title: Drug Intervention Response Predictions with PARADIGM (DIRPP) identifies drug resistant cancer cell lines and pathway mechanisms of resistance.
abstract: The revolution in sequencing techniques in the past decade has provided an extensive picture of the molecular mechanisms behind complex diseases such as cancer. The Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Project (CGP) have provided an unprecedented opportunity to examine copy number, gene expression, and mutational information for over 1000 cell lines of multiple tumor types alongside IC50 values for over 150 different drugs and drug related compounds. We present a novel pipeline called DIRPP, Drug Intervention Response Predictions with PARADIGM7, which predicts a cell line's response to a drug intervention from molecular data. PARADIGM (Pathway Recognition Algorithm using Data Integration on Genomic Models) is a probabilistic graphical model used to infer patient specific genetic activity by integrating copy number and gene expression data into a factor graph model of a cellular network. We evaluated the performance of DIRPP on endometrial, ovarian, and breast cancer related cell lines from the CCLE and CGP for nine drugs. The pipeline is sensitive enough to predict the response of a cell line with accuracy and precision across datasets as high as 80 and 88% respectively. We then classify drugs by the specific pathway mechanisms governing drug response. This classification allows us to compare drugs by cellular response mechanisms rather than simply by their specific gene targets. This pipeline represents a novel approach for predicting clinical drug response and generating novel candidates for drug repurposing and repositioning.
authors.affiliation: Case Center for Proteomics and Bioinformatics, Case Western Reserve University School of Medicine, BRB 932, 10900 Euclid Avenue, Cleveland, Ohio 44106, USA. dkb50@case.edu.
authors.forename: Douglas
authors.initials: D
authors.lastname: Brubaker
authors.forename: Analisa
authors.initials: A
authors.lastname: Difeo
authors.forename: Yanwen
authors.initials: Y
authors.lastname: Chen
authors.forename: Taylor
authors.initials: T
authors.lastname: Pearl
authors.forename: Kaide
authors.initials: K
authors.lastname: Zhai
authors.forename: Gurkan
authors.initials: G
authors.lastname: Bebek
authors.forename: Mark
authors.initials: M
authors.lastname: Chance
authors.forename: Jill
authors.initials: J
authors.lastname: Barnholtz-Sloan
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000188:drug therapy / Q000235:genetics; D045744:Cell Line, Tumor; D019295:Computational Biology; D030541:Databases, Genetic / Q000706:statistics & numerical data; D062313:Databases, Pharmaceutical / Q000706:statistics & numerical data; D055808:Drug Discovery / Q000706:statistics & numerical data; D019008:Drug Resistance, Neoplasm / Q000235:genetics; D016889:Endometrial Neoplasms / Q000188:drug therapy / Q000235:genetics; D005260:Female; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D009369:Neoplasms / Q000188:drug therapy* / Q000235:genetics* / Q000378:metabolism; D010051:Ovarian Neoplasms / Q000188:drug therapy / Q000235:genetics