Novel Systems Biology Approach to Decoding Actionable Targets to Overcome Resistance in GI Cancer Monotherapies
Abstract
In the United States, colorectal cancer (CRC) is the second most common cause of cancer deaths; however, therapeutic strategies for the disease are limited and largely consist of cytotoxic regimens. MEK inhibitors are often used as a therapeutic strategy for these patients, since about 50% of CRC patients carry genetic mutations that affect MEK-mediated function. However, CRC patient responses to the treatment are extremely heterogeneous across patient populations, and resistance to this drug therapy often occurs after a short period. Despite the robust anti-tumor effects in some CRC models, we observed that the clinical effects of MEK inhibitors were modest. Thus, it is urgent to identify drug combination targets for improving prognosis in a large number of CRC patients. CRC is more common in military members due to exposure to carcinogens in the field, including certain chemicals and ionizing radiation. This type of exposure causes mutations in DNA, which increase the rate of cancer development. The proposed combination therapy, then, is likely to be most effective in military members with CRC as their tumors are likely to carry drug-resistant mutations. 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. Dr. Sahni has been an independent faculty at University of Texas MD Anderson Cancer Center for 2 years. With the help of this award, Dr. Sahni can continue to establish her own independent lab dedicated to bringing the same type of clinically transformative combination therapy to CRC patients for whom the 5-year survival rate is currently under 10%.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Nov 19, 2019
- Source ID
- W81XWH1910703
Entities
People
- Nidhi Sahni
Organizations
- The University of Texas MD Anderson Cancer Center
- United States Army