CoChat
Sign Up
Cited by 26Metabolomics and Mass Spectrometry StudiesComputational Drug Discovery MethodsColorectal Cancer Treatments and Studies

A metabonomic approach to chemosensitivity prediction of cisplatin plus 5‐fluorouracil in a human xenograft model of gastric cancer

Open access
Wang, Xi, Yan, Shi‐Kai, Dai, Wei‐Xing, Liu, Xin‐Ru, Zhang, Wei‐Dong, Wang, Jie‐Jun·2010·International Journal of Cancer
DOI 10.1002/ijc.25294
Open this paper in CoChat — free accountOpen PDF

Abstract

The prediction of chemosensitivity is a challenging problem in the management of cancer. In the present study, a metabonomic approach was proposed to assess the feasibility of chemosensitivity prediction in a human xenograft model of gastric cancer. BALB/c‐nu/nu mice were transplanted with MKN‐45 cell line to establish the xenograft model. The mice were then randomized into treatment group (cisplatin and 5‐fluorouracil) and control group (0.9% sodium chloride), and their plasma were collected before treatment. Metabolic profiles of all plasma samples were acquired by using high‐performance liquid chromatography coupled with a quadrupole time‐of‐flight mass spectrometer (HPLC/Q‐TOF‐MS). Based on the data of metabolic profiles and k‐Nearest Neighbor algorithm, a prediction model for chemosensitivity was developed and an average accuracy of 90.4% was achieved. In addition, a series of endogenous metabolites, including 1‐acyl‐lysophosphatidycholines, polyunsaturated fatty acids and their derivatives, were determined as potential indicators of chemosensitivity. In conclusion, our results suggest that the proposed metabonomic approach allows effective chemosensitivity prediction in human xenograft model of gastric cancer. The approach presents a new concept in the chemosensitivtiy prediction of cancer and is expected to be developed as a powerful tool in the personalized cancer therapy.

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 · 10.1002/ijc.25294

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

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.

