Comprehensive comparison of molecular portraits between cell lines and tumors in breast cancer

Background: Proper cell models for breast cancer primary tumors have long been the focal point in the cancer’s research. The genomic comparison between cell lines and tumors can investigate the similarity and dissimilarity and help to select right cell model to mimic tumor tissues to properly evaluate the drug reaction in vitro. In this paper, a comprehensive comparison in copy number variation (CNV), mutation, mRNA expression and protein expression between 68 breast cancer cell lines and 1375 primary breast tumors is conducted and presented. Results: Using whole genome expression arrays, strong correlations were observed between cells and tumors. PAM50 gene expression differentiated them into four major breast cancer subtypes: Luminal A and B, HER2amp, and Basal-like in both cells and tumors partially. Genomic CNVs patterns were observed between tumors and cells across chromosomes in general. High C > T and C > G trans-version rates were observed in both cells and tumors, while the cells had slightly higher somatic mutation rates than tumors. Clustering analysis on protein expression data can reasonably recover the breast cancer subtypes in cell lines and tumors. Although the drug-targeted proteins ER/PR and interesting mTOR/GSK3/TS2/PDK1/ER_P118 cluster had shown the consistent patterns between cells and tumor, low protein-based correlations were observed between cells and tumors. The expression consistency of mRNA verse protein between cell line and tumors reaches 0.7076. These important drug targets in breast cancer, ESR1, PGR, HER2, EGFR and AR have a high similarity in mRNA and protein variation in both tumors and cell lines. GATA3 and RP56KB1 are two promising drug targets for breast cancer. A total score developed from the four correlations among four molecular profiles suggests that cell lines, BT483, T47D and MDAMB453 have the highest similarity with tumors. Conclusions: The integrated data from across these multiple platforms demonstrates the existence of the similarity and dissimilarity of molecular features between breast cancer tumors and cell lines. The cell lines only mirror some but not all of the molecular properties of primary tumors. The study results add more evidence in selecting cell line models for breast cancer research

Integrative Analysis for Identifying Multi-Layer Modules in Precision Medicine

Indiana University-Purdue University Indianapolis (IUPUI)Precision medicine aims to employ information from all modalities to develop a comprehensive view of disease progression and administer therapies tailored to the individual patient. A set of genomic features (gene CNVs, mutations, mRNA expressions, and protein abundances) is associated with each patient and it is hard to explain the phenotypic similarities such as gene essentiality or variability in drug response in a single genomic level. Thus, to extract biological principles it is critical to seek mutual information from multi-dimensional datasets. To address these concerns, we first conduct an integrated mRNA/protein analysis in both breast cancer cell lines and tumors, and most interestingly in the breast cancer subtypes. We identified cell lines that provide optimum heterogeneity models for studying the underlying biological processes of tumors. Our systematic observation across multi-omics data identifies distinct subgroups of cancer cells and patients. Based on this identified signal transduction between mRNA and RPPA, we developed a biclustering model to characterize key genetic alterations that are shared in both cancer cell lines and patients. We integrated two types of omics data including copy number variations, transcriptome, and proteome. Bi-EB adopts a data-driven statistics strategy by using Expected-Maximum (EM) algorithm to extract the foreground bicluster pattern from its background noise data in an iterative search. Using Bi-EB algorithm we selected translational gene sets that are characterized by highly correlated molecular profiles among RNA and proteins. To further investigate cell line and tissue in breast cancer we explore the relationship vii between genomic features and the phenotypic factors. Using in vitro/in vivo drug screening data, we adopt partial least square regression method and develop a multi-modular approach to predict anticancer therapy benefits for ER-negative breast cancer patients. The identified joint multi-dimensional modules here provide us new insights into the molecular mechanisms of drugs and cancer treatment.2021-12-2