Immunogenetic characterization of clonal plasma cells in systemic light-chain amyloidosis

This study was supported by the Centro de Investigación Biomédica en Red—Área de Oncología—del Instituto de Salud Carlos III (CIBERONC; CB16/12/00369; and CB16/12/00489), Instituto de Salud Carlos III/Subdirección General de Investigación Sanitaria (FIS No. PI13/02196), Asociación Española Contra el Cáncer (GCB120981SAN and the Accelerator Award), CRIS against Cancer foundation grant 2014/0120, and the Black Swan Research Initiative of the International Myeloma Foundation.Peer reviewe

Flow cytometry for fast screening and automated risk assessment in systemic light-chain amyloidosis

Early diagnosis and risk stratification are key to improve outcomes in light-chain (AL) amyloidosis. Here we used multidimensional-flow-cytometry (MFC) to characterize bone marrow (BM) plasma cells (PCs) from a series of 166 patients including newly-diagnosed AL amyloidosis (N = 94), MGUS (N = 20) and multiple myeloma (MM, N = 52) vs. healthy adults (N = 30). MFC detected clonality in virtually all AL amyloidosis (99%) patients. Furthermore, we developed an automated risk-stratification system based on BMPCs features, with independent prognostic impact on progression-free and overall survival of AL amyloidosis patients (hazard ratio: ≥ 2.9;P ≤ .03). Simultaneous assessment of the clonal PCs immunophenotypic protein expression profile and the BM cellular composition, mapped AL amyloidosis in the crossroad between MGUS and MM; however, lack of homogenously-positive CD56 expression, reduction of B-cell precursors and a predominantly-clonal PC compartment in the absence of an MM-like tumor PC expansion, emerged as hallmarks of AL amyloidosis (ROC-AUC = 0.74;P < .001), and might potentially be used as biomarkers for the identification of MGUS and MM patients, who are candidates for monitoring pre-symptomatic organ damage related to AL amyloidosis. Altogether, this study addressed the need for consensus on how to use flow cytometry in AL amyloidosis, and proposes a standardized MFC-based automated risk classification ready for implementation in clinical practice

Tumor cells in light-chain amyloidosis and myeloma show distinct transcriptional rewiring of normal plasma cell development

Although light-chain amyloidosis (AL) and multiple myeloma (MM) are characterized by tumor plasma cell (PC) expansion in bone marrow (BM), their clinical presentation differs. Previous attempts to identify unique pathogenic mechanisms behind such differences were unsuccessful, and no studies have investigated the differentiation stage of tumor PCs in patients with AL and MM. We sought to define a transcriptional atlas of normal PC development in secondary lymphoid organs (SLOs), peripheral blood (PB), and BM for comparison with the transcriptional programs (TPs) of tumor PCs in AL, MM, and monoclonal gammopathy of undetermined significance (MGUS). Based on bulk and single-cell RNA sequencing, we observed 13 TPs during transition of normal PCs throughout SLOs, PB, and BM. We further noted the following: CD39 outperforms CD19 to discriminate newborn from long-lived BM-PCs; tumor PCs expressed the most advantageous TPs of normal PC differentiation; AL shares greater similarity to SLO-PCs whereas MM is transcriptionally closer to PB-PCs and newborn BM-PCs; patients with AL and MM enriched in immature TPs had inferior survival; and protein N-linked glycosylation–related TPs are upregulated in AL. Collectively, we provide a novel resource to understand normal PC development and the transcriptional reorganization of AL and other monoclonal gammopathies

Circulating tumor cells for comprehensive and multiregional non-invasive genetic characterization of multiple myeloma

