Jalali, Shahrzad

Abstract The role and immunological constitution of the tumor microenvironment (TME) in splenic marginal zone lymphoma (sMZL) is largely unknown and studies on the phenotype and subset composition of intratumoral T cells are lacking. In the present study, we evaluated 36 sMZL spleen specimens by single cell analysis to gain a better understanding of the TME in sMZL. Using mass cytometry (CyTOF), we observed that the TME in sMZL is distinct from that of control non-malignant reactive spleen (rSP). We found that the number of TFH cells varied greatly in sMZL, ICOS+ TFH cells were more abundant in sMZL than rSP, and TFH cells positively correlated with increased numbers of memory B cells. Treg cell analysis revealed that TIGIT+ Treg cells are enriched in sMZL and correlate with suppression of TH17 and TH22 cells. Intratumoral CD8+ T cells were comprised of subsets of short-lived, exhausted and late-stage differentiated cells, thereby functionally impaired. We observed that T-cell exhaustion was present in sMZL and TIM-3 expression on PD-1low cells identified cells with severe immune dysfunction. Gene expression profiling by CITE-seq analysis validated this finding. Taken together, our data sugget that the TME as a whole, and T-cell population specifically, are heterogenous in sMZL and immune exhaustion is one of the major factors impairing T-cell function.

Abstract IgM MGUS and Waldenstrom Macroglobulinemia (WM) represent a disease spectrum with highly dissimilar therapeutic management ranging from observation to chemoimmunotherapy. The current classification relies solely on clinical features and does not explain the heterogeneity that exists within each of these conditions. To shed light on the biology that may account for the clinical differences, we used bone marrow (BM) CD19+/CD138+ sorted cells and matched BM plasma from 32 patients (pts) (7 IgM MGUS, 25 WM) and 5 healthy controls to perform the first comprehensive multiomics approach including whole exome sequencing, RNA seq, proteomics and metabolomics. Applying principal component analysis to gene expression profiling, most of WM pts clustered together, while a small subset of them grouped separately with MGUS pts, suggesting a biologic dichotomy within WM. The healthy controls formed a distinct group from most WM and MGUS. We then applied a non-negative matrix factorization consensus clustering to the gene expression data and identified three robust clusters. Cluster 1 (C1) included only WM pts, cluster 2 (C2) included both WM and MGUS pts, and cluster 3 (C3) included all controls as well as a small number of WM and MGUS pts. When mutations commonly identified in WM were analyzed, there was no difference among the three groups (excluding controls) in mutation burden of MYD88 L265P and CXCR4. Interestingly, aberrant expression of TNFAIP3 seemed a distinct feature of C1 as deletion of 6q (which encodes for TNFAIP3) and TNFAIP3 mutations were each significantly enriched in C1 (47%) compared to C2 (0%) and C3 (20%; p=0.04). Individual clusters associated with specific transcriptional signature and clinical features. While C1 displayed enrichment of cell growth, downregulation of inflammatory pathways (eg IL6 and IL8 signaling) and aggressive behavior, C2 showed increased inflammatory signaling and cell survival with indolent behavior. C3 had an intermediate feature with combined proliferative and inflammatory signatures. In accordance with the transcriptomic data, the hallmark of C1 was upregulation of proliferation-associated proteins (eg AKT, MAPK) and downregulation of inflammatory proteins (eg IL4, IL10) while the opposite was observed in C2. Once more, C3 confirmed intermediate features with combined upregulation of proliferation and inflammatory proteins. The metabolism was rewired towards fatty acid catabolism in C1, glycolysis in C2 and anabolism in C3. Accordingly, C1 showed undetectable concentration of 3-hydroxybutyric acid as opposed to C2 which had increased levels of malic and lactic acids, as end products of fatty acid oxidation and glycolysis respectively. Those metabolites had intermediate levels in C3. In conclusion, we identified three molecular clusters with distinct clinical, proteomic and metabolomic features, suggesting a potential biologic classification that may have therapeutic implications. Citation Format: Patrizia Mondello, Jonas Paludo, Joseph Novak, Kerstin Wenzl, Shahrzad Jalali, Jordan Krull, Esteban Braggio, Surendra Dasari, Michelle Manske, Jithma Abeykoon, Asher Chanan-Khan, Robert Kyle, Morie Gertz, Zhi Zhang Yang, Anne Novak, Stephen Ansell. Molecular clusters and functional drivers of IgM monoclonal gammopathies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3487.

