Year-end Sale % OFF 
Abstract
Though patient sex influences response to cancer treatments, little is known of the molecular causes, and cancer therapies are generally given irrespective of patient sex. We assessed transcriptomic differences in tumors from men and women spanning 17 cancer types, and we assessed differential expression between tumor and normal samples stratified by sex across 7 cancers. We used the LincsCloud platform to perform Connectivity Map analyses to link transcriptomic signatures identified in male and female tumors with chemical and genetic perturbagens, and we performed permutation testing to identify perturbagens that showed significantly differential connectivity with male and female tumors. Our analyses predicted that females are sensitive and males are resistant to tamoxifen treatment of lung adenocarcinoma, a finding which is consistent with known male-female differences in lung cancer. We made several novel predictions, including that CDK1 and PTPN1 knockdown would be more effective in males with hepatocellular carcinoma, and SMAD3 and HSPA4 knockdown would be more effective in females with head and neck squamous cell carcinoma. Our results provide a new resource for researchers studying male-female biological and treatment response differences in human cancer. The complete results of our analyses are provided at the website accompanying this manuscript (http://becklab.github.io/SexLinked).
Highlights
The efficacies of several existing anticancer drugs have been shown to differ between the sexes[3,4,5], identifying drugs with efficacy that varies across the sexes using conventional guess-and-check approaches is time consuming, expensive, and not scalable
Our transcriptomic analyses used gene expression (RNAseqV2) data downloaded from The Cancer Genome Atlas
We identified genes which exhibited significantly (BH < 0.05) sex-disparate expression, ranging from 28 genes in kidney chromophobe (KICH) to 1,669 genes in kidney renal clear cell carcinoma (KIRC) (Fig. 2)
Summary
The efficacies of several existing anticancer drugs have been shown to differ between the sexes[3,4,5], identifying drugs with efficacy that varies across the sexes using conventional guess-and-check approaches is time consuming, expensive, and not scalable. There would be tremendous value in the development of a systematic data-driven approach for identifying significant molecular differences in tumors in men and women, and to use this knowledge to inform the rational prioritization of cancer drugs predicted to show differential efficacy based on sex. The goal of our study is to identify transcriptomic differences between tumors in males and females, and to use this knowledge to inform drug sensitivity predictions in male and female tumors. In order to predict sex-disparate drug sensitivity, we developed a computational framework consisting of three steps: identification of genes that show differential expression in cancer with respect to patient sex; identification of genes and pathways differentially expressed between tumor and normal samples stratified by sex; and use of sex-specific gene expression signatures to prioritize chemical and genetic perturbagens predicted to have differential efficacy in males vs. females (Fig. 1)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
