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The lack of knowledge about the relationship between tumor genotypes and therapeutic responses remains one of the most critical gaps in enabling the effective use of cancer therapies. Here, we couple a multiplexed and quantitative experimental platform with robust statistical methods to enable pharmacogenomic mapping of lung cancer treatment responses in vivo. The complex map of genotype-specific treatment responses uncovered that over 20% of possible interactions show significant resistance or sensitivity. Known and novel interactions were identified, and one of these interactions, the resistance of KEAP1-mutant lung tumors to platinum therapy, was validated using a large patient response data set. These results highlight the broad impact of tumor suppressor genotype on treatment responses and define a strategy to identify the determinants of precision therapies. Significance: An experimental and analytical framework to generate in vivo pharmacogenomic maps that relate tumor genotypes to therapeutic responses reveals a surprisingly complex map of genotype-specific resistance and sensitivity.

Aberrant growth driven by genomic alterations is the defining feature of cancer, yet the temporal dynamics of this process are poorly understood. Previous efforts to model tumor growth relied on patient data or in vivo transplantation models. However, low sample sizes and sparse time points constrained these studies to simple, deterministic models that were underpowered to quantify the effect of different genotypes on tumor growth. We previously combined CRISPR/Cas9-mediated gene editing, tumor barcoding, and ultra-deep barcode sequencing with genetically engineered mouse models of human lung cancer to enable precise, high-throughput quantification of tumor sizes across many tumor genotypes in parallel. Here, we leverage this platform to systematically assay the effect of inactivating 30 known and putative tumor suppressor genes on the growth of KRASG12D_ driven lung tumors. Analyzing nearly 30 million clonal tumors within 200 mice across 19 time points between 3 and 37 weeks post-tumor initiation provided an unprecedented level of granularity and unique opportunity to evaluate the effect of tumor suppressor genotype on early lung tumor growth over time. For all tumors, we find the relative overall growth rate of tumor burden decreases monotonically with time, or alternatively the doubling time increases. Excluding the first two time points, tumor burden is approximately linear on a log-log scale, suggesting that growth scales as a power of time. For tumors driven only by oncogenic KRAS, tumor burden is approximately quadric in time. When compared to KRAS-only tumors, tumor suppressors modify tumor burden with a variety of strategies, either altering tumor number, tumor size, or a combination of both. Of the 30 tumor suppressors assayed, 8 produced a striking phenotype wherein growth during the first 10 weeks is rapid, resulting in as much as 10-fold greater tumor burden than KRAS-only tumors, but subsequently slows. Remarkably, these 8 genes included every tumor suppressor in our panel that is predicted to activate RAS signaling and downstream effector pathways (KRASWT, NF1, PTEN, STK11, and TSC1). This suggests that signaling through these key pathways limits cancer growth initially, but that this advantage lessens over time. Interestingly, inactivation of 2 tumor suppressor genes that are frequently mutated in human lung adenocarcinoma, CDKN2A and TP53, provides a sustained advantage through later time points, up to 20 weeks after tumor initiation, despite a more limited early advantage relative to other genes. Beyond characterizing the dynamics of tumor burden, the full distributions of tumor sizes of each genotype at each time point allows for quantitative stochastic modeling of the growth process. Together these results provide a detailed description of the effects of tumor suppressor genotype on the complex dynamics of early lung tumor growth. Citation Format: Alissa L. Severson, Joseph Juan, Lafia Sebastian, Vy Tran, Wensheng Nie, Ian Lai, Gregory D. Wall, Monte M. Winslow, Dmitri A. Petrov, Ian P. Winters, Michael J. Rosen. Analysis of 30 million tumor time course reveals that inactivating tumor suppressors produces complex gene-specific, time-dependent growth dynamics [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 11.

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.

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.

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.

The functional impact of most genomic alterations found in cancer, alone or in combination, remains largely unknown. Here we integrate tumor barcoding, CRISPR/Cas9-mediated genome editing and ultra-deep barcode sequencing to interrogate pairwise combinations of tumor suppressor alterations in autochthonous mouse models of human lung adenocarcinoma. We map the tumor suppressive effects of 31 common lung adenocarcinoma genotypes and identify a landscape of context dependence and differential effect strengths.

