The hallmarks of breast cancer by Raman spectroscopy

Advancements in Intraoperative Near-Infrared Fluorescence Imaging for Accurate Tumor Resection: A Promising Technique for Improved Surgical Outcomes and Patient Survival

Clear surgical margins for solid tumor resection are essential for preventing cancer recurrence and improving overall patient survival. Complete resection of tumors is often limited by a surgeon's ability to accurately locate malignant tissues and differentiate them from healthy tissue. Therefore, techniques or imaging modalities are required that would ease the identification and resection of tumors by real-time intraoperative visualization of tumors. Although conventional imaging techniques such as positron emission tomography (PET), computed tomography (CT), magnetic resonance imaging (MRI), or radiography play an essential role in preoperative diagnostics, these cannot be utilized in intraoperative tumor detection due to their large size, high cost, long imaging time, and lack of cancer specificity. The inception of several imaging techniques has paved the way to intraoperative tumor margin detection with a high degree of sensitivity and specificity. Particularly, molecular imaging using near-infrared fluorescence (NIRF) based nanoprobes provides superior imaging quality due to high signal-to-noise ratio, deep penetration to tissues, and low autofluorescence, enabling accurate tumor resection and improved survival rates. In this review, we discuss the recent developments in imaging technologies, specifically focusing on NIRF nanoprobes that aid in highly specific intraoperative surgeries with real-time recognition of tumor margins.

Raman microspectroscopy based TNM staging and grading of breast cancer

The tumor-node-metastasis (TNM) system is the most common way that doctors determine the anatomical extent of cancer on the basis of clinical and pathological criteria. In this study, a spectral histopathological study has been carried out to bridge Raman micro spectroscopy with the breast cancer TNM system. A total of seventy breast tissue samples, including healthy tissue, early, middle, and advanced cancer, were investigated to provide detailed insights into compositional and structural variations that accompany breast malignant evolution. After evaluating the main spectral variations in all tissue types, the generalized discriminant analysis (GDA) pathological diagnostic model was established to discriminate the TNM staging and grading information. Moreover, micro-Raman images were reconstructed by K-means clustering analysis (KCA) for visualizing the lobular acinar in healthy tissue and ductal structures in all early, middle and advanced breast cancer tissue groups. While, univariate imaging techniques were adapted to describe the distribution differences of biochemical components such as tryptophan, β-carotene, proteins, and lipids in the scanned regions. The achieved spectral histopathological results not only established a spectra-structure correlations via tissue biochemical profiles but also provided important data and discriminative model references for in vivo Raman-based breast cancer diagnosis.

Raman spectroscopy: current applications in breast cancer diagnosis, challenges and future prospects

Despite significant improvements in the way breast cancer is managed and treated, it continues to persist as a leading cause of death worldwide. If detected and diagnosed early, when tumours are small and localised, there is a considerably higher chance of survival. However, current methods for detection and diagnosis lack the required sensitivity and specificity for identifying breast cancer at the asymptomatic or very early stages. Thus, there is a need to develop more rapid and reliable methods, capable of detecting disease earlier, for improved disease management and patient outcome. Raman spectroscopy is a non-destructive analytical technique that can rapidly provide highly specific information on the biochemical composition and molecular structure of samples. In cancer, it has the capacity to probe very early biochemical changes that accompany malignant transformation, even prior to the onset of morphological changes, to produce a fingerprint of disease. This review explores the application of Raman spectroscopy in breast cancer, including discussion on its capabilities in analysing both ex-vivo tissue and liquid biopsy samples, and its potential in vivo applications. The review also addresses current challenges and potential future uses of this technology in cancer research and translational clinical application.

