Comprehensive benchmarking of SNV callers for highly admixed tumor data
Precision medicine attempts to individualize cancer therapy by matching tumor-specific genetic changes with effective targeted therapies. A crucial first step in this process is the reliable identification of cancer-relevant variants, which is considerably complicated by the impurity and heterogenei...
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description | Precision medicine attempts to individualize cancer therapy by matching tumor-specific genetic changes with effective targeted therapies. A crucial first step in this process is the reliable identification of cancer-relevant variants, which is considerably complicated by the impurity and heterogeneity of clinical tumor samples. We compared the impact of admixture of non-cancerous cells and low somatic allele frequencies on the sensitivity and precision of 19 state-of-the-art SNV callers. We studied both whole exome and targeted gene panel data and up to 13 distinct parameter configurations for each tool. We found vast differences among callers. Based on our comprehensive analyses we recommend joint tumor-normal calling with MuTect, EBCall or Strelka for whole exome somatic variant calling, and HaplotypeCaller or FreeBayes for whole exome germline calling. For targeted gene panel data on a single tumor sample, LoFreqStar performed best. We further found that tumor impurity and admixture had a negative impact on precision, and in particular, sensitivity in whole exome experiments. At admixture levels of 60% to 90% sometimes seen in pathological biopsies, sensitivity dropped significantly, even when variants were originally present in the tumor at 100% allele frequency. Sensitivity to low-frequency SNVs improved with targeted panel data, but whole exome data allowed more efficient identification of germline variants. Effective somatic variant calling requires high-quality pathological samples with minimal admixture, a consciously selected sequencing strategy, and the appropriate variant calling tool with settings optimized for the chosen type of data. |
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A crucial first step in this process is the reliable identification of cancer-relevant variants, which is considerably complicated by the impurity and heterogeneity of clinical tumor samples. We compared the impact of admixture of non-cancerous cells and low somatic allele frequencies on the sensitivity and precision of 19 state-of-the-art SNV callers. We studied both whole exome and targeted gene panel data and up to 13 distinct parameter configurations for each tool. We found vast differences among callers. Based on our comprehensive analyses we recommend joint tumor-normal calling with MuTect, EBCall or Strelka for whole exome somatic variant calling, and HaplotypeCaller or FreeBayes for whole exome germline calling. For targeted gene panel data on a single tumor sample, LoFreqStar performed best. We further found that tumor impurity and admixture had a negative impact on precision, and in particular, sensitivity in whole exome experiments. At admixture levels of 60% to 90% sometimes seen in pathological biopsies, sensitivity dropped significantly, even when variants were originally present in the tumor at 100% allele frequency. Sensitivity to low-frequency SNVs improved with targeted panel data, but whole exome data allowed more efficient identification of germline variants. Effective somatic variant calling requires high-quality pathological samples with minimal admixture, a consciously selected sequencing strategy, and the appropriate variant calling tool with settings optimized for the chosen type of data.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0186175</identifier><identifier>PMID: 29020110</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Alleles ; Benchmarking ; Biology and Life Sciences ; Breast cancer ; Cancer ; Cancer therapies ; Care and treatment ; Computer Simulation ; Databases, Genetic ; Datasets ; Evolution ; Exome - genetics ; Gene frequency ; Gene Frequency - genetics ; Genetic aspects ; Genomes ; Germ Cells - metabolism ; Humans ; Medicine ; Medicine and Health Sciences ; Mutation ; Neoplasms - genetics ; Physical Sciences ; Polymorphism, Single Nucleotide - genetics ; Precision medicine ; Reference Standards ; Reproducibility of Results ; Research and Analysis Methods ; Sensitivity ; Sequence Alignment ; Studies ; Tumors</subject><ispartof>PloS one, 2017-10, Vol.12 (10), p.e0186175-e0186175</ispartof><rights>COPYRIGHT 2017 Public Library of Science</rights><rights>2017 Bohnert et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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A crucial first step in this process is the reliable identification of cancer-relevant variants, which is considerably complicated by the impurity and heterogeneity of clinical tumor samples. We compared the impact of admixture of non-cancerous cells and low somatic allele frequencies on the sensitivity and precision of 19 state-of-the-art