CPTAC-GBM | The Clinical Proteomic Tumor Analysis Consortium Glioblastoma Multiforme Collection
DOI: 10.7937/K9/TCIA.2018.3RJE41Q1 | Data Citation Required | 1.6k Views | 27 Citations | Image Collection
Location | Species | Subjects | Data Types | Cancer Types | Size | Status | Updated | |
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Brain | Human | 200 | MR, CT, Histopathology | Glioblastoma Multiforme | Clinical, Genomics, Proteomics | Limited, Ongoing | 2024/07/08 |
Summary
This collection contains subjects from the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium Glioblastoma Multiforme (CPTAC-GBM) cohort. The GBM confirmatory cohort was supplemented with retrospective samples from CHOP (Children’s hospital of Philadelphia)-Upenn. CPTAC is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. Radiology and pathology images from CPTAC patients are being collected and made publicly available by The Cancer Imaging Archive to enable researchers to investigate cancer phenotypes which may correlate to corresponding proteomic, genomic and clinical data. Imaging from each cancer type will be contained in its own TCIA Collection, with the collection name "CPTAC-cancertype". Radiology imaging is collected from standard of care imaging performed on patients immediately before the pathological diagnosis, and from follow-up scans where available. For this reason the radiology image data sets are heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. Pathology imaging is collected as part of the CPTAC qualification workflow. All CPTAC cohorts are released as either a single combined cohort, or split into Discovery and Confirmatory where applicable. There are two main types of proteomic studies: discovery proteomics and targeted proteomics. The term "discovery proteomics" is in reference to "untargeted" identification and quantification of a maximal number of proteins in a biological or clinical sample. The term “targeted proteomics” refers to quantitative measurements on a defined subset of total proteins in a biological or clinical sample, often following the completion of discovery proteomics studies to confirm interesting targets selected. Commonly used proteomic technologies and platforms are different types of mass spectrometry and protein microarrays depending on the needs, throughput and sample input requirement of an analysis, with further development on nanotechnologies and automation in the pipeline in order to improve the detection of low abundance proteins, increase throughput, and selectively reach a target protein in vivo. Once the protein targets of interest are identified, high-throughput targeted assays are developed for confirmatory studies: tests to affirm that the initial tests were accurate. A summary of CPTAC imaging efforts can be found on the CPTAC Imaging Proteomics page. You can 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! Artifacts from previous webinars such as slide decks and video recordings can be found on the CPTAC SIG Webinars page. On May 13, 2020 Liang-Bo Wang and Runyu Hong presented the consortium's proteogenomic analyses of the CPTAC Glioblastoma (GBM) cohort. This deep dive into the GBM genomic and proteomic datasets will help researchers better understand how these can be correlated with features derived from the imaging data. (Download the slides)CPTAC Imaging Special Interest Group
Data Access
Some data in this collection contains images that could potentially be used to reconstruct a human face. To safeguard the privacy of participants, users must sign and submit a TCIA Restricted License Agreement to help@cancerimagingarchive.net before accessing the data.
Version 16: Updated 2024/07/08
Added radiology imaging for 4 participants. Added histopathology for 25 participants.
Title | Data Type | Format | Access Points | Subjects | License | |||
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Images | MR, CT | DICOM | Download requires NBIA Data Retriever |
70 | 154 | 1,835 | 162,934 | TCIA Restricted |
Tissue Slide Images | Histopathology | SVS | Download requires IBM-Aspera-Connect plugin |
200 | 527 | CC BY 3.0 |
Additional Resources for this Dataset
The NCI Cancer Research Data Commons (CRDC) provides access to additional data and a cloud-based data science infrastructure that connects data sets with analytics tools to allow users to share, integrate, analyze, and visualize cancer research data.
