The Drug-Gene Interaction Database (DGIdb, www.dgidb.org) is a publicly accessible resource that aggregates 102,426 gene records and 57,498 drug records from 40 drug-gene interaction data sources to aid both researchers and clinicians in identifying associations between genes of interest and available drugs and therapeutics. By using peer-reviewed data sources and publications, DGIdb represents a stand-alone resource with over 100,000 drug-gene interaction claims across 30 interaction types to drive hypothesis generation in precision medicine and interpretation pipelines. The background process that normalizes drugs to a harmonized ontological concept has been upgraded. These improvements have increased concept normalization for drugs by 20% and are now available as a stand-alone service for use (https://normalize.cancervariants.org/therapy/). Leveraging our platform’s ability to find relationships between disease-critical genes and available therapeutics, DGIdb has been used in clinical interpretation pipelines to find drugs for specific diseases with an emphasis on regulatory approval status. DGIdb now uses annotations from Drugs@FDA as an additional source to provide more accurate descriptors for market and maturity status of drugs, when available. Lastly, to enhance the annotation potential for DGIdb in precision medicine pipelines, we have updated our druggable gene category sources with an additional curated list of 2,217 genes. Used alone or in combination with existing categories-such as the heavily-utilized ‘clinically actionable’ category-this additional source will give precision medicine and interpretation pipelines the power to find concise, actionable annotations for specific diseases including pediatric cancers and epilepsy. These lists are managed and maintained as a publicly-available resource to provide up-to-date annotations on disease-associated genes as they become available. Citation Format: Matthew Cannon, James Stevenson, Kori Kuzma, Colin O'Sullivan, Katherine Miller, Olivia Grischow, Adam Coffman, Susanna Kiwala, Joshua F. McMichael, Dorian Morrissey, Kelsy Cotto, Obi Griffith, Malachi Griffith, Alex Wagner. Refining the drug-gene interaction database for precision medicine pipelines [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1197.

An evolving literature evaluates the inferential and behavioral implications of measurement error (ME) in agricultural data. We synthesize findings on the nature and sources of ME and potential remedies. We provide practical guidance for choosing among alternative approaches for detecting, obviating, or correcting for alternative sources of ME, as these have different behavioral and inferential implications. Some ME biases statistical inference and thus may require econometric correction. Other types of ME may affect (and shed light on) farmers’ decision-making processes even if farmers’ responses are objectively incorrect. Where feasible, collecting both self-reported and objectively measured data for the same variable may enrich understanding of policy-relevant agricultural and behavioral phenomena. Expected final online publication date for the Annual Review of Resource Economics, Volume 15 is October 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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Precision oncology is the practice of interpreting the clinical significance of observed molecular changes in patient neoplasms, potentially impacting medical decision making and care. This process is labor-intensive and (among other challenges) involves accurately translating between variation representation conventions from one resource to the next. For example, differences in representations of Copy Number Variation (CNV) from genomic regions, cytogenomic bands, or gene features create challenges in knowledge matching due to lack of standards covering all of these modalities of observed variation.The Global Alliance for Genomics and Health (GA4GH; ga4gh.org) is an international collaborative of genomic data sharing initiatives (Driver Projects) developing genomic data sharing standards within a human rights framework. GA4GH recently published the Variation Representation Specification (VRS; pronounced “verse”), a standard for the computational representation of biomolecular variation. VRS is a terminology, schema, and associated conventions for creating uniquely identifiable and federatable representations of molecular variation. VRS has formal data classes well-suited to differentiating between variation on a single molecule (e.g. tandem duplications) from variation measured at a systemic level (e.g. genome-wide copy number variation). In addition to molecular sequence variation, VRS also supports variation on cytogenetic coordinate systems and genes, making it well-suited to representing variation associated with cancer biomarkers.We demonstrate the use of VRS to model reported gene-associated CNVs from the AACR Project GENIE cohort, to aid in the computational discovery of evidence from clinico-genomic knowledgebases with genomic or cytogenomic CNV representations. We highlight the use case of knowledge matching to the Atlas of Genetics and Cytogenetics in Oncology and Haematology (“the Atlas”; atlasgeneticsoncology.org), a cytogenetics resource historically driven by user website navigation. Using VRS search tools we developed for the Variant Interpretation for Cancer Consortium (VICC; cancervariants.org) GA4GH Driver Project, we found that 64% of GENIE samples with reported CNVs matched clinically relevant knowledge in the Atlas. This work was enabled by programmatic search tools leveraging standard VRS object structures, demonstrating how VRS enables collection of real-world evidence across more resources without manual interpretation or custom normalization methods. We conclude with a survey of open-source tools supporting this analysis as well as search of other clinico-genomic knowledgebases with VRS, including CIViC (civicdb.org), BRCA Exchange (brcaexchange.org), and the Molecular Oncology Almanac (moalmanac.org). Citation Format: Matthew Cannon, Kori Kuzma, James Stevenson, Jiachen Liu, Colin O'Sullivan, Bimal P. Chaudhari, Matthew Brush, Robert R. Freimuth, Tristan Nelson, Michael Baudis, Obi L. Griffith, Malachi Griffith, Lawrence Babb, Melissa S. Cline, Xuelu Liu, Brian Walsh, Alex H. Wagner. Introduction of the GA4GH Variation Representation Specification (VRS) and supporting tools for discovery and exchange of clinical genomic and cytogenomic knowledge in cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1177.

Working with therapeutic terminology in the field of medicine can be challenging due to both the number of ways terms can be addressed and the ambiguity associated with different naming strategies. A therapeutic concept can be identified across many facets from ontologies and vocabularies of varying focus, including natural product names, chemical structures, development codes, generic names, brand names, product formulations, or treatment regimens. This diversity of nomenclature makes therapeutic terminology difficult to manage and harmonize. As the number and complexity of available therapeutic ontologies continues to increase, the need for harmonized cross-resource mappings is becoming increasingly apparent. Harmonized concept mappings will enable the linking together of like-concepts despite source-dependent differences in data structure or semantic representation. To support these mappings, we introduce TheraPy, a Python package and web API that constructs stable, searchable merged concepts for drugs and therapeutic terminologies using publicly available resources and thesauri. By using a directed graph approach, TheraPy can capture commonly used aliases, trade names, annotations, and associations for any given therapeutic and combine them under a single merged concept record. Using this approach, we found that TheraPy tends to normalize therapeutic concepts to their underlying active ingredients (excluding non-drug therapeutics, e.g. radiation therapy, biologics), and unifies all available descriptors regardless of ontological origin. In this report, we highlight the creation of 16,069 unique merged therapeutic concepts from 9 distinct sources using TheraPy. Further, we analyze rates of normalization for therapeutic terms taken from publicly available vocabularies.