The Multiple Myeloma dataset (MM-dataset) is a new multi-class database with 59 features for 203 patient records categorized into 9 labels stage of MM cancer which are classified by specialists on Hematology. It is made public, in order to allow comparative experiments with other research works.

The Multiple Myeloma (MM) is a type of blood cancer that affects the plasma cells in bone morrow. Its diagnosis is difficult in the early stage and depends on several medical exams and tests, thus its process is very long and can discourage patients. This may be the principal problem.

In the litereture, all the proposed reasearches to the assistance with the medical diagnosis in multiple myeloma (MM) disease, are based on genetic databases. So, we proposed our new dataset which contains the results of different MM diagnosis exams, and which can be used to detect clinical and para-clinical factors for the diagnosis of MM.

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 6, 2023. Database providing access and limited analysis to the MMGP portal data sets. These include the MMRC funded reference array comparative genomic hybridization (aCGH) and gene expression data and additional public multiple myeloma datasets. The MMGP will be updated with new features such as additional data and analysis tools as they become available.

The Multiple Myeloma Research Foundation (MMRF) runs a multi-site longitudinal clinical registry study of patients newly diagnosed with MM. This project is called CoMMpass, and collects both clinical and genomic information periodically. Researchers used interim analysis 19 (IA19) for their conclusions in "Gene interaction network analysis in multiple myeloma detects complex immune dysregulation associated with shorter survival." Includes clinical information, RNA sequencing (RNA-Seq) information, and copy number aberration (CNA), among others.

The Cancer Moonshot Biobank is a National Cancer Institute initiative to support current and future investigations into drug resistance and sensitivity and other NCI-sponsored cancer research initiatives, with an aim of improving researchers' understanding of cancer and how to intervene in cancer initiation and progression. During the course of this study, biospecimens (blood and tissue removed during medical procedures) and associated data will be collected longitudinally from at least 1000 patients across at least 10 cancer types, who are receiving standard of care cancer treatment at multiple NCI Community Oncology Research Program (NCORP) sites.

This collection contains de-identified radiology and histopathology imaging procured from subjects in NCI’s Cancer Moonshot Biobank-Multiple Myeloma (CMB-MML) cohort. Associated genomic, phenotypic and clinical data will be hosted by The Database of Genotypes and Phenotypes (dbGaP) and other NCI databases. A summary of Cancer Moonshot Biobank imaging efforts can be found on the Cancer Moonshot Biobank Imaging page.

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Multiple Myeloma Drugs Market Size 2024-2028

The multiple myeloma drugs market size is forecast to increase by USD 7.94 billion at a CAGR of 6.78% between 2023 and 2028.

The market is experiencing significant growth, driven primarily by the increasing incidence of this cancer type worldwide. Multiple myeloma is a malignant plasma cells disorder, and its prevalence is projected to rise due to an aging population and improved diagnostic methods. A key trend In the market is the emergence of nanomedicine platforms, which offer enhanced drug delivery and targeted therapy, thereby improving treatment efficacy and reducing side effects. Another trend is the growing popularity of complementary and alternative medicines, including herbal remedies and dietary supplements, as adjunct therapies for multiple myeloma. However, challenges such as high treatment costs and the limited availability of effective therapies for relapsed or refractory multiple myeloma persist. The market is segmented into immunomodulators, proteasome inhibitors, alkylating agents, and the emerging segment of XPO1 inhibitors. Companies seeking to capitalize on market opportunities and navigate challenges effectively must focus on developing innovative therapies, optimizing drug delivery systems, and exploring partnerships and collaborations to expand their product portfolios. By staying abreast of these market dynamics, stakeholders can make informed strategic decisions and operational plans.

What will be the Size of the Multiple Myeloma Drugs Market during the forecast period?

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The market encompasses a range of therapeutics designed to address this complex and often challenging blood cancer. Key drivers of market growth include the increasing number of plasma cell-related diagnoses, particularly those involving paraproteins and associated complications such as anemia, infections, and bone marrow damage. Treatment options for multiple myeloma include various modalities, including stem cell transplantation, radiation therapy, chemotherapy, targeted therapy, and biological drugs. Notable classes of multiple myeloma therapeutics include imids (lenalidomide, pomalidomide), Car-T cells, and bispecific antibodies.
Additionally, Car-T cells are a type of immunotherapy that uses genetically modified T cells to target and destroy cancer cells. The market is characterized by a pipeline of innovative oncology therapeutics, with numerous clinical trials yielding positive clinical data for new treatments. The market's size and direction reflect the ongoing efforts to improve patient outcomes and address the unmet needs of multiple myeloma patients. Multiple myeloma therapeutics continue to evolve, with a focus on developing more effective, targeted, and patient-friendly treatment options.

