Evidence map›Paper›PMID 27083415›Full record

ArticleGenome biology2016

OncoNEM: inferring tumor evolution from single-cell sequencing data.

Edith M Ross, Florian Markowetz

Abstract read
In one paragraph

Article in Genome biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 145 papers.

0numbers the graph read from it
0cells of the map it votes in
145citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

145 citing papers in PubMed.

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  19. Review
  20. Resolving tumor evolution: a phylogenetic approach.Journal of the National Cancer Center · 2024
    Review

85 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Edith M RossCancer Research UK Cambridge Institute, University of Cambridge, Robinson Way, Cambridge, UK.
Florian MarkowetzCancer Research UK Cambridge Institute, University of Cambridge, Robinson Way, Cambridge, UK. florian.markowetz@cruk.cam.ac.uk.

Funding

Cancer Research UK 19274Cancer Research UK C14303/A17197
6 · The paper itself

Abstract

Single-cell sequencing promises a high-resolution view of genetic heterogeneity and clonal evolution in cancer. However, methods to infer tumor evolution from single-cell sequencing data lag behind methods developed for bulk-sequencing data. Here, we present OncoNEM, a probabilistic method for inferring intra-tumor evolutionary lineage trees from somatic single nucleotide variants of single cells. OncoNEM identifies homogeneous cellular subpopulations and infers their genotypes as well as a tree describing their evolutionary relationships. In simulation studies, we assess OncoNEM's robustness and benchmark its performance against competing methods. Finally, we show its applicability in case studies of muscle-invasive bladder cancer and essential thrombocythemia.

Indexed as

AlgorithmsClonal EvolutionComputational BiologyEvolution, MolecularGenotypeHigh-Throughput Nucleotide SequencingHumansModels, StatisticalPhylogenyPolymorphism, Single NucleotideSingle-Cell AnalysisThrombocytopeniaUrinary Bladder NeoplasmsCancer evolutionPhylogenetic treeSingle-cell sequencingTumor evolutionTumor heterogeneity

Identifiers

PMID27083415
PMCPMC4832472

What OpenQuestion holds

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Registered trials

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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.