Composition of Immune Subpopulations Influence Multiple Myeloma Disease Progression
Authors: Ramakrishnan Rupal , Achille Alexandra , Nyugen Tuan , Taylor Ian , De Avila Gabriel , Alugubelli Raghunandanreddy , Meads Mark B. , Siqueira Silva Ariosto , Shain Kenneth H. , Brayer Jason B. 
Published: November 2019

Immune dysfunction is an important feature of multiple myeloma (MM). Characterization of lymphocytic infiltrates, comparing precursor, early & later disease stages, indicate that the anti-myeloma immune response evolves in conjunction with the progression of disease, while distinctive patterns identified in the infiltrating immune population have prognostic relevance (Pessoa de Magalhaes, Vidriales et al. 2013, Paiva, Mateos et al. 2016).The primary objective of this project is to characterize the marrow-infiltrating lymphocytic and myeloid populations to identify specific patterns of immune dysregulation & a biomarker signature that will guide predictions of efficacy of different immunotherapeutic modalities.(Willenbacher et al. 2016, Kini Bailur, Mehta et al. 2017).

Patient samples are identified for inclusion based on clinical assessment parameters: detection of a monoclonal gammopathy, the presence or absence of end-organ damage indicative of active/symptomatic MM, and <2 prior lines of therapy. 15 Healthy donor samples were acquired from volunteers with no evidence of hematologic malignancy. Disease status was categorized as precursor MGUS stage, newly diagnosed untreated MM (NDMM), pre-transplant MM (PreT-MM) and 90 days post-transplant MM (D90-MM).Infiltrating leukocyte populations are isolated from 20ml of fresh bone marrow aspirates and analyzed by multi-parameter flow cytometry. Lymphocytes are analyzed for subset composition, memory/effector maturation & exhaustion.The myeloid component analyzes the monocytic and granulocytic myeloid-derived suppressor cells, M1/M2 macrophages, dendritic cells and neutrophils. To date, 38 samples for lymphoid and 56 for the myeloid panel have been analyzed. All calculations and data analysis are performed in the industry standard bioinformatics platform, FlowJo v10.6.Dimensionality reduction is run for visualization of high parameter data using the t-distributed stochastic neighbor embedding (tSNE) algorithm. X-shift unsupervised clustering is employed to identify populations in an unbiased manner. Unsupervised clusters are compared and contrasted relative to known biomarkers of interest within the ClusterExplorer platform and batched across tSNE space in contrast to: subjects, conditions & manually defined gating. The immunologic parameters are compared to histologic, molecular and genetic features of disease as detailed by the biopsy report & to the extended longitudinal clinical course of the patient (disease progression & response to therapy).

Classical flow analysis of the myeloid panel clearly reveals significant changes occurring in the G-MDSC population with disease progression. tSNE-based unsupervised analysis show increases in CD4 T effector cells in NDMM patients, but the differences in the CD4 memory compartment in MGUS suggest that these variations in T cells may originate during premalignancy. The expression of CD95 on CD4 cells is downregulated with disease progression. In addition to changes in T cells, we observe changes in innate cells- NDMM samples show an increase in a CD194+NK cell population which may represent a transitional population expanded during pathological tumor growth. With recent studies suggesting that innate cells could be targets for immune-modulatory drugs, this opens a new avenue. Taken together, this data illustrates complex alterations in the immune landscape from a premalignant to malignant stage, implicating that engaging the immune system early in the evolution of clinical MM may improve the potential of durable remissions.

Kini Bailur, J., et al. (2017). "Changes in bone marrow innate lymphoid cell subsets in monoclonal gammopathy: target for IMiD therapy." Blood Adv1(25): 2343-2347.

Paiva, B., et al. (2016). "Immune status of high-risk smoldering multiple myeloma patients and its therapeutic modulation under LenDex: a longitudinal analysis." Blood127(9): 1151-1162.

Pessoa de Magalhaes, R. J., et al. (2013). "Analysis of the immune system of multiple myeloma patients achieving long-term disease control by multidimensional flow cytometry." Haematologica98(1): 79-86.

Willenbacher, W., et al. (2016). "Bone marrow microenvironmental CD4 + and CD8 + lymphocyte infiltration patterns define overall- and progression free survival in standard risk multiple myeloma--an analysis from the Austrian Myeloma Registry." Leuk Lymphoma57(6): 1478-1481.

Disclosures

Shain: AbbVie: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Consultancy; Amgen: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Sanofi Genzyme: Membership on an entity's Board of Directors or advisory committees. Brayer:Janssen: Consultancy, Speakers Bureau; BMS: Consultancy, Speakers Bureau.