Background: Multiple myeloma (MM) is the most common hematologic neoplasm among African Americans (AA), who have a 1.5-2-fold excess compared to Whites. Older age, male sex and obesity are also established risk factors. There is a knowledge gap regarding the tumor microenvironment (TME) in MM, especially in AA's. It is important to understand the effect of the host risk factors on the tumor microenvironment and the arrangement of immune architecture in this high-risk population group.
Methods: We assessed the tumor microenvironment (TME) of 30 diagnostic MM bone marrow samples (61 regions of interest, 516,328 cells) from AA patients who previously participated in a genome-wide association scan, using imaging mass cytometry (IMC) to identify differences linked to patient characteristics (sex, age, and obesity). Obesity was defined using the WHO categories which classified BMI into normal weight, overweight obese and severely obese.
Hypercellular regions were identified on diagnostic slides reviewed by a hematopathologist. Cores of 0.5 mm in diameter and 4 microns in thickness were taken for analysis. Antibody-metal conjugation was conducted with Maxpar labeling kit (Standard Biotools). Antigen retrieval was performed with tris-EDTA solution. The slides were incubated with metal-tagged antibodies overnight, washed, and dried before ablation.
Statistical methods: The ablated imaging data was processed into single cell objects using supervised training of multiple major cellular phenotypes including macrophages (CD68, CD163, iNOS), lymphocytes (CD3, CD4, CD8, CD20, CD45RO, FOXP3,) fibroblasts (vimentin), endothelial cells (aSMA, EprhinB2, CD31), fat (adiponectin), myeloma cells (CD138, MUM1) and bone (collagen) from the panel. The data were clustered into the major TME phenotypes identified using self-organized mapping (SOM) which were labeled using lineage markers to identify the major TME compartments. The abundance was modeled using negative-binomial regression with the cellular total as the offset; nearest neighbor distances (NND) were crossed by all pairs and compared using logistic regression. Clusters were annotated using linear mixed effects model of standardized (Z-scale) expression. Associations were estimated using multivariable linear regression in R.
Results:
The TME comprised hematopoietic cells (56%) endothelial/fibroblasts (27%), myeloma cells (12%), fat (4.3)%, and collagen representing bone (0.19%). Among hematopoietic cells there were CD45+ (βCD45=0.66 (0.56, 0.71); p<0.01) hematopoietic cells not otherwise specified (44%), cytotoxic T-cells (CTLs) (22%), macrophages (22%) B-cells (9%),FoxP3 (2%) and CD4+ cells (<1%).
There were 13 (46%) males. The average age was 59 years (sd=11.8 years). Our set consisted of 7 (26%) normal weight, 11 (41%) overweight, 6 (22%) obese and 3 (11%) morbidly obese patients.
Increasing age was positively associated with an increasing abundance of B-cells (p<0.001) and bone (p=0.02) and inversely associated with the abundance of endothelial cells (ECs) (p<0.01) and fibroblasts (p<0.01). Females had significantly higher abundance of bone (p<0.001) and ECs (p<0.001), but we did not detect differences in other TME phenotypes by sex.
Compared to normal BMI, there were no significant associations between CTLs and overweight, or obese patients' tumors. However, a significant increase in the abundance of CTLs was observed in tumors from patients who were severely obese compared to those with normal BMI (β= 1.88 (1.22, 2.9), p<0.01). Increasing abundance of ECs was associated with increasing BMI categories (β=6.6 (1.8,22.5), p<0.001).
Spatial summary
We did not detect differences in NND of any major phenotypes by age or sex. In tumors from morbidly obese individuals compared to normal BMI individuals, CTLs were significantly closer to myeloma cells (p<0.01), fibroblasts (p=0.02) and B-cells (p=0.01). There were no differences in spatial distances observed for other BMI categories.
Conclusions: Host factors, especially morbid obesity may influence the TME in AA patients with MM, with sex and age having lesser effects. Increased abundance of CTLs and endothelial cells (reflecting blood vessels) and shorter distances to myeloma cells may contribute to the poorer survival in these patients.
Vij:Janssen, Pfizer, GSK, Regeneron, Karyopharm: Other, Patents & Royalties; Sanofi, BMS, Takeda: Other, Patents & Royalties. Orlowski:AbbVie Inc, Adaptive Biotechnologies Corporation, Asylia Therapeutics Inc, BioTheryX Inc, Bristol Myers Squibb, Karyopharm Therapeutics, Meridian Therapeutics, Monte Rosa Therapeutics, Nanjing IASO Biotherapeutics, Neoleukin Therapeutics, Oncopeptides, Pf: Membership on an entity's Board of Directors or advisory committees; Bristol Myers Squibb, CARsgen Therapeutics, Exelixis Inc, Heidelberg Pharma, Janssen Biotech Inc, Sanofi, Takeda Pharmaceuticals USA Inc; Laboratory Research Funding: Asylia Therapeutics Inc, BioTheryX Inc, Heidelberg Pharma: Research Funding; Asylia Therapeutics Inc.: Current equity holder in private company, Patents & Royalties; BioTheryX: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol-Myers Squibb Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; DEM BioPharma, Inc., Karyopharm Therapeutics, Lytica Therapeutics, Meridian Therapeutics, Monte Rosa Therapeutics, Myeloma 360, Nanjing IASO Biotherapeutics, Neoleukin Corporation, Oncopeptides AB, Pfizer, Inc., Regeneron Pharmaceuticals, Inc., Sporos Bio: Membership on an entity's Board of Directors or advisory committees; Sanofi, Takeda Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding. Siddiqi:Recordati Rare Diseases: Consultancy, Speakers Bureau. Merchant:Genmab: Consultancy, Speakers Bureau; Abbvie: Consultancy, Speakers Bureau; IMMpact Bio: Research Funding; Oncovalent: Consultancy, Research Funding; Amgen: Consultancy; Innate Pharma: Research Funding; BMS: Speakers Bureau.
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