Cooperative interactions between genomic alterations in oncogenes and tumor suppressors are a hallmark of cancer. While molecular characterization of tens of thousands of primary tumors has solidified estimates of how frequently specific genomic alterations occur, identifying genetic interactions from co-mutation rates alone remains limited by the vast number of possible combinations as well as inherent biases and confounding effects. Conversely, genetically engineered mouse models can reliably uncover causal effects of specific genetic alterations but are not sufficiently high throughput to generate broad insights into the interplay between genetic alterations in tumors. Therefore, how the effects of one alteration on tumor growth change in the context of other alterations remains largely unknown. We previously addressed these technical limitations by combining CRISPR/Cas9-mediated gene editing, tumor barcoding, and ultra-deep barcode sequencing methods with genetically engineered mouse models of human lung cancer, which together enable multiplexing of tumor genotypes in individual mice and precise, high-throughput quantification of in vivo tumor fitness. Here, we leverage this platform to systematically quantify the growth effects of over 45 known and putative tumor suppressor gene alterations across different common oncogenic point mutations in Kras and Braf.

While the set of genes we identified as tumor suppressive in KrasG12D- and KrasG12C-driven lung cancers was largely consistent, inactivation of those genes typically resulted in larger tumor growth advantages in the KrasG12D model. Inactivation of most genes in BrafV600E-driven lung tumors had a strikingly different growth effect than in tumors with either Kras variant, and effects were generally far less pronounced in the BrafV600E context. Interestingly, loss of genes upstream of Braf within the Ras pathway often had strong effects in the KrasG12D or KrasG12C models—loss of Nf1, a negative regulator of Kras, or loss of the wildtype Kras allele enhanced oncogenic Kras-driven tumor growth, while loss of Shp2, a positive regulator of Kras, was mildly detrimental—but had little or no effects in the BrafV600E model.

We assessed translatability of our experimental cause-and-effect data through comparisons to correlative data from publicly available human lung cancer genomics databases, and found that genetic interactions predicted by our models and gene co-mutation rates in patient tumors largely align. Together, these findings underscore the need for a contextual understanding of the ways in which tumors are influenced by their genetics and highlight the utility of high-throughput, quantitative autochthonous mouse models in pursuing this endeavor.

Citation Format: Ian Winters, Lily Blair, Lafia Sebastian, Gabriel Grenot, Edwin Apilado, Wensheng Nie, Vy Tran, Ian Lai, Gregory Wall, Dmitri Petrov, Monte Winslow, Joseph Juan, Michael Rosen. Oncogenic context drives the landscape of tumor suppression in lung cancer [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 2196.