An IL-1ß-driven neutrophil-stromal cell axis fosters a BAFF-rich protumor microenvironment in individuals with multiple myeloma.

Human bone marrow permanently harbors high numbers of neutrophils, and a tumor-supportive bias of these cells could significantly impact bone marrow-confined malignancies. In individuals with multiple myeloma, the bone marrow is characterized by inflammatory stromal cells with the potential to influence neutrophils. We investigated myeloma-associated alterations in human marrow neutrophils and the impact of stromal inflammation on neutrophil function. Mature neutrophils in myeloma marrow are activated and tumor supportive and transcribe increased levels of IL1B and myeloma cell survival factor TNFSF13B (BAFF). Interactions with inflammatory stromal cells induce neutrophil activation, including BAFF secretion, in a STAT3-dependent manner, and once activated, neutrophils gain the ability to reciprocally induce stromal activation. After first-line myeloid-depleting antimyeloma treatment, human bone marrow retains residual stromal inflammation, and newly formed neutrophils are reactivated. Combined, we identify a neutrophil-stromal cell feed-forward loop driving tumor-supportive inflammation that persists after treatment and warrants novel strategies to target both stromal and immune microenvironments in multiple myeloma.

Identification of High-Risk Multiple Myeloma With a Plasma Cell Leukemia-Like Transcriptomic Profile.

PURPOSE: Primary plasma cell leukemia (pPCL) is an aggressive subtype of multiple myeloma, which is distinguished from newly diagnosed multiple myeloma (NDMM) on the basis of the presence of ≥ 20% circulating tumor cells (CTCs). A molecular marker for pPCL is currently lacking, which could help identify NDMM patients with high-risk PCL-like disease, despite not having been recognized as such clinically. METHODS: A transcriptomic classifier for PCL-like disease was bioinformatically constructed and validated by leveraging information on baseline CTC levels, tumor burden, and tumor transcriptomics from 154 patients with NDMM included in the Cassiopeia or HO143 trials and 29 patients with pPCL from the EMN12/HO129 trial. Its prognostic value was assessed in an independent cohort of 2,139 patients with NDMM from the HOVON-65/GMMG-HD4, HOVON-87/NMSG-18, EMN02/HO95, MRC-IX, Total Therapy 2, Total Therapy 3, and MMRF CoMMpass studies. RESULTS: High CTC levels were associated with the expression of 1,700 genes, independent of tumor burden (false discovery rate < 0.05). Of these, 54 genes were selected by leave-one-out cross-validation to construct a transcriptomic classifier representing PCL-like disease. This not only demonstrated a sensitivity of 93% to identify pPCL in the validation cohort but also classified 10% of NDMM tumors as PCL-like. PCL-like MM transcriptionally and cytogenetically resembled pPCL, but presented with significantly lower CTC levels and tumor burden. Multivariate analyses in NDMM confirmed the significant prognostic value of PCL-like status in the context of Revised International Staging System stage, age, and treatment, regarding both progression-free (hazard ratio, 1.64; 95% CI, 1.30 to 2.07) and overall survival (hazard ratio, 1.89; 95% CI, 1.42 to 2.50). CONCLUSION: pPCL was identified on the basis of a specific tumor transcriptome, which was also present in patients with high-risk NDMM, despite not being clinically leukemic. Incorporating PCL-like status into current risk models in NDMM may improve prognostic accuracy.

Molecular Signature of 18F-FDG PET Biomarkers in Newly Diagnosed Multiple Myeloma Patients: A Genome-Wide Transcriptome Analysis from the CASSIOPET Study.

