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Supplementary Table S6 from Effects of KRAS Genetic Interactions on Outcomes in Cancers of the Lung, Pancreas, and Colorectum

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posted on 2024-01-09, 08:21 authored by Isabella N. Grabski, John V. Heymach, Kenneth L. Kehl, Scott Kopetz, Ken S. Lau, Gregory J. Riely, Deborah Schrag, Rona Yaeger, Rafael A. Irizarry, Kevin M. Haigis

Supplementary Table S6: Associations between clusters and covariates of interest for NSCLC, CRC, and PDAC, reported as odds ratios except for age, which is reported as the untransformed coefficient. The 95% Bonferroni confidence intervals are included in parentheses, corrected over the number of clusters for each tested covariate.

Funding

National Cancer Institute (NCI)

United States Department of Health and Human Services

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National Institute of General Medical Sciences (NIGMS)

United States Department of Health and Human Services

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National Science Foundation Graduate Research Fellowship Program (GRFP)

Cancer Research UK (CRUK)

Mark Foundation For Cancer Research (The Mark Foundation for Cancer Research)

John and Georgia DallePezze

Ning Zhao & Ge Li Family Initiative for Lung Cancer Research and New Therapies

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ARTICLE ABSTRACT

KRAS is among the most commonly mutated oncogenes in cancer, and previous studies have shown associations with survival in many cancer contexts. Evidence from both clinical observations and mouse experiments further suggests that these associations are allele- and tissue-specific. These findings motivate using clinical data to understand gene interactions and clinical covariates within different alleles and tissues. We analyze genomic and clinical data from the AACR Project GENIE Biopharma Collaborative for samples from lung, colorectal, and pancreatic cancers. For each of these cancer types, we report epidemiological associations for different KRAS alleles, apply principal component analysis (PCA) to discover groups of genes co-mutated with KRAS, and identify distinct clusters of patient profiles with implications for survival. KRAS mutations were associated with inferior survival in lung, colon, and pancreas, although the specific mutations implicated varied by disease. Tissue- and allele-specific associations with smoking, sex, age, and race were found. Tissue-specific genetic interactions with KRAS were identified by PCA, which were clustered to produce five, four, and two patient profiles in lung, colon, and pancreas. Membership in these profiles was associated with survival in all three cancer types. KRAS mutations have tissue- and allele-specific associations with inferior survival, clinical covariates, and genetic interactions. Our results provide greater insight into the tissue- and allele-specific associations with KRAS mutations and identify clusters of patients that are associated with survival and clinical attributes from combinations of genetic interactions with KRAS mutations.

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    Cancer Epidemiology, Biomarkers & Prevention

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