Emerging technology: applications of Raman spectroscopy for prostate cancer

Fano-type discrete-continuum interaction in perovskites and its manifestation in Raman spectral line shapes

Fano resonance is one of the most significant physical phenomena that correlates microscopic processes with macroscopic manifestations for experimental observations using different spectroscopic techniques. Owing to its importance, a focused study is required to clearly understand the origin of certain modifications in spectral behaviour, the nature of which is different for different materials. This means that a careful understanding of Fano interactions can enhance the understanding of several technologically important materials, including perovskites, which are also important in the area of energy storage and conversion. In semiconductors and nano materials (including 2-D materials), Fano interactions occur due to the intervalence or interconduction band transitions. However, in perovskites, Fano interactions are dominated by the interaction between polar phonons or excitons with electronic continuum. Raman spectroscopy, being a sensitive and non-destructive tool, detects subtle scale phenomena, such as Fano interactions, by analysing the Raman line shape. Herein, different dimensions associated with the identification and thereafter the origin of the Fano resonance in perovskites, which are used in energy related areas, have been highlighted using Raman scattering.

Non-invasive biomarkers for mild cognitive impairment and Alzheimer's disease

Alzheimer's disease is the most common type of dementia in the elderly. It is a progressive degenerative disorder that may begin to develop up to 15 years before clinical symptoms appear. The identification of early biomarkers is crucial to enable a prompt diagnosis and to start effective interventions. In this work, we conducted a metabolomic study using proton Nuclear Magnetic Resonance (1H NMR) spectroscopy in serum samples from patients with neuropathologically confirmed Alzheimer's disease (AD, n = 51), mild cognitive impairment (MCI, n = 27), and cognitively healthy controls (HC, n = 50) to search for metabolites that could be used as biomarkers. Patients and controls underwent yearly clinical follow-ups for up to six years. MCI group included samples from three subgroups of subjects with different disease progression rates. The first subgroup included subjects that remained clinically stable at the MCI stage during the period of study (stable MCI, S-MCI, n = 9). The second subgroup accounted for subjects which were diagnosed with MCI at the moment of blood extraction, but progressed to clinical dementia in subsequent years (MCI-to-dementia, MCI-D, n = 14). The last subgroup was composed of subjects that had been diagnosed as dementia for the first time at the moment of sample collection (incipient dementia, Incp-D, n = 4). Partial Least Square Discriminant Analysis (PLS-DA) models were developed. Three models were obtained, one to discriminate between AD and HC samples with high sensitivity (93.75%) and specificity (94.75%), another model to discriminate between AD and MCI samples (100% sensitivity and 82.35% specificity), and a last model to discriminate HC and MCI with lower sensitivity and specificity (67% and 50%). Differences within the MCI group were further studied in an attempt to determine those MCI subjects that could develop AD-type dementia in the future. The relative concentration of metabolites, and metabolic pathways were studied. Alterations in the pathways of alanine, aspartate and glutamate metabolism, pantothenate and CoA biosynthesis, and beta-alanine metabolism, were found when HC and MCI- D patients were compared. In contrast, no pathway was found disturbed in the comparison of S-MCI with HC groups. These results highlight the potential of 1H NMR metabolomics to support the diagnosis of dementia in a less invasive way, and set a starting point for the study of potential biomarkers to identify MCI or HC subjects at risk of developing AD in the future.

Molecular Fingerprint Detection Using Raman and Infrared Spectroscopy Technologies for Cancer Detection: A Progress Review

Molecular vibrations play a crucial role in physical chemistry and biochemistry, and Raman and infrared spectroscopy are the two most used techniques for vibrational spectroscopy. These techniques provide unique fingerprints of the molecules in a sample, which can be used to identify the chemical bonds, functional groups, and structures of the molecules. In this review article, recent research and development activities for molecular fingerprint detection using Raman and infrared spectroscopy are discussed, with a focus on identifying specific biomolecules and studying the chemical composition of biological samples for cancer diagnosis applications. The working principle and instrumentation of each technique are also discussed for a better understanding of the analytical versatility of vibrational spectroscopy. Raman spectroscopy is an invaluable tool for studying molecules and their interactions, and its use is likely to continue to grow in the future. Research has demonstrated that Raman spectroscopy is capable of accurately diagnosing various types of cancer, making it a valuable alternative to traditional diagnostic methods such as endoscopy. Infrared spectroscopy can provide complementary information to Raman spectroscopy and detect a wide range of biomolecules at low concentrations, even in complex biological samples. The article concludes with a comparison of the techniques and insights into future directions.

