Evidence map›Paper›PMID 33284507›Full record

ReviewThe oncologist2021

Gamma Secretase Inhibitors in Cancer: A Current Perspective on Clinical Performance.

Tyler R McCaw, Evelyn Inga, Herbert Chen, Renata Jaskula-Sztul, Vikas Dudeja, James A Bibb, Bin Ren, J Bart Rose

Open access · greenAbstract readReview
In one paragraph

Review in The oncologist, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
82citing papers in PubMed, 1 pooled it
6.2field-weighted citation impact, top 3% of its field
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

82 citing papers in PubMed, 1 synthesis or guideline pooled it, 132 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Article
  6. Esophageal cancer: from pathogenesis to precision therapies.Signal transduction and targeted therapy · 2026
    Review
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Review
  13. Review
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  15. Article
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  18. Article
  19. Article
  20. Protein Marker-Dependent Drug Discovery Targeting Breast Cancer Stem Cells.International journal of molecular sciences · 2025
    Review

22 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

8 authors at 1 institution in 1 country.

Tyler R McCawDivisions of Surgical Oncology, The University of Alabama at Birmingham, Birmingham, Alabama, USA.ORCID 0000-0003-4097-1111
Evelyn IngaDivisions of Surgical Oncology, The University of Alabama at Birmingham, Birmingham, Alabama, USA.
Herbert ChenBreast & Endocrine Surgery, The University of Alabama at Birmingham, Birmingham, Alabama, USA.ORCID 0000-0003-2923-6079
Renata Jaskula-SztulBreast & Endocrine Surgery, The University of Alabama at Birmingham, Birmingham, Alabama, USA.
Vikas DudejaDivisions of Surgical Oncology, The University of Alabama at Birmingham, Birmingham, Alabama, USA.
James A BibbGastrointestinal Surgery, The University of Alabama at Birmingham, Birmingham, Alabama, USA.
Bin RenVascular Surgery & Endovascular Therapy, Department of Surgery, The University of Alabama at Birmingham, Birmingham, Alabama, USA.
J Bart RoseDivisions of Surgical Oncology, The University of Alabama at Birmingham, Birmingham, Alabama, USA.ORCID 0000-0002-0471-3978
University of Alabama at Birmingham · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gamma secretase inhibitors (GSIs), initially developed as Alzheimer's therapies, have been repurposed as anticancer agents given their inhibition of Notch receptor cleavage. The success of GSIs in preclinical models has been ascribed to induction of cancer stem-like cell differentiation and apoptosis, while also impairing epithelial-to-mesenchymal transition and sensitizing cells to traditional chemoradiotherapies. The promise of these agents has yet to be realized in the clinic, however, as GSIs have failed to demonstrate clinical benefit in most solid tumors with the notable exceptions of CNS malignancies and desmoid tumors. Disappointing clinical performance to date reflects important questions that remain to be answered. For example, what is the net impact of these agents on antitumor immune responses, and will they require concurrent targeting of tumor-intrinsic compensatory pathways? Addressing these limitations in our current understanding of GSI mechanisms will undoubtedly facilitate their rational incorporation into combinatorial strategies and provide a valuable tool with which to combat Notch-dependent cancers. In the present review, we provide a current understanding of GSI mechanisms, discuss clinical performance to date, and suggest areas for future investigation that might maximize the utility of these agents. IMPLICATIONS FOR PRACTICE: The performance of gamma secretase inhibitors (GSIs) in clinical trials generally has not reflected their encouraging performance in preclinical studies. This review provides a current perspective on the clinical performance of GSIs across various solid tumor types alongside putative mechanisms of antitumor activity. Through exploration of outstanding gaps in knowledge as well as reasons for success in certain cancer types, the authors identify areas for future investigation that will likely enable incorporation of GSIs into rational combinatorial strategies for superior tumor control and patient outcomes.

Indexed as

Antineoplastic AgentsNeoplasmsAmyloid Precursor Protein SecretasesApoptosisEnzyme InhibitorsHumansReceptors, NotchAmyloid Precursor Protein SecretasesAntineoplastic AgentsEnzyme InhibitorsReceptors, NotchCancerClinical trialGamma secretaseGamma secretase inhibitorsNotchSolid tumors

Identifiers

PMID33284507
PMCPMC8018325
OpenAlexW3112267132

What OpenQuestion holds

Textmetadata
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.