TieDie

TieDie links genomic perturbations to transcriptional changes in cancer by computing subnetworks via a network diffusion approach to reveal mechanistic signaling pathways and suggest subtype-specific drug targets.

Key Features:

• Network diffusion: Uses network diffusion to map relationships between genomic perturbations and transcriptional changes and to identify cancer-relevant subnetworks.
• Integration of multiple data types: Integrates protein-protein interactions, predicted transcription factor-to-target connections, and curated literature-based interactions in the network model.
• Subnetwork identification: Computes subnetworks that connect genomic alterations with gene expression profiles characteristic of cancer subtypes.
• Application to large-scale datasets: Has been applied to The Cancer Genome Atlas (TCGA) and breast cancer cell lines to identify critical signaling pathways involving oncogenes such as MYC.
• Mechanistic interpretation and drug target inference: Predicts interlinking genes within signaling networks to provide mechanistic explanations and to suggest subtype-specific drug targets with implications for predicting drug responses.

Scientific Applications:

• Cancer signaling network reconstruction: Elucidates networks linking genomic perturbations to transcriptional changes to reveal signaling mechanisms in cancer.
• Pathway and oncogene analysis: Identifies critical signaling pathways and oncogenes such as MYC in analyses of TCGA and breast cancer cell lines.
• Precision oncology and drug target discovery: Supports identification of subtype-specific drug targets and informs predictions of drug responses.

Methodology:

Computes subnetworks connecting genomic perturbations with gene expression changes using network diffusion on protein-protein interaction networks, predicted transcription factor-to-target connections, and curated literature interactions.