TIDE (Tumor Immune Dysfunction and Exclusion) is a computational framework developed to evaluate the potential of tumor immune escape from the gene expression profiles of cancer samples. This package provides a python implemented CLI, and python module with Pandas inputs and outputs. If you prefer to run TIDE online, please use Response Prediction module on our website : http://tide.dfci.harvard.edu/.
- Linux/Unix
- Python (>=3.4)
- Clone from github
$ git clone [email protected]:jingxinfu/TIDEpy.git
$ cd TIDEpy
$ pip install .
- From the pypi, a python package management tool
$ pip install tidepy
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.
Expression File
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If it's possible, please input a normalized expression file follows the instruction:
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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.
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Otherwise, We'll do the normalization for you by:
- Do the log2(x+1) transformation
- Subtract the average across your samples.
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If it's possible, please convert your gene identifier into Entrez ID based on your annotation GTF files. Otherwise, we will use our annotation GTF to do the conversion, which is gencode v27.
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).
usage: tidepy [-h] -o OUTPUT -c {Melanoma,NSCLC,Other} [--pretreat]
[--ignore_norm] [--force_normalize] [--vthres VTHRES]
expression
positional arguments:
expression Path to expression profile
optional arguments:
-h, --help show this help message and exit
-o OUTPUT, --output OUTPUT
Output Path (default: None)
-c {Melanoma,NSCLC,Other}, --cancer {Melanoma,NSCLC,Other}
Cancer Type (default: None)
--pretreat A previous immunotherapy (e.g., progressed after anti-
CTLA4 before current anti-PD1) will change the
response prediction rule. Please put the flag with
previous line of immunotherapy. However, earlier
treatments of targeted therapies or chemotherapies
should not be considered here, and please do not put
the flag. (default: False)
--ignore_norm Whether ignore the normalization step. (default:
False)
--force_normalize Whether force to do the normalization.[Require
ignore_norm to be FALSE] (default: False)
--vthres VTHRES Threshold to distinguish responder fron non-responder
based on TIDE value. (default: 0.0)
Please download the first example file under Response Prediction module on our website: http://tide.dfci.harvard.edu. The file name should be GSE78220.self_subtract.zip. To obtain the immune-related metrics for this study, you can run following code on your terminal:
$ tidepy GSE78220.self_subtract.zip -o GSE78220.txt -c MelanomaThe output file GSE78220.txt has those columns: No benefits, Responder, TIDE, IFNG, MSI Score, CD274, CD8, CTL.flag, Dysfunction, Exclusion, MDSC, CAF, TAM M2, which are exactly the same with the result outputted from our website.
$ head GSE78220.txt
No benefits Responder TIDE IFNG MSI Score CD274 CD8 CTL.flag Dysfunction Exclusion MDSC CAF TAM M2
Pt10 True False 2.9031378266316437 -2.3496942540874994 0.25805402238944347 -1.45699548307 -2.2538329160500004 False -1.1904018820543425 2.9031378266316437 0.22023489119026698 0.2086109089536346 0.021521981442711974
Pt32 True False 2.374656680742425 -1.251790579471 0.03317166493632983 -1.34396174667 -1.092551002026 False 0.7565819504165977 2.374656680742425 0.11720339003535449 0.23364173308834454 0.021349394169070356
Pt38 False False 2.3272172094686367 1.421773451414 0.369744309404354 1.44922588993 1.67154054016 True 2.3272172094686367 -3.0384590803306617 -0.16135256818744914 -0.25075626440976084 -0.054974225801339455
Pt12 True False 1.5872758043454305 -2.0323342892546665 0.42890701750431737 -1.0318990510700001 -2.235849307205 False -0.37355955596879964 1.5872758043454305 0.1187563077904593 0.14157760370392405 -0.005648658414646455
Pt14 False False 1.4617100514481065 0.08189238923924995 0.29446508349637074 2.7750512868299997 -1.004022775004 False 0.2613253541514945 1.4617100514481065 0.1495425400268765 0.09892114074144391 -0.032482034306099805
Pt20 False False 1.2040301004408855 2.682862982916667 0.8176690141355004 1.33916228496 3.756670182795 True 1.2040301004408855 -3.374284072367413-0.24958308053873887 -0.1817896254985925 -0.08581630042676716
Pt1 False False 1.0412494657529983 0.7254327628349333 0.536995843539227 -0.156163548496 0.15697680025049998 False -0.48094961955303106 1.0412494657529983 -0.046094357732277734 0.1997680886231509 0.009852881373563268
Pt9 False False 0.8010709440747635 0.2580580358761666 0.7296845983437736 -0.582410268962 0.5549609285135 False -0.11682795201511606 0.80107094407476350.05633802108085521 0.040839374451276424 0.020610152865016378
Pt7 False False 0.6718349276249306 -0.7193369628115166 0.44483998965603927 -0.8181523873759999 -0.8982304490069999 False -0.6222713974526163 0.6718349276249306 0.0767865719035387 -0.01954210031741603 0.048268448437660275
Please download the first example file under Response Prediction module on our website: http://tide.dfci.harvard.edu. The file name should be GSE78220.self_subtract.zip. To obtain the immune-related metrics for this study, you can run following code inside python console:
import pandas as pd
from tidepy.pred import TIDE
df = pd.read_csv("GSE78220.self_subtract.zip",sep='\t',index_col=0)
result = TIDE(df,cancer='Melanoma',pretreat=False,vthres=0.)
result.head(2)
"""
No benefits Responder TIDE IFNG MSI Score CD274 CD8 CTL.flag Dysfunction Exclusion MDSC CAF TAM M2
Pt10 True False 2.9031378266316437 -2.3496942540874994 0.25805402238944347 -1.45699548307 -2.2538329160500004 False -1.1904018820543425 2.9031378266316437 0.22023489119026698 0.2086109089536346 0.021521981442711974
Pt32 True False 2.374656680742425 -1.251790579471 0.03317166493632983 -1.34396174667 -1.092551002026 False 0.7565819504165977 2.374656680742425 0.11720339003535449 0.23364173308834454 0.021349394169070356
"""-
Jingxin Fu, Karen Li, Wubing Zhang, Changxin Wan, Jing Zhang§, Peng Jiang§, Xiaole Shirley Liu§. "Large-scale public data reuse to model immunotherapy response and resistance." Genome Medicine. 2020 Dec;12(1):1-8.
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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§."Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response." Nature medicine. 2018 Oct;24(10):1550-8.
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