Extensive cancer genotyping has identified a large number of putative tumor-suppressor genes. However, genome sequencing data alone are insufficient to uncover the importance and role of these genes during carcinogenesis. Moreover, whether tumor suppressors restrict the rate of tumor growth, decrease the probability of incipient tumorigenesis, or limit the emergence of particularly fast-growing clones remains unknown for even the most well-studied tumor suppressors. I will discuss the development and use of multiplexed autochthonous mouse models of human lung adenocarcinoma that integrate tumor barcoding, CRISPR/Cas9-based genome editing, and high-throughput barcode sequencing (Tuba-seq). Using these methods, the impact of large panels of putative tumor suppressor genes can be quantified in parallel. We find that many previously understudied genes have strong effects on multiple aspects of tumor growth in vivo. While some functional tumor-suppressor genes have particularly strong effects only during some but not other phases of tumorigenesis, many genes impacted multiple facets of cancer development. Tuba-seq-based studies across oncogenic contexts and with combinatorial tumor suppressor gene inactivation further highlight the context dependency of gene function during carcinogenesis. These cause-and-effect analyses uncover an unexpectedly complex taxonomy of tumor suppression. We have further employed these multiplexed and quantitative in vivo models to assess the relationship between tumor genotype and therapeutic responses at scale. We coupled Tuba-seq with robust statistical methods to enable the generation of a pharmacogenomic map of lung cancer treatment responses in vivo. We uncover a surprisingly informative map of genotype-specific therapeutic responses, which highlights the importance of tumor-suppressor genotype in driving precision treatment approaches. These blueprints of the multifaceted nature of carcinogenesis have implications for understanding cancer evolution, interpreting clinical cancer genome sequencing data, and directing approaches for precision cancer treatment. Citation Format: Hongchen Cai, Chuan Li, Su Kit Chew, Maryam Yousefi, Giorgia Foggetti, Wen-Yang Lin, Zoë N. Rogers, Ian P. Winters, Christopher D. McFarland, Katherina Politi, Charlie Swanton, Dmitri A. Petrov, Monte M. Winslow. Multiplexed functional cancer genomics [abstract]. In: Proceedings of the AACR Special Conference on the Evolving Landscape of Cancer Modeling; 2020 Mar 2-5; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2020;80(11 Suppl):Abstract nr IA26.

The kinase LKB1 is a critical tumor suppressor in sporadic and familial human cancers, yet the mechanisms by which it suppresses tumor growth remain poorly understood. To investigate the tumor-suppressive capacity of four canonical families of LKB1 substrates in vivo, we used CRISPR/Cas9-mediated combinatorial genome editing in a mouse model of oncogenic KRAS-driven lung adenocarcinoma. We demonstrate that members of the SIK family are critical for constraining tumor development. Histologic and gene-expression similarities between LKB1- and SIK-deficient tumors suggest that SIKs and LKB1 operate within the same axis. Furthermore, a gene-expression signature reflecting SIK deficiency is enriched in LKB1-mutant human lung adenocarcinomas and is regulated by LKB1 in human cancer cell lines. Together, these findings reveal a key LKB1–SIK tumor-suppressive axis and underscore the need to redirect efforts to elucidate the mechanisms through which LKB1 mediates tumor suppression. Significance: Uncovering the effectors of frequently altered tumor suppressor genes is critical for understanding the fundamental driving forces of cancer growth. Our identification of the SIK family of kinases as effectors of LKB1-mediated tumor suppression will refocus future mechanistic studies and may lead to new avenues for genotype-specific therapeutic interventions. This article is highlighted in the In This Issue feature, p. 1469

