Domain-Adversarial Neural Network and Explainable AI for Reducing Tissue-of-Origin Signal in Pan-cancer Mortality Classification
This addresses the challenge of discovering generalizable biomarkers in cancer research by reducing tissue-specific overfitting, though it is incremental as it builds on existing DANN and SHAP methods.
The paper tackled the problem of tissue-of-origin signals dominating pan-cancer gene expression, which obscures survival-relevant molecular features, by proposing a Domain-Adversarial Neural Network (DANN) to reduce tissue bias and improve mortality classification, resulting in improved low-dimensional representations for identifying survival-associated genes.
Tissue-of-origin signals dominate pan-cancer gene expression, often obscuring molecular features linked to patient survival. This hampers the discovery of generalizable biomarkers, as models tend to overfit tissue-specific patterns rather than capture survival-relevant signals. To address this, we propose a Domain-Adversarial Neural Network (DANN) trained on TCGA RNA-seq data to learn representations less biased by tissue and more focused on survival. Identifying tissue-independent genetic profiles is key to revealing core cancer programs. We assess the DANN using: (1) Standard SHAP, based on the original input space and DANN's mortality classifier; (2) A layer-aware strategy applied to hidden activations, including an unsupervised manifold from raw activations and a supervised manifold from mortality-specific SHAP values. Standard SHAP remains confounded by tissue signals due to biases inherent in its computation. The raw activation manifold was dominated by high-magnitude activations, which masked subtle tissue and mortality-related signals. In contrast, the layer-aware SHAP manifold offers improved low-dimensional representations of both tissue and mortality signals, independent of activation strength, enabling subpopulation stratification and pan-cancer identification of survival-associated genes.