Background CD8+ T-lymphocyte subsets defined by killer lectin-like receptor G1 (KLRG1) and CD127 expression have been reported to have an important role in infection, but their role in the setting of lymphoid malignancies, specifically follicular lymphoma (FL), has not been studied. Methods To characterize the phenotype of KLRG1/CD127-defined CD8+ subsets, surface and intracellular markers were measured by flow cytometry and Cytometry by time of flight (CyTOF), and the transcriptional profile of these cells was determined by CITE-Seq (Cellular Indexing of Transcriptomes and Epitopes by Sequencing). The functional capacity of each subset was determined, as was their impact on overall survival (OS) and event-free survival (EFS) of patients with FL. Results We found that intratumoral CD8+ cells in FL are skewed toward effector cell subsets, particularly KLRG+CD127- and KLRG1-CD127- cells over memory cell subsets, such as KLRG1-CD127+ and KLRG1+CD127+ cells. While effector subsets exhibited increased capacity to produce cytokines/granules when compared with memory subsets, their proliferative capacity and viability were found to be substantially inferior. Clinically, a skewed distribution of intratumoral CD8+ T cells favoring effector subtypes was associated with an inferior outcome in patients with FL. Increased numbers of CD127+KLRG1- and CD127+KLRG1+ were significantly associated with a favorable OS and EFS, while CD127-KLRG1- correlated with a poor EFS and OS in patients with FL. Furthermore, we demonstrated that interleukin (IL)-15 promotes CD127-KLRG1+ cell development in the presence of dendritic cells via a phosphoinositide 3-kinase (PI3K)-dependent mechanism, and treatment of CD8+ T cells with a PI3K inhibitor downregulated the transcription factors responsible for CD127-KLRG1+ differentiation. Conclusions Taken together, these results reveal not only a biological and prognostic role for KLRG1/CD127-defined CD8+ subsets in FL but also a potential role for PI3K inhibitors to manipulate the differentiation of CD8+ T cells, thereby promoting a more effective antitumor immune response.

Abstract Purpose: T-cell immunoglobulin and ITIM domain (TIGIT), a member of the immune checkpoint family, is important in normal T-cell biology. However, the phenotypical profile and clinical relevance of TIGIT in follicular lymphoma is largely unknown. Experimental Design: Biopsy specimens from a cohort of 82 patients with follicular lymphoma were analyzed using mass cytometry to explore the phenotype and biological and clinical significance of TIGIT+ T cells. Results: TIGIT is highly expressed on intratumoral T cells and its expression alters T-cell phenotype in follicular lymphoma. TIGIT is abundantly expressed on Treg cells, resulting in an enhanced suppressive property. TIGIT expression on non-Treg/TFH T cells defines a population that exhibits an exhausted phenotype. Clinically, increased numbers of TIGIT+ T cells are associated with inferior patient outcomes and poor survival. We observe that anti–PD-1 therapy with pembrolizumab alters the phenotype of TIGIT+ T subsets and identifies a role for CD28 expression on TIGIT+ T cells in treatment response. Conclusions: The current study provides a comprehensive analysis of the phenotypic profile of intratumoral TIGIT+ T subsets and their prognostic relevance in follicular lymphoma. Inhibition of TIGIT signaling may be an additional mechanism to prevent T-cell suppression and exhaustion in B-cell lymphoma.

