TIMER2.0 for analysis of tumor-infiltrating immune cells

TLDR

Tumor progression and immunotherapy efficacy are strongly influenced by the composition and abundance of immune cells in the tumor microenvironment, yet large‑scale public data analyses are limited by the need for computational inference of immune infiltrates from bulk transcriptomes. The authors improved their previous web platform TIMER to lower barriers for analyzing complex tumor–immune interactions. TIMER2.0 estimates immune infiltration levels for TCGA or user‑provided tumor profiles using six state‑of‑the‑art algorithms and offers four modules for exploring associations between infiltrates and genetic or clinical features, plus four modules for cancer‑related associations. Each module generates functional heatmap tables that allow users to identify significant associations across multiple cancer types, providing comprehensive analysis and visualization of tumor‑infiltrating immune cells.

Abstract

Abstract Tumor progression and the efficacy of immunotherapy are strongly influenced by the composition and abundance of immune cells in the tumor microenvironment. Due to the limitations of direct measurement methods, computational algorithms are often used to infer immune cell composition from bulk tumor transcriptome profiles. These estimated tumor immune infiltrate populations have been associated with genomic and transcriptomic changes in the tumors, providing insight into tumor–immune interactions. However, such investigations on large-scale public data remain challenging. To lower the barriers for the analysis of complex tumor–immune interactions, we significantly improved our previous web platform TIMER. Instead of just using one algorithm, TIMER2.0 (http://timer.cistrome.org/) provides more robust estimation of immune infiltration levels for The Cancer Genome Atlas (TCGA) or user-provided tumor profiles using six state-of-the-art algorithms. TIMER2.0 provides four modules for investigating the associations between immune infiltrates and genetic or clinical features, and four modules for exploring cancer-related associations in the TCGA cohorts. Each module can generate a functional heatmap table, enabling the user to easily identify significant associations in multiple cancer types simultaneously. Overall, the TIMER2.0 web server provides comprehensive analysis and visualization functions of tumor infiltrating immune cells.