Studying the pathological and biochemical features in breast cancer progression by confocal Raman microspectral imaging of excised tissue samples

Confocal Raman microspectral imaging (CRMI) has been used to detect the spectra-pathological features of ductal carcinoma in situ (DCIS) and lobular hyperplasia (LH) compared with the heathy (H) breast tissue. A total of 15-20 spectra were measured from healthy tissue, LH tissue, and DCIS tissue. One-way ANOVA and Tukey's honest significant difference (HSD) post hoc multiple tests were used to evaluate the peak intensity variations in all three tissue types. Besides that, linear discrimination analysis (LDA) algorithm was adopted in combination with principal component analysis (PCA) to classify the spectral features from tissues at different stages along the continuum to breast cancer. Moreover, by using the point-by-point scanning methodology, spectral datasets were obtained and reconstructed for further pathologic visualization by multivariate imaging methods, including K-mean clustering analysis (KCA) and PCA. Univariate imaging of individual Raman bands was also used to describe the differences in the distribution of specific molecular components in the scanning area. After a detailed spectral feature analysis from 800 to 1800 cm^-1 and 2800 to 3000 cm^-1 for all the three tissue types, the histopathological features were visualized based on the content and structural variations of lipids, proteins, phenylalanine, carotenoids and collagen, as well as the calcification phenomena. The results obtained not only allowed a detailed Raman spectroscopy-based understanding of the malignant transformation process of breast cancer, but also provided a solid spectral data support for developing Raman based breast cancer clinical diagnostic techniques.

Detection and Classification of Multi-Type Cells by Using Confocal Raman Spectroscopy

Tumor cells circulating in the peripheral blood are the prime cause of cancer metastasis and death, thus the identification and discrimination of these rare cells are crucial in the diagnostic of cancer. As a label-free detection method without invasion, Raman spectroscopy has already been indicated as a promising method for cell identification. This study uses a confocal Raman spectrometer with 532 nm laser excitation to obtain the Raman spectrum of living cells from the kidney, liver, lung, skin, and breast. Multivariate statistical methods are applied to classify the Raman spectra of these cells. The results validate that these cells can be distinguished from each other. Among the models built to predict unknown cell types, the quadratic discriminant analysis model had the highest accuracy. The demonstrated analysis model, based on the Raman spectrum of cells, is propitious and has great potential in the field of biomedical for classifying circulating tumor cells in the future.

Review of Laser Raman Spectroscopy for Surgical Breast Cancer Detection: Stochastic Backpropagation Neural Networks

Laser Raman spectroscopy (LRS) is a highly specific biomolecular technique which has been shown to have the ability to distinguish malignant and normal breast tissue. This paper discusses significant advancements in the use of LRS in surgical breast cancer diagnosis, with an emphasis on statistical and machine learning strategies employed for precise, transparent and real-time analysis of Raman spectra. When combined with a variety of "machine learning" techniques LRS has been increasingly employed in oncogenic diagnostics. This paper proposes that the majority of these algorithms fail to provide the two most critical pieces of information required by the practicing surgeon: a probability that the classification of a tissue is correct, and, more importantly, the expected error in that probability. Stochastic backpropagation artificial neural networks inherently provide both pieces of information for each and every tissue site examined by LRS. If the networks are trained using both human experts and an unsupervised classification algorithm as gold standards, rapid progress can be made understanding what additional contextual data is needed to improve network classification performance. Our patients expect us to not simply have an opinion about their tumor, but to know how certain we are that we are correct. Stochastic networks can provide that information.

A look into the use of Raman spectroscopy for brain and breast cancer diagnostics: linear and non-linear optics in cancer research as a gateway to tumor cell identity

Introduction: Currently, intensely developing of linear and non-linear optical methods for cancer detection provides a valuable tool to improve sensitivity and specificity. One of the main reasons for insufficient progress in cancer diagnostics is related to the fact that most cancer types are not only heterogeneous in their genetic composition but also reside in varying microenvironments and interact with different cell types. Until now, no technology has been fully proven for effective detecting of invasive cancer, which infiltrating the extracellular matrix.Areas covered: This review investigates the current status of Raman spectroscopy and Raman imaging for brain and breast cancer diagnostics. Moreover, the review provides a comprehensive overview of the applicability of atomic force microscopy (AFM), linear and non-linear optics in cancer research as a gateway to tumor cell identity.Expert commentary: A combination of linear and non-linear optics, particularly Raman-driven methods, has many additional advantages to identify alterations in cancer cells that are crucial for their proliferation and that distinguish them from normal cells.

