bioRxivCancer BiologyPreprint
Clinically Important sex differences in GBM biology revealed by analysis of male and female imaging, transcriptome and survival data
Wei Yang, Nicole M. Warrington, Sara J. Taylor, 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
Abstract
Sex differences in the incidence and outcome of human disease are broadly recognized but in most cases not adequately 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 temozolomide chemotherapy is more effective in female compared to male GBM patients. We then applied a novel computational algorithm to linked GBM transcriptome and outcome data, and identified novel sex-specific molecular subtypes of GBM in which cell cycle and integrin signaling were identified as the critical determinants of survival for male and female patients, respectively. The clinical utility 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 outcome for all patients might be accomplished via tailoring treatment to sex differences in molecular mechanisms.\n\nOne Sentence SummaryMale and female glioblastoma are biologically distinct and maximal chances for cure may require sex-specific approaches to treatment.
- organism: human
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- approach: machine learning (classical)
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