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Neuroblastoma, the most frequent solid tumor in infants, shows very diverse outcomes from spontaneous regression to fatal disease. When these different tumors originate and how they evolve are not known. Here we quantify the somatic evolution of neuroblastoma by deep whole-genome sequencing, molecular clock analysis and population-genetic modeling in a comprehensive cohort covering all subtypes. We find that tumors across the entire clinical spectrum begin to develop via aberrant mitoses as early as the first trimester of pregnancy. Neuroblastomas with favorable prognosis expand clonally after short evolution, whereas aggressive neuroblastomas show prolonged evolution during which they acquire telomere maintenance mechanisms. The initial aneuploidization events condition subsequent evolution, with aggressive neuroblastoma exhibiting early genomic instability. We find in the discovery cohort ( n  = 100), and validate in an independent cohort ( n  = 86), that the duration of evolution is an accurate predictor of outcome. Thus, insight into neuroblastoma evolution may prospectively guide treatment decisions. Somatic evolutionary analysis of neuroblastoma, a pediatric tumor, proposes a common fetal time of origin. Notably, high-risk tumors exhibit early genomic instability and prolonged evolution, and this evolutionary duration predicts clinical outcomes.

miRNAs can both promote and repress tumorigenesis, and directly control epithelial–mesenchymal transition (EMT). miR-9 (which is upregulated in breast cancer cells) is activated by MYC and MYCN, and regulates EMT and metastasis through direct control of E-cadherin. In contrast, tumour angiogenesis is controlled indirectly through effects on vascular endothelial growth factor (VEGF) expression. MicroRNAs (miRNAs) are increasingly implicated in regulating the malignant progression of cancer. Here we show that miR-9, which is upregulated in breast cancer cells, directly targets CDH1 , the E-cadherin-encoding messenger RNA, leading to increased cell motility and invasiveness. miR-9-mediated E-cadherin downregulation results in the activation of β -catenin signalling, which contributes to upregulated expression of the gene encoding vascular endothelial growth factor (VEGF); this leads, in turn, to increased tumour angiogenesis. Overexpression of miR-9 in otherwise non-metastatic breast tumour cells enables these cells to form pulmonary micrometastases in mice. Conversely, inhibiting miR-9 by using a 'miRNA sponge' in highly malignant cells inhibits metastasis formation. Expression of miR-9 is activated by MYC and MYCN, both of which directly bind to the mir-9-3 locus. Significantly, in human cancers, miR-9 levels correlate with MYCN amplification, tumour grade and metastatic status. These findings uncover a regulatory and signalling pathway involving a metastasis-promoting miRNA that is predicted to directly target expression of the key metastasis-suppressing protein E-cadherin.

Background RNA profiling technologies at single-cell resolutions, including single-cell and single-nuclei RNA sequencing (scRNA-seq and snRNA-seq, scnRNA-seq for short), can help characterize the composition of tissues and reveal cells that influence key functions in both healthy and disease tissues. However, the use of these technologies is operationally challenging because of high costs and stringent sample-collection requirements. Computational deconvolution methods that infer the composition of bulk-profiled samples using scnRNA-seq-characterized cell types can broaden scnRNA-seq applications, but their effectiveness remains controversial. Results We produced the first systematic evaluation of deconvolution methods on datasets with either known or scnRNA-seq-estimated compositions. Our analyses revealed biases that are common to scnRNA-seq 10X Genomics assays and illustrated the importance of accurate and properly controlled data preprocessing and method selection and optimization. Moreover, our results suggested that concurrent RNA-seq and scnRNA-seq profiles can help improve the accuracy of both scnRNA-seq preprocessing and the deconvolution methods that employ them. Indeed, our proposed method, Single-cell RNA Quantity Informed Deconvolution (SQUID), which combines RNA-seq transformation and dampened weighted least-squares deconvolution approaches, consistently outperformed other methods in predicting the composition of cell mixtures and tissue samples. Conclusions We showed that analysis of concurrent RNA-seq and scnRNA-seq profiles with SQUID can produce accurate cell-type abundance estimates and that this accuracy improvement was necessary for identifying outcomes-predictive cancer cell subclones in pediatric acute myeloid leukemia and neuroblastoma datasets. These results suggest that deconvolution accuracy improvements are vital to enabling its applications in the life sciences.

