Biology subjects

Griffith, O. L.

Publications and source records attributed to Griffith, O. L..

7 recordsLinked to original sources

bioRxiv2018

Spontaneous aggressive ERα+ mammary tumor model is driven by Kras activation

The NRL-PRL murine model, defined by mammary-selective transgenic rat prolactin ligand rPrl expression, establishes spontaneous ER+ mammary tumors, mimicking the association between elevated prolactin (PRL) and risk for development of ER+ breast cancer in postmenopausal women. Whole genome and exome sequencing in a discovery cohort (n=5) of end stage tumors revealed canonical activating mutations and copy number amplifications of Kras. The frequent mutations in this pathway were validated in an extension cohort, identifying activating Ras alterations in 79% (23/29) of tumors. Transcriptome analyses over the course of oncogenesis revealed marked alterations associated with Ras activity in established tumors, compared to preneoplastic tissues, in cell-intrinsic processes associated with mitosis, cell adhesion and invasion, as well as in the tumor microenvironment, including immune activity. These genomic analyses suggest that PRL induces a selective bottleneck for spontaneous Ras-driven tumors which may model a subset of aggressive clinical ER+ breast cancers.

cancer biology↗

bioRxiv2018

RegTools: Integrated analysis of genomic and transcriptomic data for discovery of splicing variants in cancer

Somatic mutations within non-coding regions and even exons may have unidentified regulatory consequences that are often overlooked in analysis workflows. Here we present RegTools (www.regtools.org), a computationally efficient, free, and open-source software package designed to integrate somatic variants from genomic data with splice junctions from bulk or single cell transcriptomic data to identify variants that may cause aberrant splicing. RegTools was applied to over 9,000 tumor samples with both tumor DNA and RNA sequence data. We discovered 235,778 events where a splice-associated variant significantly increased the splicing of a particular junction, across 158,200 unique variants and 131,212 unique junctions. To characterize these somatic variants and their associated splice isoforms, we annotated them with the Variant Effect Predictor (VEP), SpliceAI, and Genotype-Tissue Expression (GTEx) junction counts and compared our results to other tools that integrate genomic and transcriptomic data. While many events were corroborated by the aforementioned tools, the flexibility of RegTools also allowed us to identify novel splice-associated variants and previously unreported patterns of splicing disruption in known cancer drivers, such as TP53, CDKN2A, and B2M, as well as in genes not previously considered cancer-relevant.

bioinformatics↗

bioRxiv2018

A harmonized meta-knowledgebase of clinical interpretations of cancer genomic variants

Precision oncology relies on the accurate discovery and interpretation of genomic variants to enable individualized diagnosis, prognosis, and therapy selection. We found that knowledgebases containing clinical interpretations of somatic cancer variants are highly disparate in interpretation content, structure, and supporting primary literature, impeding consensus when evaluating variants and their relevance in a clinical setting. With the cooperation of experts of the Global Alliance for Genomics and Health (GA4GH) and six prominent cancer variant knowledgebases, we developed a framework for aggregating and harmonizing variant interpretations to produce a meta-knowledgebase of 12,856 aggregate interpretations covering 3,437 unique variants in 415 genes, 357 diseases, and 791 drugs. We demonstrated large gains in overlap between resources across variants, diseases, and drugs as a result of this harmonization. We subsequently demonstrated improved matching between a patient cohort and harmonized interpretations of potential clinical significance, observing an increase from an average of 33% per individual knowledgebase to 56% in aggregate. Our analyses illuminate the need for open, interoperable sharing of variant interpretation data. We also provide an open and freely available web interface (search.cancervariants.org) for exploring the harmonized interpretations from these six knowledgebases.

bioinformatics↗

bioRxiv2018

Standard operating procedure for somatic variant refinement of tumor sequencing data

PurposeManual review of aligned sequencing reads is required to develop a high-quality list of somatic variants from massively parallel sequencing data (MPS). Despite widespread use in analyzing MPS data, there has been little attempt to describe methods for manual review, resulting in high inter- and intra-lab variability in somatic variant detection and characterization of tumors.\n\nMethodsOpen source software was used to develop an optimal method for manual review setup. We also developed a systemic approach to visually inspect each variant during manual review.\n\nResultsWe present a standard operating procedures for somatic variant refinement for use by manual reviewers. The approach is enhanced through representative examples of 4 different manual review categories that indicate a reviewers confidence in the somatic variant call and 19 annotation tags that contextualize commonly observed sequencing patterns during manual review. Representative examples provide detailed instructions on how to classify variants during manual review to rectify lack of confidence in automated somatic variant detection.\n\nConclusionStandardization of somatic variant refinement through systematization of manual review will improve the consistency and reproducibility of identifying true somatic variants after automated variant calling.