The Nkx2-1 transcription factor promotes differentiation of lung epithelial lineages and suppresses malignant progression of lung adenocarcinoma. However, targets of Nkx2-1 that limit tumor growth and progression remain incompletely understood. Here, direct Nkx2-1 targets are identified whose expression correlates with Nkx2-1 activity in human lung adenocarcinoma. Selenium-binding protein 1 (Selenbp1), an Nkx2-1 effector that limits phenotypes associated with lung cancer growth and metastasis, was investigated further. Loss- and gain-of-function approaches demonstrate that Nkx2-1 is required and sufficient for Selenbp1 expression in lung adenocarcinoma cells. Interestingly, Selenbp1 knockdown also reduced Nkx2-1 expression and Selenbp1 stabilized Nkx2-1 protein levels in a heterologous system, suggesting that these genes function in a positive feedback loop. Selenbp1 inhibits clonal growth and migration and suppresses growth of metastases in an in vivo transplant model. Genetic inactivation of Selenbp1, using CRISPR/Cas9, also enhanced primary tumor growth in autochthonous lung adenocarcinoma mouse models. Collectively, these data demonstrate that Selenbp1 is a direct target of Nkx2-1, which inhibits lung adenocarcinoma growth in vivo. Implications: Selenbp1 is an important suppressor of lung tumor growth that functions in a positive feedback loop with Nkx2-1, and whose loss is associated with worse patient outcome. Mol Cancer Res; 16(11); 1737–49. ©2018 AACR.

Cancer genome sequencing has been instrumental in identifying the genomic alterations that occur in human tumors. However, the functional importance of the vast majority of these alterations, both alone and in combination, remains unknown. I will describe methods that integrate tumor barcoding with CRISPR/Cas9-mediated genome editing and ultradeep barcode sequencing to interrogate multiple tumor genotypes simultaneously in autochthonous mouse models of human cancer. We initially used this method to quantify the effects of eleven of the most frequently inactivated genes in human lung adenocarcinoma on tumor growth in vivo. We also investigated the in vivo fitness advantage conferred by inactivation of each of these eleven genes in combination with inactivation of p53 or Lkb1. This map of tumor-suppressive effects for >30 common lung adenocarcinoma genotypes revealed a complex landscape of context dependence and variability in effect strength. I will also describe a platform that integrates multiplexed Cas9-mediated homology-directed repair (HDR) with DNA barcoding and high-throughput sequencing to simultaneously investigate multiple oncogenic alterations in parallel. Using this approach, we introduced a library of nonsynonymous mutations into endogenous Kras in adult somatic cells to initiate tumors. High-throughput sequencing of barcoded Kras HDR alleles from bulk tumor-bearing lung uncovered surprising diversity in Kras variant oncogenicity. These in vivo approaches may redefine our ability to understand how diverse genomic alterations impact tumor initiation, growth, malignant transformation, and therapy responses. Citation Format: Ian P. Winters, Zoë N. Rogers, Christopher D. McFarland, Pranav V. Lalgudi, Shin-Heng Chiou, Mark A. Kay, Dmitri Petrov, Monte M. Winslow. Functional lung cancer genomics through in vivo genome editing [abstract]. In: Proceedings of the Fifth AACR-IASLC International Joint Conference: Lung Cancer Translational Science from the Bench to the Clinic; Jan 8-11, 2018; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(17_Suppl):Abstract nr IA03.

Pancreatic ductal adenocarcinoma (PDAC) is a genomically diverse, prevalent, and almost invariably fatal malignancy. Although conventional genetically engineered mouse models of human PDAC have been instrumental in understanding pancreatic cancer development, these models are much too labor-intensive, expensive, and slow to perform the extensive molecular analyses needed to adequately understand this disease. Here we demonstrate that retrograde pancreatic ductal injection of either adenoviral-Cre or lentiviral-Cre vectors allows titratable initiation of pancreatic neoplasias that progress into invasive and metastatic PDAC. To enable in vivo CRISPR/Cas9-mediated gene inactivation in the pancreas, we generated a Cre-regulated Cas9 allele and lentiviral vectors that express Cre and a single-guide RNA. CRISPR-mediated targeting of Lkb1 in combination with oncogenic Kras expression led to selection for inactivating genomic alterations, absence of Lkb1 protein, and rapid tumor growth that phenocopied Cre-mediated genetic deletion of Lkb1. This method will transform our ability to rapidly interrogate gene function during the development of this recalcitrant cancer.