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Introduction: The MYC gene family (MYCf), which includes MYC, MYCN, and MYCL, is deregulated in ~70% of cancers and is associated with treatment resistance. Whereas older investigational therapies for MYC amplified tumors were unsuccessful, promising novel targeted therapies are in early phase clinical trials. Unfortunately, it remains unclear how to select patients whose cancers may harbor true MYC addiction. We thus sought to characterize factors such as amplification level, focality, and clonality that may correlate with increased MYC dependence. Methods: Utilizing a center-wide next generation sequencing (NGS) program of >71,000 sequenced patients, genomic and clinical data from pediatric and adult patients with MYC, MYCN, and MYCL amplifications were identified between 2014 and 2022. Patients were characterized as harboring MYC, MYCN, and MYCL amplification based on a read-depth methodology using a DNA-based hybrid-capture NGS (MSK-IMPACT) and Fraction and Allele-Specific Copy Number Estimates from the Tumor Sequencing (FACETS). All cases underwent clinical data curation including baseline demographic, tumor characteristics, and treatment histories. Results: We identified 3911 cancers with MYCf amplification (n=3257 (82%) MYC; n=364 (9%) MYCL; n=330 (8%) MYCN) across 40 malignancies, for an overall 5.5% incidence. The most frequent tumor types with MYCf amplification were breast (22%), non-small cell lung (NSCLC) (11%), colorectal (8%), ovarian (8%), prostate (7%), brain (5%), and small cell lung cancers (SCLC) (2%). Cancers with MYC amplification had longer segment lengths than MYCL and MYCN amplification, which appeared more focal (median = 19, 4.3 and 4.5 MB, respectively, p < 0.001). MYCN amplified cancers had higher total copy number than MYC and MYCL amplified cancers (median = 19, 8, 9, respectively, p < 0.001). MYC, MYCN, and MYCL samples were predominantly clonal (median clonal fraction > 99% for all genes). Most NSCLC, squamous cell lung cancers, and pulmonary carcinoids had MYC amplifications (93%, 70%, and 67% respectively). Conversely, SCLCs most often had MYCL amplifications (49%). No concurrent targetable driver alterations were found in 33% of metastatic NSCLCs with MYC, 75% of MYCN, and 6% of MYCL amplifications. Conclusions: While MYCf amplification is observed across a broad range of cancer types, factors such as gene type (MYC, MYCN, MYCL), focality, total copy number, clonality, and concurrent oncogenic drivers vary widely. Novel MYC-directed trials may consider enrichment for a subpopulation of cancers with higher-level, focal, and clonal MYCf amplifications without concurrent other drivers. Citation Format: Monica F. Chen, Allison Richards, Patrick Evans, Patrick Lee, Adam Price, Matteo Repetto, Soo Ryum Yang, Jason Chang, Rose Brannon, Ezra Rosen, David Brown, Charles Rudin, Nitya Raj, Mark G. Kris, Jorge Reis-Filho, Mark Donoghue, Alexander E. Drilon, Noura J. Choudhury. Comprehensive clinical and genomic analysis for patients with MYC, MYCN, and MYCL amplified solid tumors [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 1394.

