A Single Gene Expression Set Derived from Artificial Intelligence Predicted the Prognosis of Several Lymphoma Subtypes; and High Immunohistochemical Expression of TNFAIP8 Associated with Poor Prognosis in Diffuse Large B-Cell Lymphoma

Abstract

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.

High Expression of Caspase-8 Associated with Improved Survival in Diffuse Large B-Cell Lymphoma: Machine Learning and Artificial Neural Networks Analyses

“…The TNFAIP8 was identified within the top 20 most relevant genes of the LLMPP series. Then, we validated the importance of TNFAIP8 by immunohistochemistry and by digital quantification using a machine-learning Weka-based segmentation method in a series of DLBCL from Tokai University Hospital, and we confirmed that high TNFAIP8 was associated with a poor overall survival of the patients [28]. TNFAIP8 acts as a negative mediator of apoptosis and may play a role in tumor progression.…”

“…In this analysis, the dependent variable was treated as a scale (continuous) because the values represent ordered categories with a meaningful metric, so that distance comparisons between values are appropriate. Of note, this differs from our previous publications in which the dependent (target) variables were dichotomic (high vs. low, or dead vs. alive) [27,28]. Another difference from our previous publications [27,28] is that we are using the values of the collapsed probes.…”

“…Of note, this differs from our previous publications in which the dependent (target) variables were dichotomic (high vs. low, or dead vs. alive) [27,28]. Another difference from our previous publications [27,28] is that we are using the values of the collapsed probes. In the setup, CASP8 was the dependent variable, while for the rest of the genes the covariates and the rescaling of the covariates were standardized.…”

“…The whole slides were also digitalized using a Hamamatsu digital slide scanner, the NanoZoomer S360, and visualized with the NDP.view2 Viewing software (Hamamatsu Photonics K.K., Hamamatsu, Japan). The representative areas of each marker were stored as a jpeg image for futher digital image quantification using the Fiji (ImageJ) image processing package, in a RGB and threshold strategy as we have recently described [28].…”

“…In comparison to the GCB, the ABC subtype is characterized by a more aggressive clinical evolution and constitutive activation of the anti-apoptotic nuclear factor kappa B (NF-kB) pathway [23][24][25][26]. We have recently described the prognostic value of a negative mediator of apoptosis in DLBCL, the tumor necrosis factor alpha-induced protein 8 (TNFAIP8) [27,28]. In this research, we had used artificial intelligence-the multilayer perceptron neural network-to analyze the gene expression of the DLBCL series of the Lymphoma/Leukemia Molecular Profiling Project (LLMPP) and to identify the genes that were associated with the overall survival of the patients.…”