Genetic mutations can alter protein behavior in various ways, including gaining new functions (neomorphs), increasing existing activity (hypermorphs), or decreasing physiological effects (hypomorphs). These researchers introduced PHNToM, a computational framework designed to classify variants of unknown functional significance (VUFS) by comparing their regulatory signatures to established mutations. By utilizing transcriptional activity networks and Gaussian mixture models, the tool identifies how specific mutations dysregulate downstream targets in a tumor-specific context. Experimental validation confirmed the method's accuracy, successfully identifying gain-of-function events in genes like FGFR2 and PIK3CA. The study also highlights how mutations in different genes can mimic one another or exhibit epistatic interactions that influence cancer development. Ultimately, this approach addresses the challenges of scaling functional assays to real-time clinical diagnostics for newly detected patient variants.

References:

• Tagore S, Tsang S, Tangermann C, et al. Pan-cancer inference and validation of hypermorphic, hypomorphic and neomorphic mutations[J]. Nature Genetics, 2026, 58(2): 329-340.