Binder, Moritz

Abstract Integrated genomic analysis including whole genome and RNA sequencing in primary multiple myeloma (MM) cells have reported a universal loss of BCL7A gene in MM patients compared to normal plasma cells (PC). Our Genetic modulation in in-vitro and in-vivo MM models have also validated the loss of BCL7A as an oncogenic event responsible for acquisition of a more proliferative phenotype in MM cells. We performed a comparative mass spectrometric analysis confirming BCL7A as a member of the canonical m-SWI/SNF chromatin remodeling complex. We therefore performed the Assay for Transposase Accessible Chromatin with high-throughput sequencing (ATAC-seq) to assess genome-wide changes in DNA accessibility upon BCL7A gain or loss. We found that loss of BCL7A in wild-type KMS12BM and NCI-H929 cells resulted in enrichment of important transcription factor motifs in the transcriptionally active sites of chromatin. We identified 36 de novo accessible and 1079 de novo inaccessible regions across the genome after BCL7A shRNA KD (knock-down). These genomic regions with altered accessibility were associated with genes involved in protein binding (FDR < 0.001), GTPase activation (FDR = 0.016), and GTPase regulation (FDR = 0.030). Candidate transcription factors for the genomic regions with altered accessibility were identified by querying a database of human ChiP-seq experiments (ReMap 2020). IRF4 was identified to be enriched in regions of accessible chromatin upon BCL7A loss. IRF4 is an oncoprotein transcription factor and is a direct target of myc, generating a feedback loop in MM cells. IRF4 dependency is central to myeloma cell proliferation. Most importantly, we found that in addition to functions within the m-SWI/SNF complex, BCL7A forms a protein complex with IRF4. Altogether these data suggest a role for BCL7A in driving IRF4 oncogenic activities in MM. To understand how BCL7A-dependent changes may influence IRF4 activity, we performed CHiP assay using IRF4 antibody in BCL7A KO (knock-out) and AB (add-back) KMS12BM and NCI-H929 cells. We observed increased binding of IRF4 to the promoter of its target genes like PRDM1, CDK6, STAG2, PIM2, SQLE, Myc, CANX, IRF4, SCD, ELL2, CASP3 in BCL7A KO cells, while the binding of these target genes was significantly decreased in AB cells. While, CHiP assay using IRF4 antibody in control and ectopically expressed BCL7A in AMO1 and KMS11 cells showed significantly low binding of IRF4 to the promoter of most of its target genes in BCL7A overexpressed cells compared to control. Integrated transcriptomic analysis following BCL7A KD and overexpression revealed the existence of a set of genes transcriptionally regulated by IRF4 to be significantly upregulated following BCL7A depletion and downregulated following ectopic expression of BCL7A. To investigate whether these genes are involved in the phenotypic and functional effects observed in MM after BCL7A depletion, we performed LOF studies (si-RNA screen) in scrambled and BCL7A KD MM cells. Among others, we observed that MM cells are highly sensitive to the inhibition of EEF1B2, RPS3A, SOX2, DCC and NDUFA1 only in the context of BCL7A loss, implicating a role as critical effector molecules downstream of the IRF4-BCL7A transcriptional network. In conclusion, we observe that BCL7A binds to IRF4, functionally restricting its activity. The universally observed down regulation of BCL7A in MM provides the necessary molecular change to allow IRF4 to exert its required transcriptional activity to induce MM cell growth. Our results now provide the basis to understand the mechanism of development of IRF4 dependency in MM. Disclosures Anderson: Pfizer: Membership on an entity's Board of Directors or advisory committees; Millenium-Takeda: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Sanofi-Aventis: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Scientific Founder of Oncopep and C4 Therapeutics: Current equity holder in publicly-traded company, Current holder of individual stocks in a privately-held company; Mana Therapeutics: Membership on an entity's Board of Directors or advisory committees. Munshi: Janssen: Consultancy; Karyopharm: Consultancy; Adaptive Biotechnology: Consultancy; Novartis: Consultancy; Legend: Consultancy; Bristol-Myers Squibb: Consultancy; Amgen: Consultancy; Abbvie: Consultancy; Takeda: Consultancy; Oncopep: Consultancy, Current equity holder in publicly-traded company, Other: scientific founder, Patents & Royalties; Celgene: Consultancy; Pfizer: Consultancy.