How is this Multiple Myeloma Drugs Industry segmented?

The industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Therapy

  Targeted therapy
  Biologic therapy
  Chemotherapy
  Others


Distribution Channel

  Hospital pharmacy
  Retail pharmacy
  E-pharmacy


Geography

  North America

    US
    Canada


  Europe

    Germany
    UK


  Asia
  Rest of World (ROW)

By Therapy Insights

The targeted therapy segment is estimated to witness significant growth during the forecast period.

Multiple myeloma, a type of blood cancer, affects the production of abnormal plasma cells In the bone marrow, leading to the accumulation of paraproteins and organ dysfunction. Anemia and infections are common complications in active multiple myeloma, while smoldering myeloma is a precursor disorder. Treatment options include chemotherapy, targeted therapy, stem cell transplantation, radiation therapy, and immunotherapy. Proteasome inhibitors, such as VELCADE (bortezomib), and histone deacetylase inhibitors, like FARYDAK (panobinostat), are targeted therapies used in multiple myeloma treatment. VELCADE blocks the action of a substance in myeloma cells that disrupts proteins, helping to kill the cells.

It was initially approved for multiple myeloma treatment following chemotherapy and later for the initial phase in 2008. However, sales have declined due to patent expiry, with an annual sales decrease of approximately 6% in 2021. Immunotherapy, including monoclonal antibodies like Sarclisa (isatuximab-irfc), immunomodulators, and protease inhibitors, is another treatment paradigm. Newer therapies, such as XPO1 inhibitors (Selinexor), CAR-T cell therapies, and bispecific antibodies, are under clinical trials. The healthcare community continues to explore treatment landscapes and paradigms to improve patient prognosis. Multiple myeloma therapeutics are available in hospitals, clinics, and through hospital p

Study Description from dbGaP: "This project was designed to describe genetic abnormalities in primary samples from patients with multiple myeloma by next generation sequencing.

We generated sequence data from multiple myeloma (MM) patients analyzing DNA both from tumor cells (purified from bone marrow using CD138 selection as a marker of plasma cells) and from normal peripheral blood cells (either whole blood or Ficoll-purified mononuclear cells). Using massively parallel sequencing technology (Illumina GA-2 or HiSeq)), we performed whole-genome sequencing (WGS) and/or whole-exome sequencing (WES). The initial set currently deposited contains data from 38 MM patients (23 patients surveyed by WGS and 16 patients by WES, with one patient analyzed by both approaches).

Genomes were sampled to high depth, obtaining an average of 33X coverage and 104X coverage for WGS and WES tumors, respectively. The normal samples had similar coverage. Our goal is to help researchers understand the complex genetic landscape of multiple myeloma and provide a resource for the generation of biological hypotheses."

The dataset includes WGS and WXS sequencing data of 408 samples.

Study Description from dbGaP: "The Multiple Myeloma Research Foundation (MMRF) CoMMpass (Relating Clinical Outcomes in MM to Personal Assessment of Genetic Profile) trial (NCT01454297) is a longitudinal observation study of 1000 newly diagnosed myeloma patients receiving various standard approved treatments that aim at collecting tissue samples, genetic information, Quality of Life (QoL) and various disease and clinical outcomes over 10 years."

The dataset includes WGS, WXS, and RNA-Seq sequencing data of 5026 samples.

This dataset contains 371 case and control RNA sequencing samples of patients with multiple myeloma. Sequencing was performed on Illumina NovaSeq 6000 and HiSeq X, HiSeq 4000 and HiSeq 2000 using TruSeq Stranded RNA and TruSeq Stranded total mRNA Kits. The sequencing was always paired.

This repository contains R Seurat objects associated with our study titled "A single-cell atlas characterizes dysregulation of the bone marrow immune microenvironment associated with outcomes in multiple myeloma".

Single cell data contained within this object comes from MMRF Immune Atlas Consortium work.

The .rds files contains a Seurat object saved with version 4.3. This can be loaded in R with the readRDS command.

Two .RDS files are included in this version of the release.

• Discovery object: MMRF_ImmuneAtlas_Full_With_Corrected_Censored_Metadata.rds contains all aliquots belonging to the 'discovery' cohort as used in the initial paper. This represents the dataset used for initial clustering, cell annotation, and analysis.
  
  
• Discovery + Validation object: COMBINED_VALIDATION_MMRF_ImmuneAtlas_Full_Censored_Metadata.rds contains both aliquots belonging to the initial 'discovery' cohort, and aliquots belonging to the 'validation' cohort. The group each cell is derived from is listed under the 'cohort' variable. Labels related to cell annotation, including doublet status, are derived from a label transfer process as described in the paper. Labels for the original 'discovery' cohort are unchanged. UMAPs have been reconstructed with both the discovery and validation cohorts integrated.

--

The discovery object contains two assays:

• "RNA" - The raw count matrix
• "RNA_Batch_Corrected" - Counts adjusted for the combination of 'Study_Site' and 'Batch'.
  • Analysis should prefer the original RNA assay, unless using pipelines which does not support adjusting for technical covariates.

Currently, the validation object only includes the uncorrected RNA assay.

--

The object contains two umaps in the reduction slot:

• umap - will render the UMAP for the full object with all cells.
• umap.sub -contains the UMAP embeddings for individual 'compartments', as indicated by 'subcluster_V03072023_compartment'

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Each sample has three different identifiers:

• public_id
  • Indicates a specific patient (n=263).
  • MMRF_####
  • This is a standard identifier which is used across all MMRF CoMMpass datasets
  • public_ids can map to multiple d_visit_specimen_ids and aliquot_ids
  • As of now, all public_ids have a single sample collected at Baseline.
    • This can be accessed by filtering for 'collection_event' %in% c("Baseline", "Screening") or VJ_INTERVAL == 'Baseline'
• d_visit_specimen_id
  • Indicates a specific visit by a patient (n=358)
  • MMRF_####_Y
    • Y is a number indicate that this is the 'Y' sample obtained from said patient. This does not correspond to a specific timepoint.
  • This is a standard identifier, which is used across all MMRF CoMMpass datasets
  • The purpose of the visit is indicated in 'collection_event' (Baseline, Relapse, Remmission, etc.). The approximate interval the visit corresponds to is in "VJ_INTERVAL"
  • d_visit_specimen_id uniquely maps to one public_id
  • d_visit_specimen_id can map to multiple aliquot_ids
• aliquot_id
  • Refers to the specific bone marrow aliquot sample processed (n=361)
  • MMRFA-######
  • This is a unique identifier for each processed scRNA-seq sample.
  • As of now, this uniquely maps to a combination of d_visit_specimen_id, Study_Site, and Batch
  • As of now, is an identifier specific to the MMRF ImmuneAtlas

Each cell has the following annotation information:

• subcluster_V03072023
  • These refer to an individual cluster derived from 'Seurat'.
  • Format is 'Compartment'.'Compartment-cluster'.'Compartment-subcluster'
    • 'NkT.2.2', indicates this cell is in the 'Natural Killer + T Cell compartment', was originally part of 'Cluster 2', and then was further separated into a refined subcluster 2.2'
    • If a parent cluster did not need to be further seprated, the 'Compartment-subcluster' part is omitted (e.g., 'NkT.6')
  • As of now, this uniquely maps to a specific cellID_short annotation.
  • Clustering was done on a per compartment basis
    • For most immune cell types, clustering was based on embeddings corrected for 'siteXbatch'. For Plasma, clustering was performed on embeddings corrected on a per-sample basis.
  • In the combined validation object, DISCOVERY.subcluster_V03072023 will contain values only for the discovery cohort, and have NA values for validation samples.
• subcluster_V03072023_compartment
  • These refer to one of five major compartments as identified roughly on the original UMAP. Clustering was performed on a per-compartment basis following a first pass rough annotation.
  • The possible compartments are
    • NkT (T cell + Natural Killer Cells)
    • Myeloid (Monocytes, Macrophages, Dendritic cells, Neutrophil/Granulocyte populations)
    • BEry (B Cell, Erythroblasts, bone marrow progenitor populations, pDCs)
    • Ery (Erythrocyte population)
    • Plasma (Plasma cell populations)
  • Each compartment has it's own UMAP generated, which can be accessed in the 'umap.sub' reduction
  • One cluster was isolated from all other populations, and was not assigned to a compartment. This cluster is labeled as 'Full.23'.
  • In the combined validation object, DISCOVERY.subcluster_V03072023_compartment will contain values only for the discovery cohort, and have NA values for validation samples.
• cellID_short
  • This is the individual annotation for each cluster.
  • Please see the 'Cell Population Annotation Dictionary' for further details.
  • If different seurat clusters were assigned similar annotations, the celltype annotation will be appended with a distinct cluster gene, or with '_b', '_c'
• lineage_group
  • This is an annotation driven grouping of clusters into major immune populations, as shown in Figure 2.
  • This includes "CD8", "CD4", "M" (Myeloid), "B" (B cell), "E" (Erythroid), "P" (Plasma), "Other" (HSC, Fibro, pDC_a), "LQ" (Doublet)
• isDoublet
  • This is a binary 'True' or 'False' derived from manual review of clusters following doublet analysis, as described in the paper.
  • True indicates the cluster was determined to be a doublet population.
  • This is derived from 'doublet_pred', in which 'dblet_cluster' and 'poss_dblet_cluster' were flagged as doublet populations for subsequent analysis.
  • In the validation object, the doublet status of new samples were inferred by if label transfer from the discovery cohort mapped the cell from the new sample as one of the previously identified doublet populations. The raw doublet scores from doublet finder, pegasus, or scrublet, are not included in this release.

