Sex differences in GBM revealed by analysis of patient imaging, transcriptome, and survival data

Wei Yang, Nicole M. Warrington, Sara J. Taylor, Paula Whitmire, Eduardo Carrasco, Kyle W. Singleton, Ningying Wu, Justin D. Lathia, Michael E. Berens, Albert H. Kim, Jill S. Barnholtz-Sloan, Kristin R. Swanson, Jingqin Luo, Joshua B. Rubin

Research output: Contribution to journalArticlepeer-review

87 Scopus citations

Abstract

Sex differences in the incidence and outcome of human disease are broadly recognized but, in most cases, not sufficiently understood to enable sex-specific approaches to treatment. Glioblastoma (GBM), the most common malignant brain tumor, provides a case in point. Despite well-established differences in incidence and emerging indications of differences in outcome, there are few insights that distinguish male and female GBM at the molecular level or allow specific targeting of these biological differences. Here, using a quantitative imaging-based measure of response, we found that standard therapy is more effective in female compared with male patients with GBM. We then applied a computational algorithm to linked GBM transcriptome and outcome data and identified sex-specific molecular subtypes of GBM in which cell cycle and integrin signaling are the critical determinants of survival for male and female patients, respectively. The clinical relevance of cell cycle and integrin signaling pathway signatures was further established through correlations between gene expression and in vitro chemotherapy sensitivity in a panel of male and female patient-derived GBM cell lines. Together, these results suggest that greater precision in GBM molecular subtyping can be achieved through sex-specific analyses and that improved outcomes for all patients might be accomplished by tailoring treatment to sex differences in molecular mechanisms.

Original languageEnglish (US)
Article numbereaao5253
JournalScience translational medicine
Volume11
Issue number473
DOIs
StatePublished - Jan 2 2019

ASJC Scopus subject areas

  • General Medicine

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