ALBERT

Description: Introduction Multiple myeloma is an incurable plasma cell malignancy with a strikingly heterogeneous genomic landscape. Other than IgH translocations and hyperdiploidy, only a few alterations are observed in large enough numbers. Amplification of the long arm of chromosome 1 (1q) is among the most common copy number alterations encountered, with a confirmed adverse effect on survival. Gene expression profiling has identified a minimal common amplified region between 1q21 and 1q23 as a probable target of the amplification event, however the actionable gene dependencies in that region have not been explored. In this study, we employ a large number of in-house and publicly available CRISPR, shRNA and drug screens in an effort to characterize the genetic dependencies of 1q-amplified myeloma and discover drugs that target them. Ultimately, we hope to propose a tailored therapeutic strategy for patients with 1q-amplified multiple myeloma. Methods To assess the genetic dependencies of 1q-amplified myeloma, we performed an shRNA screen in multiple myeloma cell lines, targeting genes in the 1q21-1q23 region. Corresponding C911 hairpins were designed for every target shRNA, and DEMETER2 was used to infer on-target effect. To that same end, we analyzed publicly available dependency data from Project Achilles (Whole-genome CRISPR screen, Avana library, 18Q4 release) and Dependency Map (combined RNAi dataset, accessed on 6/20/2018) and looked for differential dependencies in 1q-amplified multiple myeloma cell lines. Different sets of 1q-amplified and non-amplified cell lines were included in each dataset to avoid cell line-specific effects. Genes that both constituted differential dependencies and were differentially expressed were considered as hits. GSEA was used for pathway analysis. To assess differential sensitivity of 1q-amplified myeloma to drugs, we performed a drug screen utilizing the Broad Institute's Drug Repurposing Library-a library of over 5,000 drugs that have cleared varied stages of clinical testing, and compared normalized viability values between 1q-amplified and non-amplified myeloma cell lines. Utilizing publicly available patient data, we also built a 1q-amplification gene expression signature and used it to query the Connectivity Map (CMap) database. Drugs that were predicted to reverse our signature were then used in a new drug screen of myeloma cell lines. Results Through multiple dependency screens, we identified a total of 206 differential dependencies in 1q-amplified myeloma. Out of those, 46 came up in two screens (double hits), while 4 came up in all three datasets (triple hits). CLK-2, a serine/threonine and tyrosine kinase involved in mRNA splicing and POLR3C, a gene encoding a subunit of RNA-polymerase III, were among the triple hits. MCL-1, UBQLN4, CERS2, JTB, BCL9 and PEX19 were among the double hits. With at least four members affected (UBQLN4, UBE2Q1, UBAP2L and UBE2T), the ubiquitin pathway came up as an important differential dependency, while GSEA identified cell cycle as another pathway of essentiality in 1q-amplified multiple myeloma. Next, we searched for differential drug sensitivities utilizing the Drug Repurposing Library as well as a CMap-guided screen, as described above. We identified as hits several compounds targeting the MDM2 ubiquitin ligase as well as compounds related to cell cycle control, including PARP inhibitors and chemotherapeutic agents like fludarabine, thus validating the dependencies discovered in our datasets. Conclusion We employed a combination of multiple in-house and publicly available CRISPR, shRNA and drug screens, in the largest to date effort to characterize and target the genetic dependencies of 1q-amplified multiple myeloma. Cell cycle and the ubiquitin pathway came up as strong dependencies, while the drugs that target them were indeed shown to preferentially kill 1q-amplified myeloma cell lines. Thus, for the first time, our results suggest that patients with 1q-amplified myeloma might benefit from genetically tailored treatment involving cell cycle and ubiquitin inhibitors or a combination thereof. And inasmuch as 1q amplification is one of myeloma's few frequent alterations, this discovery has the exciting potential to affect change in a large number of patients. Disclosures Leleu: BMS: Honoraria, Other: steering committee membership ; Janssen: Honoraria, Other; Merk: Honoraria, Other: steering committee membership ; Takeda: Honoraria, Other: steering committee membership ; Amgen: Honoraria, Other: steering committee membership ; Sanofi: Honoraria, Other: steering committee membership steering committee membership ; Novartis: Honoraria, Other: steering committee membership ; Roche: Honoraria; Gilead: Honoraria; Incyte: Honoraria, Other: steering committee membership ; Karyopharm: Honoraria; Celgene: Honoraria, Other: steering committee membership . Ghobrial:Takeda: Consultancy; Janssen: Consultancy; Celgene: Consultancy; BMS: Consultancy.

