Genome-wide cross-cancer analysis illustrates the critical role of bimodal miRNA in patient survival and drug responses to PI3K inhibitors

Author summaryBimodal genes can be defined as those having two modes of expression within the same population. A variety of statistical methodologies have been employed to assess bimodal gene expression, but current methods and their applications have been limited. Given the advances in next-generation sequencing as well as the extensive regulatory role of miRNA, assessing bimodality in miRNA-seq data can greatly broaden our understanding of factors underlying tumor progression. The goal of the current study was to utilize a novel mixture modeling approach to identify bimodal miRNA and then demonstrate their importance in cancer by evaluating their ability to predict overall survival and drug response. Our results showed that high levels of bimodal miRNA expression was characteristic of cancer. Additionally, several bimodal miRNA were common to multiple cancer types, suggesting that certain miRNA consistently account for tumor heterogeneity and may be involved in general oncogenic processes. Our study points to the potential of bimodal miRNA to facilitate precise prognostic evaluation and effective treatment strategies. Heterogeneity of cancer means many tumorigenic genes are only aberrantly expressed in a subset of patients and thus follow a bimodal distribution, having two modes of expression within a single population. Traditional statistical techniques that compare sample means between cancer patients and healthy controls fail to detect bimodally expressed genes. We utilize a mixture modeling approach to identify bimodal microRNA (miRNA) across cancers, find consistent sources of heterogeneity, and identify potential oncogenic miRNA that may be used to guide personalized therapies. Pathway analysis was conducted using target genes of the bimodal miRNA to identify potential functional implications in cancer. In vivo overexpression experiments were conducted to elucidate the clinical importance of bimodal miRNA in chemotherapy treatments. In nine types of cancer, tumors consistently displayed greater bimodality than normal tissue. Specifically, in liver and lung cancers, high expression of miR-105 and miR-767 was indicative of poor prognosis. Functional pathway analysis identified target genes of miR-105 and miR-767 enriched in the phosphoinositide-3-kinase (PI3K) pathway, and analysis of over 200 cancer drugs in vitro showed that drugs targeting the same pathway had greater efficacy in cell lines with high miR-105 and miR-767 levels. Overexpression of the two miRNA facilitated response to PI3K inhibitor treatment. We demonstrate that while cancer is marked by considerable genetic heterogeneity, there is between-cancer concordance regarding the particular miRNA that are more variable. Bimodal miRNA are ideal biomarkers that can be used to stratify patients for prognosis and drug response in certain types of cancer.

Automated summary

This study utilizes a novel mixture modeling approach to identify bimodal miRNA in cancer and demonstrates their importance in predicting overall survival and drug response. The results reveal that high levels of bimodal miRNA expression are characteristic of cancer, and certain miRNA consistently account for tumor heterogeneity and may be involved in oncogenic processes. Bimodal miRNA can be used as biomarkers for prognosis and personalized treatment strategies in certain types of cancer.