The Impact of Stroma Admixture on Molecular Subtypes and Prognostic Gene Signatures in Serous Ovarian Cancer

Giovanni Parmigiani & Aedín C. Culhane et al. · 2019-12-23

Abstract

Background:

Recent efforts to improve outcomes for high-grade serous ovarian cancer, a leading cause of cancer death in women, have focused on identifying molecular subtypes and prognostic gene signatures, but existing subtypes have poor cross-study robustness. We tested the contribution of cell admixture in published ovarian cancer molecular subtypes and prognostic gene signatures.

Methods:

Gene signatures of tumor and stroma were developed using paired microdissected tissue from two independent studies. Stromal genes were investigated in two molecular subtype classifications and 61 published gene signatures. Prognostic performance of gene signatures of stromal admixture was evaluated in 2,527 ovarian tumors (16 studies). Computational simulations of increasing stromal cell proportion were performed by mixing gene-expression profiles of paired microdissected ovarian tumor and stroma.

Results:

Recently described ovarian cancer molecular subtypes are strongly associated with the cell admixture. Tumors were classified as different molecular subtypes in simulations where the percentage of stromal cells increased. Stromal gene expression in bulk tumors was associated with overall survival (hazard ratio, 1.17; 95% confidence interval, 1.11–1.23), and in one data set, increased stroma was associated with anatomic sampling location. Five published prognostic gene signatures were no longer prognostic in a multivariate model that adjusted for stromal content.

Conclusions:

Cell admixture affects the interpretation and reproduction of ovarian cancer molecular subtypes and gene signatures derived from bulk tissue. Elucidating the role of stroma in the tumor microenvironment and in prognosis is important.

Impact:

Single-cell analyses may be required to refine the molecular subtypes of high-grade serous ovarian cancer.

Funding
Core A: Biospecimen and High-Dimensional Data Management CoreValidation and Clinical Relevance of Ovarian Cancer Molecular SubtypesWebMeV: A Robust Platform for Intuitive Genomic Data AnalysisBasic and Translational Research Center for Reducing Health DisparitiesCancer Genomics: Integrative and Scalable Solutions in R/BioconductorPrognostic markers for ovarian cancerUnraveling the Complexities of Risk and Mechanism in CancerBioinformatics Tools for Genomic Analysis of Tumor and Stromal Pathways in CancerDiscovery, Biology and Risk of Inherited Variants in Breast CancerMeV: Software for Next Generation Genomic Data AnalysisGenomic Stratification of Ovarian Cancer PatientsDevelopmental FundsGenomic Stratification of Ovarian Cancer PatientsDevelopmental FundsBioinformatics Tools for Genomic Analysis of Tumor and Stromal Pathways in CancerPrognostic markers for ovarian cancerValidation and Clinical Relevance of Ovarian Cancer Molecular SubtypesDiscovery, Biology and Risk of Inherited Variants in Breast CancerProject 4: Statistical InnovationsBasic and Translational Research Center for Reducing Health DisparitiesBreast Cancer Research Program Grant W81XWH-15-1-0013

NCI NIH HHS

P01 CA087969

NCI NIH HHS

R03 CA191447

NCI NIH HHS

U24 CA231846

NIMHD NIH HHS

G12 MD007599

NCI NIH HHS

U24 CA180996

NCI NIH HHS

R01 CA133057

NCI NIH HHS

R35 CA220523

NCI NIH HHS

R01 CA174206

NCI NIH HHS

U19 CA148065

NCI NIH HHS

U01 CA151118

NCI NIH HHS

RC4 CA156551

NCI NIH HHS

P30 CA006516

NIH

RC4CA156551

NIH

P30CA006516

NIH

R01CA174206

NIH

R01CA133057

NIH

R03CA191447

NIH

U19CA148065

NIH

P01CA087969

NIH

G12MD007599