Auclair, Daniel

Background: Multiple myeloma is typically characterized by clonal expansion of malignant plasma cells within the bone marrow compartment. Presence of extramedullary disease (EMD) either in the form of soft tissue plasmacytoma or circulating plasma cells can be seen at diagnosis but is more common in the relapsed and refractory patients. Presence of EMD is typically associated with a poor prognosis, both in the newly diagnosed and relapsed setting, but trials designed specifically for patients with EMD are sparse. Treatment of EMD can be challenging and the responses even when seen are often short lasting, highlighting the need for developing specific treatment approaches aimed at these patients. Based on initial trials suggesting activity of pomalidomide in the setting of EMD and the increased tissue distribution with the oral proteasome inhibitor ixazomib, we designed this trial to examine if the all oral combination of ixazomib, pomalidomide and dexamethasone can be effective in the setting of EMD. Patients and Methods: Patients with previously treated multiple myeloma, with adequate hematologic and organ function were enrolled if there was evidence, at study entry, of EMD defined as one or more plasmacytomas, outside the bone marrow that were non-contiguous with a bone lesion and had a single diameter of ≥2 cm OR as plasma cell leukemia, with circulating plasma cells > 5% of peripheral blood leukocytes or at least 0.5 X 109/L or 200 cells/150000 events by flowcytometry. Patients were treated on 28-day cycles, with ixazomib 4 mg on days 1, 8, and 15 along with pomalidomide 4 mg PO daily on days 1-21 and dexamethasone 40 milligrams weekly until disease progression or unacceptable toxicity. The goals of the study were to determine confirmed response rate (≥ PR), toxicities of this combination, differential response rates and progression-free survival (PFS). Confirmed response rate, differential response rates and PFS were estimated using Duffy and Santner approach, exact binomial distributions and Kaplan Meier curves, respectively. The study was designed to accrue 30 patients. Results: This study was designed to enroll up to 30 patients but was closed after enrolling 17 patients due to slow accrual. The baseline characteristics are as indicated in Table 1. Eleven patients were enrolled with an extramedullary plasmacytoma while the remaining six patients had plasma cell leukemia. Median number of lines of prior therapy was 3 (range: 1, 7). Overall, 10 (58.8%) patients had progressive disease and 11 (64.7%) patients have died, median (range) follow-up for the live patients is 22.2 (2.1, 37.9) months. Patients were treated for a median of 2 (range 1-34) cycles. A grade 3+ AE, at least possibly attributed, was seen in 53%. Grade 3+, at least possibly related, hematologic toxicity was noted in 41%, with 29% experiencing grade 3+ neutropenia. Notable grade 3+ non-hematologic toxicities (regardless of attribution), with >10% incidence rate were anemia, hypoxia, infections and lung infection. The confirmed response rate in evaluable patients was 35% (1 CRs and 5 PRs), with a 90% CI of 6% - 37%. While 33% (4/12; 95% CI: 1-8%) of patients reported a biochemical response, 40% (2/5; 95% CI: 0-4%) of patients reported an extramedullary response. The median PFS was 4.5 (95% CI: 2-11.8) months. Conclusions: The combination of ixazomib, pomalidomide and dexamethasone resulted in disease response in a third of this group of high-risk patients with EMD; however, the responses were not very durable, with a median PFS of only 4.5 months. The trial highlights the feasibility of doing clinical trials specifically targeted towards this high-risk patient population. Future trials should explore combinations of novel agents, including monoclonal antibodies through multicenter collaborative efforts. Disclosures Kansagra: Alnylam Pharmaceuticals, Bristol Myers Squibb /Celgene, GlaxoSmithKline, Janssen, Pharmacyclics, Takeda Pharmaceuticals, Pfizer, Karyopharm Therpeutics: Other: Advisory Board. Witzig:Spectrum: Consultancy; Immune Design: Research Funding; Karyopharm Therapeutics: Research Funding; Acerta: Research Funding; Incyte: Consultancy; AbbVie: Consultancy; MorphSys: Consultancy; Celgene: Consultancy, Research Funding. Kumar:Oncopeptides: Consultancy, Other: Independent Review Committee; IRC member; Genecentrix: Consultancy; Carsgen: Other, Research Funding; Cellectar: Other; Celgene/BMS: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Dr. Reddy's Laboratories: Honoraria; AbbVie: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Takeda: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Janssen Oncology: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Merck: Consultancy, Research Funding; Amgen: Consultancy, Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments, Research Funding; Kite Pharma: Consultancy, Research Funding; Novartis: Research Funding; Adaptive Biotechnologies: Consultancy; Genentech/Roche: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; BMS: Consultancy, Research Funding; Karyopharm: Consultancy; MedImmune: Research Funding; Sanofi: Research Funding; Tenebio: Other, Research Funding.

