CRI iAtlas: an interactive portal for immuno-oncology research.

The Cancer Research Institute (CRI) iAtlas is an interactive web platform for data exploration and discovery in the context of tumors and their interactions with the immune microenvironment. iAtlas allows researchers to study immune response characterizations and patterns for individual tumor types, tumor subtypes, and immune subtypes. iAtlas supports computation and visualization of correlations and statistics among features related to the tumor microenvironment, cell composition, immune expression signatures, tumor mutation burden, cancer driver mutations, adaptive cell clonality, patient survival, expression of key immunomodulators, and tumor infiltrating lymphocyte (TIL) spatial maps. iAtlas was launched to accompany the release of the TCGA PanCancer Atlas and has since been expanded to include new capabilities such as (1) user-defined loading of sample cohorts, (2) a tool for classifying expression data into immune subtypes, and (3) integration of TIL mapping from digital pathology images. We expect that the CRI iAtlas will accelerate discovery and improve patient outcomes by providing researchers access to standardized immunogenomics data to better understand the tumor immune microenvironment and its impact on patient responses to immunotherapy

Cell-to-cell and type-to-type heterogeneity of signaling networks: insights from the crowd.

Recent technological developments allow us to measure the status of dozens of proteins in individual cells. This opens the way to understand the heterogeneity of complex multi-signaling networks across cells and cell types, with important implications to understand and treat diseases such as cancer. These technologies are, however, limited to proteins for which antibodies are available and are fairly costly, making predictions of new markers and of existing markers under new conditions a valuable alternative. To assess our capacity to make such predictions and boost further methodological development, we organized the Single Cell Signaling in Breast Cancer DREAM challenge. We used a mass cytometry dataset, covering 36 markers in over 4,000 conditions totaling 80 million single cells across 67 breast cancer cell lines. Through four increasingly difficult subchallenges, the participants predicted missing markers, new conditions, and the time-course response of single cells to stimuli in the presence and absence of kinase inhibitors. The challenge results show that despite the stochastic nature of signal transduction in single cells, the signaling events are tightly controlled and machine learning methods can accurately predict new experimental data

A community challenge for a pancancer drug mechanism of action inference from perturbational profile data

The Columbia Cancer Target Discovery and Development (CTD2) Center is developing PANACEA, a resource comprising dose-responses and RNA sequencing (RNA-seq) profiles of 25 cell lines perturbed with similar to 400 clinical oncology drugs, to study a tumor-specific drug mechanism of action. Here, this resource serves as the basis for a DREAM Challenge assessing the accuracy and sensitivity of computational algorithms for de novo drug polypharmacology predictions. Dose-response and perturbational profiles for 32 kinase inhibitors are provided to 21 teams who are blind to the identity of the compounds. The teams are asked to predict high-affinity binding targets of each compound among similar to 1,300 targets cataloged in DrugBank. The best performing methods leverage gene expression profile similarity analysis as well as deep-learning methodologies trained on individual datasets. This study lays the foundation for future integrative analyses of pharmacogenomic data, reconciliation of polypharmacology effects in different tumor contexts, and insights into network-based assessments of drug mechanisms of action.Peer reviewe

The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)

Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions.Comment: Technical report of BraSy

Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research

Every year, 11% of infants are born preterm with significant health consequences, with the vaginal microbiome a risk factor for preterm birth. We crowdsource models to predict (1) preterm birth (PTB; \u3c37 \u3eweeks) or (2) early preterm birth (ePTB; \u3c32 \u3eweeks) from 9 vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from public raw data via phylogenetic harmonization. The predictive models are validated on two independent unpublished datasets representing 331 samples from 148 pregnant individuals. The top-performing models (among 148 and 121 submissions from 318 teams) achieve area under the receiver operator characteristic (AUROC) curve scores of 0.69 and 0.87 predicting PTB and ePTB, respectively. Alpha diversity, VALENCIA community state types, and composition are important features in the top-performing models, most of which are tree-based methods. This work is a model for translation of microbiome data into clinically relevant predictive models and to better understand preterm birth

Polymorphisms of genes coding for ghrelin and its receptor in relation to colorectal cancer risk: a two-step gene-wide case-control study

<p>Abstract</p> <p>Background</p> <p>Ghrelin, an endogenous ligand for the growth hormone secretagogue receptor (GHSR), has two major functions: the stimulation of the growth hormone production and the stimulation of food intake. Accumulating evidence also indicates a role of ghrelin in cancer development.</p> <p>Methods</p> <p>We conducted a case-control study to examine the association of common genetic variants in the genes coding for ghrelin (GHRL) and its receptor (GHSR) with colorectal cancer risk. Pairwise tagging was used to select the 11 polymorphisms included in the study. The selected polymorphisms were genotyped in 680 cases and 593 controls from the Czech Republic.</p> <p>Results</p> <p>We found two SNPs associated with lower risk of colorectal cancer, namely SNPs rs27647 and rs35683. We replicated the two hits, in additional 569 cases and 726 controls from Germany.</p> <p>Conclusion</p> <p>A joint analysis of the two populations indicated that the T allele of rs27647 SNP exerted a protective borderline effect (P_trend= 0.004).</p