Multimodal convolutional neural networks based on the Raman spectra of serum and clinical features for the early diagnosis of prostate cancer

We collected surface-enhanced Raman spectroscopy (SERS) data from the serum of 729 patients with prostate cancer or benign prostatic hyperplasia (BPH), corresponding to their pathological results, and built an artificial intelligence-assisted diagnosis model based on a convolutional neural network (CNN). We then evaluated its value in diagnosing prostate cancer and predicting the Gleason score (GS) using a simple cross-validation method. Our CNN model based on the spectral data for prostate cancer diagnosis revealed an accuracy of 85.14 ± 0.39%. After adjusting the model with patient age and prostate specific antigen (PSA), the accuracy of the multimodal CNN was up to 88.55 ± 0.66%. Our multimodal CNN for distinguishing low-GS/high-GS and GS = 3 + 3/GS = 3 + 4 revealed accuracies of 68 ± 0.58% and 77 ± 0.52%, respectively.

Evaluation of the Cytotoxic Effect of Pd2Spm against Prostate Cancer through Vibrational Microspectroscopies

Regarding the development of new antineoplastic agents, with a view to assess the selective antitumoral potential which aims at causing irreversible damage to cancer cells while preserving the integrity of their healthy counterparts, it is essential to evaluate the cytotoxic effects in both healthy and malignant human cell lines. In this study, a complex with two Pd(II) centers linked by the biogenic polyamine spermine (Pd₂Spm) was tested on healthy (PNT-2) and cancer (LNCaP and PC-3) prostate human cell lines, using cisplatin as a reference. To understand the mechanisms of action of both cisplatin and Pd₂Spm at a molecular level, Fourier Transform Infrared (FTIR) and Raman microspectroscopies were used. Principal component analysis was applied to the vibrational data, revealing the major metabolic changes caused by each drug, which were found to rely on DNA, lipids, and proteins, acting as biomarkers of drug impact. The main changes were observed between the B-DNA native conformation and either Z-DNA or A-DNA, with a higher effect on lipids having been detected in the presence of cisplatin as compared to Pd₂Spm. In turn, the Pd-agent showed a more significant impact on proteins.

Raman spectroscopy: A prospective intraoperative visualization technique for gliomas

The infiltrative growth and malignant biological behavior of glioma make it one of the most challenging malignant tumors in the brain, and how to maximize the extent of resection (EOR) while minimizing the impact on normal brain tissue is the pursuit of neurosurgeons. The current intraoperative visualization assistance techniques applied in clinical practice suffer from low specificity, slow detection speed and low accuracy, while Raman spectroscopy (RS) is a novel spectroscopy technique gradually developed and applied to clinical practice in recent years, which has the advantages of being non-destructive, rapid and accurate at the same time, allowing excellent intraoperative identification of gliomas. In the present work, the latest research on Raman spectroscopy in glioma is summarized to explore the prospect of Raman spectroscopy in glioma surgery.

Discrimination of leukemias and non-leukemic cancers in blood serum samples of children and adolescents using a Raman spectral model

This study aimed to identify the differences presented in the Raman spectrum of blood serum from normal subjects compared to leukemic and non-leukemic subjects and the differences between the leukemics and non-leukemics, correlating the spectral differences with the biomolecules. Serum samples from children and adolescents were subjected to Raman spectroscopy (830 nm, laser power 350 mW; n = 566 spectra, being 72 controls, 269 leukemics, and 225 non-leukemics). Exploratory analysis based on principal component analysis (PCA) of the serum sample's spectra was performed. Classification models based on partial least squares discriminant analysis (PLS-DA) were developed to classify the spectra into normal, leukemic, and non-leukemic, as well as to discriminate spectra of leukemic from non-leukemic. The exploratory analysis showed principal components with peaks related to amino acids, proteins, lipids, and carotenoids. The spectral differences between normal, leukemic, and non-leukemic showed features assigned to proteins (serum features), amino acids, and carotenoids. The PLS-DA model classified the spectra of the normal group versus leukemic and non-leukemic groups with accuracy of 66%, sensitivity of 99%, and specificity of 57%. The PLS-DA discriminated the spectra of the leukemic and non-leukemic groups with accuracy of 67%, sensitivity of 72%, and specificity of 60%. The study showed that Raman spectroscopy is a technique that may be used for the biochemical differentiation of leukemias and other types of cancer in serum samples of children and adolescents. Nevertheless, building an extensive data library of Raman spectra from serum samples of controls, leukemics, and non-leukemics of different age groups is necessary to understand the findings better.

