A Gene Expression Model of Intrinsic Tumor Radiosensitivity: Prediction of Response and Prognosis After Chemoradiation

Purpose

Development of a radiosensitivity predictive assay is a central goal of radiation oncology. We reasoned a gene expression model could be developed to predict intrinsic radiosensitivity and treatment response in patients.

Methods and Materials

Radiosensitivity (determined by survival fraction at 2 Gy) was modeled as a function of gene expression, tissue of origin, ras status (mut/wt), and p53 status (mut/wt) in 48 human cancer cell lines. Ten genes were identified and used to build a rank-based linear regression algorithm to predict an intrinsic radiosensitivity index (RSI, high index = radioresistance). This model was applied to three independent cohorts treated with concurrent chemoradiation: head-and-neck cancer (HNC, n = 92); rectal cancer (n = 14); and esophageal cancer (n = 12).

Results

Predicted RSI was significantly different in responders (R) vs. nonresponders (NR) in the rectal (RSI R vs. NR 0.32 vs. 0.46, p = 0.03), esophageal (RSI R vs. NR 0.37 vs. 0.50, p = 0.05) and combined rectal/esophageal (RSI R vs. NR 0.34 vs. 0.48, p = 0.001511) cohorts. Using a threshold RSI of 0.46, the model has a sensitivity of 80%, specificity of 82%, and positive predictive value of 86%. Finally, we evaluated the model as a prognostic marker in HNC. There was an improved 2-year locoregional control (LRC) in the predicted radiosensitive group (2-year LRC 86% vs. 61%, p = 0.05).

Conclusions

We validate a robust multigene expression model of intrinsic tumor radiosensitivity in three independent cohorts totaling 118 patients. To our knowledge, this is the first time that a systems biology-based radiosensitivity model is validated in multiple independent clinical datasets.

Introduction

Personalized medicine holds the promise that the diagnosis, prevention, and treatment of cancer will be based on individual assessment of risk (1). Significant advances toward personalized radiation therapy (RT) have been largely achieved by physical advances in radiotherapy treatment planning and delivery (2). In contrast, the efforts in understanding the biological parameters that define intrinsic radiosensitivity have not met the same success. Thus, RT is prescribed without considering the potential individual differences in tumor and patient radiosensitivity. However, there is evidence to suggest that differences in intrinsic radiosensitivity exist (3) and understanding their biological basis could significantly impact clinical practice. Thus, a successful radiosensitivity predictive assay would be central to the development of biologically guided personalized treatment strategies in radiation oncology. A number of promising approaches have been developed in the past: (1) determination of ex vivo tumor SF2 4, 5, 6; (2) electrodes to measure tumor hypoxia (7); and (3) determination of tumor proliferative potential 8, 9. However, none has become routine in the clinic.

The advent of high-dimensional and high-throughput technologies have provided an opportunity to address the development of biomarkers from a different perspective. For example, gene expression signatures have been shown to be prognostic in breast, lung, and head-and-neck (HNC) cancer 10, 11, 12. Further, recent studies have identified biomarkers predictive of patient response to drug treatment (13). Moreover, RT may represent a common denominator in cancer therapeutics, because approximately 60% of cancer patients are treated with RT (14). We have previously shown that gene expression can predict cellular intrinsic radiosensitivity (15). In addition, we developed a systems biology model of radiation sensitivity that identified 10 hub genes (see page 497 of this issue). We reasoned a gene expression model could be developed to predict radiosensitivity in patients from these hub genes.

In this article, we apply a novel multigene expression model of intrinsic tumor radiosensitivity. The model is based on the expression of 10 hub genes identified by the systems biology model of radiosensitivity. This model predicts a radiosensitivity index (RSI) that is directly proportional to tumor radioresistance. We clinically validate the model as a predictive factor of pathological response in two independent cohorts of esophageal (n = 12) and rectal (n = 14) cancer patients treated with preoperative concurrent chemoradiation in prospective clinical trials at Moffitt Cancer Center. In addition, we find RSI is of prognostic value in a third external dataset of HNC cancer patients (HNC, n = 92) treated with definitive concurrent chemoradiation within Phase 2 and 3 clinical trials at the Netherlands Cancer Institute. In conclusion, we think this model may play a central role in individualizing therapy in radiation oncology.