Background: Neuroendocrine lung cancer carcinomas (Lung NECs), which include large cell neuroendocrine carcinomas (LCNECs) and small cell lung cancer (SCLC) tumors, are particularly aggressive lung neoplasms with limited clinical therapeutic options. Patients with lung NECs are treated with platinum-based chemotherapy as the first line of treatment, but they promptly develop resistance. Lurbinectedin has recently been approved by the FDA as a second-line treatment for SCLC due to its efficacy in metastatic chemorelapsed SCLC patients. However, there is still a large percentage of SCLC patients who do not respond to lurbinectedin and the overall survival of those who benefit from it remains very low ( Methods: We performed synergy proliferation assays in vitro. DNA damage/repair pathways were analyzed by western blotting. Cell cycle and apoptosis were analyzed by flow cytometry. Results: We explored the cytotoxic capacity of the combination of lurbinectedin with selinexor in cell linesfrom both SCLC (N=2) and LCNECs (N=3) in synergy in vitro assays. Selinexor strongly synergized with lurbinectedin in both SCLC and LCNEC settings and induced a potent increase in the DNA damage marker γ-H2A, reflective of increased DNA damage induced by the combination treatment. Mechanistically, this combination altered DNA damage and DNA repair mechanisms by decreasing CHK1 and CHK2 protein levels in short-term treatments, suggestive of the impairment of DNA response. Additionally, selinexor reduced the expression of MLH, a key regulator of DNA mismatch repair in monotherapy and in combination with lurbinectedin. Consistent with these results, selinexor in combination with lurbinectedin also induced a significant increase in apoptotic cells and promoted cell cycle arrest. Conclusions: These data indicates that inhibition of exportin 1 with selinexor in combination with lurbinectedin is a promising therapeutic strategy in lung NECs. Subsequent In vivo testing in patient-derived xenograft to study efficacy and toxicity of this combination will provide preclinical rationale for clinically exploring this combination in patients with lung NECs who have failed chemotherapy regimen. Citation Format: Esther Redin Resano, Charles Rudin, Alvaro Quintanal-Villalonga. Exportin 1 inhibition synergizes with lurbinectedin by altering the response to DNA damage in neuroendocrine lung tumors. [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 6188.

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.

Lineage plasticity is the ability for a cell to change its phenotypic state under evolutionary pressure. This process mediates acquired resistance to EGFR-targeted therapies in lung adenocarcinoma (LUAD), through transformation to small cell lung cancer (SCLC), an aggressive cancer type of neuroendocrine (NE) histology. This transformed SCLC has worse clinical outcomes than either its LUAD or de novo SCLC counterparts, with less than one-year overall survival following transformation. Classical SCLC itself can display plasticity through interconversion between classical, variant, and non-NE transcriptional subtypes, with non-classical subtypes displaying increased metastasis and chemoresistance. It is poorly understood what factors drive SCLC transformation or subtype switching. Prior studies poorly model intratumoral heterogeneity because they profile tumors in bulk, which only estimates the average phenotype. However, SCLC transformation often results in admixed LUAD/SCLC tumors. We sought to capture the full intratumoral heterogeneity of SCLC transformation and identify potential molecular determinants of plasticity by applying single-cell RNA sequencing (scRNA-seq) to 41 tumors and patient-derived xenografts from 22 patients with transformed or combined LUAD/NE histology, with matched targeted DNA sequencing and 17 de novo SCLC tumors, 10 LUAD tumors with concurrent EGFR/RB1/TP53 mutations, and 4 tumor-adjacent normal lung samples for comparison. Of 234,322 single transcriptomes assessed, we found 89,113 NE cancer cells and 18,197 LUAD cancer cells. We confirmed that NE and LUAD components within the same tumor shared clonal mutations and common ancestry. Compared to de novo, transformed SCLC harbored greater phenotypic diversity across patients (p < 1 × 10−10), driven largely by enrichment of variant and non-NE subtypes (p < 0.02), including NEUROD1, POU2F3, and YAP1-high subtypes, the latter of which was completely absent in de novo. Within each tumor, transformed SCLC displayed higher intratumoral subtype diversity than de novo SCLC (likelihood ratio p < 0.025). After adjusting for SCLC subtype, differential expression and pathway analysis demonstrated that transformed SCLC is a distinct phenotype from de novo and shares features of the ancestral clone that include residual EGFR and NSCLC gene signatures, as well as pathways in neuronal stemness, MYC targets, AKT/MTOR signaling, JAK/STAT inflammation, and chromatin remodeling. In sum, we find increased intratumoral phenotypic diversity in transformed SCLC, including variant and non-NE subtypes, that may explain worse clinical outcomes. We show that transformed SCLC is a distinct phenotype from de novo, marked by pathways that may offer new potential drug targets to constrain plasticity, with the goal of restoring original sensitivity to targeted therapies. Citation Format: Joseph Minhow Chan, Alvaro Quintanal-Villalonga, Amin Sabet, Parvathy Manoj, Ojasvi Chaudhary, Tianhao Xu, Ignas Masilionis, Jacklynn Egger, Noor Sohail, Jaeyoung Chun, Tal Nawy, Linas Mazutis, Triparna Sen, Ronan Chaligne, Helena Yu, Dana Pe'er, Charles Rudin. Single-cell transcriptomic profiling of SCLC transformation reveals increased intratumoral diversity of variant and non-neuroendocrine subtypes [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 1167.