Deep autoencoder based hybrid dimensionality reduction approach for classification of SERS for melanoma cancer diagnostics

Melanoma, a kind of fatal skin cancer, originates in melanin secreting cells of the dermis. Disease identification in the early stages assures a high survival rate for the patient. Most of the existing techniques retard the cancer detection phase. Surface-Enhanced Raman Spectroscopy (SERS) can capture fine details from the specimens that machine learning models can utilize to discriminate between healthy and diseased individuals rapidly. Our research work proposes a deep autoencoder based hybrid dimensionality reduction approach with a machine learning model on SERS spectrums of human skin fibroblast for melanoma cancer diagnostics. SERS measurements of 307 samples in total, belonging to two different classes, such as normal (157 samples) and malignant melanoma (150 samples), are used in this study. The SERS spectra measurements for both the samples lie between 100cm - 1 and 4278cm - 1. The variations in the intensity of Raman bands between both classes are intrinsically subtle. Neighborhood Component Analysis (NCA) technique has been exerted to transform 2090 dimensional spectral features into 2090 dimensional vectors and then the Deep Autoencoder (DAE) model is used to handle the nonlinearity in the data and produce the latent space, while Linear Discriminant Analysis (LDA) classifier have been employed for discriminating the normal and cancer cells. The k-fold cross-validation technique with a k value of 10 is implemented to assess the metrics of the model. The stated hybrid (NCA and DAE) model with 10-dimension latent space achieves an accuracy of 98%, the sensitivity of 99% and specificity of 97%, respectively. Due to the high-intensity nature of the SERS spectrum, the existing linear dimensionality reduction based discriminating model fails if the class label (Normal or Cancer) gets distributed on the low variance side. The proposed methodology captures both linear and nonlinear underlying structures present in the spectrums, resulting in better classification compared to the standard dimensionality reduction techniques.

Label-free discrimination of tumorigenesis stages using in vitro prostate cancer bone metastasis model by Raman imaging

Metastatic prostate cancer colonizes the bone to pave the way for bone metastasis, leading to skeletal complications associated with poor prognosis and morbidity. This study demonstrates the feasibility of Raman imaging to differentiate between cancer cells at different stages of tumorigenesis using a nanoclay-based three-dimensional (3D) bone mimetic in vitro model that mimics prostate cancer bone metastasis. A comprehensive study comparing the classification of as received prostate cancer cells in a two-dimensional (2D) model and cancer cells in a 3D bone mimetic environment was performed over various time intervals using principal component analysis (PCA). Our results showed distinctive spectral differences in Raman imaging between prostate cancer cells and the cells cultured in 3D bone mimetic scaffolds, particularly at 1002, 1261, 1444, and 1654 cm-1, which primarily contain proteins and lipids signals. Raman maps capture sub-cellular responses with the progression of tumor cells into metastasis. Raman feature extraction via cluster analysis allows for the identification of specific cellular constituents in the images. For the first time, this work demonstrates a promising potential of Raman imaging, PCA, and cluster analysis to discriminate between cancer cells at different stages of metastatic tumorigenesis.

Raman Spectroscopy: A Personalized Decision-Making Tool on Clinicians' Hands for In Situ Cancer Diagnosis and Surgery Guidance

Simple Summary

Cancer still constitutes one of the main global health challenges. Novel approaches towards understanding the molecular composition of the disease can be employed as adjuvant tools to current oncological applications. Raman spectroscopy has been contemplated and pursued to serve as a noninvasive, real time, in vivo tool which may uncover the molecular basis of cancer and simultaneously offer high specificity, sensitivity, and multiplexing capacity, as well as high spatial and temporal resolution. In this review, the potential impact of Spontaneous Raman spectroscopy in clinical applications related to cancer diagnosis and surgical removal is analyzed. Moreover, the coupling of Raman systems with modern instrumentation and machine learning methods has been explored as a prominent enhancement factor towards a personalized approach promoting objectivity and accuracy in surgical oncology.

Abstract

Accurate in situ diagnosis and optimal surgical removal of a malignancy constitute key elements in reducing cancer-related morbidity and mortality. In surgical oncology, the accurate discrimination between healthy and cancerous tissues is critical for the postoperative care of the patient. Conventional imaging techniques have attempted to serve as adjuvant tools for in situ biopsy and surgery guidance. However, no single imaging modality has been proven sufficient in terms of specificity, sensitivity, multiplexing capacity, spatial and temporal resolution. Moreover, most techniques are unable to provide information regarding the molecular tissue composition. In this review, we highlight the potential of Raman spectroscopy as a spectroscopic technique with high detection sensitivity and spatial resolution for distinguishing healthy from malignant margins in microscopic scale and in real time. A Raman spectrum constitutes an intrinsic “molecular finger-print” of the tissue and any biochemical alteration related to inflammatory or cancerous tissue state is reflected on its Raman spectral fingerprint. Nowadays, advanced Raman systems coupled with modern instrumentation devices and machine learning methods are entering the clinical arena as adjunct tools towards personalized and optimized efficacy in surgical oncology.

Recent advances in nanomaterials for prostate cancer detection and diagnosis

Despite the significant progress in the discovery of biomarkers and the exploitation of technologies for prostate cancer (PCa) detection and diagnosis, the initial screening of these PCa-related biomarkers using current...

Surface-enhanced Raman scattering (SERS) spectroscopy for prostate cancer diagnosis: A review

The present review focuses on the diagnosis of prostate cancer using surface enhanced Raman scattering (SERS) spectroscopy. On the basis of literature search, SERS-based analysis for prostate cancer detection of different sample types is reported in the present study. Prostate cancer is responsible for nearly one-tenth of all cell cancer deaths among men. Significant efforts have been dedicated to establish precise and sensitive monitoring techniques to detect prostate cancer biomarkers in different types of body samples. Among the various spectro-analytical techniques investigated to achieve this objective, SERS spectroscopy has been proven as a promising approach that provides noticeable enhancements of the Raman sensitivity when the target biomolecules interact with a nanostructured surface. The purpose of this review is to give a brief overview of the SERS-basedapproach and other spectro-analytical strategies being used for the detection and quantification of prostate cancer biomarkers. The revolutionary development of SERS methods for the diagnosis of prostate cancer has been discussed in more details based on the reported literature. It has been noticed that the SERS-based immunoassay presents reliable results for the prostate cancer quantification. The EC-SERS, which integrates electrochemistry with the SERS model, could also offer a potential ultrasensitive strategy, although its application in prostate cancer analysis has been still limited.

Dimensional reduction based on peak fitting of Raman micro spectroscopy data improves detection of prostate cancer in tissue specimens

SIGNIFICANCE

Prostate cancer is the most common cancer among men. An accurate diagnosis of its severity at detection plays a major role in improving their survival. Recently, machine learning models using biomarkers identified from Raman micro-spectroscopy discriminated intraductal carcinoma of the prostate (IDC-P) from cancer tissue with a ≥85  %   detection accuracy and differentiated high-grade prostatic intraepithelial neoplasia (HGPIN) from IDC-P with a ≥97.8  %   accuracy.

AIM

To improve the classification performance of machine learning models identifying different types of prostate cancer tissue using a new dimensional reduction technique.

APPROACH

A radial basis function (RBF) kernel support vector machine (SVM) model was trained on Raman spectra of prostate tissue from a 272-patient cohort (Centre hospitalier de l'Université de Montréal, CHUM) and tested on two independent cohorts of 76 patients [University Health Network (UHN)] and 135 patients (Centre hospitalier universitaire de Québec-Université Laval, CHUQc-UL). Two types of engineered features were used. Individual intensity features, i.e., Raman signal intensity measured at particular wavelengths and novel Raman spectra fitted peak features consisting of peak heights and widths.

RESULTS

Combining engineered features improved classification performance for the three aforementioned classification tasks. The improvements for IDC-P/cancer classification for the UHN and CHUQc-UL testing sets in accuracy, sensitivity, specificity, and area under the curve (AUC) are (numbers in parenthesis are associated with the CHUQc-UL testing set): +4  %   (+8  %  ), +7  %   (+9  %  ), +2  %   (6%), +9 (+9) with respect to the current best models. Discrimination between HGPIN and IDC-P was also improved in both testing cohorts: +2.2  %   (+1.7  %  ), +4.5  %   (+3.6  %  ), +0  %   (+0  %  ), +2.3 (+0). While no global improvements were obtained for the normal versus cancer classification task [+0  %   (-2  %  ), +0  %   (-3  %  ), +2  %   (-2  %  ), +4 (+3)], the AUC was improved in both testing sets.

CONCLUSIONS

Combining individual intensity features and novel Raman fitted peak features, improved the classification performance on two independent and multicenter testing sets in comparison to using only individual intensity features.

Nanoparticle surface-enhanced Raman spectroscopy as a noninvasive, label-free tool to monitor hematological malignancy

Aim: Monitoring minimal residual disease remains a challenge to the effective medical management of hematological malignancies; yet surface-enhanced Raman spectroscopy (SERS) has emerged as a potential clinical tool to do so. Materials & methods: We developed a cell-free, label-free SERS approach using gold nanoparticles (nanoSERS) to classify hematological malignancies referenced against two control cohorts: healthy and noncancer cardiovascular disease. A predictive model was built using machine-learning algorithms to incorporate disease burden scores for patients under standard treatment upon. Results: Linear- and quadratic-discriminant analysis distinguished three cohorts with 69.8 and 71.4% accuracies, respectively. A predictive nanoSERS model correlated (MSE = 1.6) with established clinical parameters. Conclusion: This study offers a proof-of-concept for the noninvasive monitoring of disease progression, highlighting the potential to incorporate nanoSERS into translational medicine.