Optical Imaging Approaches to Investigating Radiation Resistance

Radiation therapy is frequently the first line of treatment for over 50% of cancer patients. While great advances have been made in improving treatment response rates and reducing damage to normal tissue, radiation resistance remains a persistent clinical problem. While hypoxia or a lack of tumor oxygenation has long been considered a key factor in causing treatment failure, recent evidence points to metabolic reprogramming under well-oxygenated conditions as a potential route to promoting radiation resistance. In this review, we present recent studies from our lab and others that use high-resolution optical imaging as well as clinical translational optical spectroscopy to shine light on the biological basis of radiation resistance. Two-photon microscopy of endogenous cellular metabolism has identified key changes in both mitochondrial structure and function that are specific to radiation-resistant cells and help promote cell survival in response to radiation. Optical spectroscopic approaches, such as diffuse reflectance and Raman spectroscopy have demonstrated functional and molecular differences between radiation-resistant and sensitive tumors in response to radiation. These studies have uncovered key changes in metabolic pathways and present a viable route to clinical translation of optical technologies to determine radiation resistance at a very early stage in the clinic.

Raman Spectroscopy Applied to Parathyroid Tissues: A New Diagnostic Tool to Discriminate Normal Tissue from Adenoma

Primary hyperparathyroidism is an endocrine disorder characterized by autonomous production of parathyroid hormone. Patients with the symptomatic disease should be referred for parathyroidectomy. However, the distinction between the pathological condition and the benign one is very challenging in the surgical setting; therefore, accurate recognition is important to ensure success during minimally invasive surgery. At present, all intraoperative techniques significantly increase surgical time and, consequently, cost. In this proof-of-concept study, Raman microscopy was used to differentiate between healthy parathyroid tissue and parathyroid adenoma from 18 patients. The data showed different spectroscopic features for the two main tissue types of healthy and adenoma. Moreover, the parathyroid adenoma subtypes (chief cells and oxyphil cells) were characterized by their own Raman spectra. The partial least-squares discriminant analysis (PLS-DA) model built to discriminate healthy from adenomatous parathyroid tissue was able to correctly classify all samples in the calibration and validation data sets, providing 100% prediction accuracy. The PLS-DA model built to discriminate chief cell adenoma from oxyphil cell adenoma allowed us to correctly classify >99% of the spectra during calibration and cross-validation and to correctly predict 100% of oxyphil and 99.8% of chief cells in the external validation data set. The results clearly demonstrate the great potential of Raman spectroscopy. The final goal would be development of a Raman portable fiber probe device for intraoperative optical biopsy, both to improve the surgical success rate and reduce surgical cost.

Temporal Quantum Correlations in Inelastic Light Scattering from Water

Water is one of the most prevalent chemicals on our planet, an integral part of both our environment and our existence as a species. Yet it is also rich in anomalous behaviors. Here we reveal that water is a novel-yet ubiquitous-source for quantum correlated photon pairs at ambient conditions. The photon pairs are produced through Raman scattering, and the correlations arise from the shared quantum of a vibrational mode between the Stokes and anti-Stokes scattering events. We confirm the nonclassical nature of the produced photon pairs by showing that the cross-correlation and autocorrelations of the signals violate a Cauchy-Schwarz inequality by over 5 orders of magnitude. The unprecedented degree of violating the inequality in pure water, as well as the well-defined polarization properties of the photon pairs, points to its usefulness in quantum information.