Cancer is characterized by heterogeneous genetic alterations that drive changes in cell state that often impact responses to therapy. Recent successes in the development of therapies targeting oncogenes has dramatically improved cancer care for subsets of patients and generated a wave of interest in precision cancer medicines. However, efforts to broadly discover effective targeted therapies have been stymied by a limited understanding of how response to drugs is modulated by tumor genotype. Here, we describe an in vivo functional cancer pharmacogenomics (PGx) platform optimized for the discovery of genetic drivers of response to therapies. By ​​coupling somatic genome editing, DNA barcoding, next generation sequencing, and robust statistical methods with genetically engineered mouse models of human lung cancer, the effects of different candidate therapies can be precisely quantified across thousands of tumors of diverse tumor suppressor genotypes. These tumors are initiated de novo in the native microenvironmental context in mice with an intact adaptive immune system. To illustrate the translational value of our platform, we tested a standard-of-care chemotherapy combination of carboplatin and pemetrexed (carbo/pem) in mice with oncogenic KrasG12D-driven lung tumors with inactivation of 22 putative tumor suppressors. Treatment after 12 weeks of tumor growth for 3 weeks led to greater than 75% reduction in tumor sizes relative to vehicle controls. Surprisingly, although chemotherapy affects cell proliferation pathways rather than a defined genetic component, we identified a diverse spectrum of genotype-specific sensitivity and resistance to carbo/pem treatment in our model. We next determined the extent to which our empirical PGx data could predict human clinical outcomes by analyzing clinicogenomic data from >200 patients with KRAS mutant lung cancer who received combination chemotherapy treatment. Training a multivariate Cox proportional hazards (PH) model on overall survival from treatment initiation using our 22 cancer-associated genes as binary features in combination with baseline clinical data revealed a remarkable agreement between the Cox-PH coefficients and our empirical PGx data. Further analysis of the human survival data using multiple machine learning (ML) methods identified Random Survival Forests (RSF) as the top performing model. Importantly, using mouse empirical PGx data to rank genes for feature selection significantly improved the predictive power of the ML model. Together, these results demonstrate how leveraging our PGx platform together with human data within a machine learning framework may improve the prediction of patient clinical outcomes, and thus profoundly impact our ability to realize the promise of precision medicine. Citation Format: Michael Rosen, David Amar, Ian Winters, Hira Rizvi, Wensheng Nie, Gregory Wall, Dmitri Petrov, Monte Winslow, Charles Rudin, Joseph Juan. A quantitative in vivo pharmacogenomics platform uncovers biomarkers of therapy response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2146.