The International Myeloma Working Group recently fully incorporated 18F-FDG PET into multiple myeloma (MM) diagnosis and response evaluation. Moreover, a few studies demonstrated the prognostic value of several biomarkers extracted from this imaging at baseline. Before these 18F-FDG PET biomarkers could be fully endorsed as risk classifiers by the hematologist community, further characterization of underlying molecular aspects was necessary. Methods: Reported prognostic biomarkers (18F-FDG avidity, SUVmax, number of focal lesions, presence of paramedullary disease [PMD] or extramedullary disease) were extracted from 18F-FDG PET imaging at baseline in a group of 139 patients from CASSIOPET, a companion study of the CASSIOPEIA cohort (ClinicalTrials.gov identifier NCT02541383). Transcriptomic analyses using RNA sequencing were realized on sorted bone marrow plasma cells from the same patients. An association with a high-risk gene expression signature (IFM15), molecular classification, progression-free survival, a stringent clinical response, and minimal residual disease negativity were explored. Results:18F-FDG PET results were positive in 79.4% of patients; 14% and 11% of them had PMD and extramedullary disease, respectively. Negative 18F-FDG PET results were associated with lower levels of expression of hexokinase 2 (HK2) (fold change, 2.1; adjusted P = 0.04) and showed enrichment for a subgroup of patients with a low level of bone disease. Positive 18F-FDG PET results displayed 2 distinct signatures: either high levels of expression of proliferation genes or high levels of expression of GLUT5 and lymphocyte antigens. PMD and IFM15 were independently associated with a lower level of progression-free survival, and the presence of both biomarkers defined a group of "double-positive" patients at very high risk of progression. PMD and IFM15 were related neither to minimal residual disease assessment nor to a stringent clinical response. Conclusion: Our study confirmed and extended the association between imaging biomarkers and transcriptomic programs in MM. The combined prognostic value of PMD and a high-risk IFM15 signature may help define MM patients with a very high risk of progression.

VS38c and CD38-Multiepitope Antibodies Provide Highly Comparable Minimal Residual Disease Data in Patients With Multiple Myeloma.

OBJECTIVES: To compare flow cytometric minimal residual disease (MRD) data obtained using the EuroFlow approach, including the CD38-multiepitope (ME) antibody or the VS38c antibody. METHODS: We evaluated 29 bone marrow samples from patients with multiple myeloma (MM), of whom 15 had received daratumumab within the past 6 months. We evaluated MRD data and fluorescence intensities. RESULTS: Qualitative MRD data were 100% concordant between the 2 approaches. In MRD-positive samples (n = 14), MRD levels showed an excellent correlation (R2 = 0.999). Whereas VS38c staining was strong in both normal plasma cells and MM cells, independent of daratumumab treatment, staining intensities for CD38 were lower in MM cells compared with normal plasma cells, and on both cell types CD38 expression was significantly reduced in daratumumab-treated patients. CONCLUSIONS: Both CD38-ME and VS38c allow reliable MRD detection in MM patients, but the high expression of VS38c allows easier identification of MM cells, especially in daratumumab-treated patients.

V-Domain Ig Suppressor of T Cell Activation (VISTA) Expression Is an Independent Prognostic Factor in Multiple Myeloma.

Multiple myeloma (MM) is characterized by loss of anti-tumor T cell immunity. Despite moderate success of treatment with anti-PD1 antibodies, effective treatment is still challenged by poor T cell-mediated control of MM. To better enable identification of shortcomings in T-cell immunity that relate to overall survival (OS), we interrogated transcriptomic data of bone marrow samples from eight clinical trials (n = 1654) and one trial-independent patient cohort (n = 718) for multivariate analysis. Gene expression of V-domain Ig suppressor of T cell activation (VISTA) was observed to correlate to OS [hazard ratio (HR): 0.72; 95% CI: 0.61-0.83; p = 0.005]. Upon imaging the immune contexture of MM bone marrow tissues (n = 22) via multiplex in situ stainings, we demonstrated that VISTA was expressed predominantly by CD11b+ myeloid cells. The combination of abundance of VISTA+, CD11b+ cells in the tumor but not stromal tissue together with low presence of CD8+ T cells in the same tissue compartment, termed a high VISTA-associated T cell exclusion score, was significantly associated with short OS [HR: 16.6; 95% CI: 4.54-62.50; p < 0.0001]. Taken together, the prognostic value of a combined score of VISTA+, CD11b+ and CD8+ cells in the tumor compartment could potentially be utilized to guide stratification of MM patients for immune therapies.

