ALBERT

Description: Multiple myeloma (MM) is an incurable hematological malignancy characterized by the clonal proliferation of malignant plasma cells in the bone marrow. Like other cancers, MM is a genetically complex and heterogeneous disease. One of its distinctive characteristics is that it is preceded by a pre-malignant condition known as monoclonal gammopathy of undetermined significance (MGUS), which then progresses to asymptomatic (smoldering) multiple myeloma (SMM) and, ultimately, to late-stage MM. Its progression through these stages is determined by a sequence of genomic aberrations, starting with germline events that predispose to the disease, followed by early initiating events and the later acquisition of mutations that contribute to disease progression. Although considerable progress has been made in the past 6 years in cataloguing somatic events underlying MM development and progression, little is known about its genetic predisposition. Therefore, large-scale germline genomic variant studies are urgently needed. Recently, our group has published the largest-scale pan-cancer study of 〉10K adult and 〉1K pediatric cases that revealed new insights on germline predisposition variants across 33 cancer types (853 pathogenic or likely pathogenic variants) (Huang et al., 2018). Here, we aim to apply a similar strategy to MM cases. The CoMMpass study, promoted by MMRF (Multiple Myeloma Research Foundation) is a longitudinal, prospective observational study involving the collection and analysis of sequencing and clinical data from 〉1K MM patients at diagnosis and relapse. We performed germline variant calling on 808 normal samples from this dataset using GenomeVIP (https://github.com/ding-lab/GenomeVIP), which integrates multiple tools: VarScan2 and Genome Analysis ToolKit (GATK) for the identification of single nucleotide variants (SNVs) and indels; and Pindel for indel prediction. Variants were limited to coding regions of full length transcripts obtained from Ensembl release 70 plus the additional two base pairs flanking each exon that cover splice donor/acceptor sites. SNVs were based on the union of raw GATK and VarScan calls. Indels were required to be called by at least two out of the three callers (GATK, Pindel, VarScan). Variant calls from all tools were merged, filtered (allelic depth ≥ 5 for the alternative allele; rare variants with allele frequency ≤ 0.01 in 1000 Genomes and ExAC), and annotated using Variant Effect Predictor (VEP), resulting in an average of 1,653 variants per sample. Further, we applied CharGer (Characterization of Germline Variants, https://github.com/ding-lab/CharGer) to classify the identified germline variants as pathogenic, likely pathogenic, and prioritized variants of unknown significance (VUS). CharGer is an automatic variant classification pipeline developed by our group which adopts ACMG-AMP guidelines specifically for rare variants in cancer. Here, we were able to classify a total of 635 germline variants as pathogenic and 150 as likely pathogenic, affecting 90% of samples. Among pathogenic variants, 28 were found in known cancer predisposition genes including BRCA1 and BRCA2 - which have been previously associated with MM risk - BRIP1, CHEK2, TP53, TERT, and PMS2. Ongoing analyses include: functional characterization of these variants, identifying genes with enriched pathogenic or likely pathogenic variants in our dataset; investigation of LOH and two-hit (biallelic) events; gene and protein expression analyses in carriers of pathogenic germline variants of the respective gene; scanning for rare, germline copy number variations (CNVs); and identification of variants in post-translational modification sites that may affect protein signaling. Additionally, we are currently working on improving our CharGer tool by integrating new tumor associated data, such as DNA-Seq, RNA-Seq, Methyl-Seq and MS proteomics data, to improve variant classification. The preliminary results and analysis strategies described here will allow for efficient and cost-effective discovery of genetic changes relevant to MM etiology. Ultimately, we hope this work will impact our overall understanding of the genetics underlying MM predisposition, allowing for the development of better prevention and early detection strategies. Disclosures Vij: Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Karyopharma: Honoraria, Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: Honoraria, Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Jansson: Honoraria, Membership on an entity's Board of Directors or advisory committees.

