1170P - Identification of breast cancer-specific signatures in saliva metabolites using capillary electrophoresis mass spectrometry

Date 28 September 2014
Event ESMO 2014
Session Poster Display session
Topics Diagnostics
Breast Cancer
Translational Research
Basic Principles in the Management and Treatment (of cancer)
Presenter Hiromitsu Jinno
Citation Annals of Oncology (2014) 25 (suppl_4): iv406-iv408. 10.1093/annonc/mdu346
Authors H. Jinno1, T. Murata1, M. Sunamura2, M. Sugimoto3, T. Hayashida1, M. Takahashi1, Y. Kitagawa1
  • 1Department Of Surgery, Keio University School of Medicine, 160-8582 - Tokyo/JP
  • 2Department Of Surgery, Tohoku University School of Medicine, Sendai/JP
  • 3Institute For Advanced Biosciences, Keio University, Tsuruoka/JP



Saliva is an easily accessible and informative biological fluid which has high potential for the early diagnosis of various diseases. Saliva-based diagnostics, particularly those based on metabolomics technology, offer a promising clinical strategy by characterizing the association between salivary analytes and a particular disease. The aim of this study is to identify breast cancer-specific signatures in saliva metabolites to facilitate the early diagnosis of breast cancer.


Comprehensive metabolite analysis of saliva was conducted using capillary electrophoresis time-of-flight mass spectrometry, which can simultaneously quantify hundreds of charged metabolites. Saliva samples were obtained from 20 healthy controls and 90 breast cancer patients including 74 invasive ductal carcinoma (IDC), 2 invasive lobular carcinoma and 14 ductal carcinoma in situ (DCIS). Thirty-three patients were treated with neoadjuvant chemotherapy. Statistical analyses were performed by using a nonparametric Mann-Whitney U test, multiple logistic regression and the receiver operating characteristics (ROC) to evaluate the predictive power of biomarkers.


Totally, 205 kinds of metabolites were identified and quantified. Of these metabolites, 62 metabolites demonstrated significantly higher concentrations in breast cancer patients without any treatment comparing with healthy individuals (P < 0.05). Especially, 5 salivary biomarkers (labelled as A to E) demonstrating significant differences with P-value <0.0001, the area under the ROC curves (AUCs) were 0.765 for substance A, 0.716 for substance B, 0.809 for substance C, 0.819 for substance D and 0.850 for substance E. These 5 potential markers did not show any significant correlations with age and tumor size. The concentrations of 5 potential markers of patients with DCIS is between the patients with IDC and healthy individuals.


These data suggested that quantitative information for salivary metabolites and their combinations could be promising biomarkers for the early diagnosis of breast cancer.


All authors have declared no conflicts of interest.