Feature fusion of Raman chemical imaging and digital histopathology using machine learning for prostate cancer detection

The diagnosis of prostate cancer is challenging due to the heterogeneity of its presentations, leading to the over diagnosis and treatment of non-clinically important disease. Accurate diagnosis can directly benefit a patient's quality of life and prognosis. Towards addressing this issue, we present a learning model for the automatic identification of prostate cancer. While many prostate cancer studies have adopted Raman spectroscopy approaches, none have utilised the combination of Raman Chemical Imaging (RCI) and other imaging modalities. This study uses multimodal images formed from stained Digital Histopathology (DP) and unstained RCI. The approach was developed and tested on a set of 178 clinical samples from 32 patients, containing a range of non-cancerous, Gleason grade 3 (G3) and grade 4 (G4) tissue microarray samples. For each histological sample, there is a pathologist labelled DP-RCI image pair. The hypothesis tested was whether multimodal image models can outperform single modality baseline models in terms of diagnostic accuracy. Binary non-cancer/cancer models and the more challenging G3/G4 differentiation were investigated. Regarding G3/G4 classification, the multimodal approach achieved a sensitivity of 73.8% and specificity of 88.1% while the baseline DP model showed a sensitivity and specificity of 54.1% and 84.7% respectively. The multimodal approach demonstrated a statistically significant 12.7% AUC advantage over the baseline with a value of 85.8% compared to 73.1%, also outperforming models based solely on RCI and mean and median Raman spectra. Feature fusion of DP and RCI does not improve the more trivial task of tumour identification but does deliver an observed advantage in G3/G4 discrimination. Building on these promising findings, future work could include the acquisition of larger datasets for enhanced model generalization.

Using the Method of "Optical Biopsy" of Prostatic Tissue to Diagnose Prostate Cancer

Simple Summary

Analytical discrimination models of Raman spectra of prostate cancer tissue were constructed by using the projections onto latent structures data analysis (PLS-DA) method for different wavelengths of exciting radiation—532 and 785 nm. These models allowed us to divide the Raman spectra of prostate cancer and the spectra of hyperplasia sites for validation datasets with the accuracy of 70–80%, depending on the specificity value. Meanwhile, for the calibration datasets, the accuracy values reached 100% for the excitation of a laser with a wavelength of 785 nm. Due to the registration of Raman “fingerprints”, the main features of cellular metabolism occurring in the tissue of a malignant prostate tumor were confirmed, namely the absence of aerobic glycolysis, over-expression of markers, and a strong increase in the concentration of cholesterol and its esters, as well as fatty acids and glutamic acid.

Abstract

The possibilities of using optical spectroscopy methods in the differential diagnosis of prostate cancer were investigated. Analytical discrimination models of Raman spectra of prostate tissue were constructed by using the projections onto latent structures data analysis(PLS-DA) method for different wavelengths of exciting radiation—532 and 785 nm. These models allowed us to divide the Raman spectra of prostate cancer and the spectra of hyperplasia sites for validation datasets with the accuracy of 70–80%, depending on the specificity value. Meanwhile, for the calibration datasets, the accuracy values reached 100% for the excitation of a laser with a wavelength of 785 nm. Due to the registration of Raman “fingerprints”, the main features of cellular metabolism occurring in the tissue of a malignant prostate tumor were confirmed, namely the absence of aerobic glycolysis, over-expression of markers (FASN, SREBP1, stearoyl-CoA desaturase, etc.), and a strong increase in the concentration of cholesterol and its esters, as well as fatty acids and glutamic acid. The presence of an ensemble of Raman peaks with increased intensity, inherent in fatty acid, beta-glucose, glutamic acid, and cholesterol, is a fundamental factor for the identification of prostate cancer.

