Gene Regulatory Network Analysis Identifies Sex-Linked Differences in Colon Cancer Drug Metabolism

Abstract

Understanding sex differences in colon cancer is essential to advance disease prevention, diagnosis, and treatment. Males have a higher risk of developing colon cancer and a lower survival rate than women. However, the molecular features that drive these sex differences are poorly understood. In this study, we use both transcript-based and gene regulatory network methods to analyze RNA-seq data from The Cancer Genome Atlas for 445 patients with colon cancer. We compared gene expression between tumors in men and women and observed significant sex differences in sex chromosome genes only. We then inferred patient-specific gene regulatory networks and found significant regulatory differences between males and females, with drug and xenobiotics metabolism via cytochrome P450 pathways more strongly targeted in females. This finding was validated in a dataset of 1,193 patients from five independent studies. While targeting, the drug metabolism pathway did not change overall survival for males treated with adjuvant chemotherapy, females with greater targeting showed an increase in 10-year overall survival probability, 89% [95% confidence interval (CI), 78-100] survival compared with 61% (95% CI, 45-82) for women with lower targeting, respectively (P = 0.034). Our network analysis uncovers patterns of transcriptional regulation that differentiate male and female colon cancer and identifies differences in regulatory processes involving the drug metabolism pathway associated with survival in women who receive adjuvant chemotherapy. This approach can be used to investigate the molecular features that drive sex differences in other cancers and complex diseases.Significance: A network-based approach reveals that sex-specific patterns of gene targeting by transcriptional regulators are associated with survival outcome in colon cancer. This approach can be used to understand how sex influences progression and response to therapies in other cancers. Cancer Res; 78(19); 5538-47. ©2018 AACR.

Figure 1.. Study workflow.

Overview of the approach used to reconstruct single-sample gene regulatory networks and to perform differential targeting analysis.

Figure 2.. Differential expression and differential targeting between male and female colon cancer.

A) Expression log₂(fold-change) of the top 20 differentially expressed genes between males and females in the discovery dataset. B) Gene targeting score difference of the top 20 genes differentially targeted between males and females in the discovery dataset. C) Edge weight difference of the top 20 edges most significantly different between males and females in the discovery dataset. Positive values indicate higher levels in females, and negative values indicate higher levels in males.

Figure 3.. Differential targeting of genes in the drug metabolism pathway.

A) Subnetwork representation of the edges in the drug metabolism pathway that are most significantly different between male and female regulatory networks (FDR<0.05). B) Heatmap of gene targeting score difference for all the analyzed genes in the drug metabolism-cytochrome P450 pathway for the discovery and validation dataset analyses. For visualization purposes, the color scale was saturated at 30. Positive values indicate higher levels in females, and negative values indicate higher levels in males.

Figure 4.. Overall survival analysis based on targeting of the drug metabolism pathway.

Kaplan-Meier curve of stage III colon cancer patients not treated (A) or treated (B) with adjuvant chemotherapy. Patients were stratified by low and high targeting groups based on the median gene targeting score across all genes in the drug metabolism pathway. P-values were computed using the log-rank test to evaluate the overall survival risk between the low targeting (green) and high targeting (purple) subgroups of patients.