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

Abstract

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.

CloneSig can jointly infer intra-tumor heterogeneity and mutational signature activity in bulk tumor sequencing data

“…In recent years, a large number of methods have been developed to unravel ITH in tumors [7, 18, 8, 22, 6], and have been applied to different cohorts, including the TCGA. Recent analyses illustrate limits encountered when applying those methods to bulk WES [23, 10], as the number of observed mutations is small, the variance in read counts can be high, and a unique sample may miss the heterogeneity of the tumor. As sequencing costs are continuously decreasing, WGS, multi-sample sequencing and single cell sequencing will constitute relevant alternatives and simplify the study of ITH.…”

A practical guide to cancer subclonal reconstruction from DNA sequencing

“…Both WGS and WES have limited sensitivity for studying intra-tumor heterogeneity in single samples 73,74 . Bulk sequencing of multiple tumor regions still misses many subclones, particularly when subclones are not evenly distributed across the tumor mass.…”

Quantification of tumor heterogeneity: from data acquisition to metric generation

“…Indeed, recent results showed that the MATH score is heavily affected by the choice of mutation-calling algorithm, and its robustness and prognostic significance have been questioned [102]. Another study [103] compared the accuracy and robustness of six clonality inference methods on WES TCGA data and found that the resulting estimates were highly confounded by tumor purity or mutational load. Similar observations have been made for tumor phylogenetic reconstruction and mutation ordering methods [104].…”