Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress 2023 EHA Congress 2023 ASCO Annual Meeting
Clinical Collections
Advances in Alzheimer’s

At a glance: The ONWARDS insulin icodec trials

A round-up of the ONWARDS phase 3 clinical trials evaluating the novel weekly insulin icodec in people with diabetes.
ADVANCED SEARCH
Log in
Top
Breast Cancer Research and Treatment
Published in: Breast Cancer Research and Treatment 3/2019

01-10-2019 | Breast Cancer | Preclinical study

Salivary metabolomics with alternative decision tree-based machine learning methods for breast cancer discrimination

Authors: Takeshi Murata, Takako Yanagisawa, Toshiaki Kurihara, Miku Kaneko, Sana Ota, Ayame Enomoto, Masaru Tomita, Masahiro Sugimoto, Makoto Sunamura, Tetsu Hayashida, Yuko Kitagawa, Hiromitsu Jinno

Published in: Breast Cancer Research and Treatment | Issue 3/2019

Login to get access

Abstract

Purpose

The aim of this study is to explore new salivary biomarkers to discriminate breast cancer patients from healthy controls.

Methods

Saliva samples were collected after 9 h fasting and were immediately stored at − 80 °C. Capillary electrophoresis and liquid chromatography with mass spectrometry were used to quantify hundreds of hydrophilic metabolites. Conventional statistical analyses and artificial intelligence-based methods were used to assess the discrimination abilities of the quantified metabolites. A multiple logistic regression (MLR) model and an alternative decision tree (ADTree)-based machine learning method were used. The generalization abilities of these mathematical models were validated in various computational tests, such as cross-validation and resampling methods.

Results

One hundred sixty-six unstimulated saliva samples were collected from 101 patients with invasive carcinoma of the breast (IC), 23 patients with ductal carcinoma in situ (DCIS), and 42 healthy controls (C). Of the 260 quantified metabolites, polyamines were significantly elevated in the saliva of patients with breast cancer. Spermine showed the highest area under the receiver operating characteristic curves [0.766; 95% confidence interval (CI) 0.671–0.840, P < 0.0001] to discriminate IC from C. In addition to spermine, polyamines and their acetylated forms were elevated in IC only. Two hundred each of two-fold, five-fold, and ten-fold cross-validation using different random values were conducted and the MLR model had slightly better accuracy. The ADTree with an ensemble approach showed higher accuracy (0.912; 95% CI 0.838–0.961, P < 0.0001). These prediction models also included spermine as a predictive factor.

