ArticleNature communications2022
Machine learning-based tissue of origin classification for cancer of unknown primary diagnostics using genome-wide mutation features.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 2 of them syntheses that pooled it.
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Who cites it
53 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Precision Medicine and Artificial Intelligence in Next-Generation Cancer Surgery: A Comprehensive Analysis of Clinical Applications, Therapeutic Outcomes, and Implementation Strategies.Asian Pacific journal of cancer prevention : APJCP · 2025Pooled 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
- Integrative cfDNA profiling from low-pass whole-genome sequencing enables tissue-of-origin prediction in cancer.Molecular biomedicine · 2026Article
- Machine learning-driven cancer diagnostics with improved robustness and interpretability.Chemical science · 2026Review
- Integrating artificial intelligence into cancers of unknown primary diagnosis and treatment.iScience · 2026Review
- Multi-omics analysis identifies GINS1 as a prognostic biomarker in lung adenocarcinoma, linked to macrophage polarization and tumor cell survival.Discover oncology · 2026Article
- Metabolism pathway-based subtyping in pancreatic adenocarcinoma: an integrated study by bulk RNA-sequence and machine learning algorithms.International journal of surgery (London, England) · 2026Article
- DNA methylation biomarkers-based pan-cancer classifier: predictive modeling for cancer classification.Genome medicine · 2026Article
- Soft multiclass feature augmented deep learning to predict tumor origins using cytology or histology whole slide images.NPJ digital medicine · 2026Article
- Cancer of unknown primary genomic profiling from cell-free DNA provides insights into CUP biology and vulnerabilities.Frontiers in medicine · 2026Article
- Efficient Techniques Based on Sparse Representation for Classifying High-dimensional Multiclass Microarray Data.Journal of medical signals and sensors · 2026Article
- Passenger mutations link cellular origin and transcriptional identity in human lung adenocarcinomas.Nature genetics · 2025Article
- OptimDase: An Algorithm for Predicting DNA Binding Sites with Combined Feature Encoding.Interdisciplinary sciences, computational life sciences · 2025Article
- A multi-representation deep-learning framework for accurate multicancer classification.Journal of translational medicine · 2025Article
- Pathologist-initiated whole genome and transcriptome sequencing demonstrates diagnostic utility in resolving difficult-to-diagnose tumors.Genome medicine · 2025Article
- 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
- SurvBoard: standardized benchmarking for multi-omics cancer survival models.Briefings in bioinformatics · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancers of unknown primary (CUP) origin account for ∼3% of all cancer diagnoses, whereby the tumor tissue of origin (TOO) cannot be determined. Using a uniformly processed dataset encompassing 6756 whole-genome sequenced primary and metastatic tumors, we develop Cancer of Unknown Primary Location Resolver (CUPLR), a random forest TOO classifier that employs 511 features based on simple and complex somatic driver and passenger mutations. CUPLR distinguishes 35 cancer (sub)types with ∼90% recall and ∼90% precision based on cross-validation and test set predictions. We find that structural variant derived features increase the performance and utility for classifying specific cancer types. With CUPLR, we could determine the TOO for 82/141 (58%) of CUP patients. Although CUPLR is based on machine learning, it provides a human interpretable graphical report with detailed feature explanations. The comprehensive output of CUPLR complements existing histopathological procedures and can enable improved diagnostics for CUP patients.
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