Methylation Profile of Small Breast Cancer Tumors Evaluated by Modified MS-HRM.

The DNA methylation profile of breast cancer differs from that in healthy tissues and can be used as a diagnostic and prognostic biomarker. Aim of this study: To compare the levels of gene methylation in small malignant breast cancer tumors (<2 cm), in healthy tissue, and in fibroadenoma, and to evaluate the effectiveness of the modified Methylation Sensitive-High Resolution Melting (MS-HRM) method for this analysis. Analysis was performed using the modified MS-HRM method. For validation, the methylation levels of five genes were confirmed by pyrosequencing. The main study group included 96 breast cancer samples and the control group included 24 fibroadenoma samples and 24 healthy tissue samples obtained from patients with fibroadenoma. Breast cancer samples were divided into two subgroups (test set and validation set). The methylation of the following 15 genes was studied: MAST1, PRDM14, ZNF177, DNM2, SSH1, AP2M1, CACNA1E, CPEB4, DLGAP2, CCDC181, GCM2, ITPRIPL1, POM121L2, KCNQ1, and TIMP3. Significant differences in the validation set of samples were found for seven genes; the combination of the four genes GCM2, ITPRIPL1, CACNA1E, DLGAP2 (AUC = 0.99) showed the highest diagnostic value based on logistic regression for all breast cancer samples. Our modified MS-HRM method demonstrated that small breast cancer tumors have a specific DNA methylation profile that distinguishes them from healthy tissues and benign proliferative lesions.

Integrating Proteomics and Lipidomics for Evaluating the Risk of Breast Cancer Progression: A Pilot Study.

Metastasis is a serious and often life-threatening condition, representing the leading cause of death among women with breast cancer (BC). Although the current clinical classification of BC is well-established, the addition of minimally invasive laboratory tests based on peripheral blood biomarkers that reflect pathological changes in the body is of utmost importance. In the current study, the serum proteome and lipidome profiles for 50 BC patients with (25) and without (25) metastasis were studied. Targeted proteomic analysis for concertation measurements of 125 proteins in the serum was performed via liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM MS) using the BAK 125 kit (MRM Proteomics Inc., Victoria, BC, Canada). Untargeted label-free lipidomic analysis was performed using liquid chromatography coupled to tandem mass-spectrometry (LC-MS/MS), in both positive and negative ion modes. Finally, 87 serum proteins and 295 lipids were quantified and showed a moderate correlation with tumor grade, histological and biological subtypes, and the number of lymph node metastases. Two highly accurate classifiers that enabled distinguishing between metastatic and non-metastatic BC were developed based on proteomic (accuracy 90%) and lipidomic (accuracy 80%) features. The best classifier (91% sensitivity, 89% specificity, AUC = 0.92) for BC metastasis diagnostics was based on logistic regression and the serum levels of 11 proteins: alpha-2-macroglobulin, coagulation factor XII, adiponectin, leucine-rich alpha-2-glycoprotein, alpha-2-HS-glycoprotein, Ig mu chain C region, apolipoprotein C-IV, carbonic anhydrase 1, apolipoprotein A-II, apolipoprotein C-II and alpha-1-acid glycoprotein 1.

Validation of Breast Cancer Margins by Tissue Spray Mass Spectrometry.

Current methods for the intraoperative determination of breast cancer margins commonly suffer from the insufficient accuracy, specificity and/or low speed of analysis, increasing the time and cost of operation as well the risk of cancer recurrence. The purpose of this study is to develop a method for the rapid and accurate determination of breast cancer margins using direct molecular profiling by mass spectrometry (MS). Direct molecular fingerprinting of tiny pieces of breast tissue (approximately 1 × 1 × 1 mm) is performed using a home-built tissue spray ionization source installed on a Maxis Impact quadrupole time-of-flight mass spectrometer (qTOF MS) (Bruker Daltonics, Hamburg, Germany). Statistical analysis of MS data from 50 samples of both normal and cancer tissue (from 25 patients) was performed using orthogonal projections onto latent structures discriminant analysis (OPLS-DA). Additionally, the results of OPLS classification of new 19 pieces of two tissue samples were compared with the results of histological analysis performed on the same tissues samples. The average time of analysis for one sample was about 5 min. Positive and negative ionization modes are used to provide complementary information and to find out the most informative method for a breast tissue classification. The analysis provides information on 11 lipid classes. OPLS-DA models are created for the classification of normal and cancer tissue based on the various datasets: All mass spectrometric peaks over 300 counts; peaks with a statistically significant difference of intensity determined by the Mann-Whitney U-test (p < 0.05); peaks identified as lipids; both identified and significantly different peaks. The highest values of Q2 have models built on all MS peaks and on significantly different peaks. While such models are useful for classification itself, they are of less value for building explanatory mechanisms of pathophysiology and providing a pathway analysis. Models based on identified peaks are preferable from this point of view. Results obtained by OPLS-DA classification of the tissue spray MS data of a new sample set (n = 19) revealed 100% sensitivity and specificity when compared to histological analysis, the "gold" standard for tissue classification. "All peaks" and "significantly different peaks" datasets in the positive ion mode were ideal for breast cancer tissue classification. Our results indicate the potential of tissue spray mass spectrometry for rapid, accurate and intraoperative diagnostics of breast cancer tissue as a means to reduce surgical intervention.

Intratumoral Heterogeneity of Expression of 16 miRNA in Luminal Cancer of the Mammary Gland.

The purpose of this work is to determine the intratumoral distribution of miRNA expression profiles in luminal breast cancer (BC). The study included 33 certain BC cases of the luminal A or luminal B (Her2-) subtypes. The relative expression levels of miRNA-20a; -21; -125b; -126; -200b; -181a; -205; -221; -222; -451a; -99a; -145; -200a; -214; -30a; -191; and small nuclear RNAs U6, U54, and U58 were measured by RT-qPCR in four intratumor areas in each of 33 luminal BC specimens and in surrounding normal mammary gland tissues. Comparative analysis of miRNA expression levels between normal mammary gland tissue and different intratumor areas revealed that only four miRNAs (miRNA-21, -200b, -200a, -191) appear as consistently differentiating markers. A comparative analysis of miRNA expression levels between normal mammary gland tissue and the tumor border revealed statistically significant differences for ten miRNAs; 10 miRNAs show differential expression between normal mammary gland tissue and central tumor specimens; 9 miRNAs show differential expression between normal mammary gland tissue and tumor periphery 1; 13 miRNAs show differential expression between normal mammary gland tissue and tumor periphery 2. After comparing the tumor periphery 1 and tumor center, we found statistically significant differences in expression between five miRNAs and after comparing the tumor periphery 2 and tumor center, differences were observed for 12 miRNAs. MiRNA expression levels are subject to considerable variation, depending on the intratumor area. This may explain the inconsistency in miRNA expression estimates in BC coming from different laboratories.