Conclusions

These data indicated that combinations of salivary metabolomics with the ADTree-based machine learning methods show potential for non-invasive screening of breast cancer.
Literature
1.
2.
Cancer Incidence in Five Continents, CI5plus. IARC Cancer Base No.9. Lyon, International Agency for Research on Cancer. http://​ci5.​iarc.​fr. Accessed 11 Jan 2019
3.
4.
Matsuda A, Matsuda T, Shibata A, Katanoda K, Sobue T, Nishimoto H (2014) Cancer incidence and incidence rates in Japan in 2008: a study of 25 population-based cancer registries for the Monitoring of Cancer Incidence in Japan (MCIJ) project. Jpn J Clin Oncol 44(4):388–396CrossRefPubMed
5.
Habbema JD, van Oortmarssen GJ, van Putten DJ, Lubbe JT, van der Maas PJ (1986) Age-specific reduction in breast cancer mortality by screening: an analysis of the results of the Health Insurance Plan of Greater New York study. J Natl Cancer Inst 77(2):317–320PubMed
6.
7.
Miller AB, Wall C, Baines CJ, Sun P, To T, Narod SA (2014) Twenty five year follow-up for breast cancer incidence and mortality of the Canadian National Breast Screening Study: randomised screening trial. BMJ 348:g366CrossRefPubMedPubMedCentral
8.
Shah TA, Guraya SS (2017) Breast cancer screening programs: review of merits, demerits, and recent recommendations practiced across the world. J Microsc Ultrastruct 5(2):59–69CrossRefPubMed
9.
Johns LE, Coleman DA, Swerdlow AJ, Moss SM (2017) Effect of population breast screening on breast cancer mortality up to 2005 in England and Wales: an individual-level cohort study. Br J Cancer 116(2):246–252CrossRefPubMed
10.
Johns LE, Swerdlow AJ, Moss SM (2018) Effect of population breast screening on breast cancer mortality to 2005 in England and Wales: a nested case-control study within a cohort of one million women. J Med Screen 25(2):76–81CrossRefPubMed
11.
Kaczor-Urbanowicz KE, Martin Carreras-Presas C, Aro K, Tu M, Garcia-Godoy F, Wong DT (2017) Saliva diagnostics—Current views and directions. Exp Biol Med (Maywood) 242(5):459–472CrossRef
12.
Wang X, Kaczor-Urbanowicz KE, Wong DT (2017) Salivary biomarkers in cancer detection. Med Oncol 34(1):7CrossRefPubMed
13.
Zhang A, Sun H, Wang X (2012) Saliva metabolomics opens door to biomarker discovery, disease diagnosis, and treatment. Appl Biochem Biotechnol 168(6):1718–1727CrossRefPubMed
14.
Ishikawa S, Sugimoto M, Kitabatake K, Sugano A, Nakamura M, Kaneko M et al (2016) Identification of salivary metabolomic biomarkers for oral cancer screening. Sci Rep 6:31520CrossRefPubMedPubMedCentral
15.
Rapado-Gonzalez O, Majem B, Muinelo-Romay L, Lopez-Lopez R, Suarez-Cunqueiro MM (2016) Cancer salivary biomarkers for tumours distant to the oral cavity. Int J Mol Sci 17(9):1531CrossRefPubMedCentral
16.
de Abreu PD, Areias VR, Franco MF, Benitez MC, do Nascimento CM, de Azevedo CM et al (2013) Measurement of HER2 in saliva of women in risk of breast cancer. Pathol Oncol Res 19(3):509–513CrossRef
17.
Streckfus C, Bigler L, Tucci M, Thigpen JT (2000) A preliminary study of CA15-3, c-erbB-2, epidermal growth factor receptor, cathepsin-D, and p53 in saliva among women with breast carcinoma. Cancer Invest 18(2):101–109CrossRefPubMed
18.
Navarro MA, Mesia R, Diez-Gibert O, Rueda A, Ojeda B, Alonso MC (1997) Epidermal growth factor in plasma and saliva of patients with active breast cancer and breast cancer patients in follow-up compared with healthy women. Breast Cancer Res Treat 42(1):83–86CrossRefPubMed
19.
Brooks MN, Wang J, Li Y, Zhang R, Elashoff D, Wong DT (2008) Salivary protein factors are elevated in breast cancer patients. Mol Med Rep 1(3):375–378PubMedPubMedCentral
20.
Cavaco C, Pereira JAM, Taunk K, Taware R, Rapole S, Nagarajaram H et al (2018) Screening of salivary volatiles for putative breast cancer discrimination: an exploratory study involving geographically distant populations. Anal Bioanal Chem 410(18):4459–4468CrossRefPubMed
21.
22.
