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.

Real-time molecular navigation of tissue surgeries is an important goal at present. Combination of electrosurgical units and mass spectrometry (MS) to perform accurate molecular visualization of biological tissues has been pursued by many research groups. Determination of molecular tissue composition at a particular location by surgical smoke analysis is now of increasing interest for clinical use. However, molecular analysis of surgical smoke is commonly lacking molecular specificity and is associated with significant carbonization and chemical contamination, which are mainly related to the high temperature of smoke at which many molecules become unstable. Unlike traditional electrosurgical tools, low-temperature electrosurgical units allow tissue dissection without substantial heating. Here, we show that low-temperature electrosurgical units can be used for desorption of molecules from biological tissues without thermal degradation. The use of extractive electrospray ionization technique for the ionization of desorbed molecules allowed us to obtain mass spectra of healthy and pathological tissues with high degree of differentiation. Overall, the data indicate that the described approach has potential for intraoperative use.

Research of cancer progression mechanisms and their impact on metabolism of tumor cells and tumor microenvironment cells is an important element in drug development for cancer target therapy. In this study, changes in tumor tissue and margin tissue lipid profiles, were associated with the following clinical and morphological characteristics: tumor size, cancer stage, multifocalite, tumor grade, number of lymph node metastasis, Nottingham prognostic index, total malignancy score, level of Ki67 protein. Lipid profiling was performed by reverse-phase chromato-mass spectrometry analysis of lipid tissue extract with lipid identification by characteristic fragments. In the lipid profile of tumor tissue 13 characteristic lipids were selected. Their levels significantly correlated with at least 5 clinical and morphological features. Eight of 13 belonged to phosphatidylcholines. In lipid profile of tumor microenviroment tissue 13 lipid features were selected. Their levels significantly correlated with at least 5 clinical and morphological features. Four of 13 belonged to oxidized lipids, 4 lipid features belonged to sphingomyelins, four of 13 belonged to phosphatidylethanolamines. The tumor microenvironment tissue lipid profile correlated with tumor size, cancer stage, tumor grade, number of axillary metastases, Nottingham prognostic index. The tumor tissue lipid profile correlated with tumor size, tumor grade, total malignant score, and number of axillary metastases.

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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.

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Background: Several studies have concentrated on finding a combination of predictive parameters to establish a mathematical model that can identify patients with no axillary metastasis for whom routine lymph node dissection could be safely avoided. We developed a new model of nomogram (the Ulyanovsk Cancer Center axillary lymph node metastasis nomogram, UCC-ALNM nomogram); it employs clinically and pathologically relevant variables and offers possible advantages over the others nomograms. The purpose of the study: To assess the predictive power of UCC-ALNM nomogram. Methods: A total of 530 breast cancer patients treated between 2008 and 2010 were used as the modeling group for validating the UCC-ALNM nomogram. Clinical and pathologic features of patients were assessed by multivariable logistic regression to predict the presence of axillary metastasis in breast cancer patients. The predictive accuracy of our nomogram was measured by calculating the area under the receiver-operating characteristic (ROC) curve (AUC). Clinical factors included into analysis were: patient’s age and localization of the primary tumor. Pathological factors evaluated were: traditional pathological criteria (primary tumor size, histological type, tumor grade, HR- and Her-2 status) and new total pathological index (Ulyanovsk prognostic index - UPI), introduced by pathologists of the Ulyanovsk Regional Cancer Center. UPI is total score of six main pathological criteria that characterize the malignancy of epithelial tumors: degree of cellular differentiation, cellular polymorphism, mitotic activity, growth pattern, lymphovascular invasion, stromal reaction. Results: By the multivariate analysis, patient’s age (p=0.04), tumor size (p&lt;0.001), UPI (p&lt;0.001), PR (p&lt;0.001) and Her2 status (p=0.02) were identified as independent predictors of axillary metastasis. The nomogram was then developed using the six variables associated with axillary metastasis: age, tumor size, PR, Her2, UPI. The new model was accurate and discriminating with an AUC of 0.7510 when applied to the modeling group. Conclusions: UPI is a new predictive factor of axillary metastasis in breast cancer patients. UCC-ALNM nomogram. Citation Format: Valery Rodionov, Vlada Cometova, Sergey Panchenko, Sereda Idrisova, Yurij Savinov, Maria Rodionova. A new nomogram to predict axillary metastasis in breast cancer patients without axillary surgery [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P2-01-30.

Informative capacity analysis of immunohistochemistry (IHC) and flow cytometry (FCM) in the assessment of estrogen receptor α (ERα) expression in breast cancer tissue was performed. Similar frequencies of expression were shown by both methods: 27% of ERα-negative and 73% ERα-positive cases. However, IHC evaluation detected low levels in only 20% of ERα-positive cases, whereas low levels of ERα detected by FCM were 2 times more often (48%). Moreover, FCM revealed positive expression (23-60%) in 33% of IHC ERα-negative cases. Among IHC ER-positive cases, zero ERα expression was detected by FCM in 12.5%. The approaches to minimize errors in routine clinical determination of the estrogen receptor status were proposed.