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Acknowledgements: The KiCS program is supported by the Garron Family Cancer Centre at The Hospital for Sick Children through funding from the SickKids Foundation. A.H. received funding from the Canadian Institutes for Health Research (grant no. 162267) and is the Tier 1 Canada Research Chair in Rare Childhood Brain Tumors. D.M. is supported by the CIBC Children’s Foundation Chair in Child Health Research. A.S. is partially supported by an Early Researcher Award from the Ontario Ministry of Research and Innovation; by the Canada Research Chair in Childhood Cancer Genomics; and by funding from the V Foundation and the Robert J. Arceci Innovation Award from the St. Baldrick’s Foundation. We would like to thank the Centre for Applied Genomics, The Hospital for Sick Children, for assistance with RNA sequencing and the Treehouse Childhood Cancer Initiative, University of California, Santa Cruz, for access to their public data repository., The causes of pediatric cancers' distinctiveness compared to adult-onset tumors of the same type are not completely clear and not fully explained by their genomes. In this study, we used an optimized multilevel RNA clustering approach to derive molecular definitions for most childhood cancers. Applying this method to 13,313 transcriptomes, we constructed a pediatric cancer atlas to explore age-associated changes. Tumor entities were sometimes unexpectedly grouped due to common lineages, drivers or stemness profiles. Some established entities were divided into subgroups that predicted outcome better than current diagnostic approaches. These definitions account for inter-tumoral and intra-tumoral heterogeneity and have the potential of enabling reproducible, quantifiable diagnostics. As a whole, childhood tumors had more transcriptional diversity than adult tumors, maintaining greater expression flexibility. To apply these insights, we designed an ensemble convolutional neural network classifier. We show that this tool was able to match or clarify the diagnosis for 85% of childhood tumors in a prospective cohort. If further validated, this framework could be extended to derive molecular definitions for all cancer types.

Cancers are often defined by the dysregulation of specific transcriptional programs; however, the importance of global transcriptional changes is less understood. Hypertranscription is the genome-wide increase in RNA output. Hypertranscription’s prevalence, underlying drivers, and prognostic significance are undefined in primary human cancer. This is due, in part, to limitations of expression profiling methods, which assume equal RNA output between samples. Here, we developed a computational method to directly measure hypertranscription in 7494 human tumors, spanning 31 cancer types. Hypertranscription is ubiquitous across cancer, especially in aggressive disease. It defines patient subgroups with worse survival, even within well-established subtypes. Our data suggest that loss of transcriptional suppression underpins the hypertranscriptional phenotype. Single-cell analysis reveals hypertranscriptional clones, which dominate transcript production regardless of their size. Last, patients with hypertranscribed mutations have improved response to immune checkpoint therapy. Our results provide fundamental insights into gene dysregulation across human cancers and may prove useful in identifying patients who would benefit from novel therapies.

Leiomyosarcomas (LMS) are genetically heterogeneous tumors differentiating along smooth muscle lines. Currently, LMS treatment is not informed by molecular subtyping and is associated with highly variable survival. While disease site continues to dictate clinical management, the contribution of genetic factors to LMS subtype, origins, and timing are unknown. Here we analyze 70 genomes and 130 transcriptomes of LMS, including multiple tumor regions and paired metastases. Molecular profiling highlight the very early origins of LMS. We uncover three specific subtypes of LMS that likely develop from distinct lineages of smooth muscle cells. Of these, dedifferentiated LMS with high immune infiltration and tumors primarily of gynecological origin harbor genomic dystrophin deletions and/or loss of dystrophin expression, acquire the highest burden of genomic mutation, and are associated with worse survival. Homologous recombination defects lead to genome-wide mutational signatures, and a corresponding sensitivity to PARP trappers and other DNA damage response inhibitors, suggesting a promising therapeutic strategy for LMS. Finally, by phylogenetic reconstruction, we present evidence that clones seeding lethal metastases arise decades prior to LMS diagnosis., Heterogeneity in leiomyosarcomas (LMS) makes treatment of the disease challenging. Here the authors analyze LMS heterogeneity and molecular LMS subtypes using genomics and transcriptomics, finding origins in distinct lineages and associations with survival, in addition to the early emergence of metastatic clones.

Tumor cells may share some patterns of gene expression with their cell of origin, providing clues into the differentiation state and origin of cancer. Here, we study the differentiation state and cellular origin of 1300 childhood and adult kidney tumors. Using single cell mRNA reference maps of normal tissues, we quantify reference “cellular signals” in each tumor. Quantifying global differentiation, we find that childhood tumors exhibit fetal cellular signals, replacing the presumption of “fetalness” with a quantitative measure of immaturity. By contrast, in adult cancers our assessment refutes the suggestion of dedifferentiation towards a fetal state in most cases. We find an intimate connection between developmental mesenchymal populations and childhood renal tumors. We demonstrate the diagnostic potential of our approach with a case study of a cryptic renal tumor. Our findings provide a cellular definition of human renal tumors through an approach that is broadly applicable to human cancer., Transcriptomic analysis may provide information about the differentiation state and cell of origin of a cancer. Here, the authors assess mRNA signals in 1300 childhood and adult renal tumors and report a fetal origin of childhood tumors and no dedifferentiation of adult tumors.

Glutamine synthetase, encoded by the gene GLUL, is an enzyme that converts glutamate and ammonia to glutamine. It is expressed by endothelial cells, but surprisingly shows negligible glutamine-synthesizing activity in these cells at physiological glutamine levels. Here we show in mice that genetic deletion of Glul in endothelial cells impairs vessel sprouting during vascular development, whereas pharmacological blockade of glutamine synthetase suppresses angiogenesis in ocular and inflammatory skin disease while only minimally affecting healthy adult quiescent endothelial cells. This relies on the inhibition of endothelial cell migration but not proliferation. Mechanistically we show that in human umbilical vein endothelial cells GLUL knockdown reduces membrane localization and activation of the GTPase RHOJ while activating other Rho GTPases and Rho kinase, thereby inducing actin stress fibres and impeding endothelial cell motility. Inhibition of Rho kinase rescues the defect in endothelial cell migration that is induced by GLUL knockdown. Notably, glutamine synthetase palmitoylates itself and interacts with RHOJ to sustain RHOJ palmitoylation, membrane localization and activation. These findings reveal that, in addition to the known formation of glutamine, the enzyme glutamine synthetase shows unknown activity in endothelial cell migration during pathological angiogenesis through RHOJ palmitoylation. ispartof: NATURE vol:561 issue:7721 pages:63-+ ispartof: location:England status: published

Cryptic (hidden) pockets are sites that are not visible on unliganded target proteins' structures and only become apparent when a ligand binds. They might provide a valid alternative to classical binding sites in otherwise "undruggable" targets, but their hidden nature makes it difficult to use standard structure-based or computer-aided drug discovery approaches. Our group recently developed a Hamiltonian replica-exchange method (sampling water interfaces through scaled Hamiltonians or SWISH) that improves the sampling of hydrophobic cavities by scaling the interactions between water molecules and protein atoms. Here, we discuss further improvements to SWISH and its combination with fragment probe simulations. We tested the robustness and general applicability of the improved approach in a variety of pharmaceutically relevant targets. The chosen proteins: NPC2, p38α, LfrR, and hPNMT, represent a set of diversified and interesting targets harboring nontrivial cryptic binding sites. In all cases, the updated version of our algorithm efficiently explored the cryptic sites.