Cancer cells have altered energy demand due to their increased proliferative capacity. In Waldenstrom Macroglobulinemia (WM), an indolent yet incurable B-cell lymphoma, the homeostasis of the bone marrow (BM) environment is disturbed due to the infiltration of the lymphoplamacytic cells that continuously produce monoclonal IgM. An alteration in energy demand could skew the balance of key proteins and metabolites towards a permissive niche for WM tumor cells growth, and unfavorably effect on the immune response against tumor cells. Therefore, the aim of our study was to identify how changes in certain metabolites and/or proteins could contribute to the pathobiology of WM and whether the cytokine and chemokine composition of the BM microenvironment play a role in such changes. WM patient's samples including BM plasma, peripheral blood serum and BM cells (n=101) as well as equivalent normal counterparts (n=86) were collected and used for metabolomics analysis. Comprehensive targeted metabolomics analysis was performed using Capillary Electrophoresis Time-of-Flight Mass spectrometry (CE-TOFMS), CE-triple quadrupole mass spectrometry (CE-qQqMS) and Liquid Chromatography (LC-TOFMS). Normal and WM peripheral blood serums samples were also used for untargeted proteomics analysis using a fully automated proteomic technology platform that includes an Agilent 1200 Series Quaternary HPLC system connected to a Q Exactive Plus mass spectrometer. Real-time PCR analysis was performed to detect the gene expression of the relevant metabolite transporters located on the cell membrane. BM cells from control and WM patients' samples were used for flow cytometry analysis. IHC was used to detect the proteins on the BM tissues. Principal Component Analysis (PCA) and Hierarchal Clustering Analysis (HCA) on both metabolomics and proteomics data identified two distinct clusters for disease and normal samples, indicating that there are differentially expressed proteins and metabolites in WM versus normal samples. Furthermore, pathway analysis showed that the majority of the altered metabolites were the members of the glutathione (GSH) metabolism pathway. This finding was further validated not only by data obtained from metabolomics analysis of BM cells and BM plasma, but also by proteomic data WM patients serum, implying that GSH metabolism is key to the biology of WM. Moreover, stimulation of WM cell lines by IL-6 and IL-21, cytokines involved in inducing WM cell proliferation and IgM secretion, resulted in increased gene expression of the transporters mediating uptake of metabolites required for GSH synthesis, including SLC7A11, 4F2HC and LAT1, indicating that cytokines in the WM BM could modulate GSH metabolism. In addition, IHC staining of the BM tissues as well as flow cytometry analysis of patients' lymphoplasmacytic cells identified glutathione peroxidase as one of the major proteins modulating GSH metabolism in WM. In summary, our data highlight a central role for GSH metabolism in WM disease biology and indicate that intervening with the metabolic processes could be a potential therapeutic strategy for patients with WM. Disclosures Ansell: AI Therapeutics: Other: research funding for clinical trials; Seattle Genetics: Other: research funding for clinical trials; Regeneron: Other: research funding for clinical trials; Affimed: Other: research funding for clinical trials; Bristol Myers Squibb: Other: research funding for clinical trials; Pfizer: Other: research funding for clinical trials; Merck: Other: research funding for clinical trials; Takeda: Other: research funding for clinical trials.

Myeloid derived suppressor cells (MDSC) are a heterogeneous population of undifferentiated myeloid cells that are expanded and activated in pathological conditions and have the ability to potently suppress T-cell function and thereby contribute to immunosuppression and tumor progression. While there have been studies showing a role for MDSC in a variety of hematological malignancies, no data is available indicating that MDSCs contribute to the tumor progression in Waldenstrom's Macroglobulinemia (WM), an indolent lymphoma characterized by bone marrow (BM) infiltration of lymphoplasmacytic (LPL) cells and increased secretion of monoclonal IgM. In previous work, we have found increased GM-CSF and reduced arginine and cysteine in the BM microenvironment in WM. We hypothesized that this was due to the presence and activity of MDSCs in WM. BM aspirates from WM patients (n=17) were therefore processed to isolate LPL (CD19+/CD138+) cells from the rest