BioVLAB-Cancer-Pharmacogenomics

BioVLAB-Cancer-Pharmacogenomics analyzes multi-omics cancer tissue data to investigate intratumor heterogeneity and pharmacogenomic relationships.

Key Features:

• Multi-Omics Data Analysis: Processes genomic, transcriptomic, and epigenomic datasets to enable integrated analysis of tumor molecular profiles.
• Intratumor Heterogeneity Investigation: Leverages The Cancer Genome Atlas (TCGA) data to analyze and characterize genetic diversity within tumors.
• Pharmacogenomics Integration: Connects tumor data with Cancer Cell Line Encyclopedia (CCLE) cell line information to explore pharmacogenomic relationships and predict drug responses.
• Deconvolution and Matching: Implements a deconvolution-and-match approach using DNA methylation profiles to align tumor gene expression data with corresponding cell line profiles and identify cellular subpopulations.
• Cloud-Based Infrastructure: Utilizes Amazon Web Services (AWS) for scalable computational resources.

Scientific Applications:

• Breast Cancer Research: Applied to breast cancer datasets to investigate molecular mechanisms of tumor heterogeneity and drug resistance.
• Drug Discovery and Development: Links genetic and expression data with pharmacological responses to identify potential therapeutic targets and inform treatment strategies.
• Personalized Medicine: Supports identification of patient-specific biomarkers that predict treatment outcomes.

Methodology:

Integrates and preprocesses multi-omics datasets for compatibility with TCGA and CCLE; performs heterogeneity analyses using TCGA; applies deconvolution of DNA methylation profiles to resolve constituent cell types and matches these to CCLE gene expression profiles; and correlates genetic variations with drug response data for pharmacogenomic analysis.