Diagnosing clean margins through Raman spectroscopy in human and animal mammary tumour surgery: a short review

Breast cancer frequency in human and other mammal female populations has worryingly increased lately. The acute necessity for taxonomy of the aetiological factors along with seeking for new diagnostic tools and therapy procedures aimed at reducing mortality have yielded in an intense research effort worldwide. Surgery is a regular method to counteract extensive development of breast cancer and prevent metastases provided that negative surgical margins are achieved. This highly technical challenge requires fast, extremely sensitive and selective discrimination between malignant and benign tissues even down to molecular level. The particular advantages of Raman spectroscopy, such as high chemical specificity, and the ability to measure raw samples and optical responses in the visible or near-infrared spectral range, have recently recommended it as a means with elevated potential in precise diagnostic in oncology surgery. This review spans mainly the latter 10 years of exceptional efforts of scientists implementing Raman spectroscopy as a nearly real-time diagnostic tool for clean margins assessment in mastectomy and lumpectomy. Although greatly contributing to medical discoveries for the wealth of humanity, animals as patients have benefitted less from advances in surgery diagnostic using Raman spectroscopy. This work also dedicates a few lines to applications of surface enhanced Raman spectroscopy in veterinary oncological surgery.

Raman microspectroscopy of Hematoporphyrins. Imaging of the noncancerous and the cancerous human breast tissues with photosensitizers

Raman microspectroscopy combined with fluorescence were used to study the distribution of Hematoporphyrin (Hp) in noncancerous and cancerous breast tissues. The results demonstrate the ability of Raman spectroscopy to distinguish between noncancerous and cancerous human breast tissue and to identify differences in the distribution and photodegradation of Hematoporphyrin, which is a photosensitizer in photodynamic therapy (PDT), photodynamic diagnosis (PDD) and photoimmunotherapy (PIT) of cancer. Presented results show that Hematoporphyrin level in the noncancerous breast tissue is lower compared to the cancerous one. We have proved also that the Raman intensity of lipids and proteins doesn't change dramatically after laser light irradiation, which indicates that the PDT treatment destroys preferably cancer cells, in which the photosensitizer is accumulated. The specific subcellular localization of photosensitizer for breast tissues samples soaked with Hematoporphyrin was not observed.

Epigenetic changes in cancer by Raman imaging, fluorescence imaging, AFM and scanning near-field optical microscopy (SNOM). Acetylation in normal and human cancer breast cells MCF10A, MCF7 and MDA-MB-231

This paper examines epigenetic changes in breast cancer by Raman imaging, fluorescence imaging, AFM and SNOM and discusses how they contribute to different aspects of tumourigenesis in malignant human breast epithelial cell lines MCF7 and MDA-MB-231 compared with non-malignant MCF10A cell lines. The paper focuses on information that can be extracted from Raman microscopy and Raman imaging for the biological material of nucleoli contained within the cell nucleus and lipid droplets within the cell cytoplasm. The biochemical composition of the nuclei and lipid droplets in the non-malignant and malignant human breast epithelial cell lines has been monitored. The potential of Raman microspectroscopy to monitor acetylation processes and a prognostic value of Raman biomarkers in breast cancer have been discussed.

Label-Free Raman Imaging to Monitor Breast Tumor Signatures

Although not yet ready for clinical application, methods based on Raman spectroscopy have shown significant potential in identifying, characterizing, and discriminating between noncancerous and cancerous specimens. Real-time and accurate medical diagnosis achievable through this vibrational optical method largely benefits from improvements in current technological and software capabilities. Not only is the acquisition of spectral information now possible in milliseconds and analysis of hundreds of thousands of data points achieved in minutes, but Raman spectroscopy also allows simultaneous detection and monitoring of several biological components. Besides demonstrating a significant Raman signature distinction between nontumorigenic (MCF-10A) and tumorigenic (MCF-7) breast epithelial cells, our study demonstrates that Raman can be used as a label-free method to evaluate epidermal growth factor activity in tumor cells. Comparative Raman profiles and images of specimens in the presence or absence of epidermal growth factor show important differences in regions attributed to lipid, protein, and nucleic acid vibrations. The occurrence, which is dependent on the presence of epidermal growth factor, of new Raman features associated with the appearance of phosphothreonine and phosphoserine residues reflects a signal transduction from the membrane to the nucleus, with concomitant modification of DNA/RNA structural characteristics. Parallel Western blotting analysis reveals an epidermal growth factor induction of phosphorylated Akt protein, corroborating the Raman results. The analysis presented in this work is an important step toward Raman-based evaluation of biological activity of epidermal growth factor receptors on the surfaces of breast cancer cells. With the ultimate future goal of clinically implementing Raman-guided techniques for the diagnosis of breast tumors (e.g., with regard to specific receptor activity), the current results just lay the foundation for further label-free optical tools to diagnose the disease.

