Anderson, Emilie

Background: Beside fibril-forming proteins, amyloid deposits contain other proteins which can influence organ toxicity, disease initiation and clearance. We investigated the association between the amyloid plaque proteomic signature of diagnostic renal biopsies from patients achieving deep and sustained hematologic remissions and renal organ response in AL amyloidosis. Methods: Retrospective study of newly diagnosed AL amyloidosis patients between 2010-2020. Patients were eligible if; (1) they had a baseline renal biopsy which had undergone laser micro-dissection and mass spectrometry, (2) were assessable for renal response [RenR, non-selective proteinuria ≥1g /24 hours, predominantly albumin], (3) were not on renal replacement therapy and did not experience renal progression within 3-months of diagnosis and (4) had RenR measurements, at least twice annually in the first year from treatment and annually thereafter. Patients who did not obtain a renal complete response (RenCR) required a hematologic very good partial response (VGPR) or better within 6 months of treatment initiation, sustained for a minimum of 6 months. Hematological and renal responses were graded according to consensus criteria. Overrepresentation analyses of differentially expressed proteins were performed using the WebGestalt 2019 and the reactome pathway database. A false detection rate corrected p value <0.05 was used to identify significantly overrepresented proteins and pathways. Results: We defined two organ response categories, to capture the extreme ends of the spectrum: renal suboptimal responders (n=24), patients for whom the best renal response was ≤partial response (RenPR), and renal complete responders (n=29), patients who obtained a RenCR at best response. Overall, the median follow-up was 6.7 years (IQR: 5.4-9.2), 6.6 years (IQR: 5.4-8.1) for complete responders, and 8.4 years (IQR: 6.1-11.3) for suboptimal responders. Between renal suboptimal responders and complete responders there was no significant difference in median age (63 vs 63 years, p=0.7), lambda light chain isotype (66% vs 79%, p=0.3), first-line therapy received and renal stage (Stage III: 14% vs 8%, p=0.9). The median time to RenCR was 52-months (IQR: 31-63). Protein pathways over-represented in renal complete responders included 14-3-3 proteins (YWHAG, YWHAH, YWHAQ, YWHAZ), early components of the complement cascade and complement regulatory proteins (C3, C4A, C4B, C8A, complement factor H), keratins, cytoskeletal proteins (tubulin, actin, filamen) and collagen IV. Protein pathways over-represented in renal suboptimal responders included terminal/effector components of the complement cascade (C5, C6, C8B, C9), amyloid signature proteins (ApoA1 and E, vitronectin, clusterin), proteoglycans (heparan sulfate proteoglycan), transgelin and metallopeptidase inhibitor 3. Proteins with the highest-fold increase in renal complete responders included serine protease inhibitor (SERPINA3) and F-box and leucine rich repeat protein (FBXL6), 9.0 x 106 & 7.7-fold, respectively. Proteins with the highest-fold increase in renal suboptimal responders included complement 6 and 8B, 3.5 & 3.6-fold, respectively. Conclusion: This is the first study to identify proteomic determinants of organ response in AL amyloidosis. Molecular chaperones involved in protein aggregate homeostasis, such as the 14-3-3 proteins are upregulated in renal complete responders, similar to what we have shown in our prior work. This suggests that organ responders may have a more favorable microenvironment which “primes” them for organ response after the amyloidogenic light chain has been cleared from the circulation. Renal suboptimal responders (≤RenPR) have elevated levels of terminal complement proteins including membrane attack complex proteins and reduced levels of early components or complement regulatory proteins, which may be responsible for reno-toxic and reno-protective effects, respectively.

