Evidence map›Paper›PMID 23935036›Full record

Trial reportClinical cancer research : an official journal of the American Association for Cancer Research2013

Predicting response to bevacizumab in ovarian cancer: a panel of potential biomarkers informing treatment selection.

Fiona Collinson, Michelle Hutchinson, Rachel A Craven, David A Cairns, Alexandre Zougman, Tobias C Wind, Narinder Gahir, Michael P Messenger, Sharon Jackson, Douglas Thompson and 6 more

Open access · bronzeAbstract readClinical Trial, Phase IIIMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Clinical cancer research : an official journal of the American Association for Cancer Research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed, 3 pooled it
12.0field-weighted citation impact, top 1% 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

40 citing papers in PubMed, 3 syntheses or guidelines pooled it, 72 citations in OpenAlex.

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  14. Tyrosine kinase inhibitors (TKIs) for ovarian cancer treatment: from organic to inorganic chemotherapeutics towards selectivity-a perspective overview.Biometals : an international journal on the role of metal ions in biology, biochemistry, and medicine · 2024
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

16 authors at 5 institutions in 1 country.

Fiona CollinsonAuthors' Affiliations: Clinical Trials Research Unit, Leeds Institute of Clinical Trials Research, University of Leeds; St James's Institute of Oncology, Bexley Wing; Cancer Research UK Centre, Leeds Institute of Molecular Medicine, St James's University Hospital; Department of Clinical Biochemistry and Immunology, Leeds General Infirmary, Leeds; MRC Clinical Trials Unit; UCL Cancer Institute, University College, London; and Christie Hospital, University of Manchester, Withington, Manchester, United Kingdom.
Michelle Hutchinson
Rachel A Craven
David A Cairns
Alexandre Zougman
Tobias C Wind
Narinder Gahir
Michael P Messenger
Sharon Jackson
Douglas Thompson
Cybil Adusei
Jonathan A Ledermann
Geoffrey Hall
Gordon C Jayson
Peter J Selby
Rosamonde E Banks
Leeds General Infirmary · GBMRC Clinical Trials Unit at UCL · GBSt James's University Hospital · GBUniversity of Leeds · GBCancer Research UK · GB

Funding

Cancer Research UK A7966Cancer Research UK C2075/A7966Medical Research Council G0700549Medical Research Council G0802416
6 · The paper itself

Abstract

purposeThe aim of this study was to identify and validate novel predictive and/or prognostic serum proteomic biomarkers in patients with epithelial ovarian cancer (EOC) treated as part of the phase III international ICON7 clinical trial. EXPERIMENTAL

designICON7 was a phase III international trial in EOC which showed a modest but statistically significant benefit in progression-free survival (PFS) with the addition of bevacizumab to standard chemotherapy. Serum samples from 10 patients who received bevacizumab (five responders and five nonresponders) were analyzed by mass spectrometry to identify candidate biomarkers. Initial validation and exploration by immunoassay was undertaken in an independent cohort of 92 patients, followed by a second independent cohort of 115 patients (taken from across both arms of the trial).

resultsThree candidate biomarkers were identified: mesothelin, fms-like tyrosine kinase-4 (FLT4), and α1-acid glycoprotein (AGP). Each showed evidence of independent prognostic potential when adjusting for high-risk status in initial (P < 0.02) and combined (P < 0.01) validation cohorts. In cohort I, individual biomarkers were not predictive of bevacizumab benefit; however, when combined with CA-125, a signature was developed that was predictive of bevacizumab response and discriminated benefit attributable to bevacizumab better than clinical characteristics. The signature showed weaker evidence of predictive ability in validation cohort II, but was still strongly predictive considering all samples (P = 0.001), with an improvement in median PFS of 5.5 months in signature-positive patients in the experimental arm compared with standard arm.

conclusionsThis study shows a discriminatory signature comprising mesothelin, FLT4, AGP, and CA-125 as potentially identifying those patients with EOC more likely to benefit from bevacizumab. These results require validation in further patient cohorts.

Indexed as

Patient SelectionAdenocarcinoma, Clear CellAdenocarcinoma, MucinousAdultAgedAngiogenesis InhibitorsAntibodies, Monoclonal, HumanizedBevacizumabBiomarkers, TumorChromatography, LiquidCohort StudiesCystadenocarcinoma, SerousEndometrial NeoplasmsFallopian Tube NeoplasmsFemaleFollow-Up StudiesAngiogenesis InhibitorsAntibodies, Monoclonal, HumanizedBevacizumabBiomarkers, Tumor

Identifiers

PMID23935036
PMCPMC3780518
OpenAlexW2018889021

What OpenQuestion holds

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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.