Oncologic photodynamic diagnosis and therapy: confocal Raman/fluorescence imaging of metal phthalocyanines in human breast cancer tissue in vitro

Raman microspectroscopy and confocal Raman imaging combined with confocal fluorescence were used to study the distribution and aggregation of aluminum tetrasulfonated phthalocyanine (AlPcS4) in noncancerous and cancerous breast tissues. The results demonstrate the ability of Raman spectroscopy to distinguish between noncancerous and cancerous human breast tissue and to identify differences in the distribution and aggregation of aluminum phthalocyanine, which is a potential photosensitizer in photodynamic therapy (PDT), photodynamic diagnosis (PDD) and photoimmunotherapy (PIT) of cancer. We have observed that the distribution of aluminum tetrasulfonated phthalocyanine confined in cancerous tissue is markedly different from that in noncancerous tissue. We have concluded that Raman imaging can be treated as a new and powerful technique useful in cancer photodynamic therapy, increasing our understanding of the mechanisms and efficiency of photosensitizers by better monitoring localization in cancer cells as well as the clinical assessment of the therapeutic effects of PDT and PIT.

Label-free determination of lipid composition and secondary protein structure of human salivary noncancerous and cancerous tissues by Raman microspectroscopy

The applications of optical spectroscopic methods in cancer detection open new possibilities in oncological diagnostics.

Developing an Instrument-Independent Algorithm for Raman Spectroscopy: A Case of Cancer Detection

One of the problems in the use of Raman spectroscopy for cancer detection in clinical application is the variety of Raman instruments, producing different spectra for the same sample, due to the nature of the measurement system. This prevents the measured spectra from different systems to be compared against one another without appropriate tools and techniques. Therefore, for each instrument one needs to spend considerable amount of time to prepare a set of reference data based on which the future measurements to be interpreted. For early diagnosis of cancer by Raman spectroscopy, there is a need for an algorithm by which such diagnosis can be made by any type of Raman instrument giving rise to the same findings. In the present study we have investigated the detection of breast cancer in three classes of breast samples (normal, benign and cancer) using three different Raman instruments (Almega, Bruker and R3000) to develop an algorithm that, irrespective of the type of Raman instrument, can be applied to the spectra to extract the features necessary to arrive at the same diagnosis. In doing so, we employed different pre-processing methods to eliminate the instrument-dependent effects on the spectra enabling us to fuse such spectra obtained from different instruments. Then, we classified the data using support vector machine (SVM) and multi-layer perception (MLP) to assess the degree to which the employed methods have been able to detect cancer. The results of the study showed that the range and resolution matching using spline interpolation, and noise and fluorescence elimination using wavelet and SNV normalizations were the most sensitive and accurate procedures for eliminating the instrumental specification-based effects and fusing the data from different instruments.

Raman imaging at biological interfaces: applications in breast cancer diagnosis

BACKGROUND

One of the most important areas of Raman medical diagnostics is identification and characterization of cancerous and noncancerous tissues. The methods based on Raman scattering has shown significant potential for probing human breast tissue to provide valuable information for early diagnosis of breast cancer. A vibrational fingerprint from the biological tissue provides information which can be used to identify, characterize and discriminate structures in breast tissue, both in the normal and cancerous environment.

