Format

Send to

Choose Destination
Genome Med. 2018 Jul 27;10(1):60. doi: 10.1186/s13073-018-0564-z.

Integrative omics analyses broaden treatment targets in human cancer.

Author information

1
Division of Oncology, Department of Medicine, Washington University, St. Louis, MO, 63108, USA.
2
McDonnell Genome Institute, Washington University, St. Louis, MO, 63108, USA.
3
Division of Nephrology, Department of Medicine, Washington University, St. Louis, MO, 63108, USA.
4
Brown School, Washington University, St. Louis, MO, 63105, USA.
5
Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
6
Department of Genetics, Washington University, St. Louis, MO, 63108, USA.
7
Department of Mathematics, Washington University, St. Louis, MO, 63108, USA.
8
Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA.
9
Department of Biochemistry and Molecular Pharmacology, New York University Langone School of Medicine, New York, NY, 10016, USA.
10
Institute for Systems Genetics, New York University Langone School of Medicine, New York, NY, 10016, USA.
11
Siteman Cancer Center, Washington University, St. Louis, MO, 63108, USA.
12
Division of Nephrology, Department of Medicine, Washington University, St. Louis, MO, 63108, USA. fchen@wustl.edu.
13
Department of Genetics, Washington University, St. Louis, MO, 63108, USA. fchen@wustl.edu.
14
Division of Oncology, Department of Medicine, Washington University, St. Louis, MO, 63108, USA. lding@wustl.edu.
15
McDonnell Genome Institute, Washington University, St. Louis, MO, 63108, USA. lding@wustl.edu.
16
Department of Genetics, Washington University, St. Louis, MO, 63108, USA. lding@wustl.edu.
17
Siteman Cancer Center, Washington University, St. Louis, MO, 63108, USA. lding@wustl.edu.

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.

KEYWORDS:

Cancer and druggability; Cancer genomics; Multi-omics; Precision medicine; Proteogenomics

PMID:
30053901
PMCID:
PMC6064051
DOI:
10.1186/s13073-018-0564-z
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for BioMed Central Icon for PubMed Central
Loading ...
Support Center