Cancer reversion is a relatively newer concept where cancer cells are transformed back into normal, non-malignant states by reactivating differentiation processes. A recent study led by researchers from KAIST (Korea Advanced Institute of Science & Technology) has introduced a computational framework called BENEIN (Boolean network inference and control), which uses single-cell transcriptome data to identify master regulators of differentiation and control cancer reversion.
BENEIN applies Boolean network modeling to pinpoint critical regulators in gene expression. When tested on colorectal cancer cells, the framework identified three key targets: MYB, HDAC2, and FOXA2. Simultaneous inhibition of these regulators effectively reverted cancer cells into normal-like enterocytes, as validated through both in vitro and in vivo experiments. These findings open pathways to potentially safer, more targeted cancer therapies.
The computational model not only highlights regulatory dynamics but also aids in designing interventions. By simulating gene network responses, BENEIN revealed that disrupting MYB, HDAC2, and FOXA2 altered cancer cells' gene expression, suppressed proliferation, and induced differentiation. Animal models further confirmed a significant reduction in tumor growth when these regulators were silenced.
Beyond colorectal cancer, the study also hints at BENEIN’s versatility. Its application to different tissues demonstrated its ability to adapt and identify regulators across various biological systems. While technical barriers and cancer-specific mutations are still major challenges, the framework lays a foundation for advancing precision medicine.
This is a solid demonstration of how BENEIN can redefine cancer treatment strategies, as it shifts the focus from merely controlling malignancy to actively reversing it by restoring normal cellular function.