Evidence map›Paper›PMID 31428516›Full record

ArticleOncoimmunology

High-throughput assessment of the antibody profile in ovarian cancer ascitic fluids.

Frank Antony, Cecilia Deantonio, Diego Cotella, Maria Felicia Soluri, Olga Tarasiuk, Francesco Raspagliesi, Fulvio Adorni, Silvano Piazza, Yari Ciani, Claudio Santoro and 3 more

Open access · goldAbstract read
In one paragraph

Article in Oncoimmunology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
4.4field-weighted citation impact, top 5% 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

24 citing papers in PubMed, 47 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

13 authors at 5 institutions in 1 country.

Frank AntonyDepartment of Health Sciences, and Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Università del Piemonte Orientale, Novara, Italy.
Cecilia DeantonioDepartment of Health Sciences, and Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Università del Piemonte Orientale, Novara, Italy.
Diego CotellaDepartment of Health Sciences, and Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Università del Piemonte Orientale, Novara, Italy.ORCID 0000-0003-2714-4744
Maria Felicia SoluriDepartment of Health Sciences, and Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Università del Piemonte Orientale, Novara, Italy.
Olga TarasiukDepartment of Health Sciences, and Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Università del Piemonte Orientale, Novara, Italy.
Francesco RaspagliesiDepartment of Surgery, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
Fulvio AdorniEpidemiology Unit, Institute of Biomedical Technologies, National Research Council, Milan, Italy.
Silvano PiazzaBioinformatics and Functional Genomics Unit, Laboratorio Nazionale del Consorzio Interuniversitario per le Biotecnologie (LNCIB), Area Science Park Trieste, Italy.
Yari CianiBioinformatics and Functional Genomics Unit, Laboratorio Nazionale del Consorzio Interuniversitario per le Biotecnologie (LNCIB), Area Science Park Trieste, Italy.
Claudio SantoroDepartment of Health Sciences, and Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Università del Piemonte Orientale, Novara, Italy.
Paolo MacorDepartment of Life Science, University of Trieste, Trieste, Italy.
Delia MezzanzanicaDepartment of Research, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.ORCID 0000-0002-9664-6871
Daniele SblatteroDepartment of Life Science, University of Trieste, Trieste, Italy.ORCID 0000-0003-4309-242X
Università degli Studi del Piemonte Orientale “Amedeo Avogadro” · ITConsorzio Interuniversitario per le Biotecnologie · ITFondazione IRCCS Istituto Nazionale dei Tumori · ITUniversity of Trieste · ITInstitute of Biomedical Technologies · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The identification of effective biomarkers for early diagnosis, prognosis, and response to treatments remains a challenge in ovarian cancer (OC) research. Here, we present an unbiased high-throughput approach to profile ascitic fluid autoantibodies in order to obtain a tumor-specific antigen signature in OC. We first reported the reactivity of immunoglobulins (Igs) purified from OC patient ascites towards two different OC cell lines. Using a discovery set of Igs, we selected tumor-specific antigens from a phage display cDNA library. After biopanning, 700 proteins were expressed as fusion protein and used in protein array to enable large-scale immunoscreening with independent sets of cancer and noncancerous control. Finally, the selected antigens were validated by ELISA. The initial screening identified eight antigenic clones: CREB3, MRPL46, EXOSC10, BCOR, HMGN2, HIP1R, OLFM4, and KIAA1755. These antigens were all validated by ELISA in a study involving ascitic Igs from 153 patients (69 with OC, 34 with other cancers and 50 without cancer), with CREB3 showing the highest sensitivity (86.95%) and specificity (98%). Notably, we were able to identify an association between the tumor-associated (TA) antibody response and the response to a first-line tumor treatment (platinum-based chemotherapy). A stronger association was found by combining three antigens (BCOR, CREB3, and MRLP46) as a single antibody signature. Measurement of an ascitic fluid antibody response to multiple TA antigens may aid in the identification of new prognostic signatures in OC patients and shift attention to new potentially relevant targets.

Indexed as

ascitebiomarkerOvarian cancerprotein microarraytumor-associated antigen

Identifiers

PMID31428516
PMCPMC6685609
OpenAlexW2948633516

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.