Mechanistic Insight into Immune Cell–Cancer Gene Set Interactions Using Ai

Overview

1 in 5 individuals develops cancer in their lifetime. Due to treatment inefficiency, cancer is the leading cause of death worldwide. Unraveling immune cell-cancer cell interplay in the tumor microenvironment (TME) is crucial for developing effective cancer treatments. This research aimed to develop a novel artificial neural network (ANN) to study patterns of interplay between immune cells and cancer-associated gene sets. The model addresses hidden crosstalk among immune cells, tumor heterogeneity, and TME interconnection through its multi-layer model architecture, enabling simultaneous analysis of all components. Using data from 70 patients across six solid cancers, gene expression and bulk RNA-seq data were normalized in R programming, selecting 54,675 probes and 51,140 TPM values per sample for subsequent analysis. Gene Set Enrichment Analysis (GSEA) was then conducted to gather normalized enrichment scores (NES) for 30 of 50 hallmark gene sets. CIBERSORT was used for immune cell fractions from 21 immune cells. One ANN model was developed to study the majority of cancer-promoting gene sets and immune cells while considering crosstalk. The model was trained in TensorFlow, involving meticulous hyperparameter tuning and backpropagation. The model achieved a mean squared error (MSE) of .11 and an R-squared value of 0.90, effectively predicting the correlation between immune cells and cancer gene sets. Sensitivity analysis was conducted to reveal the impact of individual immune cell types. This model provides mechanistic insight into most immune-cancer interactions, identifying key immune cells. The study establishes a foundation for subsequent predictive modeling and personalized therapy design.

Competition history

• AJAS 2026 Category not listed

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