Mutational, immune microenvironment, and clinicopathological profiles of diffuse large B-cell lymphoma and follicular lymphoma with BCL6 rearrangement

Carreras, Joaquim; Ikoma, Haruka; Kikuti, Yara Yukie; Miyaoka, Masashi; Hiraiwa, Shinichiro; Tomita, Sakura; Kondo, Yusuke; Ito, Atsushi; Nagase, Shunsuke; Miura, Hisanobu; Kawada, Hiroshi; Roncador, Giovanna; Campo, Elias; Hamoudi, Rifat; Nakamura, Naoya

doi:10.1007/s00428-024-03774-z

Cited by 3 publications

(2 citation statements)

References 45 publications

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“…The role of RELB in the pathogenesis of DLBCL is complex [103,104,118,[123][124][125][126]. Further analysis of the impact of RELB on the prognosis of DLBCL and their relationship with known and well stablished markers such as MYC, BCL2, and BCL6 [12,127] is warranted.…”

Section: Discussionmentioning

confidence: 99%

Anomaly Detection and Artificial Intelligence Identified the Pathogenic Role of Apoptosis and RELB Proto-Oncogene, NF-kB Subunit in Diffuse Large B-Cell Lymphoma

Carreras,

Hamoudi

2024

BioMedInformatics

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Background: Diffuse large B-cell lymphoma (DLBCL) is one of the most frequent lymphomas. DLBCL is phenotypically, genetically, and clinically heterogeneous. Aim: We aim to identify new prognostic markers. Methods: We performed anomaly detection analysis, other artificial intelligence techniques, and conventional statistics using gene expression data of 414 patients from the Lymphoma/Leukemia Molecular Profiling Project (GSE10846), and immunohistochemistry in 10 reactive tonsils and 30 DLBCL cases. Results: First, an unsupervised anomaly detection analysis pinpointed outliers (anomalies) in the series, and 12 genes were identified: DPM2, TRAPPC1, HYAL2, TRIM35, NUDT18, TMEM219, CHCHD10, IGFBP7, LAMTOR2, ZNF688, UBL7, and RELB, which belonged to the apoptosis, MAPK, MTOR, and NF-kB pathways. Second, these 12 genes were used to predict overall survival using machine learning, artificial neural networks, and conventional statistics. In a multivariate Cox regression analysis, high expressions of HYAL2 and UBL7 were correlated with poor overall survival, whereas TRAPPC1, IGFBP7, and RELB were correlated with good overall survival (p < 0.01). As a single marker and only in RCHOP-like treated cases, the prognostic value of RELB was confirmed using GSEA analysis and Kaplan–Meier with log-rank test and validated in the TCGA and GSE57611 datasets. Anomaly detection analysis was successfully tested in the GSE31312 and GSE117556 datasets. Using immunohistochemistry, RELB was positive in B-lymphocytes and macrophage/dendritic-like cells, and correlation with HLA DP-DR, SIRPA, CD85A (LILRB3), PD-L1, MARCO, and TOX was explored. Conclusions: Anomaly detection and other bioinformatic techniques successfully predicted the prognosis of DLBCL, and high RELB was associated with a favorable prognosis.

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Section: Discussionmentioning

confidence: 99%

Anomaly Detection and Artificial Intelligence Identified the Pathogenic Role of Apoptosis and RELB Proto-Oncogene, NF-kB Subunit in Diffuse Large B-Cell Lymphoma

Carreras,

Hamoudi

2024

BioMedInformatics

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“…Our group has worked in predictive analytics and AI in recent years in the field of lymphoma [78] and other diseases, such as celiac disease [79] and ulcerative colitis [80]. In the lymphoma field, we identified several markers of relevance, such as ENO3 [28], TNFAIP8 [81], PD-L1 [81], CASP8 [82], CSF1R [61], immune response [83], RGS1 [26], FOXP3, PD-1, IL10, and CD163 [29,30,84], as well as BCL6 in DLBCL [85,86] and FL [87]. Therefore, we have proven that this technology is useful.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence, Lymphoid Neoplasms, and Prediction of MYC, BCL2, and BCL6 Gene Expression Using a Pan-Cancer Panel in Diffuse Large B-Cell Lymphoma

Carreras,

Nakamura

2024

Hemato

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Background: Artificial intelligence in medicine is a field that is rapidly evolving. Machine learning and deep learning are used to improve disease identification and diagnosis, personalize disease treatment, analyze medical images, evaluate clinical trials, and speed drug development. Methods: First, relevant aspects of AI are revised in a comprehensive manner, including the classification of hematopoietic neoplasms, types of AI, applications in medicine and hematological neoplasia, generative pre-trained transformers (GPTs), and the architecture and interpretation of feedforward neural net-works (multilayer perceptron). Second, a series of 233 diffuse large B-cell lymphoma (DLBCL) patients treated with rituximab-CHOP from the Lymphoma/Leukemia Molecular Profiling Project (LLMPP) was analyzed. Results: Using conventional statistics, the high expression of MYC and BCL2 was associated with poor survival, but high BCL6 was associated with a favorable overall survival of the patients. Then, a neural network predicted MYC, BCL2, and BCL6 with high accuracy using a pan-cancer panel of 758 genes of immuno-oncology and translational research that includes clinically relevant actionable genes and pathways. A comparable analysis was performed using gene set enrichment analysis (GSEA). Conclusions: The mathematical way in which neural networks reach conclusions has been considered a black box, but a careful understanding and evaluation of the architectural design allows us to interpret the results logically. In diffuse large B-cell lymphoma, neural networks are a plausible data analysis approach.

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Ulcerative Colitis, LAIR1 and TOX2 Expression, and Colorectal Cancer Deep Learning Image Classification Using Convolutional Neural Networks

Carreras,

Roncador,

Hamoudi

2024

Cancers

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Background: Ulcerative colitis is a chronic inflammatory bowel disease of the colon mucosa associated with a higher risk of colorectal cancer. Objective: This study classified hematoxylin and eosin (H&E) histological images of ulcerative colitis, normal colon, and colorectal cancer using artificial intelligence (deep learning). Methods: A convolutional neural network (CNN) was designed and trained to classify the three types of diagnosis, including 35 cases of ulcerative colitis (n = 9281 patches), 21 colon control (n = 12,246), and 18 colorectal cancer (n = 63,725). The data were partitioned into training (70%) and validation sets (10%) for training the network, and a test set (20%) to test the performance on the new data. The CNNs included transfer learning from ResNet-18, and a comparison with other CNN models was performed. Explainable artificial intelligence for computer vision was used with the Grad-CAM technique, and additional LAIR1 and TOX2 immunohistochemistry was performed in ulcerative colitis to analyze the immune microenvironment. Results: Conventional clinicopathological analysis showed that steroid-requiring ulcerative colitis was characterized by higher endoscopic Baron and histologic Geboes scores and LAIR1 expression in the lamina propria, but lower TOX2 expression in isolated lymphoid follicles (all p values < 0.05) compared to mesalazine-responsive ulcerative colitis. The CNN classification accuracy was 99.1% for ulcerative colitis, 99.8% for colorectal cancer, and 99.1% for colon control. The Grad-CAM heatmap confirmed which regions of the images were the most important. The CNNs also differentiated between steroid-requiring and mesalazine-responsive ulcerative colitis based on H&E, LAIR1, and TOX2 staining. Additional classification of 10 new cases of colorectal cancer (adenocarcinoma) were correctly classified. Conclusions: CNNs are especially suited for image classification in conditions such as ulcerative colitis and colorectal cancer; LAIR1 and TOX2 are relevant immuno-oncology markers in ulcerative colitis.

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Mutational, immune microenvironment, and clinicopathological profiles of diffuse large B-cell lymphoma and follicular lymphoma with BCL6 rearrangement

Cited by 3 publications

References 45 publications

Anomaly Detection and Artificial Intelligence Identified the Pathogenic Role of Apoptosis and RELB Proto-Oncogene, NF-kB Subunit in Diffuse Large B-Cell Lymphoma

Anomaly Detection and Artificial Intelligence Identified the Pathogenic Role of Apoptosis and RELB Proto-Oncogene, NF-kB Subunit in Diffuse Large B-Cell Lymphoma

Artificial Intelligence, Lymphoid Neoplasms, and Prediction of MYC, BCL2, and BCL6 Gene Expression Using a Pan-Cancer Panel in Diffuse Large B-Cell Lymphoma

Ulcerative Colitis, LAIR1 and TOX2 Expression, and Colorectal Cancer Deep Learning Image Classification Using Convolutional Neural Networks

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