The multiple myeloma microenvironment is defined by an inflammatory stromal cell landscape.

Progression and persistence of malignancies are influenced by the local tumor microenvironment, and future eradication of currently incurable tumors will, in part, hinge on our understanding of malignant cell biology in the context of their nourishing surroundings. Here, we generated paired single-cell transcriptomic datasets of tumor cells and the bone marrow immune and stromal microenvironment in multiple myeloma. These analyses identified myeloma-specific inflammatory mesenchymal stromal cells, which spatially colocalized with tumor cells and immune cells and transcribed genes involved in tumor survival and immune modulation. Inflammatory stromal cell signatures were driven by stimulation with proinflammatory cytokines, and analyses of immune cell subsets suggested interferon-responsive effector T cell and CD8+ stem cell memory T cell populations as potential sources of stromal cell-activating cytokines. Tracking stromal inflammation in individuals over time revealed that successful antitumor induction therapy is unable to revert bone marrow inflammation, predicting a role for mesenchymal stromal cells in disease persistence.

Preclinical activity and determinants of response of the GPRC5DxCD3 bispecific antibody talquetamab in multiple myeloma.

Cell surface expression levels of GPRC5D, an orphan G protein-coupled receptor, are significantly higher on multiple myeloma (MM) cells, compared with normal plasma cells or other immune cells, which renders it a promising target for immunotherapeutic strategies. The novel GPRC5D-targeting T-cell redirecting bispecific antibody, talquetamab, effectively kills GPRC5D+ MM cell lines in the presence of T cells from both healthy donors or heavily pretreated MM patients. In addition, talquetamab has potent anti-MM activity in bone marrow (BM) samples from 45 patients, including those with high-risk cytogenetic aberrations. There was no difference in talquetamab-mediated killing of MM cells from newly diagnosed, daratumumab-naïve relapsed/refractory (median of 3 prior therapies), and daratumumab-refractory (median of 6 prior therapies) MM patients. Tumor cell lysis was accompanied by T-cell activation and degranulation, as well as production of pro-inflammatory cytokines. High levels of GPRC5D and high effector:target ratio were associated with improved talquetamab-mediated lysis of MM cells, whereas an increased proportion of T cells expressing PD-1 or HLA-DR, and elevated regulatory T-cell (Treg) counts were associated with suboptimal killing. In cell line experiments, addition of Tregs to effector cells decreased MM cell lysis. Direct contact with bone marrow stromal cells also impaired the efficacy of talquetamab. Combination therapy with daratumumab or pomalidomide enhanced talquetamab-mediated lysis of primary MM cells in an additive fashion. In conclusion, we show that the GPRC5D-targeting T-cell redirecting bispecific antibody talquetamab is a promising novel antimyeloma agent. These results provide the preclinical rationale for ongoing studies with talquetamab in relapsed/refractory MM.

Analytical Validation of SKY92 for the Identification of High-Risk Multiple Myeloma.

Multiple myeloma (MM) is an incurable plasma cell cancer with a large variability in survival. Patients with MM classified as high risk by the SKY92 gene expression classifier are at high risk of relapse and short survival. Analytical validation of the SKY92 assay was performed with primary bone marrow specimens from 12 patients with MM and 7 reference cell line specimens. The SKY92 results were 100% concordant with the reference and/or their expected result for sensitivity, specificity, microarray stability, and RLT buffer stability. The SKY92 results were 90% concordant for primary specimen stability, 96.4% concordant for intermediate precision, and 80% to 100% concordant for RNA stability. For the cell-line reproducibility, the concordance was at least 92.9%, except for one near-cut point specimen. For the clinical specimen reproducibility, the concordance was 100%, except for two near-cut point specimens. Three independent laboratories were concordant in ≥77.8% and ≥92.9% of experiments for patient specimens and cell lines, respectively. Statistical acceptance thresholds were developed as Δ ≤1.48 (change in SKY92 score) and SD ≤0.45 (SD across SKY92 scores). Using the Clinical and Laboratory Standards Institute method of choice (EP05-A2/A3), restricted maximum likelihood, the observed Δ values (0 to 1.14) and SDs (0.22 to 0.31) passed acceptance criteria. Thus, we successfully present analytical validation for the SKY92 assay as a prognostic molecular test for individual patients with MM.

