A computational integrative approach based on alternative splicing analysis to compare immortalized and primary cancer cells

In Vivo and In Vitro Models of Hepatocellular Carcinoma: Current Strategies for Translational Modeling

Hepatocellular carcinoma (HCC) is the sixth most common cancer worldwide and the third leading cause of cancer-related death globally. HCC is a complex multistep disease and usually emerges in the setting of chronic liver diseases. The molecular pathogenesis of HCC varies according to the etiology, mainly caused by chronic hepatitis B and C virus infections, chronic alcohol consumption, aflatoxin-contaminated food, and non-alcoholic fatty liver disease associated with metabolic syndrome or diabetes mellitus. The establishment of HCC models has become essential for both basic and translational research to improve our understanding of the pathophysiology and unravel new molecular drivers of this disease. The ideal model should recapitulate key events observed during hepatocarcinogenesis and HCC progression in view of establishing effective diagnostic and therapeutic strategies to be translated into clinical practice. Despite considerable efforts currently devoted to liver cancer research, only a few anti-HCC drugs are available, and patient prognosis and survival are still poor. The present paper provides a state-of-the-art overview of in vivo and in vitro models used for translational modeling of HCC with a specific focus on their key molecular hallmarks.

Integrated Assessment of Viral Transcription, Antigen Presentation, and CD8+ T Cell Function Reveals Multiple Limitations of Class I-Selective Histone Deacetylase Inhibitors during HIV-1 Latency Reversal

Clinical trials investigating histone deacetylase inhibitors (HDACi) to reverse HIV-1 latency aim to expose reservoirs in antiretroviral (ARV)-treated individuals to clearance by immune effectors, yet have not driven measurable reductions in the frequencies of infected cells. We therefore investigated the effects of the class I-selective HDACi nanatinostat and romidepsin on various blocks to latency reversal and elimination, including viral splicing, antigen presentation, and CD8+ T cell function. In ex vivo CD4+ T cells from ARV-suppressed individuals, both HDACi significantly induced viral transcription, but not splicing nor supernatant HIV-1 RNA. In an HIV-1 latency model using autologous CD8+ T cell clones as biosensors of antigen presentation, neither HDACi-treated CD4+ T cell condition induced clone degranulation. Both HDACi also impaired the function of primary CD8+ T cells in viral inhibition assays, with nanatinostat causing less impairment. These findings suggest that spliced or cell-free HIV-1 RNAs are more indicative of antigen expression than unspliced HIV-RNAs and may help to explain the limited abilities of HDACi to generate CD8+ T cell targets in vivoIMPORTANCE Antiretroviral (ARV) drug regimens suppress HIV-1 replication but are unable to cure infection. This leaves people living with HIV-1 burdened by a lifelong commitment to expensive daily medication. Furthermore, it has become clear that ARV therapy does not fully restore health, leaving individuals at elevated risk for cardiovascular disease, certain types of cancers, and neurocognitive disorders, as well as leaving them exposed to stigma. Efforts are therefore under way to develop therapies capable of curing infection. A key focus of these efforts has been on a class of drugs called histone deacetylase inhibitors (HDACi), which have the potential of exposing hidden reservoirs of HIV-1 to elimination by the immune system. Unfortunately, clinical trial results with HDACi have thus far been disappointing. In the current study, we integrate a number of experimental approaches to build a model that provides insights into the limited activity of HDACi in clinical trials and offers direction for future approaches.

Exploiting single-cell RNA sequencing data to link alternative splicing and cancer heterogeneity: A computational approach

Cell heterogeneity studies using single-cell sequencing are gaining great significance in the era of personalized medicine. In particular, characterization of tumor heterogeneity is an emergent issue to improve clinical oncology, since both inter- and intra-tumor level heterogeneity influence the utility and application of molecular classifications through specific biomarkers. Majority of studies have exploited gene expression to discriminate cell types. However, to provide a more nuanced view of the underlying differences, isoform expression and alternative splicing events have to be analyzed in detail. In this study, we utilize publicly available single cell and bulk RNA sequencing datasets of breast cancer cells from primary tumors and immortalized cell lines. Breast cancer is very heterogeneous with well defined molecular subtypes and was therefore chosen for this study. RNA-seq data were explored in terms of genes, isoforms abundance and splicing events. The study was conducted from an average based approach (gene level expression) to detailed and deeper ones (isoforms abundance/splicing events) to perform a comparative analysis, and, thus, highlight the importance of the splicing machinery in defining the tumor heterogeneity. Moreover, here we demonstrate how the investigation of gene isoforms expression can help to identify the appropriate in vitro models. We furthermore extracted marker isoforms, and alternatively spliced genes between and within the different single cell populations to improve the classification of the breast cancer subtypes.