Accurate classification of plasma cell dyscrasias is achieved by combining artificial intelligence and flow cytometry.

Monoclonal gammopathy of unknown significance (MGUS), smouldering multiple myeloma (SMM), and multiple myeloma (MM) are very common neoplasms. However, it is often difficult to distinguish between these entities. In the present study, we aimed to classify the most powerful markers that could improve diagnosis by multiparametric flow cytometry (MFC). The present study included 348 patients based on two independent cohorts. We first assessed how representative the data were in the discovery cohort (123 MM, 97 MGUS) and then analysed their respective plasma cell (PC) phenotype in order to obtain a set of correlations with a hypersphere visualisation. Cluster of differentiation (CD)27 and CD38 were differentially expressed in MGUS and MM (P < 0·001). We found by a gradient boosting machine method that the percentage of abnormal PCs and the ratio PC/CD117 positive precursors were the most influential parameters at diagnosis to distinguish MGUS and MM. Finally, we designed a decisional algorithm allowing a predictive classification ≥95% when PC dyscrasias were suspected, without any misclassification between MGUS and SMM. We validated this algorithm in an independent cohort of PC dyscrasias (n = 87 MM, n = 41 MGUS). This artificial intelligence model is freely available online as a diagnostic tool application website for all MFC centers worldwide (https://aihematology.shinyapps.io/PCdyscrasiasToolDg/).

Study on the Relationship Between the Expression of B Cell Mature Antigen and the Classification, Stage, and Prognostic Factors of Multiple Myeloma.

The expression level of BCMA in bone marrow of 54 MM patients was detected in this study to explore the relationship between the BCMA expression and the classification, stage, and prognostic factors of MM. The BCMA expression level of the stable group and remission group was lower than that of the newly diagnosed group and relapse group (P=0.001). There was no significant difference in BCMA expression of MM patients in different types and stages (P>0.05), but it was found that for the newly diagnosed MM patients, the BCMA expression level of IgG patients was higher than that of IgA or light-chain patients (rank average 11.20 vs 5.44, P=0.014). There was no significant correlation between the BCMA expression and the age and serum creatinine of MM patients (P>0.05). And there was no significant difference in BCMA expression between patients with different levels of age and serum creatinine (P>0.05). But it was found that the BCMA expression level of the newly diagnosed MM patients was moderately positively correlated with their age (P=0.025, r=0.595). There was no significant correlation between the BCMA expression and serum ß2-microglobulin, serum lactate dehydrogenase, free kap/lam ratio, and urine ß2-microglobulin (P>0.05). But we found that the BCMA expression of patients with high serum ß2-microglobulin was higher than that of patients with low serum ß2-microglobulin (rank average 28.89 vs 17.54, P=0.017). And the BCMA expression of patients with abnormal serum free kap/lam ratio was higher than that of patients with normal ratio (rank average 28.49 vs 13.55, P=0.004). The BCMA expression was strongly positively correlated with 24-h urine protein, was moderately positively correlated with serum M protein and the percentage of plasma cells in bone marrow, was moderately negatively correlated with albumin and hemoglobin count, and was weakly positively correlated with serum corrected calcium (P<0.05). And it was found that the BCMA expression of positive serum immunofixation electrophoresis patients was higher than that of negative patients (rank average 29.94 vs 16.75, P=0.017). And we try to clarify the relationship between the bone marrow BCMA expression and the peripheral blood sBCMA expression. However, we have not found a clear correlation between them so far (P>0.05).

Phase-Specific and Lifetime Costs of Multiple Myeloma Among Older Adults in the US.