Cites

Flagged first, then by citations

Research works printed but not linked: 41

  1. Park DJ, Lenz HJ. Determinants of chemosensitivity in gastric cancer. Curr Opin Pharmacol 2006;6:337-44.
  2. Sikora K. Personalized cancer therapy-the key to the future. Pharmacogenomics 2004; 5:225-8.
  3. Jain KK. Personalised medicine for cancer: from drug development into clinical practice. Expert Opin Pharmacother 2005;6: 1463-76.
  4. Kubota T, Weisenthal L. Chemotherapy sensitivity and resistance testing: to be ''standard'' or to be individualized, that is the question. Gastric Cancer 2006;9:82-7.
  5. Blumenthal RD, Goldenberg DM. Methods and goals for the use of in vitro and in vivo chemosensitivity testing. Mol Biotechnol 2007;35:185-97.
  6. Schrag D, Garewal HS, Burstein HJ, Samson DJ, Von Hoff DD, Somerfield MR. American Society of Clinical Oncology Technology Assessment: chemotherapy sensitivity and resistance assays. J Clin Oncol 2004;22:3631-8.
  7. Lenz HJ, Leichman CG, Danenberg KD, Danenberg PV, Groshen S, Cohen H, Laine L, Crookes P, Silberman H, Baranda J, Garcia Y, Li J, et al. Thymidylate synthase mRNA level in adenocarcinoma of the stomach: a predictor for primary tumor response and overall survival. J Clin Oncol 1996;14:176-82.
  8. Boku N, Chin K, Hosokawa K, Ohtsu A, Tajiri H, Yoshida S, Yamao T, Kondo H, Shirao K, Shimada Y, Saito D, Hasebe T, et al. Biological markers as a predictor for response and prognosis of unresectable gastric cancer patients treated with 5- fluorouracil and cis-platinum. Clin Cancer Res 1998;4:1469-74.
  9. Ichikawa W. Prediction of clinical outcome of fluoropyrimidine-based chemotherapy for gastric cancer patients, in terms of the 5-fluorouracil metabolic pathway. Gastric Cancer 2006;9:145-55.
  10. Kodera Y, Ito S, Fujiwara M, Mochizuki Y, Nakayama G, Ohashi N, Koike M, Yamamura Y, Nakao A. Gene expression of 5-fluorouracil metabolic enzymes in primary gastric cancer: correlation with drug sensitivity against 5-fluorouracil. Cancer Lett 2007;252: 307-13.
  11. Metzger R, Leichman CG, Danenberg KD, Danenberg PV, Lenz HJ, Hayashi K, Groshen S, Salonga D, Cohen H, Laine L, Crookes P, Silberman H, et al. ERCC1 mRNA levels complement thymidylate synthase mRNA levels in predicting response and survival for gastric cancer patients receiving combination cisplatin and fluorouracil chemotherapy. J Clin Oncol 1998;16:309-16.
  12. Kwon HC, Roh MS, Oh SY, Kim SH, Kim MC, Kim JS, Kim HJ. Prognostic value of expression of ERCC1, thymidylate synthase, and glutathione S-transferase P1 for 5-fluorouracil/oxaliplatin chemotherapy in advanced gastric cancer. Ann Oncol 2007;18:504-9.
  13. Matsubara J, Nishina T, Yamada Y, Moriwaki T, Shimoda T, Kajiwara T, Nakajima TE, Kato K, Hamaguchi T, Shimada Y, Okayama Y, Oka T, et al. Impacts of excision repair cross- complementing gene 1 (ERCC1), dihydropyrimidine dehydrogenase, and epidermal growth factor receptor on the outcomes of patients with advanced gastric cancer. Br J Cancer 2008; 98:832-9.
  14. McLeod HL. Individualized cancer therapy: molecular approaches to the prediction of tumor response. Expert Rev Anticancer Ther 2002;2:113-9.
  15. Fareed KR, Kaye P, Soomro IN, Ilyas M, Martin S, Parsons SL, Madhusudan S. Biomarkers of response to therapy in oesophago-gastric cancer. Gut 2009;58: 127-43.
  16. Ma Y, Ding Z, Qian Y, Shi X, Castranova V, Harner EJ, Guo L. Predicting cancer drug response by proteomic profiling. Clin Cancer Res 2006;12:4583-9.
  17. Roukos DH. Innovative genomic-based model for personalized treatment of gastric cancer: integrating current standards and new technologies. Expert Rev Mol Diagn 2008;8:29-39.
  18. Schmidt C. Metabolomics takes its place as latest up-and-coming ''omic'' science. J Natl Cancer Inst 2004;96:732-4.
  19. Odunsi K, Wollman RM, Ambrosone CB, Hutson A, McCann SE, Tammela J, Geisler JP, Miller G, Sellers T, Cliby W, Qian F, Keitz B, et al. Detection of epithelial ovarian cancer using 1H-NMR-based metabonomics. Int J Cancer 2005;113:782-8.
  20. Yan SK, Wei BJ, Lin ZY, Yang Y, Zhou ZT, Zhang WD. A metabonomic approach to the diagnosis of oral squamous cell carcinoma, oral lichen planus and oral leukoplakia. Oral Oncol 2008;44:477-83.
  21. Ippolito JE, Xu J, Jain S, Moulder K, Mennerick S, Crowley JR, Townsend RR, Gordon JI. An integrated functional genomics and metabolomics approach for defining poor prognosis in human neuroendocrine cancers. Proc Natl Acad Sci USA 2005;102:9901-6.
  22. Nicholson JK, Connelly J, Lindon JC, Holmes E. Metabonomics: a platform for studying drug toxicity and gene function. Nat Rev Drug Discov 2002;1: 153-61.
  23. Lindon JC, Holmes E, Nicholson JK. Metabonomics: systems biology in pharmaceutical research and development. Curr Opin Mol Ther 2004;6:265-72.
  24. Lindon JC, Holmes E, Bollard ME, Stanley EG, Nicholson JK. Metabonomics technologies and their applications in physiological monitoring, drug safety assessment and disease diagnosis. Biomarkers 2004;9:1-31.
  25. Wold S, Kettaneh N, Tjessem K. Hierarchical multi-block PLS and PC models, for easier interpretation, and as an alternative to variable selection. J Chemometr 1996;10:463-82.
  26. Wold S, Sjostrom M, Eriksson L. PLS- regression: a basic tool of chemometrics. Chemometr Intell Lab 2001;58:109-30.
  27. Brereton RG. Chemometrics: data analysis for the laboratory and chemical plant. Chichester: John Wiley, 2003.
  28. Staunton JE, Slonim DK, Coller HA, Tamayo P, Angelo MJ, Park J, Scherf U, Lee JK, Reinhold WO, Weinstein JN, Mesirov JP, Lander ES, et al. Chemosensitivity prediction by transcriptional profiling. Proc Natl Acad Sci USA 2001;98:10787-92.
  29. Blumenthal RD. Chemosensitivity, vol. 2: in vivo models, imaging and molecular regulators. Totowa, NJ: Humana Press, 2005.
  30. Nicholson JK, Lindon JC, Holmes E. 'Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 1999;29: 1181-9.
  31. Ruzzo A, Graziano F, Kawakami K, Watanabe G, Santini D, Catalano V, Bisonni R, Canestrari E, Ficarelli R, Menichetti ET, Mari D, Testa E, et al. Pharmacogenetic profiling and clinical outcome of patients with advanced gastric cancer treated with palliative chemotherapy. J Clin Oncol 2006;24: 1883-91.
  32. Eriksson L, Antti H, Gottfries J, Holmes E, Johansson E, Lindgren F, Long I, Lundstedt T, Trygg J, Wold S. Using chemometrics for navigating in the large data sets of genomics, proteomics, and metabonomics (gpm). Anal Bioanal Chem 2004;380:419-29.
  33. Kettaneh-Wold N. Analysis of mixture data with partial least squares. Chemometr Intell Lab 1992;14:57-69.
  34. Lu X, Zhao X, Bai C, Zhao C, Lu G, Xu G. LC-MS-based metabonomics analysis. J Chromatogr B 2008; 866:64-76.
  35. Wilm M, Mann M. Analytical properties of the nanoelectrospray ion source. Anal Chem 1996;68:1-8.
  36. Smith RD, Loo JA, Edmonds CG, Barinaga CJ, Udseth HR. New developments in biochemical mass spectrometry: electrospray ionization. Anal Chem 1990; 62:882-99.
  37. Burke JE, Dennis EA. Phospholipase A2 biochemistry. Cardiovasc Drugs Ther 2009; 23:49-59.
  38. Kent C. Regulatory enzymes of phosphatidylcholine biosynthesis: a personal perspective. Biochim Biophys Acta 2005;1733:53-66.
  39. Lands WE. Stories about acyl chains. Biochim Biophys Acta 2000; 1483:1-14.
  40. Jackson SK, Abate W, Tonks AJ. Lysophospholipid acyltransferases: novel potential regulators of the inflammatory response and target for new drug discovery. Pharmacol Ther 2008;119: 104-14. Early Detection and Diagnosis 2850 Chemosensitivity prediction for gastric cancer Int. J. Cancer: 127, 2841-2850 (2010)
  41. V C 2010 UICC 10970215, 2010, 12, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ijc.25294 by Test, Wiley Online Library on [03/12/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License · DOI

