Reducing disparities is critical to promote equity of access to precision treatments for all patients with cancer. While socioenvironmental factors are a major driver behind such disparities, biological differences also are likely to contribute. The prioritization of cancer drug targets is foundational for drug discovery, yet whether ancestry-related signals in target discovery pipelines exist has not been systematically explored due to the absence of data at the appropriate scale. Here, we analyzed data from 611 genome-scale CRISPR/Cas9 viability experiments in human cell line models as part of the Cancer Dependency Map to identify ancestry-associated genetic dependencies. Surprisingly, we found that most putative associations between ancestry and dependency arose from artifacts related to germline variants that are present at different frequencies across ancestry groups. In 2-5% of genes profiled in each cellular model, germline variants in sgRNA targeting sequences likely reduced cutting by the CRISPR/Cas9 nuclease. Unfortunately, this bias disproportionately affected cell models derived from individuals of recent African descent because their genomes tended to diverge more from the consensus genome typically used for CRISPR/Cas9 guide design. To help the scientific community begin to resolve this source of bias, we report three complementary methods for ancestry-agnostic CRISPR experiments. This report adds to a growing body of literature describing ways in which ancestry bias impacts cancer research in underappreciated ways.

Genomic drivers of pediatric low-grade gliomas (pLGGs) converge on alterations that activate the MAPK pathway. However, expression of individual driver oncogenes fails to induce tumor formation with high penetrance and, paradoxically, expression of these oncogenes suppresses growth in vitro. This, combined with the non-monotonic tumor growth rate in patients, suggests that there are “hidden drivers” beyond a single driver oncogene that are necessary to support tumor growth. The goal of this project is to leverage high-throughput functional genomics strategies to identify these hidden drivers of pLGG. Our preliminary data indicates that genes which modulate differentiation are required for the survival of LGG cells, suggesting that these genes may be hidden drivers of LGG tumor growth. Additionally, we hypothesize that secreted factors in the tumor microenvironment regulate pLGG tumor growth, potentially by modulating differentiation. In total, genes which cooperate with pLGG driver oncogenes to promote tumor growth may represent a new class of therapeutic targets and may explain the complex patterns of tumor growth that are observed in patients.

Socioeconomic factors and discrimination play major roles in variable cancer treatment outcomes across individuals from different ancestral backgrounds. Tumors from patients of different ancestry groups also have divergent patterns of somatic and germline alterations. This led us to hypothesize that these molecular differences may also contribute to the variable treatment outcomes observed in the clinic. We hypothesized that the ancestry of cell lines would impact their genetic dependencies. To test this hypothesis, we leveraged The Cancer Dependency Map (DepMap) which has performed genome-wide CRISPR screens across >1,000 cell lines and >30 cancer types. We first leveraged variant calls from WES/WGS to infer cell line ancestry, then correlated these ancestries with DepMap gene dependency scores. This analysis was underpowered to detect differences in cell lines of African, American, and South Asian descent, since cell lines from these ancestry groups are poorly represented in DepMap, and in cancer research models in general. We were, however, able to detect 71 gene dependencies that were associated with either European or East Asian ancestry. Since different ancestry groups have divergent patterns of germline alterations, we reasoned that specific germline alterations may result in ancestry-associated dependencies. Surprisingly, we identified cis-QTLs for >75% of the ancestry-associated genes. We originally hypothesized that these variants would alter the function of the encoded protein, but instead found that these variants mapped to the targeting sequences of the sgRNA. This suggests that ancestry-associated mismatches within sgRNA targeting sequences can preclude Cas9-mediated genome editing. To understand this problem systematically, we mapped the germline variants that were catalogued in gnomAD to multiple genome-wide CRISPR libraries. The fraction of affected genes differed from library to library and was typically between 2-5%, but we found that individuals of African descent were consistently more affected by this problem across all CRISPR libraries. This is important because it suggests that cell lines of African descent have a higher rate of false negatives in all CRISPR-based experiments. In total, we identified a subset of genes that have ancestry-associated dependency profiles. Most of these genes are the result of ancestry-associated mismatches within the sgRNA targeting sequences. However, many of these genes are not, and require further investigation to understand the influence of ancestry on these genetic dependencies. Citation Format: Sean Alexander Misek, Aaron Fultineer, Jeremie Kalfon, Javad Noorbakhsh, Isabella Boyle, Joshua Dempster, Lia Petronio, Katherine Huang, James McFarland, Rameen Beroukhim, Jesse Boehm. Ancestry bias in CRISPR guide design impedes discovery of genetic dependencies [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 2173.

ID 55303 Poster Board 246A range of somatic alterations in both oncogenes and tumor suppressor genes promote cancer development. Synthetic lethal relationships or collateral lethalities have emerged in the context of these genetic driver mutations, revealing new targets for therapeutic development. Most variation within cancer genomes results from germline, as opposed to somatic, mutations, but the genetic dependencies engendered by these variants have not been systematically explored. We sought to define the landscape of these interactions by leveraging the genome-scale CRISPR/Cas9 screens that were performed as part of The Cancer Dependency Map (DepMap).In this study we systematically analyzed the associations between individual germline variants in 612 cancer cell lines and >16,000 genetic dependencies. Across all genes profiled in this analysis, we identified 121 genes whose dependency profiles are associated with the presence of specific germline variants. The top association in this analysis was between two functionally redundant Holliday junction resolvases. In this study we seek to define the nature of this synthetic lethal relationship, with a focus on how this variant impacts protein activity.