Abstract
Background: Clear cell renal cell carcinoma (ccRCC) is a highly malignant subtype of kidney cancer. 90% of ccRCC have inactivating mutations of VHL that stablise 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 HIF2αactivation state, not just expression levels is required as a biomarker of sensitivity to enable optimal clinical use. Methods: Two-site amplified time-resolved Förster Resonance Energy Transfer (aiFRET), with FRET-Efficiency, Ef, 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 Ef, 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 HIF1b in ccRCC patient samples. Conclusion: FuncOmap can be used to quantify heterogeneity in patient response and improve accurate patient stratification, thus enhancing the power of precision.
Original language | English |
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Article number | 10 |
Number of pages | 11 |
Journal | BJC Reports |
Early online date | 9 Feb 2024 |
DOIs | |
Publication status | Published - 9 Feb 2024 |
Funding
The work was supported by funding from MRC (MR/P010334/1) awarded to AM and the Alumni Funds University of Bath awarded to BL. Elena Safrygina is supported by a studentship from the UKRI Centre for Doctoral Training in Accountable Responsible and Transparent Artificial Intelligence (ART-AI) [grant number EP/S023437/1].
Funders | Funder number |
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Medical Research Council | MR/P010334/1 |
UKRI Centre for Doctoral Training in Accountable Responsible and Transparent Artificial Intelligence | EP/S023437/1 |