Pancreatic tumors show a large variety of genetic alterations and multiple signaling pathways controlled by reversible protein phosphorylation are responsible for the onset and progression of the disease. The central hypothesis of the project is that the overall signaling state of a pancreatic cancer cell is an important determinant for drug sensitivity or resistance and that the integration of proteomic and phosphoproteomic data with genetic information on pancreatic tumors and cell lines is a more powerful predictor of phenotypic drug sensitivity than any one approach alone. The 12 year vision is to use molecular profiling data of pancreatic cancer cell lines, organoids, spheroids and tumors to characterize their respective signaling pathways enabling the definition of subgroups and to develop subgroup-specific therapies.

Our preliminary work shows that this is a feasible vision. We have characterized the proteomes and phosphoproteomes of 60 cell lines of the NCI60 panel (representing 9 different tumor entities), a panel of 65 colorectal cancer cell lines as well as 10 pancreatic cell lines to the depth of 16,000 proteins and 55,000 phosphorylation sites. Integration of the proteomic and phosphoproteomic data using a variety of bioinformatic methods with large publically available phenotypic drug response screens have already identified markers of drug sensitivity and resistance from this data both at the level of protein expression as well as protein phosphorylation. In addition, we have characterized a number of radiation sensitive and resistant pancreatic cancer cell lines and identified proteins and active pathways that describe the observed phenotype. Importantly, bioinformatic integration of proteomic data collected for cell lines with that of primary (colorectal) tumors has shown that it is possible to identify cell lines that represent major tumor subtypes and may therefore be used to test drugs or combinations in vitro before applying these in vivo.

In the first funding period, we will extend the above proteomic and bioinformatic approaches to the analysis of ~200 low-passaged murine and ~100 human cell lines and organoids available within this CRC and integrate the data with genomic and phenotypic drug response data for a large number of cancer drugs. We will also collaborate with many other CRC projects to characterize relevant in vitro and in vivo model systems on the level of proteome expression and signaling pathway activation (i.e. phosphor-proteome) status. The in vivo models will be the focus of a second funding period so that the learnings may be translated into clinical research in a third funding period.