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Abstract Multiple myeloma (MM) develops from well-defined precursor stages; however, invasive bone marrow (BM) biopsy limits screening and monitoring strategies for patients. We enumerated circulating tumor cells (CTC) from 261 patients (84 monoclonal gammopathy of undetermined significance, 155 smoldering multiple myeloma, and 22 MM), with neoplastic cells detected in 84%. We developed a novel approach, MinimuMM-seq, which enables the detection of translocations and copy-number abnormalities through whole-genome sequencing of highly pure CTCs. Application to CTCs in a cohort of 51 patients, 24 with paired BM, was able to detect 100% of clinically reported BM biopsy events and could replace molecular cytogenetics for diagnostic yield and risk classification. Longitudinal sampling of CTCs in 8 patients revealed major clones could be tracked in the blood, with clonal evolution and shifting dynamics of subclones over time. Our findings provide proof of concept that CTC detection and genomic profiling could be used clinically for monitoring and managing disease in MM. Significance: In this study, we established an approach enabling the enumeration and sequencing of CTCs to replace standard molecular cytogenetics. CTCs harbored the same pathognomonic MM abnormalities as BM plasma cells. Longitudinal sampling of serial CTCs was able to track clonal dynamics over time and detect the emergence of high-risk genetic subclones.

Although caregivers of patients with multiple myeloma (MM) play a critical role in supporting their loved ones throughout the illness course, studies examining caregiver quality of life (QOL), psychological distress, and prognostic awareness are lacking. We conducted a cross-sectional, multisite study of patients undergoing treatment with MM and their caregivers. Eligible caregivers were enrolled to 1 of 3 cohorts based on lines of therapy. Caregivers completed validated questionnaires to assess their QOL, psychological distress, and perceptions of prognosis. We enrolled 127 caregivers of patients with MM (newly diagnosed [n = 43], 2-3 lines of therapy [n = 40], and ≥4 lines of therapy [n = 44]). Caregiver QOL and psychological distress did not differ by line of therapy. The rate of clinically significant anxiety, depression, and posttraumatic stress disorder symptoms were 44.1% (56/127), 15.8% (20/127), and 24.4% (31/127), respectively. When examined in dyads, caregivers reported higher rates of clinically significant anxiety (44.4% [55/124] vs 22.5% [28/124]) compared with patients with MM. Most caregivers (84.2%, 101/120) reported that the oncologist had informed them that the patient’s cancer was incurable; however, only 50.9% (58/114) and 53.6% (59/110) of caregivers acknowledged the patient’s cancer was terminal and incurable, respectively. Caregivers of patients undergoing treatment for MM experience substantial psychological distress across the disease continuum, particularly anxiety. The majority of caregivers of patients with MM report that knowing the patient’s prognosis is extremely important and report that the oncologist told them that the patient was incurable. Nevertheless, a significant portion of caregivers believe that the patient’s MM is curable.

MYCderegulation occurs in 67% of multiple myeloma (MM) cases and associates with progression and worse prognosis in MM. EnhancedMYCexpression is known to be driven by translocation or amplification events, but it only occurs in 40% of MM patients. Here, we describe a new mechanism ofMYCregulation, whereby epigenetic regulation ofMYCby increased accessibility of a cell-type specific enhancer leads to increasedMYCexpression. We found enhancer activity does not associate with enhancer hijacking events. We identified specific binding of c-MAF, IRF4, and SPIB transcription factors to the enhancer can activateMYC. In addition, we discovered focal amplification of this specific enhancer in approximately 4% of MM patients. Together, our findings define a new epigenetic mechanism ofMYCderegulation in MM beyond known translocations or amplifications and point to the importance of non-coding regulatory elements and their associated transcription factor networks as drivers of MM progression.

