Dataset Information

Circulating microRNAs as biomarkers of radiation-induced cardiac toxicity in non-small-cell lung cancer.

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.

SUBMITTER: Hawkins PG

PROVIDER: S-EPMC6956699 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Circulating microRNAs as biomarkers of radiation-induced cardiac toxicity in non-small-cell lung cancer.

Hawkins Peter G PG Sun Yilun Y Dess Robert T RT Jackson William C WC Sun Grace G Bi Nan N Tewari Muneesh M Hayman James A JA Kalemkerian Gregory P GP Gadgeel Shirish M SM Lawrence Theodore S TS Haken Randall K Ten RKT Matuszak Martha M MM Kong Feng-Ming Spring FS Schipper Matthew J MJ Jolly Shruti S

Journal of cancer research and clinical oncology 20190328 6

<h4>Purpose</h4>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).<h4>Methods</h4>Data from 63 patients treated on institutional trials were analyzed. Prognostic models of ...[more]

PMID: 30923943

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Project description:BackgroundRadiation-induced lung fibrosis (RILF) is an important late toxicity in patients with non-small-cell lung cancer (NSCLC) after radiotherapy (RT). Clinically significant RILF can impact quality of life and/or cause non-cancer related death. This study aimed to determine whether pre-treatment plasma cytokine levels have a significant effect on the risk of RILF and investigate the abilities of machine learning algorithms for risk prediction.MethodsThis is a secondary analysis of prospective studies from two academic cancer centers. The primary endpoint was grade≥2 (RILF2), classified according to a system consistent with the consensus recommendation of an expert panel of the AAPM task for normal tissue toxicity. Eligible patients must have at least 6 months' follow-up after radiotherapy commencement. Baseline levels of 30 cytokines, dosimetric, and clinical characteristics were analyzed. Support vector machine (SVM) algorithm was applied for model development. Data from one center was used for model training and development; and data of another center was applied as an independent external validation.ResultsThere were 57 and 37 eligible patients in training and validation datasets, with 14 and 16.2% RILF2, respectively. Of the 30 plasma cytokines evaluated, SVM identified baseline circulating CCL4 as the most significant cytokine associated with RILF2 risk in both datasets (P = 0.003 and 0.07, for training and test sets, respectively). An SVM classifier predictive of RILF2 was generated in Cohort 1 with CCL4, mean lung dose (MLD) and chemotherapy as key model features. This classifier was validated in Cohort 2 with accuracy of 0.757 and area under the curve (AUC) of 0.855.ConclusionsUsing machine learning, this study constructed and validated a weighted-SVM classifier incorporating circulating CCL4 levels with significant dosimetric and clinical parameters which predicts RILF2 risk with a reasonable accuracy. Further study with larger sample size is needed to validate the role of CCL4, and this SVM classifier in RILF2.

Project description:PurposeDose to normal lung has commonly been linked with radiation-induced lung toxicity (RILT) risk, but incorporating functional lung metrics in treatment planning may help further optimize dose delivery and reduce RILT incidence. The purpose of this study was to investigate the impact of the dose delivered to functional lung regions by analyzing perfusion (Q), ventilation (V), and combined V/Q single-photon-emission computed tomography (SPECT) dose-function metrics with regard to RILT risk in patients with non-small cell lung cancer (NSCLC) patients who received radiation therapy (RT).Methods and materialsSPECT images acquired from 88 patients with locally advanced NSCLC before undergoing conventionally fractionated RT were retrospectively analyzed. Dose was converted to the nominal dose equivalent per 2 Gy fraction, and SPECT intensities were normalized. Regional lung segments were defined, and the average dose delivered to each lung region was quantified. Three functional categorizations were defined to represent low-, normal-, and high-functioning lungs. The percent of functional lung category receiving ≥20 Gy and mean functional intensity receiving ≥20 Gy (iV20) were calculated. RILT was defined as grade 2+ radiation pneumonitis and/or clinical radiation fibrosis. A logistic regression was used to evaluate the association between dose-function metrics and risk of RILT.ResultsBy analyzing V/Q normalized intensities and functional distributions across the population, a wide range in functional capability (especially in the ipsilateral lung) was observed in patients with NSCLC before RT. Through multivariable regression models, global lung average dose to the lower lung was found to be significantly associated with RILT, and Q and V iV20 were correlated with RILT when using ipsilateral lung metrics. Through a receiver operating characteristic analysis, combined V/Q low-function receiving ≥20 Gy (low-functioning V/Q20) in the ipsilateral lung was found to be the best predictor (area under the curce: 0.79) of RILT risk.ConclusionsIrradiation of the inferior lung appears to be a locational sensitivity for RILT risk. The multivariable correlation between ipsilateral lung iV20 and RILT, as well as the association of low-functioning V/Q20 and RILT, suggest that irradiating low-functioning regions in the lung may lead to higher toxicity rates.

Dataset Information

Circulating microRNAs as biomarkers of radiation-induced cardiac toxicity in non-small-cell lung cancer.

Publications

Circulating microRNAs as biomarkers of radiation-induced cardiac toxicity in non-small-cell lung cancer.

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