CoChat
Sign Up
Cited by 115Pancreatic and Hepatic Oncology ResearchMetabolomics and Mass Spectrometry StudiesPancreatitis Pathology and Treatment

The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.

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

Open access
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·2019·JNCI: Journal of the National Cancer Institute
DOI 10.1093/jnci/djy126PMC PMC6449169
Open this paper in CoChat — free accountOpen PDF

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1093/jnci/djy126

Cites

33 of 33 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1. Schematic of study design. AUC = area under the curve; PDAC = pancreatic ductal carcinoma.
Figure 1. Schematic of study design. AUC = area under the curve; PDAC = pancreatic ductal carcinoma.CC BY · PubMed Central
Figure 2. Distributions of the five metabolite biomarkers in discovery cohorts 1 and 2. Box and whisker plots are shown for individual metabolites. Values represent the ratios of the respective metabolite relative to historical quality control reference measurement (see the “Methods”). Statistical significance was determined by two-sided Wilcoxon rank-sum test. PDAC = pancreatic ductal carcinoma.
Figure 2. Distributions of the five metabolite biomarkers in discovery cohorts 1 and 2. Box and whisker plots are shown for individual metabolites. Values represent the ratios of the respective metabolite relative to historical quality control reference measurement (see the “Methods”). Statistical significance was determined by two-sided Wilcoxon rank-sum test. PDAC = pancreatic ductal carcinoma.CC BY · PubMed Central
Figure 3. Areas under the curve (AUCs) of individual metabolites and metabolite panels in the Training and Test Sets. A) Receiver operating characteristic (ROC) curves for individual metabolites and the five-marker metabolite panel for distinguishing pancreatic ductal adenocarcinoma (PDAC; n = 29) from healthy subjects (n = 10). B) ROC curves for individual metabolites and the five-marker metabolite panel for distinguishing resectable PDAC (n = 39) from healthy subjects (n = 82; Test Set 1). AUC = area under the curve; CI = confidence interval.
Figure 3. Areas under the curve (AUCs) of individual metabolites and metabolite panels in the Training and Test Sets. A) Receiver operating characteristic (ROC) curves for individual metabolites and the five-marker metabolite panel for distinguishing pancreatic ductal adenocarcinoma (PDAC; n = 29) from healthy subjects (n = 10). B) ROC curves for individual metabolites and the five-marker metabolite panel for distinguishing resectable PDAC (n = 39) from healthy subjects (n = 82; Test Set 1). AUC = area under the curve; CI = confidence interval.CC BY · PubMed Central
Figure 4. Areas under the curve (AUCs) for protein-metabolite multiplexed panel, five-marker metabolite-panel, three-marker protein panel, and CA19-9. A and B) Receiver operating characteristic (ROC) curves for protein-metabolite multiplexed panel, five-marker metabolite-panel, three-marker (LRG, TIMP1, CA19-9) protein panel, and CA19-9 in the Training Set (29 PDAC vs 10 healthy subjects) and independent Test Set (39 PDAC vs 82 healthy subjects). AUC = area under the curve; CI = confidence interval.
Figure 4. Areas under the curve (AUCs) for protein-metabolite multiplexed panel, five-marker metabolite-panel, three-marker protein panel, and CA19-9. A and B) Receiver operating characteristic (ROC) curves for protein-metabolite multiplexed panel, five-marker metabolite-panel, three-marker (LRG, TIMP1, CA19-9) protein panel, and CA19-9 in the Training Set (29 PDAC vs 10 healthy subjects) and independent Test Set (39 PDAC vs 82 healthy subjects). AUC = area under the curve; CI = confidence interval.CC BY · PubMed Central
Figure 5. Polyamine and lipid metabolism in PDAC. A) Abundances (area units +/- SD) of N1/N8-acetylspermidine or diacetylspermine in cell lysates of 11 PDAC cell lines. B) Abundance (area units +/- SD) of N1/N8-acetylspermidine or diacetylspermine in serum-free media collected one, two, four, and six hours post conditioning from 11 PDAC cell lines. C) Network displaying enzymes involved in the biosynthesis of polyamines and their acetylated derivatives. Node color (light gray = decreased; dark gray = increased) and size depict the direction and magnitude of change in mRNA expression of respective enzymes between PDAC and adjacent control tissue. Thickened node border illustrates statistical significance (two-sided paired t test P < .05). Box and whisker plots illustrate the distribution of mRNA expression for the respective enzyme between PDAC and adjacent control tissue. mRNA expression data were obtained from Oncomine (15) and are based on the Badea data set (14). D) Percent change in serum-containing media composition of lysophosphatidylcholine(18:0), lysophosphatidylcholine(20:3), and glycerophosphocholine in PANC1 and SU8686 PDAC cell lines following 24, 48, and 72 hours of culturing. E) Heat map depicting mRNA expression of enzymes and surface receptors known to directly participate in the metabolism or binding of lysophosphatidylcholines between PDAC and adjacent control tissue. Data were obtained from Oncomine (15) and are based on the Badea data set (14). Unsupervised clustering was performed using Euclidean distance with Ward’s method.
Figure 5. Polyamine and lipid metabolism in PDAC. A) Abundances (area units +/- SD) of N1/N8-acetylspermidine or diacetylspermine in cell lysates of 11 PDAC cell lines. B) Abundance (area units +/- SD) of N1/N8-acetylspermidine or diacetylspermine in serum-free media collected one, two, four, and six hours post conditioning from 11 PDAC cell lines. C) Network displaying enzymes involved in the biosynthesis of polyamines and their acetylated derivatives. Node color (light gray = decreased; dark gray = increased) and size depict the direction and magnitude of change in mRNA expression of respective enzymes between PDAC and adjacent control tissue. Thickened node border illustrates statistical significance (two-sided paired t test P < .05). Box and whisker plots illustrate the distribution of mRNA expression for the respective enzyme between PDAC and adjacent control tissue. mRNA expression data were obtained from Oncomine (15) and are based on the Badea data set (14). D) Percent change in serum-containing media composition of lysophosphatidylcholine(18:0), lysophosphatidylcholine(20:3), and glycerophosphocholine in PANC1 and SU8686 PDAC cell lines following 24, 48, and 72 hours of culturing. E) Heat map depicting mRNA expression of enzymes and surface receptors known to directly participate in the metabolism or binding of lysophosphatidylcholines between PDAC and adjacent control tissue. Data were obtained from Oncomine (15) and are based on the Badea data set (14). Unsupervised clustering was performed using Euclidean distance with Ward’s method.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 33 references

