ArticleGenome medicine2017
Predicting cancer type from tumour DNA signatures.
Article in Genome medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Quest for Improving Treatment of Cancer of Unknown Primary (CUP) Through Molecularly-Driven Treatments: A Systematic Review.Frontiers in oncology · 2020Pooled it
- Integrating artificial intelligence into cancers of unknown primary diagnosis and treatment.iScience · 2026Review
- A multi-representation deep-learning framework for accurate multicancer classification.Journal of translational 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
- Explainable AI Model Reveals Informative Mutational Signatures for Cancer-Type Classification.Cancers · 2025Article
- Enhanced Immunohistochemistry Interpretation with a Machine Learning-Based Expert System.Diagnostics (Basel, Switzerland) · 2024Article
- Deep-Learning Model for Tumor-Type Prediction Using Targeted Clinical Genomic Sequencing Data.Cancer discovery · 2024Article
- Topical hidden genome: discovering latent cancer mutational topics using a Bayesian multilevel context-learning approach.Biometrics · 2024Article
- The practical utility of AI-assisted molecular profiling in the diagnosis and management of cancer of unknown primary: an updated review.Virchows Archiv : an international journal of pathology · 2024Review
- Comparison of the classifiers based on mRNA, microRNA and lncRNA expression and DNA methylation profiles for the tumor origin detection.Frontiers in genetics · 2024Article
- Classification of tumor types using XGBoost machine learning model: a vector space transformation of genomic alterations.Journal of translational medicine · 2023Article
- Mutation-Attention (MuAt): deep representation learning of somatic mutations for tumour typing and subtyping.Genome medicine · 2023Article
- Deep learning approach for cancer subtype classification using high-dimensional gene expression data.BMC bioinformatics · 2022Article
- New Perspectives on the Importance of Cell-Free DNA Biology.Diagnostics (Basel, Switzerland) · 2022Review
- Supervised Machine Learning Empowered Multifactorial Genetic Inheritance Disorder Prediction.Computational intelligence and neuroscience · 2022Article
- Using the "Hidden" genome to improve classification of cancer types.Biometrics · 2021Article
- Deep learning for cancer type classification and driver gene identification.BMC bioinformatics · 2021Article
- Deep learning in cancer diagnosis, prognosis and treatment selection.Genome medicine · 2021Review
- Mining mutation contexts across the cancer genome to map tumor site of origin.Nature communications · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
backgroundEstablishing the cancer type and site of origin is important in determining the most appropriate course of treatment for cancer patients. Patients with cancer of unknown primary, where the site of origin cannot be established from an examination of the metastatic cancer cells, typically have poor survival. Here, we evaluate the potential and limitations of utilising gene alteration data from tumour DNA to identify cancer types.
methodsUsing sequenced tumour DNA downloaded via the cBioPortal for Cancer Genomics, we collected the presence or absence of calls for gene alterations for 6640 tumour samples spanning 28 cancer types, as predictive features. We employed three machine-learning techniques, namely linear support vector machines with recursive feature selection, L
resultsWe found the linear support vector machine to be the most predictive model of cancer type from gene alterations. Using only 100 somatic point-mutated genes for prediction, we achieved an overall accuracy of 49.4±0.4 % (95 % confidence interval). We observed a marked increase in the accuracy when copy number alterations are included as predictors. With a combination of somatic point mutations and copy number alterations, a mere 50 genes are enough to yield an overall accuracy of 77.7±0.3 %.
conclusionsA general cancer diagnostic tool that utilises either only somatic point mutations or only copy number alterations is not sufficient for distinguishing a broad range of cancer types. The combination of both gene alteration types can dramatically improve the performance.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.