Description: Introduction: Gene fusions are the result of genomic rearrangements that create hybrid protein products or bring the regulatory elements of one gene into close proximity of another. Fusions often dysregulate gene function or expression through oncogene overexpression or tumor suppressor underexpression (Gao, Liang, Foltz, et al. Cell Rep 2018). Some fusions such as EML4--ALK in lung adenocarcinoma are known druggable targets. Fusion detection algorithms utilize discordantly mapped RNA-seq reads. Careful consideration of detection and filtering procedures is vital for large-scale fusion detection because current methods are prone to reporting false positives and show poor concordance. Multiple myeloma (MM) is a blood cancer in which rapidly expanding clones of plasma cells spread in the bone marrow. Translocations that juxtapose the highly-expressed IGH enhancer with potential oncogenes are associated with overexpression of partner genes, although they may not lead to a detectable gene fusion in RNA-seq data. Previous studies have explored the fusion landscape of multiple myeloma cohorts (Cleynen, et al. Nat Comm 2017; Nasser, et al. Blood 2017). In this study, we developed a novel gene fusion detection pipeline and post-processing strategy to analyze 742 patient samples at the primary time point and 64 samples at follow-up time points (806 total samples) from the Multiple Myeloma Research Foundation (MMRF) CoMMpass Study using RNA-seq, WGS, and clinical data. Methods and Results: We overlapped five fusion detection algorithms (EricScript, FusionCatcher, INTEGRATE, PRADA, and STAR-Fusion) to report fusion events. Our filtered call set consisted of 2,817 fusions with a median of 3 fusions per sample (mean 3.8), similar to glioblastoma, breast, ovarian, and prostate cancers in TCGA. Major recurrent fusions involving immunoglobulin genes included IGH--WHSC1 (88 primary samples), IGL--BMI1 (29), and the upstream neighbor of MYC, PVT1, paired with IGH (6), IGK (3), and IGL (11). For each event, we used WGS data when available to determine if there was genomic support of the gene fusion (based on discordant WGS reads, SV event detection, and MMRF CoMMpass Seq-FISH WGS results) (Miller, et al. Blood 2016). WGS validation rates varied by the level of RNA-seq evidence supporting each fusion, with an overall rate of 24.1%, which is comparable to previously observed pan-cancer validation rates using low-pass WGS. We calculated the association between fusion status and gene expression and identified genes such as BCL2L11, CCND1/2, LTBR, and TXNDC5 that showed significant overexpression (t-test). We explored the clinical connections of fusion events through survival analysis and clinical data correlations, and by mining potentially druggable targets from our Database of Evidence for Precision Oncology (dinglab.wustl.edu/depo) (Sun, Mashl, Sengupta, et al. Bioinformatics 2018). Major examples of upregulated fusion kinases that could potentially be targeted with off-label drug use include FGFR3 and NTRK1. We examined the evolution of fusion events over multiple time points. In one MMRF patient with a t(8;14) translocation joining the IGH locus and transcription factor MAFA, we observed IGH fusions with TOP1MT (neighbor of MAFA) at all four time points with corresponding high expression of TOP1MT and MAFA. Using non-MMRF single-cell RNA data from different patients, we were able to track cell-type composition over time as well as detect subpopulations of cells harboring fusions at different time points with potential treatment implications. Discussion: Gene fusions offer potential targets for alternative MM therapies. Careful implementation of gene fusion detection algorithms and post-processing are essential in large cohort studies to reduce false positives and enrich results for clinically relevant information. Clinical fusion detection from untargeted RNA-seq remains a challenge due to poor sensitivity, specificity, and usability. By combining MMRF CoMMpass data from multiple platforms, we have produced a comprehensive fusion profile of 742 MM patients. We have shown novel gene fusion associations with gene expression and clinical data, and we identified candidates for druggability studies. Disclosures Vij: Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Jazz Pharmaceuticals: Honoraria, Membership on an entity's Board of Directors or advisory committees; Jansson: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Karyopharma: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding.

Description: Multiple myeloma (MM) is a disease defined by clonal proliferation of abnormal plasma cells from B-cells. Improved treatments for MM have led to improving overall lifespan, but still remains incurable due to acquired resistance to therapy and tumor heterogeneity. Single-cell RNA sequencing studies (scRNA-seq) of MM patients have highlighted the significant inter-individual heterogeneity and subclonal architecture of the malignant plasma cell populations, emphasizing the importance of developing personalized therapies specific to a patients molecular pathogenesis. In this study, we have integrated scRNA-seq with single-cell proteomics (sc-Prot) for 10 plasma cells and CD4+ T cells to validate and prioritize driver events in malignant cells and evaluate the tumor microenvironment. This effort will be expanded to another 10 cases to further integrate scRNA-seq, snATAC-seq, whole exome sequencing and bulk RNA-sequencing on a fraction of the cells isolated from bone marrow. The remaining cells will be sorted using FACS to select for specific malignant and immune cells including 40 plasma cells, 15 CD4+ T and 15 CD8+ T cells. These sorted cells will be profiled with a scProt technology (BASIL nanoPOTS) to illuminate their cell-to-cell heterogeneity. In our pilot study comparing bulk and single-cell proteomic data of a single patient's plasma cells (CD138+) for 400 representative proteins, while a majority of expression signatures are concurrent between the two methods, some signaling pathways including translation and apoptotic cleavage are discordant. Our findings stress the importance of interrogating subpopulations of immune and malignant cells at the single-cell level to further refine the transcriptomic and proteomic heterogeneity of MM in a cell type specific manner. With the aid of single-cell technology, we have assessed the heterogeneity of malignant and immune cell types to evaluate transcriptomic and proteomic changes contributing to altering the interplay between the immune environment and tumor cells. Disclosures Fiala: Incyte: Research Funding. Rettig:WashU: Patents & Royalties: Patent Application 16/401,950. O'Neal:Wugen: Patents & Royalties: Patent Pending; WashU: Patents & Royalties: Patent Pending. DiPersio:WUGEN: Equity Ownership, Patents & Royalties, Research Funding; Macrogenics: Research Funding, Speakers Bureau; Cellworks Group, Inc.: Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy; Magenta Therapeutics: Equity Ownership; RiverVest Venture Partners Arch Oncology: Consultancy, Membership on an entity's Board of Directors or advisory committees; NeoImmune Tech: Research Funding; Karyopharm Therapeutics: Consultancy; Incyte: Consultancy, Research Funding; Amphivena Therapeutics: Consultancy, Research Funding; Bioline Rx: Research Funding, Speakers Bureau. Vij:Bristol-Myers Squibb: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Genentech: Honoraria; Janssen: Honoraria; Karyopharm: Honoraria; Sanofi: Honoraria; Takeda: Honoraria, Research Funding.

