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

• Published in: International journal of radiation oncology, biology, physics Vol. 75; no. 2; pp. 489 - 496
• Main Authors: Eschrich, Steven A., Ph.D, Pramana, Jimmy, M.D, Zhang, Hongling, Ph.D, Zhao, Haiyan, B.S, Boulware, David, M.S, Lee, Ji-Hyun, Dr.Ph, Bloom, Gregory, Ph.D, Rocha-Lima, Caio, M.D, Kelley, Scott, M.D, Calvin, Douglas P., M.D, Yeatman, Timothy J., M.D, Begg, Adrian C., Ph.D, Torres-Roca, Javier F., M.D
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.10.2009
• Subjects: Aged; Aged, 80 and over; ALGORITHMS; Antineoplastic Agents - administration & dosage; BIOLOGY; Cell Line, Tumor; Chemoradiation; Esophageal Neoplasms - drug therapy; Esophageal Neoplasms - genetics; Esophageal Neoplasms - radiotherapy; Female; FORECASTING; Gene expression; Gene Expression - genetics; GENES; HEAD; Head and Neck Neoplasms - drug therapy; Head and Neck Neoplasms - genetics; Head and Neck Neoplasms - radiotherapy; Hematology, Oncology and Palliative Medicine; Humans; Intrinsic radiosensitivity; Linear Models; Male; Middle Aged; Models, Genetic; NECK; NEOPLASMS; Oligonucleotide Array Sequence Analysis - methods; PATIENTS; Predictive assay; Prognosis; Prospective Studies; Radiation Tolerance - genetics; Radiology; RADIOLOGY AND NUCLEAR MEDICINE; RADIOSENSITIVITY; Rectal Neoplasms - drug therapy; Rectal Neoplasms - genetics; Rectal Neoplasms - radiotherapy; RECTUM; SPECIFICITY; SURVIVAL CURVES; Systems biology; Topotecan - administration & dosage; Predictive assay; Intrinsic radiosensitivity; Gene expression; Systems biology; Chemoradiation
• ISSN: 0360-3016; 1879-355X
• DOI: 10.1016/j.ijrobp.2009.06.014

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.