Evidence map›Paper›PMID 29867465›Full record

ArticleFrontiers in pharmacology2018

Proteomic and Bioinformatic Studies for the Characterization of Response to Pemetrexed in Platinum Drug Resistant Ovarian Cancer.

Leda Severi, Lorena Losi, Sergio Fonda, Laura Taddia, Gaia Gozzi, Gaetano Marverti, Fulvio Magni, Clizia Chinello, Martina Stella, Jalid Sheouli and 8 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in pharmacology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.6field-weighted citation impact, top 40% 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

5 citing papers in PubMed, 9 citations in OpenAlex.

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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

18 authors at 3 institutions in 2 countries.

Leda SeveriDepartment of Life Sciences, University of Modena and Reggio Emilia, Modena, Italy.
Lorena LosiDepartment of Life Sciences, University of Modena and Reggio Emilia, Modena, Italy.
Sergio FondaDepartment of Life Sciences, University of Modena and Reggio Emilia, Modena, Italy.
Laura TaddiaDepartment of Life Sciences, University of Modena and Reggio Emilia, Modena, Italy.
Gaia GozziDepartment of Biomedical Science, Metabolic Science and Neuroscience, University of Modena and Reggio Emilia, Modena, Italy.
Gaetano MarvertiDepartment of Biomedical Science, Metabolic Science and Neuroscience, University of Modena and Reggio Emilia, Modena, Italy.
Fulvio MagniDepartment of Medicine and Surgery, University of Milano Bicocca, Monza, Italy.
Clizia ChinelloDepartment of Medicine and Surgery, University of Milano Bicocca, Monza, Italy.
Martina StellaDepartment of Medicine and Surgery, University of Milano Bicocca, Monza, Italy.
Jalid SheouliDepartment of Gynecology, European Competence Center for Ovarian Cancer, Charité-Universitätsmedizin Berlin, Berlin, Germany.
Elena I BraicuDepartment of Gynecology, European Competence Center for Ovarian Cancer, Charité-Universitätsmedizin Berlin, Berlin, Germany.
Filippo GenoveseCentro Interdipartimentale Grandi Strumenti, University of Modena and Reggio Emilia, Modena, Italy.
Angela LauriolaDepartment of Life Sciences, University of Modena and Reggio Emilia, Modena, Italy.
Chiara MarracciniDepartment of Life Sciences, University of Modena and Reggio Emilia, Modena, Italy.
Alessandra GualandiDepartment of Life Sciences, University of Modena and Reggio Emilia, Modena, Italy.
Domenico D'ArcaDepartment of Biomedical Science, Metabolic Science and Neuroscience, University of Modena and Reggio Emilia, Modena, Italy.
Stefania FerrariDepartment of Life Sciences, University of Modena and Reggio Emilia, Modena, Italy.
Maria P CostiDepartment of Life Sciences, University of Modena and Reggio Emilia, Modena, Italy.
University of Modena and Reggio Emilia · ITUniversity of Milano-Bicocca · ITCharité - Universitätsmedizin Berlin · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Proteomics and bioinformatics are a useful combined technology for the characterization of protein expression level and modulation associated with the response to a drug and with its mechanism of action. The folate pathway represents an important target in the anticancer drugs therapy. In the present study, a discovery proteomics approach was applied to tissue samples collected from ovarian cancer patients who relapsed after the first-line carboplatin-based chemotherapy and were treated with pemetrexed (PMX), a known folate pathway targeting drug. The aim of the work is to identify the proteomic profile that can be associated to the response to the PMX treatment in pre-treatement tissue. Statistical metrics of the experimental Mass Spectrometry (MS) data were combined with a knowledge-based approach that included bioinformatics and a literature review through ProteinQuest™ tool, to design a protein set of reference (PSR). The PSR provides feedback for the consistency of MS proteomic data because it includes known validated proteins. A panel of 24 proteins with levels that were significantly different in pre-treatment samples of patients who responded to the therapy vs. the non-responder ones, was identified. The differences of the identified proteins were explained for the patients with different outcomes and the known PMX targets were further validated. The protein panel herein identified is ready for further validation in retrospective clinical trials using a targeted proteomic approach. This study may have a general relevant impact on biomarker application for cancer patients therapy selection.

Indexed as

bioinformaticsdrug resistancefolate pathwaymass spectrometryovarian cancerpemetrexedproteomics

Identifiers

PMID29867465
PMCPMC5952181
OpenAlexW2802894300

What OpenQuestion holds

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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.