High expression ofMYCand its target genes define a subset of germinal center B-cell diffuse large B-cell lymphoma (GCB-DLBCL) associated with poor outcomes. Half of these high-grade cases show chromosomal rearrangements between theMYClocus and heterologous enhancer-bearing loci, while focal deletions of the adjacent non-coding genePVT1are enriched inMYC-intact cases. To identify genomic drivers ofMYCactivation, we used high-throughput CRISPR-interference (CRISPRi) profiling of candidate enhancers in theMYClocus and rearrangement partner loci in GCB-DLBCL cell lines and mantle cell lymphoma (MCL) comparators that lacked common rearrangements betweenMYCand immunoglobulin (Ig) loci. Rearrangements betweenMYCand non-Ig loci were associated with unique dependencies on specific enhancer subunits within those partner loci. Notably, fitness dependency on enhancer modules within theBCL6super-enhancer (BCL6-SE) cluster regulated by a transcription factor complex of MEF2B, POU2F2, and POU2AF1 was higher in cell lines bearing a recurrentMYC::BCL6-SE rearrangement. In contrast, GCB-DLBCL cell lines withoutMYCrearrangement were highly dependent on a previously uncharacterized 3’ enhancer within theMYClocus itself (GCBME-1), that is regulated in part by the same triad of factors. GCBME-1 is evolutionarily conserved and active in normal germinal center B cells in humans and mice, suggesting a key role in normal germinal center B cell biology. Finally, we show that thePVT1promoter limitsMYCactivation by either native or heterologous enhancers and demonstrate that this limitation is bypassed by 3’ rearrangements that removePVT1from its position inciswith the rearrangedMYCgene.Key pointsCRISPR-interference screens identify a conserved germinal center B cellMYCenhancer that is essential for GCB-DLBCL lackingMYCrearrangements.Functional profiling ofMYCpartner loci reveals principles ofMYCenhancer-hijacking activation by non-immunoglobulin rearrangements.

The bone marrow (BM) microenvironment actively promotes multiple myeloma pathogenesis, and therapies targeting both cancer cells and the niche are highly effective. We were interested in identifying novel signaling pathways supporting multiple myeloma–BM cross-talk. Mutations in the transmembrane receptor Roundabout 1 (ROBO1) were recently identified in patients with multiple myeloma; however, their functional consequences are uncertain. Through protein structure–function studies, we discovered that ROBO1 is necessary for multiple myeloma adhesion to BM stromal and endothelial cells and that ROBO1 knockout (KO) compromises BM homing and engraftment in a disseminated mouse model. ROBO1 KO significantly decreases multiple myeloma proliferation in vitro and intra- and extramedullary tumor growth in vivo. Mechanistically, the ROBO1 C-terminus is cleaved in a ligand-independent fashion and is sufficient to promote multiple myeloma proliferation. Vice versa, mutants lacking the cytoplasmic domain, including the human-derived G674* truncation, act dominantly negative. Interactomic and RNA-sequencing studies suggest that ROBO1 may be involved in RNA processing, supporting further studies.Significance:ROBO1 is highly expressed in t(4;14) multiple myeloma and supports homing and dissemination to the BM niche. ROBO1 knockout causes reduced tumor growth in intramedullary and extramedullary myeloma animal models, while the ROBO1 C-terminus is cleaved in multiple fragments and it is necessary and sufficient to sustain myeloma proliferation.

Summary:The multiple myeloma treatment landscape has changed dramatically. This change, paralleled by an increase in scientific knowledge, has resulted in significant improvement in survival. However, heterogeneity remains in clinical outcomes, with a proportion of patients not benefiting from current approaches and continuing to have a poor prognosis. A significant proportion of the variability in outcome can be predicted on the basis of clinical and biochemical parameters and tumor-acquired genetic variants, allowing for risk stratification and a more personalized approach to therapy. This article discusses the principles that can enable the rational and effective development of therapeutic approaches for high-risk multiple myeloma.