Abstract: Title: Architecture of sample preparation and data governance of Immuno-genomic data collected from bone marrow and peripheral blood samples obtained from multiple myeloma patients In multiple myeloma (MM), the interactions between malignant plasma cells and the bone marrow microenvironment is crucial to fully understand tumor development, disease progression, and response to therapy. The core challenge in understanding those interactions has been the establishment of a standard process and a standard model for handling the data quality workflow and the underlying data models. Here we present the Platform (Figure 1), an integrated data flow architecture designed to create data inventory and process tracking protocols for multi-dimensional and multi-technology immune data files. This system has been designed to inventory and track peripheral blood and bone marrow samples from multiple myeloma subjects submitted for immune analysis under the MMRF Immune Atlas initiative (figure 2), and the processing and storage of Single Cell RNA-seq (scRNA-seq) and Mass Cytometry time-of-flight (CyTOF) data files derived from these immune analyses. While these methods have been previously applied on both tumor and immune populations in MM [2,3], this level of multi-institutional and multi-technology is unique. The Cloud Immune-Precision platform contains standardized protocols and bioinformatics workflows for the identification and categorization of immune cell populations and functional states based upon scRNA-seq gene signatures (ref: Bioinformatics manuscript in submission) and CyTOF protein signatures. Upon further expansion, it will contain high dimensional scRNAseq and CyTOF immune data from both bone marrow and peripheral blood samples from myeloma patients enrolled in the Multiple Myeloma Research Foundation (MMRF) CoMMpass study (NCT01454297) [1] (Figure 3). The architecture covers the automation of data governance protocols, data transformation and ETL model developments that will create an immune proteomic and profiling database and its integration into clinical and genomics databases: e.g. the MMRF CoMMpass clinical trial. This large-scale data integration will establish a cutting-edge Immune-Precision central platform supporting large scale, immune-focused advanced analytics in multiple myeloma patients. This platform will allow researchers to interrogate the relationships between immune transcriptomic and proteomic signatures and tumor genomic features, and their impact on clinical outcomes, to aid in the optimization of therapy and therapeutic sequencing. Furthermore, this platform also promotes the potential to (further) elucidate the mechanisms-of-action of approved and experimental myeloma therapies, drive biomarker discovery, and identify new targets for drug discovery. Figure 1: Cloud Immune-Precision Platform (Integrated data flow architecture designed to create data inventory and process tracking protocols for multi-dimensional and multi-technology immune data files) Figure 2: Sample tracking process architecture Figure 3: Data file creation and repository process tracking References: 1- Settino, Marzia et al. "MMRF-CoMMpass Data Integration and Analysis for Identifying Prognostic Markers." Computational Science - ICCS 2020: 20th International Conference, Amsterdam, The Netherlands, June 3-5, 2020, Proceedings, Part III vol. 12139 564-571. 22 May. 2020, doi:10.1007/978-3-030-50420-5_42 2- Ledergor, Guy et al. "Single cell dissection of plasma cell heterogeneity in symptomatic and asymptomatic myeloma." Nature medicine vol. 24,12 (2018): 1867-1876. doi:10.1038/s41591-018-0269-2 3- Hansmann, Leo et al. "Mass cytometry analysis shows that a novel memory phenotype B cell is expanded in multiple myeloma." Cancer immunology research vol. 3,6 (2015): 650-60. doi:10.1158/2326-6066.CIR-14-0236-T Figure 1 Disclosures Bhasin: Canomiiks Inc: Current equity holder in private company, Other: Co-Founder. Dhodapkar:Amgen: Membership on an entity's Board of Directors or advisory committees, Other; Celgene/BMS: Membership on an entity's Board of Directors or advisory committees, Other; Janssen: Membership on an entity's Board of Directors or advisory committees, Other; Roche/Genentech: Membership on an entity's Board of Directors or advisory committees, Other; Lava Therapeutics: Membership on an entity's Board of Directors or advisory committees, Other; Kite: Membership on an entity's Board of Directors or advisory committees, Other.

