Evidence map›Paper›PMID 21399628›Full record

ArticleNature2011

Tumour evolution inferred by single-cell sequencing.

Nicholas Navin, Jude Kendall, Jennifer Troge, Peter Andrews, Linda Rodgers, Jeanne McIndoo, Kerry Cook, Asya Stepansky, Dan Levy, Diane Esposito and 5 more

Abstract read
In one paragraph

Article in Nature, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1,454 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1,454citing papers in PubMed, 1 pooled it
–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

1,454 citing papers in PubMed, 1 synthesis or guideline pooled it.

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1,394 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

15 authors.

Nicholas NavinCold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA.
Jude Kendall
Jennifer Troge
Peter Andrews
Linda Rodgers
Jeanne McIndoo
Kerry Cook
Asya Stepansky
Dan Levy
Diane Esposito
Lakshmi Muthuswamy
Alex Krasnitz
W Richard McCombie
James Hicks
Michael Wigler

Funding

VIRAL ONCOLOGYT32CA009176 · NCI · STATE UNIVERSITY NEW YORK STONY BROOK · PI HEARING, PATRICK · 1985 to 2012
$5.2M
NCI NIH HHS T32 CA009176
6 · The paper itself

Abstract

Genomic analysis provides insights into the role of copy number variation in disease, but most methods are not designed to resolve mixed populations of cells. In tumours, where genetic heterogeneity is common, very important information may be lost that would be useful for reconstructing evolutionary history. Here we show that with flow-sorted nuclei, whole genome amplification and next generation sequencing we can accurately quantify genomic copy number within an individual nucleus. We apply single-nucleus sequencing to investigate tumour population structure and evolution in two human breast cancer cases. Analysis of 100 single cells from a polygenomic tumour revealed three distinct clonal subpopulations that probably represent sequential clonal expansions. Additional analysis of 100 single cells from a monogenomic primary tumour and its liver metastasis indicated that a single clonal expansion formed the primary tumour and seeded the metastasis. In both primary tumours, we also identified an unexpectedly abundant subpopulation of genetically diverse 'pseudodiploid' cells that do not travel to the metastatic site. In contrast to gradual models of tumour progression, our data indicate that tumours grow by punctuated clonal expansions with few persistent intermediates.

Indexed as

Evolution, MolecularBreast NeoplasmsCarcinoma, Ductal, BreastChromosome BreakpointsClone CellsDiploidyDisease ProgressionFemaleFlow CytometryGenetic HeterogeneityGenome, HumanGenomicsHumansLiver NeoplasmsLoss of HeterozygositySequence Analysis, DNA

Identifiers

PMID21399628
PMCPMC4504184

What OpenQuestion holds

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Registered trials

None linked

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.