Gain-of-Function Mutations: An Emerging Advantage for Cancer Biology

Polypyrimidine tract-binding protein 1 (PTBP1) is one of the most investigated multifunctional RNA-binding proteins (RBP), controlling almost all steps of mRNA metabolism and processing. It has been reported that PTBP1 is overexpressed in many different types of cancer and this high expression is associated with increased proliferation and poor prognoses. However, there are no reports on a putative role for PTBP1 in the molecular abnormalities and pathogenesis of hepatocellular carcinoma (HCC). Here, we identified PTBP1 as a positive regulator of human HCC growth. The expression of PTBP1 was increased in human HCC cells and tissues compared to the corresponding controls, and this high expression was positively correlated with increased tumor size and a reduced survival rate. Mechanistically, PTBP1 enhanced cyclin D3 (CCND3) translation by interacting with the 5'-untranslated region (5'-UTR) of CCND3 mRNA, consequently facilitating cell cycle progression and tumor growth. Furthermore, we found that miR-194 inhibits PTBP1 expression by binding to the 3'-UTR of PTBP1 mRNA, resulting in reduced CCND3 levels and HCC cell growth; moreover, the levels of PTBP1 were negatively correlated with miR-194 levels in HCC. Taken together, these findings identify PTBP1 as a pivotal enhancer of HCC growth; the miR-194/PTBP1/CCND3 axis seemingly has a crucial role in the development and progression of HCC and targeting the axis could be a novel therapeutic strategy against human HCC. © 2019 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

A miR-194/PTBP1/CCND3 axis regulates tumor growth in human hepatocellular carcinoma.

Accumulating evidence has demonstrated that transcriptional regulation is affected by DNA methylation. Understanding the perturbation of DNA methylation-mediated regulation between transcriptional factors (TFs) and targets is crucial for human diseases. However, the global landscape of DNA methylation-mediated transcriptional dysregulation (DMTD) across cancers has not been portrayed. Here, we systematically identified DMTD by integrative analysis of transcriptome, methylome and regulatome across 22 human cancer types. Our results revealed that transcriptional regulation was affected by DNA methylation, involving hundreds of methylation-sensitive TFs (MethTFs). In addition, pan-cancer MethTFs, the regulatory activity of which is generally affected by DNA methylation across cancers, exhibit dominant functional characteristics and regulate several cancer hallmarks. Moreover, pan-cancer MethTFs were found to be affected by DNA methylation in a complex pattern. Finally, we investigated the cooperation among MethTFs and identified a network module that consisted of 43 MethTFs with prognostic potential. In summary, we systematically dissected the transcriptional dysregulation mediated by DNA methylation across cancer types, and our results provide a valuable resource for both epigenetic and transcriptional regulation communities.

Complex impact of DNA methylation on transcriptional dysregulation across 22 human cancer types.

Low dose computed tomography (LDCT) screening, together with the recent advances in targeted and immunotherapies, have shown to improve non-small cell lung cancer (NSCLC) survival. Furthermore, screening has increased the number of early stage-detected tumors, allowing for surgical resection and multimodality treatments when needed. The need for improved sensitivity and specificity of NSCLC screening has led to increased interest in combining clinical and radiological data with molecular data. The development of biomarkers is poised to refine inclusion criteria for LDCT screening programs. Biomarkers may also be useful to better characterize the risk of indeterminate nodules found in the course of screening or to refine prognosis and help in the management of screening detected tumors. The clinical implications of these biomarkers are still being investigated and whether or not biomarkers will be included in further decision-making algorithms in the context of screening and early lung cancer management still needs to be determined. However, it seems clear that there is much room for improvement even in early stage lung cancer disease-free survival (DFS) rates; thus, biomarkers may be the key to refine risk-stratification and treatment of these patients. Clinicians' capacity to register, integrate, and analyze all the available data in both high risk individuals and early stage NSCLC patients will lead to a better understanding of the disease's mechanisms, and will have a direct impact in diagnosis, treatment, and follow up of these patients. In this review, we aim to summarize all the available data regarding the role of biomarkers in LDCT screening and early stage NSCLC from a multidisciplinary perspective. We have highlighted clinical implications, the need to combine risk stratification, clinical data, radiomics, molecular information and artificial intelligence in order to improve clinical decision-making, especially regarding early diagnostics and adjuvant therapy. We also discuss current and future perspectives for biomarker implementation in routine clinical practice.

/pdf/molecular-biomarkers-in-early-stage-lung-cancer-46dzb0zn96.pdf

Molecular biomarkers in early stage lung cancer.