SNV callers. We studied both whole exome and targeted gene panel data and up to 13 distinct parameter configurations for each tool. We found vast differences among callers. Based on our comprehensive analyses we recommend joint tumor-normal calling with MuTect, EBCall or Strelka for whole exome somatic variant calling, and HaplotypeCaller or FreeBayes for whole exome germline calling. For targeted gene panel data on a single tumor sample, LoFreqStar performed best. We further found that tumor impurity and admixture had a negative impact on precision, and in particular, sensitivity in whole exome experiments. At admixture levels of 60% to 90% sometimes seen in pathological biopsies, sensitivity dropped significantly, even when variants were originally present in the tumor at 100% allele frequency. Sensitivity to low-frequency SNVs improved with targeted panel data, but whole exome data allowed more efficient identification of germline variants. Effective somatic variant calling requires high-quality pathological samples with minimal admixture, a consciously selected sequencing strategy, and the appropriate variant calling tool with settings optimized for the chosen type of data.</description><subject>Algorithms</subject><subject>Alleles</subject><subject>Benchmarking</subject><subject>Biology and Life Sciences</subject><subject>Breast cancer</subject><subject>Cancer</subject><subject>Cancer therapies</subject><subject>Care and treatment</subject><subject>Computer Simulation</subject><subject>Databases, Genetic</subject><subject>Datasets</subject><subject>Evolution</subject><subject>Exome - genetics</subject><subject>Gene frequency</subject><subject>Gene Frequency - genetics</subject><subject>Genetic aspects</subject><subject>Genomes</subject><subject>Germ Cells - metabolism</subject><subject>Humans</subject><subject>Medicine</subject><subject>Medicine and Health Sciences</subject><subject>Mutation</subject><subject>Neoplasms - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bohnert, Regina</au><au>Vivas, Sonia</au><au>Jansen, Gunther</au><au>Galli, Alvaro</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Comprehensive benchmarking of SNV callers for highly admixed tumor data</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2017-10-11</date><risdate>2017</risdate><volume>12</volume><issue>10</issue><spage>e0186175</spage><epage>e0186175</epage><pages>e0186175-e0186175</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Precision medicine attempts to individualize cancer therapy by matching tumor-specific genetic changes with effective targeted therapies. A crucial first step in this process is the reliable identification of cancer-relevant variants, which is considerably complicated by the impurity and heterogeneity of clinical tumor samples. We compared the impact of admixture of non-cancerous cells and low somatic allele frequencies on the sensitivity and precision of 19 state-of-the-art SNV callers. We studied both whole exome and targeted gene panel data and up to 13 distinct parameter configurations for each tool. We found vast differences among callers. Based on our comprehensive analyses we recommend joint tumor-normal calling with MuTect, EBCall or Strelka for whole exome somatic variant calling, and HaplotypeCaller or FreeBayes for whole exome germline calling. For targeted gene panel data on a single tumor sample, LoFreqStar performed best. We further found that tumor impurity and admixture had a negative impact on precision, and in particular, sensitivity in whole exome experiments. At admixture levels of 60% to 90% sometimes seen in pathological biopsies, sensitivity dropped significantly, even when variants were originally present in the tumor at 100% allele frequency. Sensitivity to low-frequency SNVs improved with targeted panel data, but whole exome data allowed more efficient identification of germline variants. Effective somatic variant calling requires high-quality pathological samples with minimal admixture, a consciously selected sequencing strategy, and the appropriate variant calling tool with settings optimized for the chosen type of data.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>29020110</pmid><doi>10.1371/journal.pone.0186175</doi><tpages>e0186175</tpages><orcidid>https://orcid.org/0000-0002-8792-9407</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Alleles Benchmarking Biology and Life Sciences Breast cancer Cancer Cancer therapies Care and treatment Computer Simulation Databases, Genetic Datasets Evolution Exome - genetics Gene frequency Gene Frequency - genetics Genetic aspects Genomes Germ Cells - metabolism Humans Medicine Medicine and Health Sciences Mutation Neoplasms - genetics Physical Sciences Polymorphism, Single Nucleotide - genetics Precision medicine Reference Standards Reproducibility of Results Research and Analysis Methods Sensitivity Sequence Alignment Studies Tumors |
title | Comprehensive benchmarking of SNV callers for highly admixed tumor data |
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