ArticleNature methods2018
Detecting repeated cancer evolution from multi-region tumor sequencing data.
Article in Nature methods, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 88 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
88 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic accuracy and potential covariates of artificial intelligence for diagnosing orthopedic fractures: a systematic literature review and meta-analysis.European radiology · 2022Pooled it
- Clinical utility of PDX cohorts to reveal biomarkers of intrinsic resistance and clonal architecture changes underlying acquired resistance to cetuximab in HNSCC.Signal transduction and targeted therapy · 2022Trial
- A guide to understanding tumour evolution through the lens of population genetics.Nature reviews. Cancer · 2026Review
- Deep molecular profiling of lung neuroendocrine tumours and supra-carcinoids.Molecular cancer · 2026Article
- Integrated epidemiological and molecular data inform the relationship between precancer and cancer states of esophageal adenocarcinoma.Nature medicine · 2026Article
- Article
- Diverse genomic and transcriptomic heterogeneity in EGFR-mutant lung adenocarcinoma between exon 19 del and exon 21 L858R.Cell communication and signaling : CCS · 2026Article
- Inferring and summarizing tumor phylogenies from bulk DNA data.Algorithms for molecular biology : AMB · 2026Article
- Full spatio-temporal analyses of migration and colonization in evolution-dense 3D mapping of cancer metastases provides new insights.Molecular biology and evolution · 2026Article
- General-purpose topology-aware embedding of tumor phylogenetic trees with graph neural networks.Bioinformatics advances · 2026Article
- Integrative Genomic and Transcriptomic Analysis Reveals Targetable Vulnerabilities in Angioimmunoblastic T-Cell Lymphoma.American journal of hematology · 2025Article
- Bayesian inference of fitness landscapes via tree-structured branching processes.Bioinformatics (Oxford, England) · 2025Article
- Review
- CNRein: an evolution-aware deep reinforcement learning algorithm for single-cell DNA copy number calling.Genome biology · 2025Article
- Article
- Research hotspots and frontiers of machine learning in renal medicine: a bibliometric and visual analysis from 2013 to 2024.International urology and nephrology · 2025Review
- A hypercubic Mk model framework for capturing reversibility in disease, cancer, and evolutionary accumulation modelling.Bioinformatics (Oxford, England) · 2024Article
- A regression based approach to phylogenetic reconstruction from multi-sample bulk DNA sequencing of tumors.PLoS computational biology · 2024Article
- Article
- Article
28 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Recurrent successions of genomic changes, both within and between patients, reflect repeated evolutionary processes that are valuable for the anticipation of cancer progression. Multi-region sequencing allows the temporal order of some genomic changes in a tumor to be inferred, but the robust identification of repeated evolution across patients remains a challenge. We developed a machine-learning method based on transfer learning that allowed us to overcome the stochastic effects of cancer evolution and noise in data and identified hidden evolutionary patterns in cancer cohorts. When applied to multi-region sequencing datasets from lung, breast, renal, and colorectal cancer (768 samples from 178 patients), our method detected repeated evolutionary trajectories in subgroups of patients, which were reproduced in single-sample cohorts (n = 2,935). Our method provides a means of classifying patients on the basis of how their tumor evolved, with implications for the anticipation of disease progression.
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.