye-termination electrophoretic sequencing, relies on incorporation of nucleotides by a DNA polymerase to determine the base sequence. A DNA library with affixed adapters is denatured into single strands and grafted to a flow cell, followed by bridge amplification to form a high-densityAtty. Dkt. No.: 115872-3370 array of spots onto a glass chip. Reversible terminator methods use reversible versions of dye-terminators, adding one nucleotide at a time, detecting fluorescence at each position by repeated removal of the blocking group to allow polymerization of another nucleotide. The signal of nucleotide incorporation can vary with fluorescently labeled nucleotides, phosphate- driven light reactions and hydrogen ion sensing having all been used. Examples of SBS platforms include Illumina GA and HiSeq 2000. The MiSeq® personal sequencing system (Illumina, Inc.) also employs sequencing by synthesis with reversible terminator chemistry.

[0091] In contrast to the sequencing by synthesis method, the sequencing by ligation method uses a DNA ligase to determine the target sequence. This sequencing method relies on enzymatic ligation of oligonucleotides that are adjacent through local complementarity on a template DNA strand. This technology employs a partition of all possible oligonucleotides of a fixed length, labeled according to the sequenced position. Oligonucleotides are annealed and ligated and the preferential ligation by DNA ligase for matching sequences results in a dinucleotide encoded color space signal at that position (through the release of a fluorescently labeled probe that corresponds to a known nucleotide at a known position along the oligo). This method is primarily used by Life Technologies’ SOLiD™ sequencers. Before sequencing, the DNA is amplified by emulsion PCR. The resulting beads, each containing only copies of the same DNA molecule, are deposited on a solid planar substrate.

[0092] SMRT™ sequencing is based on the sequencing by synthesis approach. The DNA is synthesized in zero-mode wave-guides (ZMWs)-small well-like containers with the capturing tools located at the bottom of the well. The sequencing is performed with use of unmodified polymerase (attached to the ZMW bottom) and fluorescently labeled nucleotides flowing freely in the solution. The wells are constructed in a way that only the fluorescence occurring at the bottom of the well is detected. The fluorescent label is detached from the nucleotide at its incorporation into the DNA strand, leaving an unmodified DNA strand.EXAMPLESExample 1: Materials and MethodsStudy cohort and mutation profilesAtty. Dkt. No.: 115872-3370

[0093] The cohort comprised solid tumor specimens from 48,179 patients across 67 major cancer types and 498 detailed histologies that were biopsied and sequenced as part of routine clinical care at Memorial Sloan Kettering Cancer Center between November 2013 and August 2021. Matched tumor and normal sequencing was performed using the MSK- IMPACT clinical sequencing assay that profiles up to 505 cancer genes across four versions of the assay (IM3, 341 genes, n=2,302 patients; IM5, 410 genes, n=8,202; IM6, 468 genes, n=30,586; IM7, 505 genes, n=7,089)68. All samples were required to have at least lOOx sequencing coverage and a purity (pathologist estimated or FACETS derived, see below) of at least 30%. For patients with multiple biopsies, the specimen sequenced with the latest version of the panel or with the most recently sequenced biopsy was chosen. Tumor and matched blood normal specimens were sequenced to a median depth of 615X and 484X, respectively. At the time of sequencing, 56% of the patients had active metastatic disease. All somatic alterations including substitutions, small insertions / deletions, gene-level focal amplifications / homozygous deletions and, in select genes, structural rearrangements were identified using a clinically validated pipeline14. Somatic alterations were assessed to be ‘oncogenic’ or ‘driver’ if they were annotated as oncogenic or likely oncogenic by OncoKB. An alteration is defined as being at least likely oncogenic in OncoKB based on any of multiple sources of evidence suggesting its role in promoting cell proliferation or other hallmarks of cancer13,69. Briefly, for every somatic mutation identified on the MSK-IMPACT clinical assay, evidence for oncogenicity in OncoKB is curated from in silico predictions of known cancer hotspots, experimental data reported in prior studies, clinical response or resistance to targeted therapies and based on whether it has a clear biological effect13. Of note, all loss of functions mutations in tumor suppressor genes are also considered to be oncogenic.Allele-specific copy number inference

[0094] Allele-specific copy number status at each gene locus was determined using FACETS algorithm (vO.5.14)70in a two-step approach66. Briefly, the first run (low- sensitivity, cval=100) determined the log-ratio corresponding to the diploid state (total copy number of 2) of the tumor genome. Using this diploid log-ratio, a second run is generated in a high sensitivity mode (eval = 50) to infer locus specific copy number state for each gene. All copy number fits inferred via this approach were subjected to a series of filters to identify and exclude samples with low confidence fits. These include: insufficient evidenceAtty. Dkt. No.: 115872-3370 supporting the diploid state, hypersegmentation, large fraction of the genome identified as homozygous deletions, has very high ploidy (eg: > 7), has invalid purity estimates such as NA or FACETS default of 0.3, high fraction of the genome identified as being subclonal, or, has fraction of the genome where the integer copy number estimate is discordant with the allelic imbalance observed with variant allele log odds ratio. Copy number segments from FACETS calls with a minor copy number of 0 are identified as LOH events. Focal deletions are defined as copy number segments smaller than 10 megabases in size with a total copy number of 1 and harboring 10 or fewer genes71. Homozygous deletions were detected using a clinical validated pipeline as previously described68. Briefly, genes with mean segment fold-change (ratio of normalized sequencing depth in tumor to normal) of -2 or less at one or more exons with an adjusted statistical significance of p-value < 0.05 were considered as harboring homozygous deletions. Statistical significance for a homozygous deletion call is determined by comparing the fold-change of the candidate segment to the observed distribution of segments that are clustered around the segment fold-change of 1.Sample filtering for zygosity analysis

[0095] Next, several exclusion criteria were applied to retain samples suitable for robust zygosity analysis across tumor suppressors and cancer types. First, all tumor samples with purity less than 30% were excluded from this analysis. Second, all tumors with copy number fits that did not pass the copy number fit (FACETS) quality thresholds described above were excluded. Third, tumors with ‘extremely’ high tumor mutational burden in each cancer type were excluded. Since, the distribution of TMB varies across cancer types, a 90th percentile threshold of TMB was determined in each cancer type represents a reasonable cutoff above which tumors may harbor mutational processes that may confound the zygosity analysis. Tumors with microsatellite instability were also excluded72. Fourth, similar to TMB, it is believed that some tumors may harbor a high number of focal amplifications and deletions that may be introduced by high levels of genomic instability. Therefore, all patients with a total of 20 or more focal amplifications / deletions were excluded from this analysis. Finally, the analysis was limited to only the cancer types with 50 or more tumors pre-filtering. After applying all filters, 23,713 patients (49% of the cohort) were retained for zygosity analysis in this study. TSG somatic alteration and zygosity data are included in Tables S3-Atty. Dkt. No.: 115872-33705. Nearly all (99.8%, 23,666 of 23,713) of patients are included in the AACR-GENIE public cohort73.

[0096] High rates of loss of heterozygosity at loci harboring germline pathogenic variants in cancer predisposition genes may confound observations associated with evaluating biallelic inactivation associated with somatic mutations6. Specific gene and tumor sample pairs were therefore excluded in which a pathogenic germline variant was identified. This exclusion is restricted to cancer predisposition genes that have either high or moderate penetrance6. Germline variant calling for all genes was performed as previously described using a clinically validated pipeline68. Pathogenicity was assessed using an approach guided by American College of Medical Genetics and Genomics (ACMG) criteria which incorporated a combination of expert curation in a clinical setting, database annotations (ClinVar), and in silico predictions for functional impact and allele frequencies in healthy individuals (Mehine et al., )('. In all, a pathogenic variant was identified in one of 44 high or moderate penetrance genes in 1,558 patients. When assessing zygosity or signals of selection for LOH associated with somatic mutations in each individual TSG, patients with germline pathogenic variants in the corresponding gene were excluded from consideration.Assessing biallelic inactivation at each gene locus

[0097] Each gene locus is considered to be biallelically inactivated if it harbored (1) a homozygous deletion, (2) an oncogenic mutation and a structural rearrangement, (3) two somatic loss of function mutations, or (4) an oncogenic mutation with a concomitant copynumber loss of heterozygosity (MutLOH) (FIG. 1A).

[0098] A gene locus was considered to be MutLOH if any of the three criteria calculated from the allele-specific copy number inference using FACETS. First, if the total copy number (ten) at the locus with an oncogenic mutation was estimated to be 1 (minor copy number, men, has to be 0). Second, if the men was 0 and the ten was greater than 1, the mutation was required to be in at least two copies for the locus to be MutLOH. To determine this, the expected variant allele frequency (VAF) of the oncogenic mutation was first calculated as if it were present on only one of the copies:Atty. Dkt. No.: 115872-3370

[0099] (O * tcn) / ( 2 * (1 - O) + (<t> * 1) + (<t> * (ten — 1))) , where is the purity of the tumor.

[0100] Then, the locus was classified as MutLOH if the observed VAF of the oncogenic mutation is greater than the upper bound of the 95th percentile binomial confidence interval of this expected VAF if the oncogenic mutation was present on only one copy. Third, if the observed VAF of the oncogenic mutation was consistent with it being present on all copies of the locus. The expected VAF of the oncogenic mutation was calculated as if it was present on ten copies as:

[0101] (<b * tcn) / (2 * (1 — <b) + (<b * ten)).

[0102] Then, the locus was considered to be MutLOH if the observed VAF of the oncogenic mutation is greater than the lower bound of the 95th percentile binomial confidence interval of the expected VAF if it was present on all copies.

[0103] All other loci with at least one somatic mutation but those that were not biallelic by the criteria described above are considered as ‘heterozygous’. Remaining loci were considered as ‘wild-type’. These include all heterozygous copy number loss (LOH) events (mcn=0) as their oncogenic effect cannot be established to distinguish from events that are simply a consequence of genomic instability. Exemplifying this, focal LOH events arise at similar frequencies in both oncogenes and tumor suppressors (FIG. 7).Assessing enrichment for biallelic inactivation

[0104] Evaluation of selection for biallelic inactivation within TSGs and cancer types was limited to only MutLOH. Tumors with homozygous deletions, structural rearrangements (fusions) and multiple loss of function mutations (composite mutations) in TSGs were excluded from this analysis as robustly assessing the null distribution of these types of events is often intractable without whole-genome sequencing. Enrichment for MutLOH was measured by comparing the rate of LOH among tumors with oncogenic loss-of-function mutations in a given TSG to those that were wild-type using a multivariable logistic regression model that accounted for disease status (primary / metastatic), tumor mutational burden and fraction of genome altered as the measure of genomic instability. To mitigate theAtty. Dkt. No.: 115872-3370 problem of sparse-data bias, the logistic regression by applying Firth’s bias reduction was performed using the “logistf’ function in the logistf R package (vl.26.0) (doi: 10.32614 / CRAN.package.logistf). Enrichment was measured for each TSG at pan-cancer as well as cancer type level. Only the associations in which the TSG is mutated in at least 3 tumors in a given cancer type are evaluated. Genes on sex chromosomes were excluded from this analysis. In all, including pan-cancer associations, 1550 associations were evaluated for signals of selection for MutLOH among oncogenic loss of function mutations in TSGs (data not shown). Correction for multiple hypothesis testing was performed using the Benjamini- Hochberg method. Similar regression model accounting for TMB, FGA and disease status was adopted to evaluate selection for MutLOH in VUSs (FIG. 5). Only tumors with nonsynonymous substitutions that are VUSs and those with no other oncogenic loss of function alteration in the evaluated gene were considered for this analysis. In all 210 gene and cancer types with 3 or more VUS mutations were evaluated (data not shown).

[0105] For pathway analysis in FIG. 8B, the analysis specifically asked if the mutations in a given pathway in a given cancer type are statistically significantly enriched or depleted for harboring a loss of heterozygosity event. The pathway definitions and gene composition were derived from the TCGA pan-cancer study that investigated oncogenic signaling pathways in cancer74. As with analysis at gene-level (in FIG. 3), this analysis was restricted to cancer types with 50 or more mutated tumors and with at least 3 mutations in a given pathway. In all, after adjusting for tumor mutational burden, fraction of genome altered and disease status, of the 502 pathway and cancer type pairs were evaluated, 248 were significantly enriched (n=246) or depleted (n=2) for MutLOH (adjusted p-value < 0.05, data not shown, FIG. 8B).

[0106] To evaluate patterns of selection for MutLOH between primary and metastatic tumors, screens for selection for MutLOH were conducted independently in primary (n=9,789) and metastatic (n=13,337) tumor cohorts using a logistic regression model that accounted for FGA and TMB. All p-values were adjusted using the Benjamini Hochberg method. This screen was limited to 144 gene and cancer type pairs in which the respective genes are mutated in at least 10 tumors each of primary and metastasis patients in that disease (data not shown, FIG. 10A). Of these 144, 59 pairs (41%) were selected for MutLOH in both primary and metastatic cohorts while 31 pairs (22%) were statistically significant for selectionAtty. Dkt. No.: 115872-3370 for MutLOH in only the primary (n=l l) or metastatic (n=21) cohorts of the corresponding cancer type. For 27 of 31 pairs, the differences in MutLOH rates between primary and metastatic tumors was less than 20% highlighting the difficulty in biologically interpreting the differences in selection for MutLOH (FIG. 10B).Mutational timing using patients with multiple biopsies

[0107] All lung, colon, rectal and prostate adenocarcinoma patients whose tumors were profiled multiple times using MSK-IMPACT targeted sequencing were identified. All tumor specimens were required to have at least 300x coverage and a TMB that is less than 20 nonsynonymous mutations per megabase. Tumor specimens in which no mutations were identified using the clinical sequencing were excluded from this analysis. All patients with germline pathogenic mutations or those with multiple independently diagnosed cancer types were also excluded. Only specimens that shared at least one somatic mutation were considered to be clonally related and eligible for analysis. For somatic mutations not seen in every biopsy of the patient, it was reasoned that a subset may still be detectable at levels below clinical sequencing thresholds. Therefore, all mutations observed in any one of each patient’s biopsies were re-genotyped in all of their sequenced tumor biopsies. A mutation was then considered to be present in a biopsy if there were at least 5 reads supporting the variant allele. For patients with more than two clonally related biopsies, the two biopsies were preferentially selected in which the genes of interest (APC, EGFR, PIK3CA, CTNNB1, SPOP and AR) were present in one but not the other. This is particularly relevant for truncal mutations such as APC / colorectal, EGFR / lung and SPOP / prostate where the driver mutations are seen in all of the biopsies of every patient with these mutations.APC mutation zygosity in TRACERx cohort

[0108] The TRACERx cohort comprises 421 untreated, early-stage non-small cell lung cancer patients75. Bulk multi-region whole exome sequencing (WES) was performed on primary tumors, and any associated lymph node lesions that were sampled at the time of primary surgical resection. Bulk WES data is processed through a bioinformatics pipeline in order to infer mutations present in each tumor region, and tumor subclonal architecture and tumor phylogenies are reconstructed using the method CONIPHER16. CONIPHER infers mutation clusters, their prevalence in each tumor region, and their evolutionary ordering.Atty. Dkt. No.: 115872-3370Mutations themselves are classified as being ‘truncal’ if their associated mutation cluster has been assigned to the trunk node of the reconstructed phylogenetic tree. Mutations assigned to any other mutation cluster are classified as ‘ subcl onal’ . Loss of heterozygosity at the APC locus was inferred for 378 tumors evaluable for copy-number analysis. In all, 15 of 378 tumors had at least one truncal APC loss of function (LOF) mutation (two tumors were observed with two truncal APC LOF mutations each). The LOH rate among APC mutant tumors was 73% (11 of 15) while only about half (48%, 173 / 363) of APC wild-type tumors were observed with an LOH (two-sided Fisher’s exact test, p=0.065).Gene and protein expression analysis in the TCGA

[0109] Gene expression data (transcript counts) for 485 lung adenocarcinoma patients in TCGA was obtained from Genomic Data Commons77. The Bioconductor package DESeq278was used to perform differential expression analysis. The resultant normalized counts for CTNNB1 between APCWT / CTNNB 1WTand APCBi / CTNNBlWTwere compared using the Wilcoxon test to ascertain statistical significance. For protein expression analysis, the RPPA TCGA data for 351 patients with LU AD were downloaded, and compared protein expression for P-catenin between APCWT / CTNNB1WTand APCBl / CTNNBlWTpatients also with the Wilcoxon test. Same gene and protein expression data from lung adenocarcinoma patients in TCGA were used to evaluate differences in expression of key genes across tumors with different KEAP1 mutation classes (VUS vs. oncogenic). The Wilcoxon test was used to compare normalized counts for gene expression analysis and RPPA values for protein expression between: (1) KEAP1NVSand K AI3lW[patients, and (2) ^£HP7Oncogenicanc[ KEAPl^ patients, for NRF2 and NQO1.Survival analyses

[0110] Overall survival (OS) was measured as the time from the initial date of tumor sequencing to the date of last follow-up or death. For the OS analysis, all patients in the cohort with their biopsied specimens harboring a tumor purity of 15% or higher were considered. Patients with tumors exhibiting high tumor mutation burden (>90th percentile by cancer type) or copy number alteration burden (>=20 focal amplifications or deletions) as described above, were excluded. For patients with multiple sequenced biopsies, the earliest collected specimen with a FACETS fit that passed all QC criteria (see above) was used. AllAtty. Dkt. No.: 115872-3370 multivariate models of OS were performed using Cox proportional hazards models accounting for primary / metastatic disease status, sex, age at diagnosis, presence of an OncoKB Level 1 actionable alteration, FGA, TMB, and MSI score. Patients who were missing data for any covariates were excluded from multivariate models. For evaluating differences in outcomes between either mutation class (FIG. 5F, FIG. 11) or by zygosity (FIGS. 6A-6B), only the cancer types with at least 100 or more total patients were considered. For the VUS versus WT analysis (FIG. 11), each evaluated gene was required to harbor at least 10 oncogenic mutations and 10 VUSs in that cancer type, with at least 5 OS events in each group (n=290 evaluable gene and cancer type pairs). Similarly, for the outcomes by zygosity analysis in FIGs. 6A-H, the gene was required to harbor at least 10 tumors each with monoallelic and biallelic alterations and 5 patients with OS events in each group (n=150 evaluable pairs). Individual multivariate models were constructed for each pair with sufficient sample size, and p-values were adjusted using FDR correction based on the number of evaluated pairs. In FIGs. 6A-H, for all genes and cancer type pairs evaluated, except for KEAP1 in LUAD, only oncogenic alterations are considered. Based on multiple lines of evidence demonstrating that KEAP1 VUSs in LUAD phenocopy oncogenic mutations, both types of KEAP1 alterations (oncogenic and VUS) in LUAD are considered as ‘Altered’ when evaluating differences in outcomes by KEAP1 zygosity.

