Dataset Information

Discriminating somatic and germline mutations in tumor DNA samples without matching normals.

ABSTRACT: Tumor analyses commonly employ a correction with a matched normal (MN), a sample from healthy tissue of the same individual, in order to distinguish germline mutations from somatic mutations. Since the majority of variants found in an individual are thought to be common within the population, we constructed a set of 931 samples from healthy, unrelated individuals, originating from two different sequencing platforms, to serve as a virtual normal (VN) in the absence of such an associated normal sample. Our approach removed (1) >96% of the germline variants also removed by the MN sample and (2) a large number (2%-8%) of additional variants not corrected for by the associated normal. The combination of the VN with the MN improved the correction for polymorphisms significantly, with up to ?30% compared with MN and ?15% compared with VN only. We determined the number of unrelated genomes needed in order to correct at least as efficiently as the MN is about 200 for structural variations (SVs) and about 400 for single-nucleotide variants (SNVs) and indels. In addition, we propose that the removal of common variants with purely position-based methods is inaccurate and incurs additional false-positive somatic variants, and more sophisticated algorithms, which are capable of leveraging information about the area surrounding variants, are needed for optimal accuracy. Our VN correction method can be used to analyze any list of variants, regardless of sequencing platform of origin. This VN methodology is available for use on our public Galaxy server.

SUBMITTER: Hiltemann S

PROVIDER: S-EPMC4561496 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

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Discriminating somatic and germline mutations in tumor DNA samples without matching normals.

Hiltemann Saskia S Jenster Guido G Trapman Jan J van der Spek Peter P Stubbs Andrew A

Genome research 20150724 9

Tumor analyses commonly employ a correction with a matched normal (MN), a sample from healthy tissue of the same individual, in order to distinguish germline mutations from somatic mutations. Since the majority of variants found in an individual are thought to be common within the population, we constructed a set of 931 samples from healthy, unrelated individuals, originating from two different sequencing platforms, to serve as a virtual normal (VN) in the absence of such an associated normal sa ...[more]

PMID: 26209359

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Project description:BackgroundObservations of recurrent somatic mutations in tumors have led to identification and definition of signaling and other pathways that are important for cancer progression and therapeutic targeting. As tumor cells contain both an individual's inherited genetic variants and somatic mutations, challenges arise in distinguishing these events in massively parallel sequencing datasets. Typically, both a tumor sample and a "normal" sample from the same individual are sequenced and compared; variants observed only in the tumor are considered to be somatic mutations. However, this approach requires two samples for each individual.ResultsWe evaluate a method of detecting somatic mutations in tumor samples for which only a subset of normal samples are available. We describe tuning of the method for detection of mutations in tumors, filtering to remove inherited variants, and comparison of detected mutations to several matched tumor/normal analysis methods. Filtering steps include the use of population variation datasets to remove inherited variants as well a subset of normal samples to remove technical artifacts. We then directly compare mutation detection with tumor-only and tumor-normal approaches using the same sets of samples. Comparisons are performed using an internal targeted gene sequencing dataset (n = 3380) as well as whole exome sequencing data from The Cancer Genome Atlas project (n = 250). Tumor-only mutation detection shows similar recall (43-60%) but lesser precision (20-21%) to current matched tumor/normal approaches (recall 43-73%, precision 30-82%) when compared to a "gold-standard" tumor/normal approach. The inclusion of a small pool of normal samples improves precision, although many variants are still uniquely detected in the tumor-only analysis.ConclusionsA detailed method for somatic mutation detection without matched normal samples enables study of larger numbers of tumor samples, as well as tumor samples for which a matched normal is not available. As sensitivity/recall is similar to tumor/normal mutation detection but precision is lower, tumor-only detection is more appropriate for classification of samples based on known mutations. Although matched tumor-normal analysis is preferred due to higher precision, we demonstrate that mutation detection without matched normal samples is possible for certain applications.

Project description:BackgroundGermline mutations in the ataxia telangiectasia mutated (ATM) gene occur in 0.5-1% of the overall population and are associated with tumour predisposition. The clinical and pathological features of ATM-mutated prostate cancer (PC) are poorly defined but have been associated with lethal PC.ObjectiveTo report on the clinical characteristics including family history and clinical outcomes of a cohort of patients with advanced metastatic castration-resistant PC (CRPC) who were found to have germline ATM mutations after mutation detection by initial tumour DNA sequencing.Design setting and participantsWe acquired germline ATM mutation data by saliva next-generation sequencing from patients with ATM mutations in PC biopsies sequenced between January 2014 and January 2022. Demographics, family history, and clinical data were collected retrospectively.Outcome measurements and statistical analysisOutcome endpoints were based on overall survival (OS) and time from diagnosis to CRPC. Data were analysed using R version 3.6.2 (R Foundation for Statistical Computing, Vienna, Austria).Results and limitationsOverall, seven patients (n = 7/1217; 0.6%) had germline ATM mutations detected, with five of them having a family history of malignancies, including breast, prostate, pancreas, and gastric cancer; leukaemia; and lymphoma. Two patients had concomitant somatic mutations in tumour biopsies in genes other than ATM, while two patients were found to carry more than one ATM pathogenic mutation. Five tumours in germline ATM variant carriers had loss of ATM by immunohistochemistry. The median OS from diagnosis was 7.1 yr (range 2.9-14 yr) and the median OS from CRPC was 5.3 yr (range 2.2-7.3 yr). When comparing these data with PC patients sequenced by The Cancer Genome Atlas, we found that the spatial localisation of mutations was similar, with distribution of alterations occurring on similar positions in the ATM gene. Interestingly, these include a mutation within the FRAP-ATM-TRRAP (FAT) domain, suggesting that this represents a mutational hotspot for ATM.ConclusionsGermline ATM mutations are rare in patients with lethal PC but occur at mutational hotspots; further research is warranted to better characterise the family histories of these men and PC clinical course.Patient summaryIn this report, we studied the clinical and pathological features of advanced prostate cancers associated with germline mutations in the ATM gene. We found that most patients had a strong family history of cancer and that this mutation might predict the course of these prostate cancers, as well as response to specific treatments.

Dataset Information

Discriminating somatic and germline mutations in tumor DNA samples without matching normals.

Publications

Discriminating somatic and germline mutations in tumor DNA samples without matching normals.

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