ALBERT

Description: Introduction: Recent data show that Multiple Myeloma (MM) always progresses from a precursor state (monoclonal gammopathy of undetermined significance [MGUS]/smoldering multiple myeloma [SMM]) to overt MM indicating that there is continuous dissemination/clonal evolution of tumor cells from the original stages of tumor development to the time of clinical presentation. A major challenge in understanding the progression and metastasis of MM is to distinguish alterations driving the tumor growth and evolution from passenger mutations. Genetic screens are powerful tools for assaying phenotypes and identifying causal genes in various hallmarks of cancer progression. The clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated (Cas) system has emerged as a powerful technology to efficiently and simultaneously perform genome editing of multiple genes. Here we report a genome-wide CRISPR/Cas9-mediated loss of function screen in a xenograft mouse model to investigate the essential drivers of tumor growth and metastasis in MM. Methods: Lentiviral particles from 2 subpools of a human sgRNA library (Avana), each containing 1 sgRNA per gene were introduced into MM1.S (Cas9+/GFP+/Luc+) cell line with the pre-determined amount of virus to achieve 30-50% infection efficiency, corresponding to a multiplicity of infection (MOI) of ~0.5-1. Cells were selected with puromycin for 5-7 days following infection to remove uninfected cells. Selected cells were injected subcutaneously into SCID-Beige mice on both flanks. Genomic DNA from pre-transplantation cells, early primary tumors (~3 weeks post tumor cell injection), late stage primary tumors and metastatic bone marrow samples were extracted. gDNA was amplified following adaptor ligation and barcoding of the samples and PCR products were subsequently sequenced on a HiSeq2000 (Illumina). Results: To investigate the sgRNA library dynamics in different sample types (pre-transplantation cells, early primary tumor, late primary tumor, and bone marrow metastasis), we compared the overall distributions of sgRNAs from all sequenced samples. The early tumor sample replicates of both subpools on average retained 77.3% and 94.7% of the sgRNAs found in the pre-transplanted cell populations, while the late primary tumors retained 59.4% and 65.6% of the sgRNAs respectively, compared to early tumors. Interestingly, only a small fraction of sgRNAs (1.1% and 3.4% of sgRNAs in the pre-transplantation cells, 10.7% and 7.2% of sgRNAs in the late primary tumors for the 2 subpools respectively) were detected in the metastatic bone marrow samples. Using gene set enrichment analysis (GSEA), we found that the gene targets of the most enriched sgRNAs in the bone marrow samples were preferentially involved in important cellular processes, such as cell cycle regulation, protein translation, and several signaling pathways. Additionally we compared sgRNAs present in early primary tumor versus pre-transplantation cells and late primary tumor and found that many sgRNAs were depleted during tumor progression, indicating that their target genes were important for progression. These depleted sgRNAs in both stages 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. Conclusion: We established a platform for future in vivo Cas9 screens using the genome-wide CRISPR screening libraries to explore potential new targets in regulating tumor dissemination, colonization and metastasis in MM. In addition, this in vivo screening could potentially be used to investigate essential genes of response to targeted therapies or/and immunotherapies. Thus, CRISPR/Cas9-based in vivo screening is a powerful tool for functional genomics discoveries. Disclosures Roccaro: Takeda Pharmaceutical Company Limited: Honoraria. Ghobrial:BMS: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Novartis: Honoraria; Takeda: Honoraria; Noxxon: Honoraria; Amgen: Honoraria.

