Phosphorylated STAT3 as a potential diagnostic and predictive biomarker in ALK- ALCL vs. CD30high PTCL, NOS.

Aims: The differential diagnosis between ALK-negative anaplastic large cell lymphoma (ALK- ALCL) and peripheral T-cell lymphoma, not otherwise specified (PTCL, NOS) with high expression of CD30 (CD30high) are essential. However, no reliable biomarker is available in daily practice except CD30. STAT3 is characteristically activated in ALCL. We aimed to investigate whether the status of STAT3 phosphorylation could help the differential diagnosis. Methods: The status of phosphorylation of STAT3 was examined using two antibodies against pSTAT3-Y705 and pSTAT3-S727 by immunohistochemistry in ALK+ ALCL (n=33), ALK- ALCL (n=22) and PTCL, NOS (n=34). Ten PTCL, NOS with diffuse CD30 expression were defined as CD30high PTCL, NOS. Flowcytometric analysis were performed to evaluate the expression of pSTAT3-Y705/S727 in PTCL, NOS (n=3). Results: The median H-scores of pSTAT3-Y705 and S727 were 280 and 260 in ALK+ ALCL, 250 and 240 in ALK- ALCL, and 45 and 75 in CD30high subgroup, respectively. Using H score of 145 as the cutoff value, pSTAT3-S727 alone distinguished between ALK- ALCL and CD30high PTCL, NOS with a sensitivity of 100% and specificity of 83%. Additionally, pSTAT3-S727, but not pSTAT3-Y705, was also expressed by background tumor-infiltrating lymphocytes (S727TILs) in PTCL, NOS. PTCL, NOS patients with high S727TILs H score had a favorable prognosis than those with no TILs (3-year OS rate: 43% vs. 0, p=0.013) or low S727TILs (3-year OS rate: 43% vs. 0, p=0.099). Flowcytometric analysis revealed that of the three patients investigated, two had enhanced pSTAT-S727 signals in neoplastic cell populations, and all three patients were negative for pSTAT3-Y705 expression in both tumor cells and background lymphocytes. Conclusions: pSTAT3-Y705/S727 can be used to help distinguish ALK- ALCL from CD30high PTCL, NOS and pSTAT3-S727 expression by TILs predicts the prognosis of a subset of PTCL, NOS.

Agent Repurposing for the Treatment of Advanced Stage Diffuse Large B-Cell Lymphoma Based on Gene Expression and Network Perturbation Analysis.

Over 50% of diffuse large B-cell lymphoma (DLBCL) patients are diagnosed at an advanced stage. Although there are a few therapeutic strategies for DLBCL, most of them are more effective in limited-stage cancer patients. The prognosis of patients with advanced-stage DLBCL is usually poor with frequent recurrence and metastasis. In this study, we aimed to identify gene expression and network differences between limited- and advanced-stage DLBCL patients, with the goal of identifying potential agents that could be used to relieve the severity of DLBCL. Specifically, RNA sequencing data of DLBCL patients at different clinical stages were collected from the cancer genome atlas (TCGA). Differentially expressed genes were identified using DESeq2, and then, weighted gene correlation network analysis (WGCNA) and differential module analysis were performed to find variations between different stages. In addition, important genes were extracted by key driver analysis, and potential agents for DLBCL were identified according to gene-expression perturbations and the Crowd Extracted Expression of Differential Signatures (CREEDS) drug signature database. As a result, 20 up-regulated and 73 down-regulated genes were identified and 79 gene co-expression modules were found using WGCNA, among which, the thistle1 module was highly related to the clinical stage of DLBCL. KEGG pathway and GO enrichment analyses of genes in the thistle1 module indicated that DLBCL progression was mainly related to the NOD-like receptor signaling pathway, neutrophil activation, secretory granule membrane, and carboxylic acid binding. A total of 47 key drivers were identified through key driver analysis with 11 up-regulated key driver genes and 36 down-regulated key diver genes in advanced-stage DLBCL patients. Five genes (MMP1, RAB6C, ACCSL, RGS21 and MOCOS) appeared as hub genes, being closely related to the occurrence and development of DLBCL. Finally, both differentially expressed genes and key driver genes were subjected to CREEDS analysis, and 10 potential agents were predicted to have the potential for application in advanced-stage DLBCL patients. In conclusion, we propose a novel pipeline to utilize perturbed gene-expression signatures during DLBCL progression for identifying agents, and we successfully utilized this approach to generate a list of promising compounds.