Abstract
Mutant allele frequency distributions in cancer samples have been used to estimate intratumoral heterogeneity and its implications for patient survival. However, mutation calls are sensitive to the calling algorithm. It remains unknown whether the relationship of heterogeneity and clinical outcome is robust to these variations. To resolve this question, we studied the robustness of allele frequency distributions to the mutation callers MuTect, SomaticSniper, and VarScan in 4722 cancer samples from The Cancer Genome Atlas. We observed discrepancies among the results, particularly a pronounced difference between allele frequency distributions called by VarScan and SomaticSniper. Survival analysis showed little robust predictive power for heterogeneity as measured by Mutant-Allele Tumor Heterogeneity (MATH) score, with the exception of uterine corpus endometrial carcinoma. However, we found that variations in mutant allele frequencies were mediated by variations in copy number. Our results indicate that the clinical predictions associated with MATH score are primarily caused by copy number aberrations that alter mutant allele frequencies. Finally, we present a mathematical model of linear tumor evolution demonstrating why MATH score is insufficient for distinguishing different scenarios of tumor growth. Our findings elucidate the importance of allele frequency distributions as a measure for tumor heterogeneity and their prognostic role.
Highlights
A major challenge for predicting clinical outcome to cancer treatment is the heterogeneity of cell populations within each tumor, as intratumoral heterogeneity is increasingly being associated with metastasis and resistance to therapies[1,2,3,4]
We analyzed a total of 4722 samples from the The Cancer Genome Atlas (TCGA) database on a cloud computing platform to explore the effects of mutation calling by MuTect, SomaticSniper, and VarScan on allele frequency distributions and patient survival
Three different mutation calling pipelines were run for each tumor/normal pair on Amazon Web Services (AWS)[34] through the Cancer Genome Cloud (CGC) interface, and the resulting allele frequencies were calculated for each sample
Summary
A major challenge for predicting clinical outcome to cancer treatment is the heterogeneity of cell populations within each tumor, as intratumoral heterogeneity is increasingly being associated with metastasis and resistance to therapies[1,2,3,4]. Intratumoral heterogeneity develops from mutations in cells and the relative growth advantages they confer to their descendant populations[5,6] To better understand this phenomenon, multiple research groups have developed methods for the challenging problem of estimating tumor cellular composition from bulk tumor sequencing data[7,8,9,10,11,12,13]. These methods rely on the allele frequencies of mutations in each cancer, with low allele frequency mutations providing information on small subclones and high allele frequency mutations providing information on large subclones. We discuss implications of a simple evolutionary mechanism on allele frequency distributions using a mathematical modeling approach
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