A strategy for predicting the chemosensitivity of human cancers and its application to drug discovery

Abstract

The U.S. National Cancer Institute has used a panel of 60 diverse human cancer cell lines (the NCI-60) to screen >100,000 chemical compounds for anticancer activity. However, not all important cancer types are included in the panel, nor are drug responses of the panel predictive of clinical efficacy in patients. We asked, therefore, whether it would be possible to extrapolate from that rich database (or analogous ones from other drug screens) to predict activity in cell types not included or, for that matter, clinical responses in patients with tumors. We address that challenge by developing and applying an algorithm we term "coexpression extrapolation" (COXEN). COXEN uses expression microarray data as a Rosetta Stone for translating from drug activities in the NCI-60 to drug activities in any other cell panel or set of clinical tumors. Here, we show that COXEN can accurately predict drug sensitivity of bladder cancer cell lines and clinical responses of breast cancer patients treated with commonly used chemotherapeutic drugs. Furthermore, we used COXEN for in silico screening of 45,545 compounds and identify an agent with activity against human bladder cancer.

Fig. 1.

Application and performance characteristics of the COXEN algorithm for prediction of drug sensitivity in the BLA-40 human urothelial cancer cell lines. (A) Summary schematic diagram of the development and validation of chemosensitivity predictions. Step numbers relate to those of the COXEN algorithm, as described in the text. (B) Direct comparison between COXEN prediction scores and experimentally measured paclitaxel activities in the BLA-40 cell lines. The activity here and elsewhere is expressed as −log(GI50), where GI50 is the drug concentration leading to 50% growth inhibition of cells compared with control. The cell lines are ordered on the basis of their −log(GI50) values. COXEN scores and GI50 values were standardized by subtracting the overall mean and dividing by the SD across the BLA-40. The statistical significance of the set of predictions (two-tailed P = 0.006) on all 40 cells of the BLA-40 was assessed by Spearman correlation. (C) ROC analysis. ROC curves were computed for COXEN scores generated for cisplatin sensitivity from the full COXEN algorithm (steps 1–6) and for variations in which either the drug chemosensitivity signature selection step (step 3; χ² statistic P = 0.0067) or the coexpression extrapolation step (step 5; χ² statistic P = 4.0 × 10⁻⁵) was omitted.

Fig. 2.

Coclustering of NCI-60 and BLA-40 cells with and without the COXEN coexpression extrapolation step (step 5). (A) Clustered image map for the NCI-60 and BLA-40 cell lines using the first 50 chemosensitivity probe sets for cisplatin, omitting step 5. (Only the first 50 were used simply for readability of the figure.) Red, black, and green indicate high, intermediate, and low expression, respectively. Red and blue in the upper bar indicate sensitive and resistant cell types, respectively. Yellow and cyan in the lower bar indicate NCI-60 and BLA-40 cells, respectively. Most cell lines clustered on the basis of cell panel (NCI-60 vs. BLA-40) not sensitivity or resistance. Probe IDs were those provided by the commercial microarray manufacturer (Affymetrix). (B) Clustered image map for the NCI-60 and BLA-40 using the 18 COXEN probes obtained for cisplatin after step 5; cells clustered primarily on the basis of sensitivity and resistance rather than on the basis of the cell panel. (C) Normalized expression intensities of COXEN-identified genes shown for BLA-40 cells sensitive and resistant to cisplatin. The genes were selected on the basis of only NCI-60 chemosensitivity information yet showed significant differential expression between the sensitive and resistant cell lines of BLA-40. Many of the genes have been reported to be relevant to cancer (SI Table 2).

Fig. 3.

COXEN prediction of chemotherapeutic response in patients with breast cancer. (A) Schematic diagram of the prediction and validation processes. (B) Direct comparison between the COXEN predictive scores and the patients' residual tumor sizes. The scores and tumor sizes were standardized for comparison by subtracting the overall mean and dividing by the SD of each of the COXEN scores and residual tumor sizes. The statistical significance of the set of predictions (two-tailed test; P = 0.022) was assessed by Spearman correlation. (C) Kaplan–Meier survival curves for the 36 COXEN-predicted responders and the 24 COXEN-predicted nonresponders in the tamoxifen trial. The predicted responder group showed a significantly longer disease-free survival time than did the predicted nonresponder group (log-rank test; P = 0.021). (D) Normalized expression intensities of COXEN-identified genes between responder and nonresponder DOC-24 patients treated with docetaxel. The genes were selected based only on NCI-60 chemosensitivity information yet showed significant differential expression between responder and nonresponder DOC-24 patients. Many of them were found to be relevant to cancer (SI Table 3).