Integrated targeted metabolomic and lipidomic analysis: A novel approach to classifying early cystic precursors to invasive pancreatic cancer

Rogier Aäron Gaiser, Alberto Pessia, Zeeshan Ateeb, Haleh Davanian, Carlos Fernández Moro, Hassan Alkharaan, Katie Healy, Sam Ghazi, Urban Arnelo, Roberto Valente, Vidya Velagapudi, Margaret Sällberg Chen, Marco Del Chiaro

Forskningsoutput: TidskriftsbidragArtikelVetenskapligPeer review

Sammanfattning

Pancreatic cystic neoplasms (PCNs) are a highly prevalent disease of the pancreas. Among PCNs, Intraductal Papillary Mucinous Neoplasms (IPMNs) are common lesions that may progress from low-grade dysplasia (LGD) through high-grade dysplasia (HGD) to invasive cancer. Accurate discrimination of IPMN-associated neoplastic grade is an unmet clinical need. Targeted (semi)quantitative analysis of 100 metabolites and >1000 lipid species were performed on peri-operative pancreatic cyst fluid and pre-operative plasma from IPMN and serous cystic neoplasm (SCN) patients in a pancreas resection cohort (n = 35). Profiles were correlated against histological diagnosis and clinical parameters after correction for confounding factors. Integrated data modeling was used for group classification and selection of the best explanatory molecules. Over 1000 different compounds were identified in plasma and cyst fluid. IPMN profiles showed significant lipid pathway alterations compared to SCN. Integrated data modeling discriminated between IPMN and SCN with 100% accuracy and distinguished IPMN LGD or IPMN HGD and invasive cancer with up to 90.06% accuracy. Free fatty acids, ceramides, and triacylglycerol classes in plasma correlated with circulating levels of CA19-9, albumin and bilirubin. Integrated metabolomic and lipidomic analysis of plasma or cyst fluid can improve discrimination of IPMN from SCN and within PMNs predict the grade of dysplasia.
Originalspråkengelska
Artikelnummer10208
TidskriftScientific Reports
Volym9
Antal sidor12
ISSN2045-2322
DOI
StatusPublicerad - 15 jul 2019
MoE-publikationstypA1 Tidskriftsartikel-refererad

Vetenskapsgrenar

  • 3122 Cancersjukdomar

Citera det här

Gaiser, Rogier Aäron ; Pessia, Alberto ; Ateeb, Zeeshan ; Davanian, Haleh ; Fernández Moro, Carlos ; Alkharaan, Hassan ; Healy, Katie ; Ghazi, Sam ; Arnelo, Urban ; Valente, Roberto ; Velagapudi, Vidya ; Sällberg Chen, Margaret ; Del Chiaro, Marco. / Integrated targeted metabolomic and lipidomic analysis: A novel approach to classifying early cystic precursors to invasive pancreatic cancer. I: Scientific Reports. 2019 ; Vol. 9.
@article{6e89941b045f4742aaf96dd3b748d4c7,
title = "Integrated targeted metabolomic and lipidomic analysis: A novel approach to classifying early cystic precursors to invasive pancreatic cancer",
abstract = "Pancreatic cystic neoplasms (PCNs) are a highly prevalent disease of the pancreas. Among PCNs, Intraductal Papillary Mucinous Neoplasms (IPMNs) are common lesions that may progress from low-grade dysplasia (LGD) through high-grade dysplasia (HGD) to invasive cancer. Accurate discrimination of IPMN-associated neoplastic grade is an unmet clinical need. Targeted (semi)quantitative analysis of 100 metabolites and >1000 lipid species were performed on peri-operative pancreatic cyst fluid and pre-operative plasma from IPMN and serous cystic neoplasm (SCN) patients in a pancreas resection cohort (n = 35). Profiles were correlated against histological diagnosis and clinical parameters after correction for confounding factors. Integrated data modeling was used for group classification and selection of the best explanatory molecules. Over 1000 different compounds were identified in plasma and cyst fluid. IPMN profiles showed significant lipid pathway alterations compared to SCN. Integrated data modeling discriminated between IPMN and SCN with 100{\%} accuracy and distinguished IPMN LGD or IPMN HGD and invasive cancer with up to 90.06{\%} accuracy. Free fatty acids, ceramides, and triacylglycerol classes in plasma correlated with circulating levels of CA19-9, albumin and bilirubin. Integrated metabolomic and lipidomic analysis of plasma or cyst fluid can improve discrimination of IPMN from SCN and within PMNs predict the grade of dysplasia.",
keywords = "3122 Cancers, Pancreatic cancer, Intraductal papillary mucinous neoplasms, Serous cystic neoplasm, Pancreatic cyst",
author = "Gaiser, {Rogier A{\"a}ron} and Alberto Pessia and Zeeshan Ateeb and Haleh Davanian and {Fern{\'a}ndez Moro}, Carlos and Hassan Alkharaan and Katie Healy and Sam Ghazi and Urban Arnelo and Roberto Valente and Vidya Velagapudi and {S{\"a}llberg Chen}, Margaret and {Del Chiaro}, Marco",
year = "2019",
month = "7",
day = "15",
doi = "10.1038/s41598-019-46634-6",
language = "English",
volume = "9",
journal = "Scientific Reports",
issn = "2045-2322",
publisher = "Nature Publishing Group",

