High-Throughput Metabolic Spectral Imaging to Predict Response to Therapy in Colorectal Cancer
Abstract
Cetuximab and panitumumab block the epidermal growth factor receptors (EGFR) overexpressed in cancer cells and are the first line of treatment in colorectal cancer (CRC). However, the majority of patients (60%-70%) are resistant to these targeted therapies and identifying patients likely to benefit from this treatment remains challenging. Current clinical standard relies on histopathology to guide initial treatment, but this approach is often misinterpreted when presented with heterogeneous tumors resulting in inaccurate prognosis of patients with EGFR resistance. There is a critical need for an accurate high-throughput diagnostic tool that can provide rapid drug-screening in CRC patient tumor biopsies even before therapy begins to ensure patients receive the most effective treatment at the earliest time point. In this proposed work we will develop and validate an innovative platform based on Raman spectral imaging (RSI) to differentiate patients who are responsive from those resistant to targeted therapies early in the treatment regimen. RSI is an established optical technique that measures multiple metabolites (lipids, fatty acids, phospholipids, and amino acids) in patient samples with high specificity. By tracking changes in these metabolites RSI will provide treatment response within a day of starting treatment. The current clinical standard is to wait for a decrease in tumor size which takes 2 to 3 weeks and is not effective in early treatment planning. This impactful project will study the effect of cytostatic drugs, which target and decrease certain biological pathways in CRC and are overall less toxic than chemotherapies. This objective will be realized by developing RSI in human CRC (hCRC) cell lines in vitro (Aim 1) and then validate in organotypic cultures obtained from pre-treatment biopsies of CRC patients (Aim 2). The proposed work will have tremendous clinical impact on CRC patients both with early stage localized disease and late stage metastasis. Our approach will identify those resistant to EGFR even before therapy starts and guide clinical decision to an optimal treatment plan for them. For patients pre-identified as nonresponders, our approach will reduce high costs of unsuccessful treatment and severe toxicities associated with cetuximab and panitumumab. Further, RSI is a universal platform that can be easily extended to other cancer drugs approved for CRC patients such as oxaliplatin, fluorouracil, and bevacizumab among others; these drugs also change metabolism in patients. Our approach also addresses challenges with current clinical metabolic imaging techniques. Oncologists typically recommend a FDG-PET scan (18F-Fludeoxyglucose positron emission tomography) to detect metabolically active tumors, but PET is expensive and fails to identify small tumors due to poor sensitivity. PET scans also result in false positives when CRC tumors express an endothelial protein (VEGF) which enhances 18F-FDG uptake. FDG-PET is also not applicable to patients with diabetes. RSI is a cost-effective technique that can analyze drug response in all subtypes of CRC regardless of other health condition since multiple metabolites are simultaneously measured. We envision our approach can be seamlessly integrated into the existing clinical workflow allowing rapid diagnosis that will complement standard-of-care treatment planning. RSI can also be combined with other drug discovery platforms as well from DNA analysis to pharmacologic profiling to expedite the production of new drugs that will improve the survival of metastatic patients unresponsive to existing therapies. Upon successful achievement of the proposed pre-clinical studies, we expect this novel platform will achieve clinically relevant outcome in less than 10 years for advanced diagnostics. This is feasible, as Raman spectroscopy is already in human use, the cytostatic drugs chosen in this study are clinically approved for CRC patients, and our organoid culture
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 10, 2021
- Source ID
- W81XWH2010620
Entities
People
- Rizia Bardhan
Organizations
- Iowa State University
- United States Army