A Computable Phenotyping Pipeline for Rapid Learning Health Systems with the Molecular Oncology Almanac

Poster Number: P161
Presentation Time: 05:00 PM - 06:30 PM

Abstract Keywords: Precision Medicine, Bioinformatics, Clinical Decision Support, Real-World Evidence Generation
Primary Track: Applications
Programmatic Theme: Translational Bioinformatics

We extend the Molecular Oncology Almanac (MOAlmanac) to clinical panel sequencing results from the Veterans Affairs (VA) National Precision Oncology Program (NPOP) with a computable phenotyping pipeline that enables interpretation of results without preselection of diagnosis and incorporation of environmental exposures. We developed a pipeline to phenotype single-nucleotide variants (SNVs), insertion/deletions (indels), copy number variations (CNVs), and fusions present in VA NPOP data, integrate environmental exposure data and then annotate them using the MOAlmanac.

Speaker(s):
Nhan Do, MD, MS, Clinical Informatics Diplomate
Boston VA HCS

Author(s):
Theodore Feldman, PhD - VA Boston Healthcare System; Brendan Reardon; Paul Marcantonio, Computer Science; Robert Zwolinski, B.A. - VA Boston Healthcare System; Anthony Szema, MD - Zucker School of Medicine; Danne Elbers, PhD - VA Boston CSP / MAVERIC; Nathanael Fillmore, PhD - VA Boston Healthcare System; Eliezer Van Allen, MD - Dana Farber Cancer Insitute; Michael Kelley, MD - Durham VAMC; Mary Brophy, MD; Nhan Do, MD, MS, Clinical Informatics Diplomate - Boston VA HCS;