Abstract Background: Achievement of a complete response (CR) to therapy in multiple myeloma (MM) early in disease course correlates with improvement in clinical outcomes, reflective of therapeutically sensitive disease biology. While depth of response is known to impact overall survival (OS) and progression-free survival (PFS), the effect of known prognostic variables at baseline including FISH risk and International Staging System (ISS) score outside of clinical trial settings is not well described. We sought to determine the impact of known prognostic variables in MM including FISH and ISS risk in the context of NDMM patients achieving a CR to therapy and to also examine additional predictors of outcome among this cohort. Methods: A retrospective study was conducted on 1869 NDMM patients who had ≥ 2 consecutive monoclonal protein immunofixation (IFE) studies in the serum and urine available within 24 months from diagnosis. We identified the number of patients who had ≥ 2 negative serum and urine IFE as a surrogate for achieving CR (N=461), since a bone marrow biopsy is not routinely performed to confirm CR in the non-trial setting. A Kaplan-Meier model was used to compare median PFS and OS between patients achieving a CR vs. non-CR and a multivariate analysis was performed to adjust for known prognostic variables. A cox proportional hazards model was used to determine the prognostic impact of baseline FISH (high risk vs. standard risk) and ISS scores (3 vs.1 and 2) on PFS and OS, among patients achieving a CR vs. non-CR, and identify additional prognostically significant variables within the CR cohort. Results: The median length of follow-up for the entire cohort (n=1869) was 64 months (range: 1-199). The median time from diagnosis to achievement of CR among the CR cohort was 9.8 months (0.20-23.8 months). The median PFS for CR (n=461) vs. non-CR (n=1408) patients was 42.0 vs. 29.3 months (p<.0001) and median OS was 124.4 vs. 85.6 months (p<.0001), respectively (Figure 1). The impact of achieving a CR on outcomes was retained after adjusting for FISH, ISS, age, sex, autologous stem cell transplant, and involved heavy chain as follows: PFS HR 0.57 (0.43-0.76; p=0.0001); OS HR 0.56 (0.43-0.74; p<0.0001). The baseline FISH and ISS risk on PFS and OS comparing CR vs. non-CR patients are shown in Table 1, with both variables having statistically insignificant impact on PFS and OS among the CR cohort. We examined several variables present at baseline and at the time of CR to determine their impact on PFS and OS, including age, sex, and non-IgG involved heavy chain (baseline), and thrombocytopenia (<150,000), hypoalbuminemia (<3.5 g/dL), and immunoparesis (at time of CR). The following variables were found as inferior predictors on PFS within the CR cohort with hazard ratios as follows: male gender, HR 1.36 (1.05-1.75; p=0.02) and non-IgG involved heavy chain, HR 2.2 (1.55-3.2).The following variables were found as predictors for inferior outcomes on OS within the CR cohort with hazard ratios as follows: Age> 75 years, HR 2.7 (1.54-4.8; p=.0005); male gender, HR 1.5 (1.1-2.02; p=.008); serum albumin <3.5, HR 1.54 (1.1-2.08; p=.004); and non-IgG involved heavy chain, HR 1.75 (1.23-2.47; p=.002). On multivariate analysis, age, hypoalbuminemia, and non-IgG involved heavy chain retained prognostic significance for OS. Conclusion: Attainment of an early CR in NDMM confers improvement in PFS and OS. Our study confirms these findings and suggests that achievement of CR neutralizes the impact of baseline FISH and ISS risk on PFS and OS. Among the CR cohort, age> 75 years, male sex, non-IgG involved heavy chain, and hypoalbuminemia at the time of CR predict for inferior overall survival. Figure 1 Figure 1. Disclosures Dingli: Sanofi: Consultancy; Alexion: Consultancy; Novartis: Research Funding; GSK: Consultancy; Apellis: Consultancy; Janssen: Consultancy. Gertz: Akcea Therapeutics, Ambry Genetics, Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Karyopharm Therapeutics, Pfizer Inc (to Institution), Sanofi Genzyme: Honoraria; Aurora Biopharma: Other: Stock option; Ionis Pharmaceuticals: Other: Advisory Board; AbbVie Inc, Celgene Corporation: Other: Data Safetly & Monitoring; Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy. Dispenzieri: Alnylam: Research Funding; Pfizer: Research Funding; Takeda: Research Funding; Sorrento Therapeutics: Consultancy; Oncopeptides: Consultancy; Janssen: Consultancy, Research Funding. Kapoor: Karyopharm: Consultancy; Cellectar: Consultancy; BeiGene: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; Amgen: Research Funding; Ichnos Sciences: Research Funding; Regeneron Pharmaceuticals: Research Funding; Glaxo SmithKline: Research Funding; Karyopharm: Research Funding; Sanofi: Research Funding; Takeda: Research Funding; AbbVie: Research Funding. Lin: Vineti: Consultancy; Bluebird Bio: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Juno: Consultancy; Merck: Research Funding; Sorrento: Consultancy; Celgene: Consultancy, Research Funding; Gamida Cell: Consultancy; Novartis: Consultancy; Takeda: Research Funding; Kite, a Gilead Company: Consultancy, Research Funding; Legend: Consultancy. Kumar: Merck: Research Funding; Antengene: Consultancy, Honoraria; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Carsgen: Research Funding; KITE: Consultancy, 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; Bluebird Bio: Consultancy; Tenebio: Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Consultancy; Beigene: Consultancy; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Roche-Genentech: Consultancy, Research Funding; Novartis: Research Funding; BMS: Consultancy, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding.

