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Record W4380079942 · doi:10.1002/hon.3164_200

A low lymphocyte‐to‐monocyte ratio (LMR) predicts PFS, POD24 and OS in previously untreated, high tumor burden follicular lymphoma (FL): an analysis from the RELEVANCE trial

2023· article· en· W4380079942 on OpenAlexaff
Pablo Mozas, Ron Ammar, Loïc Chartier, Loretta J. Nastoupil, Emmanuel Bachy, Silvia Maria Bezsera, J. Barnes, Fontanet Bijou, André Goy, Hacène Zerazhi, Guillaume Cartron, Mario Ojeda‐Uribe, Sylvain Choquet, Bertrand Joly, Morgane Cheminant, Herbert Eradat, Rémy Gressin, Pau Abrisqueta, Anne Parcelier, María José Rodríguez Salazar, Christophe Bonnet, Michael Crump, Armando López‐Guillermo, Franck Morschhauser

Bibliographic record

VenueHematological Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsMedicineInternal medicineRituximabOncologyFollicular lymphomaLogistic regressionCohortProportional hazards modelLymphoma

Abstract

fetched live from OpenAlex

Introduction: The peripheral blood LMR has been postulated as an accessible piece of information about the composition of the tumor microenvironment. In a single-center, retrospective, unselected cohort of FL, a low LMR was shown to be associated with an older age and higher tumor burden and to predict for a shorter progression-free and overall survival (PFS, OS) and a higher risk of histological transformation and second primary malignancies (Mozas, Leuk & Lymph, 2020). We explored the impact of the LMR in patients included in the phase III RELEVANCE trial (Morschhauser, NEJM, 2018 and JCO, 2022), which compared rituximab-chemotherapy (R-chemo) with rituximab-lenalidomide (R2) in patients with previously untreated, high tumor burden FL. Methods: Xtile and the maxstat package of R software were used to find the best LMR cutoff based on PFS data and then validated using a truncated power basis spline method. Baseline characteristics, PFS per investigator assessment, early relapse (POD24) and OS were compared between LMR risk groups. Multivariable Cox regression models including the FLIPI score and the treatment arm were built for survival analyses and uni/multivariable logistic regression was used for POD24 analysis. Results: Among the 1030 patients included in the RELEVANCE study, 1018 had LMR data available. The median LMR was 2.5 (range, 0–93) and a LMR cutoff of 2 was found to best predict PFS. Patients with a LMR ≤2 (n = 372, 37%) were older and displayed higher-risk features (Figure A). In the global cohort, a LMR ≤2 was predictive of a shorter PFS (HR = 1.39 Figure B and C) and OS (HR = 1.44), but its negative impact in the multivariable model remained statistically significant solely for PFS (HR for LMR = 1.31). Likewise, a LMR ≤2 was associated with a higher risk of POD24 (univariable OR = 1.84; multivariable OR = 1.71). No significant interaction was observed in the PFS analysis between treatment arms and the LMR, despite the fact that the LMR was significantly associated to PFS only in the R-chemo arm (P = 0.001) and not in the R2 arm (P = 0.08). The research was funded by: The RELEVANCE trial was supported by Celgene, a Bristol Myers Squibb Company, and the Lymphoma Academic Research Organisation (LYSARC). Keywords: Diagnostic and Prognostic Biomarkers, Indolent non-Hodgkin lymphoma, Targeting the Tumor Microenvironment Conflicts of interests pertinent to the abstract. A. Martín García-Sancho Other remuneration: Janssen, Roche, BMS, Kyowa Kirin, Clinigen, Eusa Pharma, Novartis, Gilead/Kite, Incyte, Lilly, Takeda, ADC Therapeutics America, Miltenyi, Ideogen, Abbvie P. Abrisqueta Consultant or advisory role: Janssen, Roche, Abbvie, BMS, Astrazeneca, Gilead, Beigene Honoraria: Janssen, Roche, Abbvie, BMS, Astrazeneca, Gilead Educational grants: Janssen, Abbvie, Roche M. Crump Other remuneration: Kyte/Gilead, Novartis F. Morschhauser Consultant or advisory role: Roche, Gilead, Genmab, Novartis, Abbvie Honoraria: Chugai (scientific lectures)

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.303
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2023
Admission routes1
Has abstractyes

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