Soekojo, Cinnie Yentia

Introduction The tumor ecosystem in multiple myeloma (MM) involves complex interactions among various cell types, including clonal plasma cells (PCs) and immune cells. Previous research has indicated that the bone marrow (BM) microenvironment's metabolite and lipid profiles differ between MM patients and those with precursor conditions. This study aims to explore if differences in cell composition, metabolic phenotype of clonal PCs, and the spatial organization of various cell types are linked to the early versus late progression of high-risk smoldering MM (SMM) to MM. Methods We identified high-risk SMM patients using the IMWG 20-2-20 criteria, focusing on early progressors (EP), who progressed to MM within one year, and later progressors (LP), who progressed after more than four years. Using a high-dimensional single-cell spatial imaging platform, CODEX (CO-Detection by indEXing), we identified clonal PCs and immune cell types as well as their metabolic phenotypes at the single-cell level, in paired FFPE BM samples at SMM diagnosis and at the time of progression to MM. Our validated panel included markers such as CD3e, CD4, CD8, CD14, CD20, CD31, CD38, CD45, CD66a, CD68, CD138, HLA-DR, ASCT2, pNRF2, GLUT1, LDHA, ATPA5, CPT1A, and Citrate synthase. We employed single-cell segmentation, artifact cleanup, signal thresholding, and spatial analysis. Repeated measure models accommodated multiple regions of interest (ROIs) per patient and accounted for potential intrapatient correlation, while Poisson models estimated incidence rate ratios (RR) of cell types. Spatial statistical models evaluated cell type segregation and integration at the time of SMM diagnosis. Results Single cell events were acquired from 6 unique SMM patients who had paired FFPE BM samples from the time of their SMM diagnosis and at the time of their progression to MM. There were 3 EP SMM patients (Time to Progression (TTP):8, 8, 10 mos) and 3 LP SMM patients (TTP: 51, 58, 117 mos). The median number of single cell events identified per patient slide sample was 104,390 (Range: 24,357 - 314,877). We identified 8 main cell types: PCs, CD4+ T-cells, CD8+ T-cells, B-cells, granulocytes, Monocyte/Macrophages and two groups of other immune cells. The PCs and the remainder of the non-PCs were further classified based on the expression of the following metabolic markers: ASCT2 (Glutamine importer), CPT1A (Fatty acid metabolism), GLUT1 (Glucose importer), LDHA (Glycolysis), Citrate synthase and ATPA5 (Oxidative Phosphorylation). There was a higher RR of granulocytes (RR 2.1, CI 1.6-2.8, p<0.001) in EP compared to LP samples at the time of SMM diagnosis, but a lower RR of CD4 T cells (RR 0.40, CI 0.3-0.6, p< 0.001). Focusing on metabolic phenotypes of the PCs, we observed higher incidence RR of most metabolic markers: ASCT2 (RR=1.77; p=0.032), ATPA5 (RR=1.25; p<0.001), CPT1a (RR=2.42; p=0.015), and LDHA (RR 2.13; p<0.001) in EP compared to LP samples at the time of SMM diagnosis. However, GLUT1 had a lower incidence RR in EP patients (RR=0.79; p<0.001). The same modeling for non-PCs at the time of SMM diagnosis yielded higher expression of LDHA (RR=1.98; p<0.001) and ASCT2 (RR=3.3; p=0.002). Using the x and y coordinates and the corresponding phenotype classifications, we performed spatial statistical modeling based on spatial point processes to generate aggregated measures of how different types of cells of interest interacted. CD4 T cells were more isolated in LPs compared to EPs (p =0.046). Segregation indices for PCs and CD4 T cells indicated that they were more integrated/collocated in EPs. Granulocytes were more isolated in EPs although the difference was not as pronounced (p= 0.07). PCs were more integrated with granulocytes (p=0.06) and B cells (p=0.07) in LP patients. Further analyses of paired samples at the time of progression to MM will be reported at the time of the presentation. Conclusion This study is the first to provide whole-slide, single-cell, spatial proteomic characterization of clonal PCs and immune cells in the FFPE BMs of high-risk SMM patients with varying progression times to MM. Distinct metabolic pathway markers in clonal PCs and their spatial relationships with immune cell subsets are closely associated with the risk of progression to MM. These findings illuminate the complex interactions between the metabolic states of clonal PCs and their spatial orientation relative to immune cells in the pathogenesis of MM.

