Background
Clear cell renal cell carcinoma (ccRCC) is a highly malignant subtype of kidney cancer. Ninety percent of ccRCC have inactivating mutations of VHL that stabilise transcription factors, HIF1α and HIF2α, only stabilised in hypoxia. The varied response to HIF2 inhibition, in the preclinical and clinical settings, suggests that assessment of HIF2α activation state, not just expression levels is required as a biomarker of sensitivity to enable optimal clinical use.
Conclusion
FuncOmap can be used to quantify heterogeneity in patient response and improve accurate patient stratification, thus enhancing the power of precision.
Methods
Two-site amplified time-resolved Förster Resonance Energy Transfer (aiFRET), with FRET-Efficiency, E f<math><mi>E</mi> <mi>f</mi></math> , as its read out, provides functional proteomics quantification, a precise step forward from protein expression as a tool for patient stratification. To enhance the clinical accessibility of E f<math><mi>E</mi> <mi>f</mi></math> , we have devised a new computational approach, Functional Oncology map (FuncOmap).
Results
FuncOmap directly maps functional states of oncoproteins and allows functional states quantification at an enhanced spatial resolution. The innovative contributions in FuncOmap are the means to co-analyse and map expressional and functional state images and the enhancement of spatial resolution to facilitate clinical application. We show the spatial interactive states HIF2α and HIF1β in ccRCC patient samples.