Long noncoding RNAs (lncRNAs) are emerging as critical regulators of gene expression and play fundamental roles in various types of cancer. Current developments in transcriptome analyses unveiled the existence of lncRNAs; however, their functional characterization remains a challenge.A bioinformatics screen was performed by integration of multiple omics data in hepatocellular carcinoma (HCC) prioritizing a novel oncogenic lncRNA, LINC01132. Expression of LINC01132 in HCC and control tissues was validated by qRT-PCR. Cell viability and migration activity was examined by MTT and transwell assays. Finally, our results were confirmed in vivo mouse model and ex vivo patient derived tumor xenograft experiments to determine the mechanism of action and explore LINC01132-targeted immunotherapy.Systematic investigation of lncRNAs genome-wide expression patterns revealed LINC01132 as an oncogene in HCC. LINC01132 is significantly overexpressed in tumor and associated with poor overall survival of HCC patients, which is mainly driven by copy number amplification. Functionally, LINC01132 overexpression promoted cell growth, proliferation, invasion and metastasis in vitro and in vivo. Mechanistically, LINC01132 acts as an oncogenic driver by physically interacting with NRF and enhancing the expression of DPP4. Notably, LINC01132 silencing triggers CD8+ T cells infiltration, and LINC01132 knockdown combined with anti-PDL1 treatment improves antitumor immunity, which may prove a new combination therapy in HCC.LINC01132 functions as an oncogenic driver that induces HCC development via the NRF1/DPP4 axis. Silencing LINC01132 may enhance the efficacy of anti-PDL1 immunotherapy in HCC patients. 

/pdf/long-noncoding-rna-linc01132-enhances-immunosuppression-and-1od5smiu.pdf

Long noncoding RNA LINC01132 enhances immunosuppression and therapy resistance via NRF1/DPP4 axis in hepatocellular carcinoma

The structure of RNA, which is considered to be a second layer of information alongside the genetic code, provides fundamental insights into the cellular function of both coding and non-coding RNAs. Several high-throughput technologies have been developed to profile transcriptome-wide RNA structures, i.e., the structurome. However, it is challenging to interpret the profiling data because the observed data represent an average over different RNA conformations and isoforms with different abundance. To address this challenge, we developed an RNA structurome quantification method (RSQ) to statistically model the distribution of reads over both isoforms and RNA conformations, and thus provide accurate quantification of the isoform-specific structurome. The quantified RNA structurome enables the comparison of isoform-specific conformations between different conditions, the exploration of RNA conformation variation affected by single nucleotide polymorphism (SNP) , and the measurement of RNA accessibility for binding of either small RNAs in RNAi-based assays or RNA binding protein in transcriptional regulation. The model used in our method sheds new light on the potential impact of the RNA structurome on gene regulation.

/pdf/rsq-a-statistical-method-for-quantification-of-isoform-4d36jlvmnp.pdf

RSQ: a statistical method for quantification of isoform-specific structurome using transcriptome-wide structural profiling data [preprint]

Genetic alterations in immune-related pathways are common hallmarks of cancer. However, to realize the full potential of immunotherapy, a comprehensive understanding of immune networks and how mutations impact network structure and functional output across cancer types is instrumental. Herein we systematically interrogated somatic mutations that could express neoantigens and alter immune responses in cancer patients compared to wild-type controls. To do so, we developed a network-based immunogenomics model (NIPPER) with scoring systems to prioritize critical genes and mutations eliciting differential HLA binding affinity and alternate responses to immunotherapy. These mutations are enriched in essential protein domains and often alter tumor infiltration by immune cells, affecting T cell receptor repertoire and B cell clonal expansion. Furthermore, we devised an interactome network propagation framework integrated with drug associated gene signatures to identify potential immunomodulatory drug candidates. Together, our systems-level analysis results help interpret the heterogeneous immune responses among patients, and serve as a resource for future functional studies and targeted therapeutics.

/pdf/comprehensive-and-integrated-genomic-characterization-of-48mtg3lrdl.pdf

Comprehensive and Integrated Genomic Characterization of Human Immunome in Cancer

https://aasldpubs.onlinelibrary.wiley.com/doi/pdf/10.1002/hep.30242

MERIT: Systematic Analysis and Characterization of Mutational Effect on RNA Interactome Topology

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RSQ: a statistical method for quantification of isoform-specific structurome using transcriptome-wide structural profiling data [preprint]

Comprehensive and Integrated Genomic Characterization of Human Immunome in Cancer

MERIT: Systematic Analysis and Characterization of Mutational Effect on RNA Interactome Topology