Dataset Information

DNA-Methylome-Based Tumor Hypoxia Classifier Identifies HPV-Negative Head and Neck Cancer Patients at Risk for Locoregional Recurrence after Primary Radiochemotherapy.

ABSTRACT:

Purpose

Tumor hypoxia is a paradigmatic negative prognosticator of treatment resistance in head and neck squamous cell carcinoma (HNSCC). The lack of robust and reliable hypoxia classifiers limits the adaptation of stratified therapies. We hypothesized that the tumor DNA methylation landscape might indicate epigenetic reprogramming induced by chronic intratumoral hypoxia.

Experimental design

A DNA-methylome-based tumor hypoxia classifier (Hypoxia-M) was trained in the TCGA (The Cancer Genome Atlas)-HNSCC cohort based on matched assignments using gene expression-based signatures of hypoxia (Hypoxia-GES). Hypoxia-M was validated in a multicenter DKTK-ROG trial consisting of human papillomavirus (HPV)-negative patients with HNSCC treated with primary radiochemotherapy (RCHT).

Results

Although hypoxia-GES failed to stratify patients in the DKTK-ROG, Hypoxia-M was independently prognostic for local recurrence (HR, 4.3; P = 0.001) and overall survival (HR, 2.34; P = 0.03) but not distant metastasis after RCHT in both cohorts. Hypoxia-M status was inversely associated with CD8 T-cell infiltration in both cohorts. Hypoxia-M was further prognostic in the TCGA-PanCancer cohort (HR, 1.83; P = 0.04), underscoring the breadth of this classifier for predicting tumor hypoxia status.

Conclusions

Our findings highlight an unexplored avenue for DNA methylation-based classifiers as biomarkers of tumoral hypoxia for identifying high-risk features in patients with HNSCC tumors. See related commentary by Heft Neal and Brenner, p. 2954.

SUBMITTER: Tawk B

PROVIDER: S-EPMC10425733 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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Publications

DNA-Methylome-Based Tumor Hypoxia Classifier Identifies HPV-Negative Head and Neck Cancer Patients at Risk for Locoregional Recurrence after Primary Radiochemotherapy.

Tawk Bouchra B Rein Katrin K Schwager Christian C Knoll Maximilian M Wirkner Ute U Hörner-Rieber Juliane J Liermann Jakob J Kurth Ina I Balermpas Panagiotis P Rödel Claus C Linge Annett A Löck Steffen S Lohaus Fabian F Tinhofer Ingeborg I Krause Mechtild M Stuschke Martin M Grosu Anca Ligia AL Zips Daniel D Combs Stephanie E SE Belka Claus C Stenzinger Albrecht A Herold-Mende Christel C Baumann Michael M Schirmacher Peter P Debus Jürgen J Abdollahi Amir A

Clinical cancer research : an official journal of the American Association for Cancer Research 20230801 16

<h4>Purpose</h4>Tumor hypoxia is a paradigmatic negative prognosticator of treatment resistance in head and neck squamous cell carcinoma (HNSCC). The lack of robust and reliable hypoxia classifiers limits the adaptation of stratified therapies. We hypothesized that the tumor DNA methylation landscape might indicate epigenetic reprogramming induced by chronic intratumoral hypoxia.<h4>Experimental design</h4>A DNA-methylome-based tumor hypoxia classifier (Hypoxia-M) was trained in the TCGA (The Ca ...[more]