Description: Background . Cell-free DNA (cfDNA) sequencing enables serial temporal sampling, which offers the possibility of following the dynamics of biomarkers and clonal evolution in Multiple Myeloma (MM) over time. The use of cfDNA in clinical practice as a molecular biomarker and for monitoring response/resistance is dependent on a comprehensive profile of matched cfDNA and tumor DNA (tDNA) samples. Here we performed Ultra-Low Pass Whole Genome Sequencing (ULP-WGS) followed by whole-exome sequencing (WES) and targeted deep sequencing of matched cfDNA/tDNA samples from MM patients. Methods. We performed next generation sequencing of matched cfDNA/tDNA samples for 63 patients with newly diagnosed or relapsed MM, SMM, or MGUS. Libraries were constructed using the Kappa Hyper kit and sequenced by ultra-low-pass whole-genome sequencing (ULP-WGS, 0.1x coverage) to quantify tumor fraction within cfDNA. WES was performed on 30 matched samples cfDNA/tDNA/germline DNA from 10 patients with more than 5% of tumor fraction. Libraries were hybridized to the Nextera Rapid Capture Exome kit (Illumina) and then sequenced on HiSeq 4000 (Illumina). Targeted deep sequencing was performed on 32 matched cfDNA/tDNA samples from 16 patients using the HaloPlex HS technology (Agilent), allowing for molecular barcoding. Libraries were constructed according to the manufacturer's instructions and sequenced on HiSeq 2500 (Illumina). Sequencing data were analyzed using the Firehose pipelines, including MuTect, ABSOLUTE, ReCapSeg, GISTIC and MutSig. Results. We first used a cost-effective approach to establish the tumor content of cfDNA in a large-scale manner by ULP-WGS. Among 63 tested samples (53 MM, 6 SMM and 4 MGUS patient samples), the tumor fraction within cfDNA ranged from 0 to 81% with a mean of 10%. About 43% of these samples had tumor fraction greater than 5% within cfDNA. To assess whether cfDNA can capture the genetic diversity of MM and inform clinical management, we performed WES of matched cfDNA/tDNA/germline DNA samples for 10 patients (mean target coverage 194x). Copy number alterations (CNAs) assessed by WES (ReCapSeg) were consistent between cfDNA and tumor DNA. Similarly, focal CNAs assessed by GISTIC were consistent between tDNA and cfDNA. We then examined the overlap of somatic single nucleotide variants (SSNVs) between WES of cfDNA and matched tDNA. We found, on average, 100% of the clonal and 96% of the subclonal (range 54-100%) SSNVs that were detected in the tumor were confirmed to be present in cfDNA. Similarly, for mutations detected in the cfDNA, we found, on average, 100% of the clonal and 99% of the subclonal (range 98-100%) SSNVs were confirmed in the tumor. To assess whether targeted deep sequencing of cfDNA could be a good proxy for tumor biopsy we used a targeted deep sequencing approach of known MM driver genes. Libraries were prepared using unique molecular barcodes to avoid duplication rates, for 32 matched cfDNA/tDNA samples from 16 patients with MM. The mean target coverage was 596x. We found similar frequencies of altered MM driver genes in both cfDNA and tDNA, including KRAS, NRAS, and TP53, indicating that cfDNA can be used for precision medicine. Conclusions. Our study demonstrates that both WES and targeted deep sequencing of cfDNA are consistently representative of tumor DNA alterations in terms of CNAs, focal CNAs and SSNVs. This approach could therefore be used to longitudinally follow clonal evolution across the course of the disease and precision medicine in patients with MM. Disclosures Palumbo: Takeda: Employment, Honoraria; Janssen Cilag: Honoraria. Kumar:Noxxon Pharma: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Millennium: Consultancy, Research Funding; Skyline: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Research Funding; Kesios: Consultancy; Glycomimetics: Consultancy; BMS: Consultancy; Array BioPharma: Consultancy, Research Funding; Sanofi: Consultancy, Research Funding; AbbVie: Research Funding; Onyx: Consultancy, Research Funding. Roccaro:Takeda Pharmaceutical Company Limited: Honoraria. Facon:Amgen: Consultancy, Speakers Bureau; Novartis: Consultancy; Janssen: Consultancy, Speakers Bureau; Bristol: Consultancy; Millenium/Takeda: Consultancy; Celgene: Consultancy, Speakers Bureau; Karyopharm: Consultancy. Ghobrial:Celgene: Honoraria, Research Funding; BMS: Honoraria, Research Funding; Noxxon: Honoraria; Novartis: Honoraria; Takeda: Honoraria; Amgen: Honoraria.

