Development and Validation of the Gene Expression Predictor of High-grade Serous Ovarian Carcinoma Molecular SubTYPE (PrOTYPE)

Aline Talhouk & Michael Anglesio et al. · 2020-06-17

Abstract

Purpose:

Gene expression–based molecular subtypes of high-grade serous tubo-ovarian cancer (HGSOC), demonstrated across multiple studies, may provide improved stratification for molecularly targeted trials. However, evaluation of clinical utility has been hindered by nonstandardized methods, which are not applicable in a clinical setting. We sought to generate a clinical grade minimal gene set assay for classification of individual tumor specimens into HGSOC subtypes and confirm previously reported subtype-associated features.

Experimental Design:

Adopting two independent approaches, we derived and internally validated algorithms for subtype prediction using published gene expression data from 1,650 tumors. We applied resulting models to NanoString data on 3,829 HGSOCs from the Ovarian Tumor Tissue Analysis consortium. We further developed, confirmed, and validated a reduced, minimal gene set predictor, with methods suitable for a single-patient setting.

Results:

Gene expression data were used to derive the predictor of high-grade serous ovarian carcinoma molecular subtype (PrOTYPE) assay. We established a de facto standard as a consensus of two parallel approaches. PrOTYPE subtypes are significantly associated with age, stage, residual disease, tumor-infiltrating lymphocytes, and outcome. The locked-down clinical grade PrOTYPE test includes a model with 55 genes that predicted gene expression subtype with >95% accuracy that was maintained in all analytic and biological validations.

Conclusions:

We validated the PrOTYPE assay following the Institute of Medicine guidelines for the development of omics-based tests. This fully defined and locked-down clinical grade assay will enable trial design with molecular subtype stratification and allow for objective assessment of the predictive value of HGSOC molecular subtypes in precision medicine applications.

See related commentary by McMullen et al., p. 5271

Funding
Identifying Prognostic Markers and Therapeutic Targets for Serous Ovarian CancerMedical Research Council Grant MC_UU_12023/20Cancer Center AdministrationUCLA Clinical and Translational Science InstituteCancer Research UK Grant 15601Characterizing Molecular Subtypes of Ovarian Cancer in African-American WomenCancer Research UK Grant 22905Cancer Research UK Grant 13086UCLA Clinical and Translational Science InstituteCancer Research Career Enhancement and Related ActivitiesPathologyPCOS, Type II diabetes and ovarian cancer riskEpidemiologic factors and survival by molecular subtypes of ovarian cancerCancer Biology Research ProgramProject 2: Next Generation TOP1 Inhibition for the Treatment of Ovarian CancerIdentifying Prognostic Markers and Therapeutic Targets for Serous Ovarian CancerCanadian Institutes for Health Research FundingUnited States Department of Defense Ovarian Cancer Research Grant OC110433Smith Foundation for Health Research FundingBC Cancer Foundation FundingNIH FundingCancer Center AdministrationProject 2: Next Generation TOP1 Inhibition for the Treatment of Ovarian CancerCanada Research Chairs Program FundingMiriam and Sheldon Adelson Medical Research Foundation FundingAmerican Cancer Society Grant SIOP-06-258-01-COUNAmerican Cancer Society Grant UL1TR000124Cancer Institute NSW Grant 12/RIG/1-17Cancer Institute NSW Grant 15/RIG/1-16AOCS FundingMedical Research and Materiel Command Grant DAMD17-01-1-0729Cancer Council Victoria FundingCancer Council New South Wales FundingCancer Council South Australia FundingCancer Council Tasmania FundingNational Health and Medical Research Council Grant ID199600NHMRC Grant ID400413;National Health and Medical Research Council Grant ID400281Cancer Research UK Grant A15973Cancer Research UK Grant A15601Cancer Research UK Grant A18072Cancer Research UK Grant A17197Cancer Research UK Grant A19274Cancer Research UK Grant A19694

NCI NIH HHS

R01 CA172404

NCI NIH HHS

P30 CA034196

NCATS NIH HHS

UL1 TR000124

NCI NIH HHS

R01 CA200854

NCATS NIH HHS

UL1 TR001881

NCI NIH HHS

P30 CA042014

NCI NIH HHS

P30 CA008748

NCI NIH HHS

K22 CA193860

NCI NIH HHS

R01 CA168758

NCI NIH HHS

P30 CA071789

NCI NIH HHS

P50 CA136393

NIH

R01-CA172404

NCI

P30CA034196

Mayo Clinic

P50 CA136393