BACKGROUND Aneuploidy, defined by abnormal copy number changes of chromosomes, contributes to genome instability in multiple myeloma and has potential prognostic impact. Previous research has explored transcriptional pathways affected by aneuploidy. We aim to evaluate aneuploidy, clinical prognosis, and gene expression in RRMM patients (pts) who participated in MMRF (Multiple Myeloma Research Foundation) sequencing study at University of Michigan. We further used gene set analysis to identify enrichment and variation of genetic pathways associated with aneuploidy and overall survival (OS). This was a pilot study in view of applying similar methods to larger populations. METHODS DNA and RNA materials were obtained from 51 RRMM pts at the time of disease relapse. Targeted sequencing was performed with the Onco1700 panel and RNA sequencing was performed with a capture protocol using Agilent SureSelect All Exon V4, followed by paired end sequencing. Copy number variation (CNV) was estimated using an in-house pipeline using matched normal samples. Arm-level aneuploidy was defined as copy number status (gain, loss or neutral) with maximal proportion for each chromosomal arm. Aneuploidy score was determined by total number of arm-level CNV aberrations, with the median score defining high and low aneuploidy groups. Survival analysis was performed using Kaplan-Meier (KM) and Cox regression. RNA-seq libraries were aligned with STAR aligner to the hg38 reference and read quantification were performed with featureCounts. We used gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) to obtain genetic pathway-level summaries for 50 hallmark pathways representative of cancer from the GSEA Molecular Signatures Database (https://www.gsea-msigdb.org/). Supervised GSEA was performed, based on differential analyses comparing gene expression profiles between high versus low aneuploidy groups to identify the differences in genetic pathways in RRMM pts. Unsupervised GSVA was applied to RNAseq count data of RRMM and newly-diagnosed multiple myeloma (NDMM) pts from the MMRF CoMMpass study (797 pts). Univariate Cox proportional hazard model was used to identify pathways having significant association with OS. RESULTS Arm scale aneuploidy analysis revealed high frequency of gains and losses in multiple chromosomes (Figure 1). RRMM pts with high aneuploidy scores had worse OS (median 15.9 months since study enrollment) compared to those with low scores (median not reached, p=0.027). Multivariate analysis demonstrated that high aneuploidy score is persistently associated with poor OS (hazard ratio (HR): 3.9, p=0.006), adjusting for t(4;14) and t(11;14). Hyperdiploidy (any whole chromosome carrying >2 copies) was identified in chromosomes 3, 5, 7, 9, 11, 19, 21, with frequency >20%. Pts with hyperdiploidy had better OS via KM (p=0.04). Using RNAseq gene expression data and GSEA analysis, we found enrichment of gene markers involving the pathways for targets of E2F transcription factors (Normalized Enrichment Score (NES) = 2.07), G2-M checkpoint in the cell division cycle (NES = 1.87), MYC proto-oncogene (NES = 1.93 and 1.85), and DNA repair (NES = 1.68) in RRMM pts with high aneuploidy compared against those with low scores (Benjamini-Hochberg adjusted p-value <0.001 for all pathways, Figure 2). GSVA analysis revealed 21 genetic pathways associated with survival in NDMM and RRMM pts, with 8, 2, and 11 pathways associated with OS in NDMM only, RRMM only, and both, respectively (Figure 3). Enrichment of several biological pathways involved with cellular growth, cell cycle regulation, and stress response (E2F, G2-M, MYC, DNA repair, mTORC1 signaling, UV radiation, mitotic spindle assembly, unfolded protein response, and peroxisome) are associated with poor survival (HR>1) in both NDMM and RRMM pts. Downregulation of KRAS signaling pathway is associated with better survival (HR<1) in both groups. CONCLUSION Aneuploidy, adjusted for chromosomal translocations, is an independent adverse prognostic factor for RRMM. Aneuploidy is correlated with dysregulation of major biological pathways involving cell cycle regulation, cellular division, oncogenes, and DNA damage repair. The majority of common pathways shared between RRMM and NDMM are linked to or reflective of genomic instability, and may explain association with poor survival. Disclosures Talpaz: IMAGO: Consultancy; Takeda: Research Funding; Novartis: Research Funding; Constellation Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; BMS: Membership on an entity's Board of Directors or advisory committees. Ye:Janssen: Research Funding; Portola: Research Funding; Millennium: Research Funding; Celgene: Research Funding; Sanofi: Research Funding; Karyopharm: Research Funding; Nektar: Research Funding; AbbVie: Research Funding.

