A method to reduce ancestry related germline false positives in tumor only somatic variant calling

Rebecca F Halperin,Megan Russell,Evelyn Jaigge,John D Carpten,David W Craig,Irene Cherni,Daniel Enriquez,Jonathan Keats,Lisa A Newman,Jessica Aldrich,Ana Claasen,Max S Wicha,Seungchan Kim,Winnie S Liang,Zarko Manojlovic,Joseph Oppong,Sara Byron,Baffour Awuah

doi:10.1186/s12920-017-0296-8

Abstract

BackgroundSignificant clinical and research applications are driving large scale adoption of individualized tumor sequencing in cancer in order to identify tumors-specific mutations. When a matched germline sample is available, somatic mutations may be identified using comparative callers. However, matched germline samples are frequently not available such as with archival tissues, which makes it difficult to distinguish somatic from germline variants. While population databases may be used to filter out known germline variants, recent studies have shown private germline variants result in an inflated false positive rate in unmatched tumor samples, and the number germline false positives in an individual may be related to ancestry.MethodsFirst, we examined the relationship between the germline false positives and ancestry. Then we developed and implemented a tumor only caller (LumosVar) that leverages differences in allelic frequency between somatic and germline variants in impure tumors. We used simulated data to systematically examine how copy number alterations, tumor purity, and sequencing depth should affect the sensitivity of our caller. Finally, we evaluated the caller on real data.ResultsWe find the germline false-positive rate is significantly higher for individuals of non-European Ancestry largely due to the limited diversity in public polymorphism databases and due to population-specific characteristics such as admixture or recent expansions. Our Bayesian tumor only caller (LumosVar) is able to greatly reduce false positives from private germline variants, and our sensitivity is similar to predictions based on simulated data.ConclusionsTaken together, our results suggest that studies of individuals of non-European ancestry would most benefit from our approach. However, high sensitivity requires sufficiently impure tumors and adequate sequencing depth. Even in impure tumors, there are copy number alterations that result in germline and somatic variants having similar allele frequencies, limiting the sensitivity of the approach. We believe our approach could greatly improve the analysis of archival samples in a research setting where the normal is not available.

Highlights

Significant clinical and research applications are driving large scale adoption of individualized tumor sequencing in cancer in order to identify tumors-specific mutations
We explicitly examine how the number of private germline variants varies with ancestry and present a strategy to reduce false positives due to private germline variants in tumor-only somatic mutation calling
Current approaches for filtering out germline variants from potential somatic variants typically include comparison to databases containing large numbers of germline variants

Summary

Introduction

Significant clinical and research applications are driving large scale adoption of individualized tumor sequencing in cancer in order to identify tumors-specific mutations. When a matched germline sample is available, somatic mutations may be identified using comparative callers. Matched germline samples are frequently not available such as with archival tissues, which makes it difficult to distinguish somatic from germline variants. Generation sequencing of tumours is widely used both for discovery of biologically important somatic variants as well as for personalizing treatment based on clinically relevant somatic variants. In both cases, accurate identification of somatic variants is crucial. We have implemented a Bayesian tumour-only somatic variant caller, LumosVar, that leverages both prior knowledge of population frequencies of germline and cancer mutations, as well as the observed variant allele frequencies. We will evaluate how the tumour content, copy number alterations, read depth, and ancestry of a sample effect the ability to detect somatic variants in an unmatched tumour sample

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