A pharmacogenomic method for individualized prediction of drug sensitivity - PubMed (original) (raw)

Meta-Analysis

A pharmacogenomic method for individualized prediction of drug sensitivity

Adam L Cohen et al. Mol Syst Biol. 2011.

Abstract

Identifying the best drug for each cancer patient requires an efficient individualized strategy. We present MATCH (Merging genomic and pharmacologic Analyses for Therapy CHoice), 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.

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Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Figure 1

Figure 1

Flowchart of streamlined method to identify target population for a drug.

Figure 2

Figure 2

VPA signature. (A) The heatmap columns are the Connectivity Map samples with the 10 controls on the left and 5 treated samples on the right. Each row is a probe in the signature. Red indicates upregulation and blue indicates downregulation of the gene. (B) LOOCV from the Connectivity Map training sample. Blue samples (1–10) are the control samples. Red samples (11–15) are the VPA-treated samples. (C) Bar graph of mean and standard error of predicted VPA sensitivity on ovarian theca cells before and after treatment with VPA. (D) Graph of predicted sensitivity to VPA in Connectivity Map samples from nine independent batches. Samples are grouped as untreated controls, samples treated with a drug other than an HDAC inhibitor, samples treated with an HDAC inhibitor other than VPA, and samples treated with various doses of VPA. (E) Graph of sensitivity predictions versus actual treatment dose for Connectivity Map samples treated with various doses of VPA. The line is a best-fit sigmoidal curve excluding the two outliers. (F) ROC curve based on data from Figure 2D comparing VPA-treated samples with samples that were untreated or treated with a non-HDAC inhibitor. (G) Doughnut plot of the Gene Ontology terms for the genes in the VPA signature with Bayes factor >2. Bayes factor for each term is given on the doughnut.

Figure 3

Figure 3

Predictions across cancer types and subtypes. (A) Box-whisker plot for predicted VPA sensitivity across GSK cell lines for epithelial cancers. Median is indicated by a horizontal line. The box gives the interquartile range, and the error bars indicate the total range. (B) Box-whisker plot for VPA sensitivity across cancer types in GSE5364. Boxes for normal adjacent tissue are checkered. Median is indicated by a horizontal line. The box gives the interquartile range, and the error bars indicate the total range. (C) Heatmap of samples from 11 breast cancer data sets, divided by intrinsic subtype, with the predicted response to VPA displayed as a color, with red representing a high predicted response, and blue a low predicted response. Each column is an individual sample, and the heterogeneity of predicted response to VPA within and between subtypes is clearly visible. The percent of samples with predicted sensitivity >0.5 is given below the heatmap.

Figure 4

Figure 4

PLX4032 signature and validation. (A) The heatmap columns are the GSE20051 samples with the five controls on the left and five treated samples on the right. Each row is a probe in the signature. Red indicates upregulation and blue indicates downregulation of the gene. (B) LOOCV from the GSE20051 training set. Blue samples (1–6) are the control samples. Red samples (7–12) are the PLX4032-treated samples. (C) Box-whisker plot for predicted PLX4032 sensitivity across GSK cell lines for epithelial cancers stratified by cancer type. (D) Box-whisker plot for PLX4032 sensitivity across skin cancer types and normal skin in GSE7553. (Cancer and normal types with two or fewer samples were excluded.) Median is indicated by a horizontal line.

Figure 5

Figure 5

Correlation of actual response to targeted therapeutics and predicted response from drug response signatures. Breast cancer cell lines were treated with VPA for 96 h and proliferation was assayed using a standard MTT colorimetric method. Scatter plot shows the degree of correlation between proliferation inhibition (EC50) and predicted sensitivity for each cell line. Source data is available for this figure at

www.nature.com/msb

.

Figure 6

Figure 6

Patient tumor cell sensitivity to VPA in 3D culture. Primary tumor and pleural effusion-derived breast cancer cells were embedded in Matrigel and treated with VPA for 96 h. (A) The effect of VPA was assessed by light microscopy (right panel) and fluorescent dye (left panel) to identify live (green) and dead cells (red). (B) Correlation of EC50 of VPA and predicted response from drug response signatures in the fresh tumor samples grown in 3D. Source data is available for this figure at

www.nature.com/msb

.

Figure 7

Figure 7

In vivo validation of computationally predicted responsiveness to VPA using human breast cancer xenografts. (A) Predicted sensitivity of five breast tumors (four basal and one luminal) to VPA. The gene expression patterns of the patient tumors were analyzed using in vitro drug response signatures to VPA. (B) In vivo response to VPA treatment on xenografts generated from the primary tumors in (A). Blue and red lines: VPA group; black line: saline control group. Each group had five mice. Tumor growth rates were plotted as the mean tumor volumes of each group±s.e.m. Source data is available for this figure at

www.nature.com/msb

.

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