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CPTAC-GLIOBLASTOMA-CODEX

The Cancer Imaging Archive

CPTAC-Glioblastoma-CODEX | Multi-scale signaling and tumor evolution in high-grade gliomas

DOI: 10.7937/ce1t-ea12 | Data Citation Required | 43 Views | Analysis Result

Cancer Types Location Subjects Related Collections Size Supporting Data Updated
Glioblastoma, Astrocytoma Brain 12 148GB Clinical 2024/07/23

Summary

Co-detection by indexing (CODEX) based multiplexed imaging was performed on 18 samples from 12 patients, including 8 treatment-naive IDH-wt glioblastoma (GBM), 6 post-treatment recurrent IDH-wt GBM from matching patients, 2 IDH-mut grade 4 astrocytoma and 2 normal adjacent brain. Hematoxylin and Eosin staining on adjacent sections was also included for pathological annotation.

Patient inclusion criteria: Only histopathologically defined adult GBMs, grade 4 IDH-mutant astrocytomas and normal adjacent brain tissue were considered. Clinical data were obtained from the tissue source sites and reviewed for correctness and completeness of data.

Imaging protocol: A panel of 25 CODEX antibodies were designed for the human brain. Carrier-free antibodies were verified for their specificity by using immunofluorescence (IF) staining in multiple channels. Once verified, antibodies were conjugated using Akoya Antibody Conjugation Kit (Akoya Biosciences, SKU 7000009) with a barcode (Akoya Biosciences) assigned based on the IF staining results. Several common markers were directly purchased through Akoya Biosciences. CODEX staining and imaging were performed according to the manufacturer’s instruction (CODEX User Manual – Rev C). Briefly, 5 μm FFPE sections were placed on APTES (Sigma, #440140) coated coverslips and baked at 60°C overnight before deparaffinization. The next day, tissues were incubated in xylene, rehydrated in ethanol, and washed in ddH2O before antigen retrieval with TE buffer, pH 9 (Genemed, #10-0046) in boiling water for 10 min in a pressure cooker. Sections were then blocked using the blocking buffer (CODEX staining kit, SKU 7000008) and stained with the 25-marker antibody panel to a volume of 200 µl for 3 hours at room temperature in a humidified chamber. Imaging of the CODEX multicycle experiment was performed using Keyence fluorescence microscope (model BZ-X810) equipped with a Nikon CFI Plan Apo λ 20x/0.75 objective, the CODEX instrument (Akoya Biosciences, USA), and CODEX Instrument Manager (CIM) (Akoya Biosciences, USA). The raw images were then stitched and processed using the CODEX processor (Akoya Biosciences, USA). After multiplex imaging was completed, hematoxylin and eosin (H&E) staining was performed on the adjacent section from the same tissue.

Imaging analysis description: Multiplex images were segmented using the Mesmer pretrained nuclei + membrane segmentation model in the Deepcell cell segmentation library. The DAPI channel was used as the nuclei segmentation image, and CD45, CD8, CD31, CD4, HLA-DR, GFAP, CD68 channels were merged and used as the membrane segmentation image. Following segmentation, cells were classified with gating strategy (Endothelial cells: CD31+, macrophages: IBA1+ and/or CD163+, malignant cells: OLIG2+ and/or GFAP+, CD4 T cell: CD4+ and IBA1-, CD8 T cell: CD8+ and IBA-, monocyte/granulocyte: CD11b+). To eliminate batch effects, marker thresholds were set manually for each image.

Benefit to researchers: This dataset can serve as a resource to further understand the spatial cellular organization in IDH-mut and IDH-with high-grade gliomas, as well as primary and recurrent (post-treatment) glioblastoma.

Data Access

Version 1: Updated 2024/07/23

Title Data Type Format Access Points Subjects Studies Series Images License
Histopathology Images CODEX images, Whole Slide Image TIFF
Download requires IBM-Aspera-Connect plugin
12 52 CC BY 4.0
Clinical Data Pathology Detail, Follow-Up XLSX 12 CC BY 4.0

Collections Used In This Analysis Result

Related Collections
Related Datasets
CPTAC-GBM
UPENN-GBM
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Additional Resources For This Dataset

Supporting data: Matching clinical data and proteomic data (raw MS files and processed data files of global proteomics and PTMs) and metabolomic data from the same tumors can be accessed via the Proteomic Data Commons (PDC). Genomic, transcriptomic, and multiome snRNA-seq data files can be accessed via Genomic Data Commons (GDC). We recommend using a sample ID manifest to ensure exploration of the complete dataset including metastatic samples. Multiome snATAC-seq data files can be accessed via the Cancer Data Service (CDS) under accession phs001287.v17.p6

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

Liu, J., Cao, S., Imbach, K. J., Gritsenko, M. A., Lih, T.-S. M., Kyle, J. E., Yaron, 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 (CPTAC-Glioblastoma-CODEX) (Version 1) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/ce1t-ea12

Acknowledgements

This work was supported by grants U24CA210972, U24CA210955, U24CA210954, U24CA210985, U24CA210993, U24CA210967, U24CA210986, U01CA214125, and U24CA210979, U01CA214114, U01CA214116, U24CA271012, and U24CA271079 from the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (CPTAC), by grant R01HG009711 from National Human Genome Research Institute (NHGRI) to L.D., R01NS107833 and R01NS117149 from National Institute of Health (NIH) to M.G.C., PID2019-107043RA-I00 and RYC2019-026415-I from the Spanish Science Ministry and LABAE20038PORT from the AECC Scientific Foundation to E.P.-P. The MS-based proteomics work described herein was performed at the Environmental Molecular Sciences Laboratory (grid.436923.9), a U.S. Department of Energy (DOE) National Scientific User Facility located at the Pacific Northwest National Laboratory (PNNL) in Richland, WA. PNNL is a multi-program national laboratory operated by the Battelle Memorial Institute for the DOE under contract DE-AC05-76RL01830. Three-dimensional protein modeling and analyses were performed with UCSF Chimera, developed by the Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco, with support from NIH P41-GM103311. We thank the Philadelphia Coalition for a Cure (PC4C) for support in the procurement and coordination of longitudinal surgical samples for the study.

Related Publications

Publications by the Dataset Authors

The authors recommended the following as the best source of additional information about this dataset:

Publication Citation

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.

No other publications were recommended by dataset authors.

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