Abstract Multiple myeloma (MM) is the most common blood cancer in African American (AA) individuals. AAs have an increased risk of developing Monoclonal Gammopathy of Undetermined Significance (MGUS) and MM compared to non-Hispanic White (NHW) individuals. While several studies have shown a pivotal role for the immune tumor microenvironment (iTME) in MM pathogenesis, the composition of the iTME in AAs with MM is under investigated. Viably frozen whole bone marrow (BM) aspirates from 128 individuals (53 AA and 75 NHW) with either MM (n=65), MGUS (n=28), smoldering MM (SMM, n=13), or healthy controls (n=22) were evaluated by single cell RNA-sequencing (scRNA-seq) or mass cytometry (CyTOF). We characterized 34 (14 AA and 20 NHW) by scRNA-seq and 94 (39 AA and 55 NHW) by CyTOF. CyTOF samples were stained with Maxpar Direct Immune Profiling Assay Kit (Fluidigm), acquired on a Helios mass cytometer and analyzed in Cytobank. Data were analyzed by logistic equivalent regression model with p<0.05 for significance. ScRNA-seq samples were processed as in Yao, 2022. Data were normalized using scvi-tools and clustered by Uniform manifold approximation and projection (UMAP). Differentially expressed markers between clusters were identified using Model-based Analysis of Single-cell Transcriptomics (MAST) in Seurat R package at FDR < 0.05 and log2 fold change > 0.55. ScRNA-seq of the CD45+ non-tumor lymphocytes revealed 17 immune populations (HSC, erythroblast, MDSC, GMP, CD1c+ DC, CD4+ and CD8+ T cell, CD14+ M1 macrophage, CD16+monocyte, monocyte, pDC, naïve B, pre-B, pro-B, memory B, NK/MAIT). CD3E/D+cells were clustered into naïve/central memory (CCR7+, LEF1+, SELL+), memory (GZMK+), effector (GZMB+, GZMH+, FGFBP2+), g/d (TRDC+), MAIT (SLC4A10+, KLRB1+) and IFN CD8+ T cells (IFIT1+, IFIT2+,IFIT3+). Disease progression was associated with increases in median CD4+ and CD8+ GZMK+ T cells, memory B cells and a reduction in all monocyte clusters. Within the diseased cohort in comparison to NHW patients, AA patients had increased CD3+ T cells (CD4 and CD8+) (AA 57.5% vs. NHW 44.3%) and a reduction in overall monocytes (AA 10.5% vs. 26.5%). These results were validated by CyTOF, which confirmed an increase in CD3+ T cells (AA 53.7% vs. NHW 35.7%, p<0.001) including increases in CD8+ (AA 7.0% vs. NHW 3.2%, p<0.05) and CD4+ terminal effector T cells (AA 5.7% vs. NHW 2.7%, p<0.05) and a reduction in monocytes (AA 18.3% vs. NHW 26.6%) among AAs. After adjusting for age and sex, AAs retained an increase in T cells (OR 1.59 (95%CI 1.2-2.01, p=0.00023)) and a reduction in monocytes (OR 0.88 (95%CI 0.78-1, p=0.045)). Despite the increase in CD8+ T cells in AA patients, we identified reduced GZMK+ T cells and reductions in exhaustion markers (TIGIT, LAG3, TOX) in CD8+ T cells in comparison to NHWs. Nevertheless, the distribution of the immune populations was similar between healthy AA and NHW individuals. Our multiomics approach to characterize the iTME identified significant differences among AA compared to NHW patients with MM. Citation Format: Suganti Shivaram, Hongwei Tang, Huihuang Yan, Shulan Tian, Guyu Qin, Emilie Anderson, Neeraj Sharma, Abiola Bolarinwa, Cinthya Zepeda-Mendoza, Stacey Lehman, Felicia Gomez, Amit Mitra, Surendra Dasari, Taxiarchis Kourelis, Shaji Kumar, Linda Baughn. Characterization of the Immune Tumor Microenvironment in a Diverse Cohort of Patients with Multiple Myeloma [abstract]. In: Proceedings of the Blood Cancer Discovery Symposium; 2024 Mar 4-6; Boston, MA. Philadelphia (PA): AACR; Blood Cancer Discov 2024;5(2_Suppl):Abstract nr P06.

