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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. Multiple myeloma disease progression and therapy response are influenced by the bone marrow niche in which the tumor cells reside. To characterize this supportive niche, Cupedo and colleagues use single-cell transcriptomic analysis of bone marrow stromal cell populations from individuals with multiple myeloma. They identify a myeloma-specific inflammatory mesenchymal stromal cell (iMSC) population that spatially colocalizes with tumor cells. Anti-myeloma induction therapy does not influence iMSC presence, suggesting a role for bone marrow inflammation in myeloma persistence or relapse.

Multiple myeloma (MM) is a plasma cell malignancy with a significant heritable basis. Genome-wide association studies have transformed our understanding of MM predisposition, but individual studies have had limited power to discover risk loci. Here we perform a meta-analysis of these GWAS, add a new GWAS and perform replication analyses resulting in 9,866 cases and 239,188 controls. We confirm all nine known risk loci and discover eight new loci at 6p22.3 (rs34229995, P =1.31 × 10 −8 ), 6q21 (rs9372120, P =9.09 × 10 −15 ), 7q36.1 (rs7781265, P =9.71 × 10 −9 ), 8q24.21 (rs1948915, P =4.20 × 10 −11 ), 9p21.3 (rs2811710, P =1.72 × 10 −13 ), 10p12.1 (rs2790457, P =1.77 × 10 −8 ), 16q23.1 (rs7193541, P =5.00 × 10 −12 ) and 20q13.13 (rs6066835, P =1.36 × 10 −13 ), which localize in or near to JARID2 , ATG5 , SMARCD3 , CCAT1 , CDKN2A , WAC , RFWD3 and PREX1 . These findings provide additional support for a polygenic model of MM and insight into the biological basis of tumour development. Previous genome-wide association studies have identified loci associated with the risk of multiple myeloma. Here, the authors present a meta-analysis of six genome wide association studies of the disease and identify eight new loci; functional studies identify genes as candidates for the basis of these associations.

Abstract 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.

Bortezomib-induced peripheral neuropathy is a dose-limiting toxicity in patients with multiple myeloma, often requiring adjustment of treatment and affecting quality of life. We investigated the molecular profiles of early-onset (within one treatment cycle) versus late-onset (after two or three treatment cycles) bortezomib-induced peripheral neuropathy and compared them with those of vincristine-induced peripheral neuropathy during the induction phase of a prospective phase 3 trial. In the induction phase of the HOVON-65/GMMG-HD4 trial, patients (aged 18–65 years) with newly diagnosed Salmon and Durie stage 2 or 3 multiple myeloma were randomly assigned to three cycles of bortezomib-based or vincristine-based induction treatment. We analysed the gene expression profiles and single-nucleotide polymorphisms (SNPs) of pretreatment samples of myeloma plasma cells and peripheral blood, respectively. This study is registered, number ISRCTN64455289. We analysed gene expression profiles of myeloma plasma cells from 329 (39%) of 833 patients at diagnosis, and SNPs in DNA samples from 369 (44%) patients. Early-onset bortezomib-induced peripheral neuropathy was noted in 20 (8%) patients, and 63 (25%) developed the late-onset type. Early-onset and late-onset vincristine-induced peripheral neuropathy was noted in 11 (4%) and 17 (7%) patients, respectively. Significant genes in myeloma plasma cells from patients that were associated with early-onset bortezomib-induced peripheral neuropathy were the enzyme coding genes RHOBTB2 (upregulated by 1·59 times; p=4·5×10 −5), involved in drug-induced apoptosis, CPT1C (1·44 times; p=2·9×10 −7), involved in mitochondrial dysfunction, and SOX8 (1·68 times; p=4·28×10 −13), involved in development of peripheral nervous system. Significant SNPs in the same patients included those located in the apoptosis gene caspase 9 (odds ratio [OR] 3·59, 95% CI 1·59–8·14; p=2·9×10 −3), ALOX12 (3·50, 1·47–8·32; p=3·8×10 −3), and IGF1R (0·22, 0·07–0·77; p=8·3×10 −3). In late-onset bortezomib-induced peripheral neuropathy, the significant genes were SOD2 (upregulated by 1·18 times; p=9·6×10 −3) and MYO5A (1·93 times; p=3·2×10 −2), involved in development and function of the nervous system. Significant SNPs were noted in inflammatory genes MBL2 (OR 0·49, 95% CI 0·26–0·94; p=3·0×10 −2) and PPARD (0·35, 0·15–0·83; p=9·1×10 −3), and DNA repair genes ERCC4 (2·74, 1·56–4·84; p=1·0×10 −3) and ERCC3 (1·26, 0·75–2·12; p=3·3×10 −3). By contrast, early-onset vincristine-induced peripheral neuropathy was characterised by upregulation of genes involved in cell cycle and proliferation, including AURKA (3·31 times; p=1·04×10 −2) and MKI67 (3·66 times; p=1·82×10 −3), and the presence of SNPs in genes involved in these processes—eg, GLI1 (rs2228224 [0·13, 0·02–0·97, p=1·18×10 −2] and rs2242578 [0·14, 0·02–1·12, p=3·00×10 −2]). Late-onset vincristine-induced peripheral neuropathy was associated with the presence of SNPs in genes involved in absorption, distribution, metabolism, and excretion—eg, rs1413239 in DPYD (3·29, 1·47–7·37, 5·40×10 −3) and rs3887412 in ABCC1 (3·36, 1·47–7·67, p=5·70×10 −3). Our results strongly suggest an interaction between myeloma-related factors and the patient's genetic background in the development of treatment-induced peripheral neuropathy, with different molecular pathways being implicated in bortezomib-induced and vincristine-induced peripheral neuropathy. German Federal Ministry of Education and Research, Dutch Cancer Foundation Queen Wilhelmina, European Hematology Association, International Myeloma Foundation, Erasmus MC, and Janssen-Cilag Orthobiotech.

