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Molecular markers to predict clinical outcome and radiation induced toxicity in lung cancer.

ABSTRACT: The elucidation of driver mutations involved in the molecular pathogenesis of cancer has led to a surge in the application of novel targeted therapeutics in lung cancer. Novel oncologic research continues to lead investigators towards targeting personalized tumor characteristics rather than applying targeted therapy to broad patient populations. Several driver genes, in particular epidermal growth factor receptor (EGFR) and ALK fusions, are the earliest to have made their way into clinical trials. The avant-garde role of genomic profiling has led to important clinical challenges when adapting current standard treatments to personalized oncologic care. This new frontier of medicine requires newer biomarkers for toxicity that will identify patients at risk, as well as, new molecular markers to predict and assess clinical outcomes. Thus far, several signature genes have been developed to predict outcome as well as genetic factors related to inflammation to predict toxicity.

SUBMITTER: Palmer JD

PROVIDER: S-EPMC3968560 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

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Molecular markers to predict clinical outcome and radiation induced toxicity in lung cancer.

Palmer Joshua D JD Zaorsky Nicholas G NG Witek Matthew M Lu Bo B

Journal of thoracic disease 20140401 4

The elucidation of driver mutations involved in the molecular pathogenesis of cancer has led to a surge in the application of novel targeted therapeutics in lung cancer. Novel oncologic research continues to lead investigators towards targeting personalized tumor characteristics rather than applying targeted therapy to broad patient populations. Several driver genes, in particular epidermal growth factor receptor (EGFR) and ALK fusions, are the earliest to have made their way into clinical trial ...[more]

PMID: 24688783

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Project description:Purpose and objectivesWe previously reported that the combination of mean lung dose (MLD) and inflammatory cytokines interleukin-8 (IL-8) and transforming growth factor-β1 (TGF-β1) may provide a more accurate model for radiation-induced lung toxicity (RILT) prediction in 58 patients with non-small cell lung cancer (NSCLC). This study is to validate the previous findings with new patients and to explore new models with more cytokines.Methods and materialsOne hundred forty-two patients with stage I-III NSCLC treated with definitive radiation therapy (RT) from prospective studies were included. Sixty-five new patients were used to validate previous findings, and all 142 patients were used to explore new models. Thirty inflammatory cytokines were measured in plasma samples before RT and 2 weeks and 4 weeks during RT (pre, 2w, 4w). Grade ≥2 RILT was defined as grade 2, and higher radiation pneumonitis or symptomatic pulmonary fibrosis was the primary endpoint. Logistic regression was performed to evaluate the risk factors of RILT. The area under the curve (AUC) for the receiver operating characteristic curves was used for model assessment.ResultsSixteen of 65 patients (24.6%) experienced RILT2. Lower pre IL-8 and higher TGF-β1 2w/pre ratio were associated with higher risk of RILT2. The AUC increased to 0.73 by combining MLD, pre IL-8, and TGF-β1 2w/pre ratio compared with 0.61 by MLD alone to predict RILT. In all 142 patients, 29 patients (20.4%) experienced grade ≥2 RILT. Among the 30 cytokines measured, only IL-8 and TGF-β1 were significantly associated with the risk of RILT2. MLD, pre IL-8 level, and TGF-β1 2w/pre ratio were included in the final predictive model. The AUC increased to 0.76 by combining MLD, pre IL-8, and TGF-β1 2w/pre ratio compared with 0.62 by MLD alone.ConclusionsWe validated that a combination of mean lung dose, pre IL-8 level, and TGF-β1 2w/pre ratio provided a more accurate model to predict the risk of RILT2 compared with MLD alone.

Dataset Information

Molecular markers to predict clinical outcome and radiation induced toxicity in lung cancer.

Publications

Molecular markers to predict clinical outcome and radiation induced toxicity in lung cancer.

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