Evidence map›Paper›PMID 42079742›Full record

ArticleHemaSphere2026

Dissecting minimal residual disease dynamics to improve outcome prediction in mantle cell lymphoma: Data from the Fondazione Italiana Linfomi (FIL)-MCL0208 clinical trial.

Francesca Cordero, Simone Ferrero, Simone Pernice, Elisa Genuardi, Daniela Volpatto, Roberta Sirovich, Aurora Maria Civita, Andrea Evangelista, Simone Ragaini, Alice Di Rocco and 18 more

Abstract read
In one paragraph

Article in HemaSphere, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

28 authors.

Francesca CorderoDepartment of Computer Science University of Turin Torino Italy.ORCID https://orcid.org/0000-0002-3143-3330
Simone FerreroHematology Division, Department of Molecular Biotechnologies and Health Sciences University of Turin Torino Italy.
Simone PerniceDepartment of Computer Science University of Turin Torino Italy.
Elisa GenuardiHematology Division, Department of Molecular Biotechnologies and Health Sciences University of Turin Torino Italy.
Daniela VolpattoDepartment of Computer Science University of Turin Torino Italy.
Roberta SirovichDepartment of Mathematics "G. Peano" University of Turin Torino Italy.
Aurora Maria CivitaHematology Division, Department of Molecular Biotechnologies and Health Sciences University of Turin Torino Italy.
Andrea EvangelistaUnit of Clinical Epidemiology Azienda Ospedaliera Universitaria Città della Salute e della Scienza and CPO Piemonte Torino Italy.
Simone RagainiHematology Division, Department of Molecular Biotechnologies and Health Sciences University of Turin Torino Italy.ORCID https://orcid.org/0000-0003-3899-5755
Alice Di RoccoDepartment of Translational and Precision Medicine Sapienza University of Rome Rome Italy.
Alessandro ReDepartment of Haematology ASST-Spedali Civili Hospital Brescia Italy.
Vittorio StefoniInstitute of Haematology L. e A. Seràgnoli University of Bologna Bologna Italy.
Federica CavalloDepartment of Molecular Biotechnologies and Health Sciences Universitaria Città della Salute e della Scienza di Torino and Centre for Cancer Prevention Piemonte Torino Italy.ORCID https://orcid.org/0000-0003-2047-1099
Carola BoccominiSC Ematologia AOU "Città della Salute e della Scienza di Torino" Torino Italy.
Monica BalzarottiHematology Unit IRCCS Humanitas Research Hospital, Rozzano Milan Italy.
Vittorio Ruggero ZilioliDivision of Hematology ASST Grande Ospedale Metropolitano Niguarda Milan Italy.ORCID https://orcid.org/0000-0001-6915-2246
Maria Gomes da SilvaDepartamento de Hematologia Instituto Portugues de Oncologia de Lisboa Lisbon Portugal.
Luca ArcainiDepartment of Molecular Medicine University of Pavia Pavia Italy.ORCID https://orcid.org/0000-0002-9504-991X
Melania CelliOspedale Degli Infermi Di Rimini Rimini Italy.
Gian Maria ZaccariaDepartment of Electrical and Information Engineering (DEI) Polytechnic University of Bari Bari Italy.
Dora TortaroloDepartment of Computer Science University of Turin Torino Italy.
Marco BeccutiDepartment of Computer Science University of Turin Torino Italy.
Eva HosterInstitute of Medical Informatics, Biometry, and Epidemiology Ludwig-Maximilians-University of Munich Munich Germany.
Christiane PottSecond Medical Department University Hospital Schleswig-Holstein Kiel Germany.
Elizabeth MacintyreINSERM U1151, Institut Necker Enfants Malades (INEM) Paris France.ORCID https://orcid.org/0000-0003-0520-0493
Olivier HermineService d'Hématologie Adulte Hôpital Universitaire Necker-Enfants Malades, APHP Université Paris Cité Paris France.
Martin DreylingDepartment of Medicine III LMU Hospital Munich Germany.
Marco LadettoDivision of Hematology Antonio e Biagio e Cesare Arrigo Hospital Alessandria Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent clinical trials have underscored the value of repeated minimal residual disease (MRD) measurements as a highly sensitive method for detecting subclinical disease and enabling dynamic risk stratification in hematologic malignancies. Despite its clinical potential, the complex and heterogeneous nature of MRD kinetics presents significant challenges for interpreting and integrating it into routine clinical decision-making. In this study, we present a comprehensive, model-based workflow for the longitudinal analysis of MRD trajectories designed to improve relapse risk prediction. We applied this newly developed workflow to a cohort of patients with mantle cell lymphoma (MCL). MRD measurements were collected from both bone marrow (BM) and peripheral blood (PB) over time, stored in the Fondazione Italiana Linfomi MCL0208 clinical trial. Using our functional MRD workflow, we defined four MRD dynamics that collapsed into two clinically relevant groups: favorable (rapid, sustained negativization) and unfavorable (persistent or fluctuating MRD). Patients with unfavorable profiles showed significantly shorter time to progression (TTP), with hazard ratio (HR) = 4.18 (95% CI: 2.44-7.14) in BM and HR = 5.71 (95% CI: 2.86-11.42) in PB. External validation in the European MCL Network "Younger trial" confirmed the predictive power of this stratification, with Kaplan-Meier analyses demonstrating significant prognostic discrimination. The most informative temporal windows for patient clustering vary by tissue. Early-phase BM assessments offer greater discriminatory power, whereas late-phase assessments are most informative in PB. These findings indicate that longitudinal MRD assessment in PB represents a clinically actionable strategy that could reduce dependence on invasive BM procedures.

Identifiers

PMID42079742
PMCPMC13129670

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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.