Identification of molecular features necessary for selective inhibition of B cell lymphoma proteins using machine learning techniques

2018 ◽  
Vol 23 (1) ◽  
pp. 55-73 ◽  
Author(s):  
Ahmad Mani-Varnosfaderani ◽  
Marzieh Sadat Neiband ◽  
Ali Benvidi
Keyword(s):  
Machine Learning ◽  
B Cell ◽  
Cell Lymphoma ◽  
B Cell Lymphoma ◽  
Selective Inhibition ◽  
Machine Learning Techniques ◽  
Molecular Features ◽  
Learning Techniques

scholarly journals CT-based radiomics model with machine learning for predicting primary treatment failure in diffuse large B-cell Lymphoma

2021 ◽  
Vol 14 (10) ◽  
pp. 101188
Author(s):  
Raoul Santiago ◽  
Johanna Ortiz Jimenez ◽  
Reza Forghani ◽  
Nikesh Muthukrishnan ◽  
Olivier Del Corpo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Treatment Failure ◽  
B Cell ◽  
Cell Lymphoma ◽  
B Cell Lymphoma ◽  
Primary Treatment ◽  
Large B Cell Lymphoma ◽  
Large B Cell

Molecular features of primary mediastinal B-cell lymphoma: involvement of p16INK4A , p53 and c-myc

1999 ◽  
Vol 107 (1) ◽  
pp. 106-113 ◽  
Author(s):  
Aldo Scarpa ◽  
Patrick S. Moore ◽  
Gildas Rigaud ◽  
Giorgio Inghirami ◽  
Marina Montresor ◽  
...  
Keyword(s):  
B Cell ◽  
Cell Lymphoma ◽  
B Cell Lymphoma ◽  
Molecular Features ◽  
Lymphoma Involvement

scholarly journals Targeting mitochondrial respiration and the BCL2 family in MYC-associated B-cell lymphoma

2020 ◽  
Author(s):  
Giulio Donati ◽  
Micol Ravà ◽  
Marco Filipuzzi ◽  
Paola Nicoli ◽  
Laura Cassina ◽  
...  
Keyword(s):  
Oxidative Phosphorylation ◽  
B Cell ◽  
Complex I ◽  
Cell Lymphoma ◽  
B Cell Lymphoma ◽  
High Grade ◽  
Apoptosis Pathway ◽  
Bcl2 Family ◽  
Molecular Features ◽  
Intrinsic Apoptosis Pathway

AbstractMultiple molecular features, such as activation of specific oncogenes (e. g. MYC, BCL2) or a variety of gene expression signatures, have been associated with disease course in diffuse large B-cell lymphoma (DLBCL). Understanding the relationships between these features and their possible exploitation toward disease classification and therapy remains a major priority in the field. Here, we report that MYC activity in DLBCL is closely correlated with – and most likely a driver of – gene signatures related to Oxidative Phosphorylation (OxPhos). On this basis, we hypothesized that enzymes involved in Oxidative Phosphorylation, and in particular electron-transport chain (ETC) complexes, might constitute tractable therapeutic targets in MYC-associated lymphoma. Indeed, our data show that MYC sensitizes B-cells to IACS-010759, a selective inhibitor of ETC complex I. Mechanistically, IACS-010759 activates an ATF4-driven Integrated Stress Response (ISR), engaging the intrinsic apoptosis pathway through the transcription factor CHOP. In line with these findings, IACS-010759 shows synergy with the BCL2 inhibitor venetoclax against double-hit lymphoma (DHL), a high-grade form of DLBCL with concurrent activation of MYC and BCL2. Similarly, in BCL2-negative lymphoma cell lines, inhibition of the BCL2-related protein Mcl-1 potentiates killing by IACS-010759. Altogether, ETC complex I inhibition engages the ISR to lower the apoptotic threshold in MYC-driven lymphomas and, in combination with select BCL2-family inhibitors, provides a novel therapeutic principle against this aggressive DLBCL subset.Statement of significanceThis work points to OxPhos as a key MYC-activated process and a tractable therapeutic target toward personalized treatment of high-grade DLBCL, providing strong context-dependent cooperation with BH3-mimetic compounds.

scholarly journals MicroRNA Signatures In B-Cell Lymphomas

Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 4155-4155
Author(s):  
Lorena Di Lisio ◽  
Margarita Sanchez-Beato ◽  
Gonzalo Gomez-Lopez ◽  
Maria E. E. Rodriguez ◽  
Santiago Montes-Moreno ◽  
...  
Keyword(s):  
Cell Differentiation ◽  
B Cell ◽  
Burkitt Lymphoma ◽  
Gene Expression Regulation ◽  
Conflicts Of Interest ◽  
Cell Lymphoma ◽  
B Cell Lymphoma ◽  
Lymphoid Tissues ◽  
Molecular Features ◽  
Lymphoma Type

Abstract Abstract 4155 Beyond the conventional criteria of lymphoma classification (integrated clinical, morphological, immunophenotypic, and molecular features) additional markers are still required for a more precise differential diagnosis and a better understanding of lymphoma pathogenesis. MicroRNAs (miRNA) are non-coding small RNAs that play an important role in gene expression regulation, contributing to cell differentiation and tumorigenesis. Specifically, miRNAs have been already described to play a relevant role in B cell differentiation, and in some cases proposed to constitute lymphoma-type specific markers and possible therapeutic targets. We explore the potential diagnostic application of miRNA expression in a large series of 147 cases including all B-cell non-Hodgkin lymphomas (NHL) major types and appropriate controls. As an example of a practical application, data were also used to identify miRNAs differentially expressed when comparing Burkitt Lymphoma (BL) and Diffuse Large B-Cell Lymphoma (DLBCL) in paraffin-embedded samples. Each lymphoma type (BL, CLL, DLBCL, FL, MCL, MZL/MALT, NMZL and SMZL) was compared to the whole series of NHL by Significant Analysis of Microarray (SAM) method. The analysis identified a set of 128 characteristic miRNAs (FDR<0.01 and Fold change >1.5 log2). All lymphoma types were characterized by specific miRNA signatures, reflecting cell of origin and/or discrete oncogene alterations. Of interest is also the comparison with reactive lymphoid tissues, since it revealed a specific B-cell lymphoma miRNA profile, which includes a cluster of downregulated miRNAs, such as let7 family, miR-1 and miR-200 family. Burkitt Lymphoma was also directly compared to DLBCL, and 43 miRNA selected by SAM analysis were studied in a new series of 28 BL and 43 DLBCL samples using quantitative RT-PCRIn this second step, the differential expression of a set of 19 miRNAs was confirmed between BL and DLBCL. (FDR < 0.05 after t-test (limma)). These findings expand the potential diagnostic markers in lymphoma diagnosis and provide useful information on lymphoma pathogenesis. Disclosures: No relevant conflicts of interest to declare.

The clinicopathological and molecular features of sinusoidal large B-cell lymphoma

2020 ◽  
Author(s):  
Junpeng Xu ◽  
Peifeng Li ◽  
Jia Chai ◽  
Kangjie Yu ◽  
Tianqi Xu ◽  
...  
Keyword(s):  
B Cell ◽  
Cell Lymphoma ◽  
B Cell Lymphoma ◽  
Molecular Features ◽  
Large B Cell Lymphoma ◽  
Large B Cell

scholarly journals Predictive and interpretable models via the stacked elastic net

2020 ◽  
Author(s):  
Armin Rauschenberger ◽  
Enrico Glaab ◽  
Mark van de Wiel
Keyword(s):  
Machine Learning ◽  
R Package ◽  
Elastic Net ◽  
Machine Learning Techniques ◽  
Supplementary Information ◽  
Biomedical Sciences ◽  
Molecular Features ◽  
Learning Techniques ◽  
Meta Learning ◽  
Interpretable Models

Abstract Motivation Machine learning in the biomedical sciences should ideally provide predictive and interpretable models. When predicting outcomes from clinical or molecular features, applied researchers often want to know which features have effects, whether these effects are positive or negative, and how strong these effects are. Regression analysis includes this information in the coefficients but typically renders less predictive models than more advanced machine learning techniques. Results Here we propose an interpretable meta-learning approach for high-dimensional regression. The elastic net provides a compromise between estimating weak effects for many features and strong effects for some features. It has a mixing parameter to weight between ridge and lasso regularisation. Instead of selecting one weighting by tuning, we combine multiple weightings by stacking. We do this in a way that increases predictivity without sacrificing interpretability. Availability and Implementation The R package starnet is available on GitHub: https://github.com/rauschenberger/starnet. Supplementary information Supplementary data are available at Bioinformatics online.

Non-Hodgkin's Lymphoma: Molecular Features of B Cell Lymphoma

Hematology ◽  
2000 ◽  
Vol 2000 (1) ◽  
pp. 180-204
Author(s):  
Elizabeth Macintyre ◽  
Dennis Willerford ◽  
Stephan W. Morris
Keyword(s):  
B Cell ◽  
Large Scale ◽  
Cell Lymphoma ◽  
B Cell Lymphoma ◽  
Fusion Transcript ◽  
Molecular Techniques ◽  
Molecular Abnormalities ◽  
Lymphoid Neoplasia ◽  
Molecular Features ◽  
Hodgkin's Lymphomas

The rapid increase in the incidence of the B cell non-Hodgkin's lymphomas (NHL) and improved understanding of the mechanisms involved in their development renders timely a review of the theoretical and practical aspects of molecular abnormalities in B cell NHL. In Section I, Dr. Macintyre addresses the practical aspects of the use of molecular techniques for the diagnosis and therapeutic management of patients with B cell NHL. While detection of clonal Ig rearrangements is widely used to distinguish reactive from malignant lymphoproliferative disorders, molecular informativity is variable. The relative roles of cytogenetic, molecular and immunological techniques in the detection of genetic abnormalities and their protein products varies with the clinical situation. Consequently, the role of molecular analysis relative to morphological classification is evolving. Integrated diagnostic services are best equipped to cope with these changes. Recent evidence that large scale gene expression profiling allows improved prognostic stratification of diffuse large cell lymphoma suggests that the choice of diagnostic techniques will continue to change significantly and rapidly. In Section II, Dr. Willerford reviews current understanding of the mechanisms involved in immunoglobulin (Ig) gene rearrangement during B lymphoid development and the way in which these processes may contribute to Ig-locus chromosome translocations in lymphoma. Recent insights into the regulation of Ig gene diversification indicate that genetic plasticity in B lymphocytes is much greater than previously suspected. Physiological genomic instability, which may include isotype switching, recombination revision and somatic mutation, occurs in germinal centers in the context of immune responses and may explain longstanding clinical observations that link immunity and lymphoid neoplasia. Data from murine models and human disorders predisposing to NHL have been used to illustrate these issues. In Section III, Dr. Morris reviews the characteristics and consequences of deregulation of novel “proto-oncogenes” involved in B cell NHL, including PAX5 (chromosome 9p 13), BCL8 (15q11-q13), BCL9, MUC1, FcγRIIB and other 1q21-q22 genes and BCL10 (1p22). The AP12-MLT/MALT1 [t(11;18)(q21;q21)] fusion transcript is also described.

Immunohistochemical Expression Pattern of Germinal Center and Non-Germinal Center Markers Correlates with Prognosis in Diffuse Large B-Cell Lymphoma.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 4636-4636
Author(s):  
Carlos Chiattone ◽  
Marineide P. Carvalho ◽  
Roberto P. Paes ◽  
Karina C.B. Ribeiro ◽  
Fernando Soares
Keyword(s):  
B Cell ◽  
Germinal Center ◽  
Cell Lymphoma ◽  
International Prognostic Index ◽  
Prognostic Index ◽  
B Cell Lymphoma ◽  
Immunohistochemical Expression ◽  
Molecular Features ◽  
Large B Cell Lymphoma ◽  
Large B Cell

Abstract Recent studies have shown correlations between diffuse large B-cell lymphoma (DLCBL) prognosis and molecular features using genome profiles by cDNA microarrays. Since this analysis is not routinely used, immunohistochemical tests for prediction of DLBCL survival are gaining major importance, using markers as, CD10, BCL-6 and MUM-1 to identify germinal center B-cell (GCB) and non-GCB, respectively. The goal of this study was to evaluate the significant effect on survival within GCB and non-GCB subgroup. Patients and Methods: Seventy-four untreated pts (median age: 58 yrs: 38M/36F) with DLBCL de novo diagnosed in a single institution, treated with CHOP-like regimens. Tissue microarrays (TMA) blocks were created from paraffin-embedded, formalin-fixed block and stained with antibodies to CD20 (clone L26, Dako), CD10 (clone 56C6; Novocastra; NCL-CD10-270), BCL-6 (clone GI 191E/A8; Cell Mark; CMC 798) and MUM1 (clone MUM1p; Dako, CA; M7259). Results. Cases were subclassified using CD10, BCL-6, and MUM1 expression, and 25 cases (33.8%) were considered GCB and 49 cases (66.2%) non-GCB. The 2-year overall survival (OS) for the GCB group was 80% compared with only 38.9% for the non-GCB (p<0.001). In the multivariate analysis, only the International Prognostic Index score (IPI 3-4 HR=2.6, p=0.013) and the GCB phenotype (Non-GCB HR=2.7, p=0.054) were independent prognostic factors. In summary, immunohistochemical expression of CD10, BCL-6 and MUM1 are able to determine the GCB and non-GCB subtypes of DLBCL and predict survival.

Sign in / Sign up

Export Citation Format

Share Document