ArticlePLoS computational biology2019
Passenger mutations accurately classify human tumors.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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
33 citing papers in PubMed.
- Integrating artificial intelligence into cancers of unknown primary diagnosis and treatment.iScience · 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
- Advances in single-cell DNA sequencing enable insights into human somatic mosaicism.Nature reviews. Genetics · 2025Review
- Rethinking cancer of unknown primary: from diagnostic challenge to targeted treatment.Nature reviews. Clinical oncology · 2025Review
- Mutations in tumor signaling, metastases, and synthetic lethality establish distinct patterns.PLoS computational biology · 2025Article
- Cancer-associated loss-of-function mutations in KCNQ1 enhance Wnt/β-catenin signalling disrupting epithelial homeostasis.Oncogene · 2025Article
- Tumor Niche Influences the Activity and Delivery of Anticancer Drugs: Pharmacology Meets Chemistry.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Microenvironment and Tumor Heterogeneity as Pharmacological Targets in Precision Oncology.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Classification of non-TCGA cancer samples to TCGA molecular subtypes using compact feature sets.Cancer cell · 2025Article
- Immune evasion impacts the landscape of driver genes during cancer evolution.Genome biology · 2024Article
- Deep-Learning Model for Tumor-Type Prediction Using Targeted Clinical Genomic Sequencing Data.Cancer discovery · 2024Article
- Review
- Cell cycle gene alterations associate with a redistribution of mutation risk across chromosomal domains in human cancers.Nature cancer · 2024Article
- Multiple-instance learning of somatic mutations for the classification of tumour type and the prediction of microsatellite status.Nature biomedical engineering · 2024Article
- CDK6 as a Biomarker for Immunotherapy, Drug Sensitivity, and Prognosis in Bladder Cancer: Bioinformatics and Immunohistochemical Analysis.International journal of medical sciences · 2024Article
- Plasminogen missense variants and their involvement in cardiovascular and inflammatory disease.Frontiers in cardiovascular medicine · 2024Review
- Classification of tumor types using XGBoost machine learning model: a vector space transformation of genomic alterations.Journal of translational medicine · 2023Article
- Oncogenic context shapes the fitness landscape of tumor suppression.Nature communications · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Determining the cancer type and molecular subtype has important clinical implications. The primary site is however unknown for some malignancies discovered in the metastatic stage. Moreover liquid biopsies may be used to screen for tumoral DNA, which upon detection needs to be assigned to a site-of-origin. Classifiers based on genomic features are a promising approach to prioritize the tumor anatomical site, type and subtype. We examined the predictive ability of causal (driver) somatic mutations in this task, comparing it against global patterns of non-selected (passenger) mutations, including features based on regional mutation density (RMD). In the task of distinguishing 18 cancer types, the driver mutations-mutated oncogenes or tumor suppressors, pathways and hotspots-classified 36% of the patients to the correct cancer type. In contrast, the features based on passenger mutations did so at 92% accuracy, with similar contribution from the RMD and the trinucleotide mutation spectra. The RMD and the spectra covered distinct sets of patients with predictions. In particular, introducing the RMD features into a combined classification model increased the fraction of diagnosed patients by 50 percentage points (at 20% FDR). Furthermore, RMD was able to discriminate molecular subtypes and/or anatomical site of six major cancers. The advantage of passenger mutations was upheld under high rates of false negative mutation calls and with exome sequencing, even though overall accuracy decreased. We suggest whole genome sequencing is valuable for classifying tumors because it captures global patterns emanating from mutational processes, which are informative of the underlying tumor biology.
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.