Abstract 6139: Genetic modifiers of KRAS-mutant colorectal cancer
Bibliographic record
Abstract
Abstract Somatic driver mutations in KRAS are found in ~40% of colorectal cancers (CRCs) and are associated with worse outcomes. In the US, individuals of African ancestry with CRC are more likely to have tumors with KRAS mutations than individuals of European ancestry, even after considering differences in socioeconomic status and other risk factors. Germline variants can influence somatic mutation events and may provide a selective advantage such that a mutated cell is more likely to progress to a cancer. Germline variants have been found to associate with somatic mutations in a variety of tumors. Based on ancestry differences in KRAS tumor mutation frequency, we hypothesized that germline variants altering molecular pathways that support KRAS tumorigenesis will associate with KRAS-mutant status. To test this, we performed a two-step association study using KRAS mutation status as our phenotype in individuals with microsatellite stable CRC. First, we conducted a discovery analysis using genome-wide germline genotypes and somatic mutation data from 6,386 non-Hispanic White CRC patients from the Cancer Genome Atlas and the GECCO consortium, considering sex and tumor location as covariates. To select variants for additional study, we used in-silico tools, such as expression quantitative trait loci (eQTL), to predict how associated variants may alter gene expression and chromatin. After reviewing relevant genomic features, excluding SNVs with minor allele frequency <10% and pruning SNVs in linkage disequilibrium (r2>0.75), we identified candidate loci for a multi-ancestry independent validation analysis. In the discovery analysis, no variants met genome-wide significance thresholds (p-value <5x10-8), though 3 SNVs associated with KRAS mutation status with p-values <1x10-6 and 50 with p-values <1x10-5. Of these 50 SNVs, 21 met criteria for validation. We chose 80 additional SNVs (p-value <1x10-4) with predicted functional effect, for a total of 101 SNVs for analyses. In our preliminary validation analysis of 1,487 individuals grouped by self-reported race, no variants had a significant association in all populations. The rs726800 variant showed nominal association with KRAS mutation status (p-value=0.028) in self-reported non-Hispanic White individuals. This variant is within potential cis-regulatory distance of HS3ST3A1 and COX10-DT, both of which have been reported as cancer biomarkers. Nominal associations were identified in self-reported Black (n=7 SNVs) and Hispanic or Latino (n=3 SNVs) individuals, though both groups had limited sample sizes. Additional validation samples are being analyzed, as well as analysis with adjustment for genetic ancestry. Future functional studies of top candidate variants will add mechanistic insight. In summary, we identified germline SNVs that associate with KRAS somatic mutations in CRC and may inform the cellular context that supports development of colorectal tumors with KRAS mutations. Citation Format: Nijole P. Tjader, Johnny Ramroop, Tanish Gandhi, Peter T. Campbell, Andrew T. Chan, Steven Gallinger, Graham G. Giles, Marc J. Gunter, Tabitha A. Harrison, Michael Hoffmeister, Polly A. Newcomb, Shuji Ogino, Amanda I. Phipps, Conghui Qu, Robert E. Schoen, Andrew Pellatt, Michael O. Woods, Bethany Van Guelpen, Patrick Stevens, Heather Hampel, Ulrike Peters, Joseph P. McElroy, Amanda E. Toland. Genetic modifiers of KRAS-mutant colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6139.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".