CPDR

CPDR identifies personalized drug recommendations for cancer patients by leveraging individual disease signatures derived from transcriptomic profiles to prioritize candidate therapeutics.

Key Features:

• R package implementation: An R package that implements the computational analyses for transcriptomic-based personalized drug recommendation.
• Individual Disease Signature Identification: Identifies personalized disease signatures by analyzing transcriptomic profiles and selecting patient subgroups whose profiles closely match the input patient.
• Transcriptomic Profile Purification: Supports purification of transcriptomic data to account for high infiltration of non-cancerous cells within samples.
• In Silico Drug Efficacy Assessment: Assesses drug efficacy in silico using sensitivity data from cancer cell lines to predict potential patient responses.
• Connectivity Map (CMAP) integration: Bridges patient-specific transcriptomic signatures with the Connectivity Map (CMAP) database to prioritize candidate therapeutics.
• Demonstrated validation on GEO datasets: Workflow demonstrated on a colorectal cancer dataset from the Gene Expression Omnibus (GEO) and validated on a pancreatic cancer dataset assessing clinical responses to gemcitabine.

Scientific Applications:

• Personalized drug recommendation: Prioritizes therapeutic agents tailored to individual patients based on their transcriptomic-derived disease signatures.
• Addressing cancer heterogeneity: Identifies patient subgroups with similar molecular profiles to target therapies to subsets of patients who may benefit.
• In silico validation of therapeutics: Uses cancer cell line sensitivity data and CMAP integration to perform computational validation of candidate drugs.
• Clinical response assessment: Assesses clinical response to chemotherapeutics such as gemcitabine in pancreatic cancer using transcriptomic-based analyses.

Methodology:

Analysis of transcriptomic profiles to identify personalized disease signatures and select matched patient subgroups; purification of transcriptomic data to account for non-cancerous cell infiltration; in silico drug efficacy assessment using cancer cell line sensitivity data and integration with the Connectivity Map (CMAP); demonstrations using colorectal and pancreatic cancer datasets from GEO including assessment of gemcitabine response.