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Am Soc Clin Oncol Educ Book. 2018 May 23;38:546-553. doi: 10.1200/EDBK_200759.

Art and Challenges of Precision Medicine: Interpreting and Integrating Genomic Data Into Clinical Practice.

Author information

1
From the Innovation Center for Biomedical Informatics, Georgetown University, Washington, DC; Swedish Cancer Institute, Seattle, WA; The Ohio State University, Columbus, OH.

Abstract

Precision medicine is at the forefront of innovation in cancer care. With the development of technologies to rapidly sequence DNA from tumors, cell-free DNA, proteins, and even metabolites coupled with the rapid decline in the cost of genomic sequencing, there has been an exponential increase in the amount of data generated for each patient diagnosed with cancer. The ability to harness this explosion of data will be critical to improving treatments for patients. Precision medicine lends itself to big data or "informatics" approaches and is focused on storing, accessing, sharing, and studying these data while taking necessary precautions to protect patients' privacy. Major cancer care stakeholders have developed a variety of systems to incorporate precision medicine technologies into patient care as soon as possible and also to provide the ability to store and analyze the omics and clinical data aggregately in the future. Scaling these precision medicine programs within the confines of health care system silos is challenging, and research consortiums are being formed to overcome these limitations. Incorporating and interpreting the results of precision medicine sequencing is complex and rapidly changing, necessitating reliance on a group of experts. This is often performed at molecular tumor boards at large academic and research institutions with available in-house expertise, but alternative models clinical decision support software or of virtual tumor boards potentially expand these advances to almost any patient, regardless of site of care. The promises of precision medicine will be more quickly realized by expanding collaborations to rapidly process and interpret the growing volumes of omics data.

PMID:
30231369
DOI:
10.1200/EDBK_200759
[Indexed for MEDLINE]

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