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.

Combination of Genetic Aberration With International Staging System Classification for Stratification of Asian Multiple Myeloma Patients Undergoing Autologous Stem Cell Transplantation.

BACKGROUND/AIM: The aim of the study was to contribute to the development of adaptive risk stratification methods specific to Asian multiple myeloma (MM) patients undergoing autologous stem cell transplantation (ASCT). PATIENTS AND METHODS: We conducted this study to evaluate the prognostic impact of genetic abnormalities detected by fluorescent in situ hybridization (FISH) on survival outcomes in combination with the International Staging System (ISS) classification in 161 MM patients. This was a single-center retrospective longitudinal cohort study of newly diagnosed MM patients undergoing ASCT within 12 months from initial diagnosis. A single-center retrospective cohort study of newly diagnosed MM. RESULTS: Patients were divided into 3 groups according to risk stratification: 1) low-risk, patients without del(17p13) nor t(14;16) or t(4;14) and ISS I/II; 2) high-risk, patients with t(4;14), regardless of ISS stage; 3) intermediate-risk, all remaining patients. The median PFS for the low-risk group was 18 months versus 13 months for the intermediate group (p=0.047, HR=1.527, 95%CI=1.006-2.316) versus 10 months for the high-risk group (p<0.001, HR=2.656, 95%CI=1.572-4.490). CONCLUSION: An ISS/FISH-based prognostication strategy was developed that can predict PFS for Asian MM patients undergoing ASCT.

Classification of Plasma Cell Disorders by 21 Tesla Fourier Transform Ion Cyclotron Resonance Top-Down and Middle-Down MS/MS Analysis of Monoclonal Immunoglobulin Light Chains in Human Serum.

The current five-year survival rate for systemic AL amyloidosis or multiple myeloma is ∼51%, indicating the urgent need for better diagnosis methods and treatment plans. Here, we describe highly specific and sensitive top-down and middle-down MS/MS methods owning the advantages of fast sample preparation, ultrahigh mass accuracy, and extensive residue cleavages with 21 telsa FT-ICR MS/MS. Unlike genomic testing, which requires bone marrow aspiration and may fail to identify all monoclonal immunoglobulins produced by the body, the present method requires only a blood draw. In addition, circulating monoclonal immunoglobulins spanning the entire population are analyzed and reflect the selection of germline sequence by B cells. The monoclonal immunoglobulin light chain FR2-CDR2-FR3 was sequenced by database-aided de novo MS/MS and 100% matched the gene sequencing result, except for two amino acids with isomeric counterparts, enabling accurate germline sequence classification. The monoclonal immunoglobulin heavy chains were also classified into specific germline sequences based on the present method. This work represents the first application of top/middle-down MS/MS sequencing of endogenous human monoclonal immunoglobulins with polyclonal immunoglobulins background.

Reference intervals and diagnostic ranges for serum free κ and free λ immunoglobulin light chains vary by instrument platform: Implications for classification of patient results in a multi-center study.

BACKGROUND: Serum free light chain (FLC) immunoglobulins are key biomarkers that aid in the diagnosis, prognosis and assessment of treatment response in patients with plasma cell disorders (PCD). Here we investigated the transference of manufacturer's reported κFLC, λFLC and κ to λ FLC reference intervals (RI) and established de novo FLC RI and diagnostic ranges on four instruments at three academic medical centers. In addition, we also compared the classification of patient FLC results using manufacturer's versus established RIs and diagnostic ranges. METHODS: CLSI EP28-A3C protocol was applied to investigate transference and establishment of FLC reference intervals on the cobas (Roche), Immage (Beckman), Optilite and SPA Plus (Binding Site). Serum κ FLC and λ FLC were measured in reference sera (N = 126) with estimation of central 95% RIs and FLC ratio diagnostic range (total range). Frequencies (%) in patient FLC results (N > 380 per institution) classified above, below or within manufacturer's versus established FLC RI were compared. RESULTS: Three of four instrument platforms did not exhibit acceptable transference of manufacturer's reported κ FLC RI. The manufacturer's reported FLC total diagnostic range did not encompass all values observed in reference sera for any of the four platforms evaluated. Established FLC ratio diagnostic ranges reduced the frequency of patient results classified above range for three of four platforms evaluated. CONCLUSIONS: Transference of manufacturer's reported FLC RIs may be inappropriate for select instrument platforms. De novo establishment of FLC RIs specific to instrument platform is highly recommended in order to assure correct patient result classification.

The multiple myelomas - current concepts in cytogenetic classification and therapy.

