Evidence map›Paper›PMID 39487859›Full record

ArticleCancer immunology, immunotherapy : CII2024

Three-dimensional dynamics of mesothelin-targeted CAR.CIK lymphocytes against ovarian cancer peritoneal carcinomatosis.

Federica Galvagno, Valeria Leuci, Annamaria Massa, Chiara Donini, Ramona Rotolo, Sonia Capellero, Alessia Proment, Letizia Vitali, Andrea Maria Lombardi, Valentina Tuninetti and 7 more

Abstract read
In one paragraph

Article in Cancer immunology, immunotherapy : CII, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Article
  2. Review
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

17 authors.

Federica GalvagnoDepartment of Oncology, University of Torino, Turin, Italy.ORCID https://orcid.org/0000-0003-2245-6810
Valeria LeuciDepartment of Oncology, University of Torino, Turin, Italy.
Annamaria MassaDepartment of Oncology, University of Torino, Turin, Italy.
Chiara DoniniDepartment of Oncology, University of Torino, Turin, Italy.
Ramona RotoloDepartment of Oncology, University of Torino, Turin, Italy.
Sonia CapelleroCandiolo Cancer Institute, FPO-IRCCS, Candiolo, TO, Italy.ORCID https://orcid.org/0000-0001-9043-9014
Alessia PromentDepartment of Oncology, University of Torino, Turin, Italy.
Letizia VitaliDepartment of Oncology, University of Torino, Turin, Italy.
Andrea Maria LombardiDepartment of Oncology, University of Torino, Turin, Italy.
Valentina TuninettiDepartment of Oncology, University of Torino, Turin, Italy.ORCID https://orcid.org/0000-0002-7377-0888
Lorenzo D'AmbrosioDepartment of Oncology, University of Torino, Turin, Italy.ORCID https://orcid.org/0000-0003-3294-8819
Alessandra MerliniDepartment of Oncology, University of Torino, Turin, Italy.ORCID https://orcid.org/0000-0002-5280-1411
Elisa VignaDepartment of Oncology, University of Torino, Turin, Italy.ORCID https://orcid.org/0000-0001-9787-5732
Giorgio ValabregaDepartment of Oncology, University of Torino, Turin, Italy.ORCID https://orcid.org/0000-0001-5444-6305
Luca PrimoDepartment of Oncology, University of Torino, Turin, Italy.
Alberto Puliafito *Department of Oncology, University of Torino, Turin, Italy. alberto.puliafito@unito.it.ORCID http://orcid.org/0000-0002-0096-1536
Dario Sangiolo *Department of Oncology, University of Torino, Turin, Italy. dario.sangiolo@unito.it.ORCID http://orcid.org/0000-0002-7163-7071

Funding

CAR-T Grant RCR-2019-23669115Fondazione AIRC per la ricerca sul cancro ETS 20259Fondazione AIRC per la ricerca sul cancro ETS 23211Fondazione AIRC per la ricerca sul cancro ETS 25040Fondazione CRT 2022Italian Ministry of Health, Ricerca Corrente 2023-2024Ricerca Locale, Università degli Studi di Torino 2019Ricerca Locale, Università degli Studi di Torino 2022
6 · The paper itself

Abstract

Intraperitoneal cellular immunotherapy with CAR-redirected lymphocytes is an intriguing approach to target peritoneal carcinomatosis (PC) from ovarian cancer (OC), which is currently evaluated in clinical trials. PC displays a composite structure with floating tumor cells within ascites and solid-like masses invading the peritoneum. Therefore, a comprehensive experimental model is crucial to optimize CAR-cell therapies in such a peculiar environment. Here, we explored the activity of cytokine-induced killer lymphocytes (CIK), redirected by CAR against mesothelin (MSLN-CAR.CIK), within reductionistic 3D models resembling the structural complexity of both liquid and solid components of PC. MSLN-CAR.CIK effectively killed and were functionally efficient against OC targets. In a "floating-like" 3D context with floating OC spheroids, both tumor localization and killing by MSLN-CAR.CIK were significantly boosted by fluid flow. In a "solid-like" context, MSLN-CAR.CIK were recruited through the extracellular matrix on embedded tumor aggregates, with variable kinetics depending on the effector-target distance. Furthermore, MSLN-CAR.CIK penetrated the inner levels of OC spheroids exerting effective tumor killing. Our findings provide currently unknown therapeutically relevant information on intraperitoneal approaches with CAR.CIK, supporting further developments and improvements for clinical studies in the context of locoregional cell therapy approaches for patients with PC from OC.

Indexed as

GPI-Linked ProteinsImmunotherapy, AdoptiveMesothelinOvarian NeoplasmsPeritoneal NeoplasmsAnimalsCell Line, TumorFemaleHumansMiceReceptors, Chimeric AntigenGPI-Linked ProteinsMesothelinMSLN protein, humanMsln protein, mouseReceptors, Chimeric Antigen3D modelsCellular immunotherapyChimeric antigen receptorOvarian cancerPeritoneal carcinomatosis

Identifiers

PMID39487859
PMCPMC11531451

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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.