Check for newest update information (Update on 20200918)
What is TIDE?
TIDE stands for Tumor Immune Dysfunction and Exclusion. It is a computational framework developed to evaluate the potential of tumor immune escape from the gene expression profiles of cancer samples. We have the following functional modules:
Predict Response: The TIDE score computed for each tumor samples can serve as a surrogate biomarker to predict response to immune checkpoint blockade, including anti-PD1 and anti-CTLA4 for melanoma and NSCLC. (Tutorial: YouTube or Download).
Query Gene: The highly scored genes in TIDE signatures also present potential regulators of tumor immune escape and resistance to cancer immunotherapies (YouTube or Download).
What is the input data format for response prediction?
The input data should be a square matrix of gene expression profiles for all patients. Each column represents the patient ID, and each row represents a gene name which can be either symbol name (e.g., TGFB1) or Entrez ID (e.g., 7040). Please see some samples from anti-PD1 or anti-CTLA4 therapies in melanoma.
Note: The gene expression value should be normalized toward a control sample which could be either normal tissues related with a cancer type or mixture sample from diverse tumor samples. The log2(RPKM+1) values from a RNA-seq experiment may not be meaningful unless a good reference control is available to adjust the batch effect and cancer type difference. In our study, we used the all sample average in each study as the normalization control.
May I download the data of immunotherapy trial studies?
We keep tracking on immunotherapy trial studies with transcriptomic profiles and patient clinical characteristics
information. Please go to the Download page to see our progress and to obtain links for downloading publicly available data.
How do I cite TIDE?
Large-scale public data reuse to model immunotherapy response and resistance. Jingxin Fu, Karen Li, Wubing Zhang, Changxin Wan, Jing Zhang§, Peng Jiang§, X. Shirley Liu§
Signatures of T-cell dysfunction and exclusion predict cancer immunotherapy response. Peng Jiang*, Shengqing Gu*, Deng Pan*, Jingxin Fu, Avinash Sahu, Xihao Hu, Ziyi Li, Nicole Traugh, Xia Bu, Bo Li, Jun Liu, Gordon J. Freeman, Myles A. Brown, Kai W. Wucherpfennig§, X. Shirley Liu§
Currently, TIDE is only freely available for academic users. If you are willing to evaluate our framework in your clinical settings, we are definitely very happy to collaborate. Please contact the principal investigator Xiaole Shirley Liu and Dana Farber Innovation Office .
May I run TIDE prediction offline?
We provide a python implemented CLI, and python module with Pandas inputs and outputs.
Please see how to use our offline package here .