- Imaging Data Commons (IDC) (Imaging Data)
- Proteomic Data Commons (PDC) (Proteomic & Clinical Data)
- Genomic Data Commons (GDC) (Genomic & Clinical Data)
Citations & Data Usage Policy
Data Citation Required: Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution must include the following citation, including the Digital Object Identifier:
Data Citation |
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National Cancer Institute Clinical Proteomic Tumor Analysis Consortium (CPTAC). (2018). The Clinical Proteomic Tumor Analysis Consortium Glioblastoma Multiforme Collection (CPTAC-GBM) (Version 16) [dataset]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2018.3RJE41Q1 |
Acknowledgement |
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The CPTAC program requests that publications using data from this program include the following statement: “Data used in this publication were generated by the National Cancer Institute Clinical Proteomic Tumor Analysis Consortium (CPTAC).” |
Detailed Description
Accessing CPTAC publication cohorts
All CPTAC cohorts are released as either a single combined cohort, or split into Discovery and Confirmatory where applicable. In the case of CPTAC-GBM there was a “Discovery Cohort” release. Images associated with these cases can be downloaded using the following links:
Accessing the Proteomic & Genomic Clinical Data
To access/download the clinical data on the Proteomic Data Commons (PDC) and Genomic Data Commons (GDC), once you have identified the data of your interest, move to the ‘Clinical’ tab on the browse page. Select the checkbox to select a specific row, all rows on the page or all pages and click the export clinical manifest button in CSV or TSV format on the GDC, or TSV or JSON format on the PDC.
A Note about TCIA and CPTAC Subject Identifiers and Dates
Subject Identifiers:
A subject with radiology and pathology images stored in TCIA is identified with a de-identified project Patient ID that is identical to the Patient ID of the same subject with clinical, proteomic, and/or genomic data stored in other CPTAC databases and web sites.
Dates:
The radiology imaging data is in DICOM format. To provide temporal context information aligned with events in the clinical data set for each patient, TCIA has inserted information in DICOM tag (0012,0050) Clinical Trial Time Point ID. This DICOM tag contains the number of days from the date the patient was initially diagnosed pathologically with the disease to the date of the scan. E.g. a scan acquired 3 days before the diagnosis would contain the value -3. A follow up scan acquired 90 days after diagnosis would contain the value 90.
The DICOM date tags (be they birth dates, imaging study dates, etc.) are modified per TCIA’s standard process which offsets them by a random number of days. The offset is a number of days between 3 and 10 years prior to the real date that is consistent for each TCIA image-submitting site and collection, but that varies among sites and among collections from the same site. Thus, the number of days between a subject’s longitudinal imaging studies are accurately preserved when more than one study has been archived while still meeting HIPAA requirements.
Acknowledgements
We would like to acknowledge the individuals and institutions that have provided data for this collection:
Beaumont Health System, Royal Oak, MI - Special thanks to George D. Wilson, PhD from the Department of Radiation Oncology Research, Barbara Pruetz of the Biobank, Debra Kapczynski, MHSA, CIIP, RT(R)(CT) and Rachel Deyer from the Department of Diagnostic Radiology.
International Institute for Molecular Oncology, Poznań, Poland - Special thanks to Maciej Wiznerowicz MD, PhD and Jan Lubiński MD PhD, Tomasz Czernicki MD, PhD and Andrzej Marchel MD, PhD from Central Clinical Hospital in Warsaw, Pawel Jarmużek MD, PhD, Jakub Stawicki MD and Piotr Makarewicz MD from Karol Marcinkowski Regional Clinical Hospital in Zielona Góra; and Wojciech Szopa MD, PhD and Wojciech Kaspera MD, PhD from Regional Clinical Hospital Sosnowiec in Poland
- St. Joseph's Hospital and Medical Center, Phoenix, AZ - Special thanks to Jennifer Eschbacher, MD from the Department of Neuropathology, Catherine Seiler, PhD, Rosy Singh and Beth Hermes from the Biobank Core Facility, and Victor Sisneros, RT(R)(CT), CPSA.
- Cureline, Inc. team and clinical network, Brisbane, CA - Special thanks to Olga Potapova, Ph.D., Vladislav Golubkov, Ph.D., Victoria Fulidou, M.D., Alexander Sviridov, Dmitry Belyaev, M.D., Oxana Paklina, M.D., Dr.Sc., Galiya Setdikova, M.D., Ph.D., Denis Golbin, M.D., Ph.D.
Related Publications
Publications by the Dataset Authors
The authors recommended the following as the best source of additional information about this dataset:
Publication Citation |
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Liu, J., Cao, S., Imbach, K. J., Gritsenko, M. A., Lih, T.-S. M., Kyle, J. E., Yaron-Barir, T. M., Binder, Z. A., Li, Y., Strunilin, I., Wang, Y.-T., Tsai, C.-F., Ma, W., Chen, L., Clark, N. M., Shinkle, A., Naser Al Deen, N., Caravan, W., Houston, A., … Ding, L. (2024). Multi-scale signaling and tumor evolution in high-grade gliomas. In Cancer Cell (Vol. 42, Issue 7, pp. 1217-1238.e19). Elsevier BV. https://doi.org/10.1016/j.ccell.2024.06.004
The members of the National Cancer Institute Clinical Proteomic Tumor Analysis Consortium are listed here. |
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Research Community Publications
TCIA maintains a list of publications that leveraged this dataset. If you have a manuscript you’d like to add please contact TCIA’s Helpdesk.
