MicroRNAs as modulators of T cell functions in cancer

microRNA-378a-3p regulates the progression of hepatocellular carcinoma by regulating PD-L1 and STAT3

Programmed death ligand 1 (PD-L1) plays an essential role in the development or progression of hepatocellular carcinoma (HCC). MicroRNAs (miRNAs) are small RNA molecules that regulate gene expression during normal and pathophysiological events. Here, we explored the functions and detailed mechanisms of miR-378a-3p and PD-L1 in HCC progression. First, miR-378a-3p was selected by analyzing miRNA levels in two HCC Gene Expression Omnibus datasets. We found that miR-378a-3p levels exhibited a downward trend in HCC and were negatively correlated with PD-L1 levels. Additionally, a dual luciferase assay predicted that miR-378a-3p directly targets PD-L1. Moreover, the transfection of miR-378a-3p mimics into Li-7 and HuH-7 cells effectively decreased the PD-L1 mRNA and protein expression levels, and inhibited Treg differentiation in co-culture models by modulating the expression levels of certain cytokines. Furthermore, the overexpression of miR-378a-3p hindered cell proliferation and migration but facilitated apoptosis by repressing STAT3 signaling in HCC cells. In conclusion, miR-378a-3p appears to inhibit HCC tumorigenesis by regulating PD-L1 and STAT3 levels. Thus, miR-378a-3p may be a potential target for HCC therapy.

The Interplay Between Epigenetic Regulation and CD8+ T Cell Differentiation/Exhaustion for T Cell Immunotherapy

Effective immunotherapy treats cancers by eradicating tumourigenic cells by activated tumour antigen-specific and bystander CD8⁺ T-cells. However, T-cells can gradually lose cytotoxicity in the tumour microenvironment, known as exhaustion. Recently, DNA methylation, histone modification, and chromatin architecture have provided novel insights into epigenetic regulations of T-cell differentiation/exhaustion, thereby controlling the translational potential of the T-cells. Thus, developing strategies to govern epigenetic switches of T-cells dynamically is critical to maintaining the effector function of antigen-specific T-cells. In this mini-review, we 1) describe the correlation between epigenetic states and T cell phenotypes; 2) discuss the enzymatic factors and intracellular/extracellular microRNA imprinting T-cell epigenomes that drive T-cell exhaustion; 3) highlight recent advances in epigenetic interventions to rescue CD8⁺ T-cell functions from exhaustion. Finally, we express our perspective that regulating the interplay between epigenetic changes and transcriptional programs provides translational implications of current immunotherapy for cancer treatments.

miRNA-seq and clinical evaluation in multiple myeloma: miR-181a overexpression predicts short-term disease progression and poor post-treatment outcome

BACKGROUND

Despite significant advances in multiple myeloma (MM) therapy, disease relapse and treatment resistance remain major obstacles in clinical management. Herein, we have studied the clinical utility of miRNAs in improving patients' risk-stratification and prognosis.

METHODS

miRNA-seq was performed in CD138+ plasma cells of MM, smoldering multiple myeloma (sMM) and monoclonal gammopathy of undetermined significance (MGUS) patients. The screening MM cohort consisted of 138 patients. miRNA levels of CD138+ plasma cells were quantified by RT-qPCR following 3'-end RNA polyadenylation. Disease progression and patients' death were used as clinical end-point events. Internal validation was conducted by bootstrap analysis. Clinical net benefit on disease prognosis was assessed by decision curve analysis. Kruykov et al. 2016 served as validation cohort (n = 151).

RESULTS

miRNA-seq highlighted miR-181a to be upregulated in MM vs. sMM/MGUS, and R-ISS III vs. I patients. Screening and validation cohorts confirmed the significantly higher risk for short-term progression and worse survival of the patients overexpressing miR-181a. Multivariate models integrating miR-181a with disease established markers led to superior risk-stratification and clinical benefit for MM prognosis.

CONCLUSIONS

CD138+ overexpression of miR-181a was strongly correlated with inferior disease outcome and contributed to superior prediction of MM patients early progression, supporting personalised prognosis and treatment decisions.

The role of single nucleotide polymorphisms of miRNAs 100 and 146a as prognostic factors for prostate cancer

INTRODUCTION

Prostate cancer has a high incidence in men and is the second cause of cancer death among americans male. microRNA (miR) is becoming a potential new prognostic factor for prostate cancer. Single nucleotide polymorphisms (SNPs) are common polymorphisms, characterized by a single exchange of nitrogen based in the DNA. This polymorphism is present in the miRs, altering their function.