Introduction: Multiple myeloma (MM) is a genetically complex and clinically heterogeneous disease. Disease biology and phenotype is heavily influenced by the tumor microenvironment and the interaction between the immune milieu and malignant plasma cell population. Understanding the molecular profile of tumor along with the immune ecosystem can provide insights into key pathways that are important in disease pathobiology. Therefore, in this study, we have used single-cell RNA-Seq (scRNA-Seq) to compare the detailed maps of the bone marrow microenvironment of patients with rapid progressing disease (PFS < 18 months) with those whose disease had not progressed at the time of analysis (PFS < 4 years) Methods: MM patients (n=18) with rapid and no progression were identified from the Multiple Myeloma Research Foundation (MMRF) CoMMpass study, a longitudinal genomic study of patients with newly diagnosed, active multiple myeloma (NCT01454297). To generate a robust scRNA-Seq profile with minimal false positive, we profiled multiple technical replicates/aliquots of viably frozen CD138-negative bone marrow cells from each patient at three medical centers/universities (Beth Israel Deaconess Medical Center, Boston, Washington University in St. Louis and Mount Sinai School of Medicine, NYC using droplet-based single-cell barcoding technique. After batch correction and normalization, the cellular clusters were identified using principal component analysis and Uniform Manifold Approximation and Projection (UMAP) approach (Becht et al, 2018). Differential expression, pathways and systems biology analysis between rapid and non-progressors revealed differences for specific cell clusters (Panigrahy, Gartung et al. 2019). To determine association of plasma cell overexpressed genes with survival in CoMMpass study, survival analysis was performed using Kaplan-Meier (K-M) approach. Results: In this study, comparative analysis was performed of the bone marrow microenvironment of patients with aggressive and indolent disease by generating single-cell profiles of ~102,207 cells from 48 samples of 18 patients with MM. The UMAP approach identified multiple transcriptionally diverse clusters of plasma (CD138+), immune (PTPRC+) and erythroid (GYPA1/2+) cells (Fig 1a). Interestingly, the analysis identified CD138+ plasma/tumors cells clusters in a subset of samples from patients with rapid -progression and these clusters depicted a high degree of inter-patient heterogeneity (Fig 1a). Further characterization of plasma tumor cells depicted significant activation (Z score >2 and P-value <.001) of pathway related to "Unfolded protein response", epithelial-mesenchymal transition (EMT), and "p38 MAPK Signaling". These rapid progressions associated with plasma cells overexpressing multiple genes (e.g., Hazard ratio (HR) CCL3=1.9 95% CI= (1.5-3.9) log-rank P=0.0004, HSPA5 HR=1.4 (1-2.6), P=0.03) that are associated with poor outcome in multiple myeloma based CoMMpass data. The bone marrow microenvironment cells formed 22 clusters, comprising of cells from myeloid, macrophages, T cells, B cells, dendritic cells, Natural Killer T (NKT) cells, and erythroid lineages. The Non-progressive patients depicted enrichment of GZMB+ T and NKT cells with overexpression of genes associated with "Natural Killer Cell Signaling", "CD28 Signaling in T Helper Cells", "NF-kB Signaling" and "Th17 Activation Pathway" (Fig1b, c). Systems biology analysis depicted significant activation of TNF, STAT4, and NFATC2 regulatory signatures in NKT cells. The analysis also observed enrichment of macrophages, several types of monocytes, and myeloid cells in the samples from patients with non-progressive disease (Fig 1d). The myeloid/monocytes cluster depicted significant activation of multiple metabolic (i.e., Glycolysis, Gluconeogenesis) and immune response (i.e. IL8) pathways (Fig 1e). In summary, this multi-site study provides insights into potentially significant differences in the transcriptomic landscape of multiple myeloma patients with rapid and non-progression of disease. The non-progressive patients depict significant enrichment of activated T cells and myeloid lineage populations, suggesting their role toward better outcomes. These findings will be further expanded by ongoing single cell analyses of the CoMMpass tissue bank under the MMRF Immune Atlas initiative. Figure 1 Disclosures Bhasin: Canomiiks Inc: Current equity holder in private company, Other: Co-Founder. Dhodapkar:Roche/Genentech: Membership on an entity's Board of Directors or advisory committees, Other; Amgen: Membership on an entity's Board of Directors or advisory committees, Other; Celgene/BMS: Membership on an entity's Board of Directors or advisory committees, Other; Janssen: Membership on an entity's Board of Directors or advisory committees, Other; Kite: Membership on an entity's Board of Directors or advisory committees, Other; Lava Therapeutics: Membership on an entity's Board of Directors or advisory committees, Other. Kumar:Merck: Consultancy, Research Funding; Adaptive Biotechnologies: Consultancy; Genecentrix: Consultancy; Tenebio: Other, Research Funding; Celgene/BMS: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Genentech/Roche: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Oncopeptides: Consultancy, Other: Independent Review Committee; IRC member; Kite Pharma: Consultancy, Research Funding; Novartis: Research Funding; Sanofi: Research Funding; MedImmune: Research Funding; Karyopharm: Consultancy; BMS: Consultancy, Research Funding; Cellectar: Other; Carsgen: Other, Research Funding; Dr. Reddy's Laboratories: Honoraria; Janssen Oncology: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Takeda: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; AbbVie: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Amgen: Consultancy, Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments, Research Funding.