of the BM cells (CD19-/CD138-). Sorted (CD19-/CD138-) cells from BM of patients with WM were studied with flow cytometry. Using a sequential gating strategy (lack of lineage markers, low levels of HLADR, CD33+, CD11b+) we identified a population of MDSCs that were then subdivided using CD14 and CD15 expression into total-, monocytic-, or granulocytic- MDSCs (m-MDSC, g-MDSC). We also analyzed unsorted BM cells using cytometry by time-of-flight (CyTOF) in order to further identify and phenotypically characterize the BM MDSC population in a group of WM patients with smoldering (asymptomatic) disease, symptomatic disease, or in remission post-treatment. BM samples from normal subjects were used as a control. Flow cytometry data showed significant higher numbers of MDSC subsets expressing PD-L1 and Arginase1 in WM patients when compared to the normal samples. BM cells from WM patients (n=18) then were compared to controls (n=11), and the absolute number of the total MDSC (p=0.05), m-MDSC (p=0.002), g-MDSC (p=0.02) was increased in WM specimens. When MDSCs from WM or normal monocytes from healthy controls were co-cultured with activated T-cells, the proliferation of activated T-cells in the presence of MDSCs from WM patients was impaired compared to controls, confirming the suppressive role of MDSCs. We then performed high dimensional analysis of the total BM MDSC cells using t-SNEand identified phenotypically distinct MDSC cell populations in the BM that were differentially present when healthy controls were compared to patients with smoldering WM or those with WM needing treatment. Specifically, WM patients needing treatment had increased numbers of a distinct MDSC population that was highly positive for CD163, and CD138. Moreover, conventional markers denoting m-MDSC and g-MDSC, such as CD14 and CD15, were highly expressed in all populations and their pattern of expression did not specifically define the MDSC subtypes, indicating that high dimensional phenotyping further details the MDSC sub-compartments beyond the conventional categorization of MDSC using conventional cytometry. In summary, we find that MDSCs are increased in the BM of WM patients compared to controls. MDSCs expressing CD163 and CD138 increase when WM patients become symptomatic and require therapy. Furthermore, MDSCs in the BM of WM patients suppress T-cell function and likely contribute to progression of the disease. MDSCs in the BM therefore present a therapeutic target that should be explored in WM patients. Disclosures Ansell: Bristol Myers Squibb: Other: research funding for clinical trials; Merck: Other: research funding for clinical trials; AI Therapeutics: Other: research funding for clinical trials; Affimed: Other: research funding for clinical trials; Takeda: Other: research funding for clinical trials; Pfizer: Other: research funding for clinical trials; Regeneron: Other: research funding for clinical trials; Seattle Genetics: Other: research funding for clinical trials.

The outcomes of lymphoma patients treated with PD1 inhibitors (PD1i) vary significantly across different histologies with objective responses rates that are around 70% in classical Hodgkin lymphoma, but significantly lower, around 10%, in indolent non-Hodgkin lymphomas (iNHL). This may be due in part to the inability of PD1i to restore the function of exhausted T-cells (Tex) in the tumor microenvironment (TME). PD1 is expressed on various T-cell subsets, including effector cells and Tex. In this study we sought to identify markers of exhaustion beyond PD1 that define T-cell subsets with high reactivation potential in marginal zone lymphoma (MZL). To define different stages of exhaustion in MZL, we developed and validated a mass cytometry (CyTOF) panel which integrates T-cell markers of lineage, differentiation, activation, suppression, cytokine production and transcription factors associated with exhaustion. We analyzed 35 MZL spleens obtained at diagnosis and 6 reactive spleens. We identified a population of CD8 cells in MZL that express TIM3 without PD1, compose 1.5% (range 0.14-4.88%) of total CD8 cells and expand with activation through the TCR (6.2% of CD8, range 0.6-14.2%). Prolonged cytokine exposure has been shown to induce exhaustion in healthy T-cells, and so to determine whether PD1-TIM3+ cells are Tex, we treated healthy donor T-cells with activating beads and IL12 and analyzed them by flow cytometry. We showed that PD1-TIM3+ cells expand with repetitive activation and IL12 treatment suggesting that they are Tex. Since Tex have been shown to have a distinct