Measurement of HER2 in saliva of women in risk of breast cancer

Metabolic Features of Saliva in Breast Cancer Patients

The aim of the work was to study the metabolic characteristics of saliva in breast cancer and the subsequent assessment of the potential information content of its individual biochemical indicators. The study included 487 patients of the Omsk Clinical Oncology Center with morphologically verified breast cancer and 298 volunteers without breast pathologies. Saliva samples were obtained from all patients before the start of treatment, and the values of 34 biochemical indicators were determined. It has been shown that concentration of total protein, urea, uric acid (UA), the total content of α-amino acids and lipid peroxidation products, and the activity of metabolic and antioxidant enzymes (in particular catalase—CAT) of saliva changed significantly in breast cancer. Biochemical indicators characterizing early breast cancer have been identified, which can be used for timely diagnosis in addition to existing methods. The coefficients UA/Urea and UA·CAT/Urea are proposed, for which the maximum deviation from the norm was observed in the early stages of the disease. It was shown that for ductal breast cancer, changes in the activity of metabolic enzymes of saliva were more pronounced, while, for lobular breast cancer, the indicators of enzymatic and non-enzymatic components of antioxidant protection changed. The results confirmed the potential importance of saliva in the diagnosis of breast cancer.

Salivary biomarkers for diagnosis of systemic diseases and malignant tumors. A systematic review

Background

Saliva evaluation could be a possible alternative to blood and/or tissue analyses, for researching specific molecules associated to the presence of systemic diseases and malignancies. The present systematic review has been designed in order to answer to the question “are there significant associations between specific salivary biomarkers and diagnosis of systemic diseases or malignancies?”.

Material and Methods

The Preferred Reporting Item for Systematic Reviews and Meta-analysis (PRISMA) statement was used to guide the review. The combinations of “saliva” and “systemic diseases” or “diagnosis” or “biomarkers” or “cancers” or “carcinoma” or “tumors”, were used to search Medline, Scopus and Web of Science databases. Endpoint of research has been set at May 2019. Studies were classified into 3 groups according to the type of disease investigated for diagnosis: 1) malignant tumors; 2) neurologic diseases and 3) inflammatory/metabolic/cardiovascular diseases. Assessment of quality has been assigned according to a series of questions proposed by the National Institute of Health. Level of evidence was assessed using the categories proposed in the Oxford Center for Evidence-Based medicine (CEMB) levels for diagnosis (2011).

Results

Seventy-nine studies met the inclusion and exclusion criteria. Fifty-one (64%) investigated malignant tumors, 14 (17.5%) neurologic and 14 (18.5%) inflammatory/cardiovascular/metabolic diseases. Among studies investigating malignant tumors, 12 (23.5%) were scored as “good” and 11 of these reported statistically significant associations between salivary molecules and pathology. Two and 5 studies were found to have a good quality, among those evaluating the association between salivary biomarkers and neurologic and inflammatory/metabolic/cardiovascular diseases, respectively.

Conclusions

The present systematic review confirms the existence of some “good” quality evidence to support the role of peculiar salivary biomarkers for diagnosis of systemic diseases (e.g. lung cancer and EGFR).

Key words:Salivary diagnostics, biomarkers, systemic diseases, malignant tumors, early diagnosis.

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

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.