Cells transmit biological signals by connecting many different proteins together in various conditionally regulated combinations and quantities, culminating in protein-protein interaction (PPI) signatures. Referring to these protein combinations as `signatures' is a metaphor that can be extended, wherein PPI constitute a biochemical `language', in which proteins are members of an `alphabet' that in joining together form `words' instructing the cell to perform specific functions. Indeed, a central hypothesis of the Interactomics field is that PPI activity is distinct in healthy versus diseased states: in this model, distinct PPI signatures provide the signals that cause these opposite outcomes, and if we knew how to define those PPI signatures, we could design better drugs to halt pathologic signals, while preserving or enhancing healthy ones. Viewed from this perspective, cancer may be considered a disease of dysregulated PPI, where pathologic PPI signatures originate from mutation, unhealthy growth factor pathways, and the shutting down of the body's naturally protective immune system. To better understand these signals in cancer, the field needs technologies that expand our capability to observe PPI networks, ideally from samples as small as those routinely obtained in the clinic. Our group has recently mounted a new multiplex microsphere-based platform to address this need, termed `PiSCES'. The PiSCES platform currently focuses on T cell antigen receptor (TCR) pathway as a prototype PPI network, due to its importance in T cell-mediated eradication of tumor cells, and its possible suppression associated with the universally lethal cancer, glioblastoma multiforme (GBM). Our preliminary data already show that PiSCES can reveal distinct PPI signatures associated with functionally divergent signals, with assay sensitivity that is compatible with tiny samples originating from experimental mice or human patient biopsies. The current project is dedicated to advanced development and validation of PiSCES, as it is applied to T cells in the context of cancer immunotherapy and tumor-induced immune suppression. By showing that the PiSCES approach works for T cell signaling in cancer, we expect that this will launch the new platform in both mouse modeling and patient research communities, where it can potentially be applied to any PPI networks in any cell type of interest in cancer. Visualizing the different activities of physiologic PPI networks in cancer will be a major step toward understanding them better, and toward designing drugs to combat malignant signals or enhance the body's immune defenses against tumors.
A novel approach for assessing network signaling protein-protein interactions will undergo advanced development and validation in application to T cell signaling in cancer. The approach is called `PiSCES' analysis, and the network protein-protein activity signatures it produces already include the T cell antigen receptor (TCR) signalosome, and will be extended to include clinically relevant costimulatory and checkpoint pathways. PiSCES signatures for anti-tumor T cell signaling will be validated in a pre-clinical mouse model of anti-melanoma immunotherapy, and in the setting of human glioblastoma, with intention to advance the technology to the point of wide applicability for mouse model and patient-based cancer research.
