TY - JOUR
T1 - Interrogating Mutant Allele Expression via Customized Reference Genomes to Define Influential Cancer Mutations
AU - Grant, Adam D.
AU - Vail, Paris
AU - Padi, Megha
AU - Witkiewicz, Agnieszka K.
AU - Knudsen, Erik S.
N1 - Funding Information:
The authors thank all members of the Knudsen and Witkiewicz laboratory for thought-provoking discussion, technical assistance and help with manuscript preparation. We also thank the reviewers for their insightful comments and suggestions, which have improved the quality of this study. This project was supported by the National Institute of Health grant R01CA211878.
Publisher Copyright:
© 2019, The Author(s).
PY - 2019/12/1
Y1 - 2019/12/1
N2 - Genetic alterations are essential for cancer initiation and progression. However, differentiating mutations that drive the tumor phenotype from mutations that do not affect tumor fitness remains a fundamental challenge in cancer biology. To better understand the impact of a given mutation within cancer, RNA-sequencing data was used to categorize mutations based on their allelic expression. For this purpose, we developed the MAXX (Mutation Allelic Expression Extractor) software, which is highly effective at delineating the allelic expression of both single nucleotide variants and small insertions and deletions. Results from MAXX demonstrated that mutations can be separated into three groups based on their expression of the mutant allele, lack of expression from both alleles, or expression of only the wild-type allele. By taking into consideration the allelic expression patterns of genes that are mutated in PDAC, it was possible to increase the sensitivity of widely used driver mutation detection methods, as well as identify subtypes that have prognostic significance and are associated with sensitivity to select classes of therapeutic agents in cell culture. Thus, differentiating mutations based on their mutant allele expression via MAXX represents a means to parse somatic variants in tumor genomes, helping to elucidate a gene’s respective role in cancer.
AB - Genetic alterations are essential for cancer initiation and progression. However, differentiating mutations that drive the tumor phenotype from mutations that do not affect tumor fitness remains a fundamental challenge in cancer biology. To better understand the impact of a given mutation within cancer, RNA-sequencing data was used to categorize mutations based on their allelic expression. For this purpose, we developed the MAXX (Mutation Allelic Expression Extractor) software, which is highly effective at delineating the allelic expression of both single nucleotide variants and small insertions and deletions. Results from MAXX demonstrated that mutations can be separated into three groups based on their expression of the mutant allele, lack of expression from both alleles, or expression of only the wild-type allele. By taking into consideration the allelic expression patterns of genes that are mutated in PDAC, it was possible to increase the sensitivity of widely used driver mutation detection methods, as well as identify subtypes that have prognostic significance and are associated with sensitivity to select classes of therapeutic agents in cell culture. Thus, differentiating mutations based on their mutant allele expression via MAXX represents a means to parse somatic variants in tumor genomes, helping to elucidate a gene’s respective role in cancer.
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U2 - 10.1038/s41598-019-48967-8
DO - 10.1038/s41598-019-48967-8
M3 - Article
C2 - 31484939
AN - SCOPUS:85071747498
SN - 2045-2322
VL - 9
JO - Scientific reports
JF - Scientific reports
IS - 1
M1 - 12766
ER -