transcription profile compared to functional effector T-cells, we analyzed the expression of several transcription factors (TFs) that are upregulated in Tex by CyTOF and compared them to other subsets of T-cells. PD1-TIM3+ cells expressed higher levels of Eomes, Tbet, TOX and Helios compared to activated effector cells, confirming that they are Tex, but lower levels levels of the same TFs compared to PD1+TIM3+ cells, consistent with a less severely exhausted phenotype. To characterize the function of PD1-TIM3+ cells, we manually gated on subsets of CD8+ non-naïve cells that were stained with the CyTOF panel and compared the different subsets. We showed that PD1-TIM3+ cells arise from effector cells, maintain their polyfunctionality by producing cytokines and chemokines and have a unique cytokine production profile, including perforin, granzyme B and proinflammatory cytokines, such as IL17A. Importantly, PD1-TIM3+ cells produced higher levels of most cytokines compared to more severely Tex expressing both PD1 and TIM3 (Fig 1). We then performed tSNE analysis where similar objects are modeled by nearby points. We determined that PD1-TIM3+ cells cluster together, suggesting that they are phenotypically homogeneous cells in terms of function and expression of activation markers and are significantly different from PD1+TIM3+ cells (Fig 2). We finally blocked PD1 or TIM3 and analyzed the effect of each antibody blockade on function. We showed that TIM3 blockade was able to restore the production of most cytokines to higher degrees than PD1 blockade. In addition, TIM3 blockade led to upregulation of CD28, which is critical for the rescue of Tex through costimulation, while PD1 blockade did not. We then analyzed the clinical outcomes of all 34 MZL patients. The frequency of PD1-TIM3+ cells was lower among observed patients who failed to achieve an event-free survival (EFS) of 12 months (EFS12) (0.8% vs 1.7%, p=0.02) and among treated patients that failed an EFS of 24 months (EFS24) (0.9 vs 1.7%, p=0.03), when compared to patients that reached these endpoints. Higher levels of PD1-TIM3+ cells were also associated with a trend towards improved overall survival (OS). Five-year OS was 88% vs. 69% in patients with PD1-TIM3+ levels >2% and <2% of total CD8 cells respectively (p=0.1) (Fig 3). Our study provides insights into the mechanisms of failure of PD1i in MZL and identifies a novel population of Tex with high reactivation potential which is clinically relevant and could serve as a target for future immunotherapies in iNHL. Disclosures Anagnostou: American Society of Hematology, Mayo Clinic/Iowa Lymphoma SPORE, Mayo Clinic Immune Monitoring Core, Mayo Clinic Hematology Small Grant: Research Funding. Novak:Celgene Coorperation: Research Funding. Ansell:Mayo Clinic Rochester: Employment; Trillium: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Seattle Genetics: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Regeneron: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding.

Introduction Waldenstrom macroglobulinemia (WM) is a rare, incurable, indolent non-Hodgkin lymphoma that is preceded by an IgM monoclonal gammopathy of undetermined significance (IgM MGUS). In recent years, better understanding of the pathobiology of WM and IgM MGUS has strengthened the hypothesis that they may represent different stages of the same disease. Studying the relationship between these two conditions is the essential first step to understanding the mechanisms driving the malignant transformation from IgM MGUS to WM. Recently long non-coding RNAs (lncRNAs) have garnered significant recognition for their roles in the regulation of gene expression and cellular function. lncRNA can regulate gene expression of neighboring protein-coding genes through multiple mechanisms such as by recruiting chromatin modifiers and increasing accessibility of genes to transcription proteins, or by directly binding to enhancers or promoters. Here we present the first analysis of the lncRNA expression profile in patients with WM compared to IgM MGUS. Methods Twenty-one patients with untreated WM (n=14) and IgM MGUS (n=7) seen at Mayo Clinic were included in this analysis. Malignant cells were isolated from bone marrow samples using CD19 and CD38 positive selection. Total RNA was extracted and sequencing was performed using the Illumina HiSeq4000 NGS platform. Differentially expressed lncRNAs in WM compared to IgM MGUS were defined as log2 fold change (FC) > 1.0 (upregulated) or < -1.0 (downregulated) and a false discovery rate (FDR) < 0.05. The nearest gene to