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Metabolomics approach for predicting response to neoadjuvant chemotherapy for colorectal cancer

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Abstract

Introduction

Colorectal cancer (CRC) is a clinically heterogeneous disease, which necessitates a variety of treatments and leads to different outcomes. Only some CRC patients will benefit from neoadjuvant chemotherapy (NACT).

Objectives

An accurate prediction of response to NACT in CRC patients would greatly facilitate optimal personalized management, which could improve their long-term survival and clinical outcomes.

Methods

In this study, plasma metabolite profiling was performed to identify potential biomarker candidates that can predict response to NACT for CRC. Metabolic profiles of plasma from non-response (n = 30) and response (n = 27) patients to NACT were studied using UHPLC–quadruple time-of-flight)/mass spectrometry analyses and statistical analysis methods.

Results

The concentrations of nine metabolites were significantly different when comparing response to NACT. The area under the receiver operating characteristic curve value of the potential biomarkers was up to 0.83 discriminating the non-response and response group to NACT, superior to the clinical parameters (carcinoembryonic antigen and carbohydrate antigen 199).

Conclusion

These results show promise for larger studies that could result in more personalized treatment protocols for CRC patients.

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Abbreviations

CRC:

Colorectal cancer

NACT:

Neoadjuvant chemotherapy

MRI:

Magnetic resonance imaging

CEA:

Carcinoembryonic antigen

CA199:

Carbohydrate antigen 199

NMR:

Nuclear magnetic resonance

MS:

Mass spectrometry

TNM:

Tumor–node–metastasis

QC:

Quality control

QTOF:

Quadruple time-of-flight

RT:

Retention time

m/z:

Mass-to-charge ratio

PCA:

Principal component analysis

PLS-DA:

Partial least-squares discriminant analysis

VIP:

Variable importance in the projection

AUC:

Area under the receiver operating characteristic curve

RF:

Random forest

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Funding

This study was funded by National Natural Science Foundation of China (Grant Numbers 81773551, 81473072) and Health and Family Planning Commission of Heilongjiang Province Scientific Program (Grant Number 2014-349).

Author information

Author notes

  1. Kai Yang and Fan Zhang have contributed equally to this work.

Authors and Affiliations

  1. Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin, 150086, China

    Kai Yang, Fan Zhang, Zhuo-zhong Wang, Kui Deng, Yuan-yuan Zhang, Wei-wei Zhao, Wei Song, Yu-qing Cai & Kang Li

  2. Department of Colorectal Surgery, The Affiliated Tumor Hospital of Harbin Medical University, Harbin, 150086, China

    Peng Han & Bin-bin Cui

  3. Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 200032, China

    Zheng-Jiang Zhu

Authors
  1. Kai Yang
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  2. Fan Zhang
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  3. Peng Han
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  4. Zhuo-zhong Wang
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  9. Yu-qing Cai
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  10. Kang Li
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Corresponding authors

Correspondence to Kang Li, Bin-bin Cui or Zheng-Jiang Zhu.

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The authors declare that they have no conflict of interest.

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All procedures performed in studies involving human participants were in accordance with the Ethical Standards of the Institutional and/or National Research Committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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Cite this article

Yang, K., Zhang, F., Han, P. et al. Metabolomics approach for predicting response to neoadjuvant chemotherapy for colorectal cancer. Metabolomics 14, 110 (2018). https://doi.org/10.1007/s11306-018-1406-0

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  • DOI: https://doi.org/10.1007/s11306-018-1406-0

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