CRI iAtlas

CRI iAtlas enables exploration and analysis of tumor–immune interactions by computing and visualizing immunogenomic and spatial features to characterize the tumor immune microenvironment across cancer cohorts.

Key Features:

• Data exploration and visualization: Computes and visualizes correlations and statistics among tumor microenvironment features including cell composition, immune expression signatures, tumor mutation burden, cancer driver mutations, adaptive cell clonality, patient survival rates, expression levels of key immunomodulators, and spatial maps of tumor-infiltrating lymphocytes (TILs).
• Integration with TCGA PanCancer Atlas: Provides standardized access to immunogenomics data from the TCGA PanCancer Atlas.
• User-defined sample cohorts: Supports loading custom sample cohorts for tailored analyses.
• Immune subtype classification: Classifies expression data into defined immune subtypes.
• TIL mapping integration: Incorporates TIL mapping derived from digital pathology images for spatial analyses.

Scientific Applications:

• Immunogenomic profiling: Comparative analysis of immune expression signatures, tumor mutation burden, cancer driver mutations, and adaptive clonality across cancer cohorts.
• Tumor immune microenvironment characterization: Quantification of cell composition and expression of key immunomodulators to profile immune contexts of tumors.
• Spatial analysis: Integration of TIL maps from digital pathology with molecular data to assess spatial patterns of immune infiltration.
• Cohort stratification and subtype analysis: Classification into immune subtypes and use of user-defined cohorts to enable subgroup analyses.
• Clinical association studies: Correlation of immune features with patient survival rates and other clinical outcomes.

Methodology:

Computation and visualization of correlations and statistics among immunogenomic and spatial features, integration of TCGA PanCancer Atlas immunogenomics data, classification of expression data into immune subtypes, incorporation of TIL mapping from digital pathology images, and support for user-defined sample cohorts.