Evidence map›Paper›PMID 41383404›Full record

ReviewTherapeutic advances in medical oncology2025

Targeting synthetic lethality: an effective therapeutic approach in ovarian and endometrial cancers.

Alizée Lebeau, Athanasios Kakkos, Natacha Leroi, Vincent Bours, Katty Delbecque, Frédéric Goffin, Elodie Gonne, Christine Gennigens

Abstract readReview
In one paragraph

Review in Therapeutic advances in medical oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Alizée LebeauDepartment of Medical Oncology, CHU of Liège, Liège, Belgium.ORCID https://orcid.org/0000-0002-1144-3232
Athanasios KakkosDepartment of Gynaecology and Obstetrics, CHU of Liège, Liège, Belgium.
Natacha LeroiDepartment of Human Genetics, CHU of Liège, Liège, Belgium.
Vincent BoursDepartment of Human Genetics, CHU of Liège, Liège, Belgium.
Katty DelbecqueDepartment of Pathology, CHU of Liège, Liège, Belgium.
Frédéric GoffinDepartment of Gynaecology and Obstetrics, CHU of Liège, Liège, Belgium.
Elodie GonneDepartment of Medical Oncology, CHU of Liège, Liège, Belgium.
Christine GennigensDepartment of Medical Oncology, CHU of Liège, Avenue de l'Hôpital 1, 4000 Liège, Belgium.ORCID https://orcid.org/0000-0003-4526-6219

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Synthetic lethality (SL) is a promising therapeutic concept that relies on the indirect targeting of vulnerabilities acquired through genetic mutations. Ovarian and endometrial cancers frequently exhibit mutations in the breast cancer (BRCA), phosphatase and tensin homolog (PTEN), AT-rich interactive domain-containing protein 1A (ARID1A) and TP53 genes, as well as DNA repair pathway deficiencies. Poly(ADP-ribose) polymerase inhibitors (PARPis) have demonstrated remarkable efficacy in various cancers with BRCA mutations and homologous recombination deficiency. In addition to PARPi, there has been an expansion of drugs exploiting the selective vulnerability of cancer cells via SL, such as WEE1 kinase and Ataxia Telangiectasia and Rad3-related protein (ATR). WEE1 inhibitors have shown encouraging results in combination with chemotherapy, increasing the objective response rate in patients with platinum-resistant TP53-mutated ovarian cancer. ATR inhibitors are currently being evaluated in ARID1A-mutated tumours, with preliminary results confirming their therapeutic potential.

Indexed as

endometrial cancerinnovative treatmentovarian cancersynthetic lethalitytargeted therapy

Identifiers

PMID41383404
PMCPMC12690063

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Registered trials

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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.