The importance of proper bioinformatics analysis and clinical interpretation of tumor genomic profiling: a case study of undifferentiated sarcoma and a constitutional pathogenic BRCA2 mutation and an MLH1 variant of uncertain significance

A performance evaluation study: Variant annotation tools - The enigma of clinical next generation sequencing (NGS) based genetic testing

Dramatically expanding our ability for clinical genetic testing for inherited conditions and complex diseases such as cancer, next generation sequencing (NGS) technologies are allowing for rapid interrogation of thousands of genes and identification of millions of variants. Variant annotation, the process of assigning functional information to DNA variants based on the standardized Human Genome Variation Society (HGVS) nomenclature, is a fundamental challenge in the analysis of NGS data that has led to the development of many bioinformatic algorithms. In this study, we evaluated the performance of 3 variant annotation tools: Alamut® Batch, Ensembl Variant Effect Predictor (VEP), and ANNOVAR, benchmarked by a manually curated ground-truth set of 298 variants from the medical exome database at the Molecular Diagnostics Laboratory at Lurie Children's Hospital. Of the 3 tools, VEP produces the most accurate variant annotations (HGVS nomenclature for 297 of the 298 variants) due to usage of updated gene transcript versions within the algorithm. Alamut® Batch called 296 of the 298 variants correctly; strikingly, ANNOVAR exhibited the greatest number of discrepancies (20 of the 298 variants, 93.3% concordance with ground-truth set). Adoption of validated methods of variant annotation is critical in post-analytical phases of clinical testing.

Somatic mismatch repair testing in evaluation of Lynch syndrome: The gap between preferred and current practices

Lynch syndrome (LS) is a hereditary cancer predisposition syndrome primarily defined by increased risk for colorectal and uterine cancers. Individuals with germline pathogenic variants in the mismatch repair (MMR) genes (MLH1, MSH2/EPCAM, MSH6, and PMS2) are diagnosed with LS and recommended high-risk screening protocols to increase prevention and early detection of LS-related cancers. Tumor testing can help identify those at high risk for LS, but sometimes creates uncertainty with discordant screening and germline results, or unexplained mismatch repair deficiency (UMMRD). Somatic testing for MMR genes may help resolve UMMRD, potentially clarifying LS status and modifying cancer surveillance. However, guidelines for such testing are currently limited. This survey of cancer genetic counselors (GCs) aimed to examine current versus preferred ordering practices and interpretation of somatic MMR testing results in LS evaluation. Two hundred eligible GCs practicing in the United States and Canada were recruited from the National Society of Genetic Counselors. Participants answered questions regarding ordering practices, barriers to somatic MMR testing, theoretical scenarios, and desire for further guidelines. Statistical analysis was performed using chi-square, Fisher's exact, and Wilcoxon rank-sum tests, while themes were identified from free-text responses. Most respondents did not include somatic MMR testing in the LS work-up, despite three-quarters reporting they were 'somewhat comfortable' or 'extremely comfortable' with interpreting these results. Approximately half of participants indicated interest in ordering concurrent somatic MMR and germline testing for each of the four theoretical scenarios. Over three-quarters of individuals reported barriers to ordering somatic MMR testing, with cost and coordinating tissue samples most commonly cited. The frequently reported laboratory- and insurance-related barriers may contribute to the gap between preferred and current ordering practices for somatic MMR testing. Nearly all respondents endorsed additional guidelines for this testing, which could reduce barriers and inform screening recommendations for patients with UMMRD and their family members.

Precision Oncology in Sarcomas: Divide and Conquer

Sarcomas are a heterogeneous group of rare malignancies that exhibit remarkable heterogeneity, with more than 50 subtypes recognized. Advances in next-generation sequencing technology have resulted in the discovery of genetic events in these mesenchymal tumors, which in addition to enhancing understanding of the biology, have opened up avenues for molecularly targeted therapy and immunotherapy. This review focuses on how incorporation of next-generation sequencing has affected drug development in sarcomas and strategies for optimizing precision oncology for these rare cancers. In a significant percentage of soft tissue sarcomas, which represent up to 40% of all sarcomas, specific driver molecular abnormalities have been identified. The challenge to evaluate these mutations across rare cancer subtypes requires the careful characterization of these genetic alterations to further define compelling drivers with therapeutic implications. Novel models of clinical trial design also are needed. This shift would entail sustained efforts by the sarcoma community to move from one-size-fits-all trials, in which all sarcomas are treated similarly, to divide-and-conquer subtype-specific strategies.

A variant by any name: quantifying annotation discordance across tools and clinical databases

Background

Clinical genomic testing is dependent on the robust identification and reporting of variant-level information in relation to disease. With the shift to high-throughput sequencing, a major challenge for clinical diagnostics is the cross-identification of variants called on their genomic position to resources that rely on transcript- or protein-based descriptions.

Methods

We evaluated the accuracy of three tools (SnpEff, Variant Effect Predictor, and Variation Reporter) that generate transcript and protein-based variant nomenclature from genomic coordinates according to guidelines by the Human Genome Variation Society (HGVS). Our evaluation was based on transcript-controlled comparisons to a manually curated set of 126 test variants of various types drawn from data sources, each with HGVS-compliant transcript and protein descriptors. We further evaluated the concordance between annotations generated by Snpeff and Variant Effect Predictor and those in major germline and cancer databases: ClinVar and COSMIC, respectively.

Results

We find that there is substantial discordance between the annotation tools and databases in the description of insertions and/or deletions. Using our ground truth set of variants, constructed specifically to identify challenging events, accuracy was between 80 and 90% for coding and 50 and 70% for protein changes for 114 to 126 variants. Exact concordance for SNV syntax was over 99.5% between ClinVar and Variant Effect Predictor and SnpEff, but less than 90% for non-SNV variants. For COSMIC, exact concordance for coding and protein SNVs was between 65 and 88% and less than 15% for insertions. Across the tools and datasets, there was a wide range of different but equivalent expressions describing protein variants.

Conclusions

Our results reveal significant inconsistency in variant representation across tools and databases. While some of these syntax differences may be clear to a clinician, they can confound variant matching, an important step in variant classification. These results highlight the urgent need for the adoption and adherence to uniform standards in variant annotation, with consistent reporting on the genomic reference, to enable accurate and efficient data-driven clinical care.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-016-0396-7) contains supplementary material, which is available to authorized users.

Precision medicine in heritable cancer: when somatic tumour testing and germline mutations meet

Cancer is among the leading causes of death and disfigurement worldwide with an estimated global incidence of 14 million and ~8.2 million cancer-related deaths per annum. An estimated 5-10% of all cancers are hereditary, meaning a single gene mutation contributed to development of the cancer. In other words, inherited cancer has a worldwide incidence of ~1.4 million new cases per annum and a global prevalence of 300 million, and are often poorly recognised. The increase in genetic sequencing capability combined with the decrease in the cost of testing has altered both regulatory policy and clinical oncology practice Well-known examples of clinically important cancer susceptibility syndromes such as those caused by genetic mutations in highly penetrant genes such as BRCA1/2 hereditary breast-ovarian cancer syndrome genes have provided the framework for the practice of clinical cancer genetics. There is no question that these tests have provided clinical benefit to the patient and her/his family. However, with the expanding role of next generation sequencing in tumour profiling as well as in germline testing, clinicians are now faced with significant new challenges and potentially unexpected opportunities. Issues such as determining how best to deal with gene variants of uncertain clinical significance and the issue of incidental findings of hereditary cancer risk may be encountered during tumour genomic testing will require a concerted effort and dialogue on the part of the broad genomic community.