OBJECTIVE

To evaluate the role of SNP rs1834306 of miR100 and rs2910164 of miR146a in the development and prognosis of prostate cancer.

METHODS

One hundred patients diagnosed with prostate cancer and 68 controls were selected. The identification of SNP was rated by quantitative polymerase chain reaction from blood samples, and the analysis was performed within the presence of SNP and the prognostic variables.

RESULTS

In the SNP rs1834306 (miR100), a smaller presence of the polymorphic homozygous genotype was identified in patients with PSA >10 ng/mL, (P=0.03); when evaluating only the presence of the polymorphic allele G (P=0.09) it was compared to the presence of the wild type allele A. Among the patients with prostate cancer, SNP rs2910164 (miR146A), the polymorphic allele was more frequent in patients with a Gleason score ⩾7 than in patients with a Gleason score <7, (P=0.043). In patients with prostate cancer, miR100 was overexpressed in those with pT3 staging compared to pT2 and among those who had biochemical recurrence (P = 0.004 and P = 0.011, respectively).

CONCLUSIONS

SNP of miR146a acts as a poor prognostic factor (Gleason ⩾7), and the SNP of miR100 is linked to better prognostic data (PSA <10). MiR100 was overexpressed in prostate cancer with worse prognostic factors.

Identification of Acute Myeloid Leukemia Bone Marrow Circulating MicroRNAs

BACKGROUND

In addition to their roles in different biological processes, microRNAs in the tumor microenvironment appear to be potential diagnostic and prognostic biomarkers for various malignant diseases, including acute myeloid leukemia (AML). To date, no screening of circulating miRNAs has been carried out in the bone marrow compartment of AML. Accordingly, we investigated the circulating miRNA profile in AML bone marrow at diagnosis (AMLD) and first complete remission post treatment (AMLPT) in comparison to healthy donors (HD).

METHODS

Circulating miRNAs were isolated from AML bone marrow aspirations, and a low-density TaqMan miRNA array was performed to identify deregulated miRNAs followed by quantitative RT-PCR to validate the results. Bioinformatic analysis was conducted to evaluate the diagnostic and prognostic accuracy of the highly and significantly identified deregulated miRNA(s) as potential candidate biomarker(s).

RESULTS

We found several deregulated miRNAs between the AMLD vs. HD vs. AMLPT groups, which were involved in tumor progression and immune suppression pathways. We also identified significant diagnostic and prognostic signatures with the ability to predict AML patient treatment response.

CONCLUSIONS

This study provides a possible role of enriched circulating bone marrow miRNAs in the initiation and progression of AML and highlights new markers for prognosis and treatment monitoring.

Deconvolution of transcriptomes and miRNomes by independent component analysis provides insights into biological processes and clinical outcomes of melanoma patients

BACKGROUND

The amount of publicly available cancer-related "omics" data is constantly growing and can potentially be used to gain insights into the tumour biology of new cancer patients, their diagnosis and suitable treatment options. However, the integration of different datasets is not straightforward and requires specialized approaches to deal with heterogeneity at technical and biological levels.

METHODS

Here we present a method that can overcome technical biases, predict clinically relevant outcomes and identify tumour-related biological processes in patients using previously collected large discovery datasets. The approach is based on independent component analysis (ICA) - an unsupervised method of signal deconvolution. We developed parallel consensus ICA that robustly decomposes transcriptomics datasets into expression profiles with minimal mutual dependency.

RESULTS

By applying the method to a small cohort of primary melanoma and control samples combined with a large discovery melanoma dataset, we demonstrate that our method distinguishes cell-type specific signals from technical biases and allows to predict clinically relevant patient characteristics. We showed the potential of the method to predict cancer subtypes and estimate the activity of key tumour-related processes such as immune response, angiogenesis and cell proliferation. ICA-based risk score was proposed and its connection to patient survival was validated with an independent cohort of patients. Additionally, through integration of components identified for mRNA and miRNA data, the proposed method helped deducing biological functions of miRNAs, which would otherwise not be possible.

CONCLUSIONS

We present a method that can be used to map new transcriptomic data from cancer patient samples onto large discovery datasets. The method corrects technical biases, helps characterizing activity of biological processes or cell types in the new samples and provides the prognosis of patient survival.