A Combination of Multilayer Perceptron, Radial Basis Function Artificial Neural Networks and Machine Learning Image Segmentation for the Dimension Reduction and the Prognosis Assessment of Diffuse Large B-Cell Lymphoma

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Abstract

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.

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Engagement of the Pd-1 Immunoinhibitory Receptor by a Novel B7 Family Member Leads to Negative Regulation of Lymphocyte Activation

Gordon J. Freeman, Andrew Long, Yoshiko Iwai … (2000)

PD-1 is an immunoinhibitory receptor expressed by activated T cells, B cells, and myeloid cells. Mice deficient in PD-1 exhibit a breakdown of peripheral tolerance and demonstrate multiple autoimmune features. We report here that the ligand of PD-1 (PD-L1) is a member of the B7 gene family. Engagement of PD-1 by PD-L1 leads to the inhibition of T cell receptor–mediated lymphocyte proliferation and cytokine secretion. In addition, PD-1 signaling can inhibit at least suboptimal levels of CD28-mediated costimulation. PD-L1 is expressed by antigen-presenting cells, including human peripheral blood monocytes stimulated with interferon γ, and activated human and murine dendritic cells. In addition, PD-L1 is expressed in nonlymphoid tissues such as heart and lung. The relative levels of inhibitory PD-L1 and costimulatory B7-1/B7-2 signals on antigen-presenting cells may determine the extent of T cell activation and consequently the threshold between tolerance and autoimmunity. PD-L1 expression on nonlymphoid tissues and its potential interaction with PD-1 may subsequently determine the extent of immune responses at sites of inflammation.

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Dissection of DLBCL microenvironment provides a gene expression-based predictor of survival applicable to formalin-fixed paraffin-embedded tissue

S Ciavarella, M C Vegliante, M Fabbri … (2018)

Abstract Background Gene expression profiling (GEP) studies recognized a prognostic role for tumor microenvironment (TME) in diffuse large B-cell lymphoma (DLBCL), but the routinely adoption of prognostic stromal signatures remains limited. Patients and methods Here, we applied the computational method CIBERSORT to generate a 1028-gene matrix incorporating signatures of 17 immune and stromal cytotypes. Then, we carried out a deconvolution on publicly available GEP data of 482 untreated DLBCLs to reveal associations between clinical outcomes and proportions of putative tumor-infiltrating cell types. Forty-five genes related to peculiar prognostic cytotypes were selected and their expression digitally quantified by NanoString technology on a validation set of 175 formalin-fixed, paraffin-embedded DLBCLs from two randomized trials. Data from an unsupervised clustering analysis were used to build a model of clustering assignment, whose prognostic value was also assessed on an independent cohort of 40 cases. All tissue samples consisted of pretreatment biopsies of advanced-stage DLBCLs treated by comparable R-CHOP/R-CHOP-like regimens. Results In silico analysis demonstrated that higher proportion of myofibroblasts (MFs), dendritic cells, and CD4+ T cells correlated with better outcomes and the expression of genes in our panel is associated with a risk of overall and progression-free survival. In a multivariate Cox model, the microenvironment genes retained high prognostic performance independently of the cell-of-origin (COO), and integration of the two prognosticators (COO + TME) improved survival prediction in both validation set and independent cohort. Moreover, the major contribution of MF-related genes to the panel and Gene Set Enrichment Analysis suggested a strong influence of extracellular matrix determinants in DLBCL biology. Conclusions Our study identified new prognostic categories of DLBCL, providing an easy-to-apply gene panel that powerfully predicts patients’ survival. Moreover, owing to its relationship with specific stromal and immune components, the panel may acquire a predictive relevance in clinical trials exploring new drugs with known impact on TME.

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Differential expression of NF-kappaB target genes in MALT lymphoma with and without chromosome translocation: insights into molecular mechanism.

Yun Gong, A Appert, A. Chott … (2010)

Mucosa-associated lymphoid tissue (MALT) lymphoma is characterized by t(11;18)(q21;q21)/API2-MALT1, t(1;14)(p22;q32)/BCL10-IGH and t(14;18)(q32;q21)/IGH-MALT1, which commonly activate the nuclear factor (NF)-kappaB pathway. Gastric MALT lymphomas harboring such translocations usually do not respond to Helicobacter pylori eradication, while most of those without translocation can be cured by antibiotics. To understand the molecular mechanism of these different MALT lymphoma subgroups, we performed gene expression profiling analysis of 21 MALT lymphomas (13 translocation-positive, 8 translocation-negative). Gene set enrichment analysis (GSEA) of the NF-kappaB target genes and 4394 additional gene sets covering various cellular pathways, biological processes and molecular functions have shown that translocation-positive MALT lymphomas are characterized by an enhanced expression of NF-kappaB target genes, particularly toll like receptor (TLR)6, chemokine, CC motif, receptor (CCR)2, cluster of differentiation (CD)69 and B-cell CLL/lymphoma (BCL)2, while translocation-negative cases were featured by active inflammatory and immune responses, such as interleukin-8, CD86, CD28 and inducible T-cell costimulator (ICOS). Separate analyses of the genes differentially expressed between translocation-positive and -negative cases and measurement of gene ontology term in these differentially expressed genes by hypergeometric test reinforced the above findings by GSEA. Finally, expression of TLR6, in the presence of TLR2, enhanced both API2-MALT1 and BCL10-mediated NF-kappaB activation in vitro. Our findings provide novel insights into the molecular mechanism of MALT lymphomas with and without translocation, potentially explaining their different clinical behaviors.