Importance: Health care costs associated with diagnosis and care among older adults with multiple myeloma (MM) are substantial, with cost of care and the factors involved differing across various phases of the disease care continuum, yet little is known about cost of care attributable to MM from a Medicare perspective. Objective: To estimate incremental phase-specific and lifetime costs and cost drivers among older adults with MM enrolled in fee-for-service Medicare. Design, Setting, and Participants: A retrospective cohort study was conducted using population-based registry data from the 2007-2015 Surveillance, Epidemiology, and End Results database linked with 2006-2016 Medicare administrative claims data. Data analysis included 4533 patients with newly diagnosed MM and 4533 matched noncancer Medicare beneficiaries from a 5% sample of Medicare to assess incremental MM lifetime and phase-specific costs (prediagnosis, initial care, continuing care, and terminal care) and factors associated with phase-specific incremental MM costs. The study was conducted from June 1, 2019, to April 30, 2021. Main Outcomes and Measures: Incremental MM costs were calculated for the disease lifetime and the following 4 phases of care: prediagnosis, initial, continuing care, and terminal. Results: Of the 4533 patients with MM included in the study, 2374 were women (52.4%), 3418 (75.4%) were White, and mean (SD) age was 75.8 (6.8) years (2313 [51.0%] aged ≥75 years). The characteristics of the control group were similar; however, mean (SD) age was 74.2 (8.8) years (2839 [62.6%] aged ≤74 years). Mean adjusted incremental MM lifetime costs were $184 495 (95% CI, $183 099-$185 968). Mean per member per month phase-specific incremental MM costs were estimated to be $1244 (95% CI, $1216-$1272) for the prediagnosis phase, $11 181 (95% CI, $11 052-$11 309) for the initial phase, $5634 (95% CI, $5577-$5694) for the continuing care phase, and $6280 (95% CI, $6248-$6314) for the terminal phase. Although inpatient and outpatient costs were estimated as the major cost drivers for the prediagnosis (inpatient, 55.8%; outpatient, 40.2%), initial care (inpatient, 38.1%; outpatient, 35.5%), and terminal (inpatient, 33.0%; outpatient, 34.6%) care phases, prescription drugs (44.9%) were the largest cost drivers in the continuing care phase. Conclusions and Relevance: The findings of this study suggest that there is substantial burden to Medicare associated with diagnosis and care among older adults with MM, and the cost of care and cost drivers vary across different phases of the cancer care continuum. The study findings might aid policy discussions regarding MM care and coverage and help further the development of alternative payment models for MM, accounting for differential costs across various phases of the disease continuum and their drivers.

Gene expression derived from alternative promoters improves prognostic stratification in multiple myeloma.

Clinical and genetic risk factors are currently used in multiple myeloma (MM) to stratify patients and to design specific therapies. However, these systems do not capture the heterogeneity of the disease supporting the development of new prognostic factors. In this study, we identified active promoters and alternative active promoters in 6 different B cell subpopulations, including bone-marrow plasma cells, and 32 MM patient samples, using RNA-seq data. We find that expression initiated at both regular and alternative promoters was specific of each B cell subpopulation or MM plasma cells, showing a remarkable level of consistency with chromatin-based promoter definition. Interestingly, using 595 MM patient samples from the CoMMpass dataset, we observed that the expression derived from some alternative promoters was associated with lower progression-free and overall survival in MM patients independently of genetic alterations. Altogether, our results define cancer-specific alternative active promoters as new transcriptomic features that can provide a new avenue for prognostic stratification possibilities in patients with MM.

Biological properties of bone marrow plasma cells influence their recovery in aspirate specimens: impact on classification of plasma cell disorders and potential bias to evaluation of treatment response.

Methods to estimate bone marrow plasma cells (BMPC) basically include histopathology, cytomorphology, and flow cytometry. The present study compares the outcomes of these methods with special focus on the impact of BMPC-specific characteristics on their recovery by either method. Laboratory reports of diagnostic samples from 238 consecutive patients with suspected or known plasma cell disease were retrospectively analyzed. The median (IQR) proportion of BMPC was 30.0% (15.0-70.0%) by histological review (hBMPC), 7.0% (2.0-16.0%) by smear review (sBMPC), and 3.0% (0.8-10.0%) by flow cytometry (fBMPC). The disparity of results between core biopsy and aspirate smear was enhanced in case of poor quality of the smear, increased BM fiber content, higher grade cell atypia, expression of CD56 (all P < 0.0001), the number of cytogenetic aberrations (P = 0.0002), and abnormalities of the MYC gene (P = 0.0002). Conversely, expression of CD19 and a non-clonal plasma cell phenotype were associated with a lower difference between hBMPC and sBMPC (both P < 0.0001). The disparity between the percentages of sBMPC and fBMPC was associated with the quality of the smear (P = 0.0007) and expression of CD56 (P < 0.0001). Our results suggest that the recovery of BMPC in aspirate specimens not only is a matter of sampling quality but also depends on biological cell properties. Aspiration failure due to malignant type features of BMPC may lead to misclassification of plasma cell disorders and represent a bias for the detection of minimal residual disease after therapy.

