TIMER

TIMER: Tumor IMmune Estimation Resource

A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells

The lastest version: TIMER2.0

TIMER web server is a comprehensive resource for systematical analysis of immune infiltrates across diverse cancer types. The abundances of six immune infiltrates (B cells, CD4+ T cells, CD8+ T cells, Neutrophils, Macrophages, and Dendritic cells) are estimated by TIMER algorithm. TIMER web server allows users to input function-specific parameters, with resulting figures dynamically displayed to conveniently access the tumor immunological, clinical, and genomic features.

Go Gene module to explore the correlation between gene expression and abundance of immune infiltrates;
Go Survival module to explore the association between clinical outcome and abundance of immune infiltrates or gene expression;
Go Mutation module to explore the correlation between mutated genes and abundance of immune infiltrates;
Go SCNA module to explore the correlation between somatic CNA and abundance of immune infiltrates;
Go Diff Exp module to explore differential gene expression between tumor and normal tissue;
Go Correlation module to explore correlations between genes ;
Go Estimation module to run users' private samples by TIMER algorithm.

Suggested Citations:

Taiwen Li, Jingyu Fan, Binbin Wang, Nicole Traugh, Qianming Chen, Jun S. Liu, Bo Li, X. Shirley Liu. TIMER: A web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Research. 2017;77(21):e108-e110. [DOI] [PubMed]

Bo Li, Eric Severson, Jean-Christophe Pignon, Haoquan Zhao, Taiwen Li, Jesse Novak, Peng Jiang, Hui Shen, Jon C. Aster, Scott Rodig, Sabina Signoretti, Jun S. Liu, X. Shirley Liu. Comprehensive analyses of tumor immunity: implications for cancer immunotherapy. Genome Biology. 2016;17(1):174. [DOI] [PubMed]

Contact:

Taiwen Li: litaiwen@scu.edu.cn

X Shirley Liu: xsliu@ds.dfci.harvard.edu

Instruction

Gene module allows users to select any gene of interest and visualize the correlation of its expression with immune infiltration level in diverse cancer types.

An official gene symbol and at least one cancer type are required as inputs. The scatterplots will be generated and displayed after inputs are submitted successfully, showing the purity-corrected partial Spearman’s rho value and statistical significance. The gene expression levels against tumor purity are always displayed on the left-most panel. Because genes highly expressed in the microenvironment are expected to have negative associations with tumor purity, while the opposite is expected for genes highly expressed in the tumor cells. Due to microarray data of GBM/OV include more samples than RNA-seq data, we use microarray expression values of GBM/OV for calculation if the gene is available.

Instruction

“Survival” module allows users to explore the clinical relevance of one or more tumor immune subsets, with the flexibility to correct for multiple covariates in a multivariable Cox proportional hazard model. Covariates include clinical factors (age, gender, ethnicity, tumor stages) and gene expression. Once all inputs are defined, TIMER outputs the Cox regression results including hazard ratios and statistical significance automatically.

TIMER also draws Kaplan-Meier plots for immune infiltrates and genes to visualize the survival differences. Levels are divided into low and high levels by a user-defined slider. P-value of log-rank test for comparing survival curves of two groups is showed in each plot.

For the outputs of Cox model, Surv(CancerType)~variables is the formula of user-defined Cox's regression model. This model is fitted by function coxph() from R package 'survival'; The coefficient coef reads as a regression coefficient. HR gives you the hazard ratio, and its lower and upper 95% confidential interval are showed in 95%CI_l & 95%CI_u .

Cox Proportional Hazard Model:

Instruction

Mutation module compares the levels of immune infiltrates with or without the presence of a given mutation. In each cancer type, we chose the top 50 genes or 10% with the most frequent non-synonymous mutations as options in the “Gene with Mutation” field. Box plots are generated for each immune subset, to compare the distributions of immune infiltration levels under different gene mutation status, with statistical significance estimated using two-sided Wilcoxon rank-sum test.

Instruction

SCNA module provides the comparison of tumor infiltration levels among tumors with different somatic copy number alterations for a given gene. SCNAs are defined by GISTIC 2.0 , including deep deletion (-2), arm-level deletion (-1), diploid/normal (0), arm-level gain (1), and high amplification (2). Box plots are presented to show the distributions of each immune subset at each copy number status in selected cancer types. The infiltration level for each SCNA category is compared with the normal using a two-sided Wilcoxon rank-sum test.

Instruction

DiffExp module allows users to study the differential expression between tumor and adjacent normal tissues for any gene of interest across all TCGA tumors. Distributions of gene expression levels are displayed using box plots, with statistical significance of differential expression evaluated using the Wilcoxon test. Users can identify genes that are up- or down- regulated in the tumors compared to normal tissues for each cancer type, as displayed in gray columns when normal data are available.

Instruction

Correlation module draws the expression scatterplots between a pair of user-defined genes in a given cancer type, together with the Spearman’s rho value and estimated statistical significance. Options for partial correlation conditioned on tumor purity or age are also provided.

Instruction

1. Upload a plain text file of gene expression with HUGO gene symbols as rownames and sample IDs as colnames; (File size < 50M) File Example

2. Choose the appropriate separator of your file ("Comma" for example file);

3. Choose the corresponding cancer type; (Only TCGA cancer type is available)

4. Click the RUN button and wait for the score table to be presented below;

5. Download table matrix. (Download TCGA estimation HERE )