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Machine Learning Classification of Peripheral Blood Gene Expression Identifies at Baseline Subsets of Patients with Systemic Sclerosis Most Likely to Show Clinical Improvement in Response to Autologous Myeloablative Hematopoietic Stem Cell Transplantation

ABSTRACT: Machine Learning Classification of Peripheral Blood Gene Expression Identifies at Baseline Subsets of Patients with Systemic Sclerosis Most Likely to Show Clinical Improvement in Response to Autologous Myeloablative Hematopoietic Stem Cell Transplantation

PROVIDER: PRJNA554683 | ENA |

REPOSITORIES: ENA

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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.

Dataset Information

Machine Learning Classification of Peripheral Blood Gene Expression Identifies at Baseline Subsets of Patients with Systemic Sclerosis Most Likely to Show Clinical Improvement in Response to Autologous Myeloablative Hematopoietic Stem Cell Transplantation

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