A Plasma-Derived Protein-Metabolite Multiplexed Panel for Early-Stage Pancreatic Cancer

Johannes F. Fahrmann, Leonidas E. Bantis, Michela Capello, Ghislaine Scelo, Jennifer B. Dennison, Nikul Patel, Eunice Murage, Jody Vykoukal, Deepali L. Kundnani, Lenka Foretova, Eleonora Fabianova, Ivana Holcatova, Vladimir Janout, Ziding Feng, Michele Yip-Schneider, Jianjun Zhang, Randall Brand, Ayumu Taguchi, Anirban Maitra, Paul BrennanC. Max Schmidt, Samir Hanash

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

BACKGROUND: We applied a training and testing approach to develop and validate a plasma metabolite panel for the detection of early-stage pancreatic ductal adenocarcinoma (PDAC) alone and in combination with a previously validated protein panel for early-stage PDAC. METHODS: A comprehensive metabolomics platform was initially applied to plasmas collected from 20 PDAC cases and 80 controls. Candidate markers were filtered based on a second independent cohort that included nine invasive intraductal papillary mucinous neoplasm cases and 51 benign pancreatic cysts. Blinded validation of the resulting metabolite panel was performed in an independent test cohort consisting of 39 resectable PDAC cases and 82 matched healthy controls. The additive value of combining the metabolite panel with a previously validated protein panel was evaluated. RESULTS: Five metabolites (acetylspermidine, diacetylspermine, an indole-derivative, and two lysophosphatidylcholines) were selected as a panel based on filtering criteria. A combination rule was developed for distinguishing between PDAC and healthy controls using the Training Set. In the blinded validation study with early-stage PDAC samples and controls, the five metabolites yielded areas under the curve (AUCs) ranging from 0.726 to 0.842, and the combined metabolite model yielded an AUC of 0.892 (95% confidence interval [CI] = 0.828 to 0.956). Performance was further statistically significantly improved by combining the metabolite panel with a previously validated protein marker panel consisting of CA 19-9, LRG1, and TIMP1 (AUC = 0.924, 95% CI = 0.864 to 0.983, comparison DeLong test one-sided P= .02). CONCLUSIONS: A metabolite panel in combination with CA19-9, TIMP1, and LRG1 exhibited substantially improved performance in the detection of early-stage PDAC compared with a protein panel alone.

Original languageEnglish (US)
Pages (from-to)372-379
Number of pages8
JournalJournal of the National Cancer Institute
Volume111
Issue number4
DOIs
StatePublished - Apr 1 2019

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Pancreatic Neoplasms
Blood Proteins
Adenocarcinoma
Area Under Curve
Proteins
Pancreatic Cyst
Confidence Intervals
Lysophosphatidylcholines
Metabolomics
Validation Studies
Neoplasms

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

Cite this

Fahrmann, J. F., Bantis, L. E., Capello, M., Scelo, G., Dennison, J. B., Patel, N., ... Hanash, S. (2019). A Plasma-Derived Protein-Metabolite Multiplexed Panel for Early-Stage Pancreatic Cancer. Journal of the National Cancer Institute, 111(4), 372-379. https://doi.org/10.1093/jnci/djy126

A Plasma-Derived Protein-Metabolite Multiplexed Panel for Early-Stage Pancreatic Cancer. / Fahrmann, Johannes F.; Bantis, Leonidas E.; Capello, Michela; Scelo, Ghislaine; Dennison, Jennifer B.; Patel, Nikul; Murage, Eunice; Vykoukal, Jody; Kundnani, Deepali L.; Foretova, Lenka; Fabianova, Eleonora; Holcatova, Ivana; Janout, Vladimir; Feng, Ziding; Yip-Schneider, Michele; Zhang, Jianjun; Brand, Randall; Taguchi, Ayumu; Maitra, Anirban; Brennan, Paul; Max Schmidt, C.; Hanash, Samir.

In: Journal of the National Cancer Institute, Vol. 111, No. 4, 01.04.2019, p. 372-379.

Research output: Contribution to journalArticle

Fahrmann, JF, Bantis, LE, Capello, M, Scelo, G, Dennison, JB, Patel, N, Murage, E, Vykoukal, J, Kundnani, DL, Foretova, L, Fabianova, E, Holcatova, I, Janout, V, Feng, Z, Yip-Schneider, M, Zhang, J, Brand, R, Taguchi, A, Maitra, A, Brennan, P, Max Schmidt, C & Hanash, S 2019, 'A Plasma-Derived Protein-Metabolite Multiplexed Panel for Early-Stage Pancreatic Cancer', Journal of the National Cancer Institute, vol. 111, no. 4, pp. 372-379. https://doi.org/10.1093/jnci/djy126
Fahrmann, Johannes F. ; Bantis, Leonidas E. ; Capello, Michela ; Scelo, Ghislaine ; Dennison, Jennifer B. ; Patel, Nikul ; Murage, Eunice ; Vykoukal, Jody ; Kundnani, Deepali L. ; Foretova, Lenka ; Fabianova, Eleonora ; Holcatova, Ivana ; Janout, Vladimir ; Feng, Ziding ; Yip-Schneider, Michele ; Zhang, Jianjun ; Brand, Randall ; Taguchi, Ayumu ; Maitra, Anirban ; Brennan, Paul ; Max Schmidt, C. ; Hanash, Samir. / A Plasma-Derived Protein-Metabolite Multiplexed Panel for Early-Stage Pancreatic Cancer. In: Journal of the National Cancer Institute. 2019 ; Vol. 111, No. 4. pp. 372-379.
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T1 - A Plasma-Derived Protein-Metabolite Multiplexed Panel for Early-Stage Pancreatic Cancer