Supplementary Figure 1 showing ROBO1 expression in primary MM cells versus normal donor bone marrow plasma cells via gene expression profiling and immunohistochemistry. Supplementary Figure 2 showing distribution of ROBO1 expression in the CoMMpass database; ROBO1 expression in CoMMpass database according to cytogenetics; ROBO1 expression in IFM170 database according to cytogenetics; ROBO1 expression in t(4;14) cell lines WT or KO for MMSET. Supplementary Figure 3 showing ROBO1 protein down regulation after ROBO1 knock down via shRNA. Supplementary Figure 4 showing effects of shRNA4- and shRNA-5 mediated ROBO1 KD on cell viability across a panel of MM cell lines with western confirming proper protein down regulation. Supplementary Figure 5 showing successful ROBO1 KO across several KMS11 monoclones, but not H929 monoclones. Supplementary Figure 6 showing expression of ROBO1 FL construct in polyclonally and monoclonally-transduced OPM2 cells. Supplementary Figure 7 showing expression of FL ROBO1 addback in distinct KMS11 ROBO1 KO monoclones. Supplementary Figure 8 showing radiographic images of WT versus KO ROBO1 mu-SCID mice. Supplementary Figure 9 showing adhesion defect in ROBO1 KO KMS11 cells that is fully rescued by FL ROBO1 addback. Supplementary Figure 10 showing Kaplan-Meier survival curve of disseminated mouse model injected with ROBO1 KO versus FL OPM2 cells. Supplementary Figure 11 showing cleaved cytosolic ROBO1 fragments in 293T cells transduced with FL ROBO1 tagged with C-LAP tag and in WT MM.1S cells. Supplementary Figure 12 showing interacting partners of ROBO1 cytosolic domain as deconvoluted via STRING database. Supplementary Figure 13 showing differentially expressed genes in ROBO1 WT versus KO plasmacytoma represented via Volcano plot. Supplementary Figure 14 showing gene set enrichment analysis of differentially expressed genes in ROBO1 WT versus KO plasmacytoma. Supplementary Figure 15 showing schema of patient-derived ROBO1 mutation and expression of G674 truncation in WT and KO ROBO1 OPM2 cells. Supplementary Table 1 showing clinical annotation of newly diagnosed MM parents used in Supplementary figure 1.

Supplementary Methods

Supplementary Table from Perspectives on the Risk-Stratified Treatment of Multiple Myeloma

Supplementary data contains additional Materials and Methods used to generate results presented only in the supplementary figures and tables, as well as more detailed bioinformatics methods. Figure S1 shows correlation between clinical measures of disease pathology, survival, and circulating tumor cells enumeration. Figure S2 shows characteristics of isolated circulating tumor cells from peripheral blood of precursor disease patients. Figure S3 shows cohort-level genomic characterization of tumor in MM precursor stages with CTCs. Figure S4 shows longitudinal and tissue-matched genomic characterization of driver mutations. Figure S5 shows comparison of mutational processes between BMPCs and CTCs assigned to most likely PCAWG composite reference signature. Table S1 shows clinical characteristics and sampling of participants in this study. Table S2 shows whole-genome sequencing coverage and library metrics. Table S3 shows clinical BM FISH results and cells recovered for cohort with matched samples. Table S4 shows comparison of BCR sequence between BMPCs and CTCs. Table S5 shows clinical BM FISH results of peripheral blood only cohort and CTCs recovered. Table S6 shows enumeration of single nucleotide variants and short insertions and deletions discovered from WGS of CTCs. Table S7 shows enumeration of structural variants reconstructed from WGS of CTCs.

Multiple myeloma (MM) develops from well-defined precursor stages; however, invasive bone marrow (BM) biopsy limits screening and monitoring strategies for patients. We enumerated circulating tumor cells (CTC) from 261 patients (84 monoclonal gammopathy of undetermined significance, 155 smoldering multiple myeloma, and 22 MM), with neoplastic cells detected in 84%. We developed a novel approach, MinimuMM-seq, which enables the detection of translocations and copy-number abnormalities through whole-genome sequencing of highly pure CTCs. Application to CTCs in a cohort of 51 patients, 24 with paired BM, was able to detect 100% of clinically reported BM biopsy events and could replace molecular cytogenetics for diagnostic yield and risk classification. Longitudinal sampling of CTCs in 8 patients revealed major clones could be tracked in the blood, with clonal evolution and shifting dynamics of subclones over time. Our findings provide proof of concept that CTC detection and genomic profiling could be used clinically for monitoring and managing disease in MM.Significance:In this study, we established an approach enabling the enumeration and sequencing of CTCs to replace standard molecular cytogenetics. CTCs harbored the same pathognomonic MM abnormalities as BM plasma cells. Longitudinal sampling of serial CTCs was able to track clonal dynamics over time and detect the emergence of high-risk genetic subclones.This article is highlighted in the In This Issue feature, p. 247

Supplementary Figure Legend

Supplementary Figure 1. Hypoxia regulates WM cell formation of clumps in an E-cadherin-dependent manner. The effect of E-cadherin blocking antibody (5 μg/mL) on the size of colonies created by BCWM.1 cells in normoxia or hypoxia normalized to normoxic untreated cells (A); and on the colony formation by BCWM.1 cells as shown by representative pictures (scale bar represents 1 micrometer) (B).

