Computational drug prediction in hepatoblastoma by integrating pan-cancer transcriptomics with pharmacological response.

BACKGROUND AND AIMS: Hepatoblastoma (HB) is the predominant form of pediatric liver cancer, though it remains exceptionally rare. While treatment outcomes for children with HB have improved, patients with advanced tumors face limited therapeutic choices. Additionally, survivors often suffer from long-term adverse effects due to treatment, including ototoxicity, cardiotoxicity, delayed growth, and secondary tumors. Consequently, there is a pressing need to identify new and effective therapeutic strategies for patients with HB. Computational methods to predict drug sensitivity from a tumor's transcriptome have been successfully applied for some common adult malignancies, but specific efforts in pediatric cancers are lacking because of the paucity of data. APPROACH AND RESULTS: In this study, we used DrugSense to assess drug efficacy in patients with HB, particularly those with the aggressive C2 subtype associated with poor clinical outcomes. Our method relied on publicly available collections of pan-cancer transcriptional profiles and drug responses across 36 tumor types and 495 compounds. The drugs predicted to be most effective were experimentally validated using patient-derived xenograft models of HB grown in vitro and in vivo. We thus identified 2 cyclin-dependent kinase 9 inhibitors, alvocidib and dinaciclib as potent HB growth inhibitors for the high-risk C2 molecular subtype. We also found that in a cohort of 46 patients with HB, high cyclin-dependent kinase 9 tumor expression was significantly associated with poor prognosis. CONCLUSIONS: Our work proves the usefulness of computational methods trained on pan-cancer data sets to reposition drugs in rare pediatric cancers such as HB, and to help clinicians in choosing the best treatment options for their patients.

ß-catenin perturbations control differentiation programs in mouse embryonic stem cells.

The Wnt/ß-catenin pathway is involved in development, cancer, and embryonic stem cell (ESC) maintenance; its dual role in stem cell self-renewal and differentiation is still controversial. Here, by applying an in vitro system enabling inducible gene expression control, we report that moderate induction of transcriptionally active exogenous ß-catenin in ß-catenin null mouse ESCs promotes epiblast-like cell (EpiLC) derivation in vitro. Instead, in wild-type cells, moderate chemical pre-activation of the Wnt/ß-catenin pathway promotes EpiLC in vitro derivation. Finally, we suggest that moderate ß-catenin levels in ß-catenin null mouse ESCs favor early stem cell commitment toward mesoderm if the exogenous protein is induced only in the "ground state" of pluripotency condition, or endoderm if the induction is maintained during the differentiation. Overall, our results confirm previous findings about the role of ß-catenin in pluripotency and differentiation, while indicating a role for its doses in promoting specific differentiation programs.

ThETA: transcriptome-driven efficacy estimates for gene-based TArget discovery.

SUMMARY: Estimating efficacy of gene-target-disease associations is a fundamental step in drug discovery. An important data source for this laborious task is RNA expression, which can provide gene-disease associations on the basis of expression fold change and statistical significance. However, the simply use of the log-fold change can lead to numerous false-positive associations. On the other hand, more sophisticated methods that utilize gene co-expression networks do not consider tissue specificity. Here, we introduce Transcriptome-driven Efficacy estimates for gene-based TArget discovery (ThETA), an R package that enables non-expert users to use novel efficacy scoring methods for drug-target discovery. In particular, ThETA allows users to search for gene perturbation (therapeutics) that reverse disease-gene expression and genes that are closely related to disease-genes in tissue-specific networks. ThETA also provides functions to integrate efficacy evaluations obtained with different approaches and to build an overall efficacy score, which can be used to identify and prioritize gene(target)-disease associations. Finally, ThETA implements visualizations to show tissue-specific interconnections between target and disease-genes, and to indicate biological annotations associated with the top selected genes. AVAILABILITY AND IMPLEMENTATION: ThETA is freely available for academic use at https://github.com/vittoriofortino84/ThETA. CONTACT: vittorio.fortino@uef.fi. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Author Correction: Impaired mitophagy links mitochondrial disease to epithelial stress in methylmalonyl-CoA mutase deficiency.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Impaired mitophagy links mitochondrial disease to epithelial stress in methylmalonyl-CoA mutase deficiency.

