CC BY-NC-ND 4.0 · Yearb Med Inform 2018; 27(01): 211-222
DOI: 10.1055/s-0038-1667085
Section 11: Cancer Informatics
Survey
Georg Thieme Verlag KG Stuttgart

The Omics Revolution Continues: The Maturation of High-Throughput Biological Data Sources

Ewy Mathé
1  Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
,
John L. Hays
2  Department of Internal Medicine, The Ohio State University, Columbus, OH, USA
3  Department of Obstetrics and Gynecology, The Ohio State University, Columbus, OH, USA
,
Daniel G. Stover
2  Department of Internal Medicine, The Ohio State University, Columbus, OH, USA
,
James L. Chen
2  Department of Internal Medicine, The Ohio State University, Columbus, OH, USA
1  Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
› Author Affiliations
Further Information

Publication History

Publication Date:
29 August 2018 (online)

Summary

Objective: The aim is to provide a comprehensive review of state-of-the art omics approaches, including proteomics, metabolomics, cell-free DNA, and patient cohort matching algorithms in precision oncology.

Methods: In the past several years, the cancer informatics revolution has been the beneficiary of a data explosion. Different complementary omics technologies have begun coming into their own to provide a more nuanced view of the patient-tumor interaction beyond that of DNA alterations. A combined approach is beneficial to the patient as nearly all new cancer therapeutics are designed with an omics biomarker in mind. Proteomics and metabolomics provide us with a means of assaying in real-time the response of the tumor to treatment. Circulating cell-free DNA may allow us to better understand tumor heterogeneity and interactions with the host genome.

Results: Integration of increasingly available omics data increases our ability to segment patients into smaller and smaller cohorts, thereby prompting a shift in our thinking about how to use these omics data. With large repositories of patient omics-outcomes data being generated, patient cohort matching algorithms have become a dominant player.

Conclusions: The continued promise of precision oncology is to select patients who are most likely to benefit from treatment and to avoid toxicity for those who will not. The increased public availability of omics and outcomes data in patients, along with improved computational methods and resources, are making precision oncology a reality.