A MiR181/Sirtuin1 regulatory circuit modulates drug response in biliary cancers.

Despite recent advances, biliary tract cancer (BTC) remains one of the most lethal tumor worldwide due to late diagnosis, limited therapeutic strategies and resistance to conventional therapies. In recent years, high-throughput technologies have enabled extensive genome, and transcriptome sequencing unveiling, among others, the regulatory potential of microRNAs (miRNAs). Compelling evidence shown that miRNA are attractive therapeutic targets and promising candidates as biomarkers for various therapy-resistant tumors. The analysis of miRNA profile successfully identified miR-181c and -181d as significantly downregulated in BTC patients. Low miR-181c and -181d expression levels were correlated with worse prognosis and poor treatment efficacy. In fact, progression-free survival analysis indicated poor survival rates in miR-181c and -181d low expressing patients. The expression profile of miR-181c and -181d in BTC cell lines revealed that both miRNAs were dysregulated. Functional in vitro experiments in BTC cell lines showed that overexpression of miR-181c and -181d affected cell viability and increased sensitivity to chemotherapy compared to controls. In addition, by using bioinformatic tools we showed that the miR-181c/d functional role is determined by binding to their target SIRT1 (Sirtuin 1). Moreover, BTC patients expressing high levels of miR-181 and low SIRT1 shown an improved survival and treatment response. An integrative network analysis demonstrated that, miR-181/SIRT1 circuit had a regulatory effect on several important metabolic tumor-related processes. Our study demonstrated that miR-181c and -181d act as tumor suppressor miRNA in BTC, suggesting the potential use as therapeutic strategy in resistant cancers and as predictive biomarker in the precision medicine of BTC.

MicroRNA 483-3p overexpression unleashes invasive growth of metastatic colorectal cancer via NDRG1 downregulation and ensuing activation of the ERBB3/AKT axis.

In colorectal cancer, the mechanisms underlying tumor aggressiveness require further elucidation. Taking advantage of a large panel of human metastatic colorectal cancer xenografts and matched stem-like cell cultures (m-colospheres), here we show that the overexpression of microRNA 483-3p (miRNA-483-3p; also known as MIR-483-3p), encoded by a frequently amplified gene locus, confers an aggressive phenotype. In m-colospheres, endogenous or ectopic miRNA-483-3p overexpression increased proliferative response, invasiveness, stem cell frequency, and resistance to differentiation. Transcriptomic analyses and functional validation found that miRNA-483-3p directly targets NDRG1, known as a metastasis suppressor involved in EGFR family downregulation. Mechanistically, miRNA-483-3p overexpression induced the signaling pathway triggered by ERBB3, including AKT and GSK3ß, and led to the activation of transcription factors regulating epithelial-mesenchymal transition (EMT). Consistently, treatment with selective anti-ERBB3 antibodies counteracted the invasive growth of miRNA-483-3p-overexpressing m-colospheres. In human colorectal tumors, miRNA-483-3p expression inversely correlated with NDRG1 and directly correlated with EMT transcription factor expression and poor prognosis. These results unveil a previously unrecognized link between miRNA-483-3p, NDRG1, and ERBB3-AKT signaling that can directly support colorectal cancer invasion and is amenable to therapeutic targeting.

Targeting the MITF/APAF-1 axis as salvage therapy for MAPK inhibitors in resistant melanoma.

Melanoma is a deadly form of cancer characterized by remarkable therapy resistance. Analyzing the transcriptome of MAPK inhibitor sensitive- and resistant-melanoma, we discovered that APAF-1 is negatively regulated by MITF in resistant tumors. This study identifies the MITF/APAF-1 axis as a molecular driver of MAPK inhibitor resistance. A drug-repositioning screen identified quinacrine and methylbenzethonium as potent activators of apoptosis in a context that mimics drug resistance mediated by APAF-1 inactivation. The compounds showed anti-tumor activity in in vitro and in vivo models, linked to suppression of MITF function. Both drugs profoundly sensitize melanoma cells to MAPK inhibitors, regulating key signaling networks in melanoma, including the MITF/APAF-1 axis. Significant activity of the two compounds in inhibiting specific epigenetic modulators of MITF/APAF-1 expression, such as histone deacetylases, was observed. In summary, we demonstrate that targeting the MITF/APAF-1 axis may overcome resistance and could be exploited as a potential therapeutic approach to treat resistant melanoma.

Liver gene therapy with intein-mediated F8 trans-splicing corrects mouse haemophilia A.