Compared with traditional bulk sequencing technologies, single-cell technologies have advantages to evaluate cellular heterogeneity and investigate the evolution of cellular subpopulations from the tumor and microenvironment. Application of single-cell sequencing in Multiple Myeloma (MM) is especially beneficial given MM is a highly heterogeneous disease with uncontrolled clonal expansion of plasma cells. Single-cell RNA sequencing (scRNA-seq) has been previously utilized to understand this hematopoietic malignancy in both tumor and immune populations in MM (Ledergor G. et al., 2018, Zavidij, O. et al., 2020). Mass cytometry (CyTOF) has also been used to identify the expansion of novel memory B cells in MM. (Hansmann, L. et al., 2015). Among the various single cell techniques, cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) is a multimodal approach with simultaneous quantification of single-cell transcriptomes and surface proteins. Since these three single-cell approaches enable the identification of cell types, cell states and characterization of cellular heterogeneity at transcriptomic and/or protein levels, understanding the concordance of the measurements among these three modalities is of great interest. We applied scRNA-seq, CyTOF and CITE-seq on four baseline samples of CD138-negative 'immune cell' fractions from patients enrolled in the MMRF CoMMpass study (NCT01454297). Two subjects were fast progressors (PFS < 18 months) and two subjects were non-progressors (PFS not reached). With multi-center collaboration coordinated by Multiple Myeloma Research Foundation (MMRF), each sample was subject to scRNA-seq and CyTOF by 3 independent centers. All sites received aliquots of the same sample. On average, 1,060 immune cells were detected in each sample using scRNA-seq and >64K CD45+ cells were detected using CyTOF. CITE-seq was performed in one center and 4,805 CD138-negative immune cells were identified on average. To compare cell type abundance between scRNA-seq, CyTOF and CITE-seq, we calculated the cell subset frequency of each immune population relative to the CD45+ populations. Overall, all three approaches were concordant while there is a stronger concordance between scRNA-seq and CyTOF. Cell type abundance is especially consistent for B cells, monocytes/macrophages, and plasmacytoid dendritic cells (pDC) among the 3 methods. For the same patient of interest, natural killer (NK) cell frequency was detected at the lowest level in CyTOF relative to scRNA and CITE-seq. The T cell population showed the highest discrepancy among techniques, with highest abundance in scRNA-seq followed by CyTOF and lowest abundance in CITE-seq. Interestingly, CITE-seq detected far less CD4 T cells compared to the other two techniques while CD8 T cell frequency did not show drastic differences. In addition to cell type abundance, we further examined the concordance of expression of cell type signature genes between scRNA-seq and CyTOF. Overall, expression between RNA-level and protein-level is positively correlated with typical cell type markers highly expressed in both techniques, especially in the following cell populations: CD8+ T cells (CD3, CD8), NK cells (CD56, GranzymeB/GZMB, NKG2A/KLRC1), B cells (CD19, CD38), Monocytes (CD14, CD11c/ITGAX, CD33). To note, although the concordance in CD4+ T cells is generally good, we found the expression of CD4 is higher in CyTOF compared to scRNA-seq whereas CD127/IL7R tends to be overexpressed in scRNA-seq. This could explain why IL7R is often identified as a differentially expressed gene (DEG) in CD4+ T cell population while CD4 is barely seen in DEG list in scRNA-seq analysis. It is also interesting to notice that the expression of most NK cell markers has strong concordance between scRNA-seq and CyTOF except NKG2D/KLRK1, which has much higher expression in CyTOF relative to scRNA-seq. Our preliminary results suggested good concordance of immune cell type abundance identified in CyTOF, scRNA-seq and CITE-seq as well as concordant expression of some canonical cell type markers between RNA level and protein level. This work provides the field with reference data sets and shows more detailed examination of NK and T cell subsets is needed when handling single cell sequencing with different modalities. Disclosures Dhodapkar: Roche/Genentech: Membership on an entity's Board of Directors or advisory committees, Other; Lava Therapeutics: Membership on an entity's Board of Directors or advisory committees, Other; Kite: Membership on an entity's Board of Directors or advisory committees, Other; Janssen: Membership on an entity's Board of Directors or advisory committees, Other; Celgene/BMS: Membership on an entity's Board of Directors or advisory committees, Other; Amgen: Membership on an entity's Board of Directors or advisory committees, Other. Kumar:MedImmune: Research Funding; Amgen: Consultancy, Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments, Research Funding; AbbVie: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Janssen Oncology: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Carsgen: Other, Research Funding; Merck: Consultancy, Research Funding; Adaptive Biotechnologies: Consultancy; Celgene/BMS: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Genentech/Roche: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Oncopeptides: Consultancy, Other: Independent Review Committee; IRC member; Kite Pharma: Consultancy, Research Funding; Novartis: Research Funding; Sanofi: Research Funding; Takeda: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Tenebio: Other, Research Funding; Karyopharm: Consultancy; BMS: Consultancy, Research Funding; Genecentrix: Consultancy; Cellectar: Other; Dr. Reddy's Laboratories: Honoraria. Gnjatic:Neon Therapeutics: Consultancy, Membership on an entity's Board of Directors or advisory committees; OncoMed: Consultancy, Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Research Funding; Genentech: Research Funding; Immune Design: Research Funding; Agenus: Research Funding; Janssen R&D: Research Funding; Pfizer: Research Funding; Takeda: Research Funding; Regeneron: Research Funding; Merck: Consultancy, Membership on an entity's Board of Directors or advisory committees. Bhasin:Canomiiks Inc: Current equity holder in private company, Other: Co-Founder.