Supplementary Figure 2. Re-oxygenation induces WM cell clump-formation. The effect of hypoxia (1% O2, 48hrs) and re-oxygenation (hypoxia for 24hrs followed by normoxia for 24hrs) on BCWM.1 size of colonies normalized to hypoxic cells (A), and on the creation of clumps monitored and captured using light microscopy (scale bar represents 1 micrometer) (B).

Supplementary Figure 1. In-vivo mouse model's schema; Supplementary Figure 2. Total BLI Flux of SCID.beige myeloma xenograft model and Kaplan-Meier survival analysis of the NSG xenograft model; Supplementary Figure 3. Elotuzumab does not affect myeloma cell proliferation and homing to the bone marrow.

Elotuzumab, a recently approved antibody for the treatment of multiple myeloma, has been shown to stimulate Fcγ receptor (FcγR)-mediated antibody-dependent cellular cytotoxicity by natural killer (NK) cells toward myeloma cells. The modulatory effects of elotuzumab on other effector cells in the tumor microenvironment, however, has not been fully explored. Antibody-dependent cellular phagocytosis (ADCP) is a mechanism by which macrophages contribute to antitumor potency of monoclonal antibodies. Herein, we studied the NK cell independent effect of elotuzumab on tumor-associated macrophages using a xenograft tumor model deficient in NK and adaptive immune cells. We demonstrate significant antitumor efficacy of single-agent elotuzumab in immunocompromised xenograft models of multiple myeloma, which is in part mediated by Fc–FcγR interaction of elotuzumab with macrophages. Elotuzumab is shown in this study to induce phenotypic activation of macrophages in vivo and mediates ADCP of myeloma cells though a FcγR-dependent manner in vitro. Together, these findings propose a novel immune-mediated mechanism by which elotuzumab exerts anti-myeloma activity and helps to provide rationale for combination therapies that can enhance macrophage activity. Mol Cancer Ther; 17(7); 1454–63. ©2018 AACR.

The term monoclonal gammopathy of undetermined significance (MGUS) was coined in 1978. The recent advances in our knowledge about MGUS and smoldering multiple myeloma (SMM) have helped us better understand the pathogenesis of myeloma. It seems that myeloma evolves from a precursor state in almost all cases. We do not completely understand the multistep process from the precursor state to myeloma, but studies like whole genome sequencing continue to improve our understanding of this process. The process of transformation may not be linear acquisition of changes, but rather a branched heterogeneous process. Clinical features that are prognostic of rapid transformation have been identified, but no specific molecular markers have been identified. Even with recent advances, multiple myeloma remains an incurable disease in the vast majority, and intervening at the precursor state provides a unique opportunity to alter the natural history of the disease. A limitation is that a vast majority of patients with precursor disease, especially low-risk MGUS, will never progress to myeloma in their lifetime, and treating these patients is not only unnecessary but may be potentially harmful. The challenge is to identify a subset of patients with the precursor state that would definitely progress to myeloma and in whom interventions will have a meaningful impact. As our understanding of the molecular and genetic processes improves, these studies will guide the selection of high-risk patients more appropriately and ultimately direct a tailored management strategy to either delay progression to symptomatic myeloma or even “cure” a person at this premalignant stage. Clin Cancer Res; 19(5); 985–94. ©2012 AACR.

Supplementary Figures S1-S3.

Supplementary Methods, Tables and Figures

Supplementary Information

This file contains the legends for the supplementary figures.