[0111] Progression-free survival (PFS) on first line chemoimmunotherapy (chemo / IO) was evaluated on 556 patients with NSCLC treated at MSKCC39. After excluding tumors from patients with pathogenic germline mutations, high TMB or FGA, or insufficient purity (<15%) as described above, as well as those without mutations, PFS in 421 patients was evaluated for differences by mutation class (oncogenic, VUS or wild-type) (FIG. 5G). After excluding patients for whom clinical data was missing for any covariate, N=378 patients were included in the multivariate model of PFS by mutation class (FIG. 11C). PFS differences by zygosity was evaluated in 429 patients in whom zygosity was evaluable (FIG. 6E), withN=385 in the multivariate model (FIG. 6F). Similarly, of 923 patients withNSCLC treated with first line immunotherapy (IO) at MSK, PFS was evaluated on 654 patients with EGFRWT tumors for which zygosity was evaluable52(FIG. 6G). N=638 were included in the multivariate model (FIG. 6H) after excluding patients for whom covariate data was missing. Progression-free survival (PFS) for both lines of treatment was measured as the time from start of treatment to progression or death. For patients that did not progress, the time ofAtty. Dkt. No.: 115872-3370 last disease assessment was used as the censoring time. PFS by KEAP1 mutation status or zygosity was evaluated using Cox proportional hazards models accounting for sex, ECOG (Eastern Cooperative Oncology Group) performance status, PD-L1 level, tumor mutation burden (TMB), derived neutrophil to lymphocyte ratio (dNLR), and histology.Tumor suppressor gene classification[01121 TSGs were grouped into four classes based on their selection patterns across cancer types as described in FIG. 9A. Within a given cancer type, a given gene was considered to exhibit ‘positive selection’ for biallelic inactivation in a given cancer type if either there was a statistically significant enrichment for MutLOH (FIG. 3) or the overall biallelic rate (FIG. 2) was 80% or higher. Only enrichment scores for cancer types in which the TSG was mutated in 20 or more tumors, or biallelic rates for cancer types in which the TSG was altered (including mutations, homozygous deletions or composite mutations) in 20 or more tumors were considered for classification. TSGs were considered to undergo ‘negative selection’ for biallelic inactivation when there was significant depletion of MutLOH at the cancer-type or pan-cancer level. TSGs were determined to exhibit ‘no selection’ when no significant enrichment for MutLOH (at the cancer-type level) or depletion (at the cancertype or pan-cancer level) was observed. Genes were then classified as: Class 1, if there was positive selection in >=80% of evaluated cancer types and no negative selection; Class 2, if there was positive selection in at least one cancer type; Class 3, if there was absence of positive or negative selection in any cancer type; and finally, Class 4, if no positive selection was observed in any cancer type but negative selection was observed at either the cancer type or the pan-cancer level. In all, 66 TSGs were assigned to one of the four classes with all other TSGs remaining unclassified (data not shown). See FIG. 9 for detailed evaluation of the criteria used in the TSG classification.Plasmids

[0113] All new plasmids were generated using Gibson Assembly strategies79using NEBuilder® HiFi DNA Assembly Master Mix (NEB #E2621) following the manufacturer’s protocol. All new plasmids, along with detailed maps and sequences, will be made available through Addgene. The sequence for PE7 that was used to generate the Lenti-EF 1 a-PE7-P2A- Puro plasmid was obtained from pCMV-PE7 (Addgene #214812). The NRF2 reporterAtty. Dkt. No.: 115872-3370 plasmid was constructed by first transferring the U6-sgRNA-EFS-Blast-P2A-BFP cassette from pUSEBB80 into the higher titer pLV backbone81, then replacing the blasticidin S selection marker with a neomycin selection marker via Gibson Assembly to generate Lenti- Trono-Neo-P2A-BFP, and finally transferring the 8xARE-GFP cassette from pREP-8xARE- GFP-SV40-BFP (Addgene #134910) into the Lenti-Trono-Neo-P2A-BFP backbone, replacing the U6-sgRNA cassette to generate the final Lenti-Trono-8xARE-GFP-EFS-Neo- P2A-BFP reporter plasmid. Libraries of pegRNA-sensors were cloned into the Lenti-Trono- BR backbone, which was previously generated by transferring the U6-sgRNA-EFS-Blast- P2A-TurboRFP cassette from pUSEBR into the higher titer pLV backbone45. Lenti-PEAR- mCherry, the reporter plasmid used for validating prime editing activity, was also previously assembled45. The Lenti-UPEmS-tevo plasmid was used to assemble individual pegRNAs via Golden Gate Assembly for validation experiments45. All plasmids were validated via wholeplasmid sequencing.Prime editing sensor library design & cloning

[0114] Prime editing sensor libraries were designed using the Python package PEGG (version 2.1.0)45. As input, the 42 most frequently observed VUS in KEAP1 (40 / 42 were prime editing amenable), top 10 most frequent driver mutations (10 / 10 prime editing amenable), and 10 germline mutations (9 / 10 prime editing amenable), as well as 4 hotspot variants in NRF2, and silent substitutions (SNPs or oligonucleotide substitutions) at each mutated codon location were provided. Five pegRNAs were generated per mutation for each missense variant (when suitable NGG PAM sequences were present), prioritizing diversity among protospacers in generating these pegRNA designs. Two pegRNAs were generated for each silent substitution mutation, and included 41 non-targeting controls, whose protospacers have no target site in the genome82, and 50 safe-targeting controls, who target intergenic regions that lack annotated genomic elements83. The pegRNA-sensor cassette included a 60 nucleotide long sensor site in the reverse complement orientation relative to the protospacer, and also contained the tevopreQl motif to improve pegRNA stability84. The pegRNA-sensor cassettes were filtered to exclude EcoRI and Esp3I sites and polyT termination sequences of greater than or equal to 4 consecutive thymines. The oligonucleotide library was ordered from Twist Biosciences. The library was cloned following the same library cloning protocol presented in Gould et al.45.Atty. Dkt. No.: 115872-3370Lentivirus Production

[0115] Lentiviruses were produced by co-transfection of HEK293T cells with the relevant lentiviral transfer vector and packaging vectors psPAX2 (Addgene, catalog no. 12260) and pMD2.G (Addgene, catalog no. 12259) using Lipofectamine 2000 (Invitrogen, catalog no. 11668030). Viral supernatants were collected at 48- and 72-hours posttransfection and stored at -80°C.Cell Culture and Cell Line Generation

[0116] NCLH1299 cells were received from Koch Institute ES Cell Core, where they were mycoplasma tested and STR validated. The NCI-H1299 cells were maintained in RPMI-1640 media (Gibco, catalog no. 11875093) supplemented with 10% FBS and IX Penicillin-Streptomycin (ThermoFisher). To generate NCI-H1299-PE7 cells via spinfection, 500,000 NCI-H1299 were plated in each well of a 6-well plate with 2 mL of fresh Lenti- EFla-PE7-P2A-Puro plasmid lentivirus and polybrene transfection reagent (Sigma-Aldrich, catalog no. TR-1003) was added to a final concentration of 8 pg / mL, and the cells were spun at 800 g for 2 hours before being incubated overnight. The following day, this spinfection protocol was repeated to maximize prime editor expression, the NCI-H1299-PE7 cells were pooled, selected with 10 pg / mL puromycin, and validated for prime editing activity. Subsequently, to generate NCI-H1299-PE7-8xARE-GFP cells, the NCI-H1299-PE7 cells were transduced with Lenti-Trono-8xARE-GFP-EFS-Neo-P2A-BFP. After 3 days, the BFP positive population was sorted to isolate cells expressing this reporter construct. These cells were used for the subsequent screen.Prime editing sensor screening protocol

[0117] For each of the three replicates, one million NCI-H1299-PE7-8xARE-GFP cells were plated per well in three 6-well plates in RPMI-1640 media with 10 pg / mL puromycin, resulting in 17 million plated cells per replicate to achieve 10,000X representation assuming an MOI of 0.3. Then, 50 pL of the prime editing sensor lentivirus was added to achieve a final volume of 3 mL in each well. After 24 hours, cells from each replicate were lifted, combined, and plated in five 15-cm plates, and 10 pg / mL blasticidin S was added to the media. A small population of cells from each replicate was split and not treated withAtty. Dkt. No.: 115872-3370 blasticidin S to experimentally determine the MOI. After 72 hours, the RFP positive fraction of this unselected population was measured to quantify the MOI, which was calculated to be 0.342 (-30% of cells transduced). Puromycin and blasticidin S were maintained at 10 pg / mL throughout the screen for the rest of the cells. Cells were replated every 3 days, maintaining >10,000X representation at each time-point during the screen. Fourteen days posttransduction, a 10,000X representation cell pellet was generated from each replicate (5 million cells), and then sorted the remaining cells from each replicate into 4 equally sized bins on the basis of their GFP fluorescence level.Fluorescence-activated cell sorting and analysis

[0118] The BD FACSCelesta Cell Analyzer in tube or plate reader format, with BD FACSDiva v9.0 software used for data collection, was used for the validation of NRF2 8xARE-GFP reporter construct, the Lenti-PEAR-mCherry prime editing reporter, and validation of individual pegRNAs for ARE-GFP reporter activity. Downstream analysis was performed using FlowJo 10.9.0 to identify single cells and quantify fluorescence. The BD FACSAria III Cell Sorter was used for cell sorting.Genomic DNA extraction, Library Deconvolution & Sequencing

[0119] Genomic DNA (gDNA) was extracted from each sample using the DNEasy blood and tissue kit (Qiagen) following the manufacturer’s protocol. Then, two rounds of PCR was performed to amplify the pegRNA-sensor cassette and add barcodes and Illumina adaptors for next-generation sequencing. In the first round of PCR, 4 PCR reactions for each sample were performed, using 20 pL of gDNA, 25 pL of Q5 High Fidelity 2X Master Mix (NEB), and 2.5 pL of the forward (Fl) and reverse primers (Rl), which were at a concentration of 10 pM. The plasmid library was amplified using 10 ng of the plasmid library as a template. The sequence of the Fl primer was 5’- CGCTCTTCCGATCTCTAGCGTTCGAGTTAGGAATT-3’ (SEQ ID NO: l) and the Rl primer was 5’-CTGAACCGCTCTTCCGATCTTTGTGGAAAGGACGAAACACC-3’ (SEQ ID NO: 2). The PCR program for the first PCR amplification reaction was (1) 98°C x 2 minutes, [(2) 98°C x 10 seconds, (3) 60°C x 30 seconds, (4) 72°C x 30 seconds] x 25 cycles, (5) 72°C x 2 minutes, (6) 4°C Hold. The samples were then PCR purified by pooling samples together, using the QIAquick PCR Purification Kit (Qiagen) following theAtty. Dkt. No.: 115872-3370 manufacturer’s protocol, before gel extracting the appropriately sized band with the QIAquick Gel Extraction Kit (Qiagen), again following the manufacturer’s protocol. The PCR1 products were measured with a NanoDrop 2000 (ThermoFisher) and were then used as template for PCR2. The second round of PCR amplification (PCR2) was then performed, running two PCR reactions per sample. Each reaction contained 10 ng of purified PCR1 product as template, 25 pL of Q5 High Fidelity 2X Master Mix, 0.5 pL of the forward (F2) and reverse primers (R2), which were at a concentration of 10 pM, and were completed to 50 pL with nuclease-free water. The sequence of the F2 primer was 5’- AATGATACGGCGACCACCGAGATCTACACCGCTCTTCCGATCTCTAGCGT-3’ (SEQ ID NO: 3)., and the R2 primer was 5’- CAAGCAGAAGACGGCATACGAGATNNNNNNNNCCTGCTGAACCGCTCTTCCGA TCT-3’ (SEQ ID NO: 4)., with a different eight nucleotide barcode used to identify each sample. The PCR program for the second PCR amplification reaction (PCR2) was (1) 98 °C x 2 minutes, [(2) 98°C x 10 seconds, (3) 67°C x 30 seconds, (4) 72°C x 30 seconds] x 10 cycles, (5) 72°C x 2 minutes, (6) 4°C Hold. These PCR reactions were pooled for each sample, PCR purified and subsequently gel extracted, before being run on an Illumina MiSeq v2 sequencer with the 2x250 paired end sequencing kit. The sequencing reaction used the following custom primers: Read 1 : 5’- ACCGCTCTTCCGATCTCTAGCGTTCGAGTTAGGAATTC-3’ (SEQ ID NO: 5), Read 2: 5’-CCTGCTGAACCGCTCTTCCGATCTTTGTGGAAAGGACGAAACACCG-3’ (SEQ ID NO: 6), Index 1 : 5’- CGGTGTTTCGTCCTTTCCACAAAGATCGGAAGAGCGGTTCAGCAGG-3’ (SEQ ID NO: 7).Sequence analysis

[0120] Sequences from different samples were sorted into different fastq files on the basis of their 8 nucleotide barcodes in the index 1 read. Subsequently, reads from each sample were joined using the fastq-join (1.3.1) algorithm with the default parameters enabled (DOI: 10.2174 / 1875036201307010001). To identify the pegRNA counts in each sample, reads below an average Phred quality score of 30 were filtered and matched (with no mismatches allowed) the protospacer and 3’ extension regions of the read to the known sequences of each pegRNA in the library. For each sample, the 60 nucleotide sensor site from each read wasAtty. Dkt. No.: 115872-3370 sorted into a different fastq corresponding to each pegRNA. The last 8 nucleotides of each sensor site were used, which uniquely identified each sensor, to determine whether recombination of the sensor site had occurred, filtering out sensors without a perfect match in this region. Crispresso2 was then used to quantify the editing at each sensor site for each pegRNA in each sample85, using HDR mode with a quantification window center of 5-55. pegRNA-sensor analysis

[0121] MAGeCK (vO.5.9.5)86was used to normalize read counts between samples and determine the log2 fold-change (LFC) of each pegRNA in each sorted bin (Q1-Q4) relative the pre-sort populations, using the paired-end mode with the non-targeting control guides designated as controls. PegRNAs with a normalized control count <30 reads were then filtered to reduce spuriously enriching pegRNAs. Noise from this dataset was further removed by filtering out pegRNAs that exhibited enrichment (LFOO.l) in all of the sorted bins (Q1-Q4) relative to the pre-sort populations, as these pegRNAs likely represented PCR- amplification bias. When quantifying editing, the analysis was limited to pegRNA-sensor pairs with at least 50 sensor reads.Example 2: Characterizing somatic biallelic inactivation in tumor suppressor genes

[0122] To study the incidence and selection of biallelic alterations across cancer- associated genes, tumor profiles of 48,179 patients were analyzed across 67 major cancer subtypes using the FDA-authorized MSK-IMPACT targeted clinical sequencing assay. MSK-IMPACT profiles up to 505 cancer-associated genes, including 224 genes annotated as tumor suppressors by the precision oncology knowledgebase, OncoKB13(data not shown). To maximize sensitivity and specificity to infer allele-specific copy number alterations, the analysis to tumors was restricted with at least 30% purity and excluded tumors with high tumor mutation or copy number alteration burden (see Methods). In all, tumor profiles from 23,713 patients were retained (data not shown). Somatic alterations comprising substitutions, indels, gene-level copy number amplifications and homozygous deletions, and fusions were identified using a clinically validated pipeline14. All somatic alterations were annotated for oncogenicity by OncoKB13(see Methods, data not shown). Hereafter, all somatic alterations, unless otherwise specified, refer to OncoKB annotated oncogenic alterations. Focal copy number deletions, observed at similar rates in both oncogenes and tumor suppressors (seeAtty. Dkt. No.: 115872-3370Methods, FIG. 7). For each somatic mutation, the local zygosity state was inferred and ascribed biallelic status for those resulting in complete loss of wild-type which arise from: (1) homozygous deletions, (2) oncogenic mutation with a concomitant copy number loss on the complementary allele that resulted in a complete loss of wild-type (MutLOH), (3) oncogenic substitutions, insertions or deletions together with a genomic rearrangement event, and, (4) multiple oncogenic mutations in the same gene, referred to as “composite mutations”15(FIG. 1A, see Methods for further details).