Description: Introduction: Multiple Myeloma (MM) is a genetically complex and evolutionary process with well defined precursor states, which offer a unique opportunity to study the sequential evolution of the disease. A small number of detectable pre-malignant clones are present in early stage and continue to acquire more genomic abnormalities leading to overt disease. The interaction between cancer cells and their environment is reciprocal, multiple components in the tissue environment can influence cancer clonal evolution and cancer cells in turn can also remodel the microenvironment and further disseminate to spatially separated areas of BM. To accurately predict the course of disease with the presence of BM environment, we require methods to estimate clone-specific growth rates and define clones that have the propensity of dissemination. Methods: We developed a novel 'bone chip' MM metastatic xenograft model using fluorescent protein tagged 'rainbow' system which enables both molecular profiling and functional tracking of clonal dissemination of tumor cells by performing tumor-bearing bone chip implantation subcutaneously to SCID-beige mice (SCID-murine model). Rainbow MM cells with equal proportion of all 15 colors were injected into donor femurs and implanted into recipient mice. After paralysis, the mice were sacrificed and tumor cells were analyzed using flow cytometry and confocal microscopy. Tumor clones in the implanted bone chip (primary sites) and distant host BM (metastatic sites) were purified by sorting and underwent RNA sequencing. By intersecting differentially expressed genes, we identified a set of genes, the expression of which were altered during disease dissemination and designated this set of genes as 'metastatic signature'. In addition, we also performed genome-wide CRISPR/Cas9-mediated loss-of-function screen in a subcutaneous xenograft mouse model to investigate the essential drivers of tumor growth and metastasis in MM. The cell library infected with human sgRNA library was injected subcutaneously into SCID-Beige mice on both flanks. When metastasis was established, the fractions of each sgRNA of the primary and metastatic tumors were calculated to identify genes that facilitate tumor metastasis. Results: We found that the 15 rainbow subpopulations were present with equal distribution in the primary sites but not at the metastatic sites. Confocal imaging showed the difference in cluster structures between primary and metastatic tumors. Most of the clusters in the metastatic sites consisted of cells of single colors. RNA sequencing analysis of two human MM cell lines derived from SCID-murine model demonstrated a distinct gene expression profile of the metastatic tumors. Gene Set Enrichment Analysis of the metastatic signature in publicly available MM patient datasets (GSE6477 and GSE2658) demonstrated that this signature is significantly correlated with overall survival and with clinical progression from MGUS/smoldering MM to overt myeloma and relapsed disease. Through genome-wide CRISPR screening in vivo, we found that the gene targets of the most enriched sgRNAs in the BM samples were preferentially involved in important cellular processes, such as cell cycle regulation and several oncogenic signaling pathways. Additionally, many sgRNAs that remained the implanted sites until late stage were depleted during dissemination, indicating their targeted genes were important for progression. These depleted sgRNAs mainly targeted genes involved in mTORC1 and DNA repair pathways, many of which are regulated by MYC and cell cycle related targets of E2F transcription factors. By using a network-based inference of protein activity method, we chose 4 genes (HMGA1, KLF6, TRIM28 and PA2G4) and validated in SCID-murine model using CRISPR mediated loss-of-function screen which prioritized HMGA1 as the key regulator in MM dissemination. Conclusions: Here, we demonstrate that in vivo clonal evolution can be characterized using an in vivo model of MM. The data defines specific subclones that have a higher metastatic potential and are likely driver clones for tumor metastasis in MM. We then established a platform for future invivo CRISPR screens to investigate essential genes of response to targeted therapies and/or immunotherapies. Furthermore, a metastatic gene signature was identified and among these, HMGA1 was validated as potential regulator of MM metastasis. Disclosures Roccaro: AMGEN: Other: Advisory Board; GILEAD: Research Funding. Ghobrial:Takeda: Consultancy; Celgene: Consultancy; BMS: Consultancy; Janssen: Consultancy.