Abstract Background: Persons of African ancestry (AA) experience a 1.5-2-fold risk of multiple myeloma (MM) compared to persons of European ancestry (EA). We assembled a set of MM patients with self-reported AA in order to evaluate the contribution of genetics to etiology in this high-risk group. Methods: Here we present the results of a meta-analysis of two GWAS in 1,813 cases and 8,871 controls of AA. We also conducted an admixture mapping scan to identify risk alleles associated with local ancestry, fine-mapped the 23 known susceptibility loci to find markers that could better capture MM risk in individuals of AA, and constructed a polygenic risk score (PRS) to assess the aggregated effect of known MM risk alleles. Finally, we conducted an eQTL analysis measuring gene expression in those genes harboring a risk variant in malignant plasma cells from 292 of the patients from a single site. Results: In GWAS analysis, we identified two suggestive novel loci located at 9p24.3 and 9p13.1 at P<1 × 10-6, but no genome-wide significant association was noted. In admixture mapping, we observed a genome-wide significant inverse association between local AA at 2p24.1-23.1 and MM risk in AA individuals. 20 of the 23 known EA risk variants showed directional consistency and 9 replicated at P<0.05 in AA individuals. In eight regions, we identified markers that better capture MM risk in persons of AA. AA individuals with a PRS in the top 10% had a 1.82-fold (95%CI: 1.56, 2.11) increased MM risk compared to those with average risk (25-75%). The strongest functional association was between the risk allele for variant rs56219066 at 5q15 and lower ELL2 expression (P= 5.1 × 10–12). Conclusion: Our study shows that common genetic variation contributes to MM risk individuals of AA. This abstract is also being presented as Poster C040. Citation Format: Zhaohui Du, Niels Weinhold, Gregory Chi Song, Kristen A. Rand, David J. Van Den Berg, Amie E. Hwang, Xin Sheng, Victor Hom, Sikander Ailawadhi, Ajay K. Nooka, Seema Singhal, Karen Pawlish, Edward Peters, Cathryn Bock, Ann Mohrbacher, Alexander Stram, Sonja I. Berndt, William J. Blot, Graham Casey, Victoria L. Stevens, Rick Kittles, Phyllis J. Goodman, W. Ryan Diver, Anselm Hennis, Barbara Nemesure, Eric A. Klein, Benjamin A. Rybicki, Janet L. Stanford, John S. Witte, Lisa Signorello, Esther M. John, Leslie Bernstein, Antoinette Stroup, Owen W. Stephens, Maurizio Zangari, Frits Van Rhee, Andrew Olshan, Wei Zheng, Jennifer J. Hu, Regina Ziegler, Sarah J. Nyante, Sue Ann Ingles, Michael Press, John David Carpten, Stephen Chanock, Jayesh Mehta, Graham A Colditz, Jeffrey Wolf, Thomas G. Martin, Michael Tomasson, Mark A. Fiala, Howard Terebelo, Nalini Janakiraman, Laurence Kolonel, Kenneth C. Anderson, Loic Le Marchand, Daniel Auclair, Brian C.-H. Chiu, Elad Ziv, Daniel Stram, Ravi Vij, Leon Bernal-Mizrachi, Gareth J. Morgan, Jeffrey A. Zonder, Carol Ann Huff, Sagar Lonial, Robert Z. Orlowski, David V. Conti, Christopher A. Haiman, Wendy Cozen. A meta-analysis of genome-wide association study and eQTL analysis of multiple myeloma among African Americans [abstract]. In: Proceedings of the Twelfth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2019 Sep 20-23; San Francisco, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl_2):Abstract nr PR05.