genetics↗

bioRxiv2017

The prognostic effects of somatic mutations in ER-positive breast cancer

More than 50 genes are recurrently affected by somatic mutation in estrogen receptor positive (ER+) breast cancer but prognostic effects have not been definitively established. Primary tumor DNA was therefore subjected to targeted sequencing from 625 postmenopausal (UBC-TAM series) and 328 premenopausal (MA12 trial) hormone receptor-positive (HR+) patients. Independent validation of prognostic interactions was achieved using independent data from the METABRIC study. Associations between MAP3K1 and PIK3CA with luminal A status and TP53 mutations with Luminal B/non-luminal tumors were observed, validating the methodological approach. In UBC-TAM, NF1 frame-shift nonsense (FS/NS) mutation was validated as a poor outcome driver. For MA12, poor outcome associated with PIK3R1 mutation was similarly validated. DDR1 mutations were strongly associated with poor prognosis in UBC-TAM despite stringent false-discovery correction (q=0.0003). In conclusion, uncommon recurrent somatic mutations should be further explored to create a more complete explanation of the highly variable outcomes that typify ER+ breast cancer.

genomics↗

bioRxiv2017

Clingen Cancer Somatic Working Group: standardizing and democratizing access to cancer molecular diagnostic data to drive translational research

A growing number of academic and community clinics are conducting genomic testing to inform treatment decisions for cancer patients (1). In the last 3-5 years, there has been a rapid increase in clinical use of next generation sequencing (NGS) based cancer molecular diagnostic (MolDx) testing (2). The increasing availability and decreasing cost of tumor genomic profiling means that physicians can now make treatment decisions armed with patient-specific genetic information. Accumulating research in the cancer biology field indicates that there is significant potential to improve cancer patient outcomes by effectively leveraging this rich source of genomic data in treatment planning (3). To achieve truly personalized medicine in oncology, it is critical to catalog cancer sequence variants from MolDx testing for their clinical relevance along with treatment information and patient outcomes, and to do so in a way that supports large-scale data aggregation and new hypothesis generation. One critical challenge to encoding variant data is adopting a standard of annotation of those variants that are clinically actionable. Through the NIH-funded Clinical Genome Resource (ClinGen) (4), in collaboration with NLMs ClinVar database and >50 academic and industry based cancer research organizations, we developed the Minimal Variant Level Data (MVLD) framework to standardize reporting and interpretation of drug associated alterations (5). We are currently involved in collaborative efforts to align the MVLD framework with parallel, complementary sequence variants interpretation clinical guidelines from the Association of Molecular Pathologists (AMP) for clinical labs (6). In order to truly democratize access to MolDx data for care and research needs, these standards must be harmonized to support sharing of clinical cancer variants. Here we describe the processes and methods developed within the ClinGens Somatic WG in collaboration with over 60 cancer care and research organizations as well as CLIA-certified, CAP-accredited clinical testing labs to develop standards for cancer variant interpretation and sharing.

bioinformatics↗

bioRxiv2017

DGIdb 3.0: a redesign and expansion of the drug-gene interaction database

The Drug-Gene Interaction Database (DGIdb, www.dgidb.org) consolidates, organizes, and presents drug-gene interactions and gene druggability information from papers, databases, and web resources. DGIdb normalizes content from more than thirty disparate sources and allows for user-friendly advanced browsing, searching and filtering for ease of access through an intuitive web user interface, application programming interface (API), and public cloud-based server image. DGIdb v3.0 represents a major update of the database. Nine of the previously included twenty-eight sources were updated. Six new resources were added, bringing the total number of sources to thirty-three. These updates and additions of sources have cumulatively resulted in 56,309 interaction claims. This has also substantially expanded the comprehensive catalogue of druggable genes and antineoplastic drug-gene interactions included in the DGIdb. Along with these content updates, v3.0 has received a major overhaul of its codebase, including an updated user interface, preset interaction search filters, consolidation of interaction information into interaction groups, greatly improved search response times, and upgrading the underlying web application framework. In addition, the expanded API features new endpoints which allow users to extract more detailed information about queried drugs, genes, and drug-gene interactions, including listings of PubMed IDs (PMIDs), interaction type, and other interaction metadata.

bioinformatics↗