[HPP-2.0] Combinatory strategy using nanoscale proteomics and machine learning for T cell subtyping in peripheral blood of single multiple myeloma patients
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo Sapiens
SUBMITTER: 
Xinyou Zhang
PROVIDER: PXD049159 | iProX | Sun Feb 04 00:00:00 GMT 2024
REPOSITORIES: iProX
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Project description:T cells play crucial roles in our immunity against hematological tumors by inducing sustained immune responses. Flow cytometry-based detection of a limited number of specific protein markers has been routinely applied for basic research and clinical investigation in this area. In this study, we combined flow cytometry with the simple integrated spintip based proteomics technology (SISPROT) to characterize the proteome of primary T cell subtypes in the peripheral blood (PB) from single multiple myeloma (MM) patients.
2021-05-20 | MSV000087462 | MassIVE
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2010-05-19 | E-GEOD-15867 | biostudies-arrayexpress
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2022-11-23 | MTBLS6075 | MetaboLights
Project description:BackgroundNext-generation sequencing pipelines often perform error correction as a preprocessing step to obtain cleaned input data. State-of-the-art error correction programs are able to reliably detect and correct the majority of sequencing errors. However, they also introduce new errors by making false-positive corrections. These correction mistakes can have negative impact on downstream analysis, such as k-mer statistics, de-novo assembly, and variant calling. This motivates the need for more precise error correction tools.ResultsWe present CARE 2.0, a context-aware read error correction tool based on multiple sequence alignment targeting Illumina datasets. In addition to a number of newly introduced optimizations its most significant change is the replacement of CARE 1.0's hand-crafted correction conditions with a novel classifier based on random decision forests trained on Illumina data. This results in up to two orders-of-magnitude fewer false-positive corrections compared to other state-of-the-art error correction software. At the same time, CARE 2.0 is able to achieve high numbers of true-positive corrections comparable to its competitors. On a simulated full human dataset with 914M reads CARE 2.0 generates only 1.2M false positives (FPs) (and 801.4M true positives (TPs)) at a highly competitive runtime while the best corrections achieved by other state-of-the-art tools contain at least 3.9M FPs and at most 814.5M TPs. Better de-novo assembly and improved k-mer analysis show the applicability of CARE 2.0 to real-world data.ConclusionFalse-positive corrections can negatively influence down-stream analysis. The precision of CARE 2.0 greatly reduces the number of those corrections compared to other state-of-the-art programs including BFC, Karect, Musket, Bcool, SGA, and Lighter. Thus, higher-quality datasets are produced which improve k-mer analysis and de-novo assembly in real-world datasets which demonstrates the applicability of machine learning techniques in the context of sequencing read error correction. CARE 2.0 is written in C++/CUDA for Linux systems and can be run on the CPU as well as on CUDA-enabled GPUs. It is available at https://github.com/fkallen/CARE .
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| S-EPMC9421197 | biostudies-literature
Project description:The number of peripheral blood (PB) circulating tumor cells (CTCs) predicts risk of transformation in smoldering multiple myeloma (MM) and survival in active MM. Growing evidence suggests that as the tumor progresses and the microenvironment becomes hypoxic, clonal plasma cells (PCs) constantly invade new regions of the bone marrow (BM) through induced systemic recirculation. Of note, the frequency of CTCs is typically low and thus, it could be hypothesized that the dissemination of MM is made by few tumor cells with unique features that induce them to egress the BM and spread the disease through PB. This hypothesis has not been demonstrated because the molecular profile of CTCs in MM has not been investigated.
2019-02-26 | GSE124437 | GEO
Project description:<h4><strong>BACKGROUND:</strong> Multiple myeloma is characterized by clonal proliferation of malignant plasma cells in the bone marrow that produce monoclonal immunoglobulins. N-glycosylation changes of these monoclonal immunoglobulins have been reported in multiple myeloma, but previous studies only detected limited serum N-glycan features.</h4><h4><strong>METHODS:</strong> Here, a more detailed study of the human serum N-glycome of 91 multiple myeloma patients and 51 controls was performed. We additionally analyzed sequential samples from patients (n = 7) which were obtained at different time points during disease development as well as 16 paired blood serum and bone marrow plasma samples. N-glycans were enzymatically released and measured by mass spectrometry after linkage specific derivatization of sialic acids.</h4><h4><strong>RESULTS:</strong> A decrease in both α2,3- and α2,6-sialylation, galactosylation and an increase in fucosylation within complex-type N-glycans were found in multiple myeloma patients compared to controls, as well as a decrease in difucosylation of diantennary glycans. The observed glycosylation changes were present in all ISS stages, including the 'low-risk' ISS I. In individual patients, difucosylation of diantennary glycans decreased with development of the disease. Protein N-glycosylation features from blood and bone marrow showed strong correlation. Moreover, associations of monoclonal immunoglobulin (M-protein) and albumin levels with glycan traits were discovered in multiple myeloma patients.</h4><h4><strong>CONCLUSIONS & GENERAL SIGNIFICANCE: </strong>In conclusion, serum protein N-glycosylation analysis could successfully distinguish multiple myeloma from healthy controls. Further studies are needed to assess the potential roles of glycan trait changes and the associations of glycans with clinical parameters in multiple myeloma early detection and prognosis.</h4>
2022-04-27 | MTBLS676 | MetaboLights
Project description:A total of 40 Multiple Myeloma (MM) patients at clinical relapse who progressed during Proteasome Inhibitors (PIs) or Immunomodulating Drugs (IMiDs)-based therapies and who are assigned to antiCD38-based salvage treatments, will be enrolled. We will collect bone marrow (BM) and peripheral blood (PB) samples from patients at specific timepoints: baseline (BM, PB and buccal swab) every 3 month (PB) achievement of response (≥ Very Good Partial Response (VGPR)) (BM and PB) relapse or refractory status to antiCD38-based treatments (BM and PB) Samples will be processed and stored in the "Hematological Laboratory" located in the University of Turin (Italy) for various proposed analyses: at specific time-points CD138+ (Plasma Cells-PCs) awill be immunomagnetically enriched from the BM mononuclear cells and frozen as viable cells in dimethyl sulfoxide (DMSO); PB mononuclear cells (PBMCs) will be isolated from whole blood by density-gradient centrifugation, and frozen as above; plasma fraction from PB and BM will be obtained by centrifugation and stored frozen; Neutrophils will be obtained at the time of enrollment as a source of control germline DNA and stored frozen.
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