Abstract Purpose: Multiple myeloma (MM) is a plasma cell malignancy characterized by a collection of cytogenetic abnormalities that drive clinical presentation, response to treatment, and prognosis. The most common immunoglobulin heavy chain (IGH) translocation found in MM, t(11;14), results in CCND1 overexpression. Although t(11;14) is considered a standard risk cytogenetic abnormality, heterogeneity in patient outcomes and response to therapy within the t(11;14) group has been reported. In contrast to t(11;14) mantle cell lymphoma, where most cases (73%) have a typical, balanced t(11;14) rearrangement, only 47% of t(11;14) MM have a typical, balanced t(11;14) FISH pattern; the remaining 53% of MM are characterized by an atypical t(11;14) FISH result (Dalland, Genes Chromosomes Cancer, 2021). Here, we compared the genetic characteristics of typical vs. atypical t(11;14) MM and the association with other genomic abnormalities, CCND1 expression, IGH breakpoint location and patient outcomes. Methods: We performed a retrospective study of newly diagnosed t(11;14) MM patients seen at Mayo Clinic (N=411) or enrolled in the MMRF CoMMpass trial (N=167). The t(11;14) FISH results of the Mayo Clinic samples were segmented into two groups: typical CCND1/IGH signal pattern (n=179, 3 red, 3 green, 2 fusion signals) and atypical CCND1/IGH signal pattern (n=232, any FISH pattern deviating from typical). The CoMMpass cases had both RNAseq and long-insert WGS data and were analyzed to detect gene expression, structural and copy number variants and IGH breakpoint locations. These data were used to classify the t(11;14) as typical (n=63) or atypical (n=104) and whether there was a single t(11;14) fusion (n=107) or a gain of the t(11;14) fusion (n=60), and whether these cases were associated with changes in gene expression or breakpoint location. Differences between the groups were measured using χ2 and Wilcoxon tests. Survival curves were estimated using Kaplan Meier and compared using the Log-Rank test. Statistical significance was determined at p<0.05. Results: Of the Mayo t(11;14) cohort, 232/411 (56%) had an atypical t(11;14) FISH pattern and 179/411 (44%) had a typical t(11;14) FISH pattern. Although an atypical t(11;14) FISH pattern was not significantly associated with monosomy 17/17p deletion, MYC rearrangements, 1q gain or ISS stage III, it was significantly associated with the presence of any trisomy in comparison to typical t(11;14) cases (40/222=18% vs.12/166=7%, p=0.002). Atypical t(11;14) cases had an increase in CCND1 gene expression compared to typical t(11;14) cases (median 375.0 RPKM vs. 266.4 RPKM, p<0.0001) and cases with a gain of the t(11;14) fusion were associated with even greater CCND1 expression compared to cases with a single t(11;14) fusion (median 459.4 RPKM vs. 263.2 RPKM, p<0.0001). We next evaluated whether the t(11:14) FISH pattern was associated with differences in IGH breakpoint locations. Nearly 80.0% of atypical t(11;14) cases had a breakpoint within the constant region of the IGH locus commonly reported for MM, while only 61.0% of the typical cases had constant region breakpoints (p=0.035); the remaining cases had breakpoints involving the VDJ region. We next evaluated the impact of atypical t(11;14) FISH on OS. In Mayo cases, OS was significantly shorter in patients with atypical FISH patterns compared to typical t(11;14) FISH patterns (6.9 years [95% CI: 5.1-7.8] vs. 9.0 years [95% CI: 7.0-11.6], p=0.019). Although atypical t(11;14) was associated with increased risk of death (RR 1.45, p=0.021), this finding was not retained in a multivariate model including high risk abnormalities such as age, ISS stage, 17p deletion and 1q gain demonstrating atypical t(11;14) is not an independent predictor of poor outcome when treated with standard therapies. Conclusions: Atypical t(11;14) FISH in MM is found in approximately half of t(11;14) cases, is associated with trisomies, higher CCND1 expression and is more likely to have a breakpoint involving the constant region of the IGH locus. The enrichment of VDJ breakpoints within typical t(11;14) MM is consistent with enrichment of VDJ IGH breakpoints in MCL, which are mostly characterized by typical t(11;14) FISH patterns. While not an independent predictor of outcome with standard therapies, it remains to be evaluated whether patients with typical or atypical t(11;14) have a differential response to novel agents, such as venetoclax. Disclosures Fonseca: Janssen: Consultancy; Novartis: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; Merck: Consultancy; Juno: Consultancy; Caris Life Sciences: Membership on an entity's Board of Directors or advisory committees; Kite: Consultancy; Aduro: Consultancy; OncoTracker: Consultancy, Membership on an entity's Board of Directors or advisory committees; GSK: Consultancy; AbbVie: Consultancy; Patent: Prognosticaton of myeloma via FISH: Patents & Royalties; Mayo Clinic in Arizona: Current Employment; Amgen: Consultancy; BMS: Consultancy; Celgene: Consultancy; Takeda: Consultancy; Bayer: Consultancy; Scientific Advisory Board: Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees. Bergsagel: Pfizer: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; GSK: Consultancy, Honoraria; Oncopeptides: Consultancy, Honoraria; Genetech: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Patents & Royalties: human CRBN mouse. Kumar: Merck: Research Funding; Carsgen: Research Funding; Roche-Genentech: Consultancy, Research Funding; Beigene: Consultancy; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bluebird Bio: Consultancy; Antengene: Consultancy, Honoraria; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Consultancy; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Tenebio: Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Sanofi: Research Funding.

Abstract Background: Chronic myelomonocytic leukemia (CMML) is an aging disease that arises on the background of clonal hematopoiesis, commonly involving multiple TET2 mutations or TET2/SRSF2 co-mutations. In a prior study of 1084 CMML patients (Leukemia 2020; 34:1407-1421), we demonstrated a high prevalence of TET2 mutations (56%) in CMML, with approximately 30% of patients having >1 TET2 mutation. The cell-type and allele specific distribution of these mutations remains of great interest and are thus far poorly defined. To visualize the genotypic and immunophenotypic correlates, we performed single-cell DNA analysis and simultaneous protein profiling on 2 CMML patients with multiple TET2 mutations. Methods: After Mayo Clinic IRB approval, two CMML patients with dual TET2 mutations and available peripheral blood mononuclear cells were included in the study. Patient samples were labeled with TotalSeq TM -D Heme Oncology Cocktail (BioLegend, San Diego, CA) consisting of 45 unique oligo-conjugated cell surface markers. The samples were then washed and loaded into a Tapestri microfluidics cartridge, where single cells (3,500 cells/µL) were encapsulated, lysed and barcoded using the MissionBio Myeloid custom amplicon panel consisting of 312 amplicons in 45 genes relevant to myeloid disorders (MissionBio, San Francisco, CA). The individual barcoded DNA and protein for each cell was purified, recovered, and labeled with Illumina i5/i7 indices, quanitifed by Agilent Bioanalyzer and pooled for sequencing. The libraries were sequenced via Illumina NovaSeq by the Mayo Genomics Core, and data was recovered and analyzed using MissionBio Tapestri Insights v3.0.2. Results: Two dual TET2 mutated CMML patients were included in the study, both male, ages 79 and 83, and both with CMML-0. Case 1 had TET2 p.G1172Efs*53 (variant allele fraction/VAF-6%) and p.H1732Tfs*13 (VAF-13%) mutations, while case 2 had TET2 p.Y620Ffs* (VAF-85%), p.G898* (VAF-82%), and SRSF2 p.95H (VAF-45%) mutations, respectively. In both patients, TET2 mutations were found to be preferentially distributed in monocytes (CD11b+, CD14+, CD64+, CD13+, CD33+, HLA-DR+), with very few classically defined granulocyte (CD16+,CD13+.CD33+, HLA-DR neg), B (CD19+,CD22+)/T (CD2+,CD4+,CD5+,CD7+) lymphocytes, NK cells (CD16+/CD56+/HLA-DR neg) and plasma cells (CD38+/CD138+), demonstrating mutant TET2. Among monocytes, TET2 mutations were enriched in classical/M01 (CD11b+, CD14+,CD16-) and intermediate/M02 monocytes and were infrequent in the MO3 fraction, commensurate with monocyte repartitioning seen in CMML. Interestingly, a large number of single cells with TET2 mutations also demonstrated an aberrant surface phenotype, with universal expression of CD14+ and CD11b+, along with a mixture of other markers (CD163+, CD11c, CD4+, CD2+, and/or CD56+). With regards to allelic distribution, while several permutations and combination were seen involving heterozygosity and homozygosity for the TET2 mutations identified on bulk sequencing, data was corroborative for both TET2 mutations being located on trans alleles. Conclusions: In this study, we demonstrate that TET2 mutations in CMML are highly enriched in classical circulating monocytes and infrequently occur in classical granulocytes, B/T and NK cells; with multiple TET2 mutations most commonly co-existing on trans alleles. Analyses on additional samples along with copy number variation assessments are ongoing. Figure 1: Lollipop plot of TET2 exons with dual TET2 mutations labeled above and oxygenase domain labeled in green; below are number of cells identified with each genotype for Case 1 (A) and Case 2 (B). Mutations are labeled proximal (yellow) and distal (red) and alleles are in grey. The directionality of the arrows to different genotypes is based on decreasing cell number and genotype. Uniform manifold approximation and projection (UMAP) plot of Case 1 (C) and Case 2 (D) with cells clustered by immunophenotype and labeled by genotype (non TET-2 mutated purple, heterozygous TET2 in green, homozygous TET2 in yellow). Relative protein expression of T-cell phenotype (CD14-, CD3+, CD4+/-,CD8+/-, CD5+, CD7+, CD2+), or classical monocytes (CD14+/CD16-) for Case 1 (E & F) and Case 2 (G & H) overlayed on UMAP. Royal blue represents non TET2-mutated cells, and mutated cells are labeled in pastel blue, green and light orange by mutation type. Figure 1 Figure 1. Disclosures Durruthy-Durruthy: Mission Bio Inc.: Current Employment. Arribas-Layton: Mission Bio, Inc.: Current Employment. Patnaik: StemLine: Research Funding; Kura Oncology: Research Funding.

