miR-aculous new avenues for cancer immunotherapy.

The rising toll of cancer globally necessitates ingenuity in early detection and therapy. In the last decade, the utilization of immune signatures and immune-based therapies has made significant progress in the clinic; however, clinical standards leave many current and future patients without options. Non-coding RNAs, specifically microRNAs, have been explored in pre-clinical contexts with tremendous success. MicroRNAs play indispensable roles in programming the interactions between immune and cancer cells, many of which are current or potential immunotherapy targets. MicroRNAs mechanistically control a network of target genes that can alter immune and cancer cell biology. These insights provide us with opportunities and tools that may complement and improve immunotherapies. In this review, we discuss immune and cancer cell-derived miRNAs that regulate cancer immunity and examine miRNAs as an integral part of cancer diagnosis, classification, and therapy.

Predicting Durable Responses to Immune Checkpoint Inhibitors in Non-Small-Cell Lung Cancer Using a Multi-Feature Model.

Due to the complex mechanisms affecting anti-tumor immune response, a single biomarker is insufficient to identify patients who will benefit from immune checkpoint inhibitors (ICIs) treatment. Therefore, a comprehensive predictive model is urgently required to predict the response to ICIs. A total of 162 non-small-cell lung cancer (NSCLC) patients undergoing ICIs treatment from three independent cohorts were enrolled and used as training and test cohorts (training cohort = 69, test cohort1 = 72, test cohort2 = 21). Eight genomic markers were extracted or calculated for each patient. Ten machine learning classifiers, such as the gaussian process classifier, random forest, and support vector machine (SVM), were evaluated. Three genomic biomarkers, namely tumor mutation burden, intratumoral heterogeneity, and loss of heterozygosity in human leukocyte antigen were screened out, and the SVM_poly method was adopted to construct a durable clinical benefit (DCB) prediction model. Compared with a single biomarker, the DCB multi-feature model exhibits better predictive value with the area under the curve values equal to 0.77 and 0.78 for test cohort1 and cohort2, respectively. The patients predicted to have DCB showed improved median progression-free survival (mPFS) and median overall survival (mOS) than those predicted to have non-durable clinical benefit.

The nuclear transportation of PD-L1 and the function in tumor immunity and progression.

As the main immune checkpoint, PD-L1-PD-1 interaction plays a critical role in the dysregulation of effector T cells, which contributes to the failure of Chimeric Antigen Receptor T-cell (CAR-T) and other immunotherapies. Presently, most research focuses on the extracellular function of PD-L1. Membrane PD-L1 can interact with its receptor PD-1 and decrease T cell-induced cancer immunity. However, the function of PD-L1 in cancer cells is still unclear. Recent studies have shown the separated clinical significance of PD-L1 expression in various cancer types, showing the complexity of PD-L1 in cancer cell regulation. As a novel regulatory pathway, the nuclear translocation of PD-L1 in cancer cells receives more attention. Results of these preclinical studies demonstrated that nuclear PD-L1 has an essential role in cancer development and other immune checkpoint molecules transcription. Herein, we summarized the mechanisms involved in PD-L1 nuclear transportation and identify the key regulatory factors in this process. Furthermore, we also summarize the function of nuclear PD-L1 in cancer immunity. These findings suggested the novel PD-L1 regulation in cancer development, which showed that nuclear PD-L1 is a potential therapeutic target in future cancer therapy.