TY - JOUR
T1 - Integrative omics analyses broaden treatment targets in human cancer
AU - Sengupta, Sohini
AU - Sun, Sam Q.
AU - Huang, Kuan lin
AU - Oh, Clara
AU - Bailey, Matthew H.
AU - Varghese, Rajees
AU - Wyczalkowski, Matthew A.
AU - Ning, Jie
AU - Tripathi, Piyush
AU - McMichael, Joshua F.
AU - Johnson, Kimberly J.
AU - Kandoth, Cyriac
AU - Welch, John
AU - Ma, Cynthia
AU - Wendl, Michael C.
AU - Payne, Samuel H.
AU - Fenyö, David
AU - Townsend, Reid R.
AU - Dipersio, John F.
AU - Chen, Feng
AU - Ding, Li
N1 - Publisher Copyright:
© 2018 The Author(s).
PY - 2018/7/27
Y1 - 2018/7/27
N2 - 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.
AB - 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.
KW - Cancer and druggability
KW - Cancer genomics
KW - Multi-omics
KW - Precision medicine
KW - Proteogenomics
UR - http://www.scopus.com/inward/record.url?scp=85050619199&partnerID=8YFLogxK
U2 - 10.1186/s13073-018-0564-z
DO - 10.1186/s13073-018-0564-z
M3 - Article
C2 - 30053901
AN - SCOPUS:85050619199
SN - 1756-994X
VL - 10
JO - Genome medicine
JF - Genome medicine
IS - 1
M1 - 60
ER -