γδ T cells for immunotherapy

Employing machine learning using ferroptosis-related genes to construct a prognosis model for patients with osteosarcoma

Identifying effective biomarkers in osteosarcoma (OS) is important for predicting prognosis. We investigated the prognostic value of ferroptosis-related genes (FRGs) in OS. Transcriptome and clinical data were obtained from The Cancer Genome Atlas and Gene Expression Omnibus. FRGs were obtained from the ferroptosis database. Univariate COX regression and LASSO regression screening were performed and an FRG-based prognostic model was constructed, which was validated using the Gene Expression Omnibus cohort. The predictive power of the model was assessed via a subgroup analysis. A nomogram was constructed using clinical markers with independent prognostic significance and risk score results. The CIBERSORT algorithm was used to detect the correlation between prognostic genes and 22 tumor-infiltrating lymphocytes. The expression of prognostic genes in erastin-treated OS cell lines was verified via real-time PCR. Six prognostic FRGs (ACSL5, ATF4, CBS, CDO1, SCD, and SLC3A2) were obtained and used to construct the risk prognosis model. Subjects were divided into high- and low-risk groups. Prognosis was worse in the high-risk group, and the model had satisfactory prediction performance for patients younger than 18 years, males, females, and those with non-metastatic disease. Univariate COX regression analysis showed that metastasis and risk score were independent risk factors for patients with OS. Nomogram was built on independent prognostic factors with superior predictive power and patient benefit. There was a significant correlation between prognostic genes and tumor immunity. Six prognostic genes were differentially expressed in ferroptosis inducer-treated OS cell lines. The identified prognostic genes can regulate tumor growth and progression by affecting the tumor microenvironment.

Effects of ibrutinib on T-cell immunity in patients with chronic lymphocytic leukemia

Chronic lymphocytic leukemia (CLL), a highly heterogeneous B-cell malignancy, is characterized by tumor microenvironment disorder and T-cell immune dysfunction, which play a major role in the proliferation and survival of CLL cells. Ibrutinib is the first irreversible inhibitor of Bruton’s tyrosine kinase (BTK). In addition to targeting B-cell receptor (BCR) signaling to kill tumor cells, increasing evidence has suggested that ibrutinib regulates the tumor microenvironment and T-cell immunity in a direct and indirect manner. For example, ibrutinib not only reverses the tumor microenvironment by blocking cytokine networks and toll-like receptor signaling but also regulates T cells in number, subset distribution, T-cell receptor (TCR) repertoire and immune function by inhibiting interleukin-2 inducible T-cell kinase (ITK) and reducing the expression of inhibitory receptors, and so on. In this review, we summarize the current evidence for the effects of ibrutinib on the tumor microenvironment and cellular immunity of patients with CLL, particularly for the behavior and function of T cells, explore its potential mechanisms, and provide a basis for the clinical benefits of long-term ibrutinib treatment and combined therapy based on T-cell-based immunotherapies.