Mapping Cancer Interactomes Using Mass Spectrometry - Gingras, Anne-Claude (Association of Biomolecular Resource Facilities, 2013)
Kinases and phosphatases coordinate critical cellular decisions, including whether to grow and divide, to differentiate into a specific cell type, or to die. Deregulation of the phosphorylation balance is implicated in multiple diseases, including cancer. This deregulation can involve mutations directly in the kinase or phosphatase proteins, changes in their splicing patterns, or may involve expression modulation; all these events can lead to network rewiring. As disease sequencing projects are progressing, there is a growing disconnect between the speed of genomic data acquisition and that of functional understanding of the mutations. As kinases and phosphatases frequently associate with regulators, scaffolding molecules and substrates, a possible outcome of mutation of splicing alteration (besides modulation of intrinsic catalytic activity) is a change in these physical interactions. A strategy to rapidly identify regulated interactions in an unbiased fashion within the context of a human cell would have multiple benefits, including in the development of pharmacologically active compounds. Here we build on a well-developed experimental pipeline to express at equivalent levels allelic variants of signaling proteins. We demonstrate how this method can be used to derive static interaction networks, which reveals new protein complexes implicated in disease, including cancers and vascular malformations. We also explore the limits of data dependent acquisition for quantification of interaction dynamics, and propose the use of data independent acquisition to overcome these limitations. Notably, we have coupled affinity purification to data-independent mass spectrometric acquisition (AP-SWATH) for quantification of the changes imparted by melanoma-associated mutations in the kinase CDK4 and the splice variants of the GRK6 kinases. We further demonstrate that the approach is flexible, and enables monitoring of interactome changes imparted by drug treatments, making it an important systems biology tool that should be scalable to match genomics sequencing efforts.