Description: Introduction Multiple myeloma (MM) is an incurable hematological malignancy characterized by the clonal expansion of malignant plasma cells (PCs) within the bone marrow. MM is genetically heterogeneous with aberrations including hyperdiploidy and chromosomal translocations commonly involving the immunoglobulin heavy chain (IgH) region. Many transcription factors can revoke their normal processes and act as oncogenes when they are brought under the control of IgH regulatory regions by a chromosomal translocation. Interferon Regulatory Factor 4 (IRF4) is a transcription factor which controls plasma cell differentiation and possesses many regulatory roles including interferon response, immune cell response, cell proliferation, apoptosis, and metabolism. IRF4 has proven to be a genetic vulnerability in MM as silencing studies in a large panel of MM cell lines with various genetic etiologies have demonstrated IRF4 expression is essential for MM cell survival. Standard of care treatments that indirectly suppress IRF4 including Proteasome inhibitors and Cereblon modulators have provided the greatest clinical outcomes for patients. However, like many other transcription factors, IRF4 has been notoriously difficult to target due to the protein's lack of amenable binding pockets favored for small molecule inhibitor development. Thus, identifying novel mechanisms and compounds to target IRF4 (directly or indirectly) can provide significant clinical impacts for MM patients. Methods To discover compounds capable of depleting IRF4 levels, we performed a high-throughput drug screen utilizing the Selleckchem Drug Repurposing Library on a widely accepted IRF4-dependent cell line. This library consists of over 2,000 diverse compounds that have well validated mechanisms of actions and have additionally passed clinical phase 1 safety trials for accelerated translational use. MM.1S cells were treated for 48 hours in duplicate (n = 2) with 10 μM compound. Following treatment, the cells were fixed, permeabilized, and stained for viability and IRF4 levels. IRF4 expression and viability was acquired by using flow cytometry, with high dose lenalidomide and shRNA for IRF4 as positive controls. Compounds that reduced IRF4 levels and cell viability across both experimental runs were ranked and selected with a cutoff of 40% as promising candidate compounds for further validation. Results Our drug screen results revealed 20 compounds (undisclosed) which met our cutoff of a decrease of IRF4 levels by 40% or greater. Ten hits were selected as having greater or equal to IRF4 depleting properties of lenalidomide and moved forward to be validated by western blot. Six drugs were shown to deplete IRF4 by western blot in MM.1S and KMS-18 cells at 10 μM doses. Interestingly, 4 out of the 10 hits all belong to same compound class that selectively bind to the same target receptor (undisclosed). Additional experiments confirmed these class of compounds deplete IRF4 levels in a dose dependent manner (EC50 = 1 μM). A time course revealed that IRF4 levels decrease shortly after the binding of these drugs to their widely reported target receptor, suggesting this is a selective drug/target receptor-mediated mechanism directly altering levels of IRF4. In vitro studies demonstrated the ability to both halt cell growth and decrease the viability of a panel of 8 MM cell lines, with IC50's ranging from 1.6 - 8.5 μM. Synergy studies with Lenolidomide and Bortezomib are underway to determine any synergistic combinations with standard of care therapies. In vivo studies and RNA-sequencing are also currently underway to determine the impact of these compounds on MM tumor growth and overall survival, as well as better define the mechanism of action driving this novel class of IRF4 targeting compounds. Conclusions Despite knowledge that IRF4 is a biologically potent target in MM there have been no extensive studies highlighting drugs capable of targeting this transcription factor and its oncogenic signaling network. This screen has revealed novel compounds, some of which are clinically used, that are capable of depleting a highly dependent gene in MM. Notably, these compounds are able to deplete IRF4 in a novel mechanism which is capable of affecting survival of MM cell lines that represent the heterogeneity of myeloma, and thus holds potential for significant clinical impact. Disclosures Ghobrial: Amgen: Consultancy; Janssen: Consultancy; Celgene: Consultancy; Takeda: Consultancy; Sanofi: Consultancy; BMS: Consultancy.

