Assessing reliability of intra-tumor heterogeneity estimates from single sample whole exome sequencing data

Autoři: Judith Abécassis aff001;  Anne-Sophie Hamy aff001;  Cécile Laurent aff001;  Benjamin Sadacca aff001;  Hélène Bonsang-Kitzis aff001;  Fabien Reyal aff001;  Jean-Philippe Vert aff002
Působiště autorů: Institut Curie, PSL Research University, Translational Research Department, INSERM, U932 Immunity and Cancer, Residual Tumor & Response to Treatment Laboratory (RT2Lab), Paris, France aff001;  MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France aff002;  Institut Curie, PSL Research University, INSERM, U900, Paris, France aff003;  Institut de Mathématiques de Toulouse, UMR5219 Université de Toulouse, CNRS UPS IMT, Toulouse, France aff004;  Department of Surgery, Institut Curie, Paris, France aff005;  Google Brain, Paris, France aff006
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0224143


Tumors are made of evolving and heterogeneous populations of cells which arise from successive appearance and expansion of subclonal populations, following acquisition of mutations conferring them a selective advantage. Those subclonal populations can be sensitive or resistant to different treatments, and provide information about tumor aetiology and future evolution. Hence, it is important to be able to assess the level of heterogeneity of tumors with high reliability for clinical applications. In the past few years, a large number of methods have been proposed to estimate intra-tumor heterogeneity from whole exome sequencing (WES) data, but the accuracy and robustness of these methods on real data remains elusive. Here we systematically apply and compare 6 computational methods to estimate tumor heterogeneity on 1,697 WES samples from the cancer genome atlas (TCGA) covering 3 cancer types (breast invasive carcinoma, bladder urothelial carcinoma, and head and neck squamous cell carcinoma), and two distinct input mutation sets. We observe significant differences between the estimates produced by different methods, and identify several likely confounding factors in heterogeneity assessment for the different methods. We further show that the prognostic value of tumor heterogeneity for survival prediction is limited in those datasets, and find no evidence that it improves over prognosis based on other clinical variables. In conclusion, heterogeneity inference from WES data on a single sample, and its use in cancer prognosis, should be considered with caution. Other approaches to assess intra-tumoral heterogeneity such as those based on multiple samples may be preferable for clinical applications.

Klíčová slova:

Carcinomas – Computational pipelines – Genetic causes of cancer – Head and neck squamous cell carcinoma – Mutation detection – Prognosis – Somatic mutation


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2019 Číslo 11