Browsing by Author "Pawar, Aniruddha Vikram"

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    Classification of Breast Cancer Cell Lines into Subtypes Based on Genetic Profiles
    (2015-03-16) Pawar, Aniruddha Vikram; Li, Lang
    Today we know that there are several different types of breast cancer. Accurate identification breast cancer subtype is extremely important in treating this disease effectively. Consequently the process of invtro development of drugs to treat this disease should be naturally subtype specific. Until now several studies have identified multiple breast cancer cell lines and these cell lines have served as invaluable invitro tumor models. However very few of these cell lines are classified as per their subtypes. In this thesis an effort is made to classify 59 of such breast cancer cell lines using genetic profile comparison approach. This approach is based on comparing characteristic features such as copy number and gene expression of a given cell line to those observed from the tissue samples of different breast subtypes. The tissue data for this comparison comes from The Cancer Genome Atlas (TCGA) while cell line data is taken from Cancer Cell Line Encyclopedia (CCLE).
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    Comprehensive comparison of molecular portraits between cell lines and tumors in breast cancer
    (BioMed Central, 2016-08-22) Jiang, Guanglong; Zhang, Shijun; Yazdanparast, Aida; Li, Meng; Pawar, Aniruddha Vikram; Liu, Yunlong; Inavolu, Sai Mounika; Cheng, Lijun; Department of Medical and Molecular Genetics, IU School of Medicine
    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.