Description: Introduction: In multiple myeloma (MM), despite well-characterized precursor states such as monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM), there is a lack of sufficient biomarkers to predict mechanisms of disease progression. Most genomic analyses have sought biomarkers by study of the malignant plasma cells, however, cancers form a complex ecosystem with the immune and stromal microenvironment. Thus, to characterize the cellular composition and transcriptional programs of each component of the tumor and microenvironment at different stages of MM progression, we employed a single-cell RNA sequencing on a cohort of 22 patients and 9 healthy donors. Methods: We performed 10X droplet-based single-cell RNA sequencing using CD138-expressing plasma cells and microenvironmental populations isolated from bone marrow (BM) aspirates of patients with MGUS (n=6), low-risk SMM (n=3), high-risk SMM (n=13), newly diagnosed MM (n=8) and from 9 healthy donors (NBM). We collected a total of ~88.8K cells, comprising ~48K CD138+ cells (~36.4 from MM stages) and ~40.8K CD45+/CD138- cells (~30.8 from MM stages).Raw read data was processed using the Cell Ranger pipeline to obtain a gene-by-cell expression matrix, which was used to identify cell types and transcriptional programs by clustering and non-negative matrix factorization. Results: Expression profiles of plasma cells revealed clear tumor-specific differences including known oncogenic drivers in MM (MMSET/FGFR3, CCND1 and MAFB) as well as Lysosome-associated Membrane Protein 5 (LAMP5),Histone Cluster 1 H1 Family Member C (HIST1H1C) and Amphiregulin (AREG) distinguishing them from healthy plasma cells. We identified a subset of cycling plasma cells, observing a range of proliferative activity of the malignant fraction. Furthermore, our approach allowed a unique head-to-head comparison of gene expression changes in normal and malignant plasma cells in the MGUS and SMM patients within an individual, excluding inter-individual variation. We were able to discriminate malignant from non-malignant plasma cells and identify transcriptional alterations including known drivers, genes related to immune modulation (NKBIA) or controlling transcription and differentiation (EID1).Some alterations were patient-specific, while others, such as MHC I overexpression and CD27 loss, were recurrently observed across subsets of the cohort. Analysis of BM microenvironment in several stages of MM progression demonstrated a striking shift in the composition of immune cells with significant infiltration of natural killer cells, non-classical monocytes/macrophages, and T cells, enriched even in the earliest stages of the disease. Further investigation revealed significant upregulation of HLA expression at the mRNA level in CD14+ monocytes/macrophages. Intriguingly, comparison of healthy and patient samples by CyTOF showed downregulation of surface MHC II representation in the corresponding cell type, and moreover, co-culture with MM cell lines induced a sharp decrease of extracellular MHC II. This provided strong evidence for compromised antigen presentation by macrophages in the disease setting, hinting at a mechanism of immune evasion. Additionally, expression signatures in cytotoxic T-cells indicated a substantial skewing towards either granzyme B/H- or granzyme K-expressing memory cell-like transcriptional program. In a subgroup of patients, we found a strong simultaneous enrichment of the anti-viral/anti-bacterial gene expression signature for interferon type-1 activated genes in CD14+ monocytes/macrophages and T cells. Together, our results provide a comprehensive view at the complex interplay of the immune and malignant cells in different stages of the disease. We, for the first time, demonstrate the immune response beginning in premalignant conditions to be heterogeneous, including compromised antigen presentation as well as alterations in cellular composition and signaling. Consideration of the type of immunological response may prove valuable in determination of progression risk, as well as open up potential strategies for therapy. Disclosures Bustoros: Dava Oncology: Honoraria. Ghobrial:Celgene: Consultancy; Janssen: Consultancy; BMS: Consultancy; Takeda: Consultancy.

