Oncogenic context shapes the fitness landscape of tumor suppression

Tumors acquire alterations in oncogenes and tumor suppressor genes in an adaptive walk through the fitness landscape of tumorigenesis. However, the interactions between oncogenes and tumor suppressor genes that shape this landscape remain poorly resolved and cannot be revealed by human cancer genomics alone. Here, we use a multiplexed, autochthonous mouse platform to model and quantify the initiation and growth of more than one hundred genotypes of lung tumors across four oncogenic contexts: KRAS G12D, KRAS G12C, BRAF V600E, and EGFR L858R. We show that the fitness landscape is rugged-the effect of tumor suppressor inactivation often switches between beneficial and deleterious depending on the oncogenic context-and shows no evidence of diminishing-returns epistasis within variants of the same oncogene. These findings argue against a simple linear signaling relationship amongst these three oncogenes and imply a critical role for off-axis signaling in determining the fitness effects of inactivating tumor suppressors.

Abstract 5723: An in vivo pharmacogenomics platform replicates and extends biomarkers of therapy response identified via causal inference analysis of clinical data

Recent development of therapies targeting oncogenes have dramatically improved cancer care for subsets of patients and generated a wave of interest in cancer precision medicine. However, effective targeted therapies are scarce as we have a limited understanding of how drug responses are modulated by tumor genotype. Here, we utilize both human data and in vivo models to identify genetic drivers of therapy response in KRAS-driven non-small cell lung cancer patients treated with chemotherapy. We first present an integrative causal inference analysis of three clinical data resources: (1) the recently released Genomics Evidence Neoplasia Information Exchange, Biopharma Collaboration dataset (GENIE-BPC; n=197), (2) a selected set of patients from the Tempus Clinico-genomic Database (n=330), and (3) an additional cohort from Memorial Sloan Kettering Cancer Center (n=218). Each dataset was first analyzed separately using a causal inference pipeline. Doubly robust estimators for each variant were inferred within a counting process survival analysis model accounting for time-varying treatments and immortal time bias. Meta-analysis of the results from all three cohorts identified three commonly mutated and highly replicable genes with a significant effect on overall survival: KEAP1, SMARCA4, and CDKN2A.

As further validation, we used our murine in vivo pharmacogenomics (PGx) platform that can quantify the effects of therapies across thousands of tumors of diverse genotypes. These tumors are initiated de novo in the native microenvironmental context in mice with an intact adaptive immune system. We tested a chemotherapy combination of carboplatin and pemetrexed in mice with KrasG12D-driven lung tumors and inactivation of each of 60 putative tumor suppressors. Treatment led to >75% reduction in tumor sizes relative to tumor suppressor inactivated (matched) vehicle-treated controls. These models identified causal effects for two out of the three candidates above: KEAP1 (resistance) and CDKN2A (sensitive). Moreover, our PGx platform identified additional candidate genes beyond those found using the clinical data, which had insufficient sample size for these rarely mutated genes. Together, we demonstrate how leveraging our PGx platform together with human data within a causal inference framework may improve the stratification of patients by their clinical outcomes, profoundly advancing the promise of precision medicine.

Citation Format: David Amar, Erick Scott, Ian P. Winters, Gregory D. Wall, Dmitri A. Petrov, Monte M. Winslow, Joseph Juan, Ian K. Lai, Lafia Sebastian, Edwin A. Apilado, Gabriel Grenot, Vy B. Tran, Charles Rudin, Michael J. Rosen. An in vivo pharmacogenomics platform replicates and extends biomarkers of therapy response identified via causal inference analysis of clinical data. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5723.

Abstract 2789: Tumor suppressor genotype dramatically impacts lung cancer response to KRAS G12C inhibitors in vivo

Cancer is characterized by diverse genomic alterations that change cell state and can modify therapy responses. In some cases, the relationships between genotype and drug responses are obvious, such as the response of tumors with defined oncogenic mutations to inhibitors of those mutant proteins. However, covalent KRAS G12C inhibitors (G12Ci) induce clinical responses in less than half of patients with oncogenic KRAS G12C-driven lung cancer, illustrating that genomic alterations not obviously related to the drug mechanism can have a dramatic impact.

We have developed and optimized an in vivo platform to identify pharmacogenomic interactions that dictate lung cancer responses to therapies. By coupling somatic genome editing, tumor barcoding, ultra-deep barcode sequencing, and robust statistical methods in genetically engineered mouse models of human lung cancer, we can quantify and compare candidate therapies across thousands of clonal tumors of diverse tumor suppressor genotypes. This platform provides a uniquely high-throughput and quantitative assessment of tumors that are initiated de novo within the natural immunocompetent tissue environment.

To uncover genotypes that are particularly important in controlling the response of tumors to oncogenic KRAS inhibition, we applied this platform to quantify the impact of 59 tumor suppressor genes on KRASG12C-driven lung tumor responses to G12Ci. Treatment resulted in approximately three-quarters reduction in both average tumor sizes and overall tumor burden, with a clear drug and dose dependence. Approximately one-quarter of inactivated genes significantly and consistently altered tumor responses across numerous G12Ci and replicate studies. Interestingly, most of these genes are thought to be in pathways not directly related to RAS signaling, and they exhibit a striking pattern in which treatment sensitivity is positively correlated with strength of the tumor suppressor effect.

