Novel Systems Biology Approach to Decoding Actionable Targets to Overcome Resistance in GI Cancer Monotherapies
Abstract
Although there are many existing tools to predict cancer-causing mutations, it remains challenging to effectively and systematically identify drug combination targets for better CRC therapeutics. The reasons are multi-fold: 1) Most tumors carry multiple driver alterations that trigger diverse oncogenic events that cannot be suppressed with mono-therapies; 2) diverse confounding factors such as intra-tumor and inter-tumor heterogeneity exist across patient populations; and 3) achieving analytical sensitivity often requires generating and analyzing a massive amount of data, which poses an unprecedented computational (big-data) challenge. Our objectives in this application are to devise a network-based framework to discover drug combinations to overcome resistance and conquer cancer progression. Here we propose an integrative approach to predict synergistic drug combinations and experimentally validate their functional effects on CRC using patient-derived tumor models. Together, this application is significant and innovative, because it will provide insights in prioritizing drug combination target pairs, and uncovering patient-specific signaling mechanisms, a critical step towards personalized precision medicine in CRC therapy. This work will also experimentally validate several drug combinations in CRC, which stand to not only find their potential cures, but also save more patient lives.
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 01, 2022
- Accession Number
- AD1201588
Entities
People
- Nidhi Sahni
Organizations
- The University of Texas MD Anderson Cancer Center