Evidence map›Paper›PMID 28274726›Full record

ReviewBiochimica et biophysica acta. Reviews on cancer2017

A population genetics perspective on the determinants of intra-tumor heterogeneity.

Zheng Hu, Ruping Sun, Christina Curtis

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Biochimica et biophysica acta. Reviews on cancer, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing 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

32 citing papers in PubMed.

  1. Article
  2. Article
  3. Anti-tumor analysis of the RIG-I agonist inBiochemistry and biophysics reports · 2025
    Article
  4. Review
  5. Article
  6. Review
  7. Article
  8. Evolvability of cancer-associated genes under APOBEC3A/B selection.bioRxiv : the preprint server for biology · 2023
    Article
  9. Review
  10. Article
  11. Article
  12. Review
  13. Review
  14. Article
  15. Single-cell RNA sequencing in cancer research.Journal of experimental & clinical cancer research : CR · 2021
    Review
  16. Article
  17. Article
  18. Review
  19. Review
  20. Review
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

3 authors.

Zheng HuDepartments of Medicine and Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA 94305, USA.
Ruping SunDepartments of Medicine and Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA 94305, USA.
Christina CurtisDepartments of Medicine and Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA 94305, USA. Electronic address: cncurtis@stanford.edu.

Funding

Integrated genomic analysis and multi-scale modeling of therapeutic resistanceR01CA182514 · NCI · STANFORD UNIVERSITY · PI CURTIS, CHRISTINA N · 2014 to 2018
$2.4M
NCI NIH HHS R01 CA182514
6 · The paper itself

Abstract

Cancer results from the acquisition of somatic alterations in a microevolutionary process that typically occurs over many years, much of which is occult. Understanding the evolutionary dynamics that are operative at different stages of progression in individual tumors might inform the earlier detection, diagnosis, and treatment of cancer. Although these processes cannot be directly observed, the resultant spatiotemporal patterns of genetic variation amongst tumor cells encode their evolutionary histories. Such intra-tumor heterogeneity is pervasive not only at the genomic level, but also at the transcriptomic, phenotypic, and cellular levels. Given the implications for precision medicine, the accurate quantification of heterogeneity within and between tumors has become a major focus of current research. In this review, we provide a population genetics perspective on the determinants of intra-tumor heterogeneity and approaches to quantify genetic diversity. We summarize evidence for different modes of evolution based on recent cancer genome sequencing studies and discuss emerging evolutionary strategies to therapeutically exploit tumor heterogeneity. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.

Indexed as

Evolution, MolecularGenetic FitnessGenetic HeterogeneityAdaptation, PhysiologicalAnimalsBiomarkers, TumorCell Transformation, NeoplasticGene Expression Regulation, NeoplasticGenetic Predisposition to DiseaseGenetics, PopulationHeredityHumansModels, GeneticMutationNeoplasmsPedigreeBiomarkers, Tumor

Identifiers

PMID28274726
PMCPMC5623098

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.