Abstract Background: MASS-FIX is a novel method for detection, characterization and quantification of serum and urine monoclonal proteins in multiple myeloma (MM) which uses immunoglobulin enrichment coupled with matrix-assisted laser desorption ionization time-of-flight mass-spectrometry (MALDI-TOF). MASS-FIX has been endorsed by the International Myeloma Working Group (IMWG) as an alternative to immunofixation electrophoresis for diagnosis and disease monitoring in MM and has replaced immunofixation electrophoresis in our institution. This technique has demonstrated superior sensitivity and specificity compared to conventional gel-based methods, but its prognostic value is still unknown. We designed this study to evaluate whether serum M protein monitoring by MASS-FIX has prognostic significance in predicting disease progression in patients with MM. Methods: In the first part of the study, we included patients with MM who had a documented negative MASSFIX after first-line treatment and had serial MASS-FIX data available (n=187). We then compared the time to next treatment (TTNT) between patients who became positive by MASS-FIX and those who remained negative by last follow up. In the second part, we included patients who were positive by MASS-FIX within 90 days from diagnosis and had available serial MASS-FIX data (n=203). We compared TTNT between patients who became negative by MASS-FIX and those who remained positive by last follow up. TTNT was defined as the time of initiation of the first-line treatment to the time of initiation of second-line treatment or last follow up. Results: Part 1: We included 155 patients diagnosed with MM between January 2013 and December 2019 who had a documented negative MASSFIX with first-line treatment and had serial MASSFIX data available. After a median follow up of 3.1 (95%CI: 2.6-3.5) years, 44 (28%) patients became MASSFIX positive, while 111 (72%) still had negative MASS-FIX. The median time from treatment start to negative MASSFIX was 18.0 (IQR: 6.4-33.5) months. The median TTNT was 4.1 (95%CI: 3.2 - NR) years in patients who became MASSFIX positive and not reached (NR) (95%CI: NR-NR) in patients who were MASS-FIX negative by last follow up (P<0.001). The 2-year OS was 97% and 100% in the 2 groups, respectively (P=0.27). Part 2: We included 203 patients who had positive MASSFIX within 90 days from diagnosis and available serial MASSFIX data. The median follow-up was 1.5 (95%CI: 1.5-1.7) years. By last follow up, 39 (19%) patients became MASSFIX negative while 164 (81%) still had positive MASSFIX. The median TTNT was NR (95%CI: 2.0-NR) years in patients who developed negative MASSFIX and 2.3 (95%CI: 1.9 - NR) years in patients who were still MASS-FIX positive by last follow up (P=0.03). The 2-year OS was 97% and 94% in the 2 groups, respectively (P=0.84) Conclusion: The utilization of MASS-FIX in disease monitoring of MM patients provides prognostic information; among patients who become MASS-FIX negative after first line treatment, an earlier conversion to positive MASS-FIX predicts disease progression. Similarly, persistent MASS-FIX positivity after treatment initiation predicts earlier disease progression. Larger studies and longer follow up are needed to evaluate whether the MASS-FIX is associated with overall survival. Disclosures Murray: Mayo Clinic: Other: Has received patents for the Mass-Fix technology which has been licensed to the Binding Site with potential royalties.. Dispenzieri: Sorrento Therapeutics: Consultancy; Oncopeptides: Consultancy; Pfizer: Research Funding; Alnylam: Research Funding; Takeda: Research Funding; Janssen: Consultancy, Research Funding. Kapoor: Karyopharm: Consultancy; Cellectar: Consultancy; BeiGene: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; Amgen: Research Funding; Ichnos Sciences: Research Funding; Regeneron Pharmaceuticals: Research Funding; Glaxo SmithKline: Research Funding; Karyopharm: Research Funding; Sanofi: Research Funding; Takeda: Research Funding; AbbVie: Research Funding. Gertz: Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy; Akcea Therapeutics, Ambry Genetics, Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Karyopharm Therapeutics, Pfizer Inc (to Institution), Sanofi Genzyme: Honoraria; Ionis Pharmaceuticals: Other: Advisory Board; Aurora Biopharma: Other: Stock option; AbbVie Inc, Celgene Corporation: Other: Data Safetly & Monitoring. Dingli: Sanofi: Consultancy; GSK: Consultancy; Novartis: Research Funding; Apellis: Consultancy; Janssen: Consultancy; Alexion: Consultancy. Kumar: Amgen: Consultancy, Research Funding; Bluebird Bio: Consultancy; Antengene: Consultancy, Honoraria; BMS: Consultancy, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Beigene: Consultancy; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche-Genentech: Consultancy, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Carsgen: Research Funding; Merck: Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Consultancy; Novartis: Research Funding; Tenebio: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding.

Abstract Background: Chimeric antigen receptor T cell therapy (CAR-T) is a recent therapeutic advance for the treatment of myeloma. Among the FDA approved and investigational CAR-T, primary refractory disease post CAR-T infusion is uncommon, however most patients eventually relapse. To gain insight to the myeloma cell and immune cell characteristics associated with early relapse (PD) versus durable response (DR), we examined myeloma cells and immune cells in both the bone marrow and blood by single cell RNA-seq. Method: Bone marrow aspirate and blood samples were collected prior to BCMA targeted CAR-T therapy. CD138 + and CD138 neg cells from bone marrow (BM) and peripheral blood mononuclear cells (PBMNC) were collected for 5' based scRNA-seq (10X Genomics). CD138 + cells were clustered by Harmony integration. Top expressing genes in unique clusters for PD and DR were analyzed by Ingenuity Pathway Analyses. To profile CD138 neg BM cells and PBMNC from myeloma patients, the scRNA-seq dataset was mapped to the human PBMC reference using the Seurat v4 reference-guiding approach. Differential gene expression between responders and non-responders was performed using the FindMarkers function for each of the immune cell clusters in the BM and PB. Results: BM and PB samples from 15 patients were analyzed. Among these, 5 patients had relapsed disease more than 1-year post CAR-T infusion (Durable Response, DR), and 10 patients had relapse within 1 year (early relapse, PD). Among these, CD138+ cells were available from 3 DR and 4 PD for analysis. Multimodal expression of BCMA were seen among the CD138+ cells across all samples. Using the mode of the highest expression level as a cut-off, patients with DR had a trend toward having higher fraction of BCMA high CD138+ cells compared to pts with PD. Clustering of all CD138+ cells by Harmony integration identified 12 distinct clusters, among which two clusters were unique to pts in DR, and one was unique to patients in PD. Ingenuity pathway analysis identified the top marker genes in these 3 clusters to be enriched in pathways for IL-15 signaling, BCR signaling, and primary immunodeficiency signaling. (Figure 1A-E.) Differential gene expression for immune cell clusters from CD138 neg BM cells and PBMNC were compared between pts in PD and DR. Interestingly, different patterns were seen in immune cells in PB and BM (Figure 1G). For example, compared to pts in PD, those with DR had decreased signaling in CD16 PB monocytes for interferon signaling and differentiation to macrophages, whereas the CD16 monocytes in the BM in pts in DR had increased expression of mitochondrial genes for anti-oxidant stress, polarization to M1 macrophage and anti-apoptosis. Similarly, pts in DR had CD8 Tcm in the BM with increased expression of genes involved in cell adhesion and anti-apoptosis, and CD4 Tcm in the BM with increased expression of genes in protein metabolism. Conclusion: scRNAseq analysis of myeloma cells suggest different tumor transcriptome profile may be identified in myeloma cells that could relapse early after CAR-T therapy. In addition, host immune profile both in the BM microenvironment and in systemic PB circulation could be associated with durable clinical response. Figure 1 Figure 1. Disclosures Kapoor: Pharmacyclics: Consultancy; Glaxo SmithKline: Research Funding; Amgen: Research Funding; Sanofi: Research Funding; AbbVie: Research Funding; Ichnos Sciences: Research Funding; Sanofi: Consultancy; Regeneron Pharmaceuticals: Research Funding; Takeda: Research Funding; Karyopharm: Research Funding; Karyopharm: Consultancy; Cellectar: Consultancy; BeiGene: Consultancy. Dingli: GSK: Consultancy; Janssen: Consultancy; Sanofi: Consultancy; Novartis: Research Funding; Apellis: Consultancy; Alexion: Consultancy. Kumar: Bluebird Bio: Consultancy; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Tenebio: Research Funding; Novartis: Research Funding; BMS: Consultancy, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche-Genentech: Consultancy, Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Antengene: Consultancy, Honoraria; Carsgen: Research Funding; Oncopeptides: Consultancy; Beigene: Consultancy; Amgen: Consultancy, Research Funding; Merck: Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding. Lin: Merck: Research Funding; Kite, a Gilead Company: Consultancy, Research Funding; Takeda: Research Funding; Bluebird Bio: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Sorrento: Consultancy; Juno: Consultancy; Legend: Consultancy; Gamida Cell: Consultancy; Vineti: Consultancy; Novartis: Consultancy; Janssen: Consultancy, Research Funding.

