NMD is required for timely cell fate transitions by fine-tuning gene expression and regulating translation.

Cell fate transitions depend on balanced rewiring of transcription and translation programs to mediate ordered developmental progression. Components of the nonsense-mediated mRNA decay (NMD) pathway have been implicated in regulating embryonic stem cell (ESC) differentiation, but the exact mechanism is unclear. Here we show that NMD controls expression levels of the translation initiation factor Eif4a2 and its premature termination codon-encoding isoform (Eif4a2PTC ). NMD deficiency leads to translation of the truncated eIF4A2PTC protein. eIF4A2PTC elicits increased mTORC1 activity and translation rates and causes differentiation delays. This establishes a previously unknown feedback loop between NMD and translation initiation. Furthermore, our results show a clear hierarchy in the severity of target deregulation and differentiation phenotypes between NMD effector KOs (Smg5 KO > Smg6 KO > Smg7 KO), which highlights heterodimer-independent functions for SMG5 and SMG7. Together, our findings expose an intricate link between mRNA homeostasis and mTORC1 activity that must be maintained for normal dynamics of cell state transitions.

Cooperative genetic networks drive embryonic stem cell transition from naïve to formative pluripotency.

In the mammalian embryo, epiblast cells must exit the naïve state and acquire formative pluripotency. This cell state transition is recapitulated by mouse embryonic stem cells (ESCs), which undergo pluripotency progression in defined conditions in vitro. However, our understanding of the molecular cascades and gene networks involved in the exit from naïve pluripotency remains fragmentary. Here, we employed a combination of genetic screens in haploid ESCs, CRISPR/Cas9 gene disruption, large-scale transcriptomics and computational systems biology to delineate the regulatory circuits governing naïve state exit. Transcriptome profiles for 73 ESC lines deficient for regulators of the exit from naïve pluripotency predominantly manifest delays on the trajectory from naïve to formative epiblast. We find that gene networks operative in ESCs are also active during transition from pre- to post-implantation epiblast in utero. We identified 496 naïve state-associated genes tightly connected to the in vivo epiblast state transition and largely conserved in primate embryos. Integrated analysis of mutant transcriptomes revealed funnelling of multiple gene activities into discrete regulatory modules. Finally, we delineate how intersections with signalling pathways direct this pivotal mammalian cell state transition.

Virtual pathway explorer (viPEr) and pathway enrichment analysis tool (PEANuT): creating and analyzing focus networks to identify cross-talk between molecules and pathways.

BACKGROUND: Interpreting large-scale studies from microarrays or next-generation sequencing for further experimental testing remains one of the major challenges in quantitative biology. Combining expression with physical or genetic interaction data has already been successfully applied to enhance knowledge from all types of high-throughput studies. Yet, toolboxes for navigating and understanding even small gene or protein networks are poorly developed. RESULTS: We introduce two Cytoscape plug-ins, which support the generation and interpretation of experiment-based interaction networks. The virtual pathway explorer viPEr creates so-called focus networks by joining a list of experimentally determined genes with the interactome of a specific organism. viPEr calculates all paths between two or more user-selected nodes, or explores the neighborhood of a single selected node. Numerical values from expression studies assigned to the nodes serve to score identified paths. The pathway enrichment analysis tool PEANuT annotates networks with pathway information from various sources and calculates enriched pathways between a focus and a background network. Using time series expression data of atorvastatin treated primary hepatocytes from six patients, we demonstrate the handling and applicability of viPEr and PEANuT. Based on our investigations using viPEr and PEANuT, we suggest a role of the FoxA1/A2/A3 transcriptional network in the cellular response to atorvastatin treatment. Moreover, we find an enrichment of metabolic and cancer pathways in the Fox transcriptional network and demonstrate a patient-specific reaction to the drug. CONCLUSIONS: The Cytoscape plug-in viPEr integrates -omics data with interactome data. It supports the interpretation and navigation of large-scale datasets by creating focus networks, facilitating mechanistic predictions from -omics studies. PEANuT provides an up-front method to identify underlying biological principles by calculating enriched pathways in focus networks.

Systematic analysis of off-label and off-guideline cancer therapy usage in a real-world cohort of 165,912 US patients.