Raman spectroscopy and artificial intelligence to predict the Bayesian probability of breast cancer

This study addresses the core issue facing a surgical team during breast cancer surgery: quantitative prediction of tumor likelihood including estimates of prediction error. We have previously reported that a molecular probe, Laser Raman spectroscopy (LRS), can distinguish healthy and tumor tissue. We now report that combining LRS with two machine learning algorithms, unsupervised k-means and stochastic nonlinear neural networks (NN), provides rapid, quantitative, probabilistic tumor assessment with real-time error analysis. NNs were first trained on Raman spectra using human expert histopathology diagnostics as gold standard (74 spectra, 5 patients). K-means predictions using spectral data when compared to histopathology produced clustering models with 93.2-94.6% accuracy, 89.8-91.8% sensitivity, and 100% specificity. NNs trained on k-means predictions generated probabilities of correctness for the autonomous classification. Finally, the autonomous system characterized an extended dataset (203 spectra, 8 patients). Our results show that an increase in DNA|RNA signal intensity in the fingerprint region (600-1800 cm-1) and global loss of high wavenumber signal (2800-3200 cm-1) are particularly sensitive LRS warning signs of tumor. The stochastic nature of NNs made it possible to rapidly generate multiple models of target tissue classification and calculate the inherent error in the probabilistic estimates for each target.

Nonlinear multispectral imaging for tumor delineation

SIGNIFICANCE

In breast-preserving tumor surgery, the inspection of the excised tissue boundaries for tumor residue is too slow to provide feedback during the surgery. The discovery of positive margins requires a new surgery which is difficult and associated with low success. If the re-excision could be done immediately this is believed to improve the success rate considerably.

AIM

Our aim is for a fast microscopic analysis that can be done directly on the excised tissue in or near the operating theatre.

APPROACH

We demonstrate the combination of three nonlinear imaging techniques at selected wavelengths to delineate tumor boundaries. We use hyperspectral coherent anti-Stokes Raman scattering (CARS), second harmonic generation (SHG), and two-photon excited fluorescence (TPF) on excised patient tissue.

RESULTS

We show the discriminatory power of each of the signals and demonstrate a sensitivity of 0.87 and a specificity of 0.95 using four CARS wavelengths in combination with SHG and TPF. We verify that the information is independent of sample treatment.

CONCLUSIONS

Nonlinear multispectral imaging can be used to accurately determine tumor boundaries. This demonstration using microscopy in the epi-direction directly on thick tissue slices brings this technology one step closer to clinical implementation.

Identification of intraductal carcinoma of the prostate on tissue specimens using Raman micro-spectroscopy: A diagnostic accuracy case-control study with multicohort validation

BACKGROUND

Prostate cancer (PC) is the most frequently diagnosed cancer in North American men. Pathologists are in critical need of accurate biomarkers to characterize PC, particularly to confirm the presence of intraductal carcinoma of the prostate (IDC-P), an aggressive histopathological variant for which therapeutic options are now available. Our aim was to identify IDC-P with Raman micro-spectroscopy (RμS) and machine learning technology following a protocol suitable for routine clinical histopathology laboratories.

METHODS AND FINDINGS

We used RμS to differentiate IDC-P from PC, as well as PC and IDC-P from benign tissue on formalin-fixed paraffin-embedded first-line radical prostatectomy specimens (embedded in tissue microarrays [TMAs]) from 483 patients treated in 3 Canadian institutions between 1993 and 2013. The main measures were the presence or absence of IDC-P and of PC, regardless of the clinical outcomes. The median age at radical prostatectomy was 62 years. Most of the specimens from the first cohort (Centre hospitalier de l'Université de Montréal) were of Gleason score 3 + 3 = 6 (51%) while most of the specimens from the 2 other cohorts (University Health Network and Centre hospitalier universitaire de Québec-Université Laval) were of Gleason score 3 + 4 = 7 (51% and 52%, respectively). Most of the 483 patients were pT2 stage (44%-69%), and pT3a (22%-49%) was more frequent than pT3b (9%-12%). To investigate the prostate tissue of each patient, 2 consecutive sections of each TMA block were cut. The first section was transferred onto a glass slide to perform immunohistochemistry with H&E counterstaining for cell identification. The second section was placed on an aluminum slide, dewaxed, and then used to acquire an average of 7 Raman spectra per specimen (between 4 and 24 Raman spectra, 4 acquisitions/TMA core). Raman spectra of each cell type were then analyzed to retrieve tissue-specific molecular information and to generate classification models using machine learning technology. Models were trained and cross-validated using data from 1 institution. Accuracy, sensitivity, and specificity were 87% ± 5%, 86% ± 6%, and 89% ± 8%, respectively, to differentiate PC from benign tissue, and 95% ± 2%, 96% ± 4%, and 94% ± 2%, respectively, to differentiate IDC-P from PC. The trained models were then tested on Raman spectra from 2 independent institutions, reaching accuracies, sensitivities, and specificities of 84% and 86%, 84% and 87%, and 81% and 82%, respectively, to diagnose PC, and of 85% and 91%, 85% and 88%, and 86% and 93%, respectively, for the identification of IDC-P. IDC-P could further be differentiated from high-grade prostatic intraepithelial neoplasia (HGPIN), a pre-malignant intraductal proliferation that can be mistaken as IDC-P, with accuracies, sensitivities, and specificities > 95% in both training and testing cohorts. As we used stringent criteria to diagnose IDC-P, the main limitation of our study is the exclusion of borderline, difficult-to-classify lesions from our datasets.