Multiple myeloma (MM) patients undergo repetitive bone marrow (BM) aspirates for genetic characterization. Circulating tumor cells (CTCs) are detectable in peripheral blood (PB) of virtually all MM cases and are prognostic, but their applicability for noninvasive screening has been poorly investigated. Here, we used next-generation flow (NGF) cytometry to isolate matched CTCs and BM tumor cells from 53 patients and compared their genetic profile. In eight cases, tumor cells from extramedullary (EM) plasmacytomas were also sorted and whole-exome sequencing was performed in the three spatially distributed tumor samples. CTCs were detectable by NGF in the PB of all patients with MM. Based on the cancer cell fraction of clonal and subclonal mutations, we found that ~22% of CTCs egressed from a BM (or EM) site distant from the matched BM aspirate. Concordance between BM tumor cells and CTCs was high for chromosome arm-level copy number alterations (≥95%) though not for translocations (39%). All high-risk genetic abnormalities except one t(4;14) were detected in CTCs whenever present in BM tumor cells. Noteworthy, ≥82% mutations present in BM and EM clones were detectable in CTCs. Altogether, these results support CTCs for noninvasive risk-stratification of MM patients based on their numbers and genetic profile.This study was supported by the Centro de Investigación Biomédica en Red—Área de Oncología—del Instituto de Salud Carlos III (CIBERONC; CB16/12/00236, CB16/12/00369, CB16/12/00489, and CB16/12/00400); by Cancer Research UK [C355/A26819] and FC AECC and AIRC under the Accelerator Award Program; by the Instituto de Salud Carlos III, FCAECC and co-financed by FEDER (ERANET-TRANSCAN-2 iMMunocell AC17/00101); the Spanish Ministry of Science and Innovation and co-financed by FSE (Torres Quevedo fellowship, PTQ-16-08623); the Black Swan Research Initiative of the International Myeloma Foundation; European Research Council (ERC) under the European Commission’s H2020 Framework Programme (MYELOMANEXT, 680200); the Qatar National Research Fund (QNRF) Award No. 7-916-3-237; the AACR-Millennium Fellowship in Multiple Myeloma Research (15-40-38-PAIV); the Leukemia Research Foundation; and the Multiple Myeloma Research Foundation (MMRF) under the 2019 Research Fellowship Award

Real-world effectiveness of caplacizumab vs the standard of care in immune thrombotic thrombocytopenic purpura

Immune thrombotic thrombocytopenic purpura (iTTP) is a thrombotic microangiopathy caused by anti-ADAMTS13 antibodies. Caplacizumab is approved for adults with an acute episode of iTTP in conjunction with plasma exchange (PEX) and immunosuppression. The objective of this study was to analyze and compare the safety and efficacy of caplacizumab vs the standard of care and assess the effect of the concomitant use of rituximab. A retrospective study from the Spanish TTP Registry of patients treated with caplacizumab vs those who did not receive it was conducted. A total of 155 patients with iTTP (77 caplacizumab, 78 no caplacizumab) were included. Patients initially treated with caplacizumab had fewer exacerbations (4.5% vs 20.5%; P <.05) and less refractoriness (4.5% vs 14.1%; P <.05) than those who were not treated. Time to clinical response was shorter when caplacizumab was used as initial treatment vs caplacizumab used after refractoriness or exacerbation. The multivariate analysis showed that its use in the first 3 days after PEX was associated with a lower number of PEX (odds ratio, 7.5; CI, 2.3-12.7; P <.05) and days of hospitalization (odds ratio, 11.2; CI, 5.6-16.9; P <.001) compared with standard therapy. There was no difference in time to clinical remission in patients treated with caplacizumab compared with the use of rituximab. No severe adverse event was described in the caplacizumab group. In summary, caplacizumab reduced exacerbations and refractoriness compared with standard of care regimens. When administered within the first 3 days after PEX, it also provided a faster clinical response, reducing hospitalization time and the need for PEX

Real-world effectiveness of caplacizumab vs the standard of care in immune thrombotic thrombocytopenic purpura

