AID is a poor prognostic marker of high‐grade B‐cell lymphoma with MYC and BCL2 and/or BCL6 rearrangements

Miyaoka, Masashi; Kikuti, Yara Yukie; Carreras, Joaquim; Itou, Atsushi; Ikoma, Haruka; Tomita, Sakura; Shiraiwa, Sawako; Ando, Kiyoshi; Nakamura, Naoya

doi:10.1111/pin.13182

Cited by 8 publications

(3 citation statements)

References 37 publications

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“…There are many markers with prognostic value in DLBCL. Our group has highlighted some such as ENO3 [17,142], PTX3 and CD163 [143], RGS1 [144], CASP8 and TNFAIP8 [99], and AID [145]. All these markers will have to be validated in other series in future.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence Analysis and Reverse Engineering of Molecular Subtypes of Diffuse Large B-Cell Lymphoma Using Gene Expression Data

Carreras,

Yukie Kikuti,

Miyaoka

et al. 2024

BioMedInformatics

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Diffuse large B-cell lymphoma is one of the most frequent mature B-cell hematological neoplasms and non-Hodgkin lymphomas. Despite advances in diagnosis and treatment, clinical evolution is unfavorable in a subset of patients. Using molecular techniques, several pathogenic models have been proposed, including cell-of-origin molecular classification; Hans’ classification and derivates; and the Schmitz, Chapuy, Lacy, Reddy, and Sha models. This study introduced different machine learning techniques and their classification. Later, several machine learning techniques and artificial neural networks were used to predict the DLBCL subtypes with high accuracy (100–95%), including Germinal center B-cell like (GCB), Activated B-cell like (ABC), Molecular high-grade (MHG), and Unclassified (UNC), in the context of the data released by the REMoDL-B trial. In order of accuracy (MHG vs. others), the techniques were XGBoost tree (100%); random trees (99.9%); random forest (99.5%); and C5, Bayesian network, SVM, logistic regression, KNN algorithm, neural networks, LSVM, discriminant analysis, CHAID, C&R tree, tree-AS, Quest, and XGBoost linear (99.4–91.1%). The inputs (predictors) were all the genes of the array and a set of 28 genes related to DLBCL-Burkitt differential expression. In summary, artificial intelligence (AI) is a useful tool for predictive analytics using gene expression data.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence Analysis and Reverse Engineering of Molecular Subtypes of Diffuse Large B-Cell Lymphoma Using Gene Expression Data

Carreras,

Yukie Kikuti,

Miyaoka

et al. 2024

BioMedInformatics

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“…Similar results were found for the TNFAIP8 marker [ 12 ]. We also previously described the gene expression of High-grade B-cell lymphoma [ 35 ]. In this research, we identified seven genes that predicted the overall survival of patients of non-High-grade B-cell lymphoma cases.…”

Section: Discussionmentioning

confidence: 99%

Artificial Neural Networks Predicted the Overall Survival and Molecular Subtypes of Diffuse Large B-Cell Lymphoma Using a Pancancer Immune-Oncology Panel

Carreras

Hiraiwa

Kikuti

et al. 2021

Cancers

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Diffuse large B-cell lymphoma (DLBCL) is one of the most frequent subtypes of non-Hodgkin lymphomas. We used artificial neural networks (multilayer perceptron and radial basis function), machine learning, and conventional bioinformatics to predict the overall survival and molecular subtypes of DLBCL. The series included 106 cases and 730 genes of a pancancer immune oncology panel (nCounter) as predictors. The multilayer perceptron predicted the outcome with high accuracy, with an area under the curve (AUC) of 0.98, and ranked all the genes according to their importance. In a multivariate analysis, ARG1, TNFSF12, REL, and NRP1 correlated with favorable survival (hazard risks: 0.3–0.5), and IFNA8, CASP1, and CTSG, with poor survival (hazard risks = 1.0–2.1). Gene set enrichment analysis (GSEA) showed enrichment toward poor prognosis. These high-risk genes were also associated with the gene expression of M2-like tumor-associated macrophages (CD163), and MYD88 expression. The prognostic relevance of this set of 7 genes was also confirmed within the IPI and MYC translocation strata, the EBER-negative cases, the DLBCL not-otherwise specified (NOS) (High-grade B-cell lymphoma with MYC and BCL2 and/or BCL6 rearrangements excluded), and an independent series of 414 cases of DLBCL in Europe and North America (GSE10846). The perceptron analysis also predicted molecular subtypes (based on the Lymph2Cx assay) with high accuracy (AUC = 1). STAT6, TREM2, and REL were associated with the germinal center B-cell (GCB) subtype, and CD37, GNLY, CD46, and IL17B were associated with the activated B-cell (ABC)/unspecified subtype. The GSEA had a sinusoidal-like plot with association to both molecular subtypes, and immunohistochemistry analysis confirmed the correlation of MAPK3 with the GCB subtype in another series of 96 cases (notably, MAPK3 also correlated with LMO2, but not with M2-like tumor-associated macrophage markers CD163, CSF1R, TNFAIP8, CASP8, PD-L1, PTX3, and IL-10). Finally, survival and molecular subtypes were successfully modeled using other machine learning techniques including logistic regression, discriminant analysis, SVM, CHAID, C5, C&R trees, KNN algorithm, and Bayesian network. In conclusion, prognoses and molecular subtypes were predicted with high accuracy using neural networks, and relevant genes were highlighted.

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“…BCL6 upregulates Activation-induced cytidine deaminase (AICDA, also known as AID), which is responsible for germinal center-associated somatic hypermutation (SHM). 32 , 77 - 83 BCL6 -null mice lack germinal centers. 84 , 85 Therefore, BCL6 is a key oncogene in B-cell lymphomagenesis.…”

Section: Bcl6 Transcription Repressormentioning

confidence: 99%

The pathobiology of follicular lymphoma

Carreras

2023

J Clin Exp Hematopathol

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Follicular lymphoma is one of the most frequent lymphomas. Histologically, it is characterized by a follicular (nodular) growth pattern of centrocytes and centroblasts; mixed with variable immune microenvironment cells. Clinically, it is characterized by diffuse lymphadenopathy, bone marrow involvement, and splenomegaly. It is biologically and clinically heterogeneous. In most patients it is indolent, but others have a more aggressive evolution with relapses; and transformation to diffuse large B-cell lymphoma. Tumorigenesis includes an asymptomatic preclinical phase in which premalignant B-lymphocytes with the t(14;18) chromosomal translocation acquire additional genetic alterations in the germinal centers, and clonal evolution occurs, although not all the cells progress to the tumor stage. This manuscript reviews the pathobiology and clinicopathological characteristics of follicular lymphoma. It includes a description of the physiology of the germinal center, the genetic alterations of BCL2 and BCL6 , the mutational profile, the immune checkpoint, precision medicine, and highlights in the lymphoma classification. In addition, a comment and review on artificial intelligence and machine (deep) learning are made.

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AID is a poor prognostic marker of high‐grade B‐cell lymphoma with MYC and BCL2 and/or BCL6 rearrangements

Cited by 8 publications

References 37 publications

Artificial Intelligence Analysis and Reverse Engineering of Molecular Subtypes of Diffuse Large B-Cell Lymphoma Using Gene Expression Data

Artificial Intelligence Analysis and Reverse Engineering of Molecular Subtypes of Diffuse Large B-Cell Lymphoma Using Gene Expression Data

Artificial Neural Networks Predicted the Overall Survival and Molecular Subtypes of Diffuse Large B-Cell Lymphoma Using a Pancancer Immune-Oncology Panel

The pathobiology of follicular lymphoma

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