Purpose: The TORC1 inhibitor everolimus has previously shown significant activity as a single agent in hematologic malignancies, with reported responses of 30% to 70% in Waldenstrom macroglobulinemia. However, the specific mechanisms by which this class of mTOR inhibitors exerts anti–Waldenstrom macroglobulinemia activity have not been fully investigated. We therefore sought to dissect the mechanisms of everolimus-dependent modulation of Waldenstrom macroglobulinemia cell survival.Experimental Design: We confirmed that everolimus targets mTOR in patients treated with everolimus and responding to therapy. We evaluated the effect of everolimus on proliferation and survival of primary Waldenstrom macroglobulinemia cells, as well as of other IgM-secreting lymphoma cell lines. Everolimus-dependent mechanisms of induced apoptosis and its effect on Waldenstrom macroglobulinemia cells in the context of bone marrow microenvironment have been also evaluated. miRNA-155 loss-of-function studies were conducted. Moreover, the combinatory effect of bortezomib and rituximab has been tested.Results: We showed that everolimus targeted mTOR downstream signaling pathways, ex vivo, in patients responding to everolimus treatment. Everolimus induced toxicity in primary Waldenstrom macroglobulinemia cells, as well as in other IgM-secreting lymphoma cells, supported by cell-cycle arrest and caspase-dependent and -independent induction of apoptosis. Importantly, everolimus targeted Waldenstrom macroglobulinemia cells even in the context of bone marrow milieu, where it affected migration, adhesion, and angiogenesis. Everolimus-dependent anti–Waldenstrom macroglobulinemia activity was partially driven by miRNA-155. Moreover, everolimus synergized with bortezomib and rituximab in targeting Waldenstrom macroglobulinemia cells, as shown by synergistic inhibition of p65/ and p50/NF-κB activities.Conclusions: These findings provide a better understanding of the mechanisms that are responsible for everolimus-induced anti–Waldenstrom macroglobulinemia activity. Clin Cancer Res; 18(24); 6609–22. ©2012 AACR.

5TGM1 cells co-localized with platelets in femur of syngeneic murine model.

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Purpose:Ulocuplumab (BMS-936564) is a first-in-class fully human IgG4 monoclonal anti-CXCR4 antibody that inhibits the binding of CXCR4 to CXCL12.Patients and Methods:This phase Ib/II study aimed to determine the safety and tolerability of ulocuplumab alone and in combination with lenalidomide and dexamethasone (Arm A), or bortezomib and dexamethasone (Arm B), in patients with relapsed/refractory multiple myeloma.Results:Forty-six patients were evaluated (median age, 60 years; range, 53–67). The median number of prior therapies was 3 (range, 1–11), with 70% of subjects having received ≥3. This trial had a dose-escalation and a dose-expansion part. Using a 3+3 design on both arms of the trial, ulocuplumab's dose was escalated to a maximum of 10 mg/kg without reaching MTD. The most common treatment-related adverse events (AE) were neutropenia (13 patients, 43.3%) in Arm A and thrombocytopenia (6 patients, 37.5%) in Arm B. No deaths related to study drugs occurred. The combination of ulocuplumab with lenalidomide and dexamethasone showed a high response rate (PR or better) of 55.2% and a clinical benefit rate of 72.4%, even in patients who had been previously treated with immunomodulatory agents (IMiD).Conclusions:This study showed that blockade of the CXCR4–CXCL12 axis by ulocuplumab is safe with acceptable AEs and leads to a high response rate in combination with lenalidomide and dexamethasone in patients with relapsed/refractory myeloma, making CXCR4 inhibitors a promising class of antimyeloma drugs that should be further explored in clinical trials.

The figures show that patients received 10 mg ulocuplumab could reach higher concentration peak and larger dose-corrected area under curve (both p < 0.001).

This figure shows Absolute cell numbers in the peripheral blood of patients receiving 10mg/kg ulocuplumab (absolute count, cells/uL).

