DGIdb 5.0: rebuilding the drug-gene interaction database for precision medicine and drug discovery platforms.

The Drug-Gene Interaction Database (DGIdb, https://dgidb.org) is a publicly accessible resource that aggregates genes or gene products, drugs and drug-gene interaction records to drive hypothesis generation and discovery for clinicians and researchers. DGIdb 5.

Computational prediction of MHC anchor locations guides neoantigen identification and prioritization.

Neoantigens are tumor-specific peptide sequences resulting from sources such as somatic DNA mutations. Upon loading onto major histocompatibility complex (MHC) molecules, they can trigger recognition by T cells. Accurate neoantigen identification is thus critical for both designing cancer vaccines and predicting response to immunotherapies.

Personalized ctDNA micro-panels can monitor and predict clinical outcomes for patients with triple-negative breast cancer.

Circulating tumor DNA (ctDNA) in peripheral blood has been used to predict prognosis and therapeutic response for triple-negative breast cancer (TNBC) patients. However, previous approaches typically use large comprehensive panels of genes commonly mutated across all breast cancers. Given the reduction in sequencing costs and decreased turnaround times associated with panel generation, the objective of this study was to assess the use of custom micro-panels for tracking disease and predicting clinical outcomes for patients with TNBC.

Distinct clonal identities of B-ALLs arising after lenolidomide therapy for multiple myeloma.

Patients with multiple myeloma (MM) who are treated with lenalidomide rarely develop a secondary B-cell acute lymphoblastic leukemia (B-ALL). The clonal and biological relationship between these sequential malignancies is not yet clear. We identified 17 patients with MM treated with lenalidomide, who subsequently developed B-ALL.

A community approach to the cancer-variant-interpretation bottleneck.

As guidelines, therapies, and literature on cancer variants expand, the lack of consensus variant interpretations impedes clinical applications. CIViC is a public domain, crowd-sourced, and adaptable knowledgebase of evidence for the Clinical Interpretation of Variants in Cancer, designed to reduce barriers to knowledge sharing and alleviate the variant interpretation bottleneck.

epitope prediction analyses highlight the potential for distracting antigen immunodominance with allogeneic cancer vaccines.

Allogeneic cancer vaccines are designed to induce antitumor immune responses with the goal of impacting tumor growth. Typical allogeneic cancer vaccines are produced by expansion of established cancer cell lines, transfection with vectors encoding immunostimulatory cytokines, and lethal irradiation. More than 100 clinical trials have investigated the clinical benefit of allogeneic cancer vaccines in various cancer types.

Genomic and transcriptomic somatic alterations of hepatocellular carcinoma in non-cirrhotic livers.

Liver cancer is the second leading cause of cancer-related deaths worldwide. Hepatocellular carcinoma (HCC) risk factors include chronic hepatitis, cirrhosis, and alcohol abuse, whereby tumorigenesis is induced through inflammation and subsequent fibrotic response. However, a subset of HCC arises in non-cirrhotic livers.

Exploring the Genomic Landscape of Cancer Patient Cohorts with GenVisR.

The creation of visualizations to interpret genomics data remains an important aspect of data science within computational biology. The GenVisR Bioconductor package was created to lower the entry point for publication-quality graphics and has remained a popular suite of tools within this domain. GenVisR supports visualizations covering a breadth of topics including functions to produce visual summaries of copy-number alterations, somatic variants, sequence quality metrics, and more.

Integration of the Drug-Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts.

The Drug-Gene Interaction Database (DGIdb, www.dgidb.org) is a web resource that provides information on drug-gene interactions and druggable genes from publications, databases, and other web-based sources.

CIViCpy: A Python Software Development and Analysis Toolkit for the CIViC Knowledgebase.

Purpose: Precision oncology depends on the matching of tumor variants to relevant knowledge describing the clinical significance of those variants. We recently developed the Clinical Interpretations for Variants in Cancer (CIViC; civicdb.org) crowd-sourced, expert-moderated, and open-access knowledgebase.

Open-Sourced CIViC Annotation Pipeline to Identify and Annotate Clinically Relevant Variants Using Single-Molecule Molecular Inversion Probes.

Purpose: Clinical targeted sequencing panels are important for identifying actionable variants for patients with cancer; however, existing approaches do not provide transparent and rationally designed clinical panels to accommodate the rapidly growing knowledge within oncology.

Materials And Methods: We used the Clinical Interpretations of Variants in Cancer (CIViC) database to develop an Open-Sourced CIViC Annotation Pipeline (OpenCAP). OpenCAP provides methods to identify variants within the CIViC database, build probes for variant capture, use probes on prospective samples, and link somatic variants to CIViC clinical relevance statements.

Standard operating procedure for somatic variant refinement of sequencing data with paired tumor and normal samples.

Purpose: Following automated variant calling, manual review of aligned read sequences is required to identify a high-quality list of somatic variants. Despite widespread use in analyzing sequence data, methods to standardize manual review have not been described, resulting in high inter- and intralab variability.

Methods: This manual review standard operating procedure (SOP) consists of methods to annotate variants with four different calls and 19 tags.

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.

ORegAnno 3.0: a community-driven resource for curated regulatory annotation.

The Open Regulatory Annotation database (ORegAnno) is a resource for curated regulatory annotation. It contains information about regulatory regions, transcription factor binding sites, RNA binding sites, regulatory variants, haplotypes, and other regulatory elements. ORegAnno differentiates itself from other regulatory resources by facilitating crowd-sourced interpretation and annotation of regulatory observations from the literature and highly curated resources.