Identification of novel cancer driver mutations is crucial for targeted cancer therapy, yet a difficult task especially for low frequency drivers. To identify cancer driver mutations, we developed a machine learning (ML) model to predict cancer hotspots. Here, we applied the ML program to 32 non-receptor tyrosine kinases (NRTKs) and identified 36 potential cancer driver mutations, with high probability mutations in 10 genes, including ABL1, ABL2, JAK1, JAK3, and ACK1. ACK1 is a member of the poorly understood ACK family of NRTKs that also includes TNK1. Although ACK1 is an established oncogene and high-interest therapeutic target, the exact mechanism of ACK1 regulation is largely unknown and there is still no ACK1 inhibitor in clinical use. The ACK kinase family has a unique domain arrangement with most notably, a predicted ubiquitin association (UBA) domain at its C-terminus. While the presence of a functional UBA domain on a kinase is unique to the ACK family, but the role of the UBA domain on ACK1 is unknown. Interestingly, the ML program identified the ACK1 Mig6 homology region (MHR) and UBA domains truncating mutation p633fs* as a cancer driver mutation. Our data suggest that the ACK1 UBA domain helps activate full-length ACK1 through induced proximity. It also acts as a mechanism of negative feedback by tethering ACK1 to ubiquitinated cargo that is ultimately degraded. Indeed, our preliminary data suggest that truncation of the ACK1 UBA stabilizes ACK1 protein levels, which results in spontaneous ACK1 oligomerization and activation. Furthermore, our data suggests removal of the MHR domain hyper activates ACK1. Thus, our data provide a model to explain how human mutations in ACK1 convert the kinase into an oncogenic driver. In conclusion, our data reveal a mechanism of ACK1 activation and potential strategies to target the kinase in cancer.

Indiana University-Purdue University Indianapolis (IUPUI) / Pancreatic insulin-secreting beta cell failure is central to the development of diabetes. Therapeutic applications targeted at understanding and manipulating beta cell destruction mechanisms should enhance the preservation of functional beta cell mass and prevent diabetes. To this end, we have demonstrated that diabetogenic assaults (e.g., endoplasmic reticulum stress, glucolipotoxicity, and pro-inflammatory cytokines) attenuate the activation of beta cell pro-survival signaling pathways via a stress-inducible molecule called Mitogen-inducible gene 6 (Mig6). We discovered that the overabundance of Mig6 exacerbates stress-induced beta cell apoptosis and inhibits insulin secretion. Conversely, the deficiency of Mig6 partially protected beta cells from DNA damage-induced cell death. Further, we established that Mig6 haploinsufficient mice retained islet integrity and function and exhibited greater beta cell mass recovery following treatment with multiple low doses of the beta cell toxin streptozotocin. These data suggest that Mig6 may be a therapeutic target for beta cell preservation in diabetes.

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