Jessica W. Chen1,2, Christian A. Kunder2, Nam Bui3, James L. Zehnder2, Helio A. Costa1,2, Henning Stehr2
1Department of Biomedical Data Science, Stanford University School of Medicine
2Department of Pathology, Stanford University School of Medicine
3Department of Medicine, Stanford University School of Medicine
Email: jwrchen@stanford.edu, ckunder@stanford.edu, nambui@stanford.edu, zehdner@stanford.edu, hcosta@stanford.edu, stehr@stanford.edu
Pacific Symposium on Biocomputing 25:31-42(2020)
© 2020 World Scientific
Open Access chapter published by World Scientific Publishing Company and distributed under the terms of the Creative Commons Attribution (CC BY) 4.0 License.
Successful implementation of precision oncology requires both the deployment of nucleic acid sequencing panels to identify clinically actionable biomarkers, and the efficient screening of patient biomarker eligibility to on-going clinical trials and therapies. This process is typically performed manually by biocurators, geneticists, pathologists, and oncologists; however, this is a time-intensive, and inconsistent process amongst healthcare providers. We present the development of a feature matching algorithmic pipeline that identifies patients who meet eligibility criteria of precision medicine clinical trials via genetic biomarkers and apply it to patients undergoing treatment at the Stanford Cancer Center. This study demonstrates, through our patient eligibility screening algorithm that leverages clinical sequencing derived biomarkers with precision medicine clinical trials, the successful use of an automated algorithmic pipeline as a feasible, accurate and effective alternative to the traditional manual clinical trial curation.