Integrative Statistics, Machine Learning and Artificial Intelligence Neural Network Analysis Correlated CSF1R with the Prognosis of Diffuse Large B-Cell Lymphoma

Abstract

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.

Artificial Neural Network Analysis of Gene Expression Data Predicted Non-Hodgkin Lymphoma Subtypes with High Accuracy

“…The analysis was performed to predict all subtypes simultaneously and then each subtype against the others. The multilayer perceptron was performed as we have recently described [17,[19][20][21][22][23].…”

“…Of note, other available rescaling methods were normalized [(x − min)/(max − min)], adjusted normalized [[2 * (x − min)/(max − min)] − 1] or none. Random assignation of the cases based on the relative number of cases (training 70%, testing 30%, and holdout 0%) partitioned the data set into training, testing, and holdout samples [5,6,17,[19][20][21][22][23].…”

“…The optimization algorithm was the scaled conjugate gradient or gradient descent. The options of the training set, initial lambda (0.0000005), initial sigma (0.00005), interval center (0), and interval offset (±0.5) [5,6,17,[19][20][21][22][23].…”

“…As for stopping rules, the maximum step without a decrease in error as set to 1, the maximum training time to 15 min, the minimum relative change in training error to 0.0001, and the minimum relative change in the training error ratio to 0.001. Missing values were excluded, if present [5,6,17,[19][20][21][22][23].…”

Artificial Neural Networks Predicted the Overall Survival and Molecular Subtypes of Diffuse Large B-Cell Lymphoma Using a Pancancer Immune-Oncology Panel

“…Therefore, at diagnosis, it is important to identify and predict which patients will clinically evolve unfavorably. The prognosis of DLBCL can be assessed with several variables, such as the International Prognostic Index (IPI), which includes several clinical and biochemical variables (age, LDH, ECOG performance status, clinical stage, and extranodal sites); cell of origin molecular subtypes (gene expression profiling, Hans, Choi, and Tally algorithms, and the Lymph2Cx platform) [ 5 , 6 , 7 , 8 , 9 ]; MYC , BCL2 , and BCL6 abnormalities; and the tumor immune microenvironment [ 10 , 11 , 12 , 13 ]. Based on gene expression, three types of DLBCL have been defined: germinal center B-cell-like (GCB), activated B-cell-like (ABC), and not-otherwise-specified type 3 (i.e., unclassified, unspecified).…”