ArticleNature communications2020
A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 112 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
112 citing papers in PubMed, 3 syntheses or guidelines pooled it, 232 citations in OpenAlex.
- Deep learning approaches for resolving genomic discrepancies in cancer: a systematic review and clinical perspective.Briefings in bioinformatics · 2025Pooled it
- A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data.BMC bioinformatics · 2023Pooled it
- Circulating Clusterin Levels and Cancer Risk: A Systematic Review and Meta-Analysis.Cancer control : journal of the Moffitt Cancer CenterPooled it
- Cancer of unknown primary: the evolution of tissue of origin identification in the artificial intelligence era.Biomarker research · 2026Review
- Integrative cfDNA profiling from low-pass whole-genome sequencing enables tissue-of-origin prediction in cancer.Molecular biomedicine · 2026Article
- Integrating artificial intelligence into cancers of unknown primary diagnosis and treatment.iScience · 2026Review
- Soft multiclass feature augmented deep learning to predict tumor origins using cytology or histology whole slide images.NPJ digital medicine · 2026Article
- Graph-based deep learning approach for high-throughput protein-DNA interaction scoring.Acta pharmacologica Sinica · 2026Article
- The role of multimodality in clinical disease diagnosis: advances, challenges, and opportunities.Frontiers in public health · 2026Review
- A review on in-silico analysis of immune cell trafficking and interactions with the tumour microenvironment.Frontiers in oncology · 2026Review
- Reconstructing the lifelong history of cells and tissues via somatic mutation analysis.Cellular and molecular life sciences : CMLS · 2025Review
- Passenger mutations link cellular origin and transcriptional identity in human lung adenocarcinomas.Nature genetics · 2025Article
- A multi-representation deep-learning framework for accurate multicancer classification.Journal of translational medicine · 2025Article
- In silico generation of synthetic cancer genomes using generative AI.Cell genomics · 2025Article
- Review
- Rethinking cancer of unknown primary: from diagnostic challenge to targeted treatment.Nature reviews. Clinical oncology · 2025Review
- Large language models enable tumor-type classification and localization of cancers of unknown primary from genomic data.Cell reports. Medicine · 2025Article
- GPSai: A Clinically Validated AI Tool for Tissue of Origin Prediction during Routine Tumor Profiling.Cancer research communications · 2025Article
- Leveraging sequences missing from the human genome to diagnose cancer.Communications medicine · 2025Article
- Human-alignment influences the utility of AI-assisted decision making.Scientific reports · 2025Article
52 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
15 authors at 20 institutions in 36 countries.
Funding
Abstract
In cancer, the primary tumour's organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3% of cases a patient presents with a metastatic tumour and no obvious primary. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we train a deep learning classifier to predict cancer type based on patterns of somatic passenger mutations detected in whole genome sequencing (WGS) of 2606 tumours representing 24 common cancer types produced by the PCAWG Consortium. Our classifier achieves an accuracy of 91% on held-out tumor samples and 88% and 83% respectively on independent primary and metastatic samples, roughly double the accuracy of trained pathologists when presented with a metastatic tumour without knowledge of the primary. Surprisingly, adding information on driver mutations reduced accuracy. Our results have clinical applicability, underscore how patterns of somatic passenger mutations encode the state of the cell of origin, and can inform future strategies to detect the source of circulating tumour DNA.
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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.