[0123] The majority of oncogenic somatic alterations affecting tumor suppressors were associated with biallelic inactivation (72%), but this was not the case for oncogenes (9%) (FIG. IB). Exploring the genetic mechanisms of biallelic events, MutLOH events (i.e., mutations with a concomitant copy number loss of heterozygosity) constituted the dominant form of biallelic inactivation in both TSGs (54%) and oncogenes (83%). Expectedly, oncogenic homozygous deletions were exclusive to TSGs and comprised 30% of all biallelic events in these genes. The mechanism of loss of wild-type varied substantially by gene (FIG. 1C; data not shown): for example, the majority of biallelic events in CDKN2A (83%), TGFBR2 (65%), FAT1 (62%), and B2M (55%) were the result of homozygous deletions, whereas nearly all biallelic events in TP53 and VHP were the consequence of a mutation with a concomitant loss of heterozygosity.

[0124] Preference of mechanism for biallelic inactivation for each gene also varied by tumor lineage. Nineteen instances were found (q < 0.05) where the gene’s observed mechanism of biallelic inactivation in a given tumor lineage was significantly different from its pan-cancer pattern of biallelic inactivation (FIG. 1C, data not shown). For example, at pan-cancer resolution, PTEN biallelic losses most commonly arose via MutLOH (54% of cases), but homozygous deletions were the dominant mechanism among prostate adenocarcinomas (75%) and high grade serous ovarian tumors (91%)16 17. In contrast, composite mutations comprised 49% of biallelic PTEN events in uterine endometrioid cancers despite excluding tumors with high mutational burden (see Methods). Similarly, biallelic loss via composite mutations was also common among PIK3R1 mutated uterine endometrioid tumors (70%) compared to 23% pan-cancer. This suggests that cancer-type specific differences in mutational and copy number burden drives the adoption of specific genetic mechanisms for second hits.Atty. Dkt. No.: 115872-3370Example 3: Gene-specific variation in rates of biallelic inactivation

[0125] Inactivation of both alleles is often considered a near-obligate event for most recessive tumor suppressor genes. However, among the 174 (of 224) tumor suppressor genes that were mutated in 25 or more tumors, only 30% (52 / 174) demonstrated a biallelic rate (defined as the fraction of oncogenic alterations with evidence of biallelic alteration) of 80% or higher (data not shown). Included among these 52 genes were many well-established and highly mutated tumor suppressors in many cancers including TP53 (biallelic rate 92%), CDKN2A (96%), APC (90%), PEEN (91%) and RBI (93%). Similarly, the rate of loss of both alleles was near absolute among TSGs which are infrequently mutated pan-cancer but are highly mutated in specific lineages in which their roles in tumorigenesis is well- established. These include genes BAP1 (94%) and VHP (95%) in clear cell renal cell carcinoma, as well as CDH1 (91%) and MEN1 (86%) which are most frequent in breast invasive lobular carcinoma and pancreatic neuroendocrine tumors, respectively. Somatic loss of both alleles was also common among TSGs which are very rarely mutated in any disease. For example, CBFB and PRDM1, which are commonly altered in hematological malignancies, were rarely mutated in solid tumors (0.8% and 0.4%, respectively), but loss of wild-type was near universal (95% and 90%, respectively) in tumors with oncogenic mutations in each of these genes.

[0126] In contrast, for the remaining 70% (122 / 174) of TSGs, greater than 20% of oncogenic mutations arose without concomitant disruption of the wild-type allele. These genes clustered in certain pathways, including epigenetic regulators (22 / 24 genes in pathway, median biallelic alteration rate 47%), mediators of DNA damage repair (26 / 29, median biallelic alteration rate 48%) and NOTCH signaling (9 / 9, median biallelic alteration rate 42%) (FIG. 8A). Ascertaining whether mutations in these genes represented bona fide instances of haploinsufficiency, or alternatively are examples of cryptic biallelic alteration (e.g., by epigenetic silencing) is difficult. However, several genes identified above have been described previously as demonstrating features of functional monoallelic inactivation. Consistent with prior evidence suggesting that heterozygous mutations in ARJD1A are sufficient to drive tumor progression phenotypes in some diseases, nearly half of all AR1D1A mutated tumors in the study retained the wild-type allele18,19. In prostate cancer, heterozygous mutations in SPOP are sufficient to form heterodimeric complexes with and suppress wild-Atty. Dkt. No.: 115872-3370 type SPOP, thereby repressing functional SPOP in a dominant-negative manner20. Consistent with this proposed mechanism of action, in the cohort, SPOP mutations demonstrated the lowest biallelic inactivation rate (16%) among all tumor suppressors, with a particularly low biallelic alteration rate (6%) in 5PO -mutant prostate cancers.Example 4: Lineage dependent variation in rates of biallelic inactivation

[0127] Since most tumor suppressor genes are mutated in a lineage-specific manner, the goal was to understand how patterns of biallelic inactivation varied by tumor lineage. To do so, lineages were investigated in which a given tumor suppressor was frequently mutated (at least 1% alteration frequency and a minimum of 10 mutated tumors) also demonstrated high biallelic inactivation rates (>80%). This analysis revealed distinct classes of tumor suppressors according to their lineage-specificity and tendency for biallelic inactivation. Five widely mutated genes demonstrated universal and lineage-agnostic tendencies for high biallelic inactivation: TP53 (58 / 67 lineages), CDKN2A (43 / 45), CDKN2B (36 / 36), PTEN (28 / 29) and RBI (23 / 26) (FIG. 2). Similar patterns were observed in other bona fide TSGs such as APC (11 / 12), BAP1 (10 / 10), and the TGF-beta pathway members SMAD4 (12 / 12), SMAD2 (2 / 3) and TGFBR2 (2 / 3). In contrast to the above, several genes demonstrated varying degrees of biallelic inactivation across lineages. Some genes presented with high biallelic rates in one or select few lineages while in other lineages the biallelic events were either only moderately elevated or nearly absent. For example, SETD2 mutated renal cell carcinomas had near absolute biallelic inactivation rate (92%) but the rate of biallelic inactivation varied widely in other cancer types, (e.g., 64% of 5E D2-mutant lung adenocarcinomas and only 21% of 67 / 7 / 12-mutant glioblastomas biallelically inactivated). Similarly, TSC2 was biallelically inactivated in more than 90% of tumors of pancreatic neuroendocrine tumors and hepatocellular carcinomas, but in only a third (67%) of TSC2- mutant colon adenocarcinomas.Example 5: Selective pressure for MutLOH in TSGs

[0128] The observations above suggest that biallelic alterations to TSGs may be under evolutionary selection in specific, often disease-dependent, contexts. To quantitatively define whether the patterns of recurrent biallelic inactivation described above represented signals of evolutionary selection, the most common form of biallelic inactivation was focusedAtty. Dkt. No.: 115872-3370(representing 55% of all cases of biallelic inactivation in TSGs, FIG. IB): mutation plus loss-of-heterozygosity (MutLOH). To test for selection for MutLOH of each gene at the pancancer or cancer type level, the rate of LOH was compared at the gene locus among tumors with mutation to those without the mutation (see Methods). At the pan-cancer level, 47 of 60 (78%) tumor suppressors with mutations in 100 or more tumors showed significant evidence of selection for MutLOH (q < 0.05, data not shown) with 45 genes enriched for loss of wild-type and two genes significantly favoring the retention of the wild-type allele (FIG. 3).

[0129] However, at the individual cancer-type level, the patterns of selection for MutLOH were more varied and presented as a spectrum, ranging from TSGs that were universally biallelically inactivated across cancers to those that were preferentially monoallelic in every disease. As such, all TSGs were categorized into four classes based on their preferential loss or retention of the wild-type allele among diseases in which they were commonly mutated (that is, a disease with 20 or more mutated tumors). Of note, the genedisease pairs that showed statistically significant enrichment for MutLOH as well as those that had an overall biallelic rate (as in FIG. 2) of 80% or higher were considered as preferentially losing the wild-type allele (see Methods). As detailed below, the classification schema relied on informed but subjective thresholds; in FIG. 9 it is demonstrated that varying these thresholds produces nearly identical classification patterns across genes (also see Methods).

[0130] Class 1 TSGs were defined as those that showed preferential loss of wild-type allele in more than 80% of eligible cancer types (FIG. 7). In all, 32 genes were identified as Class 1 and included widely mutated TSGs such as TP 53 (significant for biallelic loss in 38 of 38 cancer types with 20 or more mutations), RBI (11 / 11), PTEN (10 / 10), as well as NF1 (6 / 6), SMAD4 (6 / 6), BAP1 (4 / 4), APC (4 / 4) and CDH1 (3 / 3) all of which were observed to ubiquitously prefer loss of the wild-type allele in every disease in which they were recurrently mutated (FIG. 3).

[0131] In contrast, Class 2 TSGs (n=13, including ATM, ARID1A, FBXW7, and ARTD2') showed highly lineage-specific patterns of selection for biallelic inactivation and were identified as those that preferentially lost the wild-type allele in at least one of the diseases in which they were commonly mutated. For example, while clear selection forAtty. Dkt. No.: 115872-3370MutLOH was apparent among ATM-mutant lung (OR=6.7, q=l. lxl0'9), prostate (OR=5.2, q=lxl0'3), and colon (OR=5.1, q=4.5xl0'7) cancers, no such signal was evident among bladder urothelial carcinoma (OR=2, q=0.3), despite a reasonable burden of apparently oncogenic ATM mutations (2.9% of bladder urothelial tumors with ATM mutations). The most archetypal example of this class of genes was ARID1A (mutated in 5% of all cancers). In total, 9 / 14 cancer types with frequent ARID 1 A mutations had MutLOH rates indistinguishable from those expected by chance. Among those cancer types with no evidence of selection for MutLOH of ARID 1 A were upper tract urothelial carcinoma (17% mutation rate, OR=1.1, q=0.94), colon adenocarcinoma (3.6%, OR=1.1, q=0.86), lung adenocarcinoma (3.3%, OR=1.48, q=0.25), and rectal adenocarcinoma (5.6%, OR=0.93, q=0.94). Among other Class 2 genes, enrichment for MutLOH was an exception rather than the norm. For example, 87% of FBXW7 mutated tumors in lung squamous cell carcinomas had loss-of-heterozygosity (OR=4.1, q=4.99xl0'2), whereas in six other lineages including colon (OR=1, q=0.95) and rectal (OR=1.4, q=0.39) adenocarcinomas no evidence for selection for MutLOH was observed.[01321 Next, 27% (18 / 66, FIG. 3) of TSGs that did not show selection for MutLOH in any lineage as Class 3 were classified. These included PI3K pathway members PIK3R1 and PPP2R1A, which act as negative regulators of PIK3CA and AKT 1 / 2 / 3, respectively21. None of the seven lineages in which PIK3R1 is commonly mutated showed either significant enrichment or depletion of second hits. Even among cancer types with higher rates of PIK3R1 mutations such as uterine endometrioid carcinoma (29% of tumors) and glioblastoma (8%), MutLOH rates were only 9% (q=0.8) and 9% (q=0.5), respectively, and were indistinguishable from what would be expected by chance. Consistent with prior evidence that hotspots in PPP2R1A are potentially dominant-negative22, it was found that despite the high mutation rates of PPP2R1A in numerous uterine cancer subtypes (serous (30%), mixed endometrial (25%) and carcinosarcoma (17%) subtypes of uterine cancers), only 19% of PPP2R1A mutated tumors in these diseases harbored concomitant LOH (q > 0.05). Additionally, many Class 3 TSGs were chromatin modifying and remodeling genes in the epigenetic pathway such as ARID IB, CREBBP, KMT2A, KMT2C aw / ASXLl (FIG. 8B).

[0133] Finally, genes that, when mutated, preferentially retained the wild-type allele at the pan-cancer level were designated as Class 4 (n=3). This class included genes for whichAtty. Dkt. No.: 115872-3370 both oncogenic and tumor suppressive roles have been proposed. For example, the transcription factor FOXA1 has been suggested to play a tumor suppressive role in liver and breast cancers but was recently unambiguously demonstrated to be an oncogene in prostate cancer23 26. Here, at the pan-cancer level, significant depletion for MutLOH in FOXA1- mutated cancers (OR=0.43, q=7.6'5) was found implying a dependence on the retention of the wild-type that remains to be understood. Similar depletion was also observed for GATA3 at the pan-cancer level (OR=0.29, q=3 ,2'15) with suggestive evidence for depletion for MutLOH in breast invasive ductal cancer (OR=0.62, q=0.063).Example 6: Biallelic Enrichment Identifies Rare Driver TSG Events in APC in Noncanonical Contexts

[0134] Establishing the functional significance of infrequently mutated genes is often challenging. It is hypothesized that enrichment for MutLOH (as in FIG. 3) could be used as a metric of evolutionary selection, and thereby functional relevance, of rarely mutated genes. For many of the genes selected for pan-lineage MutLOH (FIG. 3), It was also observed evidence for selection among cancer types in which these genes are rarely mutated (FIG. 4A). For example, BAP1 was under significant selection both in cancers where it is highly mutated (mesotheliomas, renal cell cancers, uveal melanomas and intrahepatic cholangiocarcinomas), as well as those with comparatively few BAP1 mutations, such as prostate adenocarcinoma (0.2% of patients, OR=163, q=3xl0'4) and lung adenocarcinoma (0.7%, OR=26, q=3xl0'5). Similarly, STK11 mutations were infrequent among cancers other than those of thoracic origin but when present were often observed with concomitant loss of the wild-type allele (e.g. in pancreatic adenocarcinoma, OR=8.3, q=1.9xl0'3; in breast invasive ductal carcinoma, OR=9.8, q=4xl0'4). Other genes that showed highly lineage specific patterns of selection for MutLOH were also selected for among infrequently mutated cancer types. In hepatocellular carcinomas, TSC1 and TSC2 mutations, while infrequent (<3%), were associated with a unique and aggressive form of HCC that is responsive oMTOR inhibition27. Consistent with this, it was found that a universal biallelic loss of TSC1 (5 / 5 mutations biallelic) and TSC2 (5 / 5 mutations biallelic) in HCC.

