Peter G Hawkins1, Yilun Sun2, Robert T Dess1, William C Jackson1, Grace Sun1, Nan Bi3, Muneesh Tewari4, James A Hayman1, Gregory P Kalemkerian5, Shirish M Gadgeel5, Theodore S Lawrence1, Randall K Ten Haken1, Martha M Matuszak1, Feng-Ming Spring Kong6, Matthew J Schipper1,2, Shruti Jolly7. 1. Department of Radiation Oncology, University of Michigan, 1500 E Medical Center Drive, UH B2 C490 SPC 5010, Ann Arbor, MI, 48109, USA. 2. Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA. 3. Department of Radiation Oncology, Cancer Hospital and Institute, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. 4. Department of Biomedical Engineering, Biointerfaces Institute, and Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA. 5. Division of Hematology/Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA. 6. Department of Radiation Oncology, Case Western Reserve University School of Medicine, Cleveland, OH, USA. 7. Department of Radiation Oncology, University of Michigan, 1500 E Medical Center Drive, UH B2 C490 SPC 5010, Ann Arbor, MI, 48109, USA. shrutij@med.umich.edu.
Abstract
PURPOSE: Radiation-induced cardiac toxicity (RICT) is an increasingly well-appreciated source of morbidity and mortality in patients receiving thoracic radiotherapy (RT). Currently available methods to predict RICT are suboptimal. We investigated circulating microRNAs (c-miRNAs) as potential biomarkers of RICT in patients undergoing definitive RT for non-small-cell lung cancer (NSCLC). METHODS: Data from 63 patients treated on institutional trials were analyzed. Prognostic models of grade 3 or greater (G3 +) RICT based on pre-treatment c-miRNA levels ('c-miRNA'), mean heart dose (MHD) and pre-existing cardiac disease (PCD) ('clinical'), and a combination of these ('c-miRNA + clinical') were developed. Elastic net Cox regression and full cross validation were used for variable selection, model building, and model evaluation. Concordance statistic (c-index) and integrated Brier score (IBS) were used to evaluate model performance. RESULTS: MHD, PCD, and serum levels of 14 c-miRNA species were identified as jointly prognostic for G3 + RICT. The 'c-miRNA and 'clinical' models yielded similar cross-validated c-indices (0.70 and 0.72, respectively) and IBSs (0.26 and 0.28, respectively). However, prognostication was not improved by combining c-miRNA and clinical factors (c-index 0.70, IBS 0.28). The 'c-miRNA' and 'clinical' models were able to significantly stratify patients into high- and low-risk groups of developing G3 + RICT. Chi-square testing demonstrated a marginally significantly higher prevalence of PCD in patients with high- compared to low-risk c-miRNA profile (p = 0.09), suggesting an association between some c-miRNAs and PCD. CONCLUSIONS: We identified a pre-treatment c-miRNA signature prognostic for G3 + RICT. With further development, pre- and mid-treatment c-miRNA profiling could contribute to patient-specific dose selection and treatment adaptation.
PURPOSE: Radiation-induced cardiac toxicity (RICT) is an increasingly well-appreciated source of morbidity and mortality in patients receiving thoracic radiotherapy (RT). Currently available methods to predict RICT are suboptimal. We investigated circulating microRNAs (c-miRNAs) as potential biomarkers of RICT in patients undergoing definitive RT for non-small-cell lung cancer (NSCLC). METHODS: Data from 63 patients treated on institutional trials were analyzed. Prognostic models of grade 3 or greater (G3 +) RICT based on pre-treatment c-miRNA levels ('c-miRNA'), mean heart dose (MHD) and pre-existing cardiac disease (PCD) ('clinical'), and a combination of these ('c-miRNA + clinical') were developed. Elastic net Cox regression and full cross validation were used for variable selection, model building, and model evaluation. Concordance statistic (c-index) and integrated Brier score (IBS) were used to evaluate model performance. RESULTS: MHD, PCD, and serum levels of 14 c-miRNA species were identified as jointly prognostic for G3 + RICT. The 'c-miRNA and 'clinical' models yielded similar cross-validated c-indices (0.70 and 0.72, respectively) and IBSs (0.26 and 0.28, respectively). However, prognostication was not improved by combining c-miRNA and clinical factors (c-index 0.70, IBS 0.28). The 'c-miRNA' and 'clinical' models were able to significantly stratify patients into high- and low-risk groups of developing G3 + RICT. Chi-square testing demonstrated a marginally significantly higher prevalence of PCD in patients with high- compared to low-risk c-miRNA profile (p = 0.09), suggesting an association between some c-miRNAs and PCD. CONCLUSIONS: We identified a pre-treatment c-miRNA signature prognostic for G3 + RICT. With further development, pre- and mid-treatment c-miRNA profiling could contribute to patient-specific dose selection and treatment adaptation.
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