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Each sample has the following information indicating shipment batches, for batch correction

• Study_Site
  • The center which processed a specific aliquot_id
  • EMORY, MSSM, WashU, MAYO
• Batch
  • The shipment batch the sample was associated with
  • Valued 1 to 3 for EMORY, MSSM, MAYO, and 1 to 4 for WashU
• siteXbatch
  • A combination of the above to variables, to be used for batch correction
• (Combined Validation Object only): cohort
  • Indicates if the sample was involved in the 'discovery' cohort, or 'validation' cohort. Samples in the 'validation' cohort will have labels inferred from label mapping

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Each public_id has limited demographic information based on publicly available information in the MMRF CoMMpass study.

• d_pt_sex
  • Patient sex (not self-identified). Male or Female
• d_pt_race_1
  • Patient self-identified race
• d_pt_ethnicity
  • Patient self-identified ethnicity
• d_dx_amm_age
  • Patient age at diagnosis.
  • Not reported for patients above 90 at diagnosis
• d_dx_amm_bmi
  • Patient BMI at diagnosis
• d_pt_height_cm
  • Patient height at diagnosis, in centimeters.
• d_dx_amm_weight_kg
  • Patient weight at diagnosis, in kilograms

d_specimen_visit_id contains two data points providing limited information about the visit

• collection_event
  • Description of why the sample was collected
    • e.g., 'Baseline' and 'Screening' indicates the sample was obtained prior to therapy
    • 'Relapse/Progression' indicates the sample was collected due to disease progression based on clinical assessment
    • 'Remission/Response' indicates the sample was collected due to patient entering remission based on clinical assessment
    • Samples may be collected for reasons independent of the above, such as 'Pre' or 'Post' ASCT, or for other unspecified reasons
• VJ_INTERVAL
  • Indicates the rough interval following start of therapy the sample is assigned to
    
    • "Baseline", "Month 3", "Year 2", etc.

All the single-cell raw data, along with outcome and cytogenetic information, is available at MMRF’s VLAB shared resource. Requests to access these data will be reviewed by data access committee at MMRF and any data shared will be released under a data transfer agreement that will protect the identities of patients involved in the study. Other information from the CoMMpass trial can also generally be

48 samples of individuals with rare germline variants in familial multiple myeloma, whole genome sequencing

Characteristics of multiple myeloma inpatients.

6 samples from individuals with multiple myeloma with selective elimination of immunosuppressive T cells, rna sequencing

BackgroundInternational collaborative research is a mechanism for improving the development of disease-specific therapies and for improving health at the population level. However, limited data are available to assess the trends in research output related to orphan diseases.Methods and FindingsWe used bibliometric mapping and clustering methods to illustrate the level of fragmentation in myeloma research and the development of collaborative efforts. Publication data from Thomson Reuters Web of Science were retrieved for 2005–2009 and followed until 2013. We created a database of multiple myeloma publications, and we analysed impact and co-authorship density to identify scientific collaborations, developments, and international key players over time. The global annual publication volume for studies on multiple myeloma increased from 1,144 in 2005 to 1,628 in 2009, which represents a 43% increase. This increase is high compared to the 24% and 14% increases observed for lymphoma and leukaemia. The major proportion (>90% of publications) was from the US and EU over the study period. The output and impact in terms of citations, identified several successful groups with a large number of intra-cluster collaborations in the US and EU. The US-based myeloma clusters clearly stand out as the most productive and highly cited, and the European Myeloma Network members exhibited a doubling of collaborative publications from 2005 to 2009, still increasing up to 2013.Conclusion and PerspectiveMultiple myeloma research output has increased substantially in the past decade. The fragmented European myeloma research activities based on national or regional groups are progressing, but they require a broad range of targeted research investments to improve multiple myeloma health care.

