Evidence map›Paper›PMID 25806106›Full record

ReviewJournal for immunotherapy of cancer2015

Immunotherapeutic approaches to ovarian cancer treatment.

Cariad Chester, Oliver Dorigo, Jonathan S Berek, Holbrook Kohrt

Open access · goldAbstract readReview
In one paragraph

Review in Journal for immunotherapy of cancer, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.

0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed
4.9field-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

42 citing papers in PubMed, 78 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Review
  10. Cabozantinib inhibits tumor growth in mice with ovarian cancer.American journal of cancer research · 2024
    Article
  11. Article
  12. Article
  13. Article
  14. Review
  15. Recent Insights into PARP and Immuno-Checkpoint Inhibitors in Epithelial Ovarian Cancer.International journal of environmental research and public health · 2022
    Review
  16. Review
  17. Article
  18. Computational modeling of ovarian cancer dynamics suggests optimal strategies for therapy and screening.Proceedings of the National Academy of Sciences of the United States of America · 2021
    Article
  19. Review
  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

4 authors at 1 institution in 1 country.

Cariad ChesterDepartment of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA USA.
Oliver DorigoStanford Women's Cancer Center, Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA 94305 U.S.A.
Jonathan S BerekStanford Women's Cancer Center, Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA 94305 U.S.A.
Holbrook KohrtDepartment of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA USA.
Stanford University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Despite advances in combinatorial chemotherapy regimens and the advent of intraperitoneal chemotherapy administration, current therapeutic options for ovarian cancer patients are inadequate. Immunotherapy offers a novel and promising therapeutic strategy for treating ovarian tumors. Following the demonstration of the immunogenicity of ovarian tumors, multiple immunotherapeutic modalities have been developed. Antibody-based therapies, immune checkpoint blockade, cancer vaccines, and chimeric antigen receptor-modified T cells have demonstrated preclinical success and entered clinical testing. In this review, we discuss these promising immunotherapeutic approaches and emphasize the importance of combinatorial treatment strategies and biomarker discovery.

Indexed as

ACTAntibodyCancer vaccineID8IDOImmune checkpoint blockadeOvarian cancerTAM

Identifiers

PMID25806106
PMCPMC4372273
OpenAlexW2141474293

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.