A pharmacogenomic method for individualized prediction of drug sensitivity

• Published in: Molecular systems biology 2011-07, Vol.7 (1), p.513-n/a
• Main Authors: Cohen, Adam L, Soldi, Raffaella, Zhang, Haiyu, Gustafson, Adam M, Wilcox, Ryan, Welm, Bryan E, Chang, Jeffrey T, Johnson, Evan, Spira, Avrum, Jeffrey, Stefanie S, Bild, Andrea H
• Format: Article
• Language: English
• Subjects: Acids; Algorithms; Animals; Biomarkers; Biomarkers, Tumor - genetics; Biotechnology; Breast cancer; Breast Neoplasms - drug therapy; Cancer; Cancer therapies; Cell culture; Cell Line, Tumor; Chemotherapy; Clinical trials; Correlation; Datasets; Drug Resistance, Neoplasm; EMBO10; EMBO24; Estrogens; Female; Gene expression; Gene Expression Profiling; Gene Expression Regulation, Neoplastic; Genome, Human; Genomes; Genomic analysis; Growth inhibition; Growth rate; Humans; In vitro methods and tests; In vivo methods and tests; Medical research; Melanoma; Mice; Oligonucleotide Array Sequence Analysis; Patients; Pharmacogenetics - methods; Pharmacogenomics; Pharmacology; Phenotype; Predictions; Raf protein; Sensitivity; Sensitivity analysis; Sensitivity and Specificity; Signatures; Target recognition; Tumor cell lines; Tumors; Valproic acid; Valproic Acid - pharmacology; Xenografts; Xenotransplantation
• ISSN: 1744-4292; 1744-4292
• DOI: 10.1038/msb.2011.47

Identifying the best drug for each cancer patient requires an efficient individualized strategy. We present MATCH ( M erging genomic and pharmacologic A nalyses for T herapy CH oice), an approach using public genomic resources and drug testing of fresh tumor samples to link drugs to patients. Valproic acid (VPA) is highlighted as a proof‐of‐principle. In order to predict specific tumor types with high probability of drug sensitivity, we create drug response signatures using publically available gene expression data and assess sensitivity in a data set of >40 cancer types. Next, we evaluate drug sensitivity in matched tumor and normal tissue and exclude cancer types that are no more sensitive than normal tissue. From these analyses, breast tumors are predicted to be sensitive to VPA. A meta‐analysis across breast cancer data sets shows that aggressive subtypes are most likely to be sensitive to VPA, but all subtypes have sensitive tumors. MATCH predictions correlate significantly with growth inhibition in cancer cell lines and three‐dimensional cultures of fresh tumor samples. MATCH accurately predicts reduction in tumor growth rate following VPA treatment in patient tumor xenografts. MATCH uses genomic analysis with in vitro testing of patient tumors to select optimal drug regimens before clinical trial initiation. Synopsis Unlike traditional chemotherapy, targeted cancer therapies are expected to work in only a subset of people with a particular cancer. However, biomarkers of response are not always known before clinical trial initiation. We present MATCH (Merging genomic and pharmacologic Analyses for Therapy CHoice), an algorithm for using genome‐wide gene expression data to identify and validate a genomic biomarker of sensitivity (see Figure 1 ). Our proof‐of‐principle example is valproic acid (VPA), but we also show that an estrogen blocking drug currently used for breast cancer and a B‐RAF inhibitor in trials for melanoma give predictions that correspond to their clinical uses. We use genome‐wide gene expression data from treated and untreated samples from the Connectivity Map to generate a VPA response signature. We validate that the VPA signature can identify treated and untreated cells in an independent data set of normal cells and in independent samples from the Connectivity Map. The AUC for the ROC curve is 0.86. We then apply the VPA signature to publically available data sets from a panel of cancer cell lines and from primary tumor and normal