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.

Monomorphic epitheliotropic intestinal T-cell lymphoma (MEITL) is a rare primary T-cell lymphoma of the digestive tract derived from intraepithelial lymphocytes and characterized by an aggressive clinical course. In this study, nine cases of Japanese MEITL were analyzed by targeted Next Generation Sequencing (NGS) and immunohistochemistry and were integrated with previously reported whole-genome copy number microarray-based assay data. The highlight of our findings is that all cases showed alterations of the tumor suppressor gene SETD2 by mutations and/or loss of the corresponding 3p21 locus. We also demonstrated that all cases showed mutations in one or more genes of JAK/STAT pathway. Therefore, the combination of epigenetic deregulation and cell signaling activation represent major oncogenic events in the pathogenesis of MEITL in Asian MEITL, similar to Western MEITL.

The prognosis of diffuse large B-cell lymphoma (DLBCL) is heterogeneous. Therefore, we aimed to highlight predictive biomarkers. First, artificial intelligence was applied into a discovery series of gene expression of 414 patients (GSE10846). A dimension reduction algorithm aimed to correlate with the overall survival and other clinicopathological variables; and included a combination of Multilayer Perceptron (MLP) and Radial Basis Function (RBF) artificial neural networks, gene-set enrichment analysis (GSEA), Cox regression and other machine learning and predictive analytics modeling [C5.0 algorithm, logistic regression, Bayesian Network, discriminant analysis, random trees, tree-AS, Chi-squared Automatic Interaction Detection CHAID tree, Quest, classification and regression (C&R) tree and neural net)]. From an initial 54,613 gene-probes, a set of 488 genes and a final set of 16 genes were defined. Secondly, two identified markers of the immune checkpoint, PD-L1 (CD274) and IKAROS (IKZF4), were validated in an independent series from Tokai University, and the immunohistochemical expression was quantified, using a machine-learning-based Weka segmentation. High PD-L1 associated with poor overall and progression-free survival, non-GCB phenotype, Epstein–Barr virus infection (EBER+), high RGS1 expression and several clinicopathological variables, such as high IPI and absence of clinical response. Conversely, high expression of IKAROS was associated with a good overall and progression-free survival, GCB phenotype and a positive clinical response to treatment. Finally, the set of 16 genes (PAF1, USP28, SORT1, MAP7D3, FITM2, CENPO, PRCC, ALDH6A1, CSNK2A1, TOR1AIP1, NUP98, UBE2H, UBXN7, SLC44A2, NR2C2AP and LETM1), in combination with PD-L1, IKAROS, BCL2, MYC, CD163 and TNFAIP8, predicted the survival outcome of DLBCL with an overall accuracy of 82.1%. In conclusion, building predictive models of DLBCL is a feasible analytical strategy.

High expression of the anti-apoptotic TNFAIP8 is associated with poor survival of the patients with diffuse large B-cell lymphoma (DLBCL), and one of the functions of TNFAIP8 is to inhibit the pro-apoptosis Caspase-8. We aimed to analyze the immunohistochemical expression of Caspase-8 (active subunit p18; CASP8) in a series of 97 cases of DLBCL from Tokai University Hospital, and to correlate with other Caspase-8 pathway-related markers, including cleaved Caspase-3, cleaved PARP, BCL2, TP53, MDM2, MYC, Ki67, E2F1, CDK6, MYB and LMO2. After digital image quantification, the correlation with several clinicopathological characteristics of the patients showed that high protein expression of Caspase-8 was associated with a favorable overall and progression-free survival (Hazard Risks = 0.3; p = 0.005 and 0.03, respectively). Caspase-8 also positively correlated with cCASP3, MDM2, E2F1, TNFAIP8, BCL2 and Ki67. Next, the Caspase-8 protein expression was modeled using predictive analytics, and a high overall predictive accuracy (>80%) was obtained with CHAID decision tree, Bayesian network, discriminant analysis, C5 tree, logistic regression, and Artificial Intelligence Neural Network methods (both Multilayer perceptron and Radial basis function); the most relevant markers were cCASP3, E2F1, TP53, cPARP, MDM2, BCL2 and TNFAIP8. Finally, the CASP8 gene expression was also successfully modeled in an independent DLBCL series of 414 cases from the Lymphoma/Leukemia Molecular Profiling Project (LLMPP). In conclusion, high protein expression of Caspase-8 is associated with a favorable prognosis of DLBCL. Predictive modeling is a feasible analytic strategy that results in a solution that can be understood (i.e., explainable artificial intelligence, “white-box” algorithms).

