IndividualizedPath

IndividualizedPath identifies personalized causal genes and risk pathways in individual cancer patients by integrating DNA copy number variations, gene expression, and KEGG pathway topology to elucidate individualized molecular mechanisms of cancer.

Key Features:

• Integration of Multi-Omic Data: Combines DNA copy number variations, gene expression data, and KEGG pathway topology to analyze individual-level effects of genetic alterations.
• Identification of Gene-Pathway Pairs: Identifies specific gene-pathway pairs that contribute to individual cancer risk, for example detecting 394 such pairs across 252 GBM patients from TCGA.
• Heterogeneity and Consistency Analysis: Characterizes inter-patient heterogeneity of genetic alterations while revealing consistent pathway-level perturbations across patients.
• Risk Pathway Identification: Links genes such as EGFR to multiple cancer-related pathways and quantifies their risk contribution in GBM.
• Survival Analysis Correlation: Correlates gene-pathway interactions with clinical outcomes, reporting that GBM patients with MET-pathway pairs had significantly shorter survival than those with only MET amplification.
• Mutual Exclusivity and Subtype Specificity: Detects mutual exclusivity patterns where different genes affect the same pathways in distinct patient groups and identifies subtype-specific gene-pathway interactions.
• Impact of Rare Genetic Alterations: Uncovers significant effects of rare copy number alterations on numerous cancer-related pathways.

Scientific Applications:

• Personalized Medicine: Supports development of individualized therapeutic strategies by identifying patient-specific gene-pathway interactions.
• Cancer Research: Facilitates discovery of biomarkers and therapeutic targets by mapping how genetic alterations perturb pathways underlying cancer heterogeneity and pathogenesis.
• Clinical Outcomes Prediction: Supports prognosis assessment by linking gene-pathway perturbations to clinical outcomes such as survival.

Methodology:

Integrates genomic DNA copy number variation data and transcriptomic gene expression data with KEGG pathway topology to map how individual genetic alterations perturb biological pathways.