Prognostic and predictive performance of R-ISS with SKY92 in older patients with multiple myeloma: the HOVON-87/NMSG-18 trial.

The standard prognostic marker for multiple myeloma (MM) patients is the revised International Staging System (R-ISS). However, there is room for improvement in guiding treatment. This applies particularly to older patients, in whom the benefit/risk ratio is reduced because of comorbidities and subsequent side effects. We hypothesized that adding gene-expression data to R-ISS would generate a stronger marker. This was tested by combining R-ISS with the SKY92 classifier (SKY-RISS). The HOVON-87/NMSG-18 trial (EudraCT: 2007-004007-34) compared melphalan-prednisone-thalidomide followed by thalidomide maintenance (MPT-T) with melphalan-prednisone-lenalidomide followed by lenalidomide maintenance (MPR-R). From this trial, 168 patients with available R-ISS status and gene-expression profiles were analyzed. R-ISS stages I, II, and III were assigned to 8%, 75%, and 7% of patients, respectively (3-year overall survival [OS] rates: 80%, 65%, 33%, P = 8 × 10-3). Using the SKY92 classifier, 13% of patients were high risk (HR) (3-year OS rates: standard risk [SR], 70%; HR, 28%; P < .001). Combining SKY92 with R-ISS resulted in 3 risk groups: SKY-RISS I (SKY-SR + R-ISS-I; 15%), SKY-RISS III (SKY-HR + R-ISS-II/III; 11%), and SKY-RISS II (all other patients; 74%). The 3-year OS rates for SKY-RISS I, II, and III are 88%, 66%, and 26%, respectively (P = 6 × 10-7). The SKY-RISS model was validated in older patients from the CoMMpass dataset. Moreover, SKY-RISS demonstrated predictive potential: HR patients appeared to benefit from MPR-R over MPT-T (median OS, 55 and 14 months, respectively). Combined, SKY92 and R-ISS classify patients more accurately. Additionally, benefit was observed for MPR-R over MPT-T in SKY92-RISS HR patients only.

Search for multiple myeloma risk factors using Mendelian randomization.

The etiology of multiple myeloma (MM) is poorly understood. Summary data from genome-wide association studies (GWASs) of multiple phenotypes can be exploited in a Mendelian randomization (MR) phenome-wide association study (PheWAS) to search for factors influencing MM risk. We performed an MR-PheWAS analyzing 249 phenotypes, proxied by 10 225 genetic variants, and summary genetic data from a GWAS of 7717 MM cases and 29 304 controls. Odds ratios (ORs) per 1 standard deviation increase in each phenotype were estimated under an inverse variance weighted random effects model. A Bonferroni-corrected threshold of P = 2 × 10-4 was considered significant, whereas P < .05 was considered suggestive of an association. Although no significant associations with MM risk were observed among the 249 phenotypes, 28 phenotypes showed evidence suggestive of association, including increased levels of serum vitamin B6 and blood carnitine (P = 1.1 × 10-3) with greater MM risk and ω-3 fatty acids (P = 5.4 × 10-4) with reduced MM risk. A suggestive association between increased telomere length and reduced MM risk was also noted; however, this association was primarily driven by the previously identified risk variant rs10936599 at 3q26 (TERC). Although not statistically significant, increased body mass index was associated with increased risk (OR, 1.10; 95% confidence interval, 0.99-1.22), supporting findings from a previous meta-analysis of prospective observational studies. Our study did not provide evidence supporting any modifiable factors examined as having a major influence on MM risk; however, it provides insight into factors for which the evidence has previously been mixed.

Transcriptome-wide association study of multiple myeloma identifies candidate susceptibility genes.

