Predicting FOXA1 gene mutation status in prostate cancer through multi-modal deep learning

Abstract

Prostate cancer stands as the foremost cause of cancer-related mortality among men globally, with its incidence and mortality rates increasing alongside the aging population. The FOXA1 gene assumes a pivotal role in prostate cancer pathology, which is potential as a prognostic indicator and a potent therapeutic target across various stages of prostate cancer. Mutations in FOXA1 have been shown to amplify, supplant, and reconfigure Androgen Receptor function, thereby fostering prostate cancer proliferation. FOXA1 is the most common molecular mutation type in Asian prostate cancer patients, with a mutation rate reaching an astonishing 41$\text{\%}$ in China. It is also an important molecular subtype in Western populations. Currently, targeted therapy for FOXA1 is rapidly developing. Therefore, effective identification of FOXA1 mutations is of great clinical significance. Gene mutation detection is usually carried out by molecular biological methods, which is expensive and has a long-time cycle. To address this problem, we proposed a multi-modal deep learning network. This network can predict the FOXA1 gene mutation status using only Hematoxylin–Eosin (H&E) stained pathological images and clinical data. Following five-fold cross-validation, our model achieved an optimal Area Under the receiver operating characteristic Curve (AUC) of 0.808, with an average predicted AUC of 0.74, surpassing other comparative models. Furthermore, we observed a discernible correlation between FOXA1 mutations and ISUP grade.