CONCLUSIONS

In this study, we developed classification models for the analysis of RμS data to differentiate IDC-P, PC, and benign tissue, including HGPIN. RμS could be a next-generation histopathological technique used to reinforce the identification of high-risk PC patients and lead to more precise diagnosis of IDC-P.

Role of artificial intelligence and vibrational spectroscopy in cancer diagnostics

INTRODUCTION

Raman and Infrared spectroscopic techniques are being used for the analysis of different types of cancers and other biological molecules. It is possible to identify cancers from normal tissues both in fresh and fixed tissues. These techniques can be used not only for the early diagnosis of cancer but also for monitoring the progression of the disease. Furthermore, chemical pathways to the progression of the disease process can be understood and followed.

AREAS COVERED

More recently, Artificial Intelligence (AI), Neural Network (NN), and Machine Learning are being combined with spectroscopy, which is making it easier to understand the chemical structural details of cancers and biological molecules more precisely and accurately. In this report, these aspects are being outlined by using breast cancer as a specific example.

EXPERT OPINION

A pathway showing to combine vibrational spectroscopy with AI and ML has immense potential in predicting various stages of different disease processes, in particular, in cancer diagnosis, staging, and designing treatment. This will result in improved patient care pathways.

Bioorthogonal SERS Nanotags as a Precision Theranostic Platform for in Vivo SERS Imaging and Cancer Photothermal Therapy

Precise detection and effective treatment are crucial to prolong cancer patients' lives. Surface-enhanced Raman scattering (SERS) imaging coupled with photothermal therapy has been considered a precise and effective strategy for cancer theranostics. Nevertheless, Raman reporters employed in the literature usually possessed multiple shift peaks in the fingerprint region, which are overlapped with background signals from endogenous biological molecules. Herein, we fabricated a new kind of bioorthogonal Raman reporter and aptamer functionalized SERS nanotags. The SERS nanotags demonstrated a strong Raman signal at 2205 cm^-1 in the biologically Raman-silent region and recognized MCF-7 breast cancer cells for Raman imaging with high specificity. Laser irradiation induced serious toxicity of MCF-7 cells due to the excellent photothermal capability of the SERS nanotags. After intravenous administration of the SERS nanotags, tumor Raman spectral detection and mapping in living mice were successfully achieved. Further in vivo antitumor experiments manifested that the aptamer-modified SERS nanotags significantly restrained tumor growth after laser irradiation with 99% inhibition rate and good biocompatibility. These results clearly revealed that the SERS nanotags could serve as a novel and precise theranostic platform for in vivo cancer diagnosis and photothermal therapy.

Raman Scattering: From Structural Biology to Medical Applications

This is a review of relevant Raman spectroscopy (RS) techniques and their use in structural biology, biophysics, cells, and tissues imaging towards development of various medical diagnostic tools, drug design, and other medical applications. Classical and contemporary structural studies of different water-soluble and membrane proteins, DNA, RNA, and their interactions and behavior in different systems were analyzed in terms of applicability of RS techniques and their complementarity to other corresponding methods. We show that RS is a powerful method that links the fundamental structural biology and its medical applications in cancer, cardiovascular, neurodegenerative, atherosclerotic, and other diseases. In particular, the key roles of RS in modern technologies of structure-based drug design are the detection and imaging of membrane protein microcrystals with the help of coherent anti-Stokes Raman scattering (CARS), which would help to further the development of protein structural crystallography and would result in a number of novel high-resolution structures of membrane proteins—drug targets; and, structural studies of photoactive membrane proteins (rhodopsins, photoreceptors, etc.) for the development of new optogenetic tools. Physical background and biomedical applications of spontaneous, stimulated, resonant, and surface- and tip-enhanced RS are also discussed. All of these techniques have been extensively developed during recent several decades. A number of interesting applications of CARS, resonant, and surface-enhanced Raman spectroscopy methods are also discussed.