BACKGROUND: While genome-wide association studies (GWAS) of multiple myeloma (MM) have identified variants at 23 regions influencing risk, the genes underlying these associations are largely unknown. To identify candidate causal genes at these regions and search for novel risk regions, we performed a multi-tissue transcriptome-wide association study (TWAS). RESULTS: GWAS data on 7319 MM cases and 234,385 controls was integrated with Genotype-Tissue Expression Project (GTEx) data assayed in 48 tissues (sample sizes, N = 80-491), including lymphocyte cell lines and whole blood, to predict gene expression. We identified 108 genes at 13 independent regions associated with MM risk, all of which were in 1 Mb of known MM GWAS risk variants. Of these, 94 genes, located in eight regions, had not previously been considered as a candidate gene for that locus. CONCLUSIONS: Our findings highlight the value of leveraging expression data from multiple tissues to identify candidate genes responsible for GWAS associations which provide insight into MM tumorigenesis. Among the genes identified, a number have plausible roles in MM biology, notably APOBEC3C, APOBEC3H, APOBEC3D, APOBEC3F, APOBEC3G, or have been previously implicated in other malignancies. The genes identified in this TWAS can be explored for follow-up and validation to further understand their role in MM biology.

Author Correction: Identification of multiple risk loci and regulatory mechanisms influencing susceptibility to multiple myeloma.

The original version of this Article contained an error in the spelling of a member of the PRACTICAL Consortium, Manuela Gago-Dominguez, which was incorrectly given as Manuela Gago Dominguez. This has now been corrected in both the PDF and HTML versions of the Article. Furthermore, in the original HTML version of this Article, the order of authors within the author list was incorrect. The PRACTICAL consortium was incorrectly listed after Richard S. Houlston and should have been listed after Nora Pashayan. This error has been corrected in the HTML version of the Article; the PDF version was correct at the time of publication.

Identification of multiple risk loci and regulatory mechanisms influencing susceptibility to multiple myeloma.

Genome-wide association studies (GWAS) have transformed our understanding of susceptibility to multiple myeloma (MM), but much of the heritability remains unexplained. We report a new GWAS, a meta-analysis with previous GWAS and a replication series, totalling 9974 MM cases and 247,556 controls of European ancestry. Collectively, these data provide evidence for six new MM risk loci, bringing the total number to 23. Integration of information from gene expression, epigenetic profiling and in situ Hi-C data for the 23 risk loci implicate disruption of developmental transcriptional regulators as a basis of MM susceptibility, compatible with altered B-cell differentiation as a key mechanism. Dysregulation of autophagy/apoptosis and cell cycle signalling feature as recurrently perturbed pathways. Our findings provide further insight into the biological basis of MM.

A multiple myeloma classification system that associates normal B-cell subset phenotypes with prognosis.

Despite the recent progress in treatment of multiple myeloma (MM), it is still an incurable malignant disease, and we are therefore in need of new risk stratification tools that can help us to understand the disease and optimize therapy. Here we propose a new subtyping of myeloma plasma cells (PCs) from diagnostic samples, assigned by normal B-cell subset associated gene signatures (BAGS). For this purpose, we combined fluorescence-activated cell sorting and gene expression profiles from normal bone marrow (BM) Pre-BI, Pre-BII, immature, naïve, memory, and PC subsets to generate BAGS for assignment of normal BM subtypes in diagnostic samples. The impact of the subtypes was analyzed in 8 available data sets from 1772 patients' myeloma PC samples. The resulting tumor assignments in available clinical data sets exhibited similar BAGS subtype frequencies in 4 cohorts from de novo MM patients across 1296 individual cases. The BAGS subtypes were significantly associated with progression-free and overall survival in a meta-analysis of 916 patients from 3 prospective clinical trials. The major impact was observed within the Pre-BII and memory subtypes, which had a significantly inferior prognosis compared with other subtypes. A multiple Cox proportional hazard analysis documented that BAGS subtypes added significant, independent prognostic information to the translocations and cyclin D classification. BAGS subtype analysis of patient cases identified transcriptional differences, including a number of differentially spliced genes. We identified subtype differences in myeloma at diagnosis, with prognostic impact and predictive potential, supporting an acquired B-cell trait and phenotypic plasticity as a pathogenetic hallmark of MM.

