A Framework for the Interoperability of Cloud Platforms: Towards FAIR Data in SAFE Environments

As the number of cloud platforms supporting scientific research grows, there is an increasing need to support interoperability between two or more cloud platforms. A well accepted core concept is to make data in cloud platforms Findable, Accessible, Interoperable and Reusable (FAIR). We introduce a companion concept that applies to cloud-based computing environments that we call a Secure and Authorized FAIR Environment (SAFE). SAFE environments require data and platform governance structures and are designed to support the interoperability of sensitive or controlled access data, such as biomedical data. A SAFE environment is a cloud platform that has been approved through a defined data and platform governance process as authorized to hold data from another cloud platform and exposes appropriate APIs for the two platforms to interoperate

Acetylation of p53 stimulates miRNA processing and determines cell survival following genotoxic stress

It is widely accepted that different forms of stress activate a common target, p53, yet different outcomes are triggered in a stress-specific manner. For example, activation of p53 by genotoxic agents, such as camptothecin (CPT), triggers apoptosis, while non-genotoxic activation of p53 by Nutlin-3 (Nut3) leads to cell-cycle arrest without significant apoptosis. Such stimulus-specific responses are attributed to differential transcriptional activation of various promoters by p53. In this study, we demonstrate that CPT, but not Nut3, induces miR-203, which downregulates anti-apoptotic bcl-w and promotes cell death in a p53-dependent manner. We find that acetylation of K120 in the DNA-binding domain of p53 augments its association with the Drosha microprocessor and promotes nuclear primary miRNA processing. Knockdown of human orthologue of Males absent On the First (hMOF), the acetyltransferase that targets K120 in p53, abolishes induction of miR-203 and cell death mediated by CPT. Thus, this study reveals that p53 acetylation at K120 plays a critical role in the regulation of the Drosha microprocessor and that post-transcriptional regulation of gene expression by p53 via miRNAs plays a role in determining stress-specific cellular outcomes

PDXNet portal: patient-derived Xenograft model, data, workflow and tool discovery.

We created the PDX Network (PDXNet) portal (https://portal.pdxnetwork.org/) to centralize access to the National Cancer Institute-funded PDXNet consortium resources, to facilitate collaboration among researchers and to make these data easily available for research. The portal includes sections for resources, analysis results, metrics for PDXNet activities, data processing protocols and training materials for processing PDX data. Currently, the portal contains PDXNet model information and data resources from 334 new models across 33 cancer types. Tissue samples of these models were deposited in the NCI\u27s Patient-Derived Model Repository (PDMR) for public access. These models have 2134 associated sequencing files from 873 samples across 308 patients, which are hosted on the Cancer Genomics Cloud powered by Seven Bridges and the NCI Cancer Data Service for long-term storage and access with dbGaP permissions. The portal includes results from freely available, robust, validated and standardized analysis workflows on PDXNet sequencing files and PDMR data (3857 samples from 629 patients across 85 disease types). The PDXNet portal is continuously updated with new data and is of significant utility to the cancer research community as it provides a centralized location for PDXNet resources, which support multi-agent treatment studies, determination of sensitivity and resistance mechanisms, and preclinical trials

A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis

Patient-derived xenografts (PDX) model human intra- and intertumoral heterogeneity in the context of the intact tissue of immunocompromised mice. Histologic imaging via hematoxylin and eosin (H&E) staining is routinely performed on PDX samples, which could be harnessed for computational analysis. Prior studies of large clinical H&E image repositories have shown that deep learning analysis can identify intercellular and morphologic signals correlated with disease phenotype and therapeutic response. In this study, we developed an extensive, pan-cancer repository of \u3e1,000 PDX and paired parental tumor H&E images. These images, curated from the PDX Development and Trial Centers Research Network Consortium, had a range of associated genomic and transcriptomic data, clinical metadata, pathologic assessments of cell composition, and, in several cases, detailed pathologic annotations of neoplastic, stromal, and necrotic regions. The amenability of these images to deep learning was highlighted through three applications: (i) development of a classifier for neoplastic, stromal, and necrotic regions; (ii) development of a predictor of xenograft-transplant lymphoproliferative disorder; and (iii) application of a published predictor of microsatellite instability. Together, this PDX Development and Trial Centers Research Network image repository provides a valuable resource for controlled digital pathology analysis, both for the evaluation of technical issues and for the development of computational image–based methods that make clinical predictions based on PDX treatment studies. Significance: A pan-cancer repository of \u3e1,000 patient-derived xenograft hematoxylin and eosin–stained images will facilitate cancer biology investigations through histopathologic analysis and contributes important model system data that expand existing human histology repositories

Comprehensive characterization of 536 patient-derived xenograft models prioritizes candidatesfor targeted treatment.

Development of candidate cancer treatments is a resource-intensive process, with the research community continuing to investigate options beyond static genomic characterization. Toward this goal, we have established the genomic landscapes of 536 patient-derived xenograft (PDX) models across 25 cancer types, together with mutation, copy number, fusion, transcriptomic profiles, and NCI-MATCH arms. Compared with human tumors, PDXs typically have higher purity and fit to investigate dynamic driver events and molecular properties via multiple time points from same case PDXs. Here, we report on dynamic genomic landscapes and pharmacogenomic associations, including associations between activating oncogenic events and drugs, correlations between whole-genome duplications and subclone events, and the potential PDX models for NCI-MATCH trials. Lastly, we provide a web portal having comprehensive pan-cancer PDX genomic profiles and source code to facilitate identification of more druggable events and further insights into PDXs\u27 recapitulation of human tumors

Sex differences in oncogenic mutational processes.

Sex differences have been observed in multiple facets of cancer epidemiology, treatment and biology, and in most cancers outside the sex organs. Efforts to link these clinical differences to specific molecular features have focused on somatic mutations within the coding regions of the genome. Here we report a pan-cancer analysis of sex differences in whole genomes of 1983 tumours of 28 subtypes as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. We both confirm the results of exome studies, and also uncover previously undescribed sex differences. These include sex-biases in coding and non-coding cancer drivers, mutation prevalence and strikingly, in mutational signatures related to underlying mutational processes. These results underline the pervasiveness of molecular sex differences and strengthen the call for increased consideration of sex in molecular cancer research

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Mechanisms of control of microRNA biogenesis

MicroRNAs (miRNAs) are a class of ∼22 nt non-coding RNAs that control diverse biological functions in animals, plants and unicellular eukaryotes by promoting degradation or inhibition of translation of target mRNAs. miRNA expression is often tissue specific and developmentally regulated. Aberrant expression of miRNAs has been linked to developmental abnormalities and human diseases, including cancer and cardiovascular disorders. The recent identification of mechanisms of miRNA biogenesis regulation uncovers that various factors or growth factor signalling pathways control every step of the miRNA biogenesis pathway. Here, we review the mechanisms that control the regulation of miRNA biogenesis discovered in human cells. Further understanding of the mechanisms that control of miRNA biogenesis may allow the development of tools to modulate the expression of specific miRNAs, which is crucial for the development of novel therapies for human disorders derived from aberrant expression of miRNAs