Background: Most of the patients with Pancreatic Ductal Adenocarcinoma (PDA) are not eligible for a curative surgical resection. For this reason there is an urgent need for personalized therapies. PDA is the result of complex interactions between tumor molecular profile and metabolites produced by its microenvironment. Despite recent studies identified PDA molecular subtypes, its metabolic classification is still lacking. Methods: We applied an integrative analysis on transcriptomic and genomic data of glycolytic genes in PDA. Data were collected from public datasets and molecular glycolytic subtypes were defined using hierarchical clustering. The grade of purity of the cancer samples was assessed estimating the different amount of stromal and immunological infiltrate among the identified PDA subtypes. Analyses of metabolomic data from a subset of PDA cell lines allowed us to identify the different metabolites produced by the metabolic subtypes. Sera of a cohort of 31 PDA patients were analyzed using Q-TOF mass spectrometer to measure the amount of metabolic circulating proteins present before and after chemotherapy. Results: Our integrative analysis of glycolytic genes identified two glycolytic and two non-glycolytic metabolic PDA subtypes. Glycolytic patients develop disease earlier, have poor prognosis, low immune-infiltrated tumors, and are characterized by a gain in chr12p13 genomic region. This gain results in the over-expression of GAPDH, TPI1, and FOXM1. PDA cell lines with the gain of chr12p13 are characterized by an higher lipid uptake and sensitivity to drug targeting the fatty acid metabolism. Our sera proteomic analysis confirms that TPI1 serum levels increase in poor prognosis gemcitabine-treated patients. Conclusions: We identify four metabolic PDA subtypes with different prognosis outcomes which may have pivotal role in setting personalized treatments. Moreover, our data suggest TPI1 as putative prognostic PDA biomarker.
Integrative Analysis of Novel Metabolic Subtypes in Pancreatic Cancer Fosters New Prognostic Biomarkers
Follia, LauraCo-first
;Ferrero, GiulioCo-first
;Mandili, Giorgia;Beccuti, Marco;Giordano, Daniele;Spadi, Rosella;Satolli, Maria Antonietta;Evangelista, Andrea;Capello, Michela;Novelli, Francesco;Cordero, Francesca
2019-01-01
Abstract
Background: Most of the patients with Pancreatic Ductal Adenocarcinoma (PDA) are not eligible for a curative surgical resection. For this reason there is an urgent need for personalized therapies. PDA is the result of complex interactions between tumor molecular profile and metabolites produced by its microenvironment. Despite recent studies identified PDA molecular subtypes, its metabolic classification is still lacking. Methods: We applied an integrative analysis on transcriptomic and genomic data of glycolytic genes in PDA. Data were collected from public datasets and molecular glycolytic subtypes were defined using hierarchical clustering. The grade of purity of the cancer samples was assessed estimating the different amount of stromal and immunological infiltrate among the identified PDA subtypes. Analyses of metabolomic data from a subset of PDA cell lines allowed us to identify the different metabolites produced by the metabolic subtypes. Sera of a cohort of 31 PDA patients were analyzed using Q-TOF mass spectrometer to measure the amount of metabolic circulating proteins present before and after chemotherapy. Results: Our integrative analysis of glycolytic genes identified two glycolytic and two non-glycolytic metabolic PDA subtypes. Glycolytic patients develop disease earlier, have poor prognosis, low immune-infiltrated tumors, and are characterized by a gain in chr12p13 genomic region. This gain results in the over-expression of GAPDH, TPI1, and FOXM1. PDA cell lines with the gain of chr12p13 are characterized by an higher lipid uptake and sensitivity to drug targeting the fatty acid metabolism. Our sera proteomic analysis confirms that TPI1 serum levels increase in poor prognosis gemcitabine-treated patients. Conclusions: We identify four metabolic PDA subtypes with different prognosis outcomes which may have pivotal role in setting personalized treatments. Moreover, our data suggest TPI1 as putative prognostic PDA biomarker.
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