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Positrons are key to the production of cold antihydrogen. Here the authors report the sympathetic cooling of positrons by interacting them with laser-cooled Be+ ions resulting in a three-fold reduction of the temperature of positrons for antihydrogen synthesis.

Abstract Endometrial cancer (EC) has four molecular subtypes with strong prognostic value and therapeutic implications. The most common subtype (NSMP; No Specific Molecular Profile) is assigned after exclusion of the defining features of the other three molecular subtypes and includes patients with heterogeneous clinical outcomes. In this study, we employ artificial intelligence (AI)-powered histopathology image analysis to differentiate between p53abn and NSMP EC subtypes and consequently identify a sub-group of NSMP EC patients that has markedly inferior progression-free and disease-specific survival (termed ‘p53abn-like NSMP’), in a discovery cohort of 368 patients and two independent validation cohorts of 290 and 614 from other centers. Shallow whole genome sequencing reveals a higher burden of copy number abnormalities in the ‘p53abn-like NSMP’ group compared to NSMP, suggesting that this group is biologically distinct compared to other NSMP ECs. Our work demonstrates the power of AI to detect prognostically different and otherwise unrecognizable subsets of EC where conventional and standard molecular or pathologic criteria fall short, refining image-based tumor classification. This study’s findings are applicable exclusively to females.

Abstract The role for routine whole genome and transcriptome analysis (WGTA) for poor prognosis pediatric cancers remains undetermined. Here, we characterize somatic mutations, structural rearrangements, copy number variants, gene expression, immuno-profiles and germline cancer predisposition variants in children and adolescents with relapsed, refractory or poor prognosis malignancies who underwent somatic WGTA and matched germline sequencing. Seventy-nine participants with a median age at enrollment of 8.8 y (range 6 months to 21.2 y) are included. Germline pathogenic/likely pathogenic variants are identified in 12% of participants, of which 60% were not known prior. Therapeutically actionable variants are identified by targeted gene report and whole genome in 32% and 62% of participants, respectively, and increase to 96% after integrating transcriptome analyses. Thirty-two molecularly informed therapies are pursued in 28 participants with 54% achieving a clinical benefit rate; objective response or stable disease ≥6 months. Integrated WGTA identifies therapeutically actionable variants in almost all tumors and are directly translatable to clinical care of children with poor prognosis cancers.

Abstract In clinical oncology, many diagnostic tasks rely on the identification of cells in histopathology images. While supervised machine learning techniques necessitate the need for labels, providing manual cell annotations is time-consuming. In this paper, we propose a self-supervised framework (enVironment-aware cOntrastive cell represenTation learning: VOLTA) for cell representation learning in histopathology images using a technique that accounts for the cell’s mutual relationship with its environment. We subject our model to extensive experiments on data collected from multiple institutions comprising over 800,000 cells and six cancer types. To showcase the potential of our proposed framework, we apply VOLTA to ovarian and endometrial cancers and demonstrate that our cell representations can be utilized to identify the known histotypes of ovarian cancer and provide insights that link histopathology and molecular subtypes of endometrial cancer. Unlike supervised models, we provide a framework that can empower discoveries without any annotation data, even in situations where sample sizes are limited.

KRAS wildtype metastatic pancreatic ductal adenocarcinoma (mPDAC) could represent a distinct molecular entity from other PDACs. Here, the authors analyse KRAS wildtype mPDAC tumours using genomics and transcriptomics and find molecular similarities with cholangiocarcinomas.

The interpretation of somatic variants in cancer is challenging due to the scale and complexity of sequencing data. Here, the authors present PORI, an open-source framework for interpreting somatic variants in cancer using graph knowledge base tools, automated reporting, and manual curation.

Several genomic features have been found for acute myeloid leukaemia (AML) but targeted clinical genetic testing fails to predict prognosis. Here, the authors generate an AML prognostic score from RNA-seq data of patients, which successfully stratifies AML patients and which may provide guidance for therapeutic strategies.

Elevated levels of interleukin-33 have been associated with poor prognosis in patients with glioma. Here the authors show that glioma-derived IL-33 modulates a pro-tumorigenic immune microenvironment by activating resident and recruiting peripheral innate immune cells.

Information on protein sequence variability and conservation can be leveraged to identify functionally important regions. Here, the authors develop new conservation measures that exploit taxonomy distances and LIST, a tool for predicting deleteriousness of human variants.

The driver mutations for the two main molecular subgroups of diffuse large B-cell lymphoma (DLBCL) are poorly defined. Here, an integrative genomics analysis identifies 3′ UTR NFKBIZ mutations within the activated B-cell DLBCL subgroup and small FCGR2B amplifications in the germinal centre B-cell DLBCL subgroup.

In melanoma, reduced ADAR1 impairs A-to-I microRNA editing. Here, the authors show that miR-378a-3p undergoes this editing in non-metastatic cells and the edited form of miR-378a-3p binds to the PARVA oncogene, inhibiting its expression and preventing melanoma progression and metastasis.

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