Dataset Information

Circulating microRNAs as prognostic therapy biomarkers in head and neck cancer patients.

ABSTRACT:

Background

The prediction of therapy response in head and neck squamous cell cancer (HNSCC) requires biomarkers, which are also a prerequisite for personalised therapy concepts. The current study aimed to identify therapy-responsive microRNAs (miRNAs) in the circulation that can serve as minimally invasive prognostic markers for HNSCC patients undergoing radiotherapy.

Methods

We screened plasma miRNAs in a discovery cohort of HNSCC patients before therapy and after treatment. We further compared the plasma miRNAs of the patients to age- and sex-matched healthy controls. All miRNAs identified as biomarker candidates were then confirmed in an independent validation cohort of HNSCC patients and tested for correlation with the clinical outcome.

Results

We identified a signature of eight plasma miRNAs that differentiated significantly (P=0.003) between HNSCC patients and healthy donors. MiR-186-5p demonstrated the highest sensitivity and specificity to classify HNSCC patients and healthy individuals. All therapy-responsive and patient-specific miRNAs in plasma were also detectable in tumour tissues derived from the same patients. High expression of miR-142-3p, miR-186-5p, miR-195-5p, miR-374b-5p and miR-574-3p in the plasma correlated with worse prognosis.

Conclusions

Circulating miR-142-3p, miR-186-5p, miR-195-5p, miR-374b-5p and miR-574-3p represent the most promising markers for prognosis and therapy monitoring in the plasma of HNSCC patients. We found strong evidence that the circulating therapy-responsive miRNAs are tumour related and were able to validate them in an independent cohort of HNSCC patients.

SUBMITTER: Summerer I

PROVIDER: S-EPMC4647540 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature

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Publications

Circulating microRNAs as prognostic therapy biomarkers in head and neck cancer patients.

Summerer I I Unger K K Braselmann H H Schuettrumpf L L Maihoefer C C Baumeister P P Kirchner T T Niyazi M M Sage E E Specht H M HM Multhoff G G Moertl S S Belka C C Zitzelsberger H H

British journal of cancer 20150609 1

<h4>Background</h4>The prediction of therapy response in head and neck squamous cell cancer (HNSCC) requires biomarkers, which are also a prerequisite for personalised therapy concepts. The current study aimed to identify therapy-responsive microRNAs (miRNAs) in the circulation that can serve as minimally invasive prognostic markers for HNSCC patients undergoing radiotherapy.<h4>Methods</h4>We screened plasma miRNAs in a discovery cohort of HNSCC patients before therapy and after treatment. We f ...[more]

PMID: 26057452

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Project description:INTRODUCTION: Circulating microRNAs (miRNAs) are easily accessible and have already proven to be useful as prognostic markers in cancer patients. However, their origin and function in the circulation is still under discussion. In the present study we analyzed changes in the miRNAs in blood plasma of head and neck squamous cell carcinoma (HNSCC) patients in response to radiochemotherapy and compared them to the changes in a cell culture model of primary HNSCC cells undergoing simulated anti-cancer therapy. MATERIALS AND METHODS: MiRNA-profiles were analyzed by qRT-PCR arrays in paired blood plasma samples of HNSCC patients before therapy and after two days of treatment. Candidate miRNAs were validated by single qRT-PCR assays. An in vitro radiochemotherapy model using primary HNSCC cell cultures was established to test the possible tumor origin of the circulating miRNAs. Microarray analysis was performed on primary HNSCC cell cultures followed by validation of deregulated miRNAs via qRT-PCR. RESULTS: Unsupervised clustering of the expression profiles using the six most regulated miRNAs (miR-425-5p, miR-21-5p, miR-106b-5p, miR-590-5p, miR-574-3p, miR-885-3p) significantly (p?=?0.012) separated plasma samples collected prior to treatment from plasma samples collected after two days of radiochemotherapy. MiRNA profiling of primary HNSCC cell cultures treated in vitro with radiochemotherapy revealed differentially expressed miRNAs that were also observed to be therapy-responsive in blood plasma of the patients (miR-425-5p, miR-21-5p, miR-106b-5p, miR-93-5p) and are therefore likely to stem from the tumor. Of these candidate marker miRNAs we were able to validate by qRT-PCR a deregulation of eight plasma miRNAs as well as miR-425-5p and miR-93-5p in primary HNSCC cultures after radiochemotherapy. CONCLUSION: Changes in the abundance of circulating miRNAs during radiochemotherapy reflect the therapy response of primary HNSCC cells after an in vitro treatment. Therefore, the responsive miRNAs (miR-425-5p, miR-93-5p) may represent novel biomarkers for therapy monitoring. The prognostic value of this exciting observation requires confirmation using an independent patient cohort that includes clinical follow-up data.

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

Circulating microRNAs as prognostic therapy biomarkers in head and neck cancer patients.

Background

Methods

Results

Conclusions

Publications

Circulating microRNAs as prognostic therapy biomarkers in head and neck cancer patients.

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