# Unraveling mechanisms of tumor suppression in lung cancer > **NIH NIH R01** · STANFORD UNIVERSITY · 2020 · $498,341 ## Abstract PROJECT SUMMARY Genome sequencing has catalogued the somatic alterations in human cancers and identified many putative tumor suppressor genes. However, human cancers generally evolve through the sequential acquisition of multiple genomic alterations and simply identifying recurrent genomic alterations does not necessarily reveal their functional importance to cancer growth. Genetically engineered mouse models uniquely enable the introduction of defined genetic alterations into normal adult cells, which results in the initiation and growth of tumors entirely within their natural in vivo setting. However, the breadth of their utility is limited by the fact that they are neither readily scalable nor sufficiently quantitative. To increase the scope and precision of in vivo cancer modeling, we previously integrated conventional genetically engineered mouse models, CRISPR/Cas9-based somatic genome engineering, and quantitative genomics with mathematical approaches. Tumor barcoding coupled with CRISPR/Cas9-mediated gene inactivation and high-throughput barcode sequencing (Tuba-seq) enables the parallel investigation of multiple tumor genotypes in individual mice and allows the large-scale analysis of pairwise tumor suppressor alterations. In Aim 1, we will employ our multiplexed and quantitative Tuba-seq approach to quantify the impact of inactivating many uncharacterized putative tumor suppressor genes on tumor growth in vivo and across time. This analysis will broaden our understanding of the driving forces of tumorigenesis and uncover the potential clinical meaning of these genomic alterations. In Aim 2, we will uncover epistatic genetic interactions between tumor suppressor genes by generating de novo tumors with pairwise combination of tumor suppressor alterations. We will generate the first broad-scale functional understanding of the combinatorial effects of genomic alterations within an autochthonous cancer model. We will uncover the epistatic interactions of these genes and pathways, illuminating novel aspects of tumorigenesis, and potentially highlighting therapeutic vulnerabilities. In Aim 3, we will uncover the molecular programs in cancer cells of different genotypes. To gain insight into how the molecular outputs of single genomic alterations relate to the effects of pairwise alteration, we will also characterize tumors with combined inactivation of cooperative tumor suppressors. This will provide a molecular framework to understand the effects of novel tumor suppressors and uncover the molecular logic that drives the pattern of genomic alterations in human cancer. Our preliminary data, novel genetic systems, and strong collaborative team make us uniquely positioned to conduct these studies. The results of this proposal will be significant because these innovative, multidisciplinary, and highly quantitative approaches will accelerate our understanding of the determinants of cancer growth and will begin the systematic deconvolution of gene ... ## Key facts - **NIH application ID:** 9936427 - **Project number:** 5R01CA234349-02 - **Recipient organization:** STANFORD UNIVERSITY - **Principal Investigator:** Dmitri Petrov - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2020 - **Award amount:** $498,341 - **Award type:** 5 - **Project period:** 2019-06-01 → 2024-05-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/9936427 ## Citation > US National Institutes of Health, RePORTER application 9936427, Unraveling mechanisms of tumor suppression in lung cancer (5R01CA234349-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9936427. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*