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Signaling networks are nonlinear and complex, involving a large ensemble of dynamic interaction states that fluctuate in space and time. However, therapeutic strategies, such as combination chemotherapy, rarely consider the timing of drug perturbations. If we are to advance drug discovery for complex diseases, it will be essential to develop methods capable of identifying dynamic cellular responses to clinically relevant perturbations. Here, we present a Bayesian dose-response framework and the screening of an oncological drug matrix, comprising 10,000 drug combinations in melanoma and pancreatic cancer cell lines, from which we predict sequentially effective drug combinations. Approximately 23% of the tested combinations showed high-confidence sequential effects (either synergistic or antagonistic), demonstrating that cellular perturbations of many drug combinations have temporal aspects, which are currently both underutilized and poorly understood.

The cancer secretome includes all of the macromolecules secreted by cells into their microenvironment. Cancer cell secretomes are significantly different to that of normal cells reflecting the changes that normal cells have undergone during their transition to malignancy. More importantly, cancer secretomes are known to be active mediators of both local and distant host cells and play an important role in the progression and dissemination of cancer. Here we have quantitatively profiled both the composition of breast cancer secretomes associated with osteotropism, and their modulation under normoxic and hypoxic conditions. We detect and quantify 162 secretome proteins across all conditions which show differential hypoxic induction and association with osteotropism. Mass Spectrometry proteomics data have been deposited to the ProteomeXchange Consortium with the dataset identifier PXD000397 and the complete proteomic, bioinformatic and biological analyses are reported in Cox et al. (2015) [1].

Cancer progresses through distinct stages, and mouse models recapitulating traits of this progression are frequently used to explore genetic, morphological, and pharmacological aspects of tumor development. To complement genomic investigations of this process, we here quantify phosphoproteomic changes in skin cancer development using the SILAC mouse technology coupled to high-resolution mass spectrometry. We distill protein expression signatures from our data that distinguish between skin cancer stages. A distinct phosphoproteome of the two stages of cancer progression is identified that correlates with perturbed cell growth and implicates cell adhesion as a major driver of malignancy. Importantly, integrated analysis of phosphoproteomic data and prediction of kinase activity revealed PAK4-PKC/SRC network to be highly deregulated in SCC but not in papilloma. This detailed molecular picture, both at the proteome and phosphoproteome level, will prove useful for the study of mechanisms of tumor progression.