Caleb F. Davis

To truly achieve personalized medicine in oncology, it is critical to catalog and curate cancer sequence variants for their clinical relevance. The Somatic Working Group (WG) of the Clinical Genome Resource (ClinGen), in cooperation with ClinVar and multiple cancer variant curation stakeholders, has developed a consensus set of minimal variant level data (MVLD). MVLD is a framework of standardized data elements to curate cancer variants for clinical utility. With implementation of MVLD standards, and in a working partnership with ClinVar, we aim to streamline the somatic variant curation efforts in the community and reduce redundancy and time burden for the interpretation of cancer variants in clinical practice. We developed MVLD through a consensus approach by i) reviewing clinical actionability interpretations from institutions participating in the WG, ii) conducting extensive literature search of clinical somatic interpretation schemas, and iii) survey of cancer variant web portals. A forthcoming guideline on cancer variant interpretation, from the Association of Molecular Pathology (AMP), can be incorporated into MVLD. Along with harmonizing standardized terminology for allele interpretive and descriptive fields that are collected by many databases, the MVLD includes unique fields for cancer variants such as Biomarker Class, Therapeutic Context and Effect. In addition, MVLD includes recommendations for controlled semantics and ontologies. The Somatic WG is collaborating with ClinVar to evaluate MVLD use for somatic variant submissions. ClinVar is an open and centralized repository where sequencing laboratories can report summary-level variant data with clinical significance, and ClinVar accepts cancer variant data. We expect the use of the MVLD to streamline clinical interpretation of cancer variants, enhance interoperability among multiple redundant curation efforts, and increase submission of somatic variants to ClinVar, all of which will enhance translation to clinical oncology practice.

Genomic deletions, inversions, and other rearrangements known collectively as structural variations (SVs) are implicated in many human disorders. Technologies for sequencing DNA provide a potentially rich source of information in which to detect breakpoints of structural variations at base-pair resolution. However, accurate prediction of SVs remains challenging, and existing informatics tools predict rearrangements with significant rates of false positives or negatives. To address this challenge, we developed ‘Structural Variation detection by STAck and Tail’ (SV-STAT) which implements a novel scoring metric. The software uses this statistic to quantify evidence for structural variation in genomic regions suspected of harboring rearrangements. To demonstrate SV-STAT, we used targeted and genome-wide approaches. First, we applied a custom capture array followed by Roche/454 and SV-STAT to three pediatric B-lineage acute lymphoblastic leukemias, identifying five structural variations joining known and novel breakpoint regions. Next, we detected SVs genome-wide in paired-end Illumina data collected from additional tumor samples. SV-STAT showed predictive accuracy as high as or higher than leading alternatives. The software is freely available under the terms of the GNU General Public License version 3 at https://gitorious.org/svstat/svstat . SV-STAT works across multiple sequencing chemistries, paired and single-end technologies, targeted or whole-genome strategies, and it complements existing SV-detection software. The method is a significant advance towards accurate detection and genotyping of genomic rearrangements from DNA sequencing data.

Characterizing large genomic variants is essential to expanding the research and clinical applications of genome sequencing. While multiple data types and methods are available to detect these structural variants (SVs), they remain less characterized than smaller variants because of SV diversity, complexity, and size. These challenges are exacerbated by the experimental and computational demands of SV analysis. Here, we characterize the SV content of a personal genome with Parliament, a publicly available consensus SV-calling infrastructure that merges multiple data types and SV detection methods. We demonstrate Parliament’s efficacy via integrated analyses of data from whole-genome array comparative genomic hybridization, short-read next-generation sequencing, long-read (Pacific BioSciences RSII), long-insert (Illumina Nextera), and whole-genome architecture (BioNano Irys) data from the personal genome of a single subject (HS1011). From this genome, Parliament identified 31,007 genomic loci between 100 bp and 1 Mbp that are inconsistent with the hg19 reference assembly. Of these loci, 9,777 are supported as putative SVs by hybrid local assembly, long-read PacBio data, or multi-source heuristics. These SVs span 59 Mbp of the reference genome (1.8%) and include 3,801 events identified only with long-read data. The HS1011 data and complete Parliament infrastructure, including a BAM-to-SV workflow, are available on the cloud-based service DNAnexus. HS1011 SV analysis reveals the limits and advantages of multiple sequencing technologies, specifically the impact of long-read SV discovery. With the full Parliament infrastructure, the HS1011 data constitute a public resource for novel SV discovery, software calibration, and personal genome structural variation analysis.

We report a case of altered mental status secondary to acute Toxoplasma Gondii encephalitis. The patient had no medical or surgical history and presented with acute onset of lethargy with no clear precipitant. A physical exam revealed no focal neurological deficits and a subsequent medicalworkup revealed multiple intracranial lesions with a biopsy confirming the diagnosis of Toxoplasma Gondii encephalitis in the setting of newly diagnosed human immunodeficiency virus (HIV). A literature review revealed that this is a unique case of toxoplasmic encephalopathy in the United States in a previously undiagnosed HIV positive patient presenting to an emergency department. [West J Emerg Med. 2012;13(6):488-490]

Gliomas, which generally have a poor prognosis, are the most common primary malignant brain tumors in adults. Recent genome-wide association studies have shown that inherited susceptibility plays a role in the development of glioma. Although first-degree relatives of patients exhibit a two-fold increased risk of glioma, the search for susceptibility loci in familial forms of the disease has been challenging because the disease is relatively rare, fatal, and heterogeneous, making it difficult to collect sufficient biosamples from families for statistical power. To address this challenge, the Genetic Epidemiology of Glioma International Consortium (Gliogene) was formed to collect DNA samples from families with two or more cases of histologically confirmed glioma. In this study, we present results obtained from 46 U.S. families in which multipoint linkage analyses were undertaken using nonparametric (model-free) methods. After removal of high linkage disequilibrium single-nucleotide polymorphism, we obtained a maximum nonparametric linkage score (NPL) of 3.39 (P = 0.0005) at 17q12-21.32 and the Z-score of 4.20 (P = 0.000007). To replicate our findings, we genotyped 29 independent U.S. families and obtained a maximum NPL score of 1.26 (P = 0.008) and the Z-score of 1.47 (P = 0.035). Accounting for the genetic heterogeneity using the ordered subset analysis approach, the combined analyses of 75 families resulted in a maximum NPL score of 3.81 (P = 0.00001). The genomic regions we have implicated in this study may offer novel insights into glioma susceptibility, focusing future work to identify genes that cause familial glioma. Cancer Res; 71(24); 7568–75. ©2011 AACR.

The identification of copy number aberration in the human genome is an important area in cancer research. We develop a model for determining genomic copy numbers using high-density single nucleotide polymorphism genotyping microarrays. The method is based on a Bayesian spatial normal mixture model with an unknown number of components corresponding to true copy numbers. A reversible jump Markov chain Monte Carlo algorithm is used to implement the model and perform posterior inference.