Background: It has been recently demonstrated that altered lipid metabolism involving increased free fatty acids (FFAs) uptake supports the growth and proliferation of MM cells (Panaroni et al. Blood 2022; Farell et al. Elife 2023). Whether the availability of FFAs in the bone marrow (BM) microenvironment is associated with the pathogenesis of MM from MGUS has not been previously evaluated. Thus, we evaluated if individual species of FAs or FA-containing lipids such as triacylglycerols (TAGs), diacylglycerols (DAGs), and monoacylglycerols (MAGs) are differentially present between the BM microenvironment of MGUS and MM patients. Methods: Lipid profiles of BM plasma samples (reflective of the BM microenvironment) from patients with a spectrum of plasma cell disorders (22 MGUS and 24 MM (15 newly diagnosed and 9 relapsed/refractory)) were acquired by liquid chromatography (LC)/tandem mass spectrometry (MS), and individual lipid species were quantified based on single-point quantification using 55 isotopically labeled internal standards. Concentration-based lipid species data was analyzed by the web-based tool MetaboAnalyst 5.0. Data was normalized by quantile normalization, log transformed and scaled by Pareto scaling. p values <0.05 were considered significant. Results: A total of 993 distinct lipid species were quantified by the MS-based targeted lipidomics assessment. Of these lipid species, the levels of individual mid-chain (≤50 carbon chain length) and long-chain (≥52 carbon chain length) polyunsaturated TAGs were the most differentially present in the BM plasma of MM compared to MGUS patients (Figure 1A). Even though a principal component analysis (PCA) involving all lipid species showed moderate separation of MM from MGUS patients, five MGUS patients (categorized as MGUS-other (MGO)) who clustered more towards MM patients compared to the remaining MGUS patients had a distinct pattern of individual mid and long-chain polyunsaturated TAG levels that were comparable to MM patients (Figure 1B). Furthermore, the proliferation rate of clonal plasma cells in the BM of these MM and MGUS as measured by the S-phase% was found to be directly correlated with the level of long-chain polyunsaturated TAGs. Conclusions: Mid and long-chain polyunsaturated TAGs are present in lower and higher levels, respectively, in the BM microenvironment of MM compared to MGUS patients. These findings continue to support the importance of FA-containing TAGs and other lipids in the pathogenesis of MM. Future confirmatory studies evaluating the biological significance of these differences are required to determine if they can be exploited for diagnostic and therapeutic purposes.

Background: Upfront autologous stem cell transplant (ASCT) is the standard-of-care for newly diagnosed multiple myeloma (MM) patients. However, relapse and the development of therapy-related myeloid neoplasms (t-MN) post ASCT results in considerable morbidity and mortality. We hypothesized that enrichment of abnormal myeloid progenitors and immune effector cells (IEC) in peripheral blood stem cells (PBSCs) of patients with MM is associated with a higher risk of relapse and development of t-MN. Methods: After institutional review board approval, we screened patients with active MM that were evaluated at Mayo Clinic, Rochester, MN between 01/01/2003 and 12/31/2020. Patients that underwent an ASCT and had cryopreserved PBSC product available for research were included. Using mass cytometry, we performed a comprehensive immunophenotyping of mobilized cryopreserved PBSCs from 54 MM patients undergoing 1 st ASCT, using validated antibody panel of 37 lymphoid- and myeloid-based markers each. Flow cytometry standard files were normalized and concatenated using the Fluidigm acquisition software. Flow cytometry standard files were uploaded to the OMIQ software from Dotmatics (www.omiq.ai, www.dotmatics.com), where transformation and cleaning (doublets, debris) were performed. To correct for between-sample technical variations (“batch effects”), we used fdaNorm within the omiq.ai platform. Clustering and visualization of normalized and concatenaded flowcytometry files were performed within the omiq.ai platform using PhenoGraph and UMAP, respectively. We performed principal component analysis (PCA) to identify