Multiple myeloma (MM) is a plasma cell neoplasm that accounts for 2% of all haematological malignancies and predominantly affects older individuals (with a median age at diagnosis of 65-70 years). MM is consistently preceded by the clinically recognized precancerous stages monoclonal gammopathy of undetermined significance and smouldering MM. Thus far, MM has been considered as a single disease entity, but the clinical presentation, response to treatment, and survival outcomes of patients with MM are quite heterogeneous and highly dependent on a set of chromosomal abnormalities that can be identified in nearly all of them. These alterations include primary cytogenetic abnormalities, such as translocations involving chromosome 14q and trisomies of odd-numbered chromosomes, as well as secondary abnormalities, such as deletion of chromosome 17p and amplification of chromosome 1q. The aetiology of myeloma is poorly understood, although different nonoverlapping disease entities can be defined on the basis of their specific primary cytogenetic abnormalities, which have a major role in determining clinical behaviour. This classification might enable the development of better treatment strategies focused on the underlying biology of each specific subtype. Herein, we describe treatment approaches that incorporate the current standard of care for patients with MM along with recommended alterations or improvements that might provide additional clinical benefit for certain subgroups of patients.

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.

Raman spectroscopy differentiates between sensitive and resistant multiple myeloma cell lines.

Current methods for identifying neoplastic cells and discerning them from their normal counterparts are often nonspecific and biologically perturbing. Here, we show that single-cell micro-Raman spectroscopy can be used to discriminate between resistant and sensitive multiple myeloma cell lines based on their highly reproducible biomolecular spectral signatures. In order to demonstrate robustness of the proposed approach, we used two different cell lines of multiple myeloma, namely MM.1S and U266B1, and their counterparts MM.1R and U266/BTZ-R subtypes, resistant to dexamethasone and bortezomib, respectively. Then, micro-Raman spectroscopy provides an easily accurate and noninvasive method for cancer detection for both research and clinical environments. Characteristic peaks, mostly due to different DNA/RNA ratio, nucleic acids, lipids and protein concentrations, allow for discerning the sensitive and resistant subtypes. We also explored principal component analysis (PCA) for resistant cell identification and classification. Sensitive and resistant cells form distinct clusters that can be defined using just two principal components. The identification of drug-resistant cells by confocal micro-Raman spectroscopy is thus proposed as a clinical tool to assess the development of resistance to glucocorticoids and proteasome inhibitors in myeloma cells.

[Towards precision medicine in myeloma: new evidence and challenges]. / Retos y avances hacia la medicina de precisión en el mieloma múltiple.

In the last few years, next-generation sequencing studies have provided insights into the mutational landscape of multiple myeloma. The identification of actionable mutations might give a precious opportunity for exploring new targeted therapies. Thus, the implementation of promising precision medicine strategies seems to be closer than ever. Throughout this review we describe the main challenges that should to be dealt with in this new era, in order to achieve the main goal of precision medicine, namely matching patients with their right drug. In addition, we provide a review of the most significant preclinical and clinical studies supporting the implementation of precision medicine nowadays. Finally, we highlight the need of clinical trials to evaluate the security and efficacy of these targeted therapies, as well as to validate predictive biomarkers that may allow an appropriate best-candidate selection and improvement of myeloma patients' survival and quality of life.

Retos y avances hacia la medicina de precisión en el mieloma múltiple / Towards precision medicine in myeloma: new evidence and challenges

Los estudios de secuenciación masiva realizados recientemente en un gran número de pacientes con mieloma múltiple han permitido profundizar el conocimiento genómico de la enfermedad. La identificación de mutaciones driver como potenciales dianas terapéuticas ofrece una oportunidad para explorar estrategias de tratamiento dirigido a nivel molecular en el mieloma. Por ello, nos encontramos a las puertas de la medicina personalizada, cuyo objetivo fundamental es administrar el tratamiento adecuado a cada paciente según las características concretas de su enfermedad. Este cambio de paradigma es prometedor, aunque a la par surgen nuevos desafíos. A lo largo de esta revisión se describirán los retos fundamentales a afrontar al aplicar la medicina personalizada en mieloma. Además se mencionarán los resultados más relevantes de estudios tanto preclínicos como clínicos con terapias dirigidas en mieloma. Finalmente, se destacará la necesidad de llevar a cabo estudios prospectivos aleatorizados con el fin de evaluar la eficacia de las nuevas terapias dirigidas, así como validar biomarcadores de respuesta que permitan seleccionar los candidatos idóneos para recibir dichos tratamientos, todo ello con el fin de mejorar la supervivencia y calidad de vida de los pacientes con mieloma.

In the last few years, next-generation sequencing studies have provided insights into the mutational landscape of multiple myeloma. The identification of actionable mutations might give a precious opportunity for exploring new targeted therapies. Thus, the implementation of promising precision medicine strategies seems to be closer than ever. Throughout this review we describe the main challenges that should to be dealt with in this new era, in order to achieve the main goal of precision medicine, namely matching patients with their right drug. In addition, we provide a review of the most significant preclinical and clinical studies supporting the implementation of precision medicine nowadays. Finally, we highlight the need of clinical trials to evaluate the security and efficacy of these targeted therapies, as well as to validate predictive biomarkers that may allow an appropriate best-candidate selection and improvement of myeloma patients' survival and quality of life.