AU - Fahrmann, Johannes F.

AU - Bantis, Leonidas E.

AU - Capello, Michela

AU - Scelo, Ghislaine

AU - Dennison, Jennifer B.

AU - Patel, Nikul

AU - Murage, Eunice

AU - Vykoukal, Jody

AU - Kundnani, Deepali L.

AU - Foretova, Lenka

AU - Fabianova, Eleonora

AU - Holcatova, Ivana

AU - Janout, Vladimir

AU - Feng, Ziding

AU - Yip-Schneider, Michele

AU - Zhang, Jianjun

AU - Brand, Randall

AU - Taguchi, Ayumu

AU - Maitra, Anirban

AU - Brennan, Paul

AU - Max Schmidt, C.

AU - Hanash, Samir

PY - 2019/4/1

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N2 - BACKGROUND: We applied a training and testing approach to develop and validate a plasma metabolite panel for the detection of early-stage pancreatic ductal adenocarcinoma (PDAC) alone and in combination with a previously validated protein panel for early-stage PDAC. METHODS: A comprehensive metabolomics platform was initially applied to plasmas collected from 20 PDAC cases and 80 controls. Candidate markers were filtered based on a second independent cohort that included nine invasive intraductal papillary mucinous neoplasm cases and 51 benign pancreatic cysts. Blinded validation of the resulting metabolite panel was performed in an independent test cohort consisting of 39 resectable PDAC cases and 82 matched healthy controls. The additive value of combining the metabolite panel with a previously validated protein panel was evaluated. RESULTS: Five metabolites (acetylspermidine, diacetylspermine, an indole-derivative, and two lysophosphatidylcholines) were selected as a panel based on filtering criteria. A combination rule was developed for distinguishing between PDAC and healthy controls using the Training Set. In the blinded validation study with early-stage PDAC samples and controls, the five metabolites yielded areas under the curve (AUCs) ranging from 0.726 to 0.842, and the combined metabolite model yielded an AUC of 0.892 (95% confidence interval [CI] = 0.828 to 0.956). Performance was further statistically significantly improved by combining the metabolite panel with a previously validated protein marker panel consisting of CA 19-9, LRG1, and TIMP1 (AUC = 0.924, 95% CI = 0.864 to 0.983, comparison DeLong test one-sided P= .02). CONCLUSIONS: A metabolite panel in combination with CA19-9, TIMP1, and LRG1 exhibited substantially improved performance in the detection of early-stage PDAC compared with a protein panel alone.

AB - BACKGROUND: We applied a training and testing approach to develop and validate a plasma metabolite panel for the detection of early-stage pancreatic ductal adenocarcinoma (PDAC) alone and in combination with a previously validated protein panel for early-stage PDAC. METHODS: A comprehensive metabolomics platform was initially applied to plasmas collected from 20 PDAC cases and 80 controls. Candidate markers were filtered based on a second independent cohort that included nine invasive intraductal papillary mucinous neoplasm cases and 51 benign pancreatic cysts. Blinded validation of the resulting metabolite panel was performed in an independent test cohort consisting of 39 resectable PDAC cases and 82 matched healthy controls. The additive value of combining the metabolite panel with a previously validated protein panel was evaluated. RESULTS: Five metabolites (acetylspermidine, diacetylspermine, an indole-derivative, and two lysophosphatidylcholines) were selected as a panel based on filtering criteria. A combination rule was developed for distinguishing between PDAC and healthy controls using the Training Set. In the blinded validation study with early-stage PDAC samples and controls, the five metabolites yielded areas under the curve (AUCs) ranging from 0.726 to 0.842, and the combined metabolite model yielded an AUC of 0.892 (95% confidence interval [CI] = 0.828 to 0.956). Performance was further statistically significantly improved by combining the metabolite panel with a previously validated protein marker panel consisting of CA 19-9, LRG1, and TIMP1 (AUC = 0.924, 95% CI = 0.864 to 0.983, comparison DeLong test one-sided P= .02). CONCLUSIONS: A metabolite panel in combination with CA19-9, TIMP1, and LRG1 exhibited substantially improved performance in the detection of early-stage PDAC compared with a protein panel alone.

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