Cell Reports (Nov 2018)

Reliability of Whole-Exome Sequencing for Assessing Intratumor Genetic Heterogeneity

  • Weiwei Shi,
  • Charlotte K.Y. Ng,
  • Raymond S. Lim,
  • Tingting Jiang,
  • Sushant Kumar,
  • Xiaotong Li,
  • Vikram B. Wali,
  • Salvatore Piscuoglio,
  • Mark B. Gerstein,
  • Anees B. Chagpar,
  • Britta Weigelt,
  • Lajos Pusztai,
  • Jorge S. Reis-Filho,
  • Christos Hatzis

Journal volume & issue
Vol. 25, no. 6
pp. 1446 – 1457

Abstract

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Summary: Multi-region sequencing is used to detect intratumor genetic heterogeneity (ITGH) in tumors. To assess whether genuine ITGH can be distinguished from sequencing artifacts, we performed whole-exome sequencing (WES) on three anatomically distinct regions of the same tumor with technical replicates to estimate technical noise. Somatic variants were detected with three different WES pipelines and subsequently validated by high-depth amplicon sequencing. The cancer-only pipeline was unreliable, with about 69% of the identified somatic variants being false positive. Even with matched normal DNA for which 82% of the somatic variants were detected reliably, only 36%–78% were found consistently in technical replicate pairs. Overall, 34%–80% of the discordant somatic variants, which could be interpreted as ITGH, were found to constitute technical noise. Excluding mutations affecting low-mappability regions or occurring in certain mutational contexts was found to reduce artifacts, yet detection of subclonal mutations by WES in the absence of orthogonal validation remains unreliable. : Shi et al. report that standard coverage whole-exome sequencing and bioinformatics pipelines cannot discriminate between genuine intratumor genetic heterogeneity and sequencing artifacts. Although aggressive minimum depth filtering would not improve the false detection rate of subclonal mutations, excluding mutations in low-mappability regions or in certain mutational contexts could help. Keywords: massively parallel sequencing, whole-exome sequencing, somatic mutations, intratumor genetic heterogeneity, multi-region profiling, breast cancer, mutational signatures, mappability, subclonal