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Methylation profiling and machine learning distinguishes primary lung squamous cell carcinomas from head and neck metastases

ABSTRACT: Head and neck squamous cell carcinoma (HNSC) patients are at risk of suffering from both pulmonary metastases or a second squamous cell carcinoma of the lung (LUSC). Differentiating pulmonary metastases from primary lung cancers is of high clinical importance, but not possible in most cases with current diagnostics. To address this, we performed DNA methylation profiling of primary tumors and trained three different machine learning methods to distinguish metastatic HNSC from primary LUSC. We developed an artificial neural network that correctly classi- fied 96.4% of the cases in a validation cohort of 279 patients with HNSC and LUSC as well as normal lung controls, outperforming support vector machines (95.7%) and random forests (87.8%). Prediction accuracies of more than 99% were achieved for 92.1% (neural network), 90% (support vector machine), and 43% (random forest) of these cases by applying thresholds to the resulting probability scores and excluding samples with low confidence. As independent clinical validation of the approach, we analyzed a series of 51 patients with a history of HNSC and a second lung tumor, demonstrating the correct classifications based on clinicopathological properties. In summary, our approach may facilitate the reliable diagnostic differentiation of pulmonary metastases of HNSC from primary LUSC to guide therapeutic decisions.

ORGANISM(S): Homo sapiens

PROVIDER: GSE124052 | GEO | 2019/09/10

REPOSITORIES: GEO

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Project description:Objectives Our goal was to evaluate the diagnostic value of DNA methylation analysis in combination with machine learning to differentiate pleural mesothelioma (PM) from important histopathological mimics. Material and methods DNA methylation data of PM, lung adenocarcinomas, lung squamous cell carcinomas and chronic pleuritis was used to train a random forest as well as a support vector machine. These classifiers were validated using an independent validation cohort including pleural carcinosis and pleomorphic variants of lung adeno- and squamous cell carcinomas. Furthermore, we used a deconvolution method to estimate the composition of the tumor microenvironment. Results T-distributed stochastic neighbor embedding clearly separated PM from lung adenocarcinomas and squamous cell carcinomas, but there was a considerable overlap between chronic pleuritis specimens and PM with low tumor cell content. While both machine learning algorithms achieved comparable accuracies in a nested cross validation on the training cohort (random forest: 94.9%; support vector machine: 95.5%), the support vector machine outperformed the random forest in distinguishing PM from chronic pleuritis. Differential methylation analysis revealed promoter hypermethylation in PM specimens, including the tumor suppressor genes BCL11B, EBF1, FOXA1, and WNK2. Furthermore, we observed comparable accuracies for the support vector machine on the validation cohort (97.1%) while the random forest performed considerably worse (89.9%). Deconvolution of the stromal and immune cell composition revealed higher rates of regulatory T-cells and endothelial cells in tumor specimens and a heterogenous inflammation including macrophages, B-cells and natural killer cells in chronic pleuritis. Conclusion DNA methylation in combination with machine learning is a promising tool to reliably differentiate PM from chronic pleuritis and lung cancer, including pleomorphic carcinomas. Furthermore, our study highlights new candidate genes for PM carcinogenesis and shows that deconvolution of DNA methylation data can provide reasonable insights into the composition of the tumor microenvironment.

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Methylation profiling and machine learning distinguishes primary lung squamous cell carcinomas from head and neck metastases

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