82. Enabling large scale precision oncology research with a new standard for genomic variants: OMOP Genomic

Aly Abdelkareem

Asieh Golozar

Asieh Golozar is Vice President, Global head of Data Science at Odysseus Data Services, a leading real-world evidence (RWE) technology vendor. She is also professor of the practice and director of clinical research at the OHDSI Center in Northeastern university. Asieh has more than more than 20 years of experience in life science research and medicine in industry, academia, and government settings. She holds a PhD in epidemiology and a Master of Health Sciences in biostatistics from the Johns Hopkins University School of Public Health, supported by a postdoctoral research fellowship award with the National Cancer Institute’s Division of Cancer Epidemiology and Genetics. She earned her medical degree from the Tehran University of Medical Sciences. After receiving her PhD, she joined the faculty at the Department of Epidemiology, JHU School of Public Health focusing on cancer and diabetes epidemiology and applying evidence-based findings to strengthen public heath infrastructure and policies. She then joined the pharmaceutical industry where she worked as pharmacoepidemiology therapeutic area lead and expert at Regeneron Pharmaceuticals, AstraZeneca and Bayer and led and contributed to the integration of effective and efficient observational research strategy into the research and development, clinical development, and life cycle management in different therapeutic areas specifically oncology. At her current role, she leads a team of data scientists, epidemiologists and bioinformaticians focusing on epidemiological research and development and application of advanced and innovative analytics across a large global network of observational data.


Asieh Golozara, Christian Reichb

aOdysseus Data Services, OHDSI, New York, NY, USA; bIQVIA, OHDSI, Boston, MA, USA

Precision Oncology requires the application of genomic variants to epidemiological methods used in clinical research. Variants are detected through traditional immunohistochemistry, microarray or PCR panels, or large-scale next generation sequencing, either for purposes of clinical care or understanding of biological processes of carcinogenesis and cancer treatment. However, we see few variants used in standard epidemiological research in the context of rich phenotypes: longitudinal observational patient data of treatment, progression and survival.

The reason for this lies in the incompatibility of data representations and methods. In a typical observational study, cohorts, exposures, and outcomes are defined through clinical events encoded by predefined concepts. Epidemiological methods use this closed system model to measure the statistical change in rates, which are calculated with the denominator of all possible clinical states. The same is very challenging for genomic variants, which work like open systems: The representation of possibly infinite variants, whether previously observed or not.

The Oncology Working Group of OHDSI has been working on the development of a canonical, comprehensive, and non-redundant representation of genomic variants that are clinically relevant for cancer. The resulting reference list, provided as part of the OMOP Standardized Vocabularies, is called ‘OMOP Genomic’. It was constructed by consolidating genomic variants from public somatic cancer variant knowledgebases and contains +95,000 variations from 575 cancer genes. Assessment of the current gap, integration of other genomic knowledgebases are essential to improve the coverage of important and clinically relevant mutations implicated in cancer.