Liver gene therapy with adeno-associated viral (AAV) vectors is under clinical investigation for haemophilia A (HemA), the most common inherited X-linked bleeding disorder. Major limitations are the large size of the F8 transgene, which makes packaging in a single AAV vector a challenge, as well as the development of circulating anti-F8 antibodies which neutralise F8 activity. Taking advantage of split-intein-mediated protein trans-splicing, we divided the coding sequence of the large and highly secreted F8-N6 variant in two separate AAV-intein vectors whose co-administration to HemA mice results in the expression of therapeutic levels of F8 over time. This occurred without eliciting circulating anti-F8 antibodies unlike animals treated with the single oversized AAV-F8 vector under clinical development. Therefore, liver gene therapy with AAV-F8-N6 intein should be considered as a potential therapeutic strategy for HemA.

Therapeutic homology-independent targeted integration in retina and liver.

Challenges to the widespread application of gene therapy with adeno-associated viral (AAV) vectors include dominant conditions due to gain-of-function mutations which require allele-specific knockout, as well as long-term transgene expression from proliferating tissues, which is hampered by AAV DNA episomal status. To overcome these challenges, we used CRISPR/Cas9-mediated homology-independent targeted integration (HITI) in retina and liver as paradigmatic target tissues. We show that AAV-HITI targets photoreceptors of both mouse and pig retina, and this results in significant improvements to retinal morphology and function in mice with autosomal dominant retinitis pigmentosa. In addition, we show that neonatal systemic AAV-HITI delivery achieves stable liver transgene expression and phenotypic improvement in a mouse model of a severe lysosomal storage disease. We also show that HITI applications predominantly result in on-target editing. These results lay the groundwork for the application of AAV-HITI for the treatment of diseases affecting various organs.

Probing the Therapeutic Potential of Marine Phyla by SPE Extraction.

The marine environment is potentially a prolific source of small molecules with significant biological activities. In recent years, the development of new chromatographic phases and the progress in cell and molecular techniques have facilitated the search for marine natural products (MNPs) as novel pharmacophores and enhanced the success rate in the selection of new potential drug candidates. However, most of this exploration has so far been driven by anticancer research and has been limited to a reduced number of taxonomic groups. In this article, we report a test study on the screening potential of an in-house library of natural small molecules composed of 285 samples derived from 57 marine organisms that were chosen from among the major eukaryotic phyla so far represented in studies on bioactive MNPs. Both the extracts and SPE fractions of these organisms were simultaneously submitted to three different bioassays-two phenotypic and one enzymatic-for cytotoxic, antidiabetic, and antibacterial activity. On the whole, the screening of the MNP library selected 11 potential hits, but the distribution of the biological results showed that SPE fractionation increased the positive score regardless of the taxonomic group. In many cases, activity could be detected only in the enriched fractions after the elimination of the bulky effect due to salts. On a statistical basis, sponges and molluscs were confirmed to be the most significant source of cytotoxic and antimicrobial products, but other phyla were found to be effective with the other therapeutic targets.

Integrated Genomics Identifies miR-181/TFAM Pathway as a Critical Driver of Drug Resistance in Melanoma.

MicroRNAs (miRNAs) are attractive therapeutic targets and promising candidates as molecular biomarkers for various therapy-resistant tumors. However, the association between miRNAs and drug resistance in melanoma remains to be elucidated. We used an integrative genomic analysis to comprehensively study the miRNA expression profiles of drug-resistant melanoma patients and cell lines. MicroRNA-181a and -181b (miR181a/b) were identified as the most significantly down-regulated miRNAs in resistant melanoma patients and cell lines. Re-establishment of miR-181a/b expression reverses the resistance of melanoma cells to the BRAF inhibitor dabrafenib. Introduction of miR-181 mimics markedly decreases the expression of TFAM in A375 melanoma cells resistant to BRAF inhibitors. Furthermore, melanoma growth was inhibited in A375 and M14 resistant melanoma cells transfected with miR-181a/b mimics, while miR-181a/b depletion enhanced resistance in sensitive cell lines. Collectively, our study demonstrated that miR-181a/b could reverse the resistance to BRAF inhibitors in dabrafenib resistant melanoma cell lines. In addition, miR-181a and -181b are strongly down-regulated in tumor samples from patients before and after the development of resistance to targeted therapies. Finally, melanoma tissues with high miR-181a and -181b expression presented favorable outcomes in terms of Progression Free Survival, suggesting that miR-181 is a clinically relevant candidate for therapeutic development or biomarker-based therapy selection.

Predictive Signatures Inform the Effective Repurposing of Decitabine to Treat KRAS-Dependent Pancreatic Ductal Adenocarcinoma.