Immune thrombotic thrombocytopenic purpura (iTTP) is a thrombotic microangiopathy caused by anti-ADAMTS13 antibodies. Caplacizumab is approved for adults with an acute episode of iTTP in conjunction with plasma exchange (PEX) and immunosuppression. The objective of this study was to analyze and compare the safety and efficacy of caplacizumab vs the standard of care and assess the effect of the concomitant use of rituximab. A retrospective study from the Spanish TTP Registry of patients treated with caplacizumab vs those who did not receive it was conducted. A total of 155 patients with iTTP (77 caplacizumab, 78 no caplacizumab) were included. Patients initially treated with caplacizumab had fewer exacerbations (4.5% vs 20.5%; P < .05) and less refractoriness (4.5% vs 14.1%; P < .05) than those who were not treated. Time to clinical response was shorter when caplacizumab was used as initial treatment vs caplacizumab used after refractoriness or exacerbation. The multivariate analysis showed that its use in the first 3 days after PEX was associated with a lower number of PEX (odds ratio, 7.5; CI, 2.3-12.7; P < .05) and days of hospitalization (odds ratio, 11.2; CI, 5.6-16.9; P < .001) compared with standard therapy. There was no difference in time to clinical remission in patients treated with caplacizumab compared with the use of rituximab. No severe adverse event was described in the caplacizumab group. In summary, caplacizumab reduced exacerbations and refractoriness compared with standard of care regimens. When administered within the first 3 days after PEX, it also provided a faster clinical response, reducing hospitalization time and the need for PEX

MALDI-TOF analysis of blood serum proteome can predict the presence of monoclonal gammopathy of undetermined significance

Monoclonal gammopathy of undetermined significance (MGUS) is a plasma cell dyscrasia that can progress to malignant multiple myeloma (MM). Specific molecular biomarkers to classify the MGUS status and discriminate the initial asymptomatic phase of MM have not been identified. We examined the serum peptidome profile of MGUS patients and healthy volunteers using MALDI-TOF mass spectrometry and developed a predictive model for classifying serum samples. The predictive model was built using a support vector machine (SVM) supervised learning method tuned by applying a 20-fold cross-validation scheme. Predicting class labels in a blinded test set containing randomly selected MGUS and healthy control serum samples validated the model. The generalization performance of the predictive model was evaluated by a double cross-validation method that showed 88% average model accuracy, 89% average sensitivity and 86% average specificity. Our model, which classifies unknown serum samples as belonging to either MGUS patients or healthy individuals, can be applied to clinical diagnosis. © 2018 Barceló et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.We are grateful to Dr. A. Gutierrez for his support and assistance in serum sample collection. We also thank C. Serret for helpful assistance in serum sample collection. We are indebted to Dr. N. Matamoros for valuable discussions on clinical issues and to Dr. J. Merino for his useful help in data analysis. The authors would like to acknowledge and thank HUSE (University Hospital Son Espases), Biobank HUSE and “Fundacio´ Banc de Sang i Teixits de les Illes Balears” (Balearic Islands Blood Bank) for providing serum samples used in this work, and the “Servicios Cientificote´cnicos” (UIB) for their assistance in providing the infrastructure to conduct the study.Peer reviewe

MALDI-TOF analysis of blood serum proteome can predict the presence of monoclonal gammopathy of undetermined significance

Monoclonal gammopathy of undetermined significance (MGUS) is a plasma cell dyscrasia that can progress to malignant multiple myeloma (MM). Specific molecular biomarkers to classify the MGUS status and discriminate the initial asymptomatic phase of MM have not been identified. We examined the serum peptidome profile of MGUS patients and healthy volunteers using MALDI-TOF mass spectrometry and developed a predictive model for classifying serum samples. The predictive model was built using a support vector machine (SVM) supervised learning method tuned by applying a 20-fold cross-validation scheme. Predicting class labels in a blinded test set containing randomly selected MGUS and healthy control serum samples validated the model. The generalization performance of the predictive model was evaluated by a double cross-validation method that showed 88% average model accuracy, 89% average sensitivity and 86% average specificity. Our model, which classifies unknown serum samples as belonging to either MGUS patients or healthy individuals, can be applied to clinical diagnosis