Minimal residual disease (MRD) status in multiple myeloma (MM) is an important prognostic biomarker. Personalized blood-based targeted mass spectrometry (MS-MRD) detecting M-proteins was shown to provide a sensitive and minimally invasive alternative to MRD assessment in bone marrow. However, MS-MRD still comprises manual steps that hamper upscaling of sample size. Here, we introduce a proof-of-concept for a novel workflow using dia-PASEF technology and automated data processing.Using automated data processing of PASEF DDA and dia-PASEF measurements, we developed a workflow that identified unique targets from MM patient sera and personalized protein sequence databases. We generated patient-specific libraries linked to dia-PASEF methods and subsequently quantitated and reported M-protein concentrations in follow-up samples from MM patients. Assay performance of prm-PASEF and dia-PASEF workflows were compared and we tested mixing patient intake sera for multiplexed target identification and selection.

MM-PSGL-1 Data Set Revised

This dataset contains 518 case and control WGS sequencing samples of patients with multiple myeloma. Sequencing was performed on Illumina NovaSeq 6000 and HiSeq X using TruSeq Nano DNA Kits. The sequencing was always paired.

Additional file 1. Figure S1: Agilent BioAnalyzer profile of a 5hmC-seq Illumina library. Figure S2: MS quantification of 5mC in genomic plasma cell DNA is independent of age and sex. (A) Dot plot of 5mC global quantification by MS in normal plasma cells from healthy donors (N=5), and of myeloma cells of patients at diagnosis (N=40). (B) Dot plot of 5mC global quantification by MS by disease stage (ISS I N=9; ISS II N=17; ISS III N=13; NA=1). 5mC (C) and 5hmC (D) dot plot of MS quantification depending on the sex of the patients. 5mC (E) and 5hmC (F) dot plot of MS quantification depending on the age of the patients. Figure S3: Survival course depending on DNA methylation (5mC) level-based separation of two risk groups of NDMM (n=20 and 20). Figure S4: 5hmC association with expression and criteria of 5hmC peaks to merge in 5hmC peak clusters. (A) Average level of 5hmC in all genes normalized to the same body length. Red line stands for average 5hmC in genes with high expression level (greater than 100 Reads per Kilobase Million (RPKM)). Orange line stands for medium expression level (between 10 and 100 RPKM). Green line represents lowly expressed genes (between 1 and 10 RPKM), and blue line stands for very lowly expressed genes (below 1 RPKM). (B) Stitching of 5hmC into 5hmC-enriched domains. The y-axis represents the number of peaks left after merging. The x-axis represents the distance between peaks to merge. Each 5hmC sample was analyzed (one color per patient). The distance 12.5 kb was chosen to stitch 5hmC peaks into the 5hmC-enriched domains that we describe in this study. (C) Fraction of overlap between 5hmC-enriched domains of this study and CpG from the Illumina 450K chip. The red bar represents overlap with hypermethylated CpGs in B cell-specific enhancers that were described by Agirre and colleagues (see Additional file 3: Methods). Blue bars represent random CpGs from the same chip. Figure S5: 5hmC allows the identification of a putative CCND2 enhancer. (A) Correlation between CCND2 expression, 5hmC at CCND2 gene body and 5hmC at the putative 5hmC enhancer across the 40 MM patients. (B) Hi-C contact map in lymphoblastoid cells (GM12878 cell line) at the CCDN2 locus showing the spatial interaction between CCND2 gene and its putative enhancer. (C) Expression of core transcription factors predicted to orchestrate core regulatory circuitries with 5hmC and RNA expression genomic data. Figure S6: MM 5hmC-enriched domains associate with H3K27ac super-enhancers. Rank ordering of the 100 strongest 5hmC-enriched domains on average in the cohort (A), in the MMSET group (B), in the CCND1 group (C) and in the hyperdiploid group (D). Color highlights domains present in only one of the ROSE plots by group. Figure S7: 5hmC signal levels at WNT5B-associated domain are increased at relapse in MM07. (A) Normalized 5hmC enrichment at WNT5B-associated domain. Point shapes match replicates. Fold change=1.3, p=0.003, FDR>0.1. (B) Gene expression levels in RPKM measured by RNA-seq at diagnosis and relapse for three genes surrounding the WNT5B-associated domain. (C) 5hmC genomic signal around WNT5B-associated domain. Colors match those of (A) and (B). 5hmC domain is depicted under signal tracks (hg38: chr12:1,517,750-1,621,200).