each lncRNA was also identified and their gene expression was detected. Using the Ingenuity Pathway Analysis software and taking into account the transcriptional expression and known function of these genes, a correlation between cell function and disease analysis was performed. Results A total of 119 lncRNAs were differently expressed between WM and IgM MGUS (17 transcripts were upregulated in WM, while 102 were downregulated in WM). Sixteen unique genes were identified as the nearest gene to 17 upregulated lncRNAs, while 18 genes were identified as the nearest genes to the top 18 downregulated lncRNAs in WM compared to IgM MGUS. Several of the upregulated lncRNA in WM were in proximity to known oncogenes. The well-known oncogenic lncRNA PVT1 was found to be upregulated in WM compared to IgM MGUS (log2 FC 1.35, FRD 0.04). PVT1 is overexpressed in several malignancies and associated with inhibition of apoptosis and increased cell proliferation. PVT1 is in close proximity to MYC and co-expression of PVT1-MYC has been described in multiple cancers. PVT1 also potentiate MYC amplification by increasing MYC protein stability, therefore promoting malignant cell growth. The lncRNA TCB1D27, also upregulated in WM compared to IgM MGUS, is located in close proximity to the oncogene TNFRSF13B (TACI), which was found to be overexpressed in WM patients (log2 FC 1.61, FDR <0.001). TACI is a receptor for BLyS/BAFF, which modulates the development of normal B-cells and plays a role in various B-cell malignancies. In fact, BLyS/BAFF level is known to be increased in WM and to promote survival and proliferation of WM B cells. The upregulated lncRNA RP11-25K21.1 in WM is located in close proximity to the oncogene FCGR2B (CD32B), which was also upregulated in WM compared to IgM MGUS (log2 FC 1.59, FDR 0.001). Overexpression of CD32B was previously shown to be associated with inferior outcomes in DLBCL and FL patients, presumably secondary to an increased internalization and clearance of rituximab due to increased CD32B expression on malignant cells. Several other upregulated lncRNAs in WM compared to IgM MGUS are also in proximity of other oncogenes such as PCED1B, POLD3, CLLU1, and SMAD7. Finally, we considered the expression of the nearest genes and their reported cell function and association with diseases. Several genes that are known to have a role in the cell proliferation and/or neoplasm development were noted to be upregulated in WM (figure 1). Conclusion We present the first report of lncRNAs expression in WM and IgM MGUS in addition to a concurrent analysis of the gene expression of the nearest protein-coding genes. Several known oncogenes are in proximity of overexpressed lncRNAs in WM compared to IgM MGUS that could indicate potential implications in the pathobiology of these diseases and the mechanism driving the malignant transformation to WM. Disclosures Paludo: Celgene: Research Funding; Celgene: Research Funding; Verily Life Sciences: Research Funding; Verily Life Sciences: Research Funding. Dasari:The Binding Site: Patents & Royalties: US Patent Rights on Mass Spectroscopy Licensing agreement with The Binding Site, Research Funding. Kapoor:Celgene: Honoraria; Cellectar: Consultancy; Janssen: Research Funding; Sanofi: Consultancy, Research Funding; Amgen: Research Funding; Takeda: Honoraria, Research Funding; Glaxo Smith Kline: Research Funding. Ailawadhi:Amgen: Consultancy, Research Funding; Pharmacyclics: Research Funding; Takeda: Consultancy; Janssen: Consultancy, Research Funding; Cellectar: Research Funding; Celgene: Consultancy. Gertz:Amyloidosis Foundation: Research Funding; i3Health: Other: Development of educational programs and materials; Medscape: Consultancy, Speakers Bureau; Ionis/Akcea: Consultancy; Celgene: Consultancy; Appellis: Consultancy; Amgen: Consultancy; Teva: Speakers Bureau; Alnylam: Consultancy; Prothena Biosciences Inc: Consultancy; Janssen: Consultancy; Spectrum: Consultancy, Research Funding; Annexon: Consultancy; Physicians Education Resource: Consultancy; Abbvie: Other: personal fees for Data Safety Monitoring board; Research to Practice: Consultancy; Johnson and Johnson: Speakers Bureau; DAVA oncology: Speakers Bureau; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Proclara: Membership on an entity's Board of Directors or advisory committees; Springer Publishing: Patents & Royalties; International Waldenstrom Foundation: Research Funding. Novak:Celgene Coorperation: Research Funding. Ansell:Affimed: Research Funding; Regeneron: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding.