Flow-cytometry-based evaluation of peripheral blood lymphocytes in prognostication of newly diagnosed DLBCL patients

Increased interleukin-17A levels promote rituximab resistance by suppressing p53 expression and predict an unfavorable prognosis in patients with diffuse large B cell lymphoma

“…Yin et al found that PBMC Th1 and Th2 cells from patients with DLBCL were influenced by the presence or absence of rituximab in the chemotherapy regimen, which was also related to the patients' response to treatment ( 29 ). Two previous studies have both shown that a low level of circulating Treg cells predicts a poor prognosis in patients with DLBCL ( 30 , 31 ); however, to the best of our knowledge, no studies to date have determined whether rituximab affects Th17 cell differentiation and IL-17 levels in patients with DLBCL.T cells present developmental plasticity in the tumor microenvironment ( 13 ). As we speculated, our results provide the first evidence to indicate that rituximab increases the proportions of Th17 and IL-17 + Foxp3 + Treg cells, and increases IL-17A levels in vivo and in vitro , probably as a result of increased IL-6 and TGF-β levels.…”

Prognostic Impact of Peripheral Blood T-Cell Subsets at the Time of Diagnosis on Survival in Patients with Diffuse Large B-Cell Lymphoma

“…The role of Tregs in patients with DLBCL is diverse because both host immune cells and lymphoma cells can be targeted [15,16]. In this study, patients with DLBCL had greater PB Treg populations than those in healthy controls.…”

“…In this study, patients with DLBCL had greater PB Treg populations than those in healthy controls. This result suggests that the expansion of the Treg population is induced by lymphoma cells, which subsequently contributes to systemic T-cell hyporesponsiveness in the host [16] and suppresses the antitumor immune response mediated by cytotoxic T cells. Tregs are also correlated with serum LDH levels, high-stage disease, and poor survival in lymphoma patients [16,17].…”

Characterization of Human B Cell Hematological Malignancies Using Protein-Based Approaches

“…Finding differentially expressed proteins between FL and MCL [95] MCL Identification of molecular signatures that differentiate MCL from B cells of the different compartments [96] Identification of proteomic biomarkers to distinguish MCL [97] Searching for specific proteomic biomarkers overexpressed in MCL tumor biopsies [98] Identification of tyrosine-phosphorylated proteins [99] Provision of insights into the dynamics of the disease and response to treatment [99] Evaluation of resistance to antinucleoside drugs [100] Discovery of neo-antigen peptides that mediate the killing of autologous lymphoma cells by circulating CD4 T cells [101] Characterization of the action mechanism of the MDM2-antagonist nutlin-3a [102] Discovery of the mechanisms involved in the pathogenesis of ocular adnexa extranodal MZL [103] Identification of biomarkers for the diagnosis of primary Sjögren's syndrome/MALT and prediction of progression [104] Establishment of the role of ID3 in regulating cell proliferation [105] MZL Study of the pharmacokinetics of umbralisib [106] BL Analysis of differentially expressed proteins between endemic and sporadic BL variants and EBV + and EBV − BL cell lines [107] Prediction of MGUS progression for an early diagnosis of MM [108] Analysis of the tumor microenvironment to identify determinants of durable responses to BCMA CAR T therapy [109] Quantification of surface proteins to identify immunotherapy targets and biomarkers associated with resistance and response to treatment [109] MM Identification of cell surface targets for immune-based therapies [110] Flow Cytometry CLL Design of panels for rapid disease diagnosis and progression assessment [111,112] Comparison of residual normal B cell profiles between CLL and MBL [113] B-ALL Evaluation of neuropilin-1/CD304 as minimal residual disease and prognostic marker [114] DLBCL Assessment of the absolute counts of B cells, T cells, and Treg cells for the prognostication of newly diagnosed DLBCL patients [115] Evaluation of the monocytic population distribution as an independent prognostic factor [116] DLBCL & FL Usage of aneuploidy and cell cycle indexing as tools for differentiating between CD10 + DLBCL and FL [117] DLBCL & BL Identification of cell markers to differentiate between BL and CD10 + DLBCL [118] MZL Distinguishing IgG4-related ophthalmic disease, idiopathic orbital inflammatio...…”

“…It is well known that the interplay between tumoral B cells and T cells influence disease development via the regulation of B lymphoma cells. In this regard, Rusak et al [115] developed a marker panel for B cells (CD19 + , CD20 + , CD22 + , and CD79a + ), T cells (CD3 + /CD4 + /CD5 + /CD8 + ), and Treg cells (CD4 + /CD25 +++ /Foxp3 high ) to determine their absolute counts and correlate such information for the prognostication of newly diagnosed DLBCL patients. They have also suggested the usage of monocytic population distribution in DLBCL patients as an independent prognostic factor [116].…”