High degree of polyclonality hinders somatic mutation calling in lung brush samples of COPD cases and controls

Chronic obstructive pulmonary disease (COPD) is induced by cigarette smoking and characterized by inflammation of airway tissue. Since smokers with COPD have a higher risk of developing lung cancer than those without, we hypothesized that they carry more mutations in affected tissue. We called somatic mutations in airway brush samples from medium-coverage whole genome sequencing data from healthy never and ex-smokers (n = 8), as well as from ex-smokers with variable degrees of COPD (n = 4). Owing to the limited concordance of resulting calls between the applied tools we built a consensus, a strategy that was validated with high accuracy for cancer data. However, consensus calls showed little promise of representing true positives due to low mappability of corresponding sequence reads and high overlap with positions harbouring known genetic polymorphisms. A targeted re-sequencing approach suggested that only few mutations would survive stringent verification testing and that our data did not allow the inference of any difference in the mutational load of bronchial brush samples between former smoking COPD cases and controls. High polyclonality in airway brush samples renders medium-depth sequencing insufficient to provide the resolution to detect somatic mutations. Deep sequencing data of airway biopsies are needed to tackle the question.

SMuRF: portable and accurate ensemble prediction of somatic mutations

Summary Somatic Mutation calling method using a Random Forest (SMuRF) integrates predictions and auxiliary features from multiple somatic mutation callers using a supervised machine learning approach. SMuRF is trained on community-curated matched tumor and normal whole genome sequencing data. SMuRF predicts both SNVs and indels with high accuracy in genome or exome-level sequencing data. Furthermore, the method is robust across multiple tested cancer types and predicts low allele frequency variants with high accuracy. In contrast to existing ensemble-based somatic mutation calling approaches, SMuRF works out-of-the-box and is orders of magnitudes faster. Availability and implementation The method is implemented in R and available at https://github.com/skandlab/SMuRF. SMuRF operates as an add-on to the community-developed bcbio-nextgen somatic variant calling pipeline. Supplementary information Supplementary data are available at Bioinformatics online.

TNscope: Accurate Detection of Somatic Mutations with Haplotype-based Variant Candidate Detection and Machine Learning Filtering

Detection of somatic mutations in tumor samples is important in the clinic, where treatment decisions are increasingly based upon molecular diagnostics. However, accurate detection of these mutations is difficult, due in part to intra-tumor heterogeneity, contamination of the tumor sample with normal tissue and pervasive structural variation. Here, we describe Sentieon TNscope, a haplotype-based somatic variant caller with increased accuracy relative to existing methods. An early engineering version of TNscope was used in our submission to the most recent ICGC-DREAM Somatic Mutation calling challenge. In that challenge, TNscope is the leader in accuracy for SNVs, indels and SVs. To further improve variant calling accuracy, we combined the improvements in the variant caller with machine learning. We benchmarked TNscope using in-silico mixtures of well-characterized Genome in a Bottle (GIAB) samples. TNscope displays higher accuracy than the other benchmarked tools and the accuracy is substantially improved by the machine learning model.

Strategy for sensitive and specific somatic SNV/InDel calling: Leveraging different callers with in-house generated gold standard variant sets.

e13104 Background: Somatic mutation calling is critical for cancer genotyping. Although rapid development of mutation detection is witnessed with the maturity of NGS, the need for high sensitivity often results in compromised specificity and manual inspection. Here, we propose a methodology that leverages different variant callers to account for specificity without compromising sensitivity. Methods: We designed a cohort of training samples (n = 22), each with known set of SNVs/InDels that were discovered by KEW CANCERPLEX platform. We assessed the performance of four prevailing mutation callers that utilize different statistical approaches and therefore have different calls, using the training samples. We optimized the parameters of the four variant callers to detect all expected variants. We then examined the intersections of every combination of the four callers and identified the best one that eliminated the highest rate of false calls. A customized tool was developed for the intersection of component SNVs and InDels and the report differences among different callers. We also collected another set of validation samples (n = 28), each with true mutations that were both curated and orthogonally validated. We used this set to further test the efficacy of the refined calling strategy. Results: From the training samples, we chose the combination that provides highest sensitivity and specificity. This refined mutation calling strategy removed ~20% false SNV calls and ~50% false InDel calls in average for each sample. This result was further confirmed by the validation samples. Conclusions: The curated set of mutations from the genetic test platforms can provide valuable gold standard to test and tune mutation callers. The conclusions drawn from the curated variants are in line with the experimentally validated variants and showcase the validity of this practice, which we applied to demonstrate that the strategy of intersecting variants from optimized variant callers will generate mutation calls of higher specificity without compromising sensitivity. This methodology reduces the number of variants for curation and improves curation procedures and turn-around time.

A Comprehensive Assessment of Somatic Mutation Calling in Cancer Genomes

The emergence of next generation DNA sequencing technology is enabling high-resolution cancer genome analysis. Large-scale projects like the International Cancer Genome Consortium (ICGC) are systematically scanning cancer genomes to identify recurrent somatic mutations. Second generation DNA sequencing, however, is still an evolving technology and procedures, both experimental and analytical, are constantly changing. Thus the research community is still defining a set of best practices for cancer genome data analysis, with no single protocol emerging to fulfil this role. Here we describe an extensive benchmark exercise to identify and resolve issues of somatic mutation calling. Whole genome sequence datasets comprising tumor-normal pairs from two different types of cancer, chronic lymphocytic leukaemia and medulloblastoma, were shared within the ICGC and submissions of somatic mutation calls were compared to verified mutations and to each other. Varying strategies to call mutations, incomplete awareness of sources of artefacts, and even lack of agreement on what constitutes an artefact or real mutation manifested in widely varying mutation call rates and somewhat low concordance among submissions. We conclude that somatic mutation calling remains an unsolved problem. However, we have identified many issues that are easy to remedy that are presented here. Our study highlights critical issues that need to be addressed before this valuable technology can be routinely used to inform clinical decision-making.