Evidence map›Paper›PMID 37248149›Full record

ReviewTrends in cancer2023

Clinical and translational relevance of intratumor heterogeneity.

Marie-Anne Goyette, Marla Lipsyc-Sharf, Kornelia Polyak

Abstract readReview
In one paragraph

Review in Trends in cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing 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

33 citing papers in PubMed.

  1. Article
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  5. Intratumor habitat aware deep learning framework for preoperative lymph node metastasis prediction in laryngeal carcinoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026
    Article
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  7. Review
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  14. Article
  15. Targeting tumor transition windows.Exploration of targeted anti-tumor therapy · 2026
    Review
  16. Frontiers in oncology · 2026
    Article
  17. Article
  18. Review
  19. Article
  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.

Marie-Anne GoyetteDepartment of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA.
Marla Lipsyc-SharfDepartment of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA.
Kornelia PolyakDepartment of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA. Electronic address: Kornelia_polyak@dfci.harvard.edu.

Funding

Targeting intratumor heterogeneity in breast cancerR35CA197623 · NCI · DANA-FARBER CANCER INST · PI KORNELIA POLYAK · 2015 to 2026
$12.4M
NCI NIH HHS R35 CA197623
6 · The paper itself

Abstract

Intratumor heterogeneity (ITH) is a driver of tumor evolution and a main cause of therapeutic resistance. Despite its importance, measures of ITH are still not incorporated into clinical practice. Consequently, standard treatment is frequently ineffective for patients with heterogeneous tumors as changes to treatment regimens are made only after recurrence and disease progression. More effective combination therapies require a mechanistic understanding of ITH and ways to assess it in clinical samples. The growth of technologies enabling the spatially intact analysis of tumors at the single-cell level and the development of sophisticated preclinical models give us hope that ITH will not simply be used as a predictor of a poor outcome but will guide treatment decisions from diagnosis through treatment.

Indexed as

NeoplasmsDisease ProgressionHumansclonal interactionintratumor heterogeneitysingle-cell technologiestreatment resistancetumor plasticity

Identifiers

PMID37248149
PMCPMC10524913

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.