Abstract Background: Systemic light chain (AL) amyloidosis is a plasma cell disorder characterized by multisystem deposition of misfolded immunoglobulin light chains produced by clonal plasma cells. Hematologic and organ responses with treatment have been shown to correlate with survival as early as 3 months from initiation of first-line treatment. Our group recently developed and validated a model integrating organ and hematologic responses for assessment of treatment outcomes at 6 months. Although current organ response criteria do not consider the depth of organ response, this has been shown to have prognostic utility in newly diagnosed patients. So, we designed this study to evaluate a composite hematologic and organ scoring system that also considers the depth of organ response at 1 to 6 months from initiation of first-line treatment. Methods: We included patients with AL amyloidosis who had at least one major organ involvement (cardiac, renal and/or liver involvement) and who had not started a second line treatment by 6 months from the time of initiating first-line treatment. For each patient, we calculated an organ response score and a hematologic response score at 1, 2, 3, 4, 5, and 6 months from initiation of first-line treatment. A score was assigned for each depth of hematologic response as follows: complete response=0, very good partial response=1, partial response=2, and no response/progressive disease=3). To calculate organ response, a score was assigned to each organ based on the depth of organ response (Muchtar et al. 2018): non-evaluable=0, complete response=1, very good partial response=2, partial response=3, and no response=4. The final organ response score was obtained by calculating the average of the individual involved organ scores. We then calculated the composite hematologic and organ response (HOR) score by adding the organ and hematologic responses at each interval and compared overall survival (OS) between patients with HOR score ≤ 5 (group 1) and those with score > 5 (group 2). Results: The cohort included 730 patients diagnosed with AL amyloidosis between February 10 th, 2006 and July 9 th, 2019. Median age was 63 (IQR: 56-69), and 65% were male. The involved light chain was Lambda in 75% of cases. Cardiac, renal, and liver involvement were found in 81%, 61%, and 17% of patients, respectively. Among all patients, 28% underwent autologous stem cell transplantation during their disease course. The median follow up in the entire cohort was 7.0 (95%CI: 6.4-8.0) years and OS was 3.6 (95%CI: 2.6-4.4) years. At 1 to 4 months, we observed a statistically significant difference in OS between patients with HOR score ≤5 vs. >5. However, there was no difference in OS between the 2 groups at 5 and 6 months. These results are presented in Table 1. Conclusion: A composite hematologic and organ response score that takes into consideration the depth of organ response can discriminate 2 groups of patients with distinct survival outcomes as early as 1 month from treatment initiation and maintains its predictive ability for up to 4 months. The lack of predictive ability beyond 4 months in this study may be due to limited sample size especially in group 2 as more patients achieve deeper responses with time. This approach can provide the basis for early changes in treatment but needs validation in future studies. Figure 1 Figure 1. Disclosures Dispenzieri: Janssen: Consultancy, Research Funding; Takeda: Research Funding; Sorrento Therapeutics: Consultancy; Oncopeptides: Consultancy; Pfizer: Research Funding; Alnylam: Research Funding. Gertz: Ionis Pharmaceuticals: Other: Advisory Board; Akcea Therapeutics, Ambry Genetics, Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Karyopharm Therapeutics, Pfizer Inc (to Institution), Sanofi Genzyme: Honoraria; Aurora Biopharma: Other: Stock option; Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy; AbbVie Inc, Celgene Corporation: Other: Data Safetly & Monitoring. Kapoor: Ichnos Sciences: Research Funding; Karyopharm: Consultancy; Glaxo SmithKline: Research Funding; Pharmacyclics: Consultancy; Amgen: Research Funding; Cellectar: Consultancy; Sanofi: Consultancy; BeiGene: Consultancy; Regeneron Pharmaceuticals: Research Funding; Karyopharm: Research Funding; Sanofi: Research Funding; Takeda: Research Funding; AbbVie: Research Funding. Dingli: GSK: Consultancy; Sanofi: Consultancy; Janssen: Consultancy; Novartis: Research Funding; Apellis: Consultancy; Alexion: Consultancy. Kumar: Oncopeptides: Consultancy; Merck: Research Funding; Antengene: Consultancy, Honoraria; Bluebird Bio: Consultancy; Roche-Genentech: Consultancy, Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Novartis: Research Funding; Carsgen: Research Funding; Tenebio: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Beigene: Consultancy; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding.

Abstract Mutations in chromatin regulator ASXL1 are frequently identified in myeloid malignancies, in particular ∼40% of patients with chronic myelomonocytic leukemia (CMML). ASXL1 mutations are associated with poor prognosis in CMML and significantly co-occur with NRAS mutations. Here, we show that concurrent ASXL1 and NRAS mutations defined a population of CMML patients who had shorter leukemia-free survival than those with ASXL1 mutation only. Corroborating this human data, Asxl1−/− accelerated CMML progression and promoted CMML transformation to acute myeloid leukemia (AML) in NrasG12D/+ mice. NrasG12D/+;Asxl1−/− (NA) leukemia cells displayed hyperactivation of MEK/ERK signaling, increased global levels of H3K27ac, upregulation of Flt3. Moreover, we find that NA-AML cells overexpressed all the major inhibitory immune checkpoint ligands: programmed death-ligand 1 (PD-L1)/PD-L2, CD155, and CD80/CD86. Among them, overexpression of PD-L1 and CD86 correlated with upregulation of AP-1 transcription factors (TFs) in NA-AML cells. An AP-1 inhibitor or short hairpin RNAs against AP-1 TF Jun decreased PD-L1 and CD86 expression in NA-AML cells. Once NA-AML cells were transplanted into syngeneic recipients, NA-derived T cells were not detectable. Host-derived wild-type T cells overexpressed programmed cell death protein 1 (PD-1) and T-cell immunoreceptor with immunoglobulin and ITIM domains (TIGIT) receptors, leading to a predominant exhausted T-cell phenotype. Combined inhibition of MEK and BET resulted in downregulation of Flt3 and AP-1 expression, partial restoration of the immune microenvironment, enhancement of CD8 T-cell cytotoxicity, and prolonged survival in NA-AML mice. Our study suggests that combined targeted therapy and immunotherapy may be beneficial for treating secondary AML with concurrent ASXL1 and NRAS mutations.

Abstract Purpose: Structural variants (SV) of the MYC gene region are common in multiple myeloma and influence disease progression. However, the prognostic significance of different MYC SVs in multiple myeloma has not been clearly established. Experimental Design: We conducted a retrospective study of multiple myeloma comparing MYC SV subtypes identified by next-generation sequencing (NGS) and FISH to MYC expression and disease survival using 140 cases from Mayo Clinic and 658 cases from the MMRF CoMMpass study. Results: MYC SVs were found in 41% of cases and were classified into nine subtypes. A correlation between the presence of a MYC SV and increased MYC expression was identified. Among the nine MYC subtypes, the non-immunoglobulin (non-Ig) insertion subtype was independently associated with improved outcomes, while the Ig insertion subtype, specifically involving the IgL gene partner, was independently associated with poorer outcomes compared with other MYC SV subtypes. Although the FISH methodology failed to detect approximately 70% of all MYC SVs, those detected by FISH were associated with elevated MYC gene expression and poor outcomes suggesting a different pathogenic role for FISH-detected MYC subtypes compared with other MYC subtypes. Conclusions: Understanding the impact of different MYC SVs on disease outcome is necessary for the reliable interpretation of MYC SVs in multiple myeloma. NGS approaches should be considered as a replacement technique for a more comprehensive evaluation of the multiple myeloma clone.