Citations

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.

Abstract INTRODUCTION: Our group has demonstrated that MASS-FIX is a quick, inexpensive, and accurate means to diagnose and monitor the serum and urine of patients with plasma cell disorders. Screening can be done with a MALDI-TOF MS and samples reflexed to microflow liquid chromatography coupled with electrospray ionization (ESI) and Q-TOF MS (microLC-ESI-Q-TOF MS). Because this technique provides a mass/charge (m/z) for a given patient's monoclonal protein, this method can provide greater sensitivity and specificity to monitor for complete response (CR), especially in patients receiving therapeutic monoclonal antibodies. Our goal was to assess the performance of miRAMM in patients with AL who have been classified as complete response using conventional means. METHODS: We identified 77 patients with AL who had both documented CR by immunofixation (Seibia) of the serum (SIFE), urine IFE (UIFE), and serum free light chain (FLC; The Binding Site) and paired serum samples to test by MALDI-TOF and ESI-TOF. No urine samples were available to test. Paired serum samples from baseline and approximately one year post-therapy were immunoaffinity purified using nanobodies targeting kappa, lambda, alpha, gamma and mu as previously described. For the MALDI-TOF (Bruker Microflex, LT), a range of 9,000 to 32,000 m/z was acquired. The m/z distribution was then visually inspected for the presence of a peak that was distinct from the polyclonal background in both the M+1 and M+2 light chain mass ranges. For the ESI-TOF, spectra were also collected on an TripleTOF 5600 quadrupole time-of-flight mass spectrometer (ABSciex, Vaughan ON, CA) in ESI positive mode with a Turbo V dual ion source with an automated calibrant delivery system. TOF MS scans were acquired from m/z 600−2500 with an acquisition time of 100 ms. RESULTS: Median age of the cohort was 58 (range 42, 81). Fifty-eight percent were male. No test was 100% sensitive at baseline with positive results as follows: abnormal FLC ratio, 82%; positive SIFE, 70%; 73% positive UIFE; positive serum MASS-FIX, 71%; and ESI-TOF, 79% . There was light chain isotype agreement in all cases, except for 2 patients for whom the SIFE and the FLCr disagreed. Of the 56 patients with baseline positive MASS-FIX, there was evidence of the original monoclonal protein in 5 patients (see figure for example of spectra illustrating same m/z before and after therapy). Of the 63 patients who had baseline positive ESI-TOF, there was evidence of the original monoclonal protein in 8 patients. Small unrelated monoclonal proteins were seen both by SIFE and by mass spectrometry techniques. With the spectrometry techniques, however, these transient oligoclonal bands could be distinguished from the original monoclonal protein when they shared the same isotype based on the difference in m/z. DISCUSSION: At baseline, the MALDI was able to identify the baseline monoclonal protein in a comparable number of patients as SIFE, UIFE, and FLC. Approximately 10% of patients thought to be in CR using routine screening tests were found to have persistence of their original clone by MALDI and by ESI-TOF. Small post-therapy unrelated monoclonal proteins were also seen both by IFE and by MALDI, but either the presence of a different isotype or in the case of MALDI, a different m/z, made it clear that the monoclonal protein was not related to the original clone. The sensitivity of the assay will improve significantly when we formalize the introduction of free light chain magnetic beads to capture the serum FLCs. Routine use of MASS-FIX of the urine will also increase performance characteristics. Figure. Figure. Disclosures Dispenzieri: Celgene, Takeda, Prothena, Jannsen, Pfizer, Alnylam, GSK: Research Funding. Gertz:Prothena: Honoraria; celgene: Consultancy; spectrum: Consultancy, Honoraria; Teva: Consultancy; Physicians Education Resource: Consultancy; annexon: Consultancy; Apellis: Consultancy; Alnylam: Honoraria; Medscape: Consultancy; Amgen: Consultancy; janssen: Consultancy; Research to Practice: Consultancy; Abbvie: Consultancy; Ionis: Honoraria. Kumar:Novartis: Research Funding; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding. Dingli:Alexion Pharmaceuticals, Inc.: Other: Participates in the International PNH Registry (for Mayo Clinic, Rochester) for Alexion Pharmaceuticals, Inc.; Alexion Pharmaceuticals, Inc.: Other: Participates in the International PNH Registry (for Mayo Clinic, Rochester) for Alexion Pharmaceuticals, Inc.; Millennium Takeda: Research Funding; Millennium Takeda: Research Funding. Kapoor:Takeda: Research Funding; Celgene: Research Funding. Russell:Vyriad: Equity Ownership.

