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.
Highlights
According to the WHO Classification of Tumors of Hematopoietic and Lymphoid Tissues, revised 4th edition, published in 2017, there are 21 disease groups
The overall survival was different between the different lymphoma subtypes and acute myeloid leukemia (AML)
The computerized neural networks that are known as perceptrons consist of neuron-like units
Summary
According to the WHO Classification of Tumors of Hematopoietic and Lymphoid Tissues, revised 4th edition, published in 2017, there are 21 disease groups. Among them we can identify the groups of acute myeloid leukemia (AML) and the mature B-cell neoplasms. The relative frequencies of the B-cell lymphoma subtypes are diffuse large B-cell lymphoma (DLBCL) (37%), follicular lymphoma (FL). (29%), chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL) (12%), extranodal marginal zone lymphoma of mucosa-associated lymphoid tissue (MALT lymphoma) (9%), mantle cell lymphoma (MCL) (7%) and others with a frequency below the 3% such as primary mediastinal large. The prognosis of these subtypes is heterogeneous, e.g., being more indolent in CLL/SLL and FL, and more aggressive in AML, MCL and DLBCL [1]. In DLBCL GEP has classified the patients into different molecular subtypes: germinal center B-cell-like (GCB) and activated B-cell-like (ABC) [2]
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