Project description:Despite decades of effort, pancreatic adenocarcinoma (PDAC) remains an intractable clinical challenge. An insufficient understanding of mechanisms underlying tumor cell responses to chemotherapy contributes significantly to the lack of effective treatment regimens. Here, paclitaxel, a first-line chemotherapeutic agent, was observed to interact synergistically with birinapant, a Second Mitochondrial-derived Activator of Caspases mimetic. Therefore, we investigated molecular-level drug interaction mechanisms using comprehensive, reproducible, and well-controlled ion-current-based MS1 quantification (IonStar). In a set of 40 biological samples, we compared temporal proteomic responses of PDAC cells treated with birinapant and paclitaxel, alone and combined. Using stringent criteria (e.g. strict false-discovery-rate FDR control, 2 peptides/protein), we quantified 4069 unique proteins confidently (99.8% without any missing data), and 541 proteins were significantly altered in the three treatment groups with a FDR of <1%. Interestingly, most of these proteins were altered only by combined birinapant/paclitaxel, and these predominantly represented three biological processes: mitochondrial function, cell growth and apoptosis, and cell cycle arrest. Proteins responsible for activation of oxidative phosphorylation, fatty acid β-oxidation, and inactivation of aerobic glycolysis were altered largely by combined birinapant/paclitaxel compared to single drugs, suggesting that the Warburg effect, which is employed by PDAC cells for survival and proliferation, was alleviated by the combination treatment. Metabolic profiling confirmed substantially greater suppression of the Warburg effect by the combined agents compared to either drug alone. Western blot analysis confirmed changes in apoptosis/survival signaling pathways, such as inhibition of PI3K/AKT, JAK/STAT, and MAPK/ERK signal transduction, as well as induction of G2/M arrest, and the drug combination induced much more apoptosis than did single agents. Overall, this in-depth, large-scale proteomics study provided novel insights into molecular mechanisms underlying synergy of combined birinapant/paclitaxel, and describes a proteomics/informatics pipeline that can be applied broadly to the development of cancer drug combination regimens.

Project description:Quantitative proteomics in large cohorts is highly valuable for biomedical/pharmaceutical investigations but often suffers from suboptimal reliability, accuracy, and reproducibility. Here we describe a new ultra-high-resolution (UHR) IonStar method achieving precise/reproducible protein measurement in large-cohorts while eliminating accuracy-related problems such as ratio compression, by taking advantage of the exceptional selectivity of UHR-MS1 detection (240k_FWHM). Using mixed-proteome sets reflecting large-cohort analysis using technical or biological replicates (N=56), we comprehensively compared the quantitative performances of UHR-IonStar with a state-of-the-art SWATH-MS method. The SWATH-MS employs a cutting-edge TripleTOF and a SpectronautTM package and was meticulously optimized to maximize sensitivity, reproducibility and proteome-coverage, by developing a highly extensive, customized spectral-library, reproducible/robust capillary-LC separation and narrow, variable-window acquisition. Comparing quantitative performances of the two distinct methods (i.e. MS1-vs.MS2-based) affords interesting and valuable observations. While the performances for higher-abundance proteins (i.e. higher 75% proteins in abundance) are quite similar by the two methods, UHR-IonStar showed markedly better quantitative accuracy, precision and reproducibility (e.g. R2>0.9 by UHR-IonStar vs. R2<0.4 by SWATH-MS) for proteins of lower 25% in abundance, and much improved sensitivity/selectivity for discovering significantly-altered proteins, especially these with changes ≤2.5 folds. Furthermore, UHR-IonStar showed more accurate protein quantification in single analysis of each individual sample in a large set, which is an inadequately-investigated albeit highly critical parameter for large-cohort analysis. Finally, we compared the two methods in measuring time courses of altered proteins in cancer cells (N=36) treated by paclitaxel, where dysregulated biological processes have been well-established. UHR-IonStar discovered more altered-proteins in biological processes and pathways that are known to be induced by paclitaxel, with substantially better significance scores. Additionally, UHR-IonStar showed markedly superior ability in accurately depicting the time courses of tubulin-α/β isoforms which are well-known to be consistently induced by paclitaxel. In summary, UHR-IonStar represents a reliable, robust and cost-effective solution for large-cohort proteomics quantification with excellent accuracy and precision.

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Dataset Information

MIA PaCa-2 cells treated with Gemcitabine

Dataset's files

Publications

Comparative Proteomic Analysis Identifies Key Metabolic Regulators of Gemcitabine Resistance in Pancreatic Cancer.

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