The Cancer Genome Atlas (TCGA) team now presents the Pan-Cancer Atlas, investigating different aspects of cancer biology by analyzing the data generated during the 10+ years of the TCGA project.
The ...Cancer Genome Atlas (TCGA) team now presents the Pan-Cancer Atlas, investigating different aspects of cancer biology by analyzing the data generated during the 10+ years of the TCGA project.
When it comes to precision oncology, proteogenomics may provide better prospects to the clinical characterization of tumors, help make a more accurate diagnosis of cancer, and improve treatment for ...patients with cancer. This perspective describes the significant contributions of The Cancer Genome Atlas and the Clinical Proteomic Tumor Analysis Consortium to precision oncology and makes the case that proteogenomics needs to be fully integrated into clinical trials and patient care in order for precision oncology to deliver the right cancer treatment to the right patient at the right dose and at the right time.
Multiomics approaches that collectively integrate proteomics and genomics provide new layers into our understanding of the mechanisms of tumorigenesis across diverse cancer types and patients and provide the path towards treatment through rationalized precision oncology.
Finding better therapies for the treatment of brain tumors is hampered by the lack of consistently obtained molecular data in a large sample set and the ability to integrate biomedical data from ...disparate sources enabling translation of therapies from bench to bedside. Hence, a critical factor in the advancement of biomedical research and clinical translation is the ease with which data can be integrated, redistributed, and analyzed both within and across functional domains. Novel biomedical informatics infrastructure and tools are essential for developing individualized patient treatment based on the specific genomic signatures in each patient's tumor. Here, we present Repository of Molecular Brain Neoplasia Data (Rembrandt), a cancer clinical genomics database and a Web-based data mining and analysis platform aimed at facilitating discovery by connecting the dots between clinical information and genomic characterization data. To date, Rembrandt contains data generated through the Glioma Molecular Diagnostic Initiative from 874 glioma specimens comprising approximately 566 gene expression arrays, 834 copy number arrays, and 13,472 clinical phenotype data points. Data can be queried and visualized for a selected gene across all data platforms or for multiple genes in a selected platform. Additionally, gene sets can be limited to clinically important annotations including secreted, kinase, membrane, and known gene-anomaly pairs to facilitate the discovery of novel biomarkers and therapeutic targets. We believe that Rembrandt represents a prototype of how high-throughput genomic and clinical data can be integrated in a way that will allow expeditious and efficient translation of laboratory discoveries to the clinic.
The Rembrandt brain cancer dataset includes 671 patients collected from 14 contributing institutions from 2004-2006. It is accessible for conducting clinical translational research using the open ...access Georgetown Database of Cancer (G-DOC) platform. In addition, the raw and processed genomics and transcriptomics data have also been made available via the public NCBI GEO repository as a super series GSE108476. Such combined datasets would provide researchers with a unique opportunity to conduct integrative analysis of gene expression and copy number changes in patients alongside clinical outcomes (overall survival) using this large brain cancer study.
Gaps in the translation of research findings to clinical management have been recognized for decades. They exist for the diagnosis as well as the management of cancer. The international standards for ...cancer diagnosis are contained within the World Health Organization (WHO) Classification of Tumours, published by the International Agency for Research on Cancer (IARC) and known worldwide as the WHO Blue Books. In addition to their relevance to individual patients, these volumes provide a valuable contribution to cancer research and surveillance, fulfilling an important role in scientific evidence synthesis and international standard setting. However, the multidimensional nature of cancer classification, the way in which the WHO Classification of Tumours is constructed, and the scientific information overload in the field pose important challenges for the translation of research findings to tumour classification and hence cancer diagnosis. To help address these challenges, we have established the International Collaboration for Cancer Classification and Research (IC3R) to provide a forum for the coordination of efforts in evidence generation, standard setting and best practice recommendations in the field of tumour classification. The first IC3R meeting, held in Lyon, France, in February 2019, gathered representatives of major institutions involved in tumour classification and related fields to identify and discuss translational challenges in data comparability, standard setting, quality management, evidence evaluation and copyright, as well as to develop a collaborative plan for addressing these challenges.
What's new?
The World Health Organization Classification of Tumours has been informing cancer research and clinical practice by providing an international consensus on the tumour criteria for cancer diagnosis. In a new initiative coordinated by the International Agency for Research on Cancer, major institutions are now joining forces to address the remaining challenges in translational research and facilitate the application of research results to clinical practice. The newly‐established International Collaboration for Cancer Classification and Research (IC3R) aims to provide a forum for the coordination of efforts in generating evidence, setting standards, and providing best practice recommendations for tumor classification and cancer research.
Despite similarities between tumor-initiating cells with stem-like properties (TICs) and normal neural stem cells, we hypothesized that there may be differences in their differentiation potentials. ...We now demonstrate that both bone morphogenetic protein (BMP)-mediated and ciliary neurotrophic factor (CNTF)-mediated Jak/STAT-dependent astroglial differentiation is impaired due to EZH2-dependent epigenetic silencing of BMP receptor 1B (
BMPR1B) in a subset of glioblastoma TICs. Forced expression of BMPR1B either by transgene expression or demethylation of the promoter restores their differentiation capabilities and induces loss of their tumorigenicity. We propose that deregulation of the BMP developmental pathway in a subset of glioblastoma TICs contributes to their tumorigenicity both by desensitizing TICs to normal differentiation cues and by converting otherwise cytostatic signals to proproliferative signals.
Abstract
Fully automated machine learning (AutoML) for predictive modeling is becoming a reality, giving rise to a whole new field. We present the basic ideas and principles of Just Add Data Bio ...(JADBio), an AutoML platform applicable to the low-sample, high-dimensional omics data that arise in translational medicine and bioinformatics applications. In addition to predictive and diagnostic models ready for clinical use, JADBio focuses on knowledge discovery by performing feature selection and identifying the corresponding biosignatures, i.e., minimal-size subsets of biomarkers that are jointly predictive of the outcome or phenotype of interest. It also returns a palette of useful information for interpretation, clinical use of the models, and decision making. JADBio is qualitatively and quantitatively compared against Hyper-Parameter Optimization Machine Learning libraries. Results show that in typical omics dataset analysis, JADBio manages to identify signatures comprising of just a handful of features while maintaining competitive predictive performance and accurate out-of-sample performance estimation.
Abstract The National Institutes of Health–US Food and Drug Administration Joint Leadership Council Next-Generation Sequencing and Radiomics Working Group was formed by the National Institutes of ...Health–Food and Drug Administration Joint Leadership Council to promote the development and validation of innovative next-generation sequencing tests, radiomic tools, and associated data analysis and interpretation enhanced by artificial intelligence and machine learning technologies. A 2-day workshop was held on September 29-30, 2021, to convene members of the scientific community to discuss how to overcome the “ground truth” gap that has frequently been acknowledged as 1 of the limiting factors impeding high-quality research, development, validation, and regulatory science in these fields. This report provides a summary of the resource gaps identified by the working group and attendees, highlights existing resources and the ways they can potentially be employed to accelerate growth in these fields, and presents opportunities to support next-generation sequencing and radiomic tool development and validation using technologies such as artificial intelligence and machine learning.
The Cancer Genome Atlas (TCGA) is one of the most ambitious and successful cancer genomics programs to date. The TCGA program has generated, analyzed, and made available genomic sequence, expression, ...methylation, and copy number variation data on over 11,000 individuals who represent over 30 different types of cancer. This chapter provides a brief overview of the TCGA program and detailed instructions and tips for investigators on how to find, access, and download this data.
Rapid and affordable tumor molecular profiling has led to an explosion of clinical and genomic data poised to enhance the diagnosis, prognostication and treatment of cancer. A critical point has now ...been reached at which the analysis and storage of annotated clinical and genomic information in unconnected silos will stall the advancement of precision cancer care. Information systems must be harmonized to overcome the multiple technical and logistical barriers to data sharing. Against this backdrop, the Global Alliance for Genomic Health (GA4GH) was established in 2013 to create a common framework that enables responsible, voluntary and secure sharing of clinical and genomic data. This Perspective from the GA4GH Clinical Working Group Cancer Task Team highlights the data-aggregation challenges faced by the field, suggests potential collaborative solutions and describes how GA4GH can catalyze a harmonized data-sharing culture.
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