This pharmacogenomic profile for G12Ci was categorically different than the profiles we have observed for chemotherapy and other therapies. Overall, these results provide direct causal evidence that certain tumor suppressor genotypes dramatically shift the effectiveness of G12Ci in vivo and generate hypotheses about patients likely to benefit from G12Ci relative to alternatives. Analysis of available human data provides early clinical support for some of these hypotheses. Ongoing efforts to define pharmacogenomic profiles of combination therapies could be of even greater importance. Platforms that can accurately predict how tumor genotype drives responses have the potential to transform precision cancer therapy, enabling more effective patient stratification and therapy combinations.

Citation Format: Paul J. McMurdie, Ian P. Winters, Lily M. Blair, Lafia Sebastian, Vy Tran, Gabriel Grenot, Edwin A. Apilado, Edwin A. Apilado, Ian K. Lai, Gregory D. Wall, Dmitri A. Petrov, Monte M. Winslow, Michael J. Rosen, Joseph Juan. Tumor suppressor genotype dramatically impacts lung cancer response to KRAS G12C inhibitors in vivo [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2789.

Abstract 1172: Oncogenic context shapes the fitness landscape of tumor suppression

Cancer progression is a quintessential example of a walk on an adaptive fitness landscape, with tumor growth depending on the cooperation of multiple driver mutations. While sequencing of tens of thousands of clinical samples has revealed a vast set of cancer drivers, far less is known about the interactions of oncogene-tumor suppressor pairs. Due to patient-level selection bias, confounding biological factors and limited sample sizes, a map of these interactions cannot be generated from human data alone and, instead, requires direct perturbational experiments and functional genomics approaches.

Here, we model and quantify tumors using an autochthonous mouse platform and Tubaseq, which integrates barcoded lentiviral-sgRNA/Cre vectors and high-throughput barcode sequencing to uncover the number of neoplastic cells in each tumor of each genotype. Across four oncogenic contexts—KRAS G12D, KRAS G12C, BRAF V600E, and EGFR L858R—we analyzed >10,000,000 tumors to estimate the tumorigenesis potential of the oncogene alone as well as the effect of inactivating 28 tumor suppressor genes.

We discovered that despite KRAS G12D producing >10x the tumors as G12C, tumor suppressor inactivations provide similar growth effects on both backgrounds. In contrast, although the intrinsic abilities of BRAF V600E and EGFR L858R to drive tumor development fall within the range of the KRAS variants, tumor suppressive effects are categorically different in the context of each oncogene.

Many tumor suppressors show clear sign epistasis with the oncogenes, whereby inactivation is advantageous in one context and neutral or deleterious in another. Inactivation of some of the strongest tumor suppressors (e.g., Lkb1, Setd2, and Kmt2d) in KRAS-driven tumors strongly decreases tumor growth in the presence of oncogenic EGFR. While some of these epistatic effects are consistent with a textbook understanding of the RAS pathway, most cannot be predicted based on the linear oncogenic EGFR → KRAS → BRAF pathway model.

Analyses of clinical genomics data from AACR Project GENIE confirm that high rates of passenger mutations in KRAS- and BRAF-driven lung tumors, among other factors, prevent the discovery of these interactions from human data alone. However, for EGFR-mutant lung cancers, which are less confounded by high mutational burden, the rates of coincident tumor suppressor mutation are highly correlated with tumor growth effects in our in vivo model.

Thus, we find via causal experiments that the landscape of tumor suppression is highly dependent on oncogenic context, with a minority of tumor suppressive effects robust to changes in the oncogene. These findings suggest that the utility of a specific cancer mutation as a prognostic or predictive biomarker of patient outcomes will be dependent on coincident mutations in the tumor and highlight the utility of high-throughput, quantitative autochthonous mouse models in advancing our understanding of cancer biology.

Citation Format: Lily M. Blair, Joseph M. Juan, Lafia Sebastian, Vy B. Tran, Wensheng Nie, Gregory D. Wall, Mehmet Gerceker, Ian K. Lai, Edwin A. Apilado, Gabriel Grenot, David Amar, Giorgia Foggetti, Mariana Do Carmo, Zeynep Ugur, Debbie Deng, Alex Chenchik, Maria Paz Zafra, Lukas E. Dow, Katerina Politi, Jonathan J. MacQuitty, Dmitri A. Petrov, Monte M. Winslow, Michael J. Rosen, Ian P. Winters. Oncogenic context shapes the fitness landscape of tumor suppression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1172.

Biased clustered substitutions in the human genome: the footprints of male-driven biased gene conversion

We examined fixed substitutions in the human lineage since divergence from the common ancestor with the chimpanzee, and determined what fraction are AT to GC (weak-to-strong). Substitutions that are densely clustered on the chromosomes show a remarkable excess of weak-to-strong "biased" substitutions. These unexpected biased clustered substitutions (UBCS) are common near the telomeres of all autosomes but not the sex chromosomes. Regions of extreme bias are enriched for genes. Human and chimp orthologous regions show a striking similarity in the shape and magnitude of their respective UBCS maps, suggesting a relatively stable force leads to clustered bias. The strong and stable signal near telomeres may have participated in the evolution of isochores. One exception to the UBCS pattern found in all autosomes is chromosome 2, which shows a UBCS peak midchromosome, mapping to the fusion site of two ancestral chromosomes. This provides evidence that the fusion occurred as recently as 740,000 years ago and no more than approximately 3 million years ago. No biased clustering was found in SNPs, suggesting that clusters of biased substitutions are selected from mutations. UBCS is strongly correlated with male (and not female) recombination rates, which explains the lack of UBCS signal on chromosome X. These observations support the hypothesis that biased gene conversion (BGC), specifically in the male germline, played a significant role in the evolution of the human genome.