Evidence map›Paper›PMID 27241520›Full record

ReviewCurrent oncology reports2016

Genomic Characterization of High-Grade Serous Ovarian Cancer: Dissecting Its Molecular Heterogeneity as a Road Towards Effective Therapeutic Strategies.

Lorenza Mittempergher

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current oncology reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
4.4field-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

23 citing papers in PubMed, 33 citations in OpenAlex.

  1. Review
  2. Review
  3. Article
  4. Observational
  5. Genomic instability andFrontiers in oncology · 2025
    Article
  6. Review
  7. Overview of Tumor Heterogeneity in High-Grade Serous Ovarian Cancers.International journal of molecular sciences · 2023
    Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Review
  19. Article
  20. Article
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

1 author at 1 institution in 1 country.

Lorenza MittempergherDivision of Molecular Carcinogenesis, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands. l.mittempergher@nki.nl.
The Netherlands Cancer Institute · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-grade serous ovarian carcinoma (HGSOC) accounts for the majority of the ovarian cancer deaths, but over the last years little improvement in overall survival has been achieved. HGSOC is a molecularly and clinically heterogeneous disease. At genomic level, it represents a C-class malignancy having frequent gene losses (NF1, RB1, PTEN) and gains (CCNE1, MYC). HGSOC shows a simple mutational profile with TP53 nearly always mutated and with other genes mutated at low frequency. Importantly, 50 % of all HGSOCs have genetic features indicating a homologous recombination (HR) deficiency. HR deficient tumors are highly sensitive to PARP inhibitor anticancer agents, which exhibit synthetic lethality with a defective HR pathway. Transcriptionally, HGSOCs can be grouped into different molecular subtypes with distinct biology and prognosis. Molecular stratification of HGSOC based on these genomic features may result in improved therapeutic strategies.

Indexed as

Molecular Targeted TherapyAntineoplastic AgentsCystadenocarcinoma, SerousFemaleHumansNeoplasm GradingNeoplasm ProteinsOvarian NeoplasmsPrognosisAntineoplastic AgentsNeoplasm ProteinsCCNE1CDK12GenomicsHigh-grade serous ovarian carcinoma (HGSOC)Homologous recombination (HR)miRNAMolecular heterogeneityMolecular subtypesPARP inhibitorsTP53

Identifiers

PMID27241520
OpenAlexW2408848642

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.