ArticleFrontiers in genetics2020
Predicting Cancer Tissue-of-Origin by a Machine Learning Method Using DNA Somatic Mutation Data.
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- New techniques to identify the tissue of origin for cancer of unknown primary in the era of precision medicine: progress and challenges.Briefings in bioinformatics · 2024Pooled it
- Cancer of unknown primary: the evolution of tissue of origin identification in the artificial intelligence era.Biomarker research · 2026Review
- TumorOriginPredictor: A Clinically Validated and Cloud-Deployed AI Platform for Tissue-of-Origin Identification in Cancer of Unknown Primary Using Somatic Mutation Profiles.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Cancer of unknown primary: the hunt for its elusive tissue-of-origin - is it time to call off the search?British journal of cancer · 2025Review
- GPSai: A Clinically Validated AI Tool for Tissue of Origin Prediction during Routine Tumor Profiling.Cancer research communications · 2025Article
- Explainable AI Model Reveals Informative Mutational Signatures for Cancer-Type Classification.Cancers · 2025Article
- A cross-cohort computational framework to trace tumor tissue-of-origin based on RNA sequencing.Scientific reports · 2023Article
- Molecular characterization of colorectal adenoma and colorectal cancer via integrated genomic transcriptomic analysis.Frontiers in oncology · 2023Article
- A deep learning model to classify neoplastic state and tissue origin from transcriptomic data.Scientific reports · 2022Article
- Genomic pan-cancer classification using image-based deep learning.Computational and structural biotechnology journal · 2021Article
- Evaluating DNA Methylation, Gene Expression, Somatic Mutation, and Their Combinations in Inferring Tumor Tissue-of-Origin.Frontiers in cell and developmental biology · 2021Article
Corrections and comments
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Authors and funding
11 authors.
Funding
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Abstract
Patients with carcinoma of unknown primary (CUP) account for 3-5% of all cancer cases. A large number of metastatic cancers require further diagnosis to determine their tissue of origin. However, diagnosis of CUP and identification of its primary site are challenging. Previous studies have suggested that molecular profiling of tissue-specific genes could be useful in inferring the primary tissue of a tumor. The purpose of this study was to evaluate the performance somatic mutations detected in a tumor to identify the cancer tissue of origin. We downloaded the somatic mutation datasets from the International Cancer Genome Consortium project. The random forest algorithm was used to extract features, and a classifier was established based on the logistic regression. Specifically, the somatic mutations of 300 genes were extracted, which are significantly enriched in functions, such as cell-to-cell adhesion. In addition, the prediction accuracy on tissue-of-origin inference for 3,374 cancer samples across 13 cancer types reached 81% in a 10-fold cross-validation. Our method could be useful in the identification of cancer tissue of origin, as well as the diagnosis and treatment of cancers.
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