PMID: 37058257

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Project description:PurposeA reliable locoregional recurrence (LRR) prediction model is important for the personalized management of head and neck cancers (HNC) patients who received radiotherapy. This work aims to develop a delta-radiomics feature-based multi-classifier, multi-objective, and multi-modality (Delta-mCOM) model for post-treatment HNC LRR prediction. Furthermore, we aim to adopt a learning with rejection option (LRO) strategy to boost the reliability of Delta-mCOM model by rejecting prediction for samples with high prediction uncertainties.MethodsIn this retrospective study, we collected PET/CT image and clinical data from 224 HNC patients who received radiotherapy (RT) at our institution. We calculated the differences between radiomics features extracted from PET/CT images acquired before and after radiotherapy and used them in conjunction with pre-treatment radiomics features as the input features. Using clinical parameters, PET radiomics features, and CT radiomics features, we built and optimized three separate single-modality models. We used multiple classifiers for model construction and employed sensitivity and specificity simultaneously as the training objectives for each of them. Then, for testing samples, we fused the output probabilities from all these single-modality models to obtain the final output probabilities of the Delta-mCOM model. In the LRO strategy, we estimated the epistemic and aleatoric uncertainties when predicting with a trained Delta-mCOM model and identified patients associated with prediction of higher reliability (low uncertainty estimates). The epistemic and aleatoric uncertainties were estimated using an AutoEncoder-style anomaly detection model and test-time augmentation (TTA) with predictions made from the Delta-mCOM model, respectively. Predictions with higher epistemic uncertainty or higher aleatoric uncertainty than given thresholds were deemed unreliable, and they were rejected before providing a final prediction. In this study, different thresholds corresponding to different low-reliability prediction rejection ratios were applied. Their values are based on the estimated epistemic and aleatoric uncertainties distribution of the validation data.ResultsThe Delta-mCOM model performed significantly better than the single-modality models, whether trained with pre-, post-treatment radiomics features or concatenated BaseLine and Delta-Radiomics Features (BL-DRFs). It was numerically superior to the PET and CT fused BL-DRF model (nonstatistically significant). Using the LRO strategy for the Delta-mCOM model, most of the evaluation metrics improved as the rejection ratio increased from 0% to around 25%. Utilizing both epistemic and aleatoric uncertainty for rejection yielded nonstatistically significant improved metrics compared to each alone at approximately a 25% rejection ratio. Metrics were significantly better than the no-rejection method when the reject ratio was higher than 50%.ConclusionsThe inclusion of the delta-radiomics feature improved the accuracy of HNC LRR prediction, and the proposed Delta-mCOM model can give more reliable predictions by rejecting predictions for samples of high uncertainty using the LRO strategy.

Project description:BackgroundSecuring negative surgical margins is a critical goal for head and neck surgery. Local recurrence develops even in some patients who have histologically negative surgical margins. Minimal residual tumor cells may lead to locoregional recurrence despite clear histologic margins reported at the time of resection of head and neck squamous cell carcinoma (HNSCC). To identify subclinical residual disease, the authors analyzed deep margin imprint samples collected on 1-layer nitrocellulose sheets.MethodsBisulfite-treated DNA samples from 73 eligible patients were amplified by quantitative methylation-specific polymerase chain reaction (QMSP) targeting 6 genes (deleted in colorectal cancer [DCC], endothelin receptor type B [EDNRB], homeobox protein A9 [HOXA9], kinesin family member 1A [KIF1A], nidogen-2 [NID2], and N-methyl D-aspartate receptor subtype 2B [NR2B]). QMSP values were dichotomized as positive or negative. Associations between the QMSP status of deep margin samples and clinical outcomes were evaluated.ResultsTwo-gene methylation combinations among the genes DCC, EDNRB, and HOXA9 were associated with decreased locoregional recurrence-free survival, recurrence-free survival, and overall survival. The methylated gene combination of EDNRB and HOXA9 in margin imprints was the most powerful predictor of poor locoregional recurrence-free survival (hazard ratio [HR], 3.31; 95% confidence interval [CI], 1.30-8.46; P = .012) independent of standard histologic factors. In addition, methylation of both EDNRB and HOXA9 indicated a trend toward reduced recurrence-free survival (HR, 2.74; 95% CI, 0.90-8.33; P = .075) and reduced OS (HR, 5.78; 95% CI, 0.75-44.7; P = .093) in multivariable analysis.ConclusionsA panel of gene methylation targets in deep surgical margin imprints provides a potential predictive marker of postoperative locoregional recurrence. Intraoperative use of molecular margin imprint analysis may assist surgeons in obtaining rigorously negative surgical margins and improve the outcome of head and neck surgery.

Dataset Information

DNA-Methylome-Based Tumor Hypoxia Classifier Identifies HPV-Negative Head and Neck Cancer Patients at Risk for Locoregional Recurrence after Primary Radiochemotherapy.

Purpose

Experimental design

Results

Conclusions

Publications

DNA-Methylome-Based Tumor Hypoxia Classifier Identifies HPV-Negative Head and Neck Cancer Patients at Risk for Locoregional Recurrence after Primary Radiochemotherapy.

Similar Datasets

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