Expression of ribosomal and actin network proteins and immunochemotherapy resistance in diffuse large B cell lymphoma patients

Ednersson, Susanne Bram; Stenson, Martin; Stern, Mimmie; Enblad, Gunilla; Fagman, Henrik; Nilsson‐Ehle, Herman; Hasselblom, Sverker; Andersson, Per‐Ola

doi:10.1111/bjh.15259

Cited by 13 publications

(9 citation statements)

References 55 publications

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“…Defensin Beta, Interferon, and Keratin Associated Proteins are known to be strong cancer driver genes [25,26]. Actin and Ribonuclease has been shown to play a significant role in some cancer types [27]. Further work is needed to determine the impact of olfactory genes on cancer as the current research is conflicted [28,29].…”

Section: Resultsmentioning

confidence: 99%

The Efficacy of Various Machine Learning Models for Multi-class Classification of RNA-Seq Expression Data

Ramroach

John

Joshi

2019

Advances in Intelligent Systems and Computing

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Late diagnosis and high costs are key factors that negatively impact the care of cancer patients worldwide. Although the availability of biological markers for the diagnosis of cancer type is increasing, costs and reliability of tests currently present a barrier to the adoption of their routine use. There is a pressing need for accurate methods that enable early diagnosis and cover a broad range of cancers. The use of machine learning and RNA-seq expression analysis has shown promise in the classification of cancer type. However, research is inconclusive about which type of machine learning models are optimal. The suitability of five algorithms were assessed for the classification of 17 different cancer types. Each algorithm was fine-tuned and trained on the full array of 18,015 genes per sample, for 4,221 samples (75 % of the dataset). They were then tested with 1,408 samples (25 % of the dataset) for which cancer types were withheld to determine the accuracy of prediction. The results show that ensemble algorithms achieve 100% accuracy in the classification of 14 out of 17 types of cancer. The clustering and classification models, while faster than the ensembles, performed poorly due to the high level of noise in the dataset. When the features were reduced to a list of 20 genes, the ensemble algorithms maintained an accuracy above 95% as opposed to the clustering and classification models.

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Section: Resultsmentioning

confidence: 99%

The Efficacy of Various Machine Learning Models for Multi-class Classification of RNA-Seq Expression Data

Ramroach

John

Joshi

2019

Advances in Intelligent Systems and Computing

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“…Large-scale proteomics enabled clear separation of cell lines representing GCB and ABC subtypes [6,7]. Follow-up clinical studies on FFPE tumor tissues [8][9][10] have provided promising results. Nevertheless, the advantage of proteome profiling over gene expression profiling based on FFPE tumor tissue is still unclear.…”

Section: Introductionmentioning

confidence: 99%

Proteomic Landscape of Extracellular Vesicles for Diffuse Large B-Cell Lymphoma Subtyping

Carvalho

Baeta

Henriques

et al. 2021

IJMS

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The role of extracellular vesicles (EVs) proteome in diffuse large B-cell lymphoma (DLBCL) pathology, subclassification, and patient screening is unexplored. We analyzed by state-of-the-art mass spectrometry the whole cell and secreted extracellular vesicles (EVs) proteomes of different molecular subtypes of DLBCL, germinal center B cell (GCB subtype), and activated B cell (ABC subtype). After quality control assessment, we compared whole-cell and secreted EVs proteomes of the two cell-of-origin (COO) categories, GCB and ABC subtypes, resulting in 288/1115 significantly differential expressed proteins from the whole-cell proteome and 228/608 proteins from EVs (adjust p-value < 0.05/p-value < 0.05). In our preclinical model system, we demonstrated that the EV proteome and the whole-cell proteome possess the capacity to separate cell lines into ABC and GCB subtypes. KEGG functional analysis and GO enrichment analysis for cellular component, molecular function, and biological process of differential expressed proteins (DEP) between ABC and GCB EVs showed a significant enrichment of pathways involved in immune response function. Other enriched functional categories for DEPs constitute cellular signaling and intracellular trafficking such as B-cell receptor (BCR), Fc_gamma R-mediated phagocytosis, ErbB signaling, and endocytosis. Our results suggest EVs can be explored as a tool for patient diagnosis, follow-up, and disease monitoring. Finally, this study proposes novel drug targets based on highly expressed proteins, for which antitumor drugs are available suggesting potential combinatorial therapies for aggressive forms of DLBCL. Data are available via ProteomeXchange with identifier PXD028267.

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“…This study allowed identifying 102 DA proteins and the authors could confirm the up-regulation of proteins involved in actin regulation in chemosensitive patients. Interestingly, they managed to highlight a potential role for ribosomal proteins in treatment-resistance as these proteins were largely represented in those found to be overexpressed in chemorefractory patients 39 . In comparison with these previously published works, our study points out the potential role of the microenvironment in drug-resistance in DLBCL.…”

Section: Resultsmentioning

confidence: 99%

Multi-omics dataset to decipher the complexity of drug resistance in diffuse large B-cell lymphoma

Fornecker

Muller

Bertrand

et al. 2019

Sci Rep

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The prognosis of patients with relapsed/refractory (R/R) diffuse large B-cell lymphoma (DLBCL) remains unsatisfactory and, despite major advances in genomic studies, the biological mechanisms underlying chemoresistance are still poorly understood. We conducted for the first time a large-scale differential multi-omics investigation on DLBCL patient’s samples in order to identify new biomarkers that could early identify patients at risk of R/R disease and to identify new targets that could determine chemorefractoriness. We compared a well-characterized cohort of R/R versus chemosensitive DLBCL patients by combining label-free quantitative proteomics and targeted RNA sequencing performed on the same tissues samples. The cross-section of both data levels allowed extracting a sub-list of 22 transcripts/proteins pairs whose expression levels significantly differed between the two groups of patients. In particular, we identified significant targets related to tumor metabolism (Hexokinase 3), microenvironment (IDO1, CXCL13), cancer cells proliferation, migration and invasion (S100 proteins) or BCR signaling pathway (CD79B). Overall, this study revealed several extremely promising biomarker candidates related to DLBCL chemorefractoriness and highlighted some new potential therapeutic drug targets. The complete datasets have been made publically available and should constitute a valuable resource for the future research.

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Expression of ribosomal and actin network proteins and immunochemotherapy resistance in diffuse large B cell lymphoma patients

Cited by 13 publications

References 55 publications

The Efficacy of Various Machine Learning Models for Multi-class Classification of RNA-Seq Expression Data

The Efficacy of Various Machine Learning Models for Multi-class Classification of RNA-Seq Expression Data

Proteomic Landscape of Extracellular Vesicles for Diffuse Large B-Cell Lymphoma Subtyping

Multi-omics dataset to decipher the complexity of drug resistance in diffuse large B-cell lymphoma

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