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Precision oncology: lessons learned and challenges for the future

Authors Yang HT, Shah RH, Tegay D, Onel K

Received 12 January 2019

Accepted for publication 8 July 2019

Published 7 August 2019 Volume 2019:11 Pages 7525—7536

DOI https://doi.org/10.2147/CMAR.S201326

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Amy Norman

Peer reviewer comments 3

Editor who approved publication: Dr Alexandra R. Fernandes


Hsih-Te Yang,1 Ronak H Shah,1,2 David Tegay,1 Kenan Onel3

1Medical Genetics and Human Genomics, Department of Pediatrics, Northwell Health, New York, NY, USA; 2Center for Research Informatics and Innovation, The Feinstein Institute for Medical Research, Northwell Health, New York, NY, USA; 3The Icahn School of Medicine at Mount Sinai, Department of Genetics and Genomic Sciences, New York, NY, USA

Abstract: The decreasing cost of and increasing capacity of DNA sequencing has led to vastly increased opportunities for population-level genomic studies to discover novel genomic alterations associated with both Mendelian and complex phenotypes. To translate genomic findings clinically, a number of health care institutions have worked collaboratively or individually to initiate precision medicine programs. These precision medicine programs involve designing patient enrollment systems, tracking electronic health records, building biobank repositories, and returning results with actionable matched therapies. As cancer is a paradigm for genetic diseases and new therapies are increasingly tailored to attack genetic susceptibilities in tumors, these precision medicine programs are largely driven by the urgent need to perform genetic profiling on cancer patients in real time. Here, we review the current landscape of precision oncology and highlight challenges to be overcome and examples of benefits to patients. Furthermore, we make suggestions to optimize future precision oncology programs based upon the lessons learned from these “first generation” early adopters.

Keywords: next-generation sequencing, pathogenic variant, driver mutation, actionable mutation, cancer disparities
 

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