Evidence map›Paper›PMID 33191568›Full record

ArticleThe oncologist2021

More Insights on the Use of γ-Secretase Inhibitors in Cancer Treatment.

Pilar López-Nieva, Laura González-Sánchez, María Ángeles Cobos-Fernández, Raúl Córdoba, Javier Santos, José Fernández-Piqueras

Open access · hybridAbstract read
In one paragraph

Article in The oncologist, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 1 pooled it
1.4field-weighted citation impact, top 18% 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

25 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.

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  14. IncreasedInternational journal of molecular sciences · 2024
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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

6 authors at 4 institutions in 1 country.

Pilar López-Nieva *Centro de Biología Molecular Severo Ochoa (CBMSO), Consejo Superior de Investigaciones Científicas-Universidad Autónoma de Madrid (CSIC-UAM), Madrid, Spain.ORCID 0000-0001-6369-2444
Laura González-Sánchez *Centro de Biología Molecular Severo Ochoa (CBMSO), Consejo Superior de Investigaciones Científicas-Universidad Autónoma de Madrid (CSIC-UAM), Madrid, Spain.
María Ángeles Cobos-FernándezCentro de Biología Molecular Severo Ochoa (CBMSO), Consejo Superior de Investigaciones Científicas-Universidad Autónoma de Madrid (CSIC-UAM), Madrid, Spain.
Raúl CórdobaIIS Fundación Jiménez Díaz, Madrid, Spain.
Javier SantosCentro de Biología Molecular Severo Ochoa (CBMSO), Consejo Superior de Investigaciones Científicas-Universidad Autónoma de Madrid (CSIC-UAM), Madrid, Spain.ORCID 0000-0002-4168-6251
José Fernández-PiquerasCentro de Biología Molecular Severo Ochoa (CBMSO), Consejo Superior de Investigaciones Científicas-Universidad Autónoma de Madrid (CSIC-UAM), Madrid, Spain.ORCID 0000-0003-4520-6785
Centre for Biomedical Network Research on Rare Diseases · ESHospital Universitario Fundación Jiménez Díaz · ESCentro de Biología Molecular Severo Ochoa · ESConsejo Superior de Investigaciones Científicas · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The NOTCH1 gene encodes a transmembrane receptor protein with activating mutations observed in many T-cell acute lymphoblastic leukemias (T-ALLs) and lymphomas, as well as in other tumor types, which has led to interest in inhibiting NOTCH1 signaling as a therapeutic target in cancer. Several classes of Notch inhibitors have been developed, including monoclonal antibodies against NOTCH receptors or ligands, decoys, blocking peptides, and γ-secretase inhibitors (GSIs). GSIs block a critical proteolytic step in NOTCH activation and are the most widely studied. Current treatments with GSIs have not successfully passed clinical trials because of side effects that limit the maximum tolerable dose. Multiple γ-secretase-cleavage substrates may be involved in carcinogenesis, indicating that there may be other targets for GSIs. Resistance mechanisms may include PTEN inactivation, mutations involving FBXW7, or constitutive MYC expression conferring independence from NOTCH1 inactivation. Recent studies have suggested that selective targeting γ-secretase may offer an improved efficacy and toxicity profile over the effects caused by broad-spectrum GSIs. Understanding the mechanism of GSI-induced cell death and the ability to accurately identify patients based on the activity of the pathway will improve the response to GSI and support further investigation of such compounds for the rational design of anti-NOTCH1 therapies for the treatment of T-ALL. IMPLICATIONS FOR PRACTICE: γ-secretase has been proposed as a therapeutic target in numerous human conditions, including cancer. A better understanding of the structure and function of the γ-secretase inhibitor (GSI) would help to develop safe and effective γ-secretase-based therapies. The ability to accurately identify patients based on the activity of the pathway could improve the response to GSI therapy for the treatment of cancer. Toward these ends, this study focused on γ-secretase inhibitors as a potential therapeutic target for the design of anti-NOTCH1 therapies for the treatment of T-cell acute lymphoblastic leukemias and lymphomas.

Indexed as

Amyloid Precursor Protein SecretasesPrecursor T-Cell Lymphoblastic Leukemia-LymphomaCell Line, TumorHumansMutationReceptor, Notch1Receptors, NotchSignal TransductionAmyloid Precursor Protein SecretasesReceptor, Notch1Receptors, NotchBroad-spectrum γ-secretase inhibitorsMYC gene dosageNew resistance factorPF-03084014 treatmentSelective γ-secretase inhibitorsT-cell lymphoblastic cell lines

Identifiers

PMID33191568
PMCPMC7873333
OpenAlexW3100844668

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.