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Record 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 on 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

Bibliographic record

VenueBlood · 2025
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsIntegrity Testing Laboratory (Canada)
Fundersnot available
KeywordsWaldenstrom macroglobulinemiaMonoclonal gammopathy of undetermined significanceMultiple myelomaBone marrowAsymptomaticMacroglobulinemiaImmunoglobulin MMonoclonal

Abstract

fetched live from 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.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.224
Teacher spread0.220 · 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 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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Published2025
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