Tumor-associated macrophages (TAMs) of the immune microenvironment play an important role in the Diffuse Large B-cell Lymphoma (DLBCL) pathogenesis. This research aimed to characterize the expression of macrophage colony-stimulating factor 1 receptor (CSF1R) at the gene and protein level in correlation with survival. First, the immunohistochemical expression of CSF1R was analyzed in a series of 198 cases from Tokai University Hospital and two patterns of histological expression were found, a TAMs, and a diffuse B-lymphocytes pattern. The clinicopathological correlations showed that the CSF1R + TAMs pattern associated with a poor progression-free survival of the patients, disease progression, higher MYC proto-oncogene expression, lower MDM2 expression, BCL2 translocation, and a MYD88 L265P mutation. Conversely, a diffuse CSF1R + B-cells pattern was associated with a favorable progression-free survival. Second, the histological expression of CSF1R was also correlated with 10 CSF1R-related markers including CSF1, STAT3, NFKB1, Ki67, MYC, PD-L1, TNFAIP8, IKAROS, CD163, and CD68. CSF1R moderately correlated with STAT3, TNFAIP8, CD68, and CD163 in the cases with the CSF1R + TAMs pattern. In addition, machine learning modeling predicted the CSF1R immunohistochemical expression with high accuracy using regression, generalized linear, an artificial intelligence neural network (multilayer perceptron), and support vector machine (SVM) analyses. Finally, a multilayer perceptron analysis predicted the genes associated with the CSF1R gene expression using the GEO GSE10846 DLBCL series of the Lymphoma/Leukemia Molecular Profiling Project (LLMPP), with correlation to the whole set of 20,683 genes as well as with an immuno-oncology cancer panel of 1790 genes. In addition, CSF1R positively correlated with SIRPA and inversely with CD47. In conclusion, the CSF1R histological pattern correlated with the progression-free survival of the patients of the Tokai series, and predictive analytics is a feasible strategy in DLBCL.

Objective: We have recently identified using multilayer perceptron analysis (artificial intelligence) a set of 25 genes with prognostic relevance in diffuse large B-cell lymphoma (DLBCL), but the importance of this set in other hematological neoplasia remains unknown. Methods and Results: We tested this set of genes (i.e., ALDOB, ARHGAP19, ARMH3, ATF6B, CACNA1B, DIP2A, EMC9, ENO3, GGA3, KIF23, LPXN, MESD, METTL21A, POLR3H, RAB7A, RPS23, SERPINB8, SFTPC, SNN, SPACA9, SWSAP1, SZRD1, TNFAIP8, WDCP and ZSCAN12) in a large series of gene expression comprised of 2029 cases, selected from available databases, that included chronic lymphocytic leukemia (CLL, n = 308), mantle cell lymphoma (MCL, n = 92), follicular lymphoma (FL, n = 180), DLBCL (n = 741), multiple myeloma (MM, n = 559) and acute myeloid leukemia (AML, n = 149). Using a risk-score formula we could predict the overall survival of the patients: the hazard-ratio of high-risk versus low-risk groups for all the cases was 3.2 and per disease subtype were as follows: CLL (4.3), MCL (5.2), FL (3.0), DLBCL not otherwise specified (NOS) (4.5), multiple myeloma (MM) (5.3) and AML (3.7) (all p values < 0.000001). All 25 genes contributed to the risk-score, but their weight and direction of the correlation was variable. Among them, the most relevant were ENO3, TNFAIP8, ATF6B, METTL21A, KIF23 and ARHGAP19. Next, we validated TNFAIP8 (a negative mediator of apoptosis) in an independent series of 97 cases of DLBCL NOS from Tokai University Hospital. The protein expression by immunohistochemistry of TNFAIP8 was quantified using an artificial intelligence-based segmentation method and confirmed with a conventional RGB-based digital quantification. We confirmed that high protein expression of TNFAIP8 by the neoplastic B-lymphocytes associated with a poor overall survival of the patients (hazard-risk 3.5; p = 0.018) as well as with other relevant clinicopathological variables including age >60 years, high serum levels of soluble IL2RA, a non-GCB phenotype (cell-of-origin Hans classifier), moderately higher MYC and Ki67 (proliferation index), and high infiltration of the immune microenvironment by CD163-positive tumor associated macrophages (CD163+TAMs). Conclusion: It is possible to predict the prognosis of several hematological neoplasia using a single gene-set derived from neural network analysis. High expression of TNFAIP8 is associated with poor prognosis of the patients in DLBCL.