Dataset Information

ERCC1 and XRCC1 as biomarkers for lung and head and neck cancer.

ABSTRACT: Advanced stage non-small cell lung cancer and head and neck squamous cell carcinoma are both treated with DNA damaging agents including platinum-based compounds and radiation therapy. However, at least one quarter of all tumors are resistant or refractory to these genotoxic agents. Yet the agents are extremely toxic, leading to undesirable side effects with potentially no benefit. Alternative therapies exist, but currently there are no tools to predict whether the first-line genotoxic agents will work in any given patient. To maximize therapeutic success and limit unnecessary toxicity, emerging clinical trials aim to inform personalized treatments tailored to the biology of individual tumors. Worldwide, significant resources have been invested in identifying biomarkers for guiding the treatment of lung and head and neck cancer. DNA repair proteins of the nucleotide excision repair pathway (ERCC1) and of the base excision repair pathway (XRCC1), which are instrumental in clearing DNA damage caused by platinum drugs and radiation, have been extensively studied as potential biomarkers of clinical outcomes in lung and head and neck cancers. The results are complex and contradictory. Here we summarize the current status of single nucleotide polymorphisms, mRNA, and protein expression of ERCC1 and XRCC1 in relation to cancer risk and patient outcomes.

SUBMITTER: Vaezi A

PROVIDER: S-EPMC3513219 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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ERCC1 and XRCC1 as biomarkers for lung and head and neck cancer.

Vaezi Alec A Feldman Chelsea H CH Niedernhofer Laura J LJ

Pharmacogenomics and personalized medicine 20110720

Advanced stage non-small cell lung cancer and head and neck squamous cell carcinoma are both treated with DNA damaging agents including platinum-based compounds and radiation therapy. However, at least one quarter of all tumors are resistant or refractory to these genotoxic agents. Yet the agents are extremely toxic, leading to undesirable side effects with potentially no benefit. Alternative therapies exist, but currently there are no tools to predict whether the first-line genotoxic agents wil ...[more]

PMID: 23226053

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Project description:Introduction"Radiomics" extracts and mines a large number of medical imaging features in a non-invasive and cost-effective way. The underlying assumption of radiomics is that these imaging features quantify phenotypic characteristics of an entire tumor. In order to enhance applicability of radiomics in clinical oncology, highly accurate and reliable machine-learning approaches are required. In this radiomic study, 13 feature selection methods and 11 machine-learning classification methods were evaluated in terms of their performance and stability for predicting overall survival in head and neck cancer patients.MethodsTwo independent head and neck cancer cohorts were investigated. Training cohort HN1 consisted of 101 head and neck cancer patients. Cohort HN2 (n?=?95) was used for validation. A total of 440 radiomic features were extracted from the segmented tumor regions in CT images. Feature selection and classification methods were compared using an unbiased evaluation framework.ResultsWe observed that the three feature selection methods minimum redundancy maximum relevance (AUC?=?0.69, Stability?=?0.66), mutual information feature selection (AUC?=?0.66, Stability?=?0.69), and conditional infomax feature extraction (AUC?=?0.68, Stability?=?0.7) had high prognostic performance and stability. The three classifiers BY (AUC?=?0.67, RSD?=?11.28), RF (AUC?=?0.61, RSD?=?7.36), and NN (AUC?=?0.62, RSD?=?10.52) also showed high prognostic performance and stability. Analysis investigating performance variability indicated that the choice of classification method is the major factor driving the performance variation (29.02% of total variance).ConclusionOur study identified prognostic and reliable machine-learning methods for the prediction of overall survival of head and neck cancer patients. Identification of optimal machine-learning methods for radiomics-based prognostic analyses could broaden the scope of radiomics in precision oncology and cancer care.

Dataset Information

ERCC1 and XRCC1 as biomarkers for lung and head and neck cancer.

Publications

ERCC1 and XRCC1 as biomarkers for lung and head and neck cancer.

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