Identifying New Subgroups of Colorectal Cancer Patients with the BRAF-V600E Mutation to Improve Treatment Response Using Genetic Analysis

Colorectal cancer (CRC) is a type of cancer that begins in the colon or rectum, which are parts of the digestive system. It is one of the most common cancers worldwide and is the second leading cause of cancer deaths. CRC usually starts as small growths called polyps, which can become cancerous over time. If not detected early, the cancer can spread to other parts of the body, making it harder to treat. Approximately 12% of CRC patients have a change in a mutation (gene) called BRAF, which makes the disease more aggressive and harder to treat. These patients often do not respond well to chemotherapy, and if the cancer spreads, survival rates are very low.
In recent years, new drugs have been developed for patients with this BRAF mutation, leading to some improvements in survival. However, not all patients respond the same way, and it is unclear why there are differences in treatment outcomes. This project aims to better understand those differences and improve treatment for these patients.
Researchers have learned that CRC patients can be grouped based on changes in how their genes are expressed. Gene expression is when a gene is "turned on" in a cell to create a product, like a protein, which affects how the cell works. However, the current systems used to classify these patients are not perfect—they are based on data from too few people or do not focus specifically on those with a BRAF gene mutation. Now, with newer technology, researchers can study gene expression in individual cells, which is more accurate than looking at the average expression of genes in many cells at once.
The goal of this research is to create a more accurate system for classifying CRC patients with a BRAF mutation by using newer, more precise methods. The research will use data from large collections of patient samples, including the BEACON study, which is the largest trial to date for BRAF-mutated CRC patients. Additionally, artificial intelligence (computer systems that can analyze large amounts of data and make predictions) will be used to combine all the data and identify meaningful patterns. We will also look at gene expression at the single-cell level to further refine these classifications. We will then test how different drugs work for each of the newly identified patient groups using laboratory-grown cancer cells and animal models.
By creating a better classification system, this research could help doctors understand the differences between patients with BRAF-mutated CRC. This could lead to more personalized treatments that are more effective than the current “one-size-fits-all” approach, ultimately improving the survival and quality of life for patients with this challenging form of cancer.