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Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer
Published on: September 18, 2020
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Predicting cancer origins with a DNA methylation-based deep neural network model
1Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America.
Plos One
|May 9, 2020
Summary
A new deep neural network (DNN) model accurately predicts cancer origin using DNA methylation data. This advance offers improved diagnostic capabilities for metastatic cancers and circulating tumor cells.
Area of Science:
- Genomics and Bioinformatics
- Computational Biology
- Oncology
Background:
- Accurate cancer origin determination is crucial for effective metastatic cancer treatment and improved patient outcomes.
- Current pathology and gene expression-based diagnostic methods often exhibit performance limitations.
- The Cancer Genome Atlas (TCGA) provides a valuable resource for large-scale cancer genomic data analysis.
Purpose of the Study:
- To develop and evaluate a deep neural network (DNN)-based classifier for predicting cancer origin.
- To utilize DNA methylation data for enhanced cancer origin prediction.
- To compare the performance of the DNN classifier against existing diagnostic techniques.
Main Methods:
- Development of a deep neural network (DNN) classifier using DNA methylation data from 7,339 patients across 18 cancer origins (TCGA).
- Rigorous model evaluation through 10-fold cross-validation, testing on hold-out data (1,468 patients), analysis of metastatic cancer patients (143), and validation on an independent dataset (581 samples).
- Comparative analysis with pathology and gene expression-based techniques.
Main Results:
- The DNN model demonstrated high diagnostic accuracy across multiple evaluation strategies, achieving specificities above 99.47% and sensitivities ranging from 92.59% to 95.95%.
- Performance metrics consistently exceeded those of existing pathology and gene expression-based methods.
- The model showed robust performance on independent and metastatic cancer patient datasets.
Conclusions:
- DNA methylation-based DNN classifiers offer a highly accurate and potentially more effective approach for cancer origin prediction.
- The developed DNN model shows significant promise for diagnosing cancer of unknown primary (CUP) and identifying cancer cell types in circulating tumor cells (CTCs).
- The classifier's ease of implementation suggests clinical utility and improved patient management strategies.
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