Distribution-based measures of tumor heterogeneity are sensitive to mutation calling and lack strong clinical predictive power.
Sci Rep 2018 Jul 30; 8(1):11445
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.
Noorbakhsh, Javad; Kim, Hyunsoo; Namburi, Sandeep; and Chuang, Jeffrey H, "Distribution-based measures of tumor heterogeneity are sensitive to mutation calling and lack strong clinical predictive power." (2018). Faculty Research 2018. 162.
JN would like to thank Joshy George, Francesca Menghi, and Ziming Zhao for helpful comments, Luc Morris for useful discussion and sharing of his previous results, Anna Lisa Lucido for manuscript edits, and Jane Cha for graphics design. JN and SN would like to thank Gaurav Kaushik for instructions on the Cancer Genome Cloud platform.
This open access article is licensed under a Creative Commons Attribution 4.0 International License.