Abstract 3379: Cancer dependency signatures for line of sight genomic alterations in squamous cell lung cancer yield rational targets

The Cancer Dependency Map Portal enables combined analyses of genomic and phenomic data such as genomic alterations and CRISPR gene dependencies but focuses on single genes or cell lines. This is well suited for verifying previously generated hypotheses such as the dependency of cells on PRMT5 in the setting of loss of function mutations in MTAP. To identify additional actionable de-novo vulnerabilities in-silico, we refined the genomic status of 1000 cell lines by 1. focusing on variants (driver mutations and copy number alterations) that have higher prevalence in a given cancer type in TCGA and 2. reclassifying variants as actionable only if they cause loss of function in known tumour suppressor genes or gain of function in oncogenes. This gives a clear line of sight, a more manageable set of variants and is analogous to Mendelian randomization. This approach was applied to lung squamous cell carcinoma to form a binary event matrix for the shortlisted genes and copy number alterations across a panel of lung squamous cell lines. For each of the variants, we used machine learning approaches to define a dependency signature from CRISPR dependencies for the matching cell lines that distinguish altered vs non-altered cell lines. The top ranking dependencies were further examined for biological relevance and potential toxicity, yielding novel targets to follow up on. The approach generalises well and can be applied to any other cancers with unmet needs. All data in this study was derived from public databases. By combining TCGA cancer specific prevalent genomic alterations in tumor suppressor and oncogenes we drew clear line of sight and associated these alterations to CRISPR based vulnerabilities. This yielded a manageable set of alterations, and for each of these we identified a set of essential genes/pathways to use in machine learning. These signatures were further refined by biological enrichment, targetability and toxicity in order to build a process for rational novel target ID generation. Citation Format: Miika Ahdesmaki, Joshua Armenia, Krishna Bulusu, Ben Sidders, Jonathan Dry, Ultan McDermott. Cancer dependency signatures for line of sight genomic alterations in squamous cell lung cancer yield rational targets [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3379.