247 research outputs found
AACR Project GENIE: Powering Precision Medicine through an International Consortium
No full textAbstract
The AACR Project GENIE is an international data-sharing consortium focused on generating an evidence base for precision cancer medicine by integrating clinical-grade cancer genomic data with clinical outcome data for tens of thousands of cancer patients treated at multiple institutions worldwide. In conjunction with the first public data release from approximately 19,000 samples, we describe the goals, structure, and data standards of the consortium and report conclusions from high-level analysis of the initial phase of genomic data. We also provide examples of the clinical utility of GENIE data, such as an estimate of clinical actionability across multiple cancer types (>30%) and prediction of accrual rates to the NCI-MATCH trial that accurately reflect recently reported actual match rates. The GENIE database is expected to grow to >100,000 samples within 5 years and should serve as a powerful tool for precision cancer medicine.
Significance: The AACR Project GENIE aims to catalyze sharing of integrated genomic and clinical datasets across multiple institutions worldwide, and thereby enable precision cancer medicine research, including the identification of novel therapeutic targets, design of biomarker-driven clinical trials, and identification of genomic determinants of response to therapy. Cancer Discov; 7(8); 818–31. ©2017 AACR.
See related commentary by Litchfield et al., p. 796.
This article is highlighted in the In This Issue feature, p. 783</jats:p
AACR Project GENIE Consortium Members from Analysis and Visualization of Longitudinal Genomic and Clinical Data from the AACR Project GENIE Biopharma Collaborative in cBioPortal
No full textList of AACR Project GENIE Consortium Members</p
New genetic loci link adipose and insulin biology to body fat distribution
No full textBody fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms
The GENIE Is Out of the Bottle: Landmark Cancer Genomics Dataset Released
No full textAbstract
Summary: In this issue of Cancer Discovery, an overview of the AACR Project GENIE, a landmark study in cancer genomics, is presented by The AACR Project GENIE Consortium. A summary of the goals and objectives of this ambitious program is provided, together with an analysis of the phase I cohort of 19,000 samples. Cancer Discov; 7(8); 796–8. ©2017 AACR.
See related article by The AACR Project GENIE Consortium, p. 818.</jats:p
GENIE-NF-AI: Identifying Neurofibromatosis Tumors using Liquid Neural Network (LTC) trained on AACR GENIE Datasets
No full textIn recent years, the field of medicine has been increasingly adopting
artificial intelligence (AI) technologies to provide faster and more accurate
disease detection, prediction, and assessment. In this study, we propose an
interpretable AI approach to diagnose patients with neurofibromatosis using
blood tests and pathogenic variables. We evaluated the proposed method using a
dataset from the AACR GENIE project and compared its performance with modern
approaches. Our proposed approach outperformed existing models with 99.86%
accuracy. We also conducted NF1 and interpretable AI tests to validate our
approach. Our work provides an explainable approach model using logistic
regression and explanatory stimulus as well as a black-box model. The
explainable models help to explain the predictions of black-box models while
the glass-box models provide information about the best-fit features. Overall,
our study presents an interpretable AI approach for diagnosing patients with
neurofibromatosis and demonstrates the potential of AI in the medical field.Comment: The authors would like to acknowledge the American Association for
Cancer Research and its financial and material support in the development of
the AACR Project GENIE registry, as well as members of the consortium for
their commitment to data sharing. Interpretations are the responsibility of
study author
AACR Project GENIE: 100,000 cases and beyond.