Cited by

Sorted by citations

See all 115

Related

Metadata

Authors
Fahrmann, J. F., Bantis, L. E., Capello, M., Scelo, G., Dennison, J. B., Patel, N., Murage, E., Vykoukal, J., Kundnani, D. L., 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.
Affiliations
  • The University of Texas MD Anderson Cancer Center (US)
  • International Agency for Research on Cancer (IARC), Lyon, France
  • Department of Cancer Epidemiology and Genetics, Masaryk Memorial Cancer Institute, Brno, Czech Republic
  • Regional Authority of Public Health in Banska Bystrica, Banska Bystrica, Slovakia
  • Institute of Public Health and Preventive Medicine, 2nd Faculty of Medicine, Charles University, Prague, Czech Republic
  • Faculty of Medicine, Palacky University, Olomouc, Czech Republic
  • Indiana University Indianapolis (US)
  • University of Pittsburgh (US)
  • Centre international de recherche sur le cancer (FR)
  • Department Surgery, Indiana University School of Medicine, Indianapolis, IN
Corresponding author
Hanash, Samir
ORCID
Venue
JNCI: Journal of the National Cancer Institute
Publisher
Oxford University Press (OUP)
Published
2018-08-18
Volume
111
Issue
4
Pages
372-379
ISSN
  • 0027-8874 (ISSN-L)
  • 1460-2105
DOI
10.1093/jnci/djy126
PMC
PMC6449169
OpenAlex
W2885196877
Cited by
115
References
33
Citations by year
2026: 8 · 2025: 12 · 2024: 10 · 2023: 15 · 2022: 16 · 2021: 24 · 2020: 15 · 2019: 14 · 2018: 1
FWCI
5.07
Type
journal article
Language
en
Open access
Licence
Locations
  • JNCI Journal of the National Cancer Institute (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • IUScholarWorks (Indiana University) (submittedVersion)
Funders
  • National Cancer Institute Early Detection Network
  • Pancreatic Cancer Action Network
  • Cancer- Lustgarten Foundation (SU2C-AACR-DT25-17)
  • National Institutes of Health (IR32CA209366-01, 1R21CA209366-01, 1U01CA200468, 1U01CA196403, R03 CA123546-02)
  • National Cancer Institute
  • Development of Research Organization of the Ministry of Health of the Czech Republic (MMCI, 00209805, 9422-3, 8090-3)
  • Epidemiological Study on Pancreatic Cancer, Regional Authority of Public Health in Banska Bystrica (MZSR2007/17-RUVZBB-02)
Topics
  • Health Sciences › Medicine › Oncology › Pancreatic and Hepatic Oncology Research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
  • Health Sciences › Medicine › Surgery › Pancreatitis Pathology and Treatment
Keywords
pancreatic ductal adenocarcinoma, early-stage cancer detection, CA19-9, LRG1, TIMP-1, lysophosphatidylcholine, intraductal papillary mucinous neoplasm, benign pancreatic cysts
MeSH terms
Adenocarcinoma, Mucinous, Carcinoma, Papillary, Follow-Up Studies, Humans, Neoplasm Invasiveness, Neoplasm Staging, Pancreatic Neoplasms, Biomarkers, Tumor, Case-Control Studies, Carcinoma, Pancreatic Ductal, Metabolome, Transcriptome

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review