Large-scale proteome-wide analysis with high accuracy/precision to guide pancreatic cancer therapy development

Quantitative profiling of the expressed proteome of cancer/cancer-associated cells has potentially transforma- tive applications in cancer research, treatment development, and individualization of therapy, potentially provid- ing (i) a molecular-level understanding of cancer subtypes and how they respond to standard-of-care (SoC) therapy or new agents, (ii) insights into tumor cell/stromal interactions that govern responses to therapy, and (iii) how signaling and drug response networks are `wired' in both genetically-mutated cancer cells and in non- cancer stromal cells. Given the prevalent discordance between genetic/transcriptomic analysis and expressed tissue proteomes, we hypothesize that large scale, quantitative proteomic analysis that interrogates the expressed proteome can provide an in-depth, high-resolution understanding of tumor and associated cells respond to chemotherapy drugs. We hypothesize that detailed understanding of SoC chemotherapy responses will also assist in selection or repurposing of new agents into treatment regimens to enhance response by influencing specific cellular response networks. We will employ a novel workflow, IonStar, that incorporates analytical, technological and informatics advances to enable robust simultaneous quantification of a majority of the expressed tumor proteome at high sensitivity, accuracy, and reproducibility, in large sample cohorts, in order to capture concentration- and time-dependent responses of patient-derived xenograft (PDX) models of pancreatic adenocarcinoma (PDAC) to SoC regimens. IonStar is a robust, well-validated sample processing, analysis, and informatics workflow that currently quantifies 5-7,000 proteins in 80-100 biological sample batches, with >95% of proteins free of missing data, at high accuracy and reproducibility, and a