No full textThe American Association for Cancer Research (AACR) Project GENIE is an international pan-cancer registry with the goal to inform cancer research and clinical care worldwide. Founded in late 2015, the milestone GENIE 9.1-public release contains data from \u3e110,000 tumors from \u3e100,000 people treated at 19 cancer centers from USA, Canada, the United Kingdom, France, Netherlands, and Spain. Here, we demonstrate use of these real-world data, harmonized through a centralized data resource to accurately predict enrollment on genome-guided trials, discover driver alterations in rare tumors, and identify cancer types without actionable mutations that could benefit from comprehensive genomic analysis. The extensible data infrastructure and governance framework support additional deep patient phenotyping through biopharma collaborations, and expansion to include new data types such as cell-free DNA sequencing. AACR Project GENIE continues to serve a global precision medicine knowledgebase of increasing impact to inform clinical decision making and bring together cancer researchers internationally
Supplemental File 1 from AACR Project GENIE: Powering Precision Medicine through an International Consortium
No full textAACR GENIE Data Guide</p
Abstract LB-102: Landscape analysis of the initial data release from AACR Project GENIE
No full textAbstract
AACR Project Genomics Evidence Neoplasia Information Exchange (GENIE) is a multi-phase, multi-year, international data-sharing consortium whose goal is to generate an evidence base for precision cancer medicine by integrating and linking clinical-grade cancer genomic data with clinical outcome data for tens of thousands of cancer patients treated at multiple institutions worldwide. The project fulfills an unmet need in oncology by providing the statistical power necessary to identify novel therapeutic targets, to understand genomic determinants of response to therapy, to design new biomarker-driven clinical trials and ultimately, to improve clinical decision-making and the care delivered to patients. Here we describe the goals, structure and data standards of the GENIE consortium and conclusions from a high-level analysis of the first public release of genomic and limited clinical data from approximately 19,000 patients treated at eight cancer centers obtained during this initial phase of the project. We also explore the clinical utility of these genomic data by examining rates of clinical actionability across multiple cancer types and by estimating patient enrollment rates to the NCI MATCH Trial. Based on yearly rates of sequencing at each of the eight founding institutions, together with the planned addition of new members, we estimate the GENIE database could grow to &gt;100,000 samples within five years. Consistent with the goals of the proposed Cancer Moonshot National Cancer Data Ecosystem, GENIE is committed to the principles of generating interoperable, open access data that can be widely shared across the entire scientific community.
Citation Format: Ethan Cerami, Alexander S. Baras, Justin Guinney, Eva Lepisto, Trevor J. Pugh, Nikolaus Schultz, Thomas Stricker, Shawn M. Sweeney, Laura J. van't Veer, Gerrit A. Meijer, Fabrice Andre, Victor E. Velculescu, Kenna R. Shaw, Mia A. Levy, Philippe L. Bedard, Barrett J. Rollins, Charles L. Sawyers, on behalf of the AACR Project GENIE Consortium. Landscape analysis of the initial data release from AACR Project GENIE [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr LB-102. doi:10.1158/1538-7445.AM2017-LB-102</jats:p
Genie: A Population Genetics Simulation Built with JavaScript
No full textabstract: The modern web presents an opportunity for educators and researchers to create tools that are highly accessible. Because of the near-ubiquity of modern web browsers, developers who hope to create educational and analytical tools can reach a large au- dience by creating web applications. Using JavaScript, HTML, and other modern web development technologies, Genie was developed as a simulator to help educators in biology, genetics, and evolution classrooms teach their students about population genetics. Because Genie was designed for the modern web, it is highly accessible to both educators and students, who can access the web application using any modern web browser on virtually any device. Genie demonstrates the efficacy of web devel- opment technologies for demonstrating and simulating complex processes, and it will be a unique educational tool for educators who teach population genetics
Genie, sparrows and asteroids: the three wishes of Octávio C.
No full textNeste artigo tratamos de Os Três Desejos de Octávio C., 2008, do autor português Pedro Eiras. A partir da conhecida história sobre o gênio da lâmpada que concede três desejos a quem esfrega o artefato, analisamos a obra observando os significados de “génios, parcas e asteroides” e ao revelar um protagonista a querer mudar o mundo, procurando impedir a sua queda embora, com isso, acelere todo o seu processo. Ao revisitar a ideia de um génio da lâmpada capaz de satisfazer todos os desejos do seu amo, o autor opta por apresentar um Aladino moderno e cultiva a crise ao criar problemas sem solução.In this article we are dealing with The Three Desires of Octávio C., 2008, by Portuguese author Pedro Eiras. From the well-known story about the genie of the lamp that grants three wishes to the one who rubs the artifact, we analyze the work observing the meanings of "genie, sparrows and asteroids" and revealing a protagonist to want to change the world, trying to prevent its fall although this will speed up your entire process. In revisiting the idea of a lamp genie capable of satisfying all the wishes of his master, the author chooses to present a modern Aladdin and cultivates the crisis by creating problems without solution
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