iATMEcell

iATMEcell identifies abnormal tumor microenvironment (TME) cells associated with clinical outcomes in cancer by analyzing cell–cell crosstalk networks.

Key Features:

• Systematic Analysis: Provides a systematic approach to analyze cellular interactions within the tumor microenvironment (TME).
• Network-Based Methodology: Constructs a weighted cell–cell crosstalk network using gene signatures of TME cell types collected from multiple published studies, contextualized with cancer bulk tissue transcriptome data, and integrates biological function similarity and transcriptional dysregulation activities of shared gene signatures between cells.
• Network Propagation Algorithm: Employs a network propagation algorithm to pinpoint significantly dysregulated TME cells that are potentially abnormal and associated with outcomes such as patient survival and response to immunotherapy.
• Validation and Pan-Cancer Analysis: Validated using the Cancer Genome Atlas (TCGA) Bladder Urothelial Carcinoma training set and two independent validation sets, and applied in a pan-cancer analysis identifying four common abnormal immune cells influencing prognosis across multiple cancer types.

Scientific Applications:

• Identification of Prognostic Cells: Identifies TME cell types whose dysregulation correlates with patient survival across cancer cohorts.
• Immunotherapy Response Analysis: Associates abnormal TME cells with response to immunotherapy to inform immune-related outcome studies.
• Study of Tumor Progression and Immune Evasion: Reveals cell–cell interactions that contribute to tumor growth and immune escape mechanisms.
• Pan-Cancer Biomarker Discovery: Enables cross-cancer identification of common abnormal immune cells relevant to patient prognosis.

Methodology:

Constructs weighted cell–cell crosstalk networks from published TME gene signatures, contextualizes networks with cancer bulk tissue transcriptome data, integrates biological function similarity and transcriptional dysregulation activities of shared gene signatures between cells, applies a network propagation algorithm to identify dysregulated TME cells, and validates findings on TCGA Bladder Urothelial Carcinoma and two independent validation sets with a subsequent pan-cancer analysis.