Diversification at the transcriptome 3′end is an important and evolutionarily conserved layer of gene regulation associated with differentiation and dedifferentiation processes. Here, we identify extensive transcriptome 3′end-alterations in neuroblastoma, a tumour entity with a paucity of recurrent somatic mutations and an unusually high frequency of spontaneous regression. Utilising extensive RNAi-screening we reveal the landscape and drivers of transcriptome 3′end-diversification, discovering PCF11 as critical regulator, directing alternative polyadenylation (APA) of hundreds of transcripts including a differentiation RNA-operon. PCF11 shapes inputs converging on WNT-signalling, and governs cell cycle, proliferation, apoptosis and neurodifferentiation. Postnatal PCF11 down-regulation induces a neurodifferentiation program, and low-level PCF11 in neuroblastoma associates with favourable outcome and spontaneous tumour regression. Our findings document a critical role for APA in tumorigenesis and describe a novel mechanism for cell fate reprogramming in neuroblastoma with potentially important clinical implications. We provide an interactive data repository of transcriptome-wide APA covering > 170 RNAis, and an APA-network map with regulatory hubs. In gene regulation, diversification at the transcriptome 3′end is linked to differentiation and dedifferentiation. Here, the authors discover extensive transcriptome 3′end-alterations in neuroblastoma, regulated by PCF11, and provide an interactive data repository of transcriptome-wide alternative polyadenylation.

BackgroundNeuroblastoma is the most common malignancy in infancy, accounting for 15% of childhood cancer deaths. Outcome for the high-risk disease remains poor. DNA-methylation patterns are significantly altered in all cancer types and can be utilised for disease stratification.MethodsGenome-wide DNA methylation (n = 223), gene expression (n = 130), genetic/clinical data (n = 213), whole-exome sequencing (n = 130) was derived from the TARGET study. Methylation data were derived from HumanMethylation450 BeadChip arrays. t-SNE was used for the segregation of molecular subgroups. A separate validation cohort of 105 cases was studied.ResultsFive distinct neuroblastoma molecular subgroups were identified, based on genome-wide DNA-methylation patterns, with unique features in each, including three subgroups associated with known prognostic features and two novel subgroups. As expected, Cluster-4 (infant diagnosis) had significantly better 5-year progression-free survival (PFS) than the four other clusters. However, in addition, the molecular subgrouping identified multiple patient subsets with highly increased risk, most notably infant patients that do not map to Cluster-4 (PFS 50% vs 80% for Cluster-4 infants, P = 0.005), and allowed identification of subgroup-specific methylation differences that may reflect important biological differences within neuroblastoma.ConclusionsMethylation-based clustering of neuroblastoma reveals novel molecular subgroups, with distinct molecular/clinical characteristics and identifies a subgroup of higher-risk infant patients.

Ferroptosis and apoptosis are key cell‐death pathways implicated in several human diseases including cancer. Ferroptosis is driven by iron‐dependent lipid peroxidation and currently has no characteristic biomarkers or gene signatures. Here a continuous phenotypic gradient between ferroptosis and apoptosis coupled to transcriptomic and metabolomic landscapes is established. The gradual ferroptosis‐to‐apoptosis transcriptomic landscape is used to generate a unique, unbiased transcriptomic predictor, the Gradient Gene Set (GGS), which classified ferroptosis and apoptosis with high accuracy. Further GGS optimization using multiple ferroptotic and apoptotic datasets revealed highly specific ferroptosis biomarkers, which are robustly validated in vitro and in vivo. A subset of the GGS is associated with poor prognosis in breast cancer patients and PDXs and contains different ferroptosis repressors. Depletion of one representative, PDGFA‐assaociated protein 1(PDAP1), is found to suppress basal‐like breast tumor growth in a mouse model. Omics and mechanistic studies revealed that ferroptosis is associated with enhanced lysosomal function, glutaminolysis, and the tricarboxylic acid (TCA) cycle, while its transition into apoptosis is attributed to enhanced endoplasmic reticulum(ER)‐stress and phosphatidylethanolamine (PE)‐to‐phosphatidylcholine (PC) metabolic shift. Collectively, this study highlights molecular mechanisms underlying ferroptosis execution, identified a highly predictive ferroptosis gene signature with prognostic value, ferroptosis versus apoptosis biomarkers, and ferroptosis repressors for breast cancer therapy. Transcriptomic, metabolomics, mechanistic, and computational analyses of programmed ferroptosis‐to‐apoptosis transition landscape identified ferroptosis selective biomarkers, revealed ferroptosis repressors as potential targets for breast cancer therapy, and highlighted ferroptosis‐toapoptosis molecular switches, including the PE/PC lipidomic shift.

Neuroblastoma is the most frequently occurring solid tumour in children, with an incidence of 1·3 cases per 100 000 children aged 0–14 years. Despite many advances during the past three decades, neuroblastoma has remained an enigmatic challenge to clinical and basic scientists. 20 years ago, the MYCN gene was found to be amplified in neuroblastomas, and research since then has focused on the search for other genetic markers. It has emerged that neuroblastoma cells, like cells of many other tumour types, often suffer from extensive, non-random genetic damage at multiple genetic loci. Elucidation of the exact molecular make-up of neuroblastomas will enable researchers to analyse how much specific markers, alone or in combination, can help to stratify disease in prospective studies; at present, stratification is based on age, stage, MYCN, and Shimada pathology. Neuroblastoma may be one of the first examples of the use of genetic tumour markers as a tool for defining tumour behaviour and to aid clinical staging.

Neuroblastomas encompass malignant cells with varying degrees of differentiation, ranging from adrenergic (adr) cells resembling the sympathoadrenal lineage to undifferentiated, stem-cell-like mesenchymal (mes) cancer cells. Relapsed neuroblastomas, which often have mesenchymal features, have a poor prognosis and respond less to anticancer therapies, necessitating the development of novel treatment strategies. To identify novel treatment options, we analyzed the sensitivity of 91 pediatric cell models, including patient-derived tumoroid cultures, to a drug library of 76 anti-cancer drugs at clinically relevant concentrations. This included 24 three-dimensionally cultured neuroblastoma cell lines representing the range of mesenchymal to adrenergic subtypes. High-throughput ATP-based luminescence measurements were compared to high-content confocal imaging. With machine learning-supported imaging analysis, we focused on changes in the lysosomal compartment as a marker for therapy-induced senescence and assessed the basal lysosomal levels in a subset of untreated mesenchymal versus adrenergic cells. We correlated these findings with pathway activity signatures based on bulk RNA and scRNAseq. Comprehensive image-based synergy screens with spheroid cultures validated the combined effects of selected drugs on proliferation and cytotoxicity. Mesenchymal models presented high basal lysosomal levels correlating with senescence-associated secretory phenotype (SASP) and sphingolipid metabolism pathways. Chemotherapy treatment further increased lysosome numbers, indicative of therapy-induced senescence. Furthermore, the mesenchymal subtypes correlated with MAPK activity and sensitivity to MAPK pathway inhibitors. Lysosomal and SASP signaling is druggable by inhibitors of lysosomal acid sphingomyelinase (SLMi) or senolytics, including BCL2-family inhibitors. Especially the sequential combination of MEK inhibitors (MEKi) with BCL2-family inhibitors was the most effective on relapsed neuroblastoma cell lines. Gene expression analysis of 223 patient samples, drug sensitivity profiling of five patient-derived fresh tissue cultures, and in vivo zebrafish embryo neuroblastoma xenograft models confirmed these findings. Inhibition of MAPK signaling in combination with BCL2-family inhibitors is a novel treatment option for patients suffering from relapsed neuroblastomas. Highlights Identification of specific vulnerabilities in neuroblastomas through comprehensive drug screening, aiming to address the poor prognosis and limited treatment options for relapsed neuroblastomas, focusing on mesenchymal and adrenergic subtypes. Complementation of high-throughput screening by high-content confocal imaging and imaging-based synergy screens, focusing on cell morphology and lysosomal response to drug treatment. Repurposing of clinically approved drugs identified MEK inhibitors and the combination of MEK inhibitors with BCL2-family inhibitors as a promising treatment option for relapsed neuroblastomas in vitro (cell lines), ex vivo (patient-derived organoid-like cultures), and in vivo (zebrafish embryo xenograft model).

ABSTRACT This paper analyses the Bank for International Settlements (BIS) triennial survey on foreign exchange trading, conducted in April of 2019 and 2022—before and close to the end of the COVID-19 pandemic. Did the evolution of Renminbi (RMB) trading change during these years, and did this episode have an effect on the Renminbi’s path to establish itself as a major currency on world-financial markets? Compared to the previous survey period from 2016 to 2019, analysed in Cheung et al. (2021), we find the following new patterns: (i) While policies to promote the use of RMB trading and macro-factors played a role in the internationalisation of the RMB in the 2016 to 2019 period, these elements were not prominent drivers in the 2019 to 2022 period. (ii) Furthermore, there is only limited evidence that pandemic related factors, such as restrictions on international mobility, or business closures, had a lasting effect on the geographic distribution of RMB trading. (iii) Instead, the largest share of variation in the data is explained by market forces, such as the RMB’s long-term convergence to the share of all currencies. The latter result is reminiscent to the findings in Cheung et al. (2019) for the 2013 to 2016 period. Furthermore, we document some new insights from the decomposition of the data by region and by instrument. We find that the speed of convergence is particularly strong in the options market, and in the financial centres of North America and Europe.

The purpose of this study was to develop a method of classifying cancers to specific diagnostic categories based on their gene expression signatures using artificial neural networks (ANNs). We trained the ANNs using the small, round blue-cell tumors (SRBCTs) as a model. These cancers belong to four distinct diagnostic categories and often present diagnostic dilemmas in clinical practice. The ANNs correctly classified all samples and identified the genes most relevant to the classification. Expression of several of these genes has been reported in SRBCTs, but most have not been associated with these cancers. To test the ability of the trained ANN models to recognize SRBCTs, we analyzed additional blinded samples that were not previously used for the training procedure, and correctly classified them in all cases. This study demonstrates the potential applications of these methods for tumor diagnosis and the identification of candidate targets for therapy.