Abstract INTRODUCTION: Traditional cytometry methods have been unable to capture the immense heterogeneity of the tumor immune microenvironment in various malignancies including multiple myeloma (MM). Cytometry by time of flight (CyTOF) can, to some extent, overcome this limitation. However, the computational challenges that come with analyzing these complex datasets in a reproducible manner remain. In this study, using a large cohort of patients, we compare the bone marrow immunomes from patients with MGUS, multiple myeloma (MM), smoldering MM (SMM) and light chain amyloidosis (AL) at diagnosis, after induction therapy with lenalidomide and dexamethasone and after autologous transplant (ASCT). METHODS: We studied a total of 118 cryopreserved samples as follows: 14 healthy donors, 43 AL (27 newly diagnosed-ND, of which 13 with <10% bone marrow plasma cells, 16 matched samples post ASCT), 12 with ND MGUS, 11 with SMM (of which 6 were ND), 14 with ND MM, 13 paired MM samples post induction therapy and 11 paired MM samples post ASCT. Our CyTOF surface staining panel included the following 33 markers: CD45, HLA-DR, CD19, CD3, CD4, CD8, CD14, CCR6, CD11a, Cd123, CCR5, CD7, ICOS, CD25, CD57, CD45RA, CD163, PD-1, PDL-1, CXCR3, CCR4, CCR7, CD28, CTLA4, CD11c, CD56, CD45RO, CD44, CD27, CD138, CD38, CD-127 and CD16. Data processing and analysis was performed using Cytobank. Live cells were identified based on Pt195 and Ir193 staining. Myeloma cells and CD45- cells were excluded and only CD45+ cells were used for subsequent analyses. Single-cell data were downsampled using VisNE, a permutation of t-Distributed Stochastic Neighbor Embedding (tSNE) and clustered using CITRUS (using 10,000 events per sample with a minimum cluster size of 1%). A Significance analysis of microarrays (SAM) analysis was performed to ascertain differences between groups. Significance was inferred for a false discovery rate <1% . All CITRUS analyses were repeated at least 3 times and only clusters found to differ consistently across runs were considered. RESULTS: The proportions of immune subsets identified to vary by CITRUS before and after induction therapy and ASCT for MM and AL are shown in the table. No differences were identified between MGUS, SMM and NDMM. The proportion of a subset (369850) of CD14+/C16- monocytes, a group of cells shown to correlate positively with survival and response to therapy in solid malignancies, increased after induction therapy in MM. Naïve B cells increased dramatically post ASCT in both AL (429918) and MM (369948), consistent with expected immune reconstitution patterns, although a CCR6+ B Cell subset (429940) shown to mediate effective antibody responses, decreased (in grey). A subset (369980) of functionally exhausted (PD1/CTLA4+) central memory (CM) CD4 T cells decreased after induction therapy in MM but recovered early post ASCT whereas a CM CD4+ subset (369986) lacking major activation markers (CD28, CD25) decreased gradually with therapy and post ASCT. A subset (369953) of naïve CD8 T cells shown to be actively recruited in tumor sites (CXCR3+) decreased after ASCT. CD57+ senescent effector memory (EM) CD8 T cells (369962) decreased with induction therapy but recovered post ASCT. EM CD8 T cells associated with long term immune memory (CCR5+, CD127+, cluster 369959), also decreased after ASCT. LIMITATIONS: Include a) No barcoding b) batch effects were difficult to avoid when processing large number of samples c) use of cryopreserved samples d) need for downsampling to cope with the computational burden. The latter could have been one of the reasons we did not identify any differences between MGUS, SMM and MM. At the time of the meeting we will present confirmatory analyses using conceptually different methods of clustering and of performing across group comparisons as well as analyses that do not include downsampling. CONCLUSIONS: Mass cytometry can provide a more granular view of the bone marrow immunome in plasma cell dyscrasias. Novel agent induction therapy can create an immunologically favorable anti-tumor microenvironment although, in some cases, these favorable immunomic shifts are temporarily reversed by ASCT. Confirmatory analyses, baseline immune profiles, comparisons with healthy donors and correlation with other clinically relevant patient characteristics and outcomes will be reported at the meeting. Disclosures Dispenzieri: Celgene, Takeda, Prothena, Jannsen, Pfizer, Alnylam, GSK: Research Funding. Kumar:Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding.

Abstract ATTRwt (formerly known as senile) amyloidosis and plasma cell disorders share a common demographic of occurrence with increasing prevalence with age. We aimed to identify the prevalence of plasma cell disorders in patients diagnosed with ATTRwt. We retrospectively reviewed all patients seen at Mayo Clinic Rochester between 1st January 2009 and 31st of December 2017 who were diagnosed with ATTRwt. The start date of the study was chosen to coincide with routine clinical availability of laser microdissection mass spectrometry (LCMS) for subtyping of amyloidosis. 492 patients with ATTRwt were evaluated during the study period. Serum immunofixation electrophoresis (SIFE), urine immunofixation electrophoresis (UIFE) and serum free light chain (FLC) assay testing was performed in 74% (n=362), 53% (n=261) and 79% (n=386) respectively. Of the 492 ATTRwt seen during this period, 139 (28%) had abnormal monoclonal protein studies, the vast majority (92%, n=128) of which were monoclonal gammopathy of undetermined significance (MGUS). Monoclonal protein testing in this cohort included SIFE, UIFE and FLC in 96% (n=134), 71% (n=99) and 99% (n=137) respectively. The non-MGUS diagnoses included smoldering multiple myeloma in <1%% (n=2), multiple myeloma (MM) in 6% (n=8), AL amyloidosis in 2% (n=3), and lymphoplasmacytic lymphoma (LPL) in 3% (n=4). MM and LPL developed after progression from MGUS in 2 and 3 patients respectively. Two patients with MGUS also had a diagnosis of low grade non Hodgkin lymphoma (chronic lymphocytic leukemia in one and marginal zone lymphoma in the other). The abnormal monoclonal protein studies included SIFE in 55%, UIFE in 26% and an abnormal serum free light chain ratio (FLCr) in 73%. The organ biopsied for the diagnosis of ATTRwt was heart in 43% (n=60), bone marrow in 24% (n=33), fat in 9% (n=12) and other sites in 5% (n=8, 3 lung, 2 lip, 2 bladder and 1 small bowel). LCMS confirmed the subtype of amyloidosis as ATTRwt in all patients with a biopsy positive for amyloidosis by congo red staining. 19% (n=26) of patients were diagnosed with ATTRwt based on clinical features and a strongly positive PYP scan. PYP scan was positive for ATTR in 55/56 patients in whom the test was performed. The most common monoclonal protein isotypes detected were light chain (45%) and IgG (28%). Light chain subtype was kappa in the majority of patients (80%). Plasma cell disorders are not uncommon in patients with ATTRwt amyloidosis. The diagnosis of a plasma cell disorder and ATTRwt are not mutually exclusive, and their coexistence can confuse the diagnosis if amyloid typing is not performed. Patients with ATTRwt may be misclassified as AL amyloidosis due to the presence of amyloid and a clonal plasma cell disorder with significant prognostic and therapeutic implications. Disclosures Kumar: Novartis: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Research Funding; Merck: Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding. Lacy:Celgene: Research Funding. Dingli:Alexion Pharmaceuticals, Inc.: Other: Participates in the International PNH Registry (for Mayo Clinic, Rochester) for Alexion Pharmaceuticals, Inc.; Millennium Takeda: Research Funding; Millennium Takeda: Research Funding; Alexion Pharmaceuticals, Inc.: Other: Participates in the International PNH Registry (for Mayo Clinic, Rochester) for Alexion Pharmaceuticals, Inc.. Kapoor:Takeda: Research Funding; Celgene: Research Funding. Gertz:Ionis: Honoraria; Research to Practice: Consultancy; Amgen: Consultancy; Prothena: Honoraria; Alnylam: Honoraria; Medscape: Consultancy; celgene: Consultancy; Teva: Consultancy; Abbvie: Consultancy; janssen: Consultancy; spectrum: Consultancy, Honoraria; annexon: Consultancy; Apellis: Consultancy; Physicians Education Resource: Consultancy. Dispenzieri:Celgene, Takeda, Prothena, Jannsen, Pfizer, Alnylam, GSK: Research Funding.

Abstract Proteomics with great sensitivity and specificity effectively analyzes proteins by prior tryptic digestion and subsequent analysis by LC-MS/MS of the tryptic digest. We have utilized this approach in the development of a LC-MS/MS method to characterize minimum residual disease in multiple myeloma. The abundant antibodies produced by multiple myeloma plasma cells are identical and appear as a spike (M-spike) upon protein electrophoresis. The M-spike and histological examination of the bone marrow constitute part of the myeloma diagnostic repertoire. Upon treatment, myeloma cells and the antibodies they produce are reduced in number and amount and become increasingly hard to detect. The bone marrow biopsy serves as the “gold standard” test for clinical remission. We have developed a sensitive test for the presence of the monoclonal antibody produced by the plasma cells which may serve as a substitute for invasive bone marrow biopsy. We have focused on tryptic peptides comprising the variable CDR regions of the Ig light chains that are unique to each patients antibody clone. Utilizing 2-5 µL of patient plasma/serum from the initial M-spike sample we have utilized SDS-PAGE to yield a crudely purified immunoglobulin light chain. The light chain band is isolated, reduced, alkylated and trypsin digested with subsequent LC-MS/MS analysis to identify CDR specific tryptic peptide(s). These can be identified as variable peaks (elution time and mass) in a base peak ion chromatogram where constant region peptides of either lambda or kappa light chains (clone dependent) serve as “internal standards” for identifying the CDR tryptic peptides. The peptide’s mass and its corresponding MS/MS spectra are unique to this CDR tryptic peptide and this patient’s clone. The unique diagnostic peptide is isolated in subsequent samples by immunoaffinity purification of the target kappa/lambda clone, SDS-PAGE separation and LC-MS/MS analysis of the light chain gel band. An extracted ion chromatogram is generated based upon the CDR peptides identified in the initial analysis of the M-spike sample. In patients in clinical remission the presence of a significant signal from the targeted peptides indicates that targeting a CDR peptide from the M-spike protein is more sensitive than currently available diagnostic tools including immunofixation. Comparison of this test to the gold standard bone marrow biopsy will be examined. Disclosures: Barnidge: Mayo Foundation: provisional patent application for technology, provisional patent application for technology Patents & Royalties.

Abstract Background Immunoglobulin light chain-associated amyloidosis (AL) is caused by deposition of immunoglobulin light chain molecules with unique, clonotypic variable (V) regions. Detection and identification of the V region, however, has been challenging due heterogeneity inherent in V regions. We developed a new method for detecting IGKV and IGLV region fragments in AL deposits using protein mass spectrometry and novel bioinformatics approach. We report distinct IGKV and IGLV usage in localized versus systemic AL. Methods Shotgun protein mass spectrometry on amyloid plaques was performed as previously described (Blood. 2009; 114: 4957-9). Peptide tandem mass spectra (MS/MS) were searched against a composite sequence database containing SwissProt's human complete proteome augmented with 1764 IG V region sequences obtained from ImMunoGeneTics database and a Mayo Clinic internal database. Reversed sequences were appended to the database for estimating identification false discovery rates (FDRs). Peptide identifications were processed with Scaffold software. Confident protein identifications (probability > 0.9) with at least one unique peptide identifications and five MS/MS matches were considered. Detected variable gene family (if any) with most number of peptide and spectral matches is considered to be present in the deposit. The method was first validated in 8 cases of AL amyloidosis with known IGKV and IGLV region sequences and then applied on 1238 systemic and 393 localized AL cases. Differences between groups were measured by generating hypergeometric p-values. Results In all 8 AL cases with known IGKV and IGLV region usage, gene family identified in the AL deposit via proteomics matched the gene family inferred from bone marrow plasma cells by Sanger sequencing of IGLC genes. Armed with this method, we analyzed the amyloid proteomics data from 1238 patients with systemic AL amyloidosis and 393 patients with localized AL amyloidosis. The anatomical site distribution in systemic cohort was 296 GI tract, 364 heart, 225 kidney, 81 liver and 272 fat aspirate; Localized cohort has 78 bladder, 158 lung and 157 skin cases. Figure 1 shows the normalized frequency of the variable gene families detected between the systemic and localized AL amyloidosis. For AL-kappa, KV1 was more prevalent in systemic cases when compared to localized cases (p=7.2E-12) suggesting KV1 clones as a hallmark for systemic AL-kappa amyloidosis. KV3 was more frequently seen in localized AL-kappa cases when compared to systemic AL-kappa cases (p =2.8E-11). For AL-lambda, LV1 and LV2 gene families were more prevalent in localized cases when compared to systemic cases (p= 0.039 and 2.7E-05). LV6 was more prevalent in systemic AL-lambda cases (p=1.4E-07). We also detected a higher incidence of mixed AL/AH type in localized AL (18%) when compared to systemic AL (5%; Odds Ratio=4.2673, p<0.0001). We next turned to the organ specific IGKV and IGLV gene family usage patterns in patients with localized amyloidosis. For 147 localized AL-kappa cases, KV3 was most dominant in lung (64%) and KV1 was dominant in skin (38%). For 246 localized AL-lambda cases, LV2 was most prevalent in lung (34%) and LV3 was most prevalent in skin (36%). For bladder, both LV1 and LV2 had comparable prevalence (42% and 32%). For 361 systemic AL-kappa cases, KV1 consistently ranked at the top of gene families used in GI tract (55%), heart (71%), kidney (59%), liver (75%) and fat aspirate (50%). For 877 systemic AL-lambda cases, LV2 was predominant in GI tract (30%), LV3 in heart (35%) and LV6 in kidney (33%). The prevalence of LV1, LV2 and LV3 in liver is comparable (36%, 22% and 31%). LV6 and LV3 had comparable prevalence (22% and 24%) in AL-lambda fat aspirates. Conclusion The novel proteomics method detects IGLC V family usage in large cohorts of AL patients. It identifies unique profiles in systemic and localized cases, and in different organ sites. This information will be helpful in determining systemic versus localized nature of AL amyloidosis at diagnosis and to assess risk of specific end organ involvement. We found also a strong association between IGKV and IGLV gene usage and organ involvement. Disclosures: No relevant conflicts of interest to declare.

Abstract Amyloidosis is a group of hereditary and acquired disorders of diverse pathogenesis united by end organ damage due to tissue deposition of misfolded proteins. The diagnosis is typically based on demonstrating Congo red positive amyloid deposits in tissue biopsies of clinically involved sites. Because treatment options for amyloidosis patients are highly varied and intricately tied to the identity of the protein that constitutes the amyloid deposits, adequate diagnostic evaluation now requires identification of the amyloidogenic protein. Traditionally, immunohistochemistry with antibodies to amyloid-associated proteins was the clinical technique of choice for subtyping amyloid deposits in tissue sections. This technique suffers from poor analytical sensitivity and specificity and is a suboptimal technique on which to base treatment decisions for amyloidosis patients. We developed a highly accurate and reproducible liquid chromatography electrospray tandem mass spectrometery (LC-MS/MS)-based shotgun proteomics assay for subtyping amyloid deposits (Blood. 2009; 114: 4957-9) from microdissected paraffin embedded tissue samples. Implementation of this assay in a CAP/CLIA clinical laboratory has subsequently transformed the standard of amyloidosis care at Mayo Clinic. Since 2008, we utilized the assay in routine practice to identify 20 different amyloid types in tissue samples from diverse anatomic sites from 4139 patients (Table 1). Although AL and ATTR were most common, the cases include a large variety of unusual, and in many cases clinically unsuspected amyloid types. In addition, the amyloid protein subtypes had different frequencies in different anatomic sites (A partial list of tissue sites and their amyloid types analyzed in this study includes: bone marrow: AL 82%, ATTR 15%, AA 2%, other 1%; kidney: AL 51%, ALect2 18%, AA 17%, AH 5%, AFib 4%, other 5%; liver: AL 64%, ALect2 23%, AApoA1 6%, AA 3%, ATTR 2%, other 2%; heart: ATTR 60%, AL 37%, AANF1%, other 2%; GI tract: AL 78%, ATTR 13%, AA 6% AApoA4 1%, other 2%; eye [primarily adnexae] AL 63%; eye [primarily cornea] TGFB induced protein ig-h3 34%, other 3%). One caveat is that these distributions of amyloid types may not accurately reflect population prevalence. More likely, the findings reflected a composite prevalence that included challenging amyloidosis cases.Table 1Frequency of amyloid subtypes observed in the Mayo Amyloid Cohort (n=4139). Asterisk denotes non-canonical subtypesAmyloid SubtypeNumber (%) of casesAL2553 (61.68)ATTR1015 (24.52)AA151 (3.65)ALect2148 (3.58)AIns48 (1.09)Keratin*36 (0.87)AApoA130 (0.72)AH27 (0.65)AFib26 (0.63)TGFB1-IP*22 (0.53)AApoA420 (0.48)AANF14 (0.34)Ab2M12 (0.29)AGel12 (0.29)ASem112 (0.29)APro7 (0.17)ALys3 (0.07)ACal2 (0.05)Enfuvirtide*2 (0.05)AIAPP2 (0.05) The prevalence of diverse and unexpected amyloid types in many tissues suggest that shotgun proteomic-based analysis may be the only way to accurately assign amyloid subtype in tissue samples. The technique avoids the analytical problems of immunohistochemistry and the bias introduced by assigning probable amyloid type based on clinical presentation and disease distribution. It provides the clinician with an amyloid subtype that allows for confident individualized patient management. Accurate amyloid subtyping by LC-MS/MS is not only possible, but has proven to be a robust routine clinical tool for the evaluation of numerous amyloidosis patients in our practice. Disclosures: No relevant conflicts of interest to declare.

Abstract Background: Peripheral T-cell lymphomas (PTCLs) are aggressive non-Hodgkin lymphomas with marked clinical, pathological, and molecular heterogeneity. Outcomes following standard therapy generally are poor; however, few candidate therapeutic targets have been identified for precision medicine approaches. Retinoic acid receptor alpha (RARA) is a transcription factor that modulates cell growth and differentiation in response to natural or synthetic retinoids. Retinoids have been used successfully to treat acute promyelocytic leukemia and some cutaneous T-cell lymphomas (CTCLs). However, the function of RARA and the action of retinoids in PTCL have not been defined. Methods:Based on identification of a PTCL patient with a non-synonymous point mutation, RARA R394Q, identified in the Mayo Clinic Center for Individualized Medicine, we sought to characterize the role of RARA in PTCL cells. To investigate the role of wild-type and mutant RARA, we constructed expression vectors containing either wild-type RARA or RARA R394Q coding sequences, and also used siRNAs targeting RARA to study the role of native RARA expression. Cell lines derived from post-thymic T-cell malignancies were used for in vitro studies, including HuT78 and Mac-1 (both derived from circulating tumor cells from CTCL patients) and Karpas 299 (from an ALK-positive anaplastic large cell lymphoma). Following RARA overexpression or knockdown, we measured cell growth, cell cycle regulation, and sensitivity to synthetic retinoids. In addition, RNA sequencing and pathway analysis were performed to profile the transcriptomic response to retinoids in malignant T cells. Results:In two RARAlow cell lines, Karpas 299 and HuT78, overexpression of wild-type RARA or RARA R394Q significantly increased cell growth (p<0.001), with a greater increase observed from mutant versus wild-type RARA in Karpas 299 (136% of control versus 122%; p=0.04). Accordingly, knockdown of wild-type RARA in the RARAhigh cell line, Mac-1, resulted in a 22% inhibition of cell growth (p=0.0002). This inhibition specifically was associated with G1 cell cycle arrest (120% of control; p=0.004) and decreased protein expression of the G1-S-associated cyclin-dependent kinases, CDK2, CDK4, and CDK6. These kinases were up-regulated by overexpression of RARA in RARAlow HuT78 cells. The relatively RARA-specific retinoid, AM80 (tamibarotene), and the less specific retinoid, all-trans retinoic acid (ATRA), resulted in RARA protein degradation, cell growth inhibition that was both dose-dependent and proportional to baseline RARA expression, G1 arrest, and CDK protein up-regulation. Gene-set enrichment analysis (GSEA) of transcriptome data confirmed that genes down-regulated by AM80 were highly enriched for regulators of cell cycle and particularly G1-S transition. Finally, overexpressing RARA in RARAlow Karpas 299 and HuT78 cell lines significantly increased the ability of AM80 to inhibit CDK2/4/6 expression and cell growth (16% to 23% greater growth inhibition than control; p<0.05). Conclusions:RARA drives cyclin-dependent kinase expression and G1-S transition in malignant T cells, and promotes cell growth. These functions may be enhanced by specific RARA gene mutations. Synthetic retinoids inhibit these functions in a dose-dependent fashion, and are most effective in cells with high RARA expression. These data suggest RARA as a candidate therapeutic target in some PTCL patients. Disclosures Nowakowski: Celgene: Research Funding; Morphosys: Research Funding; Bayer: Consultancy, Research Funding.

Abstract Background: Peripheral T-cell lymphomas (PTCLs) comprise a heterogeneous group of relatively uncommon and generally aggressive non-Hodgkin lymphomas. Recent studies have begun to reveal their mutational landscape, such as the recurrent mutations affecting RHOA, IDH2, and TET2 in angioimmunoblastic T-cell lymphomas (AITLs) and related PTCLs. Here, we studied the exomes of 63 PTCLs to address several unanswered questions: (1) How frequently do PTCLs have mutations with a strong rationale for being actionable using clinically available targeted therapies? (2) What pathways are enriched in PTCLs that could point to additional targeted therapies? (3) What additional recurrent mutations exist that may provide insight into PTCL pathogenesis and classification? Methods: We performed exome capture and sequencing in 63 frozen PTCL samples (with matched germline controls from 22), including 13 PTCLs, not otherwise specified; 6 AITLs; 37 anaplastic large cell lymphomas (ALCLs: 12 ALK+, 19 ALK-, and 6 cutaneous); 4 extranodal NK/T-cell lymphomas; 2 cutaneous T-cell lymphomas, and 1 enteropathy associated T-cell lymphoma. Following primary variant calling, data were filtered by removing variants that were: outside the capture region, polymorphic (0.1% in ExAC), seen in paired germline or 50 healthy controls, within repetitive elements, or synonymous. Potentially actionable variants required published evidence for altering protein function and demonstrated in vivo or in vitro sensitivity to an available targeted agent (including open clinical trials). Mutated cancer genes (COSMIC) were examined for enrichment of KEGG canonical signaling pathways. A recurrent mutation encoding MSC E116K was validated in tumor DNA and MSC protein was examined by immunohistochemistry. Results: Of 25 unique variants with published evidence suggesting actionability (including ErbB, JAK/STAT, and RAS genes), at least one variant was present in 16/63 patients (25%). Variants in any COSMIC gene were seen in 55/63 samples (87%). Top genes were STAT3, RHOA, ARID1A, MLL2 (KMT2D), PTPRB, TP53, and TET2. The KEGG ErbB pathway was most significantly enriched for mutated genes in our dataset (EGFR, ERBB2, ERBB3, ERBB4, and others; q=3.88x10-3), followed by neurotrophin and mitogen-activated protein kinase signaling pathways. Among 25 recurrent non-synonymous variants, we identified MSC E116K with predicted gain-of-function exclusively in 3 ALK-ALCLs and 1 cutaneous ALCL. All mutated cases strongly expressed MSC protein. Among 96 cases, 13/18 ALK-ALCLs (72%) were positive for MSC, compared to 9/78 (12%) from other PTCL subtypes. Conclusions: Mutations that were potentially actionable based on published variant-specific data were identified in 25% of PTCLs, indicating promise for the role of sequencing in individualizing therapy. Additionally, remaining variants were enriched for several targetable pathways, most significantly ErbB. A novel recurrent mutation, MSC E116K, was seen exclusively in ALK-ALCLs (systemic and cutaneous). MSC protein, a repressor of E2A-mediated transcription of targets such as MYC, was expressed in most ALK- ALCLs and generally was absent in other PTCL subtypes. This finding extends evidence for a genetic relationship between systemic and cutaneous ALCLs and suggests MSC may help distinguish ALK- ALCLs from other PTCLs. Disclosures Link: Genentech: Consultancy, Research Funding; Kite Pharma: Research Funding.

Abstract Background The lymphoma proteome is the phenotypic representation of the underlying genetic and epigenetic makeup of each individual patient's tumor. The proteome is rich in druggable targets and offers a unique opportunity for the hematologist to personalize therapy. We developed a deep proteomic profiling method using ion exchange fractionation and tandem mass spectrometry. In this pilot study, we applied this method to detect differentially expressed proteins in anaplastic large cell lymphoma (ALCL) cell lines with previously known (ALK-positive) and unknown (ALK-negative) therapeutic targets. We then asked whether integrative informatic analysis of these data could be used to predict drug sensitivity in each of the cell lines. Methods To examine reproducibility of our method, proteins were extracted independently from 4 pellets each of FE-PD (ALK-negative) and Karpas 299 (ALK-positive) ALCL cells, reduced with dithiothreitol, alkylated with iodoacetamide, and digested with trypsin. Resulting peptides were separated into 6 fractions using strong anion exchange (SAX) chromatography. Peptides in each fraction were analyzed via shotgun proteomics on a QExactive mass spectrometer. Peptide mass spectra (MS/MS) were matched against a RefSeq human protein sequence database using MyriMatch software. Reversed sequences were added to the database to measure identification false discovery rates (FDRs). IDPicker filtered the peptide identifications at 2% FDR. Proteins with at least two unique peptide identifications and five MS/MS matches were considered to be present in the sample. Filtered protein identifications and corresponding spectral counts were used as input to QuasiTel software, which was configured to use proteins with at least one spectrum per biological replicate. Proteins that were significantly differentially expressed (quasi p-value < 0.05) with an absolute log2 fold-change of at least 0.5 fold were loaded into the Ingenuity Pathway Analysis (IPA) software, and a master list of drugs and corresponding gene targets was assembled using PharmGKB database and the Drug-Gene Interaction Database (DGID). The resulting drug-gene target list was merged with the differentially expressed protein identifications. Candidate targets were validated by Western blot and candidate drugs were assessed in viability assays. Results The SAX-LC-MS/MS method identified 10,111 proteins from all replicate analyses of FE-PD and Karpas 299 samples, and 93% of the identified proteome was detected in all 4 replicate analyses. The detected proteome was well represented by key transcription factors, phophatases, kinases, translation regulators and transmembrane regulators. There were 1369 proteins differentially expressed between the 2 cell lines, 709 up regulated in Karpas 299 cell line and 673 up regulated in FE-PD. Differentially expressed proteins also showed consistent expression across the biological replicates (Figure 1). Our integrated approach to identify candidate targets and drugs “rediscovered” ALK in Karpas299 and unexpectedly identified relative overexpression of the IL2-IL2RA-STAT5A-STAT5B network in ALK-negative FE-PD cells. Western blot confirmed these findings. As expected, 50-100 nM crizotinib (ALK inhibitor) decreased Karpas 299 viability (p =0.016) but had no effect on FE-PD (Figure 2). In contrast, 50-100 nM of the experimental STAT5 inhibitor 573108 (EMD Millipore) decreased FE-PD viability (p=0.002) but had no effect on Karpas 299 (Figure 2). Conclusion The lymphoma proteome is complex with 10,000 proteins and contains druggable targets that can be reproducibly identified using SAX-LC-MS/MS. These targets vary across samples and integrated informatic analysis can predict target-drug combinations that have efficacy in an experimental model. These data suggest that SAX-LC-MS/MS could be used to personalize treatment regimens in lymphoma patients. Figure 1: Spectral counts of 1369 differentially expressed proteins between Karpas299 and FE-PD cell lines were normalized and plotted in a heat map. Red color indicates down regulation and yellow color indicates up regulation. Disclosures: No relevant conflicts of interest to declare.