[0135] Focusing on genes mutated in <10% of samples in a particular cancer type (i.e. in a non-canonical cancer lineage), it was noted that enrichment for biallelic loss of APC in prostate (OR=36, q=9xlO'30) and lung adenocarcinoma (OR=34, p-value=2xl0'12)Atty. Dkt. No.: 115872-3370 corresponded to two of the most statistically significant findings (FIG. 4A). Biallelic loss of APC (APCB1) is an early and obligate event for tumorigenesis in most colorectal cancers that promotes stabilization and nuclear translocation of the transcription factor P-catenin28. In total, seven tumor lineages (glioblastomas, cutaneous melanomas and adenocarcinomas of prostate, lung, pancreas, stomach and esophagus) were identified in which APC was mutated in fewer than 10% of cases but was significantly enriched for biallelic alterations via MutLOH, which has been described in part in prior work17,29’30. It was confirmed that in tumor samples profiled by The Cancer Genome Atlas (TCGA), prostate adenocarcinomas (OR=48, p-value=4.9xl0'6) were again enriched for biallelic loss of APC (FIG. 4B). However, statistical significance was not reached for the lung adenocarcinoma patients in TCGA (OR=2.7, p-value=0.18) or TRACERx (TRAcking Cancer Evolution through therapy (Rx), OR=3, p-value=0.065) cohorts, likely due to small sample sizes (9 total cases of oncogenic APC mutations in TCGA and 15 cases in TRACERx). Despite this, consistent with selection for biallelic loss of APC, lung adenocarcinomas in the TCGA with biallelic alterations, but not heterozygous alterations, in APC showed significantly higher levels of P- catenin protein while CTNNB1 gene expression levels remain unchanged (FIG. 4C). These data suggest that APC is a rare but important driver gene in both prostate and lung adenocarcinomas.Example 7: WNT pathway mutations arise late in lung and prostate adenocarcinomas

[0136] WNT pathway mutations (including those in APC, CTNBB1, AMER1, AXIN1 / 2, etc.) are overall infrequent in adenocarcinomas of lung (8%) and prostate (10%)17 31compared to tumors of colon (83%) and rectum (80%). To understand the contexts in which these mutations arise in lung and prostate cancers, identification of their co-mutation patterns with other drivers in these cancer types was sought. As well noted in colorectal cancers32, near universal mutual exclusivity between mutations APC, CTNNB1 and other WNT genes (FIGS. 4D-4E) have been observed. Shared patterns of co-mutations are also observed between different WNT genes and other canonical drivers. For example, in lung cancer, both APCMTand CTNNB1MTtumors were significantly enriched for mutations in EGFR, SMAD4 and PTEN compared to tumors wild-type for WNT alterations. In prostate cancer, SPOP mutations were significantly enriched in APCMTtumors compared to WNT-wild-type tumorsAtty. Dkt. No.: 115872-3370(41% vs. 11%, BH-corrected p-value = 9xl0'25) and were elevated in CTNNB1MTtumors(18%, BH-corrected p-value = 0.1).

[0137] Since APC mutations are tumor-initiating driver mutations in colorectal cancers4, the goal is to understand if they also arose early in lung and prostate adenocarcinomas. To do so, patients were examined with two or more clonally related tumor specimens that were collected and sequenced longitudinally over the course of their clinical care. For these patients, it was examined whether key mutations were present or absent in each of the two sequenced specimens, reasoning that absence of a mutation in an early specimen (but presence in a later specimen) implied late emergence of a mutation (FIG. 4F, see Methods). Consistent with their role as early drivers, no instances were found of exclusively late-arising EGFA-mutant lung cancers (0 / 280 patients), / FGF-mutant prostate adenocarcinoma (0 / 23 patients), or APC-mutant colorectal cancers (0 / 118 patients) (FIG. 4G). In contrast, in patients who acquired specific resistance mutations in response to treatment in lung33, colorectal34and prostate cancers35, these mutations were detected in only the later sequenced biopsies (FIG. 4G). Unlike truncal / early APC mutations in colorectal cancers, robust evidence was found for late arising APC and CTNNB1 mutations in both lung and prostate cancers. In nearly a third of each of the lung (35%, 8 / 23) and prostate (30%, 7 / 23) adenocarcinomas, APC mutations were observed in only one of the sequenced biopsies (FIGS. 4G-H). Similarly, CTNNB1 mutations were also late arising in 54% (21 / 39) and 38% (5 / 13) lung and prostate adenocarcinomas, respectively (FIGS. 4G-4H). Despite the later acquisition of APC mutations, both the biallelic rates and the selection pressure to lose the wild-type were indistinguishably high in both primary and metastatic tumors of both lung and prostate adenocarcinomas (FIGs. 10A and 10B, data not shown, Methods). Together, this suggests that WNT pathway mutations significantly co-occur with canonical drivers in lung UX' JI'R) and prostate (SPOP) cancers, and that in many cases these mutations arise late in tumor evolution, suggesting they mediate tumor progression or treatment resistance.Example 8: Selection for MutLOH Reclassifies Functional Variants of Unknown Significance

[0138] Mutational recurrence at a residue is a singularly important determinant for in silico prediction of oncogenic potential of mutant alleles. Unlike in oncogenes, missense mutations in TSGs often do not cluster at single residues and are rather dispersed across theAtty. Dkt. No.: 115872-3370 length of the gene36, rendering many putative oncogenic alleles (i.e., nonsynonymous mutations) to be classified as variants of unknown significance (VUS)13. This consequently affects patients with certain diseases in which specific TSGs have an important role in disease stratification or therapeutic eligibility. Here, it was hypothesized that selection for biallelic inactivation could be used as a metric to identify genes with strong enrichment for putatively functional VUSs. Therefore the analysis in FIG. 3 was expanded, quantifying selection for MutLOH in TSG-subtype pairs with at least 10 VUSs as well as 10 OncoKB-annotated oncogenic alleles (FIG. 5A).

[0139] Fifteen gene / subtype pairs with evidence of positive selection were ultimately identified for MutLOH in the context of a VUS (BH-corrected p-value <0.05, data not shown). Lung adenocarcinoma had the highest number of genes (ATM, KEAP1, STK11 and CDKN2A) with significant enrichment for MutLOH among VUSs. Interestingly, three examples of universal MutLOH were identified among VUSs, including MEN1 VUSs in pancreatic neuroendocrine cancers (25 / 25 cases with MutLOH, q = 4.2xl0'6), a tumor type in which loss of MEN1 is a pathognomonic genetic event, and CBFB in breast invasive ductal carcinoma (30 / 30, q = 8.6xl0'6). Thus, while in the majority of genes VUSs demonstrate no widespread selective pressure for loss of the wild-type allele, in select genes and lineages, selection for biallelic losses among VUSs appear to be near-complete.

[0140] KEAP1 is a well-established oncogenic driver gene in lung adenocarcinoma (LUAD) with 8% (374 of 4794) of all patients with LUAD harboring OncoKB-annotated oncogenic mutations m KEAP! in the MSK-IMPACT cohort, and an additional 5% of patients (259 of 4794) presented with missense VUSs in KEAPP Emblematic of the scattered prevalence of somatic mutations in TSGs, missense mutations in KEAP1 were dispersed across the length of the gene with only 17% (32 of 190) of all nonsynonymous substitutions classified as oncogenic by OncoKB (FIG. 5B). Interestingly, both KEAP1 oncogenic mutations and VUSs showed strong selection for biallelic inactivation with more than 90% of mutations in each category presenting with a concomitant loss of the wild-type allele in both the MSK-IMPACT and the TCGA cohorts (FIG. 5C). Of note, the strong selection pressure for LOH among the tumors in TCGA cohort, which are treatment-naive, indicates that 7N / U biallelic loss is an early event.Atty. Dkt. No.: 115872-3370

[0141] In homeostatic conditions, KEAP1 mediates the ubiquitination and subsequent degradation of the transcription factor NRF2 (also known as NFE2L2). However, in the presence of reactive oxygen species or loss-of-function mutations in KEAP1, NRF2 is able to translocate to the nucleus and bind antioxidant response elements (ARE) to activate transcription of cytoprotective antioxidant genes37. Consistent with its function in regulating NRF2 at the protein level, TCGA tumors in the LU AD cohort with KEAP1 VUSs, as well as those with KEAP1 oncogenic mutations, showed significant increases in NRF2 protein, but not RNA, abundance relative to WT (p=2xl0‘5, FIG. 5D). Similarly, both RNA and protein expression of the NRF2 target gene NQO1 were also elevated (RNA VUS vs. WT: p=7xl0‘20; protein VUS vs. WT: p=7xl0'11). Furthermore, at the molecular subtype level, both KEAP1 oncogenic mutations and KEAP1 VUSs showed similar patterns of co-mutations with other key drivers of LU AD (FIG. 5E). Altogether, these data imply that KEAP1 VUSs in lung cancer phenocopy aspects of KEAP1 oncogenic alleles by activating NRF2 signaling.

[0142] KEAP1 mutations (often in conjunction with STK! I) define a genomically distinct group of lung adenocarcinomas with a poor response to systemic therapy and overall poor prognosis38. Based on the functional and genomic data above, it was hypothesized that patients with KEAP1 VUSs would show clinical outcomes indistinguishable from those with KEAP1 oncogenic alleles. Consistent with this, MSK-IMPACT patients with LUAD harboring KEAP1 VUSs had similarly worse overall survival as patients with KEAP1 oncogenic mutations when compared to patients with KEAP1 wild-type tumors (VUS vs. WT : median overall survival, mOS of 17 mo. vs. 44 mo., HRadj=1.7 [95% CI: 1.4-2.0], p- value=2.1xl0'9; oncogenic vs. WT: mOS 13 mo. vs. 44 mo., HRadj=1.7 [95% CL 1.4-1.9], p-value= 1.5x1 O'11) indicating that the majority of the KEAP1 VUSs in LUAD are likely oncogenic (FIG. 5F, data not shown, FIG. 11). This analysis was then extended to all other TSG / cancer type pairs with sufficient sample size to evaluate OS (see Methods); however, KEAP1 in LUAD was the only pair in which VUS were significantly associated with prognosis compared to wild-type (FIGS. 11A-11B). In addition to OS, whether KEAP1 VUS would be similarly associated with poor progression-free survival (PFS) on standard of care chemoimmunotherapy in advanced NSCLC (chemo-IO cohort, n=421 patients)39was evaluated. Again, patients with tumors harboring either VUS or oncogenic driver mutations in KEAP1 had significantly worse PFS compared to KEAPl^ (VUS vs. WT: mPFS of 4 mo. vs. 6.8 mo., HRadj=2.0 [95% CL 1.3-3.0], p-value=1.3xl0'3, oncogenic vs. WT: mPFSAtty. Dkt. No.: 115872-33703 mo. vs. 6.8 mo., HRadj=1.7 [95% CI: 1.3-2.3], p-value=5.8xl0'4, further suggesting that most KEA Pl VUS are likely oncogenic (FIG. 5G, FIG. 11C).Example 9: Prime Editing Screen Establishes that KEAP1 VUSs Activate NRF2

[0143] It was sought to experimentally test the hypothesis that KEAP1 VUS phenocopy annotated driver variants, as suggested by patterns of MutLOH, by using prime editing to engineer and screen KEAP1 variants. To do so, a reporter system was adapted to quantify the effects of loss-of-function somatic alterations in KEAP1 on activation of NRF2- induced transcription40(FIG. 5H). To assess the ability of various KEAP1 mutations to suppress or activate NRF2, we constructed a genetic reporter containing eight copies of the NRF2-targeted ARE elements next to a minimal promoter linked to GFP (8x ARE-GFP), such that GFP expression indicates functional NRF2 transcriptional activity (FIG. 5H). This reporter was transduced into NCI-H1299 non-small cell lung cancer cells stably expressing PE7, a recently developed prime editor41. These cells carry wild type copies of both KEAP1 and NFE2L2 allowing the NRF2 reporter to be validated by treating them with tertbutylhydroquinone (tBHQ), an activator of NRF2 that interacts with thiol groups in KEAP1 to prevent it from repressing NRF243’44. As expected, a strong, dose-dependent induction of 8x ARE-driven GFP expression (FIG. 12A) was observed, validating the reporter. The prime editing activity of NCI-H1299-PE7 cells was also validated with Lenti-PEAR-mCherry, a reporter construct where GFP is turned on in the event of successful prime editing45’46(FIG.12B)

[0144] The high variance in prime editing efficiency among different prime editing guide RNAs (pegRNAs) prompted a recently-described prime editing ‘sensor’ approach to be adopted for high throughput screening. Prime editing sensors contain a pegRNA and a synthetic copy of the endogenous target site where the guide is predicted to install specific mutations, thereby providing an integrated readout of the efficiency of a given pegRNA45,47. We constructed a library of 500 pegRNA-sensors targeting 59 missense variants in KEAP1, including 47 VUS and 12 annotated oncogenic variants. 4 NRF2 hotspot mutations, silent substitution control mutations were further included for each mutated codon, and both safe- and non-targeting control pegRNAs, with multiple pegRNAs designed for each variant (FIGS. 12C-12D) This pegRNA-sensor library was delivered to NCI-H1299 cells stably expressing PE7 and the 8x ARE-GFP reporter followed by antibiotic selection of cells withAtty. Dkt. No.: 115872-3370 successful integrations. After allowing 14 days for editing to occur, fluorescence-activated cell sorting was used to isolate cells into four equally sized bins based on GFP expression levels (Q1-Q4) followed by extraction of genomic DNA and sequencing of integrated pegRNA-sensor cassettes (FIG. 5H). Next-generation DNA sequencing was used to quantify the abundance of pegRNAs and their editing efficiency at their cognate sensor sites in both the pre-sort and sorted cell populations, identifying efficient pegRNAs for many of the missense-inducing pegRNAs in the library (FIG. 51, FIGS. 12E-12F). Given that NRF2 activation requires the loss-of-function of both KEAP1 alleles (either through LOH or mutations in both alleles), The analysis was restricted to five pegRNAs with at least 40% editing efficiency with a high likelihood of installing the desired edit at both KEAP1 alleles (FIG. 5J, K, FIG. 12G). Consistent with the hypothesis that KEAP1 VUS phenocopy known oncogenic driver mutations, it was found that highly efficient pegRNAs that generated oncogenic mutations or VUS (but not silent substitutions) all exhibited NRF2 activation, as evidenced by their enrichment in the highest GFP-expressing bin (Q4) (FIG. 5J, K).

[0145] The variant that was most strongly enriched in the high GFP-expressing population (Q4) was A184G, a novel VUS that was observed in one patient, and also had the highest efficiency pegRNA (>80% sensor editing) (FIG. 51, J, K). This variant was further tested, alongside the highest scoring annotated oncogenic driver mutation mKEAPl (G186C) and silent substitution-generating pegRNAs (A184A and G186G), by individually transducing these pegRNAs into NCI-H1299-PE7-8xARE-GFP cells. It was validated that the A184G and G186C pegRNAs introduced their edits at the endogenous KEAP1 locus (FIG. 12H) followed by flow cytometric assessment of their relative induction of the 8x ARE-GFP reporter. It was found that both A184G and G186C exhibited significant GFP expression relative to A184A and G186G, respectively, and that A184G activated the ARE- GFP reporter more strongly than G186C (FIG. 5L). To assess whether the difference in reporter activity between A184G, a VUS, and G186C, an annotated oncogenic variant, could be explained by differences in editing efficiency, these results were normalized by the expected number of homozygous edits for each pegRNA, based on their sensor editing rates (FIG. 121) With this normalization, there is no significant difference between the reporter activity of the two variants, indicating that both the VUS (A184G) and oncogenic variant (G186C) result in KEAP1 LOF and NRF2-dependent transcriptional activation of ARE- containing target genes. Together with patient clinical data, these results provide substantialAtty. Dkt. No.: 115872-3370 functional support to the claim that KEAP1 VUSs in lung cancer phenocopy known oncogenic driver mutations, and that the zygosity of KEAP1 mutations should be considered in the stratification of KEAP1 mutations.Example 10: KEAP1 mutant zygosity is a prognostic biomarker to standard therapies in LU AD

[0146] KEAP1 mutations are characteristically associated with inferior outcomes and therapeutic resistance in lung cancer48,49. These observations are one of many examples of somatic mutations in numerous genes which have been reported to be prognostic of overall survival in specific cancers50,51. However, the impact of mutation zygosity on outcomes remains poorly understood. Here, in an unsupervised manner, the association between OS and mutation status / zygosity was evaluated for all genes that presented with at least 10 monoallelic and 10 biallelic alterations in a given cancer type (in all, n=150 gene / cancer type pairs; see Methods). Significant associations were determined using a multivariable Cox proportional hazards model that adjusted for disease status, age of diagnosis, sex, fraction of genome altered (FGA), tumor mutation burden (TMB), MSI status and the presence of clinically targetable alterations. When considering the alteration status alone (i.e., irrespective of zygosity), 18 gene-cancer type pairs are associated with worse (n=l 5) or better (n=3) overall survival compared to wild-type tumors (FIG. 6A, data not shown). These associations included genes such as TP53 (n=4 cancer types), PTEN (n=2) and CDKN2A (n=2). Interestingly, when carrying out this analysis in subgroups defined by whether inactivation of a given gene was biallelic or monoallelic, several instances were observed in which biallelic, but not monoallelic, alterations exhibited significantly different outcomes compared to wild-type patients (FIG. 6A, data not shown). For example, in prostate adenocarcinomas, when compared to wild-type tumors, only the patients with biallelic but not monoallelic alterations in TP53 showed worse outcomes of overall survival (biallelic vs. WT: HRadj=1.8 , q-value=8xl0'14; monoallelic vs. WT: HRadj=l.l, q-value=0.6).

[0147] It was sought to directly identify gene and cancer type pairs in which the outcome differences between tumors with biallelic and monoallelic alterations were statistically significant (FIG. 6B). In addition to TP53 in prostate adenocarcinomas, tumors with biallelic alterations in KEAP1 and SMARCA4 in lung adenocarcinomas had significantly worse OS compared to tumors with monoallelic alterations in these respective genes (KEAP1Atty. Dkt. No.: 115872-3370 biallelic vs. monoallelic: HRadj=1.8, q-value=4xl0'4; SMARCA4 biallelic vs. monoallelic: HRadj=2.5, q-value=3xl0'4). Strikingly, the overall survival among KEAP1 monoallelic tumors was indistinguishable from that of KEAP1 wild-type tumors (monoallelic vs. wildtype: mOS of 48 mo. vs. 44 mo., HRadj=1.0, p-value=0.99) (FIGS. 6C-D). These data directly indicate that KEAP1 zygosity is a critical determinant of the prognostic value of KEAP1 mutations, independent of other clinical covariates.

[0148] KEAP1 mutated tumors have been shown to reduce the efficacy of standard- of-care first-line immune checkpoint inhibitors both as monotherapy or in combination with chemotherapy (referred to as chemoimmunotherapy)38’39. Based on the data in FIGS. 6A-D, it was hypothesized that KEAP1 zygosity status may also be prognostic of responses to these therapies and sought to evaluate progression-free survival (PFS) in two cohorts of advanced NSCLC patients treated with chemoimmunotherapy (chemo-IO cohort as in FIG. 5G, n=385 patients with evaluable zygosity)39or anti-PD(L)l immunotherapy alone (IO-cohort, n=638 patients with evaluable zygosity)52. In the chemo-IO cohort, after adjusting for ECOG performance status, PD-L1 expression, TMB, derived neutrophil to lymphocyte ratio (dNLR), smoking history, and histology, only the patients with tumors harboring biallelic alterations in KEAP1 demonstrated significantly worse PFS compared to those with KEAP1 wild-type tumors (mPFS of 2.7 mo. vs. 6.7 mo., HRadj=1.8, p=3.1xl0'5) (FIGS. 6E-F). No difference in PFS between patients with tumors harboring monoallelic KEAP1 alterations vs. wild-type was observed (mPFS of 11 mo. vs. 6.7 mo., HRadj=0.98, p=0.96). More striking differences in PFS by KEAP1 zygosity were observed in the IO-cohort (FIGS. 6G-H). While patients with KEAP1 biallelic tumors showed significantly worse PFS compared to those with wildtype tumors (mPFS of 1.8 mo. vs. 2.7 mo., HRadj=1.6, p=1.04xl0'4), monoallelic KEAP1 inactivation was associated with favorable outcomes on IO when compared to the same wildtype tumors (mPFS of 11 mo. vs. 2.7 mo., HRadj=0.6, p=0.027). In total, these data argue that the zygosity of KEAP1 mutations, rather than KEAP1 mutation itself, may be an important prognostic biomarker of overall survival in lung cancer and, more importantly, may be a predictive biomarker of response to standard-of-care immunotherapies.

[0149] Loss-of-function somatic alterations to TSGs represent one of the two fundamental genetic events underlying oncogenesis. While complete loss (biallelic inactivation) is the cornerstone for the two-hit model of TSG-mediated tumorigenesis, theAtty. Dkt. No.: 115872-3370 extent to which this model applies to most TSGs has remained incompletely understood. Here, allele-specific analysis of somatic mutation and copy number data was used to conduct a census of the incidence of diverse mechanisms of biallelic loss across 224 TSGs and 96 detailed cancer types.

[0150] The results broadly stratified all TSGs into four classes based on their tendency for biallelic inactivation. The majority of Class 1 TSGs underwent near universal biallelic inactivation in every cancer type in which they were mutated, suggesting that loss of both alleles in these genes is obligate for impairing their tumor suppressive effect. In contrast, Class 2 TSGs (e.g., ATM, ARID 1 A, FBXW7), while broadly mutated across cancers, exhibited highly lineage-restricted and divergent preferences for retention of the wild-type across lineages. While these genes may demonstrate haploinsufficiency or dosage sensitivity in some contexts18,53~56, the data directly implicate selection for biallelic inactivation in at least some lineages, and argue for a context-dependent role in oncogenesis for these tumorsuppressive genes. In contrast, Class 3 and 4 TSGs invariably showed a lack of selection for biallelic inactivation in every disease in which they were commonly mutated. While biological mechanism of action for a majority of these genes remains to be understood, some of these TSGs (e.g. PPP2R1A and SPOP) have been shown to acquire dominant negative oncogenic mutations that constitutively inhibit their wild-type alleles without requiring additional somatic hits to the gene locus22,57. Other genes in these classes may acquire specific mutations that alter the gene function. For example, PIK3R1 loss-of-function mutations may promote transformation by increasing JNK and ERK phosphorylation through a neomorphic scaffold-based mechanism58. Interestingly, Class 4 genes demonstrated a particular preference for retaining the wild-type allele, the biological rationale for which remains to be understood. Taken together, these data suggest that the propensity to lose the wild-type allele is dictated by the biochemical mode of TSG activity in affecting tumor suppression.

[0151] Haploinsufficiency or dosage sensitivity of TSGs has been proposed to explain the retention of heterozygosity observed in many TSGs10. While the study was not designed to evaluate haploinsufficiency, the data nevertheless challenge how the evidence on dosage sensitivity of key TSGs that arose in murine models translates to human cancers. For example, heterozygous loss of PTEN has been suggested to be sufficient to promote tumorAtty. Dkt. No.: 115872-3370 development in several murine models of prostate cancer8, breast cancer59and astrocytomas60. However, in the patients, strong selection for biallelic losses was seen for PTEN in each of these diseases (prostate: 93% biallelic loss of PTEN, breast: 85%, and astrocytomas: 100%) suggesting a dependence on the complete loss of the protein. A plausible reconciliation of the two observations is that partial losses of PTEN are oncogenic early in tumor initiation (as observed in mice) but as tumors progress there is an ultimate dependence on loss of wild-type11. These findings thus argue for careful interpretation of haploinsufficiency in tumor suppressors in human cancers.

[0152] It was demonstrated that signals of selection for biallelic inactivation of genes such as APC in unexpected lineages can reveal new insights into the progression of those cancers. While WNT pathway dysregulation in lung cancers has been shown to be associated with metastasis61, poor outcomes62, resistance to cisplatin63and EGFR inhibitors64, WNT activation in this disease is often thought to be mediated by increased secretion of WNT ligands by the surrounding ‘niche’ cells65. It is reported here that WNT pathway mutations, while still being infrequent in lung cancers, show hallmarks of late arising and biologically important mutations.[01531 Finally, signatures of selection were also leveraged for biallelic inactivation to identify potentially functional alleles currently overlooked by contemporary, clinically operational frameworks for inference of mutant-allele oncogenicity (e.g. OncoKB). Although such frameworks combine information on mutational recurrence with functional predictions (e.g., whether a mutation is likely to introduce a truncating allele to a putative TSG), they face significant difficulty in classifying non-recurrent missense mutations. Using selection for biallelic inactivation, several genes were identified, including KEAP1, in which variants of uncertain significance show both (1) compelling evidence of function and (2) patient outcomes that mirror those of oncogenic alleles, prompting their reclassification. Most importantly, it was demonstrated that only biallelically-inactivated KEAP1 mutant tumors, but not those KEAP1 mutant tumors which retained the wild-type allele, were associated with poor overall survival and response to chemoimmunotherapy. The discoveries here complement both prior observations in TSGs (SMARCA4 lung adenocarcinoma51) as well as in oncogenes (KRAS66,67) that mutant allele dosage can mediate response to both targeted therapies as well as chemo- and immuno-therapies. These data therefore support the routineAtty. Dkt. No.: 115872-3370 clinical reporting of loss-of-heterozygosity as a guide to interpretation of the clinical significance of mutant TSG alleles in cancer patients.

[0154] Supplementary Note 1: Evaluation of criteria used in TSG classification

[0155] The robustness of the TSG classifications was evaluate that was presented in the study to different criteria used for class assignments. These criteria / thresholds include: (a) the biallelic rate threshold of >=80%, (b) the minimum number of mutated / altered tumors threshold (of 20) to consider a gene and cancer type pair in the TSG classification, and (c) the fraction of cancer types exhibiting positive selection for a TSG to be considered as Class I. Ultimately, across the varying thresholds, the TSG classifications remain qualitatively identical.

[0156] Biallelic rate threshold. The specific threshold for biallelic inactivation rate was chosen (>=80%) is rooted in the analysis of the distribution of biallelic rates. Of the 142 gene and cancer type pairs that showed significant selection for MutLOH (adj. p < 0.05) and that were used for TSG classification, a majority (n=115, 81%) also had a biallelic inactivation rate of 80% or higher (FIG. 9B). The justification for the biallelic rate threshold becomes strikingly apparent when evaluating the distributions of biallelic inactivation rates of those genes with and without statistically significant enrichment for MutLOH in each evaluated cancer type (FIG. 9C). That is, the dominant peak of biallelic rates for statistically significant gene / cancer type pairs falls above the heuristic threshold of 80%. Moreover, the distribution for pairs without significant selection is bimodal, suggesting that there is a population of genes where biallelic inactivation commonly occurs in certain cancer types but which may not have been statistically significant due to other dominant mechanisms of inactivation besides MutLOH and / or insufficient sample size. Consistent with this, the majority (20 of 39) of the gene and cancer type pairs that were included in the TSG classification solely based on the biallelic inactivation rate had a dominant mechanism of inactivation that is other than MutLOH. These include lineage agnostic bona fide Class 1 TSGs such as CDKN2A (n=8), RBI (n=4) and PTEN (n=3) in which we already demonstrated statistically significant enrichment of homozygous deletions in several cancer types (FIG. 1C)Atty. Dkt. No.: 115872-3370

[0157] It was also investigated more generally the variability of biallelic alteration fraction across different classes of TSGs. FIG. 9D shows the distribution of biallelic rates, now grouped by gene and colored by TSG classification, classified using the 80% thresholds. This visualization supports the categorization of Class 1 genes as those with most (>=80%) cancer types exhibiting positive selection for biallelic inactivation, with most Class 1 genes having median biallelic rates >80% and low variance. Class 2 genes also demonstrate highly context-specific biallelic inactivation, shown by the wide ranges of biallelic rates. The purpose of this analysis was to investigate the possibility of edge cases, whereby (for example) a Class 1 TSG nevertheless demonstrated exceptionally high variability in biallelic alteration rate in certain diseases. Carrying out this analysis, we identified no such edge cases: in fact, the average pan-cancer rate biallelic alteration rate correlated strongly with the typical (i.e. median) cancer-specific biallelic alteration rate for a given gene, supporting the use of these heuristics for classification.

[0158] To investigate the extent to which this 80% threshold affects TSG classification, the TSG class assignments were compared to those determined using 70% and 90% thresholds. It was observed that nearly all (95%) of classified genes using either the 70% (62 / 65) or 90% (62 / 65) thresholds retained the classification obtained using an 80% threshold. Thus, TSG classification does not depend strongly on the choice of biallelic alteration- threshold (FIG. 9E).

[0159] Minimum mutation / alteration count threshold. It was required that each gene and cancer type included in the TSG classification to have at least 20 mutated or altered tumors. This is to ensure that each pair included in the classification had sufficient power to determine both the presence or absence of selection. Setting this threshold too low can lead to inclusion of many cancer types with higher rate of false negativity to detect selection, and thereby increasing the likelihood of a Class 1 TSG to be identified as Class 2. On the contrary, too high of a threshold will disproportionately limit the number of genes that can be classified to only those that are highly mutated in a small number of diseases. While this choice of cutoff will unavoidably be subjective, a conservative threshold of 20 was chosen as the median total sample size across the individual disease cohorts is 81 tumors.

[0160] To evaluate the impact of this cut-off on TSG classifications, the class assignments were compared to those derived from when using a threshold of either 10 or 30Atty. Dkt. No.: 115872-3370 mutations or alterations per disease. The differences in the TSG class assignments were minor when using the lower threshold of 10 (55 of 65 concordant with threshold of 20) or a higher threshold of 30 (48 of 59 concordant) (FIG. 9F). Among the small number of discordances, three expected patterns were evident. First, while the majority of the Class 1 genes retained their class assignment with varying threshold, expectedly, lowering the threshold to 10 led to inclusion of infrequently mutated cancer types in which sufficient power was lacking to infer selection. Exemplifying this is the well-established two-hit TSG gene, TSC1, in clear cell renal cell carcinoma (ccRCC). While 9 of 11 TSC1 mutated tumors in ccRCC harbored an LOH, the statistical significance for MutLOH was not reached (adjusted p-value = 0.09) and consequently, this contributed to TSC1 now being classified as Class 2 at this cut-off threshold. Increasing this threshold did not change the assignments of Class 1 genes but led to some genes remaining unclassified (n=4). Second, lowering the threshold (to 10) increased the sensitivity to detect cancer types exhibiting selection for MutLOH leading to a small number of Class 3 genes now (with cut-off of 20) being assigned to Class 2. It was expected that as large numbers of disease types and tumors are profiled in future studies, some genes in Class 3 (which currently do not show evidence of selection for MutLOH in any disease type) will be classified as Class 2. Third, increasing the threshold (to 30) restricted the gene and cancer type pairs to only those diseases with large sample sizes leading to qualitatively incorrect assignment of Class 2 (lineage-specific two-hit) genes to Class 1 (bona fide, lineage-agnostic two-hit) genes. For example, SMARCA4 is infrequently mutated in both lung adenocarcinomas (4%, n=86 mutated tumors) and bladder urothelial carcinomas (3%, n=22) with widely different patterns of selection for MutLOH. While 86% of SMARCA4 mutated lung adenocarcinomas had a concomitant LOH (adjusted p-value = 3e-9), no such pattern of selection was evident among SMARCA4 mutated bladder cancers in which the LOH rate was only 23% (adjusted p-value = 0.43). Therefore, the pattern of MutLOH in SMARCA4 across cancers is entirely consistent with it being a Class 2 gene. However, using a higher minimum mutation count cut-off of 30 the sensitivity in identifying this as a Class 2 gene is lost.

[0161] Fraction o f cancer types threshold. An important factor in the classification schema is the breadth of selection for MutLOH across diseases. As described in the text, Class 1 TSGs were defined “as those that showed preferential loss of wild-type allele in more than 80% of eligible cancer types”. This 80% threshold was selected simply to distinguishAtty. Dkt. No.: 115872-3370 between Class 1 TSGs, where biallelic inactivation was selected for in “most” cancer types, and Class 2 TSGs, where the selection was more cancer type-specific. To evaluate the sensitivity of the classification to this numeric threshold, we varied the threshold for the minimum fraction of cancer types with positive selection (statistically significant enrichment or high biallelic rate) for a gene to be considered Class 1. Changing the threshold to either 70% or 90% had almost no impact on the TSG classification (FIG. 9G). Only one gene (PBRMl) was elevated to Class 1 upon lowering the threshold.Example 11: KEAP1, and STK11 mutation zygosity in mediating responses to immune checkpoint blockade in lung cancers

[0162] It has been shown that while KEAP1, STK11, and SMARCA4 mutations are associated with poor outcomes on PD(L)-1 immunotherapy in lung cancers, KEAP1 and STK11 mutated lung cancers benefit from dual immune checkpoint blockade (ICB) therapies compared to wild-type tumors. It is shown that this clinical benefit from dual ICB in KEAP1, or STK11 mutated tumors is restricted to only the patients that also harbor a loss of the wildtype allele (MutLOH) (see preliminary data on FIGs. 13A-14D).

[0163] REAP land STK11 are mutated in 20% of all lung cancers with robust early evidence in literature suggesting that these patients would significantly benefit from PD(L)- 1 / CTLA4 combination therapy. The preliminary data involving patients indicates that this clinical benefit is restricted to patients with biallelic loss of KEAP1 or STK11. It is estimated that 15-20% of tumors with mutations in KEAP1 have an intact wild-type allele, and it is hypothesized that patients with tumors harboring these heterozygous mutations would not benefit from dual ICB. Based on these preliminary findings, it is expected that these findings in KEAP1 to be translated to STK11 mutations. Therefore, treatment guidelines for ~2% of all lung cancers would be impacted. The work would enable a novel biomarker driven approach to select a patient population traditionally associated with poor prognosis who would derive benefit from escalation of therapy to dual ICB and prevent overtreatment and associated toxicity in patients with heterozygous alterations.Supporting Data / BackgroundAtty. Dkt. No.: 115872-3370

[0164] In lung cancers, mutations in KEAP1, STK11 and SMARCA4 are associated with poor outcomes including worse outcomes on immune checkpoint blockade (ICB) therapies (Alessi et al., J Thorac Oncol. 2023; Ricciuti et al., J Thorac Oncol. 2022). It was shown that allelic status of mutations in these genes is an important determinant of these poor outcomes (Zucker et al., Cell 2025). More specifically, only biallelic losses (mutation with a concomitant loss of wild-type allele) in these genes are associated with worse clinical outcomes. This was robustly demonstrated through evaluation of overall survival as well as progression-free survival among cohorts of patients treated with immunotherapy alone or in combination with chemotherapy. Biologically, phenotypic differences are shown in gene and protein activity of factors downstream of KEAP1 to establish differences in tumors with monoallelic and biallelic losses of KEAP1.

[0165] Dual ICB treatments are infrequently administered in lung cancers due to their high toxicity and limited responses. This clinical benefit may only be observed among tumors with biallelic losses in these genes given that the monoallelic tumors differ in NRF2 pathway activity.

[0166] The preliminary analysis confirms the hypothesis.

[0167] The following analyses will be performed in non-small cell lung cancer patients treated with anti-PD(L)-l and / or anti-CTLA4 therapies (treatments and combinations listed below in “Supplementary Note”). Clinical outcomes of overall survival (OS), time on treatment (TOT) and time to next treatment (TNT) will be evaluated.Aim 1 : Evaluate the outcomes on ICB in lung cancer patients with tumors harboring mutations m KEAPl, STK11 and / or SMARCA4.

[0168] Outcomes will be evaluated on anti-PD(L)-l alone or in combination with CTLA4 in patients with mutations in KEAP1, STK11 and / or SMARCA4 compared to wildtype. We will need to account for a number of co-variates such as KRAS mutation status, PDL1 expression, fraction of genome altered (if available), tumor mutational burden, ECOG status and other relevant covariates that are available. In addition, we will aim to restrict the cohort to Stage IV patients who receive these treatments as front-line therapy. All tumorsAtty. Dkt. No.: 115872-3370 with targetable alterations in MAPK pathway (EGFR sensitizing mutations, RET / ROS / ALK fusions, etc) will be excluded.

[0169] Each of the following analyses will be performed within the patient groups treated with: (a) PD(L)-1 alone, (b) PD(L)-1 + CTLA4, (c) PD(L)-1 + chemo, (d) PD(L)-1 + CTLA4 + chemo:1. KEAP1 mutated (any mutation) vs. wild-type - Prevalence, OS, TOT, TNT2. STK11 mutated (any mutation) vs. wild-type - Prevalence, OS, TOT, TNT3. SMARCA4 mutated (only oncogenic / likely oncogenic mutations) vs. wild-type - Prevalence, OS, TOT, TNT4. Mutation in any of KEAP1, STK11, SMARCA4 vs. wild-type - Prevalence, OS, TOT, TNTAim 2: Evaluate the predictive role of mutation zygosity in mediating outcomes on ICB in KEAP1, STK11 and / or SMARCA4 mutated lung cancers.

[0170] Outcomes will be evaluated on anti-PD(L)-l alone or in combination with CTLA4 in patients with different zygosity status (biallelic, monoallelic and wild-type) in KEAP1, STK11 and / or SMARCA4. A mutation will be considered as biallelic if there is (1) a co-occurring loss of heterozygosity (LOH) at the corresponding gene locus, or (2) a second somatic mutation. If a mutation is observed without a corresponding LOH at the locus or a second somatic mutation in the gene, the specific mutation will be considered as heterozygous. If there is no mutation observed in a gene, then the gene is considered as wildtype (even if it was observed to have an LOH). We note that the three genes are all on chromosome 19p and within 11Mb of each other. LOH may include focal deletions as well as chromosome / arm-level losses. The prevalence of chromosome and arm-level losses contributing to biallelic mutations should be reported along with focal deletions.

[0171] Each of the following analyses will be performed within patient groups treated with: (a) PD(L)-1 alone, (b) PD(L)-1 + CTLA4, (c) PD(L)-1 + chemo, (d) PD(L)-1 + CTLA4 + chemo:Atty. Dkt. No.: 115872-3370

[0172] KEAP1 biallelic vs. KEAP1 monoallelic vs. wild-type - Prevalence, OS, TOT, TNT

[0173] STK11 biallelic vs. STK11 monoallelic vs. wild-type - Prevalence, OS, TOT, TNT

[0174] SMARCA4 biallelic vs. SMARCA4 monoallelic vs. wild-type - Prevalence, OS, TOT, TNT

[0175] Biallelic in any of REAP I, STKU, SMARCA4 vs. monoallelic in KEAP1, STK11, SMARCA4 vs. wild-type - Prevalence, OS, TOT, TNT

[0176] The specific mutation classes may considered for each gene. For example, for KEAP1 and STK17, we will consider all types of nonsynonymous mutations, whereas, for SMARCA4, we will only consider Oncogenic and Likely Oncogenic mutations. Moreover, for the SMARCA4 analyses, all tumors with only VUSs in SMARCA4 will also be excluded from the wild-type group.

[0177] Supplementary note 1: Inferring zygosity associated with each mutation

[0178] Biallelic and monoallelic status will be inferred for each mutation using the VAF and the allele-specific copy number output from CNVkit. Specifically, the following columns from the CNVkit output will be used to infer the zygosity (monoallelic / biallelic) for each somatic mutation: "cn", "baf1, "cnl", "cn2".

[0179] Criteria for biallelic inactivation:• Has non-synonymous mutation, and if min(cnl, cn2) == 0• Has 2 non-synonymous mutations

[0180] Criteria for monoallelic inactivation:• Has non-synonymous mutation, and if min(cnl, cn2) >= 1

[0181] Criteria for wild-type:No mutation in gene. And cn >= 2Atty. Dkt. No.: 115872-3370[01821 Supplementary note 2: specific treatment combinations

[0183] PD(L)-1 + Chemo:• Pembrolizumab + carboplatin + pemetrexed• Pembrolizumab + cisplatin + pemetrexed• Pembrolizumab + carboplatin + paclitaxel• Pembrolizumab + carboplatin + albumin-bound paclitaxel• Pembrolizumab + Pemetrexed• Pembrolizumab + Gemcitabine• Cemiplimab-rwlc + carboplatin + pemetrexed• Cemiplimab-rwlc + cisplatin + pemetrexed• Cemiplimab-rwlc + carboplatin + paclitaxel• Cemiplimab-rwlc + cisplatin + paclitaxel• Cemiplimab-rwlc + paclitaxel + carboplatin• Cemiplimab-rwlc + paclitaxel + cisplatin• Cemiplimab-rwlc + pemetrexed• Atezolizumab + carboplatin + paclitaxel + bevacizumab• Atezolizumab + carboplatin + albumin-bound paclitaxel

[0184] PD(L)-1 + CTLA4 + Chemo:• Nivolumab + ipilimumab + carboplatin + pemetrexed• Nivolumab + ipilimumab + cisplatin + pemetrexed• Nivolumab + ipilimumab + carboplatin + paclitaxelAtty. Dkt. No.: 115872-3370• Tremelimumab-actl + durvalumab + carboplatin + albumin-bound paclitaxel• Tremelimumab-actl + durvalumab + carboplatin + pemetrexed• Tremelimumab-actl + durvalumab + cisplatin + pemetrexed• Tremelimumab-actl + durvalumab + carboplatin + gemcitabine• Tremelimumab-actl + durvalumab + cisplatin + gemcitabine• Off-label: dual checkpoint regimen + any other agent

[0185] PD(L)-1 only (no chemo):• Pembrolizumab• Atezolizumab• Cemiplimab-rwlc• Durvalumab• Nivolumab

[0186] PD(L)-1 + CTLA4 only (no chemo):• Tremelimumab-actl + durvalumab• Nivolumab + ipilimumabExample 12: Systems and Methods for Selecting Cancer Patients for Treatments Based on Detecting Biallelic Or Monoallelic Alterations in Gene Profde Data of Cancer Patients

[0187] Referring now to FIG. 15, depicted is a block diagram of a system 100 for selecting cancer patients for treatments based on detecting biallelic or monoallelic alterations in gene data of cancer patients. In brief overview, the system 100 may include at least one data processing system 105, at least one sample profiler 110, and at least one computing device 115, communicatively coupled with one another via at least one network 120. The data processing system 105 may include at least one dataset retriever 125, at least profileAtty. Dkt. No.: 115872-3370 analyzer 130, at least one mutant evaluator 135, at least one loss evaluator 140, at least one response predictor 145, at least one output handler 150, and at least one database 155, among others. The system 100 may include any of the components and may be used to implement or perform any of the methods as detailed in any of the Examples detailed herein Each of the components in the system 100, as detailed herein, may be implemented using hardware (e.g., one or more processors coupled with memory) or a combination of hardware and software, as detailed herein in conjunction with FIG. 18.

[0188] In further overview, the data processing system 105 (sometimes herein generally referred to as a computing system or a server) may be any computing device including one or more processors coupled with memory and software and capable of performing the various processes and tasks described herein. The data processing system 105 can be in communication with the sample profiler 110, the computing device 115, the database 155, and other devices, via the network 120. The data processing system 105 may be situated, located, or otherwise associated with at least one server group. The server group may correspond to a data center, a branch office, or a site at which one or more servers corresponding to the data processing system 105 are situated. In some embodiments, the sample profile 110 and the computing device 115 may be part of the data processing system 105.

[0189] The data processing system 105 may include one or more subsystems, modules, or components to execute the various processes and tasks detailed herein. On the data processing system 105, the data retriever 125 may obtain a dataset of reads generated from a sample from a subject. The sequence analyzer 130 may identify portion of the data corresponding to a tumor suppressor gene. The mutant evaluator 135 may determine whether a mutant allele is present in the tumor suppressor gene. The loss evaluator 140 may determine whether a copy number loss is presented in the tumor suppressor gene. The response predictor 145 may determine whether the subject is a predicted responder to combination therapy or monotherapy. The output handler 150 may generate and provide an instruction based on the determination for presentation via the computing device 115.

[0190] The sample profiler 110 may be any device to perform genetic sequencing on a gene segment (e.g., genomic deoxyribonucleic acid (DNA), complementary DNA, ribonucleic acid (RNA), or a messenger RNA (mRNA) sample) in a sample taken from aAtty. Dkt. No.: 115872-3370 subject and to generate sequencing datasets using the genetic sequencing. The genetic sequencing carried out may be a high throughput, massively parallel sequencing technique (sometimes herein referred to as next-generation sequencing), such as whole genome sequencing (WGS), pyrosequencing, reversible dye-terminator sequencing, SOLiD sequencing, Ion semiconductor sequencing, or Helioscope single molecule sequencing, among others. Using the genetic sequencing data, the sample profiler 110 may generate a genomic dataset profiling the subject. In generating the genomic dataset, the sample profiler 110 may execute read alignment, variant calling, and gene expression quantification, among others. In some embodiments, the sample profiler 110 may be part of a genomic profiling platform, such as IMPACT, GENIE, or FoundationOne platform, among others. The sample profiler 110 may use the gene sequencing to generate a sequencing dataset. The sequencing dataset may be maintained using one or more files according to a format (e.g., FASTQ, BAM, SAM, BCL, or VCF formats).

[0191] The computing device 115 (sometimes herein referred to as a computing device) may be any computing device comprising one or more processors coupled with memory and software and capable of performing the various processes and tasks described herein. The computing device 115 may be in communication with the data processing system 105 and the sample profiler 110 via the network 120. The computing device 115 may have at least one display. The computing device 115 may be associated with an entity (e.g., a clinician) examining the subject or gene data from the subject. The display may present information about the subject provided by the data processing system 105.[01921 The database 155 may store and maintain various resources and data associated with the data processing system 105, the sample profiler 110, and the administrative device 115, among others. The database 155 may include a database management system (DBMS) to arrange and organize the data maintained thereon. The database 155 may be in communication with the data processing system 105, the sample profiler 110, and the computing device 115, via the network 120. While running various operations, the data processing system 105, the sample profiler 110, and the computing device 115 may access the database 155 to retrieve identified data therefrom. The data processing system 105, the sample profiler 110, and the computing device 115 may also write data onto the database 155 from running such operations.Atty. Dkt. No.: 115872-3370

[0193] Referring now to FIG. 16 depicts a block diagram of a process 200 for processing gene data of cancer patients to detect alterations in the system 100 for selecting cancer patients for treatments. Under the process 200, the data processing system 105 and the sample profiler 110 may be used to assess at least one subject 205. The subject 205 (sometimes referred to herein as a cancer subject or a cancer patient) may be at risk of, suffering from, or diagnosed with cancer. The cancer may include, for example, lung cancer, pancreatic cancer, or breast cancer, among others. The lung cancer may include, for example, non-small cell lunger cancer (NSCLC) (e.g., adenocarcinoma, squamous cell carcinoma, or large cell carcinoma) or small cell lunger cancer (SCLS), among others. The pancreatic cancer may include, for example, exocrine pancreatic cancer (e.g., pancreatic ductal adenocarcinoma (PDAC) or acinar cell carcinoma) or neuroendocrine tumor (pNET) (e.g., insulinoma, glucagonoma, or gastrinoma), among others. The breast cancer may include, for example, invasive ductal carcinoma (IDC), invasive lobular carcinoma (ILC), or ductal carcinoma in situ (DCIS), among others.

[0194] The cancer (e.g., lung cancer) may be at any level of disease progression in terms of severity (e.g., tumor growth). The lung cancer may be, for example, at least one of an early stage (e.g., Stage I, with cancer confined to the lung), a localized stage (e.g., Stage II, with cancer spread to lymph nodes or adjacent regions within the lung), an advanced stage (e.g., Stage III, with cancer spread throughout the chest region), or a metastasized stage (e.g., Stage IV, with cancer spread throughout the body), among others. The subject 205 may be at any age range, such as above or equal to 18 years old (e.g., an adult) or less than 18 years old (e.g., a minor).

[0195] At least one biological sample 210 may be taken, isolated, or otherwise obtained from the subject 205. The biological sample 210 may include, for example, at least one of a fresh tissue sample, a frozen tissue sample, or a fixed-formalin paraffin- embedded tissue sample, among others. The biological sample 210 may have genomic DNA, cDNA, RNA, and / or mRNA, among others. The biological sample 210 may be obtained from a primary cancer site or a metastatic site associated with the cancer. For example, the primary cancer site may correspond to a lung region from which the lung cancer originated in the subject 205. The metastatic site may correspond to anotherAtty. Dkt. No.: 115872-3370 anatomical site in the subject 205 to which the lung cancer spread from the primary cancer site.

[0196] The sample profiler 110 may execute, carry out, or otherwise perform genome sequencing on the biological sample 210. The sequencing may be in accordance with at least one of next-generation sequencing, PCR, real-time quantitative PCR (qPCR), digital PCR (dPCR), Southern blotting, Reverse transcriptase-PCR (RT-PCR), Northern blotting, microarray, dot or slot blots, in situ hybridization, or fluorescent in situ hybridization (FISH), among others. From performing the sequencing, the sample profiler 110 may create, produce, or otherwise generate at least one dataset 215. The dataset 215 may include genomic data about the genomic DNA, cDNA, RNA, and / or mRNA in the biological sample 210. The genomic data may include, for example, somatic mutations or copy-number alterations, among others. In performing sequencing, the sample profiler 110 may apply filtering based on quality metrics.

[0197] With the generation of the dataset 215, the sample profiler 110 may send, transmit, or otherwise provide the dataset 215 to the data processing system 105. In some embodiments, the sample profiler 110 may send the dataset 215 for storage on the database 155. The dataset retriever 125 may identify, receive, or otherwise obtain the dataset 215 from the sample profiler 110 or the database 155. With receipt, the dataset retriever 125 may process or parse the dataset 215 to extract or identify the genomic data for the biological sample 210 of the subject 205.

[0198] From parsing the genomic data of the dataset 215, the profile analyzer 130 may extract or identify data corresponding to at least one tumor suppressor gene in the biological sample 210. The tumor suppressor gene may be KEAP1 or STK11. To identify, the profile analyzer 130 may parse through the genomic data of the dataset 215 to find a match for the target tumor suppressor gene. When a match is detected, the profile analyzer 130 may determine a presence of the tumor suppressor gene in the biological sample 210, and may continue processing the genomic data of the dataset 215. Otherwise, when no match is detected, the profile analyzer 130 may determine an absence of the tumor suppressor gene in the biological sample 210, and may refrain from processing the genomic data of the dataset 215.Atty. Dkt. No.: 115872-3370

[0199] Using the genomic data of the dataset 215, the mutant evaluator 135 may identify or determine a presence or absence of at least one mutant allele in the tumor suppressor gene. The mutant allele may be detected via next-generation sequencing, PCR, real-time quantitative PCR (qPCR), digital PCR (dPCR), Southern blotting, Reverse transcriptase-PCR (RT-PCR), Northern blotting, microarray, dot or slot blots, in situ hybridization, or fluorescent in situ hybridization (FISH). In some embodiments, the at least one mutant allele may include one or more of: a variant of unknown significance (VUS), a frameshift mutation, a missense mutation, a deletion, an insertion, a nonsense mutation, an inversion, or a translocation (e.g., structural rearrangement), among others. In some embodiments, the at least one mutant allele may include a somatic alteration including at least one of a substitution, an insertion, a deletion, a focal amplification, a homozygous deletion, or a translocation, among others.

[0200] In determining, the mutant evaluator 135 may parse the genomic data to find a match with the at least one mutant allele. When at least one mutant allele is detected, the mutant evaluator 135 may determine or detect a presence of the at least one mutant allele in the tumor suppressor gene. In some embodiments, the mutant evaluator 135 may identify a number of mutant alleles in the tumor suppressor gene. The number may be one or two mutant alleles. When there are no mutant alleles, the mutant evaluator 135 may identify or determine an absence of any mutant alleles in the tumor suppressor gene in the biological sample 210.

[0201] The loss evaluator 140 may identify, determine, or detect a presence of at least one copy number loss of the tumor suppressor gene using the genomic data of the dataset 215. The copy number loss may be in complementary allele to the mutant allele or may be independent of any mutant allele. The copy loss number may be detected via next-generation sequencing, PCR, real-time quantitative PCR (qPCR), digital PCR (dPCR), Southern blotting, Reverse transcriptase-PCR (RT-PCR), Northern blotting, microarray, dot or slot blots, in situ hybridization, or fluorescent in situ hybridization (FISH). The copy loss number may include, for example, at least one of: a copy number segment with a minor copy number of 0 corresponding to a loss of heterozygosity (LOH), or a focal deletion corresponding to a copy number segment smaller than 10 megabases with a total copy number of 1 and harboring 10 or fewer genes, among others.Atty. Dkt. No.: 115872-3370

[0202] In determining, the loss evaluator 140 may parse the genomic data to find a match with the at least one copy number loss. When at least copy number loss is detected, the loss evaluator 140 may determine or detect a presence of the at least one copy number loss in the tumor suppressor gene. In some embodiments, the loss evaluator 140 may determine or identify whether the copy number loss is in a complementary allele to the mutant allele detected in the tumor suppressor gene. When the copy number loss is in the complementary allele, the loss evaluator 140 may identify the copy number loss as in the complementary allele of the tumor suppressor gene. Otherwise, when the copy number loss is not in the complementary allele, the loss evaluator 140 may identify the copy number loss as in an allele of the tumor suppressor gene. In some embodiments, the mutant evaluator 135 may identify a number of copy number losses in the tumor suppressor gene. The number may be one or two copy number losses. When there are copy number losses, the loss evaluator 140 may identify or determine an absence of any copy number losses in the tumor suppressor gene in the biological sample 210.

[0203] The response predictor 145 may identify or determine whether a type of alteration in the tumor suppressor gene is one of a biallelic or monoallelic. The response detector 145 may determine that the type of alteration is biallelic, when the at least one mutant allele and / or at least one copy loss number in the complementary allele are detected in the tumor suppressor gene. In some embodiments, the response detector 145 may determine that the type of alteration is biallelic, when two mutant alleles corresponding to a homozygous deletion are detected in the tumor suppressor gene. In some embodiments, the response detector 145 may determine that the type of alteration is biallelic, when two mutant alleles corresponding to a point mutation and a translocation are detected in the tumor suppressor gene. In some embodiments, the response detector 145 may determine that the type of alteration is biallelic, when two copy number losses are detected in the complementary allele of the tumor suppressor gene. In some embodiments, the response detector 145 may determine that the type of alteration is biallelic, when a single mutant allele in the tumor suppressor gene and a single copy number loss in the complementary allele of the tumor suppressor gene are detected. Conversely, the response detector 145 may determine that the type of alteration is monoallelic, when single first mutant allele is detected in the tumor suppressor gene and the subject 205 does not harbor a copy number loss in a complementary allele of the tumor suppressor gene. The response detector 145 may determine that the typeAtty. Dkt. No.: 115872-3370 of alteration is monoallelic, when single copy number loss is detected in an allele of the tumor suppressor gene and the subject 205 does not comprise another mutant allele in a complementary allele (e.g., to the copy loss number) of the tumor suppressor gene.

[0204] Based on the type of alteration, the response predictor 145 may identify or determine the subject 205 as a predicted responder to a combination therapy or a monotherapy. The combination therapy may include anti-CTLA4 and anti-PD-Ll / PD-1 ICB therapy. In some embodiments, the anti-CTLA4 and anti-PD-Ll / PD-1 ICB therapy may include an anti-PD-Ll inhibitor selected from among Atezolizumab, Avelumab, and Durvalumab or an anti-PD-1 inhibitor selected from among Pembrolizumab, Cemiplimab, and Nivolumab. In some embodiments, the dual anti-CTLA4 and anti-PD-Ll / PD-1 ICB therapy comprises an anti-CTLA4 inhibitor selected from among Ipilimumab and Tremelimumab. The monotherapy may include anti-PD-Ll or an anti-PD-1 ICB monotherapy. In some embodiments, the anti-PD-Ll ICB monotherapy may include Atezolizumab, Avelumab, or Durvalumab. In some embodiments, the anti-PD-1 ICB monotherapy may include Pembrolizumab, Cemiplimab, and Nivolumab.

[0205] When the type of alteration is identified as biallelic, the response predictor 145 may identify the subject 205 as the predicted responder to the combination therapy. In some embodiments, the response predictor 145 may identify the subject 205 as a candidate for administration of the combination therapy. In some embodiments, the response predictor 145 may select the combination therapy for the subject 205. On the other hand, when the type of alteration is identified as monoallelic, the response predictor 145 may identify the subject 205 as the predicted responder to the monotherapy. In some embodiments, the response predictor 145 may identify the subject 205 as a candidate for administration of the monotherapy. In some embodiments, the response predictor 145 may select the monotherapy for the subject 205.

[0206] The output handler 150 may create, produce, or otherwise generate at least one instruction 225 based on the determination of the subject 205 as the predicted responder to combination therapy (also referred herein as dual therapy) or monotherapy. When the subject 205 is identified as the predicted responder to combination therapy, the output handler 150 may generate the instruction 225 to indicate administration of the combination therapy (e.g., an effective amount of dual anti-CTLA4 and anti-PD-Ll / PD-1 ICB therapy). In someAtty. Dkt. No.: 115872-3370 embodiments, when the subject 205 is identified as the candidate for combination therapy or when the combination therapy is selected, the output handler 150 may generate the instruction 225 to indicate administration of the combination therapy. Conversely, when the subject 205 is identified as the predicted responder to monotherapy, the output handler 150 may generate the instruction 225 to indicate administration of the monotherapy (e.g., an effective amount of anti-PD-Ll or an anti-PD-1 ICB monotherapy). In some embodiments, when the subject 205 is identified as the candidate for monotherapy or when the monotherapy is selected, the output handler 150 may generate the instruction 225 to indicate administration of the monotherapy therapy. With the generation, the output handler 150 may send, transmit, or otherwise provide the instruction 225 for presentation via the computing device 115.

[0207] The computing device 115 may retrieve, identify, or otherwise receive the instruction 225 from the data processing system 105. With receipt, the computing device 115 may display, render, or otherwise present the instruction 225 for a user (e.g., a clinician examining the subject 205) via a user interface. Using the information presented via the computing device 115, the clinician examining the subject 205 may identify which of the combination therapy or monotherapy to provide or administer to the subject 205. When the instruction 225 indicates administration of the combination therapy, the subject 205 may be administered (e.g., by the clinician) with a therapeutically effective amount of the combination therapy (e.g., dual anti-CTLA4 and an anti-PD-Ll / PD-1 ICB therapy). Otherwise, when the instruction 225 indicates administration of the monotherapy, the subject 205 may be administered with a therapeutically effective amount of the monotherapy (e.g., anti-PD-Ll / PD-1 ICB monotherapy).

[0208] Referring now to FIG. 17, depicted a flow diagram of a method 300 of selecting cancer patients for treatments based on detecting biallelic or monoallelic alterations in gene data of cancer patients. The method 300 may be implemented using or performed by any of the components described herein, such as the system 100. Under the method 300, a computing system may receive a genomic dataset of a biological sample from a subject (305). The computing system may identify a tumor suppressor gene in the genomic dataset (310). The computing system may determine an absence or presence at least one mutant allele in the tumor suppressor gene (315). The computing system may determine an absence or presenceAtty. Dkt. No.: 115872-3370 at least one copy number loss (CNL) in an allele (e.g., complementary or non-complementary to the mutant allele) the tumor suppressor gene (320).

[0209] The computing system may determine whether an alteration in the tumor suppressor gene is biallelic or monoallelic based on the determinations regarding the mutant allele and the CNL (325). When the alteration is determined as biallelic, the computing system may identify the subject as a responder to a combination therapy (330). The computing system may select the combination therapy for the subject (335). Conversely, when the alteration is determined as monoallelic, the computing system may identify the subject as a responder to a monotherapy (340). The computing system may select the monotherapy for the subject (345). The computing system may provide an output based on the selection of the therapy (350). Based on the indicated therapy, the subject may be administered with a therapeutically effective amount of the therapy (355).Example 13: Computing and Network Environment

[0210] Various operations described herein can be implemented on computer systems. FIG. 18 shows a simplified block diagram of a representative server system 400, client computing system 414, and network 426 usable to implement certain embodiments of the present disclosure. In various embodiments, server system 400 or similar systems can implement services or servers described herein or portions thereof. Client computing system 414 or similar systems can implement clients described herein. The system 400 described herein can be similar to the server system 400. Server system 400 can have a modular design that incorporates a number of modules 402 (e.g., blades in a blade server embodiment); while two modules 402 are shown, any number can be provided. Each module 402 can include processing unit(s) 404 and local storage 406.

[0211] Processing unit(s) 404 can include a single processor, which can have one or more cores, or multiple processors. In some embodiments, processing unit(s) 404 can include a general-purpose primary processor as well as one or more special-purpose co-processors such as graphics processors, digital signal processors, or the like. In some embodiments, some or all processing units 404 can be implemented using customized circuits, such as application specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs). In some embodiments, such integrated circuits execute instructions that are stored on theAtty. Dkt. No.: 115872-3370 circuit itself. In other embodiments, processing unit(s) 404 can execute instructions stored in local storage 406. Any type of processors in any combination can be included in processing unit(s) 404.

[0212] Local storage 406 can include volatile storage media (e.g., DRAM, SRAM, SDRAM, or the like) and / or non-volatile storage media (e.g., magnetic or optical disk, flash memory, or the like). Storage media incorporated in local storage 406 can be fixed, removable or upgradeable as desired. Local storage 406 can be physically or logically divided into various subunits such as a system memory, a read-only memory (ROM), and a permanent storage device. The system memory can be a read-and-write memory device or a volatile read-and-write memory, such as dynamic random-access memory. The system memory can store some or all of the instructions and data that processing unit(s) 404 need at runtime. The ROM can store static data and instructions that are needed by processing unit(s) 404. The permanent storage device can be a non-volatile read-and-write memory device that can store instructions and data even when module 402 is powered down. The term “storage medium” as used herein includes any medium in which data can be stored indefinitely (subject to overwriting, electrical disturbance, power loss, or the like) and does not include carrier waves and transitory electronic signals propagating wirelessly or over wired connections.

[0213] In some embodiments, local storage 406 can store one or more software programs to be executed by processing unit(s) 404, such as an operating system and / or programs implementing various server functions such as functions of the system 100 of FIG. 15 or any other system described herein, or any other server(s) associated with system 400 or any other system described herein.

[0214] Software” refers generally to sequences of instructions that, when executed by processing unit(s) 404 cause server system 400 (or portions thereof) to perform various operations, thus defining one or more specific machine embodiments that execute and perform the operations of the software programs. The instructions can be stored as firmware residing in read-only memory and / or program code stored in non-volatile storage media that can be read into volatile working memory for execution by processing unit(s) 404. Software can be implemented as a single program or a collection of separate programs or program modules that interact as desired. From local storage 406 (or non-local storage describedAtty. Dkt. No.: 115872-3370 below), processing unit(s) 404 can retrieve program instructions to execute and data to process in order to execute various operations described above.

[0215] In some server systems 400, multiple modules 402 can be interconnected via a bus or other interconnect 408, forming a local area network that supports communication between modules 402 and other components of server system 400. Interconnect 408 can be implemented using various technologies including server racks, hubs, routers, etc.

[0216] A wide area network (WAN) interface 410 can provide data communication capability between the local area network (interconnect 408) and the network 426, such as the Internet. Technologies can be used, including wired (e.g., Ethernet, IEEE 402.3 standards) and / or wireless technologies (e.g., Wi-Fi, IEEE 402.24 standards).

[0217] In some embodiments, local storage 406 is intended to provide working memory for processing unit(s) 404, providing fast access to programs and / or data to be processed while reducing traffic on interconnect 408. Storage for larger quantities of data can be provided on the local area network by one or more mass storage subsystems 412 that can be connected to interconnect 408. Mass storage subsystem 412 can be based on magnetic, optical, semiconductor, or other data storage media. Direct attached storage, storage area networks, network-attached storage, and the like can be used. Any data stores or other collections of data described herein as being produced, consumed, or maintained by a service or server can be stored in mass storage subsystem 412. In some embodiments, additional data storage resources may be accessible via WAN interface 410 (potentially with increased latency).

[0218] Server system 400 can operate in response to requests received via WAN interface 410. For example, one of modules 402 can implement a supervisory function and assign discrete tasks to other modules 402 in response to received requests. Work allocation techniques can be used. As requests are processed, results can be returned to the requester via WAN interface 410. Such operation can generally be automated. Further, in some embodiments, WAN interface 410 can connect multiple server systems 400 to each other, providing scalable systems capable of managing high volumes of activity. Other techniques for managing server systems and server farms (collections of server systems that cooperate) can be used, including dynamic resource allocation and reallocation.Atty. Dkt. No.: 115872-3370

[0219] Server system 400 can interact with various user-owned or user-operated devices via a wide-area network such as the Internet. An example of a user-operated device is the client computing system 414. Client computing system 414 can be implemented, for example, as a consumer device such as a smartphone, other mobile phone, tablet computer, wearable computing device (e.g., smart watch, eyeglasses), desktop computer, laptop computer, and so on.

[0220] For example, client computing system 414 can communicate via WAN interface 410. Client computing system 414 can include computer components such as processing unit(s) 416, storage device 418, network interface 420, user input device 422, and user output device 424. Client computing system 414 can be a computing device implemented in a variety of form factors, such as a desktop computer, laptop computer, tablet computer, smartphone, other mobile computing device, wearable computing device, or the like.

[0221] Processing unit(s) 416 and storage device 418 can be similar to processing unit(s) 404 and local storage 406 described above. Suitable devices can be selected based on the demands to be placed on client computing system 414; for example, client computing system 414 can be implemented as a “thin” client with limited processing capability or as a high-powered computing device. Client computing system 414 can be provisioned with program code executable by processing unit(s) 416 to enable various interactions with server system 400.

[0222] Network interface 420 can provide a connection to the network 426, such as a wide area network (e.g., the Internet) to which WAN interface 410 of server system 400 is also connected. In various embodiments, network interface 420 can include a wired interface (e.g., Ethernet) and / or a wireless interface implementing various RF data communication standards such as Wi-Fi, Bluetooth, or cellular data network standards (e.g., 4G, 5G, LTE, etc.).

[0223] User input device 422 can include any device (or devices) via which a user can provide signals to client computing system 414; client computing system 414 can interpret the signals as indicative of particular user requests or information. In various embodiments, user input device 422 can include any or all of a keyboard, touch pad, touchAtty. Dkt. No.: 115872-3370 screen, mouse or other pointing device, scroll wheel, click wheel, dial, button, switch, keypad, microphone, and so on.

[0224] User output device 424 can include any device via which client computing system 414 can provide information to a user. For example, user output device 424 can include a display to present images generated by or delivered to client computing system 414. The display can incorporate various image generation technologies, e.g., a liquid crystal display (LCD), light-emitting diode (LED) including organic light-emitting diodes (OLED), projection system, cathode ray tube (CRT), or the like, together with supporting electronics (e.g., digital-to-analog or analog-to-digital converters, signal processors, or the like). Some embodiments can include a device such as a touchscreen that function as both input and output device. In some embodiments, other user output devices 424 can be provided in addition to or instead of a display. Examples include indicator lights, speakers, tactile “display” devices, printers, and so on.

[0225] Some embodiments include electronic components, such as microprocessors, storage and memory that store computer program instructions in a computer-readable storage medium. Many of the features described in this specification can be implemented as processes that are specified as a set of program instructions encoded on a computer-readable storage medium. When these program instructions are executed by one or more processing units, they cause the processing unit(s) to perform various operation indicated in the program instructions. Examples of program instructions or computer code include machine code, such as is produced by a compiler, and files including higher-level code that are executed by a computer, an electronic component, or a microprocessor using an interpreter. Through suitable programming, processing unit(s) 404 and 416 can provide various functionality for server system 400 and client computing system 414, including any of the functionality described herein as being performed by a server or client, or other functionality.

[0226] It will be appreciated that server system 400 and client computing system 414 are illustrative and that variations and modifications are possible. Computer systems used in connection with embodiments of the present disclosure can have other capabilities not specifically described here. Further, while server system 400 and client computing system 414 are described with reference to particular blocks, it is to be understood that these blocks are defined for convenience of description and are not intended to imply a particular physicalAtty. Dkt. No.: 115872-3370 arrangement of component parts. For instance, different blocks can be but need not be located in the same facility, in the same server rack, or on the same motherboard. Further, the blocks need not correspond to physically distinct components. Blocks can be configured to perform various operations, e.g., by programming a processor or providing appropriate control circuitry, and various blocks might or might not be reconfigurable depending on how the initial configuration is obtained. Embodiments of the present disclosure can be realized in a variety of apparatus including electronic devices implemented using any combination of circuitry and software.

[0227] While the disclosure has been described with respect to specific embodiments, one skilled in the art will recognize that numerous modifications are possible. Embodiments of the disclosure can be realized using a variety of computer systems and communication technologies including but not limited to the specific examples described herein. Embodiments of the present disclosure can be realized using any combination of dedicated components and / or programmable processors and / or other programmable devices. The various processes described herein can be implemented on the same processor or different processors in any combination. Where components are described as being configured to perform certain operations, such configuration can be accomplished, e.g., by designing electronic circuits to perform the operation, by programming programmable electronic circuits (such as microprocessors) to perform the operation, or any combination thereof. Further, while the embodiments described above may make reference to specific hardware and software components, those skilled in the art will appreciate that different combinations of hardware and / or software components may also be used and that particular operations described as being implemented in hardware might also be implemented in software or vice versa.

[0228] Computer programs incorporating various features of the present disclosure may be encoded and stored on various computer-readable storage media; suitable media include magnetic disk or tape, optical storage media such as compact disk (CD) or DVD (digital versatile disk), flash memory, and other non-transitory media. Computer-readable media encoded with the program code may be packaged with a compatible electronic device, or the program code may be provided separately from electronic devices (e.g., via Internet download or as a separately packaged computer-readable storage medium).Atty. Dkt. No.: 115872-3370

[0229] Thus, although the disclosure has been described with respect to specific embodiments, it will be appreciated that the disclosure is intended to cover all modifications and equivalents within the scope of the following claims.REFERENCES

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[0316] The present technology is not to be limited in terms of the particular embodiments described in this application, which are intended as single illustrations ofAtty. Dkt. No.: 115872-3370 individual aspects of the present technology. Many modifications and variations of this present technology can be made without departing from its spirit and scope, as will be apparent to those skilled in the art. Functionally equivalent methods and apparatuses within the scope of the present technology, in addition to those enumerated herein, will be apparent to those skilled in the art from the foregoing descriptions. Such modifications and variations are intended to fall within the scope of the present technology. It is to be understood that this present technology is not limited to particular methods, reagents, compounds compositions or biological systems, which can, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting.

[0317] In addition, where features or aspects of the disclosure are described in terms of Markush groups, those skilled in the art will recognize that the disclosure is also thereby described in terms of any individual member or subgroup of members of the Markush group.

[0318] As will be understood by one skilled in the art, for any and all purposes, particularly in terms of providing a written description, all ranges disclosed herein also encompass any and all possible subranges and combinations of subranges thereof. Any listed range can be easily recognized as sufficiently describing and enabling the same range being broken down into at least equal halves, thirds, quarters, fifths, tenths, etc. As a non-limiting example, each range discussed herein can be readily broken down into a lower third, middle third and upper third, etc. As will also be understood by one skilled in the art all language such as “up to,” “at least,” “greater than,” “less than,” and the like, include the number recited and refer to ranges which can be subsequently broken down into subranges as discussed above. Finally, as will be understood by one skilled in the art, a range includes each individual member. Thus, for example, a group having 1-3 cells refers to groups having 1, 2, or 3 cells. Similarly, a group having 1-5 cells refers to groups having 1, 2, 3, 4, or 5 cells, and so forth.

[0319] All patents, patent applications, provisional applications, and publications referred to or cited herein are incorporated by reference in the entirety, including all figures and tables, to the extent they are not inconsistent with the explicit teachings of this specification.

Claims

1. Atty. Dkt. No.: 115872-3370WHAT IS CLAIMED IS:

1. A method of selecting a cancer patient for treatment with dual anti-CTLA4 and anti-PD- Ll / PD-1 immune checkpoint blockade (ICB) therapy comprising:(a) detecting (i) at least one mutant allele in a tumor suppressor gene and / or (ii) at least one copy number loss in a complementary allele of the tumor suppressor gene in a biological sample obtained from a cancer patient, wherein the tumor suppressor gene is KEAP1 or STK11; and(b) administering to the cancer patient an effective amount of dual anti-CTLA4 and anti-PD-Ll / PD-1 ICB therapy.

2. The method of claim 1, wherein the at least one mutant allele in the tumor suppressor gene comprises at least one of a variant of unknown significance (VUS), a frameshift mutation, a missense mutation, a deletion, an insertion, a nonsense mutation, an inversion, or a translocation.

3. The method of claim 1 or 2, wherein the dual anti-CTLA4 and anti-PD-Ll / PD-1 ICB therapy comprises an anti-PD-1 inhibitor selected from among Pembrolizumab, Cemiplimab, and Nivolumab.

4. The method of any one of claims 1-3, wherein the at least one copy number loss comprises at least one of: a copy number segment with a minor copy number of 0 corresponding to a loss of heterozygosity (LOH), or a focal deletion corresponding to a copy number segment smaller than 10 megabases with a total copy number of 1 and harboring 10 or fewer genes.

5. The method of any one of claims 1-4, wherein the cancer patient suffers from or is diagnosed with lung cancer, pancreatic cancer, or breast cancer.

6. The method of any one of claims 1-5, wherein the dual anti-CTLA4 and anti-PD-Ll / PD- 1 ICB therapy comprises an anti-PD-Ll inhibitor selected from among Atezolizumab, Avelumab, and Durvalumab.Atty. Dkt. No.: 115872-33707. The method of any one of claims 1-6, wherein the dual anti-CTLA4 and anti-PD-Ll / PD- 1 ICB therapy comprises an anti-CTLA4 inhibitor selected from among Ipilimumab and Tremelimumab.

8. The method of any one of claims 1-7, wherein the biological sample is a fresh tissue sample, a frozen tissue sample, or a fixed-formalin paraffin-embedded tissue sample.

9. The method of any one of claims 1-8, wherein the biological sample comprises at least one of genomic DNA, cDNA, RNA, and / or mRNA.

10. The method of any one of claims 1-9, wherein the mutation or copy number loss is detected via next-generation sequencing, PCR, real-time quantitative PCR (qPCR), digital PCR (dPCR), Southern blotting, Reverse transcriptase-PCR (RT-PCR), Northern blotting, microarray, dot or slot blots, in situ hybridization, or fluorescent in situ hybridization (FISH).

11. The method of any one of claims 1-10, wherein step (a) comprises at least one of: detecting two mutant alleles corresponding to a homozygous deletion in the tumor suppressor gene; detecting two mutant alleles corresponding to a point mutation and a translocation in the tumor suppressor gene; detecting two copy number losses in the alleles of the tumor suppressor gene; or detecting a single mutant allele in the tumor suppressor gene and a single copy number loss in the complementary allele of the tumor suppressor gene.

12. The method of any one of claims 1-11, further comprising retrieving, by one or more processors, a dataset comprising genomic data of the biological sample obtained from the cancer patient, wherein step (a) further comprises parsing the genomic data in the dataset to detect the at least one mutant allele and / or the at least one copy number loss.Atty. Dkt. No.: 115872-337013. A system for selecting a cancer patient for treatment with dual anti-CTLA4 and anti-PD- Ll / PD-1 immune checkpoint blockade (ICB) therapy comprising: one or more processors coupled with memory, configured to:(a) detect (i) at least one mutant allele in a tumor suppressor gene and / or (ii) at least one copy number loss in a complementary allele of the tumor suppressor gene in a biological sample obtained from a cancer patient, wherein the tumor suppressor gene is KEAP1 or STK11; and(b) provide an instruction to administer to the cancer patient an effective amount of dual anti-CTLA4 and anti-PD-Ll / PD-1 ICB therapy.

14. The system of claim 13, wherein the at least one mutant allele in the tumor suppressor gene comprises at least one of a variant of unknown significance (VUS), a frameshift mutation, a missense mutation, a deletion, an insertion, a nonsense mutation, an inversion, or a translocation.

15. The system of any one of claims 13 or 14, wherein the dual anti-CTLA4 and PD-L1 / PD- 1 ICB therapy comprises an anti-PD-1 inhibitor selected from among Pembrolizumab, Cemiplimab, and Nivolumab.

16. The system of any one of claims 13-15, wherein the at least one copy number loss comprises at least one of: a copy number segment with a minor copy number of 0 corresponding to a loss of heterozygosity (LOH), or a focal deletion corresponding to a copy number segment smaller than 10 megabases with a total copy number of 1 and harboring 10 or fewer genes.

17. The system of any one of claims 13-16, wherein the cancer patient suffers from or is diagnosed with lung cancer, pancreatic cancer, or breast cancer.

18. The system of any one of claims 13-17, wherein the dual anti-CTLA4 and PD-L1 / PD-1 ICB therapy comprises an anti-PD-Ll inhibitor selected from among Atezolizumab, Avelumab, and Durvalumab.Atty. Dkt. No.: 115872-337019. The system of any one of claims 13-18, wherein the dual anti-CTLA4 and anti-PD- Ll / PD-1 ICB therapy comprises an anti-CTLA4 inhibitor selected from among Ipilimumab and Tremelimumab.

20. The system of any one of claims 13-19, wherein the biological sample comprises at least one of a fresh tissue sample, a frozen tissue sample, or a fixed-formalin paraffin- embedded tissue sample.

21. The system of any one of claims 13-20, wherein the biological sample comprises at least one of genomic DNA, cDNA, RNA, and / or mRNA.

22. The system of any one of claims 13-21, wherein the mutation or copy number loss is detected via next-generation sequencing, PCR, real-time quantitative PCR (qPCR), digital PCR (dPCR), Southern blotting, Reverse transcriptase-PCR (RT-PCR), Northern blotting, microarray, dot or slot blots, in situ hybridization, or fluorescent in situ hybridization (FISH).

23. The system of any one of claims 13-22, wherein detecting comprises at least one of: detecting two mutant alleles corresponding to a homozygous deletion in the tumor suppressor gene; detecting two mutant alleles corresponding to a point mutation and the translocation in the tumor suppressor gene; detecting two copy number losses in the alleles of the tumor suppressor gene; or detecting a single mutant allele in the tumor suppressor gene and a single copy number loss in the complementary allele of the tumor suppressor gene.

24. The system of any one of claims 13-23, wherein the one or more processors are further configured to: retrieve a dataset comprising genomic data of the biological sample obtained from the cancer patient; andAtty. Dkt. No.: 115872-3370 parse the genomic data in the dataset to detect the at least one mutant allele and / or the at least one copy number loss.

25. A method of selecting a cancer patient for treatment with anti-PD-Ll / PD-1 immune checkpoint blockade (ICB) monotherapy comprising:(a) detecting(i) a single first mutant allele in a tumor suppressor gene in a biological sample obtained from a cancer patient, wherein the cancer patient does not harbor a copy number loss in a complementary allele of the tumor suppressor gene or(ii) a single copy number loss in an allele of the tumor suppressor gene obtained from the cancer patient, wherein the cancer patient does not comprise a second mutant allele in a complementary allele of the tumor suppressor gene, and wherein the tumor suppressor gene is KEAP1 or STK11; and(b) administering to the cancer patient an effective amount of anti-PD-Ll / PD-1 ICB monotherapy.

26. The method of claim 25, wherein the single first mutant allele in the tumor suppressor gene is a frameshift mutation, a missense mutation, a deletion, an insertion, a nonsense mutation, an inversion, or a translocation.

27. The method of claim 25 or 26, wherein the anti-PD-1 ICB monotherapy comprises Pembrolizumab, Cemiplimab, or Nivolumab.

28. The method of any one of claims 25-27, wherein the single copy number loss comprises at least one of: a copy number segment with a minor copy number of 0 corresponding to a loss of heterozygosity (LOH), or a focal deletion corresponding to a copy number segment smaller than 10 megabases with a total copy number of 1 and harboring 10 or fewer genes.

29. The method of claim 25-28, wherein the cancer patient suffers from or is diagnosed with lung cancer, pancreatic cancer, or breast cancer.Atty. Dkt. No.: 115872-337030. The method of any one of claims 25-29, wherein the anti-PD-Ll ICB monotherapy comprises Atezolizumab, Avelumab, or Durvalumab.

31. The method of any one of claims 25-30, wherein the biological sample comprises at least one of a fresh tissue sample, a frozen tissue sample, or a fixed-formalin paraffin- embedded tissue sample.

32. The method of any one of claims 25-31, wherein the biological sample comprises at least one of genomic DNA, cDNA, RNA, and / or mRNA.

33. The method of any one of claims 25-32, wherein the single first mutation or the first copy number loss is detected via next-generation sequencing, PCR, real-time quantitative PCR (qPCR), digital PCR (dPCR), Southern blotting, Reverse transcriptase-PCR (RT- PCR), Northern blotting, microarray, dot or slot blots, in situ hybridization, or fluorescent in situ hybridization (FISH).

34. The method of any one of claims 25-32, further comprising retrieving, by one or more processors, a dataset comprising a genomic data of the biological sample obtained from the cancer patient, wherein step (a) further comprises parsing the genomic data in the dataset to detect the single first mutant allele or the single copy number.

35. A system for selecting a cancer patient for treatment with anti-PD-Ll / PD-1 immune checkpoint blockade (ICB) monotherapy comprising: one or more processors coupled with memory, configured to:(a) detect (i) a single first mutant allele in a tumor suppressor gene in a biological sample obtained from a cancer patient, wherein the cancer patient does not harbor a copy number loss in a complementary allele of the tumor suppressor gene or (ii) a single copy number loss in an allele of the tumor suppressor gene obtained from the cancer patient, wherein the cancer patient does not comprise a second mutant allele in a complementaryAtty. Dkt. No.: 115872-3370 allele of the tumor suppressor gene, and wherein the tumor suppressor gene is KEAP1 or STK11; and(b) provide an instruction to administer to the cancer patient an effective amount of anti-PD-Ll / PD-1 ICB monotherapy.

36. The system of claim 35, wherein the single first mutant allele in the tumor suppressor gene comprises at least one of a frameshift mutation, a missense mutation, a deletion, an insertion, a nonsense mutation, an inversion, or a translocation.

37. The system of claims 35 or 36, wherein the anti-PD-1 ICB monotherapy comprises Pembrolizumab, Cemiplimab, or Nivolumab.

38. The system of any one of claims 35-37, wherein the single copy number loss comprises at least one of: a copy number segment with a minor copy number of 0 corresponding to a loss of heterozygosity (LOH), or a focal deletion corresponding to a copy number segment smaller than 10 megabases with a total copy number of 1 and harboring 10 or fewer genes.

39. The system of any one of claims 35-38, wherein the cancer patient suffers from or is diagnosed with lung cancer, pancreatic cancer, or breast cancer.

40. The system of any one of claims 35-39, wherein the anti-PD-Ll ICB monotherapy comprises Atezolizumab, Avelumab, or Durvalumab.

41. The system of any one of claims 35-40, wherein the biological sample comprises at least one of a fresh tissue sample, a frozen tissue sample, or a fixed-formalin paraffin- embedded tissue sample.

42. The system of any one of claims 35-41, wherein the biological sample comprises at least one of genomic DNA, cDNA, RNA, and / or mRNA.Atty. Dkt. No.: 115872-337043. The system of any one of claims 35-42, wherein the single first mutation or the first copy number loss is detected via next-generation sequencing, PCR, real-time quantitative PCR (qPCR), digital PCR (dPCR), Southern blotting, Reverse transcriptase-PCR (RT- PCR), Northern blotting, microarray, dot or slot blots, in situ hybridization, or fluorescent in situ hybridization (FISH).

44. The system of any one of claims 35-43, wherein the one or more processors are further configured to: retrieve a dataset comprising genomic data of the biological sample obtained from the cancer patient, parse the genomic data in the dataset to detect the single first mutant allele or the single copy number.

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