主 题: A multidimensional network approach reveals microRNAs as determinants of the mesenchymal colorectal cancer subtype
报告人: Prof. Xin Wang (City University of Hong Kong)
时 间: 2018-03-29 14:00-15:00
地 点: Room 1114, Sciences Building No. 1
Abstract: Colorectal cancer (CRC) is a heterogeneous disease posing a challenge for accurate classification and treatment of this malignancy. There is no common genetic molecular feature that would allow for the identification of patients at risk for developing recurrences and thus selecting patients that would benefit from more stringent therapies still poses a major clinical challenge. Recently, an international multicenter consortium (CRC Subtyping Consortium) was established aiming at the classification of CRC patients in biologically homogeneous CRC subtypes. Four consensus molecular subtypes (CMS) were identified, of which the mesenchymal CMS4 presented with worse prognosis signifying the importance of identifying these patients. Despite the large number of samples analyzed and their clear association with unifying biological programs and clinical features, single driver mutations could not be identified and patients are heterogeneous with regard to currently used clinical markers. We therefore set out to define the regulatory mechanisms underlying the distinct gene expression profiles using a network-based approach involving multiple molecular modalities such as gene expression, methylation levels, and microRNA (miR) expression. The miR-200 family presented as the most powerful determinant of CMS4-specific gene expression, tuning the majority of genes differentially expressed in the poor prognosis subtype including genes associated with the epithelial-mesenchymal transition program. Furthermore, our data show that two epigenetic marks, namely the methylation of the two miR-200 promoter regions, can identify tumors belonging to the mesenchymal subtype and is predictive of disease-free survival in CRC patients. Importantly, epigenetic silencing of the miR-200 family is also detected in epithelial CRC cell lines that belong to the mesenchymal CMS. We thus show that determining regulatory networks is a powerful strategy to define drivers of distinct cancer subtypes, which possess the ability to identify subtype affiliation and to shed light on biological behavior.
About the speaker: Dr Wang is an Assistant Professor at the Department of Biomedical Sciences, City University of Hong Kong. He received his PhD at the University of Cambridge Department of Oncology and Cancer Research UK Cambridge Institute in 2013, where his research was concerned with how to infer intracellular signalling pathways from phenotyping screens. In collaboration with experimental oncologists, he identified molecularly distinct colon cancer subtypes using an unsupervised classification approach. From 2013 to 2015, Dr Wang did his postdoc training at Harvard Medical School Department of Biomedical Informatics, where he applied advanced techniques in next-generation sequencing data analysis to study the role of BRD4-NUT fusion gene in NUT midline carcinoma and characterized LRF as an independent repressive transcription factor of fetal hemoglobin. Dr Wang's current major research interest is to better understand the biology underlying cancer using novel machine learning approaches. In collaboration with cancer biologists, he is working on dissecting molecular heterogeneity and subtype-specific regulatory mechanisms in major malignancies such as breast, colon, pancreatic, ovarian and liver cancers.