Yilun Sun1, Peter G Hawkins2, Nan Bi3, Robert T Dess2, Muneesh Tewari4, Jason W D Hearn2, James A Hayman2, Gregory P Kalemkerian5, Theodore S Lawrence2, Randall K Ten Haken2, Martha M Matuszak2, Feng-Ming Kong6, Shruti Jolly7, Matthew J Schipper8. 1. Department of Biostatistics, University of Michigan, Ann Arbor, Michigan. 2. Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan. 3. Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan; Department of Radiation Oncology, Cancer Hospital and Institute, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking, People's Republic of China. 4. Division of Hematology/Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan; Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan; Biointerfaces Institute, University of Michigan, Ann Arbor, Michigan; Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan. 5. Division of Hematology/Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan. 6. Department of Radiation Oncology, Indiana University, Indianapolis, Indiana. 7. Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan. Electronic address: shrutij@med.umich.edu. 8. Department of Biostatistics, University of Michigan, Ann Arbor, Michigan; Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan.
Abstract
PURPOSE: To assess the utility of circulating serum microRNAs (c-miRNAs) to predict response to high-dose radiation therapy for locally advanced non-small cell lung cancer (NSCLC). METHODS AND MATERIALS: Data from 80 patients treated from 2004 to 2013 with definitive standard- or high-dose radiation therapy for stages II-III NSCLC as part of 4 prospective institutional clinical trials were evaluated. Pretreatment serum levels of 62 miRNAs were measured by quantitative reverse transcription-polymerase chain reaction array. We combined miRNA data and clinical factors to generate a dose-response score (DRS) for predicting overall survival (OS) after high-dose versus standard-dose radiation therapy. Elastic net Cox regression was used for variable selection and parameter estimation. Model assessment and tuning parameter selection were performed through full cross-validation. The DRS was also correlated with local progression, distant metastasis, and grade 3 or higher cardiac toxicity using Cox regression, and grade 2 or higher esophageal and pulmonary toxicity using logistic regression. RESULTS: Eleven predictive miRNAs were combined with clinical factors to generate a DRS for each patient. In patients with low DRS, high-dose radiation therapy was associated with significantly improved OS compared to treatment with standard-dose radiation therapy (hazard ratio 0.22). In these patients, high-dose radiation also conferred lower risk of distant metastasis and local progression, although the latter association was not statistically significant. Patients with high DRS exhibited similar rates of OS regardless of dose (hazard ratio 0.78). The DRS did not correlate with treatment-related toxicity. CONCLUSIONS: Using c-miRNA signature and clinical factors, we developed a DRS that identified a subset of patients with locally advanced NSCLC who derive an OS benefit from high-dose radiation therapy. This DRS may guide dose escalation in a patient-specific manner.
PURPOSE: To assess the utility of circulating serum microRNAs (c-miRNAs) to predict response to high-dose radiation therapy for locally advanced non-small cell lung cancer (NSCLC). METHODS AND MATERIALS: Data from 80 patients treated from 2004 to 2013 with definitive standard- or high-dose radiation therapy for stages II-III NSCLC as part of 4 prospective institutional clinical trials were evaluated. Pretreatment serum levels of 62 miRNAs were measured by quantitative reverse transcription-polymerase chain reaction array. We combined miRNA data and clinical factors to generate a dose-response score (DRS) for predicting overall survival (OS) after high-dose versus standard-dose radiation therapy. Elastic net Cox regression was used for variable selection and parameter estimation. Model assessment and tuning parameter selection were performed through full cross-validation. The DRS was also correlated with local progression, distant metastasis, and grade 3 or higher cardiac toxicity using Cox regression, and grade 2 or higher esophageal and pulmonary toxicity using logistic regression. RESULTS: Eleven predictive miRNAs were combined with clinical factors to generate a DRS for each patient. In patients with low DRS, high-dose radiation therapy was associated with significantly improved OS compared to treatment with standard-dose radiation therapy (hazard ratio 0.22). In these patients, high-dose radiation also conferred lower risk of distant metastasis and local progression, although the latter association was not statistically significant. Patients with high DRS exhibited similar rates of OS regardless of dose (hazard ratio 0.78). The DRS did not correlate with treatment-related toxicity. CONCLUSIONS: Using c-miRNA signature and clinical factors, we developed a DRS that identified a subset of patients with locally advanced NSCLC who derive an OS benefit from high-dose radiation therapy. This DRS may guide dose escalation in a patient-specific manner.
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Authors: Joe Y Chang; Suresh Senan; Marinus A Paul; Reza J Mehran; Alexander V Louie; Peter Balter; Harry J M Groen; Stephen E McRae; Joachim Widder; Lei Feng; Ben E E M van den Borne; Mark F Munsell; Coen Hurkmans; Donald A Berry; Erik van Werkhoven; John J Kresl; Anne-Marie Dingemans; Omar Dawood; Cornelis J A Haasbeek; Larry S Carpenter; Katrien De Jaeger; Ritsuko Komaki; Ben J Slotman; Egbert F Smit; Jack A Roth Journal: Lancet Oncol Date: 2015-05-13 Impact factor: 41.316
Authors: Peter G Hawkins; Yilun Sun; Robert T Dess; William C Jackson; Grace Sun; Nan Bi; Muneesh Tewari; James A Hayman; Gregory P Kalemkerian; Shirish M Gadgeel; Theodore S Lawrence; Randall K Ten Haken; Martha M Matuszak; Feng-Ming Spring Kong; Matthew J Schipper; Shruti Jolly Journal: J Cancer Res Clin Oncol Date: 2019-03-28 Impact factor: 4.553