MCL1 gene co-expression module stratifies multiple myeloma and predicts response to proteasome inhibitor-based therapy.

Multiple myeloma (MM) is the second most common hematologic cancer, characterized by abnormal accumulation of plasma cells in the bone marrow. The extensive biological and clinical heterogeneity of MM hinders effective treatment and etiology research. Several molecular classification systems of prognostic impact have been proposed, but they do not predict the response to treatment nor do they correlate to plasma cell development pathways. Here we describe the classification of MM into two distinct subtypes based on the expression levels of a gene module coexpressed with MCL1 (MCL1-M), a regulator of plasma cell survival. The classification system enabled prediction of the prognosis and the response to bortezomib-based therapy. Moreover, the two MM subtypes were associated with two different plasma cell differentiation pathways (enrichment of a preplasmablast signature versus aberrant expression of B cell genes). 1q gain, harboring 63 of the 87 MCL1-M members including MCL1, was found in about 80% of the MM with upregulated MCL1-M expression. Clonal analysis showed that 1q gain tended to occur as an early clonal event. Members of MCL1-M captured both MM cell-intrinsically acting signals and the signals regulating the interaction between MM cells with bone marrow microenvironment. MCL1-M members were co-expressed in mouse germinal center B cells. Together, these findings indicate that MCL1-M may play previously inadequately recognized, initiating role in the pathogenesis of MM. Our findings suggest that MCL1-M signature-based molecular clustering of MM constitutes a solid framework toward understanding the etiology of this disease and establishing personalized care. Article Summary: A pathogenic mechanism-guided molecular classification would facilitate treatment decision and etiology research of multiple myeloma. On the basis of the expression levels of a gene module coexpressed with MCL1, we have established a classification scheme assigning multiple myeloma into two subtypes with distinct prognosis, treatment responses and pathogenic backgrounds.

Genetic correlation between multiple myeloma and chronic lymphocytic leukaemia provides evidence for shared aetiology.

The clustering of different types of B-cell malignancies in families raises the possibility of shared aetiology. To examine this, we performed cross-trait linkage disequilibrium (LD)-score regression of multiple myeloma (MM) and chronic lymphocytic leukaemia (CLL) genome-wide association study (GWAS) data sets, totalling 11,734 cases and 29,468 controls. A significant genetic correlation between these two B-cell malignancies was shown (Rg = 0.4, P = 0.0046). Furthermore, four of the 45 known CLL risk loci were shown to associate with MM risk and five of the 23 known MM risk loci associate with CLL risk. By integrating eQTL, Hi-C and ChIP-seq data, we show that these pleiotropic risk loci are enriched for B-cell regulatory elements and implicate B-cell developmental genes. These data identify shared biological pathways influencing the development of CLL and, MM and further our understanding of the aetiological basis of these B-cell malignancies.

Multiple myeloma.

Multiple myeloma is a malignancy of terminally differentiated plasma cells, and patients typically present with bone marrow infiltration of clonal plasma cells and monoclonal protein in the serum and/or urine. The diagnosis of multiple myeloma is made when clear end-organ damage attributable to the plasma cell proliferative disorder or when findings that suggest a high likelihood of their development are present. Distinguishing symptomatic multiple myeloma that requires treatment from the precursor stages of monoclonal gammopathy of undetermined significance and smouldering multiple myeloma is important, as observation is the standard for those conditions. Much progress has been made over the past decade in the understanding of disease biology and individualized treatment approaches. Several new classes of drugs, such as proteasome inhibitors and immunomodulatory drugs, have joined the traditional armamentarium (corticosteroids, alkylating agents and anthracyclines) and, along with high-dose therapy and autologous haemopoietic stem cell transplantation, have led to deeper and durable clinical responses. Indeed, an increasing proportion of patients are achieving lasting remissions, raising the possibility of cure for this disease. Success will probably depend on using combinations of effective agents and treating patients in the early stages of disease, such as patients with smouldering multiple myeloma.