Description: Introduction. Multiple myeloma (MM) is a complex and heterogeneous malignancy of plasma cells that has two precursor states: monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). MGUS and SMM are asymptomatic states that eventually give rise to overt MM, with some patients progressing, while others do not. Recent studies in MM pathobiology have highlighted epigenetic alterations that contribute to the onset, progression and heterogeneity of MM. Global hypomethylation of DNA, including tumor suppressor genes, and hypermethylation of B-cell specific enhancers, abnormal histone methylation patterns due to the overexpression of histone methyltransferases such as MMSET, and deregulation of non-coding RNAs along with mutations in different classes of chromatin modulators underline a potential for epigenetic biomarkers in disease prognosis and treatment. This study aimed to define epigenetic pathways that lead to the dynamic regulation of gene expression in MM pathogenesis. Methods. We performed ATAC-seq (Assay for Transposase-Accessible Chromatin using sequencing) and RNA-seq on 10 MM cell lines and CD138+ plasma cells isolated from bone marrow aspirates of 3 healthy donors, 9 SMM, 8 newly diagnosed MM (NDMM) and 9 relapsed (RRMM) patients. ATAC-seq reads were trimmed of adapters, aligned to hg19 using bowtie2, and filtered for mapping quality 〉=Q30 using the ENCODE ATAC-seq pipeline. Reads mapping to promoter regions, defined as -400 to +250 bases from a refseq transcription start site, were counted using bedtools for each sample. Promoter read counts were then normalized by the total number of reads in promoters in the sample, scaled to 1 million total reads, and converted to log10(x+1) space. Results. To characterize the epigenetic contribution to disease progression in MM, we first identified accessible promoter regions in normal plasma cells (NPC), SMM, NDMM and RRMM patients and found regions displaying differential accessibility in MM progression. Next, we intersected the list of differential accessible regions (DARs) with matched transcriptome data and observed two main clusters: genes with unaltered transcription profiles and genes in which the dynamics of open chromatin regions (OCRs) correlated with gene expression. Transcriptomic analysis revealed that a large portion of the differentially expressed (DE) genes in SMM remain DE in NDMM as compared to NPCs (882 genes out of 1642 and 1150 DE genes in SMM and NDMM, respectively). Those genes were significantly enriched for pathways like epithelial mesenchymal transition, cell cycle checkpoints and mitosis, KRAS signaling and interleukin-JAK-STAT pathways. To investigate the genes that behaved differently among the stages of disease, we looked at differential accessibility and expression in NDMM and SMM samples, and integrated them with Whole-Genome Bisulfite-Sequencing and 450K DNA-methylation data from MM patients and healthy donors (BLUEPRINT). This analysis led to the identification of novel genes in MM progression, such as the transcriptional repressor ZNF254 and IRAK3, a negative regulator of the TLR/IL1R signaling pathway. Although gene expression data for these genes showed comparable mRNA levels in SMM and NPCs, followed by a significant decrease in NDMM/ RRMM, ATAC-seq revealed a striking drop in promoter accessibility in SMM, NDMM and RRMM cases. Comparison of ATAC-seq peaks to DNA methylation and ChIP-seq data revealed that the altered OCR of IRAK3 is actually hypermethylated in MM patients and marked by H3K4me3, a marker of active promoters, in MM cell lines. Hypermethylation of IRAK3 has been described in hepatocellular carcinoma, where it is associated with poor prognosis. Together, our data suggest that the identified IRAK3 OCR may act as a bivalent domain that loses accessibility in the precursor states and gains DNA methylation in MM progression. Hence, IRAK3 methylation could be a novel prognostic marker in MM. Conclusion. We have generated a global epigenetic map of primary tumors from patients at the smoldering, newly diagnosed and relapsed/refractory stage of multiple myeloma. Integrative analysis of ATAC-seq data with DNA methylome, transcriptome and whole-genome map of active and repressive histone marks in our study led to the identification of IRAK3 as a novel epigenetic biomarker of disease progression. Disclosures Licht: Celgene: Research Funding. Ghobrial:Takeda: Consultancy; BMS: Consultancy; Celgene: Consultancy; Janssen: Consultancy.