Purpose: Tumor cell–platelet interactions contribute to tumor progression and metastasis in solid tumors. However, the role of platelets in hematological malignancies is not clear. We investigated the association of platelet activation status with clinical stages in multiple myeloma (MM) patients and explored the role of platelets in MM progression.Experimental Design: Platelets were obtained from healthy donors and MM patients. We examined platelet activation status in MM patients by flow cytometry and transmission electron microscopy. We also observed the enriched pathways that are involved with platelet activation in RNA sequencing of platelets. MM cell lines were used to assess the effect of platelets on MM cell proliferation in vitro and their engraftment in vivo. RNA sequencing of MM cell lines was performed to explore molecular mechanisms underlying MM cell–platelet interaction and a CRISPR/Cas9 knockout approach was used for validation.Results: Platelets from MM patients were highly activated with disease progression. RNA sequencing of platelets revealed that genes involved in platelets were enriched in patients with smoldering MM (SMM) or MM. Platelets promoted MM cell proliferation in vitro and contributed to tumor engraftment in bone marrow in vivo. RNA sequencing revealed that IL-1β was upregulated in MM cell lines co-cultured with platelets, whereas IL-1β knockout in MM cell lines abrogated the effects of platelets on MM cell proliferation and engraftment in vivo.Conclusions: Platelets from MM patients were highly activated with disease progression. IL-1β is critical to platelet-mediated MM progression and might be a potential target for MM treatment. Clin Cancer Res; 24(10); 2430–9. ©2018 AACR.

Purpose: Waldenstrom macroglobulinemia is a lymphoplasmacytic lymphoma characterized by widespread involvement of the bone marrow. Despite different options of therapy, Waldenstrom macroglobulinemia is still incurable. Src tyrosine kinase has been shown to play a central role in the regulation of a variety of biological processes, such as cell proliferation, migration, adhesion, and survival in solid tumors. We sought to determine whether the protein tyrosine kinase Src regulates adhesion, migration, and survival in Waldenstrom macroglobulinemia.Experimental Design: We tested the expression of Src tyrosine kinase in Waldenstrom macroglobulinemia and normal cells, and the effect of the specific Src inhibitor AZD0530 on the adhesion, migration, cell cycle, and survival of a Waldenstrom macroglobulinemia cell line and patient samples. Moreover, we tested the effect of AZD0530 on cytoskeletal and cell cycle signaling in Waldenstrom macroglobulinemia.Results: We show that Src is overexpressed in Waldenstrom macroglobulinemia cells compared with control B cells, and that the use of the Src inhibitor AZD0530 led to significant inhibition of adhesion, migration, and cytoskeletal signaling induced by SDF1. Moreover, inhibition of Src activity induced G1 cell cycle arrest; however, it had minimal effect on survival of Waldenstrom macroglobulinemia cells, and no significant effect on survival of normal cells.Conclusions: Taken together, these results delineate the role of Src kinase activity in Waldenstrom macroglobulinemia and provide the framework for future clinical trials using Src inhibitors in combination with other drugs to improve the outcome of patients with Waldenstrom macroglobulinemia. (Clin Cancer Res 2009;15(19):6035–41)

Supplementary Table S1. Summary statistics of the estimated slopes and turning points for the model fitting of the six cohorts from the three trials. Supplementary Table S2. Information on the number of data points in the pre-treatment phase, phase during administration of therapy, and posttreatment phase. Supplementary Table S3. Summary statistics of slopes and turning points for the treatment response data of the VISTA, MMY-2001, and APEX trials. Supplementary Table S4. Dependence of MM stage, prior administration of steroids, and the best-fitting model. Supplementary Table S5. Counts of death for single-phasic and 2-phasic patients as well as different sub-cohorts based on modeling parameters in the VISTA MP and VISTA VMP cohort during treatment. Supplementary Table S6. Summary statistics of the day differences between scheduled and actual visit dates for during-treatment response data for the VISTA MP and VISTA VMP cohort. Supplementary Table S7. Parameter values used for the hybrid mathematical model for all three cohorts.

Supplementary Data from Src Tyrosine Kinase Regulates Adhesion and Chemotaxis in Waldenstrom Macroglobulinemia

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This figure shows (A) Proportion of circulating CD34+ stem cells (% of living cells, all patients combined). (B) Proportion of circulating CD138+ myeloma cells (% of living cells, all patients combined) (C) Distribution of CD34+ stem cells in responding vs. non-responding group (% of living cells). (D) Distribution of CD138+ myeloma cells in responding vs. non-responding group (% of living cells).