Purpose: Multiple myeloma (MM) treatment has changed tremendously, with significant improvement in patient out-comes. One group with a suboptimal benefit is patients with high-risk cytogenetics, as tested by conventional karyotyping or fluorescence in situ hybridization (FISH). Methodology for these tests has been published, but not necessarily standardized. Methods: We address variability in the testing and reporting methodology for MM cytogenetics in the United States using the ongoing African American Multiple Myeloma Study (AAMMS). We evaluated clinical and cytogenetic data from 1,221 patients (1,161 with conventional karyotyping and 976 with FISH) tested between 1998 and 2016 across 58 laboratories nationwide. Results: Interlab and intralab variability was noted for the number of cells analyzed for karyotyping, with a significantly higher number of cells analyzed in patients in whom cytogenetics were normal (P 5.0025). For FISH testing, CD138-positive cell enrichment was used in 29.7% of patients and no enrichment in 50% of patients, whereas the remainder had unknown status. A significantly smaller number of cells was analyzed for patients in which CD138 cell enrichment was used compared with those without such enrichment (median, 50 v 200; P, .0001). A median of 7 loci probes (range, 1-16) were used for FISH testing across all laboratories, with variability in the loci probed even within a given laboratory. Chromosome 13–related abnormalities were the most frequently tested abnormality (n5956; 97.9%), and t(14;16) was the least frequently tested abnormality (n 5 119; 12.2%). Conclusions: We report significant variability in cytogenetic testing across the United States for MM, potentially leading to variability in risk stratification, with possible clinical implications and personalized treatment approaches.

Abstract In an effort to identify novel genetic abnormalities in multiple myeloma (MM) we analyzed an initial set, as part of the Multiple Myeloma Research Consortium (MMRC) Genomics Initiative, of 94 MM patient samples on high-resolution array-based comparative genomic hybridization (aCGH) and gene expression profiling (GEP) technologies. The MMRC Genomics Initiative is a three-year research program spanning a full-spectrum of genomic technologies with the goal of analyzing 250 MM patient samples by 2009. Raw data from the Initiative are released in near real time through the MMRC Multiple Myeloma Genomics Portal. This initial aCGH dataset from the Agilent 244K platform represents the highest resolution gene copy number analysis with corresponding GEP released to date in MM. It has confirmed the cytogenetic definitions of MM with the data set being split between patients with hyperdiploid and non-hyperdiploid MM. Furthermore, the 244K platform has helped to refine minimal regions of change present on 1p, 1q, 6q, 8p, 13q, 14q, and 16q. A comprehensive screen of the dataset for genes residing in regions of homozygous deletion identified 105 genes. The most commonly affected gene was CDKN2C(p18) (8.3%) while 12 other regions were recurrently targeted. Several of these regions were recently implicated in the pathogenesis of MM but the large majority represents novel observations. The aCGH and GEP data were integrated using two different programs developed by the core institutions (The Broad Institute of Harvard & MIT and Translational Genomics Research Institute). First, Genomic Identification of Significant Targets in Cancer (GISTIC), which identifies highly significant regions of change and a limited set of candidate genes within these regions, was used to identify 285 genes associated with DNA content alterations. Second, Breakpoint Expression Correlation (BEC), which identifies dysregulated genes on either side of a copy number alteration, was used to identify 194 genes with expression changes associated with a breakpoint. This latter gene list includes all known IgH translocation target genes identified to date in myeloma. Overlapping the gene lists generated by GISTIC, BEC, and the homozygous deletion screen identified a number of genes previously implicated in MM such as CDKN2C(p18), CYLD, BIRC2/BIRC3, and two novel genes, FAM46C and RNF6. Ongoing data integration with RNAi and high content sequencing projects of the current Initiative holds great promise in furthering our understanding of MM. The MMRC Genomics Initiative and resulting efforts from the myeloma research community will increase our understanding of MM at the molecular level, help to identify new targets, and ultimately lead to the development of better, more effective therapies for myeloma.