Description: Multiple myeloma (MM) is a hematological cancer of the antibody-secreting plasma cells. Despite therapeutic advancements, MM remains incurable due to high incidence of drug-resistant relapse. In recent years, targeted immunotherapies, which take advantage of the immune system's cytotoxic defenses to specifically eliminate tumor cells expressing certain cell surface and intracellular proteins have shown promise in combating this and other B cell hematologic malignancies. A major limitation in the development of these therapies lies in the discovery of optimal candidate targets, which require both high expression in tumor cells as well as stringent tissue specificity. In an effort to identify potential myeloma-specific target antigens, we performed an unbiased search for genes with specific expression in plasma and/or B cells using single-cell RNA-sequencing (scRNAseq) of 53 bone marrow samples taken from 42 patients. By comparing 〉40K plasma cells to 〉97K immune cells across our cohort, we were able to identify a total of 181 plasma cell-associated genes, including 65 that encode cell-surface proteins and 116 encoding intracellular proteins. Of particular interest is that the plasma cells from each patient were shown to be transcriptionally distinct with unique sets of genes expressed defining each patient's malignant plasma cells. Using pathway enrichment analysis, we found significant overrepresentation of cellular processes related to B-Cell receptor (BCR) signaling, protein transport, and endoplasmic reticulum (ER) stress, involving genes such as DERL3, HERPUD1, PDIA4, PDIA6, RRBP1, SSR3, SSR4, TXNDC5, and UBE2J1. To note, our strategy successfully captured several of the most promising MM therapeutic targets currently under pre-clinical and clinical trials, including TNFRSF17(BCMA), SLAMF7, and SDC1 (CD138). Among these, TNFRSF17 showed very high plasma cell expression, with concomitant sharp exclusion of other immune cell types. To ascertain tissue specificity of candidate genes outside of the bone marrow, we analyzed gene and protein expression data from the Genotype-Tissue Expression (GTEx) portal and Human Protein Atlas (HPA). We found further support for several candidates (incl. TNFRSF17,SLAMF7, TNFRSF13B (TACI), and TNFRSF13C) as being both exclusively and highly expressed in lymphoid tissues. While several surface candidates were not found to be lymphocyte-restricted at the protein level, they remain relevant considerations as secondary targets for bi-specific immunotherapy approaches currently under development. To further investigate potential combinatorial targeting, we examine sample-level patterns of candidate co-expression and mutually-exclusive expression using correlation analysis. As the majority of our detected plasma cell-specific genes encode intracellular proteins, we investigated the potential utility of these epitopes as therapeutic targets via MHC presentation. Highly expressed candidates include MZB1, SEC11C, HLA-DOB, POU2AF1, and EAF2. We analyzed protein sequences using NetMHC and NETMHCII to predict high-affinity peptides for common class-I and class-II HLA alleles. To correlate MHC allelic preference with candidate expression in our cohort, we performed HLA-typing for 29 samples using Optitype. To support our scRNAseq-driven findings, we cross-referenced gene expression data with 907 bulk RNA-sequencing samples, including 15 from internal studies and 892 from the Multiple Myeloma Research Foundation (MMRF), as well as bulk global proteomics data from 4 MM cell lines (TIB.U266, RPMI8226, OPM2, MM1ST) and 4 patients. We see consistent trends across both cohorts, with high positive correlation (Pearson R ranging between 0.60 and 0.99) for a majority of genes when comparing scRNA and bulk RNA expression in the same samples. Our experimental design and analysis strategies enabled the efficient discovery of myeloma-associated therapeutic target candidates. In conclusion, this study identified a set of promising myeloma CAR-T targets, providing novel treatment options for myeloma patients. Disclosures Goldsmith: Wugen Inc.: Consultancy. DiPersio:Magenta Therapeutics: Membership on an entity's Board of Directors or advisory committees.