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Record W2953047243 · doi:10.1002/hon.108_2629

DECIPHERING THE CONTRIBUTION OF MACROPHAGES TO FOLLICULAR LYMPHOMA PATHOGENESIS: NEW INSIGHTS INTO THERAPY

2019· article· en· W2953047243 on OpenAlexaff
Patricia Pérez Galán, Juan García Valero, Alba Matas‐Céspedes, Vanina Rodríguez, Fabián Arenas, Joaquim Carreras, Neus Serrat, Martina Guerrero‐Hernández, M. Corbera, A. Yahiaoui, Silvia Martín, Alfredo Rivas‐Delgado, Stacey Tannheimer, María C. Cid, Elı́as Campo, Armando López‐Guillermo, Dolors Colomer

Bibliographic record

VenueHematological Oncology · 2019
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsGilead Sciences (Canada)
Fundersnot available
KeywordsCD163Follicular lymphomaFollicular dendritic cellsCancer researchIn vivoM2 MacrophageCD68Tumor microenvironmentCell cultureCD20LymphomaTumor-associated macrophageChemistryMolecular biologyMacrophageMedicineImmunologyBiologyImmunohistochemistryIn vitroT cellImmune systemAntigen-presenting cellTumor cells

Abstract

fetched live from OpenAlex

Introduction: Follicular Lymphoma (FL) represents the paradigm of a lymphoid neoplasia depending on microenvironment. Early studies by gene expression profiling (GEP) in FL tumor biopsies found that those cases enriched in certain genes expressed mainly in macrophages and Follicular Dendritic Cells (FDC) showed an inferior outcome (Dave et al, NEJM 2004). In the current study we have analyzed the three-way crosstalk of FL-FDC-M2, the correlation of CSF1-R expression, the M-CSF receptor essential for macrophage differentiation, with FL clinical data, and explored the potential antitumor effect of FL-M2 disruption using a specific CSF1-R inhibitor in combination with anti-B cell therapies. Methods: Primary FL cells (n=5) were co-culture (48h) with M2 macrophages at 4:1 ratio, generated from peripheral blood monocytes of healthy donors (100ng/mL M-CSF). Purified B cells (CD20 beads, Miltenyi) were subjected to GEP using HG-U219 microarray (Affymetrix). Data mining analysis was done with GSEA software. IHC analysis of CSF1-R and CD163 expression were performed in FFPE of FL patients (grade 0-2 (n=59) grade 3a-3b (n=28)). In vivo FL-FDC mouse model was generated by sc inoculation of the FL cell lines RL or WSU-FSCCL w/wo the FDC non-immortalized cell line HK in SCID mice. Results: In vivo, FDC significantly increased FL cell lines tumorigenicity (p<0.001), being these FL-FDC co-xenografts highly infiltrated with mouse M2 macrophages (CD206+). Remarkably, macrophage depletion (liposomal clodronate) significantly (p< 0.001) decreased tumor growth, supporting the contribution of macrophages to FL progression. Cell culture supernatants from FL-FDC co-cultures were enriched in pro-angiogenic factors and in the macrophage-attractant CCL2, favoring in vitro monocyte recruitment to the tumor. Moreover, primary cultures of FL cells induce monocyte differentiation towards M2-like macrophages (Fig.1). FL viability was significantly increased in FL-M2 co-cultures, and gene sets related to migration, adhesion and invasion were enriched in FL cells (Fig.2). Interestingly, in FL tumor biopsies, while the well-established M2 maker CD163 did not correlate with FL clinical parameters, CSF1-R expression correlated with the histological grade. The CSF1-R inhibitor Pexidartinib (PLX3397) changed M2 integrin profile decreasing their adhesion, and switching the M2 macrophage polarization towards a M1 phenotype (Fig.3). PLX3397 hampers the in vitro pro-survival effect provided by M2 to FL primary cells. In vivo, the simultaneous targeting of FL tumor cells with anti-CD20 Rituximab and Macrophages with PLX3397, led to a cooperative and significant reduction of tumor growth (Fig.4). Keywords: dendritic cells; follicular lymphoma (FL); macrophages.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.019
GPT teacher head0.299
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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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Citations2
Published2019
Admission routes1
Has abstractyes

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