Description: Introduction: Multiple Myeloma (MM) is an incurable malignancy characterized by the proliferation of clonal plasma cells in the bone marrow (BM). MM almost always progresses from the precursor states of monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM), which indicates the presence of a gradual clonal evolution underlying progression from the original stages of tumor development to the time of clinical presentation. Clonal heterogeneity adds another layer of complexity to that, by introducing interclonal competition in the context of disease progression or therapeutic bottlenecks. Here we developed a mouse model to investigate the impact of multiple clonal mutations on tumor development, as well as the competitive expansion of individual clones. Methods: Primary mouse MM Vk*Myc cells stably expressing Cas9 were infected with validated sgRNAs to knockout (KO) genes of interest (P53, Cyld, Rb1, Dis3, Prdm1, Traf3 and Fam46c) that are significantly mutated in human MM. KO cells were mixed at a 1:1 ratio with control cells infected with control sgRNA and injected intravenously into 8-week-old RAG2 KO mice. Vk*Myc cells were then isolated from bone marrow and spleen through CD138 positive selection, followed by genomic DNA extraction and NGS sequencing to understand the dynamic changes in abundance of mutants from injection to early and late timepoints. Results: In vitro, most knockout Vk*Myc cells had a similar proliferation rate to control cells with the exception of P53 and Rb1 knockout cells, which grew faster as expected; both P53 and RB1 are known cell cycle regulators. However, when co-injected into RAG2 KO mice (Vk*Myc cells constructed with Cas9 do not engraft in C57BL/6 mice), although P53 and Rb1 knockout cells remained the strongest competitors, occupying the majority of the tumor, most KO cells exhibited significantly enhanced proliferation over control cells. These results indicate that certain mutations only become advantageous in the context of the tumor microenvironment, while mutations that directly affect the tumor cell's proliferation rate give rise to more flexible, potent clones. To better understand these differences, we took advantage of the CRISPR-induced heterogeneous pool of genomic edits per gene, and looked at clonal abundancy rates within each knockout population separately. Interestingly, we found mutants with certain insertions/deletions grew faster than others and were overrepresented at the late stage of disease, even when they were generated from the same double-stranded break. Although it is well established that mutations in different regions of the same gene might have different effects, these results indicate that different mutations in the exact same spot can give rise to clones of variable potency and beg the question of whether mutation sequence is as important as mutation hotspot. Conclusion: We established a mouse model to study clonal competition in vivo, utilizing the CRISPR-Cas9 genome editing toolset. Through our model, we were able to witness a range of competitive potential among genes that are significantly mutated in multiple myeloma, with P53- and RB1-mutants as the strongest competitors. Furthermore, we observed that competitive potential can be conditional, with certain mutants conferring fitness advantage only in the context of tumor microenvironment. Adding another layer of complexity to differential fitness, we found that different mutations in the same spot of the same gene give rise to clones of varied potency, implicating mutation sequence as a novel fitness variable. In this study, we thus demonstrate that mutational candidates can be prioritized based on competitive potential, a process of the utmost importance given multiple myeloma's marked genetic heterogeneity. Disclosures Ghobrial: Celgene: Consultancy; Takeda: Consultancy; Janssen: Consultancy; BMS: Consultancy.

Description: Background. Waldenström macroglobulinemia (WM) is a low-grade non-Hodgkin's lymphoplasmacytic lymphoma associated with overproduction of monoclonal IgM protein. It is preceded by an asymptomatic stage, called Smoldering Waldenström Macroglobulinemia (SWM), associated with a high risk of progression to overt disease. Current understanding of progression risk in SWM is based on a few small studies, and it is still unclear how to distinguish the asymptomatic patients who will progress from those who will not. Patients and Methods. We obtained clinical data of all WM patients who had been diagnosed and followed up at Dana-Farber Cancer Institute from 1982 to the end of 2014. Only patients with asymptomatic disease at the time of diagnosis were included in this study to identify risk factors for disease progression. Patients who received chemotherapy for a second cancer, before or after asymptomatic WM diagnosis (n =24), were excluded as chemotherapy might have affected the natural course of disease. Patients who progressed to or were diagnosed later with other types of B-cell lymphoproliferative disorders or Amyloidosis (n =71) and patients with myeloproliferative disorders or thalassemia (n = 4) were all excluded from our cohort. Furthermore, we excluded patients with no morphologic evidence of lymphoplasmacytic infiltration in the bone marrow biopsy (n =37), those without a bone marrow biopsy done at time of diagnosis (n =21), and those who were treated for peripheral neuropathy alone (n =13). Progression was defined based on the Consensus Panel recommendations of the Second International Workshop on WM. Survival analysis was performed using the Kaplan-Meier method and differences between the curves were tested by log-rank test. Effects of potential risk factors on progression rates was examined using Cox proportional-hazards models, with hazard ratios (HRs) and associated 95% confidence intervals (CIs). Results. A total of 439 patients were included in the study. During the 35-year study period and a median follow up of 7.8 years, 317 patients (72.2%) progressed to symptomatic WM. The median time to progression was 3.9 (95% CI 3.2-4.6) years. In the multivariate analysis, IgM ≥ 4,500 mg/dL (adjusted HR 4.65; 95% CI 2.52-8.58; p 〈 0.001), BM lymphoplasmacytic infiltration ≥ 70% (adjusted HR 2.56; 95% CI 1.69-3.87; p 〈 0.001), β2-microglobulin ≥ 4.0 mg/dL (adjusted HR 2.31; 95% CI 1.19-4.49; p = 0.014), and albumin 〈 3.5 g/dL (adjusted HR 2.78; 95% CI 1.52-5.09; p = 0.001) were all identified as independent predictors of disease progression, suggesting those thresholds could be clinically useful for determining high-risk patients. On the other hand, given the continuous nature of these variables, we built a proportional hazards model based on four variables (Bone marrow infiltration percentage, serum IgM, albumin, β2-microglobulin). The model divided the cohort into 3 distinct risk groups: a high-risk group with a median time to progression (TTP) of 1.9 years (95% CI 1.64-2.13), an intermediate-risk group with median TTP of 4.6 years (95% CI 4.31-5.15), and a low-risk group with a median TTP of 8.1 years (95% CI 7.33-8.13)(See Figure). To enhance its clinical applicability, we made the model available as user interface through a webpage and mobile application, where clinicians can enter an individual SWM patient's lab values and get information regarding their risk group and estimated individual risk of progression to symptomatic WM. Conclusion. We have assembled the largest cohort of SWM patients to date, which allowed us to identify four independent predictors of progression to overt disease: BM infiltration ≥ 70%, IgM ≥ 4,500 mg/dL, b2m ≥ 4.0 mg/dL and albumin 〈 3.5 g/dL. Using those variables in a proportional hazards model, we developed a robust, flexible classification system based on risk of progression to symptomatic WM. This system stratifies SWM patients into low-, intermediate- and high-risk groups and thus has the potential to inform patient monitoring and care. Most importantly, it can help identify high-risk patients who might benefit from early intervention in this rare malignancy. Figure 1. Figure 1. Disclosures Bustoros: Dava Oncology: Honoraria. Kastritis:Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Prothena: 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. Soiffer:Jazz Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees. Treon:Johnson & Johnson: Consultancy; Janssen: Consultancy, Other: Travel, Accommodations, Expenses; BMS: Research Funding; Pharmacyclics: Consultancy, Other: Travel, Accommodations, Expenses, Research Funding. Castillo:Genentech: Consultancy; Millennium: Research Funding; Abbvie: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Beigene: Consultancy, Research Funding; Pharmacyclics: Consultancy, Research Funding. Dimopoulos:Amgen: Honoraria; Janssen: Honoraria; Takeda: Honoraria; Celgene: Honoraria; Bristol-Myers Squibb: Honoraria. Ghobrial:BMS: Consultancy; Janssen: Consultancy; Takeda: Consultancy; Celgene: Consultancy.