Previous Versions
Version 15: Updated 2023/05/10
11 pathology patients removed from study.
Title | Data Type | Format | Access Points | License | ||||
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Images | CT, MR | DICOM | Download requires NBIA Data Retriever |
66 | 148 | 1,752 | 156,259 | TCIA Restricted |
Tissue Slide Images | Histopathology | SVS | Download requires IBM-Aspera-Connect plugin |
178 | 462 | CC BY 3.0 |
Version 14: Updated 2023/02/24
Radiology modality data cleanup to remove extraneous scans.
Title | Data Type | Format | Access Points | License | ||||
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Images | DICOM | Download requires NBIA Data Retriever |
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Tissue Slide Images | SVS | |||||||
Proteomics | WEB |
Version 13: Updated 2020/09/03
Changed to new Aspera download link for histopathology slides.
Title | Data Type | Format | Access Points | License | ||||
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Images | DICOM | Download requires NBIA Data Retriever |
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Tissue Slide Images | SVS | |||||||
Proteomics | WEB |
Version 12: Updated 2020/02/28
Corrected 1 radiology subject cancer type, and added 3 radiology subjects.
Title | Data Type | Format | Access Points | License | ||||
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Images | DICOM | Download requires NBIA Data Retriever |
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Tissue Slide Images | SVS | |||||||
Proteomics | WEB |
Version 11: Updated 2020/02/14
Added 8 new pathology subjects & 37 new pathology slides
Title | Data Type | Format | Access Points | License | ||||
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Images | DICOM | Download requires NBIA Data Retriever |
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Tissue Slide Images | SVS | |||||||
Proteomics | WEB |
Version 10: Updated 2019/12/16
Added 5 new radiology and 2 pathology subjects
Title | Data Type | Format | Access Points | License | ||||
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Images | DICOM | Download requires NBIA Data Retriever |
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Tissue Slide Images | SVS | |||||||
Proteomics | WEB |
Version 9: Updated 2019/10/30
Added new subjects
Title | Data Type | Format | Access Points | License | ||||
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Images | DICOM | Download requires NBIA Data Retriever |
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Tissue Slide Images | SVS | |||||||
Proteomics | WEB |
Version 8: Updated 2019/09/30
Added new subjects
Title | Data Type | Format | Access Points | License | ||||
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Images | DICOM | Download requires NBIA Data Retriever |
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Tissue Slide Images | SVS | |||||||
Proteomics | WEB |
Version 7: Updated 2019/06/30
Added new subjects
Title | Data Type | Format | Access Points | License | ||||
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Images | DICOM | Download requires NBIA Data Retriever |
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Tissue Slide Images | SVS | |||||||
Proteomics | WEB |
Version 6: Updated 2019/03/31
Added new subjects
Title | Data Type | Format | Access Points | License | ||||
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Images | DICOM | Download requires NBIA Data Retriever |
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Tissue Slide Images | WEB | |||||||
Proteomics | WEB |
Version 5: Updated 2018/12/31
Added new subjects.
Title | Data Type | Format | Access Points | License | ||||
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Images | DICOM | Download requires NBIA Data Retriever |
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Tissue Slide Images | WEB | |||||||
Proteomics | WEB |
Version 4: Updated 2018/10/24
Added new subjects.
Title | Data Type | Format | Access Points | License | ||||
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Images | DICOM | Download requires NBIA Data Retriever |
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Tissue Slide Images | WEB | |||||||
Proteomics | WEB |
Version 3: Updated 2018/06/30
Added new subjects.
Title | Data Type | Format | Access Points | License | ||||
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Images | DICOM | Download requires NBIA Data Retriever |
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Tissue Slide Images | WEB | |||||||
Proteomics | WEB |
Version 2: Updated 2018/04/26
Added new subjects.
Title | Data Type | Format | Access Points | License | ||||
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Images | DICOM | Download requires NBIA Data Retriever |
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Tissue Slide Images | WEB | |||||||
Proteomics | WEB |
Version 1: Updated 2018/01/10
Title | Data Type | Format | Access Points | License | ||||
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Images | DICOM | Download requires NBIA Data Retriever |
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Tissue Slide Images | WEB | |||||||
Proteomics | WEB |