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: Background Increased bone marrow (BM) microvessel density (MVD) has been associated with progression of multiple myeloma (MM). Endothelial progenitor cells (EPCs) are circulating precursors with the capacity to differentiate into endothelial lineage cells through a process known as “vasculogenesis” thus contributing to vessel formation. The role of these cells in MM pathogenesis remains largely unexplored. We studied EPC BM mobilization in several MM mouse models (5TGM1, MM1S and Vk*MYC) during disease progression and quantified these cells in peripheral blood (PB) from patients at different stages of MM disease. Methods EPCs were quantified using flow cytometry (circulating CD34+ VEGFR2+ cells) in PB from mice injected intravenously (i.v.) with either murine MM 5TGM1-turboRFP+ cells or human MM1S-GFP+/luc+ cells. Circulating EPCs were also enumerated in PB from mice previously transplanted with BM from SCID/GFP mice (GFP-BM SCID mice) after the i.v. injection of 5TGM1-turboRFP+ cells as circulating GFP+ CD34+ VEGFR2+ cells. Peripheral blood samples were obtained from transgenic Vk*MYC mice affected by smoldering (s) MM (M-spike less than 6% of total protein on SPEP); active (a) MM (M-spike higher than 6% of total protein on SPEP); or healthy syngeneic mice, and examined for EPC levels through flow-cytometry (circulating CD34+ VEGFR2+ cells). Finally, the level of EPCs was evaluated in PB from healthy controls, smoldering (s) MM patients, in remission (r) and active (a) MM patients by using flow-cytometry (CD34+VEGFR2+ cells) and in vitro by performing endothelial colony forming assays [endothelial cells colony forming units (EC-CFUs) and endothelial colony forming cells (ECFCs)]. Results An increase in EPCs was evident starting one week after i.v. injection of tumor cells in both murine 5TGM1 and human MM1S orthotopic models. Compared to control mice, this EPC increase became significant (P

Description: Key Points LAPTM5 c403t and HCLS1g496a are potentially novel contributors for the genetic predisposition to familial WM. LAPTM5 c403t and HCLS1g496a represent possible candidates for screening in familial WM.

Description: Key Points Knockdown of the sialyl-transferase, ST3GAL6, in MM inhibits in vivo homing and prolongs survival in xenograft mice. In MM patients, high expression of ST3GAL6 is associated with inferior overall survival.

Description: Platelets play important roles in both physiological and pathological conditions. For instance, platelets may facilitate cancer metastasis by protecting circulating tumor cells (CTCs) from shear stress and immunological assault during their intravascular phase and by supporting CTCs extravasation. Moreover, soluble factors released from activated platelets enhance proliferation and migration of endothelial cells, thereby promoting tumor angiogenesis. Correlations between increased platelet counts and shorter survival time have been described for many solid tumors. However, the role of platelets/megakaryocytes (MKs) in regulating tumor progression and dissemination in Multiple myeloma (MM) has not been previously examined. We measured the platelet aggregation-inducing abilities of MM cell lines and found MM cell lines (MM.1S, OPM-2, KMS-11, U266, and H929) induced platelet aggregation. This was not observed using leukemia cells (K562) and primary healthy donor-derived peripheral blood mononuclear cells. We next investigated whether platelets interact with MM cells within the bone marrow (BM) niche, in vivo. In order to identify platelets/MKs, femurs were harvested from MM.1S GFP+-harboring mice and stained with a DyLight649-conjugated anti-GPIb-beta antibody. The femurs were rendered transparent by the CUBIC method as previously described (Cell 159, 911-24 (2014)) and examined using confocal microscopy; and found that platelets/MKs co-localized within MM.1S GFP+-infiltrated BM niches. Further confirmation of the co-localization of MM cells and MKs was performed using immunohistochemistry (CD138+ and GPIb-alpha+). We next investigated the effect of platelets on MM cell proliferation: MM cells, where co-cultured with platelets and found that platelets enhanced the proliferation rate of MM cells in a platelet number-dependent manner, as shown by using BrdU (p

