Evidence map›Paper›PMID 34518297›Full record

ReviewMolecular cancer therapeutics2021

Synthetic Lethality in Ovarian Cancer.

Akshaya Chandrasekaran, Kevin M Elias

Open access · hybridAbstract readReview
In one paragraph

Review in Molecular cancer therapeutics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
2.6field-weighted citation impact, top 7% 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

30 citing papers in PubMed, 39 citations in OpenAlex.

  1. Review
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  10. Dual‑regulated oncolytic adenovirus carryingMolecular medicine reports · 2024
    Article
  11. Review
  12. The two sides of chromosomal instability: drivers and brakes in cancer.Signal transduction and targeted therapy · 2024
    Review
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  15. Review
  16. Article
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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

2 authors at 1 institution in 1 country.

Akshaya ChandrasekaranDivision of Gynecologic Oncology, Department of Obstetrics and Gynecology, and Reproductive Biology, Brigham and Women's Hospital, Boston Massachusetts.ORCID 0000-0003-0535-6623
Kevin M EliasDivision of Gynecologic Oncology, Department of Obstetrics and Gynecology, and Reproductive Biology, Brigham and Women's Hospital, Boston Massachusetts. kelias@bwh.harvard.edu.ORCID 0000-0003-1502-5553
Brigham and Women's Hospital · US

Funding

REPRODUCTIVE SCIENTIST TRAINING PROGRAMK12HD000849 · NICHD · WASHINGTON UNIVERSITY · PI Danny J Schust · 1988 to 2026
$33.2M
NICHD NIH HHS K12 HD000849
6 · The paper itself

Abstract

Ovarian cancers include several distinct malignancies which differ with respect to clinicopathologic features and prognosis. High-grade serous cancer is the most common histologic subtype and accounts for most ovarian cancer-related deaths. High-grade serous ovarian cancer (HGSOC) is treated with surgery and platinum-based chemotherapy, but most patients relapse and succumb to chemoresistant disease. The genetic concept of synthetic lethality, in which the synergy of mutations in multiple genes results in cell death, provides a framework to design novel therapeutic approaches to overcome chemoresistance in ovarian cancer. Recent progress in understanding the genomic architecture and hereditary drivers of ovarian cancer has shown potential for synthetic lethality strategies designed around homologous DNA repair. Clinical trials have validated high response rates for PARP inhibitors in patients with

Indexed as

FemaleHumansOvarian NeoplasmsPoly(ADP-ribose) Polymerase InhibitorsSynthetic Lethal MutationsPoly(ADP-ribose) Polymerase Inhibitors

Identifiers

PMID34518297
PMCPMC8571039
OpenAlexW3199748251

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.