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Metabolomics approach for predicting response to neoadjuvant chemotherapy for breast cancer.

ABSTRACT: Breast cancer is a clinically heterogeneous disease, which necessitates a variety of treatments and leads to different outcomes. As an example, only some women will benefit from chemotherapy. Identifying patients who will respond to chemotherapy and thereby improve their long-term survival has important implications to treatment protocols and outcomes, while identifying non responders may enable these patients to avail themselves of other investigational approaches or other potentially effective treatments. In this study, serum metabolite profiling was performed to identify potential biomarker candidates that can predict response to neoadjuvant chemotherapy for breast cancer. Metabolic profiles of serum from patients with complete (n = 8), partial (n = 14) and no response (n = 6) to chemotherapy were studied using a combination of nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography-mass spectrometry (LC-MS) and statistical analysis methods. The concentrations of four metabolites, three (threonine, isoleucine, glutamine) from NMR and one (linolenic acid) from LC-MS were significantly different when comparing response to chemotherapy. A prediction model developed by combining NMR and MS derived metabolites correctly identified 80% of the patients whose tumors did not show complete response to chemotherapy. These results show promise for larger studies that could result in more personalized treatment protocols for breast cancer patients.

SUBMITTER: Wei S

PROVIDER: S-EPMC5528483 | biostudies-literature | 2013 Jun

REPOSITORIES: biostudies-literature

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Metabolomics approach for predicting response to neoadjuvant chemotherapy for breast cancer.

Wei Siwei S Liu Lingyan L Zhang Jian J Bowers Jeremiah J Gowda G A Nagana GA Seeger Harald H Fehm Tanja T Neubauer Hans J HJ Vogel Ulrich U Clare Susan E SE Raftery Daniel D

Molecular oncology 20121025 3

Breast cancer is a clinically heterogeneous disease, which necessitates a variety of treatments and leads to different outcomes. As an example, only some women will benefit from chemotherapy. Identifying patients who will respond to chemotherapy and thereby improve their long-term survival has important implications to treatment protocols and outcomes, while identifying non responders may enable these patients to avail themselves of other investigational approaches or other potentially effective ...[more]

PMID: 23142658

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Project description:Objective:This study aims to identify effective gene networks and biomarkers to predict response and prognosis for HER2-negative breast cancer patients who received sequential taxane-anthracycline neoadjuvant chemotherapy. Materials and Methods:Transcriptome data of training dataset including 310 HER2-negative breast cancer who received taxane-anthracycline treatment and an independent validation set with 198 samples were analyzed by weighted gene co-expression network analysis (WGCNA) approach in R language. Gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis were performed for the selected genes. Module-clinical trait relationships were analyzed to explore the genes and pathways that associated with clinicopathological parameters. Log-rank tests and COX regression were used to identify the prognosis-related genes. Results:We found a significant correlation of an expression module with distant relapse-free survival (HR = 0.213, 95% CI [0.131-0.347], P = 4.80E-9). This blue module contained genes enriched in biological process of hormone levels regulation, reproductive system, response to estradiol, cell growth and mammary gland development as well as pathways including estrogen, apelin, cAMP, the PPAR signaling pathway and fatty acid metabolism. From this module, we further screened and validated six hub genes (CA12, FOXA1, MLPH, XBP1, GATA3 and MAGED2), the expression of which were significantly associated with both better chemotherapeutic response and favorable survival for BC patients. Conclusion:We used WGCNA approach to reveal a gene network that regulate HER2-negative breast cancer treatment with taxane-anthracycline neoadjuvant chemotherapy, which enriched in pathways of estrogen signaling, apelin signaling, cAMP signaling, the PPAR signaling pathway and fatty acid metabolism. In addition, genes of CA12, FOXA1, MLPH, XBP1, GATA3 and MAGED2 might serve as novel biomarkers predicting chemotherapeutic response and prognosis for HER2-negative breast cancer.

Dataset Information

Metabolomics approach for predicting response to neoadjuvant chemotherapy for breast cancer.

Publications

Metabolomics approach for predicting response to neoadjuvant chemotherapy for breast cancer.

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