An integrated approach to the prediction of chemotherapeutic response in patients with breast cancer

Abstract

Background: A major challenge in oncology is the selection of the most effective chemotherapeutic agents for individual patients, while the administration of ineffective chemotherapy increases mortality and decreases quality of life in cancer patients. This emphasizes the need to evaluate every patient's probability of responding to each chemotherapeutic agent and limiting the agents used to those most likely to be effective.

Methods and results: Using gene expression data on the NCI-60 and corresponding drug sensitivity, mRNA and microRNA profiles were developed representing sensitivity to individual chemotherapeutic agents. The mRNA signatures were tested in an independent cohort of 133 breast cancer patients treated with the TFAC (paclitaxel, 5-fluorouracil, adriamycin, and cyclophosphamide) chemotherapy regimen. To further dissect the biology of resistance, we applied signatures of oncogenic pathway activation and performed hierarchical clustering. We then used mRNA signatures of chemotherapy sensitivity to identify alternative therapeutics for patients resistant to TFAC. Profiles from mRNA and microRNA expression data represent distinct biologic mechanisms of resistance to common cytotoxic agents. The individual mRNA signatures were validated in an independent dataset of breast tumors (P = 0.002, NPV = 82%). When the accuracy of the signatures was analyzed based on molecular variables, the predictive ability was found to be greater in basal-like than non basal-like patients (P = 0.03 and P = 0.06). Samples from patients with co-activated Myc and E2F represented the cohort with the lowest percentage (8%) of responders. Using mRNA signatures of sensitivity to other cytotoxic agents, we predict that TFAC non-responders are more likely to be sensitive to docetaxel (P = 0.04), representing a viable alternative therapy.

Conclusions: Our results suggest that the optimal strategy for chemotherapy sensitivity prediction integrates molecular variables such as ER and HER2 status with corresponding microRNA and mRNA expression profiles. Importantly, we also present evidence to support the concept that analysis of molecular variables can present a rational strategy to identifying alternative therapeutic opportunities.

Figure 1. mRNA (a) and miRNA (b) gene signatures of sensitivity to paclitaxel, 5-fluorouracil, adriamycin, and cyclophosphamide.

Gene number is displayed on the vertical axes, while sample number is listed horizontally.

Figure 2. Development and validation of chemotherapeutic response predictors.

(a) Strategy for generating the chemotherapeutic response predictors. (b) Prediction of single-agent chemotherapy response in patient breast samples. Probability values of non-responders (NR) are shown in red, while probability values of responders (R) are shown in blue. Response was defined as complete pathologic response upon completion of TFAC neoadjuvant therapy. (c) Combined prediction of sensitivity to the TFAC chemotherapy regimen separated by non-responders (n = 99, red) and responders (n = 34, blue).

Figure 3. Effect of molecular variables on combined TFAC prediction.

Left, TFAC probability values of basal-like (HER2, ER, and PR negative) and non basal-like patients as separated by non-responders (NR) and responders (R). Right, TFAC probability values of non-responders and responders separated by ER score less than or greater than 50, PR status, and HER2 status.

Figure 4. Effect of HER2 status and Topoisomerase IIA expression levels on adriamycin prediction.

Left, patients were divided depending on HER2 negative or positive status, and predictive probability values of sensitivity to adriamycin were plotted for non-responders (NR) and responders (R). Right, patients were divided on the basis of whether their expression of Topoisomerase IIA (obtained using two different probes, 201291_s_at and 201292_at, in the U133A platform) was above or below the median value. Non-responders and responders were separated and their predictive probability values plotted.

Figure 5. Patterns of predicted oncogenic pathway activation and alternative chemotherapeutic options in human breast cancer tumors.

Above, hierarchical clustering of a collection of breast tumors (n = 133) according to patterns of oncogenic pathway activation. Below, predictions of sensitivity to other commonly used chemotherapeutic agents in breast tumors. Predictions were plotted as heatmaps in which a high probability of sensitivity (or response) is indicated by red, and low probability (or resistance) is indicated in blue. The percentage of responders in each cluster is reported.