Translational Cancer Medicine
Livio Trusolino, M.D. Ph.D.
University of Torino Medical School
There is a paradox associated with the use of cancer therapeutics directed against catalytically active oncoproteins. When the therapy is efficacious, the speed and magnitude of clinical responses are usually remarkable. However, at least for solid malignancies, the individuals that benefit from such targeted therapies are typically very few, and tumors with similar histopathological features and even similar expression levels of the targeted enzyme display different biological sensitivity to drug activity. It is now well established that the major determinant of responsiveness to targeted therapeutics is the presence of a constitutively hyperactive form of the druggable molecule, which usually arises as a consequence of genetic alterations. In this minority of genetically-defined responsive tumors the targeted oncogene is both necessary and sufficient to maintain the transformed phenotype so that when the oncogene is therapeutically inactivated, the cancer cells experience cell-cycle arrest and/or cell death and the overall tumor mass undergoes regression. This reliance of some tumors on the activity of a single oncogene for continued cell proliferation and survival is described as oncogene addiction. Our objective is to unravel the signaling pathways and genomic makeups that selectively mediate drug sensitivity and therapeutic responsiveness in oncogene-addicted tumors, with a special emphasis on colorectal and breast cancer. To this aim, we use different technological platforms (phosphoproteomics, gene expression profiling, deep DNA and RNA sequencing, gene copy number analysis) and various experimental settings (cell lines, xenopatients, i.e. xenografts of patient-derived material, and genetically modified animal models). Our experimental pipeline involves the use of integrated, large-scale genomic data for discovery and hypothesis generation, followed by cell-based mechanistic insight and preclinical validation in animal models. This knowledge will form a predictive basis for the rational identification of novel tumor targets and will provide hints for molecularly-driven patient stratification.