Liu X, Yu H, Qiao Y, Yang J, Shu J, Zhang J et al (2018) Salivary glycopatterns as potential biomarkers for screening of early-stage breast cancer. EBioMedicine 28:70–79CrossRefPubMedPubMedCentral
23.
Zhang L, Xiao H, Karlan S, Zhou H, Gross J, Elashoff D et al (2010) Discovery and preclinical validation of salivary transcriptomic and proteomic biomarkers for the non-invasive detection of breast cancer. PLoS ONE 5(12):e15573CrossRefPubMedPubMedCentral
24.
Sugimoto M, Wong DT, Hirayama A, Soga T, Tomita M (2010) Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral, breast and pancreatic cancer-specific profiles. Metabolomics 6(1):78–95CrossRefPubMed
25.
Takayama T, Tsutsui H, Shimizu I, Toyama T, Yoshimoto N, Endo Y et al (2016) Diagnostic approach to breast cancer patients based on target metabolomics in saliva by liquid chromatography with tandem mass spectrometry. Clin Chim Acta 452:18–26CrossRefPubMed
26.
Tsutsui H, Mochizuki T, Inoue K, Toyama T, Yoshimoto N, Endo Y et al (2013) High-throughput LC-MS/MS based simultaneous determination of polyamines including N-acetylated forms in human saliva and the diagnostic approach to breast cancer patients. Anal Chem 85(24):11835–11842CrossRefPubMed
27.
Wang X, Zhao X, Chou J, Yu J, Yang T, Liu L et al (2018) Taurine, glutamic acid and ethylmalonic acid as important metabolites for detecting human breast cancer based on the targeted metabolomics. Cancer Biomark 23(2):255–268CrossRefPubMed
28.
Zhong L, Cheng F, Lu X, Duan Y, Wang X (2016) Untargeted saliva metabonomics study of breast cancer based on ultra performance liquid chromatography coupled to mass spectrometry with HILIC and RPLC separations. Talanta 158:351–360CrossRefPubMed
29.
Cheng F, Wang Z, Huang Y, Duan Y, Wang X (2015) Investigation of salivary free amino acid profile for early diagnosis of breast cancer with ultra performance liquid chromatography-mass spectrometry. Clin Chim Acta 447:23–31CrossRefPubMed
30.
Asai Y, Itoi T, Sugimoto M, Sofuni A, Tsuchiya T, Tanaka R et al (2018) Elevated polyamines in saliva of pancreatic cancer. Cancers 10(2):43CrossRefPubMedCentral
31.
Asai Y, Itoi T, Sugimoto M, Sofuni A, Tsuchiya T, Tanaka R et al (2018) Elevated polyamines in saliva of pancreatic cancer. Cancers (Basel) 10(2):43CrossRef
32.
Tomita A, Mori M, Hiwatari K, Yamaguchi E, Itoi T, Sunamura M et al (2018) Effect of storage conditions on salivary polyamines quantified via liquid chromatography-mass spectrometry. Sci Rep 8(1):12075CrossRefPubMedPubMedCentral
33.
Ishikawa S, Sugimoto M, Kitabatake K, Tu M, Sugano A, Yamamori I et al (2017) Effect of timing of collection of salivary metabolomic biomarkers on oral cancer detection. Amino Acids 49(4):761–770CrossRef
34.
Sugimoto M, Saruta J, Matsuki C, To M, Onuma H, Kaneko M et al (2013) Physiological and environmental parameters associated with mass spectrometry-based salivary metabolomic profiles. Metabolomics 9(2):454–463CrossRef
35.
Sugimoto M, Kawakami M, Robert M, Soga T, Tomita M (2012) Bioinformatics tools for mass spectroscopy-based metabolomic data processing and analysis. Curr Bioinform 7(1):96–108CrossRefPubMedPubMedCentral
36.
Freund Y, Mason L (1999) The alternating decision tree learning algorithm. Icml 1999:124–133
37.
Wang Q, Gao P, Wang X, Duan Y (2014) Investigation and identification of potential biomarkers in human saliva for the early diagnosis of oral squamous cell carcinoma. Clin Chim Acta 427:79–85CrossRefPubMed
38.
Irene LW, James JD, Bernard F, Eleftherios PM, Stewart JA, Thomas BJ et al (2011) Long-term outcome of invasive ipsilateral breast tumor recurrences after lumpectomy in NSABP B-17 and B-24 randomized clinical trials for DCIS. J Natl Cancer Inst 103(6):478–488CrossRef
39.
40.
41.
Elshof LE, Tryfonidis K, Slaets L, van Leeuwen-Stok AE, Skinner VP, Dif N et al (2015) Feasibility of a prospective, randomised, open-label, international muticentre, phase III, non-inferiority trial to assess the saftey of active surveillance for low risk ductal carcinoma in site The LORD study. Eur J Cancer 51(12):1497–1510CrossRefPubMed
