Identification of direct oncogenic kinase-substrate relation

The development and pathology of tumors are governed by a multitude of genetic alterations in cancer cells. In particular, tyrosine kinase activity is often observed to be upregulated. Consequently, tyrosine kinase inhibitors are the first-line therapeutic option for example for the treatment of acute lymphoblastic leukemia (ALL) and chronic myeloid leukemia (CML), which are frequently driven by an oncogenic fusion of two proteins, BCR and the tyrosine kinase ABL. The fusion protein BCR-ABL displays markedly elevated kinase activity; however, the precise oncogenic processes that are altered through this activity remain unclear. This is, in part, due to the lack of knowledge regarding the oncogenic substrates of kinases. In general, determining kinase substrate relationships is a challenging problem. In this project, Saccharomyces cerevisiae (yeast) is employed as a host organism for the expression of the oncogenic fusion protein kinase BCR-ABL. The protein is of considerable size, yet our findings indicate that it exhibits robust tyrosine kinase activity in yeast and can be considered a suitable model for human cells. The yeast strain expressing BCR-ABL can now be employed to test thousands of individual human proteins in a parallel, large-scale approach, with each human protein being tested individually to determine whether it will undergo phosphorylation by the oncogenic kinase. The phosphorylation of human proteins by BCR-ABL in yeast is quantified through the use of mass spectrometry. A significant benefit of this methodology is that yeast lacks tyrosine phosphorylation, allowing for unambiguous assignment of kinase-substrate relationships, which is not feasible when working directly in human cancer cells. Computational approaches will play a pivotal role in identifying the crucial BCR-ABL substrates in cancer. This approach has the potential to comprehensively study the dysregulation of understudied kinases in cancer and significantly advance our understanding of how signaling processes go wrong in cancer.