Over the past decade the accumulation of large-scale systems level data sets has occurred at an accelerating pace. Unfortunately, to date this massive accumulation of biological and medical information has rarely translated into truly efficacious therapies that dramatically alter the course of disease. Clearly new informatics approaches are needed that will enable the identification of transformative therapeutics. The central goal of this proposal is to develop an experimental-theoretical approach that defines, with high accuracy, the altered protein network structures present in each cancer malignancy. We propose to integrate quantitative mass spectrometry- based protein and protein phosphorylation measurements with surprisal analysis, a thermodynamic-based information theory approach, to resolve altered protein network structure in each malignancy. An altered network in each patients' tumor may comprise several distinct, sometimes rewired, protein subnetworks that drive the molecular imbalance in cancer tissue. Identification of unbalanced subnetworks will highlight molecular nodes that will be targeted in each patient to either restore the basal, non-transformed state or to decrease tumor cell viability. To demonstrate the ability of this approach to define unbalanced subnetworks and their associated therapeutic targets, the proposal is divided into three phases with increasing complexity and physiological relevance. In the first phase, RTK networks in breast cancer cell lines representing different subtypes will be stimulated with natural ligands to induce well characterized unbalanced processes to validate the ability of surprisal analysis to identify these networks. In the second phase, unbalanced processes present in the basal, unstimulated state of each cell line will be defined. Therapeutic targeting of these processes, alone or in combination, at high and low dose, will be performed to assess the effect of complete vs. incomplete inhibition. Unbalanced processes mediating the development of therapeutic resistance during long-term low- dose treatment will be quantified at various time points to predict combination therapies to abrogate resistance. Finally, surprisal analysis will be used to identify unbalanced processes associated with chemotherapeutic resistance in vivo in triple negative breast cancer patient derived xenograft tumors. Nodes in these imbalanced networks will be targeted to decrease tumor viability. Combination with chemotherapy may further sensitize tumor cells to treatment. Through these efforts we aim to demonstrate the ability of this combined proteomic- surprisal analysis strategy to rationally design, with high-precision, patient-specific drug cocktails that prevent drug resistance development.
Despite the continued accumulation of systems-level data sets, a significant challenge remains to gain biological insight regarding potential therapeutic targets that will dramatically alter the course of disease. To this end, here we propose a combined experimental-theoretical approach in which cutting-edge proteomic and phosphoproteomic data acquisition is coupled to surprisal analysis, a thermodynamic-based theoretical approach to identify altered network structures that underlie disease phenotypes in each individual patient. We will apply this strategy to identify altered network structures in a range of in vitro breast cancer cell lines, and explore the patient-specific development of therapeutic resistance in these same lines following long-term low-dose drug treatment. Finally, we will assess the ability of surprisal analysis to provide translational insight by predicting patient specific therapies in chemotherapeutic resistant TNBC PDX tumors.