RESULTS

The paper reviews recent progress in understanding structure and interactions at biological interfaces of the human tissue by using confocal Raman imaging and IR spectroscopy. The important differences between the noncancerous and cancerous human breast tissues were found in regions characteristic for vibrations of carotenoids, fatty acids, proteins, and interfacial water. Particular attention was paid to the role played by unsaturated fatty acids and their derivatives as well as carotenoids and interfacial water.

CONCLUSIONS

We demonstrate that Raman imaging has reached a clinically relevant level in regard to breast cancer diagnosis applications. The results presented in the paper may have serious implications on understanding mechanisms of interactions in living cells under realistically crowded conditions of biological tissue.

Raman spectroscopy and imaging: applications in human breast cancer diagnosis

The applications of spectroscopic methods in cancer detection open new possibilities in early stage diagnostics. Raman spectroscopy and Raman imaging represent novel and rapidly developing tools in cancer diagnosis. In the study described in this paper Raman spectroscopy has been employed to examine noncancerous and cancerous human breast tissues of the same patient. The most significant differences between noncancerous and cancerous tissues were found in regions characteristic for the vibrations of carotenoids, lipids and proteins. Particular attention was paid to the role played by unsaturated fatty acids in the differentiation between the noncancerous and the cancerous tissues. Comparison of Raman spectra of the noncancerous and the cancerous tissues with the spectra of oleic, linoleic, α-linolenic, γ-linolenic, docosahexaenoic and eicosapentaenoic acids has been presented. The role of sample preparation in the determination of cancer markers is also discussed in this study.

Raman 'optical biopsy' of human breast cancer

Raman imaging (RI) is a novel method of medical diagnostics of human breast cancer and has a potential to become a routine optical biopsy. Up to date the present study is the most statistically reliable Raman analysis based on data of normal, benign, and cancerous breast tissues for 146 patients. This paper present the first Raman 'optical biopsy' images of the normal and cancerous breast tissue of the same patient. The results presented here demonstrate the ability of Raman spectroscopy to accurately characterize cancer tissue and distinguish between normal (noncancerous), and cancerous types. The results provide evidence that carotenoids and lipids composition of cancerous breast tissues differs significantly from that of the surrounding noncancerous breast tissue and may be a key factor responsible for mechanisms of carcinogenesis. We have found that fatty acid composition of the cancerous breast tissue is markedly different from that of the surrounding noncancerous breast tissue. The cancerous breast tissue seems to be dominated by the metabolism products of the arachidonic acid - derived cyclic eicosanoids catalyzed by cyclooxygenase, while the noncancerous breast tissue is dominated by monounsaturated oleic acid and its derivatives.

Raman Spectroscopy for Clinical Oncology

Cancer is one of the leading causes of death throughout the world. Advancements in early and improved diagnosis could help prevent a significant number of these deaths. Raman spectroscopy is a vibrational spectroscopic technique which has received considerable attention recently with regards to applications in clinical oncology. Raman spectroscopy has the potential not only to improve diagnosis of cancer but also to advance the treatment of cancer. A number of studies have investigated Raman spectroscopy for its potential to improve diagnosis and treatment of a wide variety of cancers. In this paper the most recent advances in dispersive Raman spectroscopy, which have demonstrated promising leads to real world application for clinical oncology are reviewed. The application of Raman spectroscopy to breast, brain, skin, cervical, gastrointestinal, oral, and lung cancers is reviewed as well as a special focus on the data analysis techniques, which have been employed in the studies.

Phase transitions in oleic acid and in human breast tissue as studied by Raman spectroscopy and Raman imaging

We present the results of differential scanning calorimetry (DSC) and Raman studies in the temperature range of 293-77 K on vibrational properties of the oleic acid and the human breast tissue as a function of temperature. We have found that vibrational properties are very sensitive indicators to specify phases and phase transitions at the molecular level. We have found that water content confined in the cancerous tissue is markedly different from that in the noncancerous tissue. The OH stretching vibrations of water are useful as potential Raman biomarkers to distinguish between the cancerous and the noncancerous human breast tissues. Our results provide experimental evidence on the role of lipid profile and cell hydration as factors of particular significance in differentiation of the noncancerous and cancerous breast tissues.