Patients with cancer may be given treatments that are not officially approved (off-label) or recommended by guidelines (off-guideline). Here we present a data science framework to systematically characterize off-label and off-guideline usages using real-world data from de-identified electronic health records (EHR). We analyze treatment patterns in 165,912 US patients with 14 common cancer types. We find that 18.6% and 4.4% of patients have received at least one line of off-label and off-guideline cancer drugs, respectively. Patients with worse performance status, in later lines, or treated at academic hospitals are significantly more likely to receive off-label and off-guideline drugs. To quantify how predictable off-guideline usage is, we developed machine learning models to predict which drug a patient is likely to receive based on their clinical characteristics and previous treatments. Finally, we demonstrate that our systematic analyses generate hypotheses about patients' response to treatments.

Clinico-Genomic Characterization of Patients with Metastatic Urothelial Carcinoma in Real-World Practice Identifies a Novel Bladder Immune Performance Index (BIPI).

PURPOSE: This retrospective analysis of the largest available clinico-genomic database used de-identified patient-level electronic health record-derived real-world data (RWD) combined with FoundationOne comprehensive genomic profiling (CGP) to characterize patients with metastatic urothelial carcinoma (mUC) treated in the real-world setting, detect potential biomarkers, and develop a bladder immune performance index (BIPI). EXPERIMENTAL DESIGN: Patients with mUC who started front-line single-agent immune checkpoint inhibitors (ICI) and an unmatched group treated with front-line platinum-based chemotherapy between January 1, 2011, and September 30, 2019, were selected. Clinical and genomic data were correlated with overall survival (OS). A novel BIPI predicting outcome with ICIs was developed using machine learning methods and validated using data from a phase II trial (NCT02951767). RESULTS: In ICI-treated patients (n = 118), high tumor mutational burden (≥10 mutations/megabase) was associated with improved OS (HR, 0.58; 95% CI, 0.35-0.95; P = 0.03). In chemotherapy-treated patients (n = 268), those with high APOBEC mutational signature had worse OS (HR, 1.43; 95% CI, 1.06-1.94; P = 0.02). Neither FGFR3 mutations nor DNA damage-repair pathway alterations were associated with OS. A novel BIPI combining clinical and genomic variables (nonmetastatic at initial diagnosis, normal or above normal albumin level at baseline, prior surgery for organ-confined disease, high tumor mutational burden) identified ICI-treated patients with longest OS and was validated in an independent dataset. CONCLUSIONS: Contemporary RWD including FoundationOne CGP can be used to characterize outcomes in real-world patients according to biomarkers beyond PD-L1. A validated, novel clinico-genomic BIPI demonstrated satisfactory prognostic performance for OS in patients with mUC receiving front-line ICI therapy.

Machine Learning Prediction of Clinical Trial Operational Efficiency.

Clinical trials are the gatekeepers and bottlenecks of progress in medicine. In recent years, they have become increasingly complex and expensive, driven by a growing number of stakeholders requiring more endpoints, more diverse patient populations, and a stringent regulatory environment. Trial designers have historically relied on investigator expertise and legacy norms established within sponsor companies to improve operational efficiency while achieving study goals. As such, data-driven forecasts of operational metrics can be a useful resource for trial design and planning. We develop a machine learning model to predict clinical trial operational efficiency using a novel dataset from Roche containing over 2,000 clinical trials across 20 years and multiple disease areas. The data includes important operational metrics related to patient recruitment and trial duration, as well as a variety of trial features such as the number of procedures, eligibility criteria, and endpoints. Our results demonstrate that operational efficiency can be predicted robustly using trial features, which can provide useful insights to trial designers on the potential impact of their decisions on patient recruitment success and trial duration.

Systematic pan-cancer analysis of mutation-treatment interactions using large real-world clinicogenomics data.

Quantifying the effectiveness of different cancer therapies in patients with specific tumor mutations is critical for improving patient outcomes and advancing precision medicine. Here we perform a large-scale computational analysis of 40,903 US patients with cancer who have detailed mutation profiles, treatment sequences and outcomes derived from electronic health records. We systematically identify 458 mutations that predict the survival of patients on specific immunotherapies, chemotherapy agents or targeted therapies across eight common cancer types. We further characterize mutation-mutation interactions that impact the outcomes of targeted therapies. This work demonstrates how computational analysis of large real-world data generates insights, hypotheses and resources to enable precision oncology.