The functional epigenetic landscape of aberrant gene expression in molecular subgroups of newly diagnosed multiple myeloma.

BACKGROUND: Multiple Myeloma (MM) is a hematological malignancy with genomic heterogeneity and poor survival outcome. Apart from the central role of genetic lesions, epigenetic anomalies have been identified as drivers in the development of the disease. METHODS: Alterations in the DNA methylome were mapped in 52 newly diagnosed MM (NDMM) patients of six molecular subgroups and matched with loci-specific chromatin marks to define their impact on gene expression. Differential DNA methylation analysis was performed using DMAP with a ≥10% increase (hypermethylation) or decrease (hypomethylation) in NDMM subgroups, compared to control samples, considered significant for all the subsequent analyses with p<0.05 after adjusting for a false discovery rate. RESULTS: We identified differentially methylated regions (DMRs) within the etiological cytogenetic subgroups of myeloma, compared to control plasma cells. Using gene expression data we identified genes that are dysregulated and correlate with DNA methylation levels, indicating a role for DNA methylation in their transcriptional control. We demonstrated that 70% of DMRs in the MM epigenome were hypomethylated and overlapped with repressive H3K27me3. In contrast, differentially expressed genes containing hypermethylated DMRs within the gene body or hypomethylated DMRs at the promoters overlapped with H3K4me1, H3K4me3, or H3K36me3 marks. Additionally, enrichment of BRD4 or MED1 at the H3K27ac enriched DMRs functioned as super-enhancers (SE), controlling the overexpression of genes or gene-cassettes. CONCLUSIONS: Therefore, this study presents the underlying epigenetic regulatory networks of gene expression dysregulation in NDMM patients and identifies potential targets for future therapies.

Genotype-immunophenotype analysis reveals the immunogenomic subtype and prognosis of multiple myeloma.

Immune dysfunction plays an important role in tumour development, recurrence, therapeutic responses and overall survival (OS). Multiple myeloma (MM) is a clonal B-cell malignancy which characterized by anti-tumoural immune dysfunction. In this study, we analysed 28 tumour-immune-related pathways and calculated the immune pathway score through published microarray data from the Gene Expression Omnibus (GEO) data portal. A training set of 345 patients and a validation set of 214 patients with primary MM were chosen. We performed least absolute shrinkage and selection operator (LASSO) analysis to identify prognostic factors. Then, we used cluster analysis to divide patients into three immunogenomic subtypes, which named abnormal immune activated type, common type and anti-myeloma immune activated type. Log­rank tests showed that anti-myeloma immune activated type had the best prognosis and abnormal immune activated type had the shortest OS (P = 0.000) and event-free survival (EFS) (P = 0.000). Multivariate Cox also indicated that the immunogenomic subtype was an independent predictor of OS (P = 0.001) and EFS (P = 0.000). We also analysed the characteristics and the immune-response patterns of different subtypes. Then, we established a mathematical model to classify patients in the validation set. In the validation set, patients with different immunogenomic subtypes also had a significantly different OS (P = 0.001) and EFS (P = 0.005). Our study explored tumour-immune-related pathways at a multi-dimensional level and found the immunogenomic subtype of MM. Potential mechanisms on the genetic level of how tumour-immunity influences the prognosis and therapeutic responses are provided. The immunogenomic subtype may be feasible for deciding clinical treatment in the future.

Genomic profiling of multiple myeloma: New insights and modern technologies.

Advances in technologies for genomic profiling, primarily with next generation sequencing, have lead to a better understanding of the complex genomic landscape in multiple myeloma. Integrated analysis of whole genome, exome and transcriptome sequencing has lead to new insights on disease drivers including translocations, copy number alterations, somatic mutations, and altered gene expression. Disease progression in multiple myeloma is largely driven by structural variations including the traditional immunoglobulin heavy chain (IGH) translocations and hyperdiploidy which are early events in myelomagenesis as well as more complex events spanning over multiple chromosomes and involving amplifications and deletions. In this review, we will discuss recent insights on the genomic landscape of multiple myeloma and their implications for disease progression and personalized treatment. We will review how sequencing assays compare to current clinical methods and give an overview of modern technologies for interrogating genomic aberrations.