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Proteomics profiling of research models for studying pancreatic ductal adenocarcinoma

ABSTRACT: Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, with a five-year survival rate of 10-15% due to late-stage diagnosis and limited efficacy of existing treatments. This study utilized proteomics-based system modelling to generate multimodal datasets from various research models, including PDAC cells, spheroids, organoids, and tissues derived from murine and human samples. Identical mass spectrometry-based proteomics was applied across the different models. Preparation and validation of the research models and the proteomics were described in detail. The assembly datasets we present here may contribute to the data collection on PDAC, which will be useful for systems modeling, data mining, knowledge discovery in databases, and bioinformatics of individual models. Further data analysis may lead to generation of research hypotheses, predictions of targets for diagnosis and treatment and relationships between data variables. bridging the gap between preclinical research and clinical trials, thus enhancing the possibilities for discovering early diagnostic biomarkers and effective therapeutic targets.

INSTRUMENT(S): timsTOF Pro

ORGANISM(S): Mus Musculus (mouse)

SUBMITTER: Animesh Sharma

LAB HEAD: Lars Hagen

PROVIDER: PXD057804 | Pride | 2024-12-18

REPOSITORIES: Pride

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			Action	DRS
	220427_Mathilde_15_Slot2-1_1_1585.d.tar	Other
	220427_Mathilde_30_Slot2-2_1_1586.d.tar	Other
	220427_Mathilde_60_Slot2-3_1_1587.d.tar	Other
	txt.tar	Other

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Project description:Pancreatic ductal adenocarcinoma (PDAC) remains a major unsolved health problem. Most drugs that pass preclinical tests fail in these patients, emphasizing the need of appropriate preclinical models to test novel anticancer strategies. We developed four orthotopic mouse models employing primary human PDAC cells expressing Firefly and Gaussia luciferases, enabling bioluminescence monitoring of tumor growth and metastasis formation. Additional tumor characterization was performed using MR and high frequency ultrasound imaging. Genomic and immunohistochemical analysis revealed c-Met amplification and overexpression in one of four models. Analysis of c-Met inhibitors in vitro showed that crizotinib had the most potent effect. Moreover, we demonstrated synergistic effects between crizotinib and gemcitabine M-bM-^@M-^S the standard of care therapeutic in PDAC patients - in vitro and in vivo. Importantly, crizotinib reduced the cytidine deaminase activity in PDAC cells causing prolonged activity of gemcitabine due to diminished metabolic inactivation, as measured by LC-MS/MS. This might at least in part explain the observed prolonged survival of concomitantly treated mice with PDAC tumors and metastases. In conclusion, our orthotopic PDAC models enabled PDAC tumor imaging, and showed genetic, histopathological and metastatic features similar to their originator tumors. This allowed the identification of c-Met as a potential therapeutic target in PDAC, and revealed a cytidine deaminase-mediated synergistic mechanism between crizotinib and gemcitabine, a combination of drugs that warrants further investigation for the potential treatment of PDAC patients. 12 test samples in total [4 PDAC models (PDAC-1, PDAC-2, PDAC-3, PDAC-4; in three panel: primary human PDAC, primary tumor culture and mouse sample)] and reference sample (healthy control (mix/pool of healthy volunteers DNA) were analyzed as following (8 hybridizations); PDAC-1 primary human-Cy3 vs PDAC-4 cultured cells -Cy5 PDAC-1 cultured cells-Cy3 vs PDAC-4 mouse-Cy5 PDAC-1 mouse-Cy3 vs reference-Cy5 PDAC-2 primary human-Cy3 vs reference-Cy5 PDAC-2_cultured cells-Cy3 vs PDAC-2 mouse-Cy5 PDAC-3_primary human-Cy3 vs reference-Cy5 PDAC-3_cultured cells-Cy3 vs PDAC-3 mouse-Cy5 PDAC-4 primary human-Cy5 vs reference-Cy3 Normalized log2 ratio of (sample/references) data were calculated for 12 samples [*txt files on Series records]. Description of experimental/analysis design in r-program to generate 12 samples from 8 raw data files is provided in README.txt [Series supplementary file]

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Proteomics profiling of research models for studying pancreatic ductal adenocarcinoma

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