Researchers have developed a novel computational framework that may help identify key molecular switches capable of reverting colorectal cancer cells to a more normal-like state, according to a recent study.
The system, called REVERT (REVERse Transition), analyzes single-cell transcriptome data to reconstruct core molecular regulatory networks and identify potential therapeutic targets.
In the study, published in Advanced Science, the researchers, led by Kwang-Hyun Cho, PhD, of the Korea Advanced Institute of Science and Technology, demonstrated that inhibiting USP7, a protein identified through their framework, could effectively reduce cancer cell growth and promote characteristics of normal colon cells in patient-derived organoids.
"Cells in the transition state share similar genetic backgrounds but display heterogeneous transcriptional profiles, reflecting diverse phenotypic behaviors," the study authors wrote. They focused on analyzing these transition states between normal and cancerous cells to identify potential intervention points.
The framework identified a combination of transcription factors MYC and YY1 as key regulators in the cancer reversion process. However, because of challenges in directly targeting these factors, the researchers investigated their downstream targets, ultimately identifying USP7 as a more practical therapeutic candidate.
When testing USP7 inhibition using the compound P22077 in colorectal cancer organoids, the researchers observed significant reduction in organoid growth rates and noted molecular changes indicating a shift toward more normal-like cell states. Gene set enrichment analysis revealed that USP7 inhibition was associated with downregulation of genes related to the MYC and MAPK pathways, which are crucial in tumor formation.
"REVERT provides an opportunity for developing targeted therapeutic strategies for cancer reversion and advancing our understanding for cellular reprogramming processes," the study authors noted.
The research utilized patient-derived matched organoids of colorectal cancer and normal colorectal tissue, allowing for direct comparison of cellular states and transitions. The computational framework integrated single-cell RNA sequencing data with copy number variation analysis to identify cells in various states of transition between normal and cancerous phenotypes.
The methodology could have broader implications beyond colorectal cancer. The researchers suggested that REVERT could be applied to investigate various cell fate transition phenomena, potentially offering new approaches to cancer treatment through cellular reprogramming.
The study followed previous research demonstrating that cancer cells can potentially be reprogrammed to normal-like states and presented a computational approach for identifying specific molecular targets for this transition.
The study's limitations included the need for further validation in different cancer types and additional investigation of the long-term stability of the reverted cell states. The researchers noted that copy number variations represent permanent genomic alterations that are inherently irreversible. However, they suggested that their findings provided a promising framework for developing new therapeutic strategies focused on cancer reversion rather than traditional cytotoxic approaches.
The authors declared no conflict of interest.