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.

Novel therapies are needed for patients with multiple myeloma (MM) and extramedullary plasmacytomas. The prospective, Phase II EMN19 study assessed the efficacy and safety of daratumumab plus bortezomib, cyclophosphamide, and dexamethasone (DaraVCD) in 40 patients with newly diagnosed MM (NDMM; n = 29) or at first relapse (RMM; n = 11) and positron emission tomography or computed tomography (PET/CT)‐confirmed extramedullary plasmacytomas (extraosseous [EMD] and/or paraosseous [PS]). DaraVCD was administered until disease progression or up to 3 years. The primary endpoint was hematological complete response (CR). Among patients, 22 (55.0%), 4 (10.0%), and 14 (35.0%) had EMD, EMD/PS, and PS plasmacytomas, respectively. Median patient age was 58.0 years, and 16 (40.0%), 12 (30.0%), and 10 (25.0%) patients were at International Staging System (ISS) Stages I, II, and III, respectively. Median circulating tumor cell (CTC) level was 0.002% (range, 0.000–0.353), significantly higher (P < 0.05) in patients with ISS Stage III and those with plasma cells > 60%. At a median follow‐up of 30.0 months, all patients completed treatment (median duration: 19.8 months). The overall hematologic ≥CR rate was 47.5% (19/40; NDMM patients: 58.6% [17/29]; RMM patients: 18.2% [2/11]). Of patients with ≥CR, 80.0% (15/19) achieved minimal residual disease (MRD) negativity, and 68.4% (13/19) combined MRD negativity and complete metabolic response (CMR) on PET/CT. The overall median progression‐free survival was 25.8 months, significantly longer in patients achieving hematologic ≥CR and/or CMR than others (not reached and 4.8 months, respectively; P < 0.001). DaraVCD showed encouraging efficacy in patients with MM and extramedullary plasmacytomas. Notably, this is the first report on CTC levels in EMD, and they were lower than previously reported NDMM thresholds.

Given the evolving understanding of genetic risk factors in multiple myeloma (MM), this paper assesses whether next‐generation sequencing (NGS) could complement or even replace fluorescence in situ hybridization (FISH) at diagnosis. A structured consensus process within European Myeloma Network (EMN) clinical and laboratory groups was conducted to establish recommendations on routine clinical deployment of NGS in MM risk assessment. Four key questions were addressed: (1) should NGS be used in addition to, or alternatively to FISH in identifying prognostic genetic markers, (2) which prognostic markers are most relevant for analysis by NGS, (3) which patients should be offered NGS testing, and (4) what is the optimal timing for performing NGS. The panel reviewed current literature, evaluated available NGS technologies, and compared their performance with that of FISH‐based methodologies. The paper reviews current standard NGS protocols, quality control measures, and provides practical points for the implementation of an NGS diagnosis in MM. While NGS shows promise in improving risk stratification, challenges such as cost, accessibility, and clinical workflow integration must be addressed. The consensus supports the initial incorporation of NGS as a complementary tool to FISH. Recommendations emphasize that: a broader list of genetic events should be incorporated into such a test than what currently requested by risk scores; the test should be offered at least to the fit patients who could be candidates for modern triplet or quadruplet treatments; the test should be repeated at the time relapse, especially in the future when targeted treatments may mandate the use of predictive markers of response. This consensus provides a foundation for future research and policy development, guiding the adoption of NGS in MM risk assessment.

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.

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.