42.
Francis A, Thomas J, Fallowfield L, Wallis M, Bartlett JM, Brookes C et al (2015) Addressing overtreatment of screen detected DCIS; the LORIS trial. Eur J Cancer 51(16):2296–2303CrossRefPubMed
43.
44.
45.
Gerner EW, Meyskens FL Jr (2004) Polyamines and cancer: old molecules, new understanding. Nat Rev Cancer 4(10):781–792CrossRefPubMed
46.
Satoh K, Yachida S, Sugimoto M, Oshima M, Nakagawa T, Akamoto S et al (2017) Global metabolic reprogramming of colorectal cancer occurs at adenoma stage and is induced by MYC. Proc Natl Acad Sci USA 114(37):E7697–E7706CrossRefPubMed
47.
Hiramatsu K, Takahashi K, Yamaguchi T, Matsumoto H, Miyamoto H, Tanaka S et al (2005) N(1), N(12)-Diacetylspermine as a sensitive and specific novel marker for early- and late-stage colorectal and breast cancers. Clin Cancer Res 11(8):2986–2990CrossRefPubMed
48.
Takahashi Y, Sakaguchi K, Horio H, Hiramatsu K, Moriya S, Takahashi K et al (2015) Urinary N1, N12-diacetylspermine is a non-invasive marker for the diagnosis and prognosis of non-small-cell lung cancer. Br J Cancer 113(10):1493–1501CrossRefPubMedPubMedCentral
49.
Nakajima T, Katsumata K, Kuwabara H, Soya R, Enomoto M, Ishizaki T et al (2018) Urinary polyamine biomarker panels with machine-learning differentiated colorectal cancers, benign disease, and healthy controls. Int J Mol Sci 19(3):756CrossRefPubMedCentral
50.
Wikoff WR, Hanash S, DeFelice B, Miyamoto S, Barnett M, Zhao Y et al (2015) Diacetylspermine is a novel prediagnostic serum biomarker for non-small-cell lung cancer and has additive performance with pro-surfactant protein B. J Clin Oncol 33(33):3880–3886CrossRefPubMedPubMedCentral
51.
Vargas AJ, Ashbeck EL, Thomson CA, Gerner EW, Thompson PA (2014) Dietary polyamine intake and polyamines measured in urine. Nutr Cancer 66(7):1144–1153CrossRefPubMed
52.
Park MH, Igarashi K (2013) Polyamines and their metabolites as diagnostic markers of human diseases. Biomol Ther (Seoul) 21(1):1–9CrossRef
Metadata
Title
Salivary metabolomics with alternative decision tree-based machine learning methods for breast cancer discrimination
Authors
Takeshi Murata
Takako Yanagisawa
Toshiaki Kurihara
Miku Kaneko
Sana Ota
Ayame Enomoto
Masaru Tomita
Masahiro Sugimoto
Makoto Sunamura
Tetsu Hayashida
Yuko Kitagawa
Hiromitsu Jinno
Publication date
01-10-2019
Publisher
Springer US
Published in
Breast Cancer Research and Treatment / Issue 3/2019
Print ISSN: 0167-6806
Electronic ISSN: 1573-7217
DOI
https://doi.org/10.1007/s10549-019-05330-9

Other articles of this Issue 3/2019

Breast Cancer Research and Treatment 3/2019 Go to the issue

Epidemiology

Quantifying the relationship between age at diagnosis and breast cancer-specific mortality

Review

Awareness of residents’ technical ability can affect margin status in breast conserving operations

Epidemiology

A generalizable relationship between mortality and time-to-death among breast cancer patients can be explained by tumour dormancy

Epidemiology

Mammographic screening in male patients at high risk for breast cancer: is it worth it?

Preclinical study

Genomic comparison of paired primary breast carcinomas and lymph node macrometastases using the Oncotype DX Breast Recurrence Score® test

Epidemiology

Mode of detection and breast cancer mortality by follow-up time and tumor characteristics among screened women in Cancer Prevention Study-II
Webinar | 19-02-2024 | 17:30 (CET)

Keynote webinar | Spotlight on antibody–drug conjugates in cancer

Watch now

Antibody–drug conjugates (ADCs) are novel agents that have shown promise across multiple tumor types. Explore the current landscape of ADCs in breast and lung cancer with our experts, and gain insights into the mechanism of action, key clinical trials data, existing challenges, and future directions.

Dr. Véronique Diéras
Prof. Fabrice Barlesi
Developed by: Springer Medicine
Image Credits