Integrative analysis of novel metabolic subtypes in pancreatic cancer fosters new prognostic biomarkers

Laura Follia; Giulio Ferrero; Giorgia Mandili; Marco Beccuti; Daniele Giordano; Rosella Spadi; Maria Antonietta Satolli; Andrea Evangelista; Hiroyuki Katayama; Wang Hong; Amin A. Momin; Michela Capello; Samir M. Hanash; Francesco Novelli; Francesca Cordero

doi:10.3389/fonc.2019.00115

Integrative analysis of novel metabolic subtypes in pancreatic cancer fosters new prognostic biomarkers

Laura Follia, Giulio Ferrero, Giorgia Mandili, Marco Beccuti, Daniele Giordano, Rosella Spadi, Maria Antonietta Satolli, Andrea Evangelista, Hiroyuki Katayama, Wang Hong, Amin A. Momin, Michela Capello, Samir M. Hanash, Francesco Novelli, Francesca Cordero

Research output: Contribution to journal › Article › peer-review

29 Scopus citations

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.

Original language	English (US)
Article number	115
Journal	Frontiers in Oncology
Volume	9
Issue number	FEB
DOIs	https://doi.org/10.3389/fonc.2019.00115
State	Published - 2019

Keywords

Cancer subtypes
Glycolysis
Metabolism
Pancreatic cancer
Transcriptomic data

ASJC Scopus subject areas

Oncology
Cancer Research

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10.3389/fonc.2019.00115

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Follia, L., Ferrero, G., Mandili, G., Beccuti, M., Giordano, D., Spadi, R., Satolli, M. A., Evangelista, A., Katayama, H., Hong, W., Momin, A. A., Capello, M., Hanash, S. M., Novelli, F., & Cordero, F. (2019). Integrative analysis of novel metabolic subtypes in pancreatic cancer fosters new prognostic biomarkers. Frontiers in Oncology, 9(FEB), Article 115. https://doi.org/10.3389/fonc.2019.00115

Follia, L, Ferrero, G, Mandili, G, Beccuti, M, Giordano, D, Spadi, R, Satolli, MA, Evangelista, A, Katayama, H, Hong, W, Momin, AA, Capello, M, Hanash, SM, Novelli, F & Cordero, F 2019, 'Integrative analysis of novel metabolic subtypes in pancreatic cancer fosters new prognostic biomarkers', Frontiers in Oncology, vol. 9, no. FEB, 115. https://doi.org/10.3389/fonc.2019.00115

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title = "Integrative analysis of novel metabolic subtypes in pancreatic cancer fosters new prognostic biomarkers",

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.",

keywords = "Cancer subtypes, Glycolysis, Metabolism, Pancreatic cancer, Transcriptomic data",

author = "Laura Follia and Giulio Ferrero and Giorgia Mandili and Marco Beccuti and Daniele Giordano and Rosella Spadi and Satolli, {Maria Antonietta} and Andrea Evangelista and Hiroyuki Katayama and Wang Hong and Momin, {Amin A.} and Michela Capello and Hanash, {Samir M.} and Francesco Novelli and Francesca Cordero",

note = "Publisher Copyright: Copyright {\textcopyright} 2019 Follia, Ferrero, Mandili, Beccuti, Giordano, Spadi, Satolli, Evangelista, Katayama, Hong, Momin, Capello, Hanash, Novelli and Cordero.",

year = "2019",

doi = "10.3389/fonc.2019.00115",

language = "English (US)",

volume = "9",

journal = "Frontiers in Oncology",

issn = "2234-943X",

publisher = "Frontiers Media S. A.",

number = "FEB",

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TY - JOUR

T1 - Integrative analysis of novel metabolic subtypes in pancreatic cancer fosters new prognostic biomarkers

AU - Follia, Laura

AU - Ferrero, Giulio

AU - Mandili, Giorgia

AU - Beccuti, Marco

AU - Giordano, Daniele

AU - Spadi, Rosella

AU - Satolli, Maria Antonietta

AU - Evangelista, Andrea

AU - Katayama, Hiroyuki

AU - Hong, Wang

AU - Momin, Amin A.

AU - Capello, Michela

AU - Hanash, Samir M.

AU - Novelli, Francesco

AU - Cordero, Francesca

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Cancer subtypes

KW - Glycolysis

KW - Metabolism

KW - Pancreatic cancer

KW - Transcriptomic data

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DO - 10.3389/fonc.2019.00115

M3 - Article

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AN - SCOPUS:85063256181

SN - 2234-943X

VL - 9

JO - Frontiers in Oncology

JF - Frontiers in Oncology

IS - FEB

M1 - 115

ER -

Integrative analysis of novel metabolic subtypes in pancreatic cancer fosters new prognostic biomarkers

Abstract

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