Pharmacogenomic and High-Throughput Data Analysis to Overcome Triple Negative Breast Cancers Drug Resistance

Abstract : Given the large number of treatment-resistant triple-negative breast cancers, it is essential to understand the mechanisms of resistance and to find new effective molecules. First, we analyze two large-scale pharmacogenomic datasets. We propose a novel classification based on transcriptomic profiles of cell lines, according to a biological network-driven gene selection process. Our molecular classification shows greater homogeneity in drug response than when cell lines are grouped according to their original tissue. It also helps identify similar patterns of treatment response. In a second analysis, we study a cohort of patients with triple-negative breast cancer who have resisted to neoadjuvant chemotherapy. We perform complete molecular analyzes based on RNAseq and WES. We observe a high molecular heterogeneity of tumors before and after treatment. Although we highlighted clonal evolution under treatment, no recurrent mechanism of resistance could be identified Our results strongly suggest that each tumor has a unique molecular profile and that that it is increasingly important to have large series of tumors. Finally, we are improving a method for testing the overrepresentation of known RNA binding protein motifs in a given set of regulated sequences. This tool uses an innovative approach to control the proportion of false positives that is not realized by the existing algorithm. We show the effectiveness of our approach using two different datasets.
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Benjamin Sadacca. Pharmacogenomic and High-Throughput Data Analysis to Overcome Triple Negative Breast Cancers Drug Resistance. Quantitative Methods [q-bio.QM]. Université Paris-Saclay, 2017. English. ⟨NNT : 2017SACLS538⟩. ⟨tel-01956586⟩

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