Mappings of tumor-infiltrating lymphocytes (TILs), based on H&E images from 13 of The Cancer Genome Atlas (TCGA) tumor types are available here. These TIL maps are derived through computational staining, using a convolutional neural network trained to classify patches of images. In addition to the TIL Maps, the analysis codes and the software used to extract TILs are also available. The accompanying paper contains detailed information about our methods and our findings. The source histopathology, molecular correlates and clinical data used in this study can be found on the Genomic Data Commons. More information about the tools used to generate these results can be found on the QuIP Software Stack and TIL Classification Software pages. Answers to commonly asked questions about these data are contained in this FAQs document.
TCGA Tumor Types Used in this Study
- BLCA Bladder urothelial carcinoma
- BRCA Breast invasive carcinoma
- CESC Cervical squamous cell carcinoma and endocervical adenocarcinoma
- COAD Colon adenocarcinoma
- LUAD Lung adenocarcinoma
- LUSC Lung squamous cell carcinoma
- PAAD Pancreatic adenocarcinoma
- PRAD Prostate adenocarcinoma
- READ Rectum adenocarcinoma
- SKCM Skin Cutaneous Melanoma
- STAD Stomach adenocarcinoma
- UCEC Uterine Corpus Endometrial Carcinoma
- UVM Uveal Melanoma
|Data Type||Download all or Query/Filter||License|
|Histopathology TIL Maps|
Click the Versions tab for more info about data releases.
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.
The following external resources have been made available by the data submitters. These are not hosted or supported by TCIA, but may be useful to researchers utilizing this collection.
Collections Used in this Third Party Analysis
Below is a list of TCIA Collections used in these analyses:
Please contact email@example.com with any questions regarding usage.
Citations & Data Usage Policy
Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution should include references to the following citations:
Saltz, J., Gupta, R., Hou, L., Kurc, T., Singh, P., Nguyen, V., Samaras, D., Shroyer, K. R., Zhao, T., Batiste, R., Van Arnam, J., The Cancer Genome Atlas Research Network, Shmulevich, I., Rao, A. U. K., Lazar, A. J., Sharma, A., & Thorsson, V. (2018). Tumor-Infiltrating Lymphocytes Maps from TCGA H&E Whole Slide Pathology Images [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2018.Y75F9W1
Saltz, J., Gupta, R., Hou, L., Kurc, T., Singh, P., Nguyen, V., Samaras, D., Shroyer, K. R., Zhao, T., Batiste, R., Van Arnam, J., The Cancer Genome Atlas Research Network, Shmulevich, I., Rao, A. U. K., Lazar, A. J., Sharma, A., Thorsson, V. (2018). Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images. Cell Reports, 23(1), 181-193.e7. https://doi.org/10.1016/j.celrep.2018.03.086
Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. Journal of Digital Imaging, 26(6), 1045–1057. https://doi.org/10.1007/s10278-013-9622-7