Join the CPTAC Imaging Special Interest Group to be notified of webinars & data releases, collaborate on common data wrangling tasks and seek out partners to explore research hypotheses! Announcements about upcoming webinars and artifacts from previous events can be found below.
Webinar schedule:
Imaging-Omic correlation studies utilizing CPTAC data (September 9, 2019)
Please join us for our next CPTAC Imaging SIG webinar at Monday, Sep 9, 2019 12:00 pm Eastern. In this session we'll have two speakers who will summarize their work linking the imaging, genomics and proteomics data from CPTAC patients.
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.
2) Runyu Hong is a computational biomedicine PhD student working in Dr. David Fenyö’s lab of NYU School of Medicine’s Institute for Systems Genetics. He will speak about their project which trained a deep learning model to distinguish STK11 mutated and wild type pathology slides from CPTAC-LUAD (lung adenocarcinoma) patients. The STK11 gene provides instructions for making a tumor suppressor, serine/threonine kinases 11. Multi-omics analyses of CPTAC non-small-cell lung cancer (LUAD) datasets have found that STK11-mutated patients showed less immune response than other patients. To visualize and validate this pattern in histopathology images, they trained an InceptionV3-architectured convolutional neural network model that can achieve high performance (AUC=0.94444) in predicting STK11 mutations based on histopathology images. By extracting the model’s last layer activation maps of a random-sampled tiled pieces of images from the test set, they clustered these tiles with dimensional reduction method for features’ visualization. An experienced pathologist examined the tiles in the positively and negatively predicted clusters and was able to conclude that tiles in the STK11-postive clusters generally show plenty of cancer cells, but very few immune cells compared to the ones in STK11-negative clusters. This experiment supports the finding in multi-omics analyses and suggests that their model used immune cells as an important feature to distinguish STK11 mutated images.
Connection details:
URL: https://cbiit.webex.com/cbiit/j.php?MTID=m46af7437b801588996cf7f73600c8d9e
Phone: 650-479-3207
Access code: 736 153 817
Accessing CPTAC data via Jupyter Notebooks (August 6, 2019)
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)
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)