Introduction: The combination of chemotherapy and immunotherapy, commonly referred to as chemo-immunotherapy (CIT), has long been a cornerstone in the treatment of lymphoma. Although this approach has resulted in successful treatments for many patients (pts), a significant number continue to experience unfavorable results, underscoring the urgency for improved treatments. There is still a significant gap in knowledge about the immune profile of lymphoma pts, specifically in terms of (1) how their immune phenotype compare to those of healthy individuals and (2) how treatment affects their immune system. This study aimed to compare the immune profiles of lymphoma pts (at baseline) to that of matched healthy controls, and track the changes in the immune system during and after lymphoma treatment. Methods: Pts with newly diagnosed, untreated lymphoma, without prior history of cancer or autoimmunity were enrolled in the Dyna-RISC study. Whole blood samples were collected at baseline (BL), on day 1 of cycle 2 (C2D1), on day 1 of last cycle (CLD1), and 6 months after completion of therapy (6mo). Age- and gender-matched healthy individuals provided blood for baseline comparison. Samples were stained fresh with a pre-mixed lyophilized cocktail containing 30 metal-tagged monoclonal antibodies (Maxpar Direct Immune Profile Assay), fixed and cryopreserved. Samples were barcoded and acquired in batches on the Helios CyTOF (cytometry by time-of-flight) system. Cytometry data was analyzed using OMIQ. Differential abundance analysis was performed using relative frequencies of cell subsets (% of parent) using a false-discovery rate of 0.05 to account for multiple comparisons. Results: Between 11/2020 and 06/2021, 15 lymphoma pts were enrolled. Median age was 62 years (range 43-86), 5 pts (33%) were female. Five pts had follicular lymphoma (FL), 3 had diffuse large B-cell lymphoma (DLBCL),1 had primary mediastinal B-cell lymphoma (PBML), 1 had classic Hodgking lymphoma (cHL), 1 had mantle cell lymphoma (MCL), 1 had plasmablastic lymphoma (PBL), 1 had splenic marginal zone lymphoma (SpMZL), 1 had ALK- anaplastic large cell lymphoma (ALCL), 1 had subcutaneous panniculitis-like T-cell lymphoma (SPTCL). Four pts received bendamustine plus rituximab (BR) and 7 pts received R-CHOP. In total, more than 30 million cellular events were acquired, resulting in over 18 million analyzable live single-cell events after expert manual gating. A total of 27 cell subsets were identified within 9 major immune cell compartments. At baseline, lymphoma pts showed significant reduction (p<0.05) of CD4+ naïve T cells (median 1.37%, range 0.33%-9.01%) compared to controls (median 9.28%, range 3.44-21.3%). Concurrently, lymphoma patients at baseline had significant elevation (p<0.05) of intermediate monocytes (median 2.1%, range 0.64%-5.73%) compared to controls (median 0.8%, range 0.2%-2.73%). Naïve cytotoxic T cells were significantly increased (p<0.05) in lymphoma pts at the 6mo timepoint (median 2.08%, range 0.7%-7.46%) compared to baseline (median 0.23%, range 0.03%-4.79%). A sub-group analysis restricted to patients who received either BR or R-CHOP with 6mo timepoint samples available (n=10), showed a significant reduction (p<0.05) in the frequency of CD4+ central memory T cells in the BR group (median 0.76%, range 0.06%-1.02%) compared to the R-CHOP group (median 3.3%, range 2.19%-5.0%). Additional results describing the details of the immune profile dynamics at intermediate timepoints (C2D1, CLD1) will be presented at the conference. Conclusion: Our results suggest that lymphoma patients have an unfavorable immune profile (high intermediate monocytes combined with low naïve CD4+ T cells) at the time of diagnosis, compared to matched healthy individuals. Their immune profile is reshaped after treatment with increase in naïve cytotoxic T cells 6 months after therapy. Bendamustine-based treatment seems to particularly affect T cell recovery post-therapy, leading to a reduction in CD4+ central memory T cells, compared to patients treated with R-CHOP. These findings highlight the importance of studying baseline and treatment-mediated immune changes in lymphoma pts. This characterization is of special interest in the context of therapies that rely on the fitness of immune cells to mediate an effect, such as chimeric antigen receptor (CAR) T cell therapy and bispecific T cell engagers (BiTEs).

High-dose therapy (HDT) and autologous stem-cell transplantation (ASCT) has historically been an essential part of multiple myeloma (MM) management since early studies demonstrated its efficacy in relapsed disease, and subsequent phase III trials demonstrated better responses and improved survival with this modality compared with standard chemotherapy. With further advances in the MM treatment landscape, including the development of potent novel agents, there has been an increasing debate around various aspects of ASCT, including the optimal timing, role of single versus tandem ASCT, and the practice of consolidation and maintenance therapy post-ASCT. Routine incorporation of the novel agents at each of the treatment phases, induction, consolidation when used, and maintenance has led to better responses as reflected by increasing rates of minimal residual disease (MRD) negativity, longer progression-free survival (PFS) with improvement in overall survival (OS) and in some of the trials. The phase III trials over the last decade have provided significant clarity on the current approach, and have raised important questions regarding the applicability of this modality in all patients. This review aims to summarize the latest literature in the field and discusses how these findings impact the practice of ASCT today.