Mutated KRAS protein is a pivotal tumor driver in pancreatic cancer. However, despite comprehensive efforts, effective therapeutics that can target oncogenic KRAS are still under investigation or awaiting clinical approval. Using a specific KRAS-dependent gene signature, we implemented a computer-assisted inspection of a drug-gene network to in silico repurpose drugs that work like inhibitors of oncogenic KRAS. We identified and validated decitabine, an FDA-approved drug, as a potent inhibitor of growth in pancreatic cancer cells and patient-derived xenograft models that showed KRAS dependency. Mechanistically, decitabine efficacy was linked to KRAS-driven dependency on nucleotide metabolism and its ability to specifically impair pyrimidine biosynthesis in KRAS-dependent tumors cells. These findings also showed that gene signatures related to KRAS dependency might be prospectively used to inform on decitabine sensitivity in a selected subset of patients with KRAS-mutated pancreatic cancer. Overall, the repurposing of decitabine emerged as an intriguing option for treating pancreatic tumors that are addicted to mutant KRAS, thus offering opportunities for improving the arsenal of therapeutics for this extremely deadly disease. SIGNIFICANCE: Decitabine is a promising drug for cancer cells dependent on RAS signaling.

Intein-mediated protein trans-splicing expands adeno-associated virus transfer capacity in the retina.

Retinal gene therapy with adeno-associated viral (AAV) vectors holds promises for treating inherited and noninherited diseases of the eye. Although clinical data suggest that retinal gene therapy is safe and effective, delivery of large genes is hindered by the limited AAV cargo capacity. Protein trans-splicing mediated by split inteins is used by single-cell organisms to reconstitute proteins. Here, we show that delivery of multiple AAV vectors each encoding one of the fragments of target proteins flanked by short split inteins results in protein trans-splicing and full-length protein reconstitution in the retina of mice and pigs and in human retinal organoids. The reconstitution of large therapeutic proteins using this approach improved the phenotype of two mouse models of inherited retinal diseases. Our data support the use of split intein-mediated protein trans-splicing in combination with AAV subretinal delivery for gene therapy of inherited blindness due to mutations in large genes.

Combining Pathway Identification and Breast Cancer Survival Prediction via Screening-Network Methods.

Breast cancer is one of the most common invasive tumors causing high mortality among women. It is characterized by high heterogeneity regarding its biological and clinical characteristics. Several high-throughput assays have been used to collect genome-wide information for many patients in large collaborative studies. This knowledge has improved our understanding of its biology and led to new methods of diagnosing and treating the disease. In particular, system biology has become a valid approach to obtain better insights into breast cancer biological mechanisms. A crucial component of current research lies in identifying novel biomarkers that can be predictive for breast cancer patient prognosis on the basis of the molecular signature of the tumor sample. However, the high dimension and low sample size of data greatly increase the difficulty of cancer survival analysis demanding for the development of ad-hoc statistical methods. In this work, we propose novel screening-network methods that predict patient survival outcome by screening key survival-related genes and we assess the capability of the proposed approaches using METABRIC dataset. In particular, we first identify a subset of genes by using variable screening techniques on gene expression data. Then, we perform Cox regression analysis by incorporating network information associated with the selected subset of genes. The novelty of this work consists in the improved prediction of survival responses due to the different types of screenings (i.e., a biomedical-driven, data-driven and a combination of the two) before building the network-penalized model. Indeed, the combination of the two screening approaches allows us to use the available biological knowledge on breast cancer and complement it with additional information emerging from the data used for the analysis. Moreover, we also illustrate how to extend the proposed approaches to integrate an additional omic layer, such as copy number aberrations, and we show that such strategies can further improve our prediction capabilities. In conclusion, our approaches allow to discriminate patients in high-and low-risk groups using few potential biomarkers and therefore, can help clinicians to provide more precise prognoses and to facilitate the subsequent clinical management of patients at risk of disease.

Cancer Markers Selection Using Network-Based Cox Regression: A Methodological and Computational Practice.

International initiatives such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) are collecting multiple datasets at different genome-scales with the aim of identifying novel cancer biomarkers and predicting survival of patients. To analyze such data, several statistical methods have been applied, among them Cox regression models. Although these models provide a good statistical framework to analyze omic data, there is still a lack of studies that illustrate advantages and drawbacks in integrating biological information and selecting groups of biomarkers. In fact, classical Cox regression algorithms focus on the selection of a single biomarker, without taking into account the strong correlation between genes. Even though network-based Cox regression algorithms overcome such drawbacks, such network-based approaches are less widely used within the life science community. In this article, we aim to provide a clear methodological framework on the use of such approaches in order to turn cancer research results into clinical applications. Therefore, we first discuss the rationale and the practical usage of three recently proposed network-based Cox regression algorithms (i.e., Net-Cox, AdaLnet, and fastcox). Then, we show how to combine existing biological knowledge and available data with such algorithms to identify networks of cancer biomarkers and to estimate survival of patients. Finally, we describe in detail a new permutation-based approach to better validate the significance of the selection in terms of cancer gene signatures and pathway/networks identification. We illustrate the proposed methodology by means of both simulations and real case studies. Overall, the aim of our work is two-fold. Firstly, to show how network-based Cox regression models can be used to integrate biological knowledge (e.g., multi-omics data) for the analysis of survival data. Secondly, to provide a clear methodological and computational approach for investigating cancers regulatory networks.