The development of in vitro
diagnostics to predict response to therapeutic agents is a central goal of molecular medicine (1
). In this study, we validate a robust systems biology-based multigene expression model of intrinsic tumor radiosensitivity in three independent datasets totaling 118 patients. Although previous studies have shown that radiosensitivity signatures were possible (15
), this is the first time to our knowledge that a systems biology-based radiosensitivity model is validated in multiple independent clinical datasets. We show that RSI when analyzed as a continuous variable is correlated with pathological response in rectal and esophageal cancer patients treated with preoperative concurrent chemoradiation. Furthermore, the receiver-operating characteristic analysis proposes a cut point (RSI = 0.46) where the test predictive accuracy is encouraging. The sensitivity, specificity, and positive predictive value of the assay were 80%, 82%, and 86%, respectively. Importantly, we also show that RSI is of prognostic significance in a cohort of 92 patients with locally advanced HNC. The applicability of the model in three different disease sites strongly suggests that the model captures commonalities that define radiosensitivity across disease sites. Therefore, it is possible that the model might be generally applicable to other disease sites (e.g.
, lung, prostate, cervix cancer). However, it should be emphasized that no disease-specific conclusions should be made at this juncture given the small nature of two of the validating clinical cohorts (rectal, esophageal).
In the molecular medicine era, high-throughput technologies (e.g.
, microarrays, proteomics) have led to the identification of numerous molecular signatures of prognostic or predictive significance (10
). However the initial enthusiasm that these signatures would lead to personalized medicine has been dampened by lack of robustness (44
). The robustness of the radiosensitivity model is supported by several lines of evidence. First, the algorithm was validated in three independent prospectively collected datasets in three different diseases. Second, the model was valid across different gene expression platforms. The model is originally developed on an Affymetrix HU-6800 platform, but clinically validated in two different gene expression platforms (i.e.
, Affymetrix U133-Plus for esophageal and rectal cohorts, NKI cDNA array for HNC). This observation suggests the model can be transferred to a more practical clinical platform (i.e.
, RT-PCR/formalin-fixed tissue). Third, all patients in the validating clinical cohorts were treated with concurrent chemoradiation, because we were unable to obtain a dataset of patients treated with radiation alone. However, the algorithm was based on cellular radiosensitivity. Thus, in spite of this potential source of inaccuracy, the model was still validated. Finally, the model showed both predictive and prognostic value.
False negatives (predicted radioresistant that responded) were the main inaccuracy when the model was dichotomized in the esophageal and rectal datasets. This population represented 60% of the misclassified cases in these cohorts. This inaccuracy may be due to the radiosensitization effect of chemotherapy. The proportion of individuals that are classified in this group (11.5%) is consistent with the observed improvement in clinical responses with concurrent chemotherapy over radiotherapy alone (45
). Therefore, this effect may be addressed by analyzing gene expression differences between R and NR that share a predicted radioresistant phenotype.
The model in this study is designed to predict tumor radiosensitivity. Interestingly RSI was prognostic in the HNC dataset, suggesting that the biologic factors that determine radiosensitivity are related to disease prognosis after treatment. This is not surprising because complete pathological response has been shown to have strong prognostic significance in several studies (17
This model may play a central role in the individualization of therapy in radiation oncology. For example, the model may provide an opportunity to individualize radiation dose parameters based on intrinsic radiosensitivity. Because higher doses of RT are associated with higher toxicity rate (50
), dose personalization would result in a therapeutic ratio benefit. There is also a role for identifying patients that are likely to be downstaged, particularly in rectal cancer. For example, this knowledge might lead to better counseling of patients with low-lying rectal tumors where sphincter-sparing surgery is being considered. In addition, the model may provide a unique framework to understand the differences between R and NR that share a predicted radioresistant phenotype. This may allow the accurate identification of patients that benefit from the addition of concurrent chemotherapy.
In conclusion, we present evidence to support the clinical validity of a multigene expression model of intrinsic tumor radiosensitivity. To our knowledge, this is the first systems biology-based radiosensitivity model to have validation in multiple independent datasets. The model is versatile and robust as demonstrated by both its predictive and prognostic ability in three different disease sites using two different gene expression microarray platforms. The data presented justify further development and optimization of this technology in larger clinical populations.