MultiVarNet - Predicting Tumour Mutational Status at the Protein Level

Deep learning research in medical image analysis demonstrated the capability of predicting molecular information, including tumour mutational status, from cell and tissue morphology extracted from standard histology images. While this capability holds the promise of revolutionising pathology, it is of critical importance to go beyond gene-level mutations and develop methodologies capable of predicting precise variant mutations. Only then will it be possible to support important clinical applications, including specific targeted therapies. To address this need we developed MultiVarNet which allows us to decipher complex genomic patterns, facilitating precise predictions of hotspot alterations at the protein level. For the first time we demonstrate that we can achieve notable success in identifying over 20 mutation variants across major oncogenes. This study introduces a novel approach that underscores the importance of incorporating the underlying molecular biology of tumours to enhance algorithm accuracy, moving us towards more personalized and advanced targeted treatment options for patients.