title: Genomic signatures for paclitaxel and gemcitabine resistance in breast cancer derived by machine learning.
abstract: Increasingly, the effectiveness of adjuvant chemotherapy agents for breast cancer has been related to changes in the genomic profile of tumors. We investigated correspondence between growth inhibitory concentrations of paclitaxel and gemcitabine (GI50) and gene copy number, mutation, and expression first in breast cancer cell lines and then in patients. Genes encoding direct targets of these drugs, metabolizing enzymes, transporters, and those previously associated with chemoresistance to paclitaxel (n = 31 genes) or gemcitabine (n = 18) were analyzed. A multi-factorial, principal component analysis (MFA) indicated expression was the strongest indicator of sensitivity for paclitaxel, and copy number and expression were informative for gemcitabine. The factors were combined using support vector machines (SVM). Expression of 15 genes (ABCC10, BCL2, BCL2L1, BIRC5, BMF, FGF2, FN1, MAP4, MAPT, NFKB2, SLCO1B3, TLR6, TMEM243, TWIST1, and CSAG2) predicted cell line sensitivity to paclitaxel with 82% accuracy. Copy number profiles of 3 genes (ABCC10, NT5C, TYMS) together with expression of 7 genes (ABCB1, ABCC10, CMPK1, DCTD, NME1, RRM1, RRM2B), predicted gemcitabine response with 85% accuracy. Expression and copy number studies of two independent sets of patients with known responses were then analyzed with these models. These included tumor blocks from 21 patients that were treated with both paclitaxel and gemcitabine, and 319 patients on paclitaxel and anthracycline therapy. A new paclitaxel SVM was derived from an 11-gene subset since data for 4 of the original genes was unavailable. The accuracy of this SVM was similar in cell lines and tumor blocks (70-71%). The gemcitabine SVM exhibited 62% prediction accuracy for the tumor blocks due to the presence of samples with poor nucleic acid integrity. Nevertheless, the paclitaxel SVM predicted sensitivity in 84% of patients with no or minimal residual disease.
authors.affiliation: Department of Biochemistry, Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, Canada.
authors.forename: Stephanie N
authors.initials: SN
authors.lastname: Dorman
authors.affiliation: Department of Biochemistry, Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, Canada.
authors.forename: Katherina
authors.initials: K
authors.lastname: Baranova
authors.affiliation: Department of Pathology and Laboratory Medicine, Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, Canada; Molecular Diagnostics Division, Laboratory Medicine Program, London Health Sciences Centre, ON, Canada; Cytognomix Inc., London, ON, Canada.
authors.forename: Joan H M
authors.initials: JH
authors.lastname: Knoll
authors.affiliation: Department of Physiology and Pharmacology, Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, Canada.
authors.forename: Brad L
authors.initials: BL
authors.lastname: Urquhart
authors.affiliation: Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
authors.forename: Gabriella
authors.initials: G
authors.lastname: Mariani
authors.affiliation: Department of Diagnostic and Laboratory Pathology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
authors.forename: Maria Luisa
authors.initials: ML
authors.lastname: Carcangiu
authors.affiliation: Department of Biochemistry, Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, Canada; Cytognomix Inc., London, ON, Canada; Department of Computer Science, University of Western Ontario, London, ON, Canada; Department of Oncology, Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, Canada. Electronic address: progan@uwo.ca.
authors.forename: Peter K
authors.initials: PK
authors.lastname: Rogan
mesh_terms: D000970:Antineoplastic Agents / Q000627:therapeutic use*; D001943:Breast Neoplasms / Q000188:drug therapy* / Q000235:genetics; D045744:Cell Line, Tumor; D003841:Deoxycytidine / Q000031:analogs & derivatives* / Q000627:therapeutic use; D019008:Drug Resistance, Neoplasm / Q000235:genetics*; D005260:Female; D006801:Humans; D000069550:Machine Learning; D017239:Paclitaxel / Q000627:therapeutic use*; D060388:Support Vector Machine; D000093542:Gemcitabine
chemical_list: D000970:Antineoplastic Agents; D003841:Deoxycytidine; D017239:Paclitaxel; D000093542:Gemcitabine
keywords: Breast cancer; Drug sensitivity; Gemcitabine; Genomic profiles; Paclitaxel; Resistance