Challenge: In the last decade, cancer genomics has shown that not all patient tumours are identical, and conversely that they will not respond similarly to anticancer agents. The key, therefore, is personalized (or precision) medicine whereby specific drugs will be given to a patient carrying a gene that will make their tumours sensitive to such drugs.
However, the identification of genes expressing proteins that could represent therapeutic targets still remains elusive in many cancers.

Solution: The team developed quantitative Transcriptome and Affinity-Proteomics (qTAP), a transformative diagnostic platform that integrates quantitative interaction proteomics with whole transcriptomics to provide a global view of an individual patient’s tumour. The platform allows the analysis of the human protein network, providing a functional 3D view of protein-protein interactions in patient tumours. Such molecular information will make it possible to identify drugs that are most effective at treating
specific cancers. As a proof of concept, the team used this technology to identify candidate peptides to inhibit important nodes of the Smurf and Hippo signaling pathways, which are involved in multiple drug resistance in cancer.

Achievements/Impact: At completion of the project, the team successfully provided tools to analyze and probe patient tumors, including (i) affinity-purified mass spectrometry (AP-MS)-based proteomics strategies to map the dynamic state of signaling networks in vivo and (ii) qTAP to instruct single and combined targeted therapeutic strategies. The project also led to the identification of small molecule antagonists for the morphogen pathways, Smurf and Hippo pathways, that could be further investigated.
These technologies offer new avenues for the discovery of novel therapeutic targets for treating patients whose tumors fail to respond to existing therapies and understanding the drug resistance mechanisms.