Different classes of antibiotics exhibit disparate negative impacts on the therapeutic efficacy of immune checkpoint inhibitors in advanced non-small cell lung cancer patients.

It has been reported that antibiotics (ATBs) have adverse effect on the efficacy of treatment with immune checkpoint inhibitors (ICIs) in cancer patients. Since different classes of ATBs have different antibacterial spectrum, we aimed to study whether all ATBs had similar or different negative effects on the clinical outcomes of ICIs in patients with advanced non-small cell lung cancer (NSCLC). Patients with advanced NSCLC who received ICIs were included in this retrospective study and grouped by the class of ATBs they had used around the ICIs treatment time. The overall survival (OS) and the progression free survival (PFS) of patients among these groups were compared using Kaplan-Meier method and Cox proportional hazards model. A total of 148 eligible patients were enrolled, and 80 patients used ATBs. The results indicated that quinolones had no significant negative consequence on the clinical outcomes, while ß-lactams significantly shortened the OS and PFS of patients. Furthermore, patients exposed to the combination of ß-lactams and quinolones suffered the worst OS and PFS. Moreover, the subgroup analysis of ß-lactams revealed that only penicillins, but not carbapenems and cephalosporins, markedly reduced both OS and PFS. In addition to the class of ATBs used, the time frame of ATBs used also affected the clinical outcomes of ICIs therapy. Patients receiving ATBs within 60 days prior to and 30 days after the initiation of ICI treatment had significantly shorter OS and PFS compared with those who did not use ATBs. This study demonstrated that different classes of ATBs had disparate negative impacts on the clinical outcomes, and the use of ß-lactams, especially penicillins, should be avoided in advanced NSCLC patients who are receiving or scheduled to receive ICIs within 60 days.

TREM2: Keeping Pace With Immune Checkpoint Inhibitors in Cancer Immunotherapy.

To date, immune checkpoint inhibitors have been successively approved and widely used in clinical cancer treatments, however, the overall response rates are very low and almost all cancer patients eventually progressed to drug resistance, this is mainly due to the intricate tumor microenvironment and immune escape mechanisms of cancer cells. One of the main key mechanisms leading to the evasion of immune attack is the presence of the immunosuppressive microenvironment within tumors. Recently, several studies illustrated that triggering receptor expressed on myeloid cells-2 (TREM2), a transmembrane receptor of the immunoglobulin superfamily, was a crucial pathology-induced immune signaling hub, and it played a vital negative role in antitumor immunity, such as inhibiting the proliferation of T cells. Here, we reviewed the recent advances in the study of TREM2, especially focused on its regulation of tumor-related immune signaling pathways and its role as a novel target in cancer immunotherapy.

Construction of an RNA-Binding Protein-Related Prognostic Model for Pancreatic Adenocarcinoma Based on TCGA and GTEx Databases.

Background: Recently, RNA-binding proteins (RBPs) were reported to interact with target mRNA to regulate gene posttranscriptional expression, and RBP-mediated RNA modification can regulate the expression and function of proto-oncogenes and tumor suppressor genes. We systematically analyzed the expression of RBPs in pancreatic adenocarcinoma (PAAD) and constructed an RBP-associated prognostic risk model. Methods: Gene expression data of normal pancreatic samples as well as PAAD samples were downloaded from TCGA-PAAD and GTEx databases. Wilcoxon test and univariate Cox analysis were, respectively, applied to screen differential expression RBPs (DE-RBPs) and prognostic-associated RBPs (pRBPs). Functional enrichment was analyzed by GO, KEGG, and GSEA. Protein-protein interaction (PPI) network was constructed by STRING online database. Modeling RBPs were selected by multivariate Cox analysis. Kaplan-Meier survival and Cox analysis were applied to evaluate the effects of risk score on the overall survival of PAAD patients. ROC curves and validation cohort were applied to verify the accuracy of the model. Nomogram was applied for predicting 1-, 3-, and 5-year overall survival (OS) of PAAD patients. At last, modeling RBPs were further analyzed to explore their differential expression, prognostic value, as well as enrichment pathways in PAAD. Results: RBPs (453) were differentially expressed in normal and tumor samples, besides, 28 of which were prognostic associated. DE-RBPs (453) are functionally associated with ribosome, ribonuclease, spliceosome, etc. Eight RBPs (PABPC1, PRPF6, OAS1, RBM5, LSM12, IPO7, FXR1, and RBM6) were identified to construct a prognostic risk model. Higher risk score not only predicted poor prognosis but also was an independent poor prognostic indicator, which was verified by ROC curves and validation cohort. Eight modeling RBPs were confirmed to be significantly differentially expressed between normal and tumor samples from RNA and protein level. Besides, all of eight RBPs were related with overall survival of PAAD patients. Conclusions: We successfully constructed an RBP-associated prognostic risk model in PAAD, which has a potential clinical application prospect.