Integrative omics analyses broaden treatment targets in human cancer

Sohini Sengupta†,
Sam Q. Sun†,
Kuan-lin Huang,
Clara Oh,
Matthew H. Bailey,
Rajees Varghese,
Matthew A. Wyczalkowski,
Jie Ning,
Piyush Tripathi,
Joshua F. McMichael,
Kimberly J. Johnson,
Cyriac Kandoth,
John Welch,
Cynthia Ma,
Michael C. Wendl,
Samuel H. Payne,
David Fenyö,
Reid R. Townsend,
John F. Dipersio,
Feng ChenEmail author and
Li DingEmail author View ORCID ID profile

†Contributed equally

Genome Medicine201810:60

https://doi.org/10.1186/s13073-018-0564-z

Received: 15 December 2017
Accepted: 28 June 2018
Published: 27 July 2018

Abstract

Background

Although large-scale, next-generation sequencing (NGS) studies of cancers hold promise for enabling precision oncology, challenges remain in integrating NGS with clinically validated biomarkers.

Methods

To overcome such challenges, we utilized the Database of Evidence for Precision Oncology (DEPO) to link druggability to genomic, transcriptomic, and proteomic biomarkers. Using a pan-cancer cohort of 6570 tumors, we identified tumors with potentially druggable biomarkers consisting of drug-associated mutations, mRNA expression outliers, and protein/phosphoprotein expression outliers identified by DEPO.

Results

Within the pan-cancer cohort of 6570 tumors, we found that 3% are druggable based on FDA-approved drug-mutation interactions in specific cancer types. However, mRNA/phosphoprotein/protein expression outliers and drug repurposing across cancer types suggest potential druggability in up to 16% of tumors. The percentage of potential drug-associated tumors can increase to 48% if we consider preclinical evidence. Further, our analyses showed co-occurring potentially druggable multi-omics alterations in 32% of tumors, indicating a role for individualized combinational therapy, with evidence supporting mTOR/PI3K/ESR1 co-inhibition and BRAF/AKT co-inhibition in 1.6 and 0.8% of tumors, respectively. We experimentally validated a subset of putative druggable mutations in BRAF identified by a protein structure-based computational tool. Finally, analysis of a large-scale drug screening dataset lent further evidence supporting repurposing of drugs across cancer types and the use of expression outliers for inferring druggability.

Conclusions

Our results suggest that an integrated analysis platform can nominate multi-omics alterations as biomarkers of druggability and aid ongoing efforts to bring precision oncology to patients.