The Return of Actionable Variants Empirical (RAVE) Study,a Mayo Clinic Genomic Medicine Implementation Study: Design and Initial Results

Objectives

To identify clinically actionable genetic variants from targeted sequencing of 68 disease-related genes, estimate their penetrance, and assess the impact of disclosing results to participants and providers.

BAP1 mutations define a homogeneous subgroup of hepatocellular carcinoma with fibrolamellar-like features and activated PKA

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Optimized variant calling for estimating kinship

One of the fundamental goals of forensic genetics is sample attribution, i.e., whether an item of evidence can be associated with some person or persons. The most common scenario involves a direct comparison, e.g., between DNA profiles from an evidentiary item and a sample collected from a person of interest. Less common is an indirect comparison in which kinship is used to potentially identify the source of the evidence. Because of the sheer amount of information lost in the hereditary process for comparison purposes, sampling a limited set of loci may not provide enough resolution to accurately resolve a relationship. Instead, whole genome techniques can sample the entirety of the genome or a sufficiently large portion of the genome and as such they may effect better relationship determinations. While relatively common in other areas of study, whole genome techniques have only begun to be explored in the forensic sciences. As such, bioinformatic pipelines are introduced for estimating kinship by massively parallel sequencing of whole genomes using approaches adapted from the medical and population genomic literature. The pipelines are designed to characterize a person’s entire genome, not just some set of targeted markers. Two different variant callers are considered, contrasting a classical variant calling algorithm (BCFtools) to a more modern deep convolution neural network (DeepVariant). Two different bioinformatic pipelines specific to each variant caller are introduced and evaluated in a titration series. Filters and thresholds are then optimized specifically for the purposes of estimating kinship as determined by the KING-robust algorithm. With the appropriate filtering and thresholds in place both tools perform similarly, with DeepVariant tending to produce more accurate genotypes, though the resultant types of inaccuracies tended to produce slightly less accurate overall estimates of relatedness     

Genomic,pathological, and clinical heterogeneity as drivers of personalized medicine in prostate cancer

Prostate cancer (CaP) is the most commonly diagnosed malignancy in men in the Western world. In North America, more than 275,000 men are diagnosed annually, whereby approximately 1 in 6 men will be diagnosed with CaP in their lifetime, and 1 in 34 men will die from castration-resistant metastatic disease. Unfortunately, current clinical prognostic factors explain only a proportion of the observed variation in clinical outcome from patient to patient. Furthermore, overtreatment of indolent and low-risk cancers leads to inappropriate morbidity following radiotherapy or surgery. As such, better predictors of individualized prognosis and treatment response are urgently needed to triage patients to customized and intensified CaP treatment. Recent developments in next-generation sequencing have made it possible to identify prognostic and predictive signatures based on genomic profiles. We discuss the genetic basis of CaP progression from localized to systemic disease (e.g., point mutations, copy-number alterations, and structural variants) in relation with unique features of CaP biology, including intraprostatic and interprostatic heterogeneity, multifocality and multiclonality, TMPRSS2:ERG, and other ETS-family gene fusions. Finally, we focus on the use of genomic markers as prognostic factors for local failure and for systemic disease, as novel risk-stratification tools, in triaging patients to existing treatment options, and ultimately the potential of genomics for the identification of molecular targets for therapy of CaP.