Cited by

Sorted by citations

Authors
Wang, X., Yan, S., Dai, W., Liu, X., Zhang, W., & Wang, J.
Affiliations
Second Military Medical University (CN)
Corresponding author
Wang, Jie‐Jun
ORCID
Venue
International Journal of Cancer
Publisher
Wiley
Published
2010-03-03
Volume
127
Issue
12
Pages
2841-2850
ISSN
  • 0020-7136 (ISSN-L)
  • 1097-0215
DOI
10.1002/ijc.25294
OpenAlex
W2047012062
Cited by
26
References
2
Citations by year
2026: 1 · 2024: 1 · 2023: 1 · 2021: 2 · 2020: 2 · 2019: 2 · 2018: 3 · 2017: 1 · 2016: 3 · 2015: 2 · 2014: 3 · 2013: 1 · 2012: 3
FWCI
0.65
Type
journal article
Language
en
Open access
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations
  • International Journal of Cancer (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • National Natural Science Foundation of China (2007CB507400, 973 Program, 863 Program, 2006BAI02A05)
  • National High-tech Research and Development Program
  • National Key Research and Development Program of China (2007CB507400)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Health Sciences › Medicine › Oncology › Colorectal Cancer Treatments and Studies
Keywords
chemotherapy response prediction, gastric cancer, cisplatin, 5-fluorouracil, plasma metabolic profiling, metabolomics, MKN45 cells, polyunsaturated fatty acids, predictive biomarkers, precision oncology
MeSH terms
Adenocarcinoma, Animals, Antineoplastic Combined Chemotherapy Protocols, Cisplatin, Fluorouracil, Humans, Mice, Inbred BALB C, Mice, Nude, Stomach Neoplasms, Tumor Cells, Cultured, Biomarkers, Tumor, Drug Resistance, Neoplasm, Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization, Xenograft Model Antitumor Assays, Mice, Metabolomics

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