}

Gaiser, RA, Pessia, A, Ateeb, Z, Davanian, H, Fernández Moro, C, Alkharaan, H, Healy, K, Ghazi, S, Arnelo, U, Valente, R, Velagapudi, V, Sällberg Chen, M & Del Chiaro, M 2019, 'Integrated targeted metabolomic and lipidomic analysis: A novel approach to classifying early cystic precursors to invasive pancreatic cancer', Scientific Reports, vol. 9, 10208. https://doi.org/10.1038/s41598-019-46634-6

Integrated targeted metabolomic and lipidomic analysis: A novel approach to classifying early cystic precursors to invasive pancreatic cancer. / Gaiser, Rogier Aäron; Pessia, Alberto; Ateeb, Zeeshan; Davanian, Haleh; Fernández Moro, Carlos; Alkharaan, Hassan; Healy, Katie; Ghazi, Sam; Arnelo, Urban; Valente, Roberto; Velagapudi, Vidya; Sällberg Chen, Margaret; Del Chiaro, Marco.

I: Scientific Reports, Vol. 9, 10208, 15.07.2019.

Forskningsoutput: TidskriftsbidragArtikelVetenskapligPeer review

TY - JOUR

T1 - Integrated targeted metabolomic and lipidomic analysis: A novel approach to classifying early cystic precursors to invasive pancreatic cancer

AU - Gaiser, Rogier Aäron

AU - Pessia, Alberto

AU - Ateeb, Zeeshan

AU - Davanian, Haleh

AU - Fernández Moro, Carlos

AU - Alkharaan, Hassan

AU - Healy, Katie

AU - Ghazi, Sam

AU - Arnelo, Urban

AU - Valente, Roberto

AU - Velagapudi, Vidya

AU - Sällberg Chen, Margaret

AU - Del Chiaro, Marco

PY - 2019/7/15

Y1 - 2019/7/15

N2 - Pancreatic cystic neoplasms (PCNs) are a highly prevalent disease of the pancreas. Among PCNs, Intraductal Papillary Mucinous Neoplasms (IPMNs) are common lesions that may progress from low-grade dysplasia (LGD) through high-grade dysplasia (HGD) to invasive cancer. Accurate discrimination of IPMN-associated neoplastic grade is an unmet clinical need. Targeted (semi)quantitative analysis of 100 metabolites and >1000 lipid species were performed on peri-operative pancreatic cyst fluid and pre-operative plasma from IPMN and serous cystic neoplasm (SCN) patients in a pancreas resection cohort (n = 35). Profiles were correlated against histological diagnosis and clinical parameters after correction for confounding factors. Integrated data modeling was used for group classification and selection of the best explanatory molecules. Over 1000 different compounds were identified in plasma and cyst fluid. IPMN profiles showed significant lipid pathway alterations compared to SCN. Integrated data modeling discriminated between IPMN and SCN with 100% accuracy and distinguished IPMN LGD or IPMN HGD and invasive cancer with up to 90.06% accuracy. Free fatty acids, ceramides, and triacylglycerol classes in plasma correlated with circulating levels of CA19-9, albumin and bilirubin. Integrated metabolomic and lipidomic analysis of plasma or cyst fluid can improve discrimination of IPMN from SCN and within PMNs predict the grade of dysplasia.

AB - Pancreatic cystic neoplasms (PCNs) are a highly prevalent disease of the pancreas. Among PCNs, Intraductal Papillary Mucinous Neoplasms (IPMNs) are common lesions that may progress from low-grade dysplasia (LGD) through high-grade dysplasia (HGD) to invasive cancer. Accurate discrimination of IPMN-associated neoplastic grade is an unmet clinical need. Targeted (semi)quantitative analysis of 100 metabolites and >1000 lipid species were performed on peri-operative pancreatic cyst fluid and pre-operative plasma from IPMN and serous cystic neoplasm (SCN) patients in a pancreas resection cohort (n = 35). Profiles were correlated against histological diagnosis and clinical parameters after correction for confounding factors. Integrated data modeling was used for group classification and selection of the best explanatory molecules. Over 1000 different compounds were identified in plasma and cyst fluid. IPMN profiles showed significant lipid pathway alterations compared to SCN. Integrated data modeling discriminated between IPMN and SCN with 100% accuracy and distinguished IPMN LGD or IPMN HGD and invasive cancer with up to 90.06% accuracy. Free fatty acids, ceramides, and triacylglycerol classes in plasma correlated with circulating levels of CA19-9, albumin and bilirubin. Integrated metabolomic and lipidomic analysis of plasma or cyst fluid can improve discrimination of IPMN from SCN and within PMNs predict the grade of dysplasia.

KW - 3122 Cancers

KW - Pancreatic cancer

KW - Intraductal papillary mucinous neoplasms

KW - Serous cystic neoplasm

KW - Pancreatic cyst

U2 - 10.1038/s41598-019-46634-6

DO - 10.1038/s41598-019-46634-6

M3 - Article

VL - 9

JO - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

M1 - 10208

ER -