Abstract Table 1. Samples to p53 deletions p 53 deletion FISH aCGH FISH and aCGH MGUS 142 - - SMM 108 - - Newly diagnosed MM - 182 - Relapsed/refractory MM 62 156 19 HMCLs - 48 - Total 312 386 19 Background: Inactivation of p53 by mutation or allelic loss is a rare event in multiple myeloma (MM) at the time of disease diagnosis and believed to be more common in the late stages of the disease. Here we defined the prevalence of p53 deletions in monoclonal gammopathy of undetermined significance (MGUS), smoldering multiple myeloma (SMM), newly diagnosed MM, relapsed MM and human myeloma cell lines (HMCLs). Indirect mechanisms of p53 inactivation such as amplification of MDM2, or deletion of CDKN2A (p14ARF) were also investigated. Patients and Methods: A total of 631 MM samples and 48 HMCLs were analyzed for p53 abnormalities (Table 1) and compared with a cohort of plasma cell leukemia (PCL) previously published by us. Interphase fluorescence in situ hybridization (FISH) and highresolution array-based comparative genomic hybridization (aCGH) were used to detect p53 deletions. MDM2 amplification and p14ARF loss were evaluated by aCGH. Overall survival (OS) was estimated through Kaplan Meier method. Results: The prevalence of p53 deletions was 1.4%, 3.7%, 8.9%, 20% and 87.5% in MGUS, SMM, newly diagnosed MM, relapsed MM and HMCLs respectively (Figure 1). Of interest patients in first relapse showed a p53 deletion prevalence of 18% in comparison with 33% for patients in second relapse or more. We obtained p53 deletion status in 8 patients noted to have deletion at the time of relapse and in whom we had previous samples stored. In 7 of 8 cases deletions had been acquired (absent in the previous samples). The median OS for relapsed MM patients with or without p53 deletion were 4.2 months and 37.8 months respectively (p <0.001). MDM 2 amplification and p14ARF loss was detected in 0.4% and 2% of new diagnosed MM, 0.6% and 5.1% of relapsed MM and 2% and 29% of HMCLs respectively. Discussion: Emergence of p53 deletion/mutations denotes progression genetic events in MM. Importantly we show for the first time the overriding importance of p53 deletions as prognostic markers in relapsed MM. A molecular staging system that is based on the presence of p53 inactivation may be more powerful than classifying patients based on the loss of p53. Other abnormalities involving p53 pathway regulators like MDM2 gain and p14ARF loss are infrequent events in MM, even in advanced disease. Figure 1. Prevalence of p53 deletions Figure 1. Prevalence of p53 deletions

Abstract The Multiple Myeloma Research Consortium (MMRC) Genomics Initiative was instigated to harness the power of multiple genomic approaches to further the understanding of multiple myeloma. To date, 137 samples from patients with newly diagnosed and relapsed myeloma (out of an estimated final total of 250 by the end of the year) have been subjected to expression profiling and array comparative genomic hybridization. To identify regions of recurrent copy number alteration with a high degree of confidence, we have used the Genomic Identification of Significant Targets In Cancer (GISTIC) algorithm, which detects such regions and assigns a probability to each. Application of GISTIC to the MMRC collection identified 14 significant regions of amplification and 15 significant regions of deletion. The algorithm further detects peaks of copy number change that contribute to each region’s significance. Of genes that were expressed in our dataset, a total of 64 across the genome were found to lie within the boundaries of significant amplification peaks and 30 were found to lie within significant deletion peaks. Given the likelihood that this gene collection is highly enriched for genes important in the pathogenesis of myeloma and potential therapeutic targets, it has been prioritized for further validation with the amplified genes submitted for arrayed RNAi and the deleted genes expedited for re-sequencing. In order to define a poor prognosis group, we developed and applied a model based on the gene expression dataset of Shaughnessy and colleagues. To identify pathways activated in poor prognosis disease, we applied Gene Set Enrichment Analysis with the Molecular Signatures Database. This demonstrated enrichment of multiple canonical pathways within the poor prognosis group, including those associated with proliferation, cell cycle progression and DNA repair. A search for recurrent copy number events associated with poor prognosis disease revealed that by far the most significant event, and the only one surviving correction for multiple hypothesis testing, was mono- or bi-allelic deletion of CDKN2C (p18). Interestingly, the poor prognosis group also demonstrated higher expression of p18 in those samples without bi-allelic deletion and of CDKN2A (p16). Deletion and increased expression of p18 in high proliferation index myeloma samples has previously been described. However, our analyses demonstrate the pre-eminence of p18 loss for defining poor prognosis disease compared to other recurrent copy number changes. Furthermore, it expands on work in other tumors demonstrating feedback mechanisms between cyclin dependent kinase inhibitor pathways, suggesting a more complex model of interplay between the pathways than previously described. In conclusion, the MMRC reference collection is proving an important tool in the fight to understand key genetic events in multiple myeloma and would ultimately be anticipated to contribute towards development of improved therapy for the disease.