Description: Background. LIN28B regulates developmental processes and cellular reprogramming by suppressing let-7 microRNAs (miRNAs). A role for LIN28B has been reported in cancers, however the LIN28B/let-7 axis has not been studied in multiple myeloma (MM). Methods. LIN28B level expression in MM patients was studied using previously published gene expression profiling (GEP) datasets. Knockdown (KD) of LIN28B was performed on MM cell lines (U266, MOLP-8) using 2 shRNA and validated using 2 sgRNA by CRISPR knockout (KO). Downstream regulations were assessed by qRT-PCR and western blots, as well as RNA sequencing. For RNA sequencing, control and Lin28B CRISPR cells were used for library preparation (NEBNext kit) and sequencing on a HiSeq 2000. Proliferation of KD and KO cells were evaluated in vitro and in vivo in a xenograft mouse model. An LNA-GapmeR technology was used to develop a let-7 mimic in vivo in SCID mice. Findings. Two independent GEP datasets (GSE16558; GSE2658) were analyzed for LIN28B expression, showing a significantly higher level in MM patients compared to healthy controls. In addition, high LIN28B levels correlated with a shorter overall survival (p = 0.0226), along with an enrichment of let-7 target genes by Gene Set Enrichment Analyses (GSEA). LIN28B KD cells had a significantly increased expression level of let-7 family members and were associated with down-regulation of let-7 target genes Myc and Ras at the protein level. We further confirmed downstream regulation of MYC and RAS in a LIN28B CRISPR KO model in MM cells (MOPL-8). We next validated the role of LIN28B in MM in vivo by using a xenograft tumor model showing a decreased tumor burden in LIN28B KD mice compared to scramble control (p =0.0045). In addition, we performed a RNA sequencing from the CRISPR LIN28B KO and control cells and observed a central role by GSEA for both MYC and E2F cell cycle pathways in LIN28B-engineered cells. LIN28B activity in regulating MYC and cell proliferation was further defined to be dependent on let-7 by performing a rescue experiment in MM1S cells. Moreover, we explored the possibility to therapeutically regulate MYC expression through let-7 with an LNA-GapmeR containing a let-7b mimic, in vivo, and showed that high levels of let-7 expression represses tumor growth in SCID mice by regulating MYC expression compared to control GapmeR treated mice (p = 0.0026). Conclusions. These findings reveal the essential role of LIN28B/let-7 in regulating two essential oncogenic pathways in MM, MYC and RAS. Interference with this pathway may represent an efficient option for targeting MYC in cancer. Disclosures No relevant conflicts of interest to declare.

Description: Background Quantification of monoclonal immunoglobulins in serum is the foundation of IMWG multiple myeloma (MM) response criteria for patients with intact immunoglobulin disease. However, electrophoretic methods for quantifying M-Ig in serum are subject to well documented limitations including inaccuracy at high concentrations (due to dye saturation) and poor sensitivity at low concentrations. Moreover, the methods require skilled interpretation and can be time consuming; however their clinical utility is well established. Recently heavy/light chain (HLC) assays, which quantify kappa and lambda isotypes of intact immunoglobulins have become available. Here we compare responses assigned by the immunoassays to IMWG criteria and evaluate the clinical impact of discordance. Methods Sequential sera, from enrolment to progression, were available for 107 MM patients (59 IgGκ, 29 IgGλ, 12 IgAκ, 7 IgAλ) enrolled onto either the IFM 2009-02 end-stage relapsed or refractory MM or the IFM 2010-02 in del17p and t(4;14) relapsed or refractory MM trials. The inclusion criterion was a measurable intact immunoglobulin MM according to IMWG criteria (M spike ≥ 10 g/L), using serum and/or urine protein electrophoresis. IgA HLC (IgAκ and IgAλ) and IgG HLC (IgGκ and IgGλ) analysis was compared to historic SPEP, IFE, UPEP, uIFE and serum free light chain (sFLC) results (measured using polyclonal antisera based assays). Responses were assigned at approximately 90 days (median 90, range 61-107 days) and at maximum response (if different) according to IMWG criteria using changes in monoclonal protein concentrations measured either by SPEP or dHLC (clonal - non clonal). Complete response was assigned either by the absence of monoclonal protein on IFE or by a normal HLC ratio. Results At the time of enrolment, 87/88 IgG and 17/17 IgA patients had abnormal HLC ratios which were concordant with IFE results, and quantification of the monoclonal protein by dHLC was similar (median (range): 29.2 (2.5-77.5) g/L) and SPEP (30.7 (1.8-66.9) g/L). After 3 months of treatment, responses assigned by HLC/FLC assays showed near-perfect agreement with responses assigned using SPEP and IFE (Weighted Kappa 〉0.81, p