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Enregistrement W4417019462 · doi:10.1182/blood-2025-3559

Spatial profiling of the bone marrow microenvironment in patients with IgM gammopathies reveals a T cell–Enriched tumor niche associated with asymptomatic waldenstrom macroglobulinemia progression

2025· article· en· W4417019462 sur OpenAlexaff
David Cordas dos Santos, Kane Foster, Daniel Heilpern-Mallory, Sophia Schroeder, Meirong Su, Vidhi Patel, Mohammed Rahman, Jacqueline Perry, Daniel Zangrando, Nina J. Lane, Yoshinobu Konishi, Steven Treon, Gad Getz, Irene M. Ghobrial

Notice bibliographique

RevueBlood · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueChronic Lymphocytic Leukemia Research
Établissements canadiensIntegrity Testing Laboratory (Canada)
Organismes subventionnairesnon disponible
Mots-clésWaldenstrom macroglobulinemiaMonoclonal gammopathy of undetermined significanceMultiple myelomaBone marrowAsymptomaticMacroglobulinemiaImmunoglobulin MMonoclonal

Résumé

récupéré en direct d'OpenAlex

Abstract INTRODUCTION While the genetic drivers of Waldenström macroglobulinemia (WM) are well characterized, the contribution of the bone marrow (BM) microenvironment to disease initiation and progression is less well understood. Prior studies have relied on BM aspirates, which are prone to dilution and fail to capture spatial context. In this study, we used spatial proteomics to profile the BM microenvironment across patients with IgM monoclonal gammopathy of undetermined significance (MGUS), asymptomatic WM (AWM), and symptomatic WM, aiming to identify spatial features linked to disease progression. METHODS We analyzed 80 bone marrow biopsies from 70 patients with IgM gammopathies enrolled in the PCROWD study at Dana-Farber Cancer Institute. Spatial proteomic profiling was performed using imaging mass cytometry (IMC; Hyperion XTi, Standard BioTools). Cell segmentation was performed using cellpose-sam. Phenotype markers were used to annotate 31 cell types in 733,010 single cells. Cellular neighborhoods, defined as the 10-μm radius around each cell, were calculated on a spatial distance graph and grouped using K-means clustering. Clinical data were integrated, including MYD88 mutational status (assessed from peripheral blood via institutional panel). P values were adjusted using FDR correction. RESULTS The study included 44 patients with IgM MGUS, 34 with AWM, and 2 with symptomatic WM at initial diagnosis. The median age was 65 years, and 49% were female. Among IgM MGUS patients, 43% were MYD88-mutated, 46% wild-type, and 11% untested. In AWM, 68% were mutated, 12% wild-type, and 21% unknown. Over a median follow-up of 8.2 years, 30% of patients progressed to symptomatic disease (IgM MGUS: 10%; AWM: 41.2%). The median time from diagnosis to BM sampling was 6.1 months (IQR 1.0–28.9). At the time of collection, 5 IgM MGUS patients had progressed to AWM, and 2 IgM MGUS and 4 AWM patients had developed symptoms. Pathologist-reported BM infiltration was similar in AWM and WM (median 40%) and strongly correlated with IMC-based quantification of CD45⁺CD19⁺ WM cells (R=0.78, p<0.001). Analysis of the overall BM composition revealed a significantly higher fraction of WM cells in AWM compared to IgM MGUS (q<0.001), accompanied by lower proportions of myeloid-lineage cells (q<0.001), erythroid-lineage cells, and hematopoietic stem cells (both q=0.002). These findings aligned with higher hemoglobin levels in IgM MGUS vs AWM (p<0.01) at sample collection, reflecting the preserved hematopoiesis in precursor states. Surprisingly, despite greater lymphomatous involvement, the AWM samples exhibited a higher abundance of T cells (q=0.02), predominantly driven by the CD8⁺ fraction (q=0.004). Phenotypic analysis in AWM showed increased Tregs (q=0.002); activated CD4⁺ T cells (q=0.02); early activated (q=0.007) and exhausted CD8⁺ T cells (Tex) (q=0.03); and reduced GZB⁺Ki67⁺ CD8⁺ effectors (q=0.003), suggesting a progressively immunosuppressed T cell microenvironment in later disease stages. Cell neighborhood analysis revealed two spatially adjacent but distinct WM neighborhoods within the same samples. The two WM neighborhoods exhibited similar compositions across most cell types but differed in their proportions of WM and T cells: one was characterized by a higher WM cell content and lower T cell infiltration (62% WM, 17% T cells), while the other showed reduced WM density alongside increased T cell presence (52% WM, 23% T cells). WM cells in the T cell–enriched neighborhood displayed elevated expression of Ki-67 (q=0.004), HLA-DR (q=0.0005), and PD-L1 (q=0.001). Within the T cell compartment, this neighborhood was enriched for activated CD8⁺ and CD4⁺ T cells (q<0.0001 and q=0.0009), CD8⁺ Tex (q=0.0002), and Treg (q=0.002). Notably, the fraction of HLA-DR–expressing WM cells correlated with the abundance of CD4⁺ Tregs (R=0.46, p=0.004) and CD8⁺ Tex cells (R=0.57, p<0.0001), supporting the notion that the T cell–enriched WM neighborhood represents a “hot” immune microenvironment. Patients with above-median enrichment of this neighborhood showed a trend toward higher progression to symptomatic WM (p=0.06), whereas the T cell–low WM neighborhood was not associated with progression risk. CONCLUSIONS By mapping spatial microenvironmental changes across IgM gammopathies, this study reveals a T cell–enriched WM niche associated with disease progression, supporting a model in which localized tumor–immune interactions shape the course of WM pathogenesis.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,003

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,004
Tête enseignante GPT0,224
Écart entre enseignants0,220 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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