This repository contains 10x Chromium linked-read WGS (lrWGS), RNAseq and H3K27Ac ChIPseq from multiple myeloma.

The data consists of fastq files from lrWGS of 37 individuals with data from tumor and matched normal tissue from 32 of them. Additionally, it contains fastq files from RNAseq of 32 of the 37 patients and H3K27Ac ChIPseq data from select patients.

The data set contains sensitive human genomic data and is under restricted access. Request for access can be made to datacentre@scilifelab.se.

BCMA-targeted CAR-T therapy has shown potent treatment outcomes in treating multiple myeloma (MM), a disease characterized by malignant bone marrow (BM) plasma cells. However, the remodeling of MM microenvironment after CAR-T therapy remains poorly understood. Here, we report the reconstitution of MM microenvironment by obtaining single-cell transcriptomes for paired BM specimens (n = 14) from 7 MM patients before (i.e., baseline, ''day −4'') and after (i.e., ''day 28'') post-lymphodepleted BCMA CAR-T therapy. Our analysis revealed heterogeneity in driver gene expression among MM cells, even those harboring the same cytogenetic abnormalities. The best overall responses of patients over the 15-month follow-up are positively correlated with the abundance and targeted cytotoxic activity of CD8+ effector CAR-T cells on day 28 after CAR-T cell infusion. Additionally, favorable responses are associated with attenuated immunosuppression mediated by regulatory T cells (Tregs), enhanced CD8+ eff..., The collected paired BM specimens from MM patients before and after BCMA CAR-T therapy were isolated into single cell suspensions, and 3'-scRNA-seq (Chromium Single Cell 3′ v3 Libraries) analysis was performed on each sample. The analysis included 7 MM patients (P1-P7). We collected baseline BM aspirate specimens (P1_B, P2_B, P3_B, P4_B, P5_B, P6_B, and P7_B) from each patient before (i.e., baseline, ''day −4'') BCMA CAR-T cell infusion (i.e., ''day 0''), and the BM aspirate specimens (P1_R, P2_R, P3_R, P4_R, P5_R, P6_R, and P7_R) after BCMA CAR-T therapy were collected on day 28. These patients had received 2 or 3 previous lines of therapies, including two patients (P2 and P6) with extramedullary disease; the other patients did not experience extramedullary progression. All patients received cyclophosphamide-mediated lymphodepletion on day −3 with the aim of potentiating the expansion of CAR-T cells. Efficacy assessments based on the International Myeloma Working Group (IMWG) criteria ..., , # Reconstitution of the Multiple Myeloma Microenvironment Following Lymphodepletion with BCMA CAR-T Therapy

https://doi.org/10.5061/dryad.44j0zpcn7

The collected paired BM specimens from MM patients before and after BCMA CAR-T therapy were isolated into single cell suspensions, and 3'-scRNA-seq (Chromium Single Cell 3' v3 Libraries) analysis was performed on each sample.

Description of the data and file structure

The dataset includes '.mtx' files, each of which is for creating a Seurat object in R.

The naming convention of the '.mtx' files was following the sampling time points of the MM patients receiving BCMA CAR-T therapy. Specifically, the 14 BM specimens that performed scRNA-seq were collected from 7 relapsed or refractory MM patients (i.e. 'P1, P2, P3, P4, P5, P6, and P7') before and after BCMA CAR-T therapy. For these specimens, the baseline (i.e. 'B') specimens (P1_B, P2_B, P3_B, P4_B, P5_B, P6_B, and P7_B) were collected from ...

Data and analysis scripts for the paper: Widespread amyloidogenicity potential of multiple myeloma patient-derived immunoglobulin light chains.

The context of the data files is described in the paper and the material deposited here is intended for reproducibility of the analysis performed in the paper.