Description: Introduction: Multiple Myeloma (MM) is a clonal plasma cell malignancy, accounting for 10% of all hematological malignancies. Genetic analyses of large populations revealed that blood-specific somatic mutations in hematopoietic stem cells (HSCs) are commonly acquired during aging, a new entity labeled: clonal hematopoiesis of indeterminate potential (CHIP). We sought to determine the role of CHIP on survival of MM patients, specifically those receiving immunomodulator (IMiD) maintenance (Lenalidomide or Thalidomide) post autologous stem cell transplant (ASCT). Methods: We collected the cryopreserved, growth factor mobilized peripheral blood of 629 MM patients who underwent ASCT between 2003 and 2011 at the Dana-Farber Cancer Institute (DFCI). Then, we performed targeted next-generation sequencing using a 224-gene panel at a mean depth of coverage of 978X and ultra-low pass whole-genome sequencing at 0.1X to account for tumor contamination. We downloaded (dbGAP # phs000748.v6.p4) the whole-exome sequencing (WES) data of a cohort of 1144 newly diagnosed, untreated MM patients from the Multiple Myeloma Research Foundation (MMRF) Clinical Outcomes in MM to Personal Assessment of Genetic Profile (CoMMpass, NCT0145429) study (MMRC) and the WES data of a cohort of 205 newly diagnosed, untreated MM patients from the Broad Institute dataset. We analyzed their peripheral blood (average coverage of 108X) and tumor (average coverage of 107X) data separately, looking for the same CHIP genes included in our target bait panel. Results: The DFCI cohort had a median age of 58 years [range, 24-83] at time of ASCT and median follow up post ASCT of 8 years [range, 0.1-14.5]. 204 patients (32%) in the DFCI cohort had CHIP at time of ASCT. The most commonly detected mutated genes were DNMT3A, TET2, TP53, ASXL1 and PPM1D. 24 patients (3.8%) developed a second hematological malignancy at a median of 4 years [range, 1-10] post ASCT, half of whom had CHIP. Around 48% of the DFCI cohort received IMiDs as part of induction therapy. Different induction regimens had no effect on CHIP prevalence at time of ASCT. Around 56% of the DFCI cohort received IMiD maintenance, 22% of which received maintenance for at least 3 years [range, 0.06-12.8]. Among those who did not receive IMiD maintenance, patients with CHIP had worse progression free survival (PFS) (p-value 〈 0.001) and overall survival (OS) (p-value = 0.005). In patients receiving IMiD maintenance, having CHIP had no effect on PFS or OS. On the other hand, the MMRF cohort had a median age of 63 years [range, 27-93] and median follow up of 3.03 years [range, 0-5.9] from time of diagnosis. Around 52% of that cohort underwent ASCT and around 76% of those received IMiD maintenance with a median follow up of 2.7 years [range, 0-5.5] from time of ASCT. Furthermore, 200 patients of the MMRF cohort have follow-up samples of both tumor and peripheral blood that had targeted sequencing done by a 562-gene panel that included our genes of interest. Similarly, when studying the genomic results of 139 out of 1144 MMRF patients, as well as the 205 patients from the Broad Institute dataset, we detected CHIP in 25.6% of them and the top 5 most commonly mutated genes were similar to those of our cohort. Conclusion: CHIP is a common entity among MM patients, reaching a prevalence of up to 32%, that predicts a worse PFS and OS in those who do not receive IMiD maintenance therapy post ASCT. As expected, IMiD maintenance improves outcome in MM patients, with and without CHIP. In patients with CHIP, the use of IMiDs abrogated the deleterious effect imposed by CHIP to a point that outcome is identical to that of patients without CHIP. Figure Figure. Disclosures Bustoros: Dava Oncology: Honoraria. Munshi:OncoPep: Other: Board of director. Anderson:Gilead: Membership on an entity's Board of Directors or advisory committees; OncoPep: Equity Ownership, Other: Scientific founder; Celgene: Consultancy; C4 Therapeutics: Equity Ownership, Other: Scientific founder; Bristol Myers Squibb: Consultancy; Millennium Takeda: Consultancy. Richardson:Oncopeptides: Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: 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; Amgen: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding. Soiffer:Jazz Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees. Ghobrial:Celgene: Consultancy; Janssen: Consultancy; BMS: Consultancy; Takeda: Consultancy.