Lung adenocarcinoma (LUAD), constituting 50% of non-small cell lung cancer, is classically defined by the presence of driver mutations in oncogenes such as KRAS, EGFR, and BRAF. While the development of driver-targeted therapies has significantly improved survival in subsets of patients with LUAD, studies examining tumor sequencing data and clinical outcomes have shown that intra-driver genetic and phenotypic heterogeneity underlie differential patient responses to these therapies. In particular, co-occurring alterations in the genes SMARCA4 (BRG1), STK11 (LKB1), and KEAP1 are predictive of exceptionally poor prognosis and worse overall survival in metastatic LUAD patients. SMARCA4 loss is also a hallmark of undifferentiated, highly chemoresistant NSCLC tumors. While these co-mutations most often appear in KRAS-driven tumors, their association with poor outcomes is maintained regardless of driver status. Studies exploring the molecular features of SMARCA4-deficient and STK11/KEAP1 co-mutant LUAD have yielded translational insights specific to these subsets, establishing a rationale to characterize the unique biology of SMARCA4/STK11/KEAP1 (BLK) triple-mutant LUAD. To this end, we are profiling BLK LUAD through a combination of genomic, transcriptomic, epigenomic, and phenotypic assays in in vitro models. We have generated paired isogenic in vitro models of BLK LUAD via CRISPR-Cas9 knockout. Phenotypic analyses of these cell lines, whose engineered genotypes model varying combinations of SMARCA4, KEAP1, and STK11 loss, have revealed growth rate differences in vitro, and pairwise comparisons of RNA-sequencing in these lines show downregulation of lung differentiation markers and upregulation of metastasis-associated genes concomitant with SMARCA4 loss. In conclusion, we have generated isogenic preclinical models showing molecular and phenotypic features consistent with observations reported in BLK LUADs. Further molecular and functional characterization of these models may reveal mechanistic insights into the biology of this subset of aggressive tumors. Citation Format: Emily Costa, Corrin Wohlheiter, Samuel Tischfield, Alister Funnell, JT Poirier, Álvaro Quintanal Villalonga, Triparna Sen, Charles Rudin. Characterizing SMARCA4/STK11/KEAP1 co-mutant lung adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 859.

Background: Despite recent treatment advances, malignant pleural mesothelioma (MPM) is an aggressive, recalcitrant malignancy. Currently, histologic subtype (epithelioid/non-epithelioid/biphasic) is the primary prognostic factor; other potential biomarkers to guide therapeutic strategies remain elusive. Even with multimodality therapies, recurrence is high in early-stage disease. In the unresectable/metastatic setting, there are only two FDA approved regimens, both in the first line setting: cisplatin/pemetrexed and ipilimumab/nivolumab. Unfortunately, most who respond to first line treatment experience disease progression within a year. A few established MPM cell lines, with inherent limitations, provide minimal preclinical insight. The relative lack of model systems that accurately reflect MPM tumorigenesis is a barrier to therapeutic and diagnostic advances in MPM. Methods: We developed a diverse library of 22 extensively annotated patient-derived xenograft (PDX) models from 22 patients with MPM. Multi-omic analyses including, targeted tumor next-generation sequencing by MSK-IMPACT, RNA-sequencing, and immunohistochemistry was performed. We deconvoluted the mutational landscapes, global expression profiles, and molecular subtypes of these MPM models and further compared the PDXs to MPM clinical specimens, including matched PDX and primary tumor pairs. Results: The mutational landscapes of PDX models strongly correlated with paired tumor samples. There were some differences in CDKN2A/B mutations and relative enrichment of NF2 with fewer BAP1 alterations, the significance of which is being investigated. When compared by histological subtype, we observed an upregulation of genes involved in NOTCH and EMT signaling in the epithelioid models. Models derived from patients with shorter overall survival or poor response to platinum doublet had higher expression of WNT/β-catenin signaling, hedgehog pathway, and epithelial-mesenchymal transition signaling as well as downregulation of immune-activation pathways, including type I and II interferon signaling and inflammatory response pathways. Conclusions: This library of MPM PDXs, the largest to date, effectively mimics human disease and provides unprecedented insight into the genomic, transcriptomic, and protein landscape of MPM. These PDX models will inform future clinical investigations and provide an important new preclinical resource. Citation Format: Michael Offin, Jennifer L. Sauter, Jacklyn V. Egger, Elisa deStanchina, John T. Poirier, Marjorie G. Zauderer, Charles Rudin, Triparna Sen. Multiomic profiling of patient-derived xenografts established from patients with malignant pleural mesothelioma proposes pathways associated with poor prognosis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 51.