ArticleNature medicine2023
Machine learning for genetics-based classification and treatment response prediction in cancer of unknown primary.
Article in Nature medicine, 2023. 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 67 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
67 citing papers in PubMed, 3 syntheses or guidelines pooled it, 102 citations in OpenAlex.
- Diagnostic predictive models for cancer-related fatigue: current evidence and future directions.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Pooled it
- SEOM-GECOD clinical guideline for cancer of unknown primary (update 2025).Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Guideline
- Imaging of cancer of unknown primary: a systematic literature review of the past, present, and future.The British journal of radiology · 2025Pooled it
- Decoding the microbiome: artificial intelligence-targeted gut microenvironment breakthroughs in personalized cancer therapy.Gut microbes · 2026Review
- 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
- MoESurv: a zero-sample and transferable survival prediction framework for rare cancers using mixture of experts.Bioinformatics (Oxford, England) · 2026Article
- Integrating artificial intelligence into cancers of unknown primary diagnosis and treatment.iScience · 2026Review
- Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.Nature reviews. Cancer · 2026Review
- Artificial intelligence in medicine: from molecular data analysis to clinical decision-making.Croatian medical journal · 2026Article
- From Disease-Specific Models to Broad Clinical Utility: A Perspective on AI Hybrid Ensemble Frameworks.Advanced genetics (Hoboken, N.J.) · 2026Article
- Convergence of machine learning and genomics for precision oncology.Nature reviews. Cancer · 2026Review
- Discovery of predictive biomarkers for cancer therapy through computational approaches.Nature reviews. Clinical oncology · 2026Review
- Comparative cross-methodological analysis of the IDH-wildtype glioblastoma tumor microenvironment.Journal of cancer research and clinical oncology · 2026Article
- Beyond origin: multimodal AI synthesis to resolve cancers of unknown primary.Cancer biology & medicine · 2026Article
- Diagnosis of Lung Diseases in the Artificial Intelligence Era.International journal of molecular sciences · 2026Article
- Machine learning-based integration of tumor deposit molecular signatures improves prognostic stratification in colon adenocarcinoma.International journal of colorectal disease · 2026Article
- Advances and challenges in single-cell RNA sequencing data analysis: a comprehensive review.Briefings in bioinformatics · 2026Review
- Management and survival of patients with cancer of unknown primary discussed by a French national multidisciplinary tumour board: a retrospective analysis.The Lancet regional health. Europe · 2026Article
- 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
7 more citing papers are in PubMed but not listed here.
Corrections and comments
- Commented on by
- Erratum issued
- Update of
Authors and funding
9 authors at 5 institutions in 1 country.
Funding
Abstract
Cancer of unknown primary (CUP) is a type of cancer that cannot be traced back to its primary site and accounts for 3-5% of all cancers. Established targeted therapies are lacking for CUP, leading to generally poor outcomes. We developed OncoNPC, a machine-learning classifier trained on targeted next-generation sequencing (NGS) data from 36,445 tumors across 22 cancer types from three institutions. Oncology NGS-based primary cancer-type classifier (OncoNPC) achieved a weighted F1 score of 0.942 for high confidence predictions ([Formula: see text]) on held-out tumor samples, which made up 65.2% of all the held-out samples. When applied to 971 CUP tumors collected at the Dana-Farber Cancer Institute, OncoNPC predicted primary cancer types with high confidence in 41.2% of the tumors. OncoNPC also identified CUP subgroups with significantly higher polygenic germline risk for the predicted cancer types and with significantly different survival outcomes. Notably, patients with CUP who received first palliative intent treatments concordant with their OncoNPC-predicted cancers had significantly better outcomes (hazard ratio (HR) = 0.348; 95% confidence interval (CI) = 0.210-0.570; P = [Formula: see text]). Furthermore, OncoNPC enabled a 2.2-fold increase in patients with CUP who could have received genomically guided therapies. OncoNPC thus provides evidence of distinct CUP subgroups and offers the potential for clinical decision support for managing patients with CUP.
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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.