major immunomic drivers of variability across patients. We explored the associations between immune subsets and the dichotomized clinical outcomes of interest: progression-free (PFS), myeloid neoplasm-free (MNFS) and overall survival (OS) from ASCT. A 2-sided false discovery rate adjusted P-value of <0.05 was considered significant when multiple comparisons were performed; otherwise, a P-value of <0.05 was considered significant. Results: The most abundant cell populations were T-cells (49% of the CD45 +) and myeloid cells (43% of the CD45 + cells). Clustering on the CD45 + cells revealed 30 unique lymphoid clusters and 27 unique myeloid clusters. Subclustering on NK-cells identified an additional 16 NK-cell clusters. On PCA, principal components-1 (PC-1) and 2 (PC-2) explained 15.2% and 12.5% of the variability in the data, respectively. A high PC-2 score was associated with inferior median PFS (20 vs. 52 months; P=0.005, Figure 1A), MNFS (52 vs. 85 months, P=0.03, Figure 1B) and OS (64 vs. 106 months, P=0.01) from ASCT. Immune subsets enriched in the high PC-2 cohort included the ‘NKT-like’ (CD56 +) T-cell subset which expressed the inhibitory CD159a receptor and TIGIT; a terminally differentiated (CD27/CD28 -, CD57/KLRG1 +) and exhausted (TIGIT/PD-1 +) T cell subset, 2 exhausted (TIGIT/PD-1 +) T cell subsets; an immunosenescent (CD27/CD28 -, CD57/KLRG1 +) T cell subset as well as a terminally exhausted (C27/C28 -, KLRG1/PD-1/TIGIT +) TCRgamma-delta subset. Immune subsets with high loadings within PC-1 (associated with improved clinical outcomes) consisted exclusively of CD4 + cells with early memory phenotypes (CD27/CD28/CD127 +). Abnormal expression of CD7 and HLA-DR expression on myeloid progenitor cell subsets was associated with an inferior PFS, MNFS, and OS. A NK-cell subcluster, characterized by an immature (CD56 high), inhibitory (CD159a +, NKG2A) phenotype, was enriched in patients developing t-MN (0.33% vs. 0.17%, P=0.006). Conclusion: We identify abnormal myeloid and immune effector cells in the PBSCs of patients with MM, even prior to ASCT. Patients with a PBSC product that is enriched in immunosuppressive T and myeloid cell subsets, and with a lower proportion of antigen presenting cells, progenitor populations, and anti-tumor macrophages have a shorter PFS and MNFS. This suggests that the predisposition to t-MN and inferior outcomes for MM are possibly determined early in the disease course. These findings need external validation and have the potential to guide sequencing of future treatment modalities.

As part of the Multiple Myeloma Research Foundation (MMRF) immune atlas pilot project, we compared immune cells of multiple myeloma bone marrow samples from 18 patients assessed by single-cell RNA sequencing (scRNA-seq), mass cytometry (CyTOF), and cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) to understand the concordance of measurements among single-cell techniques. Cell type abundances are relatively consistent across the three approaches, while variations are observed in T cells, macrophages, and monocytes. Concordance and correlation analysis of cell type marker gene expression across different modalities highlighted the importance of choosing cell type marker genes best suited to particular modalities. By integrating data from these three assays, we found International Staging System stage 3 patients exhibited decreased CD4+ T/CD8+ T cells ratio. Moreover, we observed upregulation of RAC2 and PSMB9, in natural killer cells of fast progressors compared with those of nonprogressors, as revealed by both scRNA-seq and CITE-seq RNA measurement. This detailed examination of the immune microenvironment in multiple myeloma using multiple single-cell technologies revealed markers associated with multiple myeloma rapid progression which will be further characterized by the full-scale immune atlas project. Significance: scRNA-seq, CyTOF, and CITE-seq are increasingly used for evaluating cellular heterogeneity. Understanding their concordances is of great interest. To date, this study is the most comprehensive examination of the measurement of the immune microenvironment in multiple myeloma using the three techniques. Moreover, we identified markers predicted to be significantly associated with multiple myeloma rapid progression.

