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Proteogenomic analysis
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of Lung Adenocarcinoma (July 22, 2020)
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Shankha Satpathy presents about the consortium's proteogenomic analyses of the
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CPTAC Lung Adenocarcinoma (LUAD) cohort. This deep dive into the
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LUAD genomic and proteomic datasets will help researchers better understand how these can be correlated with features derived from the imaging data.
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Proteogenomic analysis of Glioblastoma (
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May 13, 2020
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Liang-Bo Wang and Runyu Hong present
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Proteogenomic analysis of Lung Adenocarcinoma (March 3, 2020)
Please join us at 12pm Eastern on March 3, 2020 for Dr. Michael Gillette's presentation about the consortium's proteogenomic analyses of the CPTAC Lung Adenocarcinoma (LUAD)CPTAC Glioblastoma (GBM) cohort. This deep dive into the LUAD GBM genomic and proteomic datasets will help researchers better understand how these can be correlated with features derived from the imaging data.
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NCI-OCCPR Webinar: The Cancer Imaging Archive (March 18, 2020)
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Meeting number: 738 359 215
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Phone: 1-650-479-3207 Call-in toll number (US/Canada)
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In collaboration with NCI's Office of Cancer Clinical Proteomics Research (OCCPR), Justin Kirby presents an overview of The Cancer Imaging Archive (TCIA) and its support for the Clinical Proteomics Tumor Analysis Consortium (CPTAC) imaging data. In this webinar he provides an overview of TCIA functionality and datasets with a special emphasis on the >1500 radiology and histology cases associated with the CPTAC patient cohorts. He also discusses how to access clinical, proteomic, and genomic data found in other CPTAC resources to facilitate correlation studies across multiple data types. (Download the slides)
Proteogenomic analysis of Clear Cell Renal Cell Carcinoma (February 4, 2020)
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Agenda & Slides
Dr. David Clarke presents the consortium's proteogenomic analyses of the CPTAC CPTAC Clear Cell Renal Cell Carcinoma Carcinoma (CCRCC) cohort cohort. This deep dive into the CCRCC genomic and proteomic datasets will help researchers better understand how these can be correlated with features derived from the imaging data. (Download the slides)
Proteogenomic analysis of Uterine Corpus Endometrial Carcinoma (January 14, 2020)
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Agenda & Slides
Emily Kawaler presents the consortium's proteogenomic analyses of the CPTAC Uterine Corpus Endometrial Carcinoma (UCEC) cohort. This deep dive into the UCEC genomic and proteomic datasets will help researchers better understand how they can be correlated with features derived from the imaging data. (Download the slides)
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Agenda & Slides
1) Dr. Olivier Gevaert is an assistant professor at Stanford University focused on developing machine-learning methods for biomedical decision support from multi-scale data. His lab lab develops machine learning methods including Bayesian, kernel methods, regularized regression and deep learning to integrate, clinical, molecular and biomedical image data. His presentation will show an example of how to process proteomic data from CPTAC Phase 2 projects (breast, ovarian and colorectal) with emphasis on how to use, preprocess and subsequently model proteomic data using bioinformatics algorithms. He will show an example of linking protein data to DNA methylation and mRNA gene expression data, and how proteomic data can be integrated with medical image data. (Download the slides)
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During this webinar you'll learn about a python-based open source tool (https://github.com/PayneLab/CPTAC) being developed by members of Sam Payne's lab at BYU which saves researchers the trouble of having to individually navigate the various websites where proteomic, genomic, and clinical data are stored. The slide deck is available here: CPTAC SIG - 2019-08-06 - Easy Data Dissemination by Sam Payne.pptx
Program overview & data access tutorials (July 1, 2019)
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Agenda & Slides
- Welcome (Bob Nordstrom)
- Clinical Proteomic Tumor Analysis Consortium (CPTAC) Project Overview (Chris Kinsinger, 15min)
- CPTAC radiology & pathology image data at TCIA – live demo (Justin Kirby, 15min)
- CPTAC Data Portal and the Proteomics Data Commons (R. Rajesh Thangudu, 30min)
- CPTAC genomic data at the Genomics Data Commons (Ana Robles, 15min)
- Q&A (15min)
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