Discrimination of parotid neoplasms from the normal parotid gland by use of Raman spectroscopy and support vector machine

Preoperative diagnosis of neoplasms in the parotid gland is essential for successful surgical treatment. The purpose of this study is to apply Raman spectroscopy in order to distinguish the spectral differences between pleomorphic adenoma and Warthin tumor from that of normal parotid gland tissues. Furthermore we establish the diagnostic model of the Raman spectra of neoplasms in parotid gland by employing support vector machine (SVM) with Gaussian radial basis function. Firstly, Raman spectra from different histopathological tissues were obtained by near-infrared Raman microscope, SVM was then employed to analyze the different spectra and establish a discriminating model. As a result, the differences of peaks in the region 800-1800 cm(-1) demonstrated the biochemical molecular alterations between different histopathological tissues. Compared with normal parotid gland tissues, the content of proteins, lipids and DNA increased in pleomorphic adenoma. The content of DNA increased but proteins and lipids decreased in Warthin tumor. SVM displayed a powerful role in the classification of three different groups. The sensitivities and specificities of discrimination between different groups reached above 95% and 99%, respectively. Raman spectroscopy combined SVM algorithm could have great potential for providing a noninvasive, effective and accurate diagnostic technology for neoplasm diagnosis in the parotid gland.

Detection of minor salivary glands affected in Sjogren's syndrome by Raman spectroscopy

Sjogren's syndrome(SS) is defined as a slowly processing autoimmune inflammatory exocrinopathy affecting the salivary and lachrymal glands. Due to the difficulty in reaching a diagnosis in some SS cases, there are many criteria published for classification and diagnosis. Among the most of criteria, lip biopsy plays an important role in clinical diagnosis, but it is an invasive and complex method which always causes patients' pain and discomfort. Raman spectroscopy is a noninvasive and real-time vibrational spectroscopic technique applied in the clinical detection and diagnosis of diseases. The diagnostic sensitivity and specificity are both high. The hypothesis is to detect the minor salivary glands affected in SS by the noninvasive Raman spectroscopy instead of lip biopsy. Therefore, Raman spectroscopy may increase the diagnostic sensitivity and specificity, meanwhile prevent patients from pain caused by biopsy.

BRMS1 expression alters the ultrastructural, biomechanical and biochemical properties of MDA-MB-435 human breast carcinoma cells: an AFM and Raman microspectroscopy study

Restoring BReast cancer Metastasis Suppressor 1 (BRMS1) expression suppresses metastasis in MDA-MB-435 human breast carcinoma cells at ectopic sites without affecting tumor formation at orthotopic site in the body. BRMS1 expression induces many phenotypic alterations in 435 cells such as cell adhesion, cytoskeleton rearrangement, and the down regulation of epidermal growth factor receptor (EGFR) expression. In order to better understand the role of cellular biomechanics in breast cancer metastasis, the qualitative and quantitative detection of cellular biomechanics and biochemical composition is urgently needed. In the present work, using atomic force microscopy (AFM) and fluorescent microscopy we revealed that BRMS1 expression in 435 cells induced reorganization of F-actin and caused alteration in cytoarchitectures (cell topography and ultrastructure). Results from AFM observed increase in biomechanical properties which include cell adhesion, cellular spring constant, and Young's modulus in 435/BRMS1 cells. Raman microspectroscopy showed weaker vibrational spectroscopic bands in 435/BRMS1 cells, implying decrease in concentration of cellular biochemical components in these cells. This was despite the similar spectral patterns observed between 435 and 435/BRMS1 cells. This work demonstrated the feasibility of applying AFM and Raman techniques for in situ measurements of the cellular biomechanics and biochemical components of breast carcinoma cells. It provides vital clues in understanding of the role of cellular biomechanics in cancer metastasis, and further the development of new techniques for early diagnosis of breast cancer.