Introduction: The routine incorporation of proteasome inhibitors, immunomodulatory drugs (IMIDs), and monoclonal antibody drugs (mAbs) in managing multiple myeloma (MM) has improved patients' prognosis. Unfortunately, triple refractory patients continue to do poorly. Immunotherapeutic approaches (mAbs, CAR-T cells) have shown the most promise in these patients. However, the efficacy of these therapies depends on a functional tumor immune microenvironment (iTME), which is extensively reshaped by existing MM therapies and disease evolution. Understanding how the iTME and malignant clone co-evolve over the disease course is crucial to optimize the use of immune-based treatments and understand their mechanisms of action. Methods: Newly diagnosed MM patients (NDMM), MM patients relapsing prior to anti-CD38 antibody exposure (RMM), and triple refractory MM patients (TRMM) were identified from the Mayo Clinic biospecimen database. We performed CyTOF (with a 37 marker lymphoid panel on CD138- bone marrow [BM] mononuclear cell samples) to assess the cellular iTME, Luminex (using BM plasma) to assess the humoral iTME (cytokines and chemokines) and RNAseq analysis (using BM CD138+ cells) to assess the malignant clone transcriptome. Astrolabe was used to identify immune cell clusters. Gene set enrichment analysis (GSEA) was used to identify gene pathways with differential expression across groups. JMP was used to perform unsupervised hierarchical clustering and descriptive statistics. An FDR corrected p value of <0.05 was considered statistically significant for all comparisons. Results: This study included 39 patients (13 NDMM, 11 RMM, and 15 TRMM). Samples were hierarchically clustered by cellular iTME, humoral iTME and malignant cell transcriptome, separately. The cellular iTME best recapitulated known disease biology and was used as the "anchoring" variable for subsequent analyses. Three distinct immune clusters were identified (figure 1): cluster 1, comprised mainly of NDMM and RMM patients; and clusters 2 and 3, comprised primarily of TRMM patients. In cluster 2, 4/5 patient samples were collected immediately after progression on daratumumab-IMID therapy, whereas all 7 TRMM patient samples in cluster 3 were collected at a median of 2.8 (range 0-17.2) months after last daratumumab exposure (table 1). Most naïve T cell subsets (including the TIGIT+ T5 and the C38/CD127+ subsets T1, T2, and T12) were highest in cluster 1 compared to clusters 2 and 3. Several unfavorable T cell subsets were elevated in Cluster 2: 1) CD161+ T6, T7 and T17 subsets, which are thought to be Th17 polarized cells that promote MM growth; 2) T7, T9 CCR5+ subsets, which are thought to represent tumor-specific exhausted (PD-1+) T cell subsets; 3) senescent subsets (T-10, T14, T16,T18, T19); and 4) TCR-gd subsets with features of senescence (KLR, CD57), exhaustion (PD-1, TIGIT) and Th17 polarization (CD161). There were no significant differences in cytokine and chemokine expression based on disease status (NDMM, RMM, or TRMM) or iTME (immune clusters 1, 2, or 3). GSEA was performed to compare clusters 2 and 3 (TRMM enriched) with cluster 1. The most up-regulated gene pathways in clusters 2 and 3 related to cell cycle targets of E2F transcription factors and MYC proliferation pathways. The most down-regulated gene pathways in clusters 2 and 3 were IFN-gamma, IFN-alpha and TNF-alpha response and signalling. Conclusion: Changes in the cellular iTME mirror the disease course better than changes in the malignant clone or humoral iTME. Patients with more refractory MM have a smaller pool of naïve T cells which express TIGIT or CD127 and could be "revived" with anti-TIGIT antibodies or IL-7 prior to manufacturing CART cells or administering BCMA-based therapies. Significant heterogeneity exists within the more refractory subgroups, with some patients having more dysfunctional T cell subsets which may be less responsive to immunotherapeutic approaches. The malignant transcriptome of samples in clusters 2 and 3 parallels the aggressive clinical course, with myc upregulation and downregulation of cytokine pathways that mediate an antitumor immune response. This study suggests that the MM iTME becomes more dysfunctional with therapy and may explain the differential sensitivity of patients to immunotherapeutic approaches. Disclosures Kumar: MedImmune: Research Funding; Janssen Oncology: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; BMS: Consultancy, Research Funding; Tenebio: Other, Research Funding; Genecentrix: Consultancy; Sanofi: Research Funding; Genentech/Roche: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Oncopeptides: Consultancy, Other: Independent Review Committee; IRC member; Adaptive Biotechnologies: Consultancy; Takeda: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Merck: Consultancy, Research Funding; AbbVie: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Dr. Reddy's Laboratories: Honoraria; Amgen: Consultancy, Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments, Research Funding; Carsgen: Other, Research Funding; Novartis: Research Funding; Cellectar: Other; Kite Pharma: Consultancy, Research Funding; Karyopharm: Consultancy; Celgene/BMS: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments.