Dataset Information

Patterson2022 - Tumour mutation data driven Random Forest model to predict immune checkpoint inhibitor therapy benefit in metastatic melanoma

ABSTRACT: A Random Forest model is developed to incorporate tumor mutation data within the context of the biological process known as leukocyte proliferation regulation. This model aims to predict a patient's response to anti-PD1 treatment. The authors conducted experiments using four different types of classifiers: Random Forest, Gradient Boosting, Feed Forward Neural Network, and Long Short-Term Memory (LSTM) recurrent neural network. Among these classifiers, the Random Forest algorithm yielded the best predictive performance when modeling gene mutation data associated with the 'leukocyte proliferation regulation' biological process. Hence, this curated version of the model focuses on the Random Forest model trained specifically on the 'Leukocyte Proliferation Regulation' process. In this model, a value of '0' is assigned to NonResponders, while a value of '1' is assigned to Responders. Please note that to obtain predictions, users should provide mutation data containing only the genes corresponding to the 'GO_REGULATION_OF_LEUKOCYTE_PROLIFERATION' process keyword, as specified in the 'GO_test_genes_dict_intersection' dictionary.

SUBMITTER: Divyang Deep Tiwari

PROVIDER: BIOMD0000001073 | BioModels | 2023-07-03

REPOSITORIES: BioModels

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Publications

Mutated processes predict immune checkpoint inhibitor therapy benefit in metastatic melanoma.

Patterson Andrew A Auslander Noam N

Nature communications 20220919 1

Immune Checkpoint Inhibitor (ICI) therapy has revolutionized treatment for advanced melanoma; however, only a subset of patients benefit from this treatment. Despite considerable efforts, the Tumor Mutation Burden (TMB) is the only FDA-approved biomarker in melanoma. However, the mechanisms underlying TMB association with prolonged ICI survival are not entirely understood and may depend on numerous confounding factors. To identify more interpretable ICI response biomarkers based on tumor mutatio ...[more]

PMID: 36123351

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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

Patterson2022 - Tumour mutation data driven Random Forest model to predict immune checkpoint inhibitor therapy benefit in metastatic melanoma

Publications

Mutated processes predict immune checkpoint inhibitor therapy benefit in metastatic melanoma.

Similar Datasets

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