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Distribution-based measures of tumor heterogeneity are sensitive to mutation calling and lack strong clinical predictive power

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 i...

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Autores principales: Noorbakhsh, Javad, Kim, Hyunsoo, Namburi, Sandeep, Chuang, Jeffrey H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6065409/
https://www.ncbi.nlm.nih.gov/pubmed/30061557
http://dx.doi.org/10.1038/s41598-018-29154-7
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author Noorbakhsh, Javad
Kim, Hyunsoo
Namburi, Sandeep
Chuang, Jeffrey H.
author_facet Noorbakhsh, Javad
Kim, Hyunsoo
Namburi, Sandeep
Chuang, Jeffrey H.
author_sort Noorbakhsh, Javad
collection PubMed
description 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.
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spelling pubmed-60654092018-08-06 Distribution-based measures of tumor heterogeneity are sensitive to mutation calling and lack strong clinical predictive power Noorbakhsh, Javad Kim, Hyunsoo Namburi, Sandeep Chuang, Jeffrey H. Sci Rep Article 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. Nature Publishing Group UK 2018-07-30 /pmc/articles/PMC6065409/ /pubmed/30061557 http://dx.doi.org/10.1038/s41598-018-29154-7 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Noorbakhsh, Javad
Kim, Hyunsoo
Namburi, Sandeep
Chuang, Jeffrey H.
Distribution-based measures of tumor heterogeneity are sensitive to mutation calling and lack strong clinical predictive power
title Distribution-based measures of tumor heterogeneity are sensitive to mutation calling and lack strong clinical predictive power
title_full Distribution-based measures of tumor heterogeneity are sensitive to mutation calling and lack strong clinical predictive power
title_fullStr Distribution-based measures of tumor heterogeneity are sensitive to mutation calling and lack strong clinical predictive power
title_full_unstemmed Distribution-based measures of tumor heterogeneity are sensitive to mutation calling and lack strong clinical predictive power
title_short Distribution-based measures of tumor heterogeneity are sensitive to mutation calling and lack strong clinical predictive power
title_sort distribution-based measures of tumor heterogeneity are sensitive to mutation calling and lack strong clinical predictive power
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6065409/
https://www.ncbi.nlm.nih.gov/pubmed/30061557
http://dx.doi.org/10.1038/s41598-018-29154-7
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