Psychometric properties of the Alcohol Use Disorders Identification Test (AUDIT) across cross-cultural subgroups, genders, and sexual orientations: Findings from the International Sex Survey (ISS)

© 2023 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).INTRODUCTION: Despite being a widely used screening questionnaire, there is no consensus on the most appropriate measurement model for the Alcohol Use Disorders Identification Test (AUDIT). Furthermore, there have been limited studies on its measurement invariance across cross-cultural subgroups, genders, and sexual orientations. AIMS: The present study aimed to examine the fit of different measurement models for the AUDIT and its measurement invariance across a wide range of subgroups by country, language, gender, and sexual orientation. METHODS: Responses concerning past-year alcohol use from the participants of the cross-sectional International Sex Survey were considered (N = 62,943; M age: 32.73; SD = 12.59). Confirmatory factor analysis, as well as measurement invariance tests were performed for 21 countries, 14 languages, three genders, and four sexual-orientation subgroups that met the minimum sample size requirement for inclusion in these analyses. RESULTS: A two-factor model with factors describing 'alcohol use' (items 1-3) and 'alcohol problems' (items 4-10) showed the best model fit across countries, languages, genders, and sexual orientations. For the former two, scalar and latent mean levels of invariance were reached considering different criteria. For gender and sexual orientation, a latent mean level of invariance was reached. CONCLUSIONS: In line with the two-factor model, the calculation of separate alcohol-use and alcohol-problem scores is recommended when using the AUDIT. The high levels of measurement invariance achieved for the AUDIT support its use in cross-cultural research, capable also of meaningful comparisons among genders and sexual orientations.Peer reviewe

Proapoptotic activity of Ukrain is based on Chelidonium majus L. alkaloids and mediated via a mitochondrial death pathway

BACKGROUND: The anticancer drug Ukrain (NSC-631570) which has been specified by the manufacturer as semisynthetic derivative of the Chelidonium majus L. alkaloid chelidonine and the alkylans thiotepa was reported to exert selective cytotoxic effects on human tumour cell lines in vitro. Few clinical trials suggest beneficial effects in the treatment of human cancer. Aim of the present study was to elucidate the importance of apoptosis induction for the antineoplastic activity of Ukrain, to define the molecular mechanism of its cytotoxic effects and to identify its active constituents by mass spectrometry. METHODS: Apoptosis induction was analysed in a Jurkat T-lymphoma cell model by fluorescence microscopy (chromatin condensation and nuclear fragmentation), flow cytometry (cellular shrinkage, depolarisation of the mitochondrial membrane potential, caspase-activation) and Western blot analysis (caspase-activation). Composition of Ukrain was analysed by mass spectrometry and LC-MS coupling. RESULTS: Ukrain turned out to be a potent inducer of apoptosis. Mechanistic analyses revealed that Ukrain induced depolarisation of the mitochondrial membrane potential and activation of caspases. Lack of caspase-8, expression of cFLIP-L and resistance to death receptor ligand-induced apoptosis failed to inhibit Ukrain-induced apoptosis while lack of FADD caused a delay but not abrogation of Ukrain-induced apoptosis pointing to a death receptor independent signalling pathway. In contrast, the broad spectrum caspase-inhibitor zVAD-fmk blocked Ukrain-induced cell death. Moreover, over-expression of Bcl-2 or Bcl-x(L )and expression of dominant negative caspase-9 partially reduced Ukrain-induced apoptosis pointing to Bcl-2 controlled mitochondrial signalling events. However, mass spectrometric analysis of Ukrain failed to detect the suggested trimeric chelidonine thiophosphortriamide or putative dimeric or monomeric chelidonine thiophosphortriamide intermediates from chemical synthesis. Instead, the Chelidonium majus L. alkaloids chelidonine, sanguinarine, chelerythrine, protopine and allocryptopine were identified as major components of Ukrain. Apart from sanguinarine and chelerythrine, chelidonine turned out to be a potent inducer of apoptosis triggering cell death at concentrations of 0.001 mM, while protopine and allocryptopine were less effective. Similar to Ukrain, apoptosis signalling of chelidonine involved Bcl-2 controlled mitochondrial alterations and caspase-activation. CONCLUSION: The potent proapoptotic effects of Ukrain are not due to the suggested "Ukrain-molecule" but to the cytotoxic efficacy of Chelidonium majus L. alkaloids including chelidonine

The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20\%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a landmark community benchmark event with a successful history of 12 years of resource creation for the segmentation and analysis of adult glioma. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which represents the first BraTS challenge focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on benchmarking the development of volumentric segmentation algorithms for pediatric brain glioma through standardized quantitative performance evaluation metrics utilized across the BraTS 2023 cluster of challenges. Models gaining knowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training data will be evaluated on separate validation and unseen test mpMRI dataof high-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors