Check for newest update information (Update on 20200918)

TIDE:   Tumor Immune Dysfunction and Exclusion

Based on tumor pre-treatment expression profiles, this TIDE module can estimate multiple published transcriptomic biomarkers to predict patient response.

Tutorial video of response prediction

Download the video

Please input your gene expression file. Compressed formats of ".gz" and ".zip" are accepted.

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.

Cancer type:

We validated TIDE performance on predicting anti-PD1 and anti-CTLA4 response across several melanoma datasets and a limited dataset of non-small cell lung cancer (NSCLC). TIDE may not work on cancer types other than melanoma and NSCLC (e.g., glioblastoma, or renal cell carcinoma) and therapies other than anti-PD1 and anti-CTLA4 (e.g., anti-PDL1, or Car T).

Previous immunotherapy:

A previous immunotherapy (e.g., progressed after anti-CTLA4 before current anti-PD1) will change the response prediction rule. Please select "Yes" with previous line of immunotherapy. However, earlier treatments of targeted therapies or chemotherapies should not be considered here, and please select "No".

You can download some test samples of pre-treatment melanoma tumor expression profiles of patients. Please make sure Entrez ID or Hugo Symbols are used for human gene names. Please look at our sample gene expression files. Note: This web application of TIDE is developed for research purpose. Please use it at your own risk for clinical diagnosis.

Dana Farber Cancer Institute & Harvard University © 2018-2020