Deregulation of mitochondrial network in terminally differentiated cells contributes to a broad spectrum of disorders. Methylmalonic acidemia (MMA) is one of the most common inherited metabolic disorders, due to deficiency of the mitochondrial methylmalonyl-coenzyme A mutase (MMUT). How MMUT deficiency triggers cell damage remains unknown, preventing the development of disease-modifying therapies. Here we combine genetic and pharmacological approaches to demonstrate that MMUT deficiency induces metabolic and mitochondrial alterations that are exacerbated by anomalies in PINK1/Parkin-mediated mitophagy, causing the accumulation of dysfunctional mitochondria that trigger epithelial stress and ultimately cell damage. Using drug-disease network perturbation modelling, we predict targetable pathways, whose modulation repairs mitochondrial dysfunctions in patient-derived cells and alleviate phenotype changes in mmut-deficient zebrafish. These results suggest a link between primary MMUT deficiency, diseased mitochondria, mitophagy dysfunction and epithelial stress, and provide potential therapeutic perspectives for MMA.

Publisher Correction: Prioritizing target-disease associations with novel safety and efficacy scoring methods.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

The TRAPP complex mediates secretion arrest induced by stress granule assembly.

The TRAnsport Protein Particle (TRAPP) complex controls multiple membrane trafficking steps and is strategically positioned to mediate cell adaptation to diverse environmental conditions, including acute stress. We have identified the TRAPP complex as a component of a branch of the integrated stress response that impinges on the early secretory pathway. The TRAPP complex associates with and drives the recruitment of the COPII coat to stress granules (SGs) leading to vesiculation of the Golgi complex and arrest of ER export. The relocation of the TRAPP complex and COPII to SGs only occurs in cycling cells and is CDK1/2-dependent, being driven by the interaction of TRAPP with hnRNPK, a CDK substrate that associates with SGs when phosphorylated. In addition, CDK1/2 inhibition impairs TRAPP complex/COPII relocation to SGs while stabilizing them at ER exit sites. Importantly, the TRAPP complex controls the maturation of SGs. SGs that assemble in TRAPP-depleted cells are smaller and are no longer able to recruit RACK1 and Raptor, two TRAPP-interactive signaling proteins, sensitizing cells to stress-induced apoptosis.

Prioritizing target-disease associations with novel safety and efficacy scoring methods.

Biological target (commonly genes or proteins) identification is still largely a manual process, where experts manually try to collect and combine information from hundreds of data sources, ranging from scientific publications to omics databases. Targeting the wrong gene or protein will lead to failure of the drug development process, as well as incur delays and costs. To improve this process, different software platforms are being developed. These platforms rely strongly on efficacy estimates based on target-disease association scores created by computational methods for drug target prioritization. Here novel computational methods are presented to more accurately evaluate the efficacy and safety of potential drug targets. The proposed efficacy scores utilize existing gene expression data and tissue/disease specific networks to improve the inference of target-disease associations. Conversely, safety scores enable the identification of genes that are essential, potentially susceptible to adverse effects or carcinogenic. Benchmark results demonstrate that our transcriptome-based methods for drug target prioritization can increase the true positive rate of target-disease associations. Additionally, the proposed safety evaluation system enables accurate predictions of targets of withdrawn drugs and targets of drug trials prematurely discontinued.

The Golgi complex in disease and therapy.

The Golgi complex occupies a strategic position in the endomembrane system and acts not only as a key trafficking and sorting station and a vital biosynthetic center for glycoproteins and lipids, but also as an active signaling hub. As such, the Golgi complex participates in the establishment and maintenance of cell compartmentalization and in general, cell processes such as cell growth and apoptosis. The different functions of the Golgi complex are executed by composite molecular machineries that have been exhaustively dissected over the last three decades. These machineries can become dysfunctional as a result of mutations in the respective encoding genes or may be hijacked by infectious agents or misregulated in the course of multifactorial diseases such as neurodegeneration and cancer. Small molecules targeting components of these machineries have been instrumental in dissecting their functions in in vitro studies and some of them have been developed or are currently under development for clinical use.