Quantitative Histopathology of Stained Tissues using Color Spatial Light Interference Microscopy (cSLIM)

Tissue biopsy evaluation in the clinic is in need of quantitative disease markers for diagnosis and, most importantly, prognosis. Among the new technologies, quantitative phase imaging (QPI) has demonstrated promise for histopathology because it reveals intrinsic tissue nanoarchitecture through the refractive index. However, a vast majority of past QPI investigations have relied on imaging unstained tissues, which disrupts the established specimen processing. Here we present color spatial light interference microscopy (cSLIM) as a new whole-slide imaging modality that performs interferometric imaging on stained tissue, with a color detector array. As a result, cSLIM yields in a single scan both the intrinsic tissue phase map and the standard color bright-field image, familiar to the pathologist. Our results on 196 breast cancer patients indicate that cSLIM can provide stain-independent prognostic information from the alignment of collagen fibers in the tumor microenvironment. The effects of staining on the tissue phase maps were corrected by a mathematical normalization. These characteristics are likely to reduce barriers to clinical translation for the new cSLIM technology.

Raman spectroscopy for cancer detection and cancer surgery guidance: translation to the clinics

Oncological applications of Raman spectroscopy have been contemplated, pursued, and developed at academic level for at least 25 years. Published studies aim to detect pre-malignant lesions, detect cancer in less invasive stages, reduce the number of unnecessary biopsies and guide surgery towards the complete removal of the tumour with adequate tumour resection margins. This review summarizes actual clinical needs in oncology that can be addressed by spontaneous Raman spectroscopy and it provides an overview over the results that have been published between 2007 and 2017. An analysis is made of the current status of translation of these results into clinical practice. Despite many promising results, most of the applications addressed in scientific studies are still far from clinical adoption and commercialization. The main hurdles are identified, which need to be overcome to ensure that in the near future we will see the first Raman spectroscopy-based solutions being used in routine oncologic diagnostic and surgical procedures.

Applications of Raman spectroscopy in cancer diagnosis

Novel approaches toward understanding the evolution of disease can lead to the discovery of biomarkers that will enable better management of disease progression and improve prognostic evaluation. Raman spectroscopy is a promising investigative and diagnostic tool that can assist in uncovering the molecular basis of disease and provide objective, quantifiable molecular information for diagnosis and treatment evaluation. This technique probes molecular vibrations/rotations associated with chemical bonds in a sample to obtain information on molecular structure, composition, and intermolecular interactions. Raman scattering occurs when light interacts with a molecular vibration/rotation and a change in polarizability takes place during molecular motion. This results in light being scattered at an optical frequency shifted (up or down) from the incident light. By monitoring the intensity profile of the inelastically scattered light as a function of frequency, the unique spectroscopic fingerprint of a tissue sample is obtained. Since each sample has a unique composition, the spectroscopic profile arising from Raman-active functional groups of nucleic acids, proteins, lipids, and carbohydrates allows for the evaluation, characterization, and discrimination of tissue type. This review provides an overview of the theory of Raman spectroscopy, instrumentation used for measurement, and variation of Raman spectroscopic techniques for clinical applications in cancer, including detection of brain, ovarian, breast, prostate, and pancreatic cancers and circulating tumor cells.

Raman spectroscopy with a 1064-nm wavelength laser as a potential molecular tool for prostate cancer diagnosis: a pilot study

Raman spectroscopy is widely used to investigate the structure and property of the molecules from their vibrational transitions and may allow for the diagnosis of cancer in a fast, objective, and nondestructive manner. This experimental study aims to propose the use of the 1064-nm wavelength laser in a Raman spectroscopy and to evaluate its discrimination capability in prostate cancer diagnosis. Seventy-four spectra from patients who underwent radical prostatectomy were evaluated. The acquired signals were filtered, normalized, and corrected for possible oscillations in the laser intensity and fluorescence effects. Wilcoxon tests revealed significant differences between the benign and malign samples associated with the deformation vibration characteristic of nucleic acids, proteins, and lipids. A classifier based on support vector machines was able to predict the Gleason scores of the samples with 95% of accuracy, opening a perspective for the use of the 1064-nm excitatory wavelength in prostatic cancer diagnosis.

Lipid profiles of prostate cancer cells

Lipids are important cellular components which can be significantly altered in a range of disease states including prostate cancer. Here, a unique systematic approach has been used to define lipid profiles of prostate cancer cell lines, using quantitative mass spectrometry (LC-ESI-MS/MS), FTIR spectroscopy and fluorescent microscopy. All three approaches identified significant difference in the lipid profiles of the three prostate cancer cell lines (DU145, LNCaP and 22RV1) and one non-malignant cell line (PNT1a). Specific lipid classes and species, such as phospholipids (e.g., phosphatidylethanolamine 18:1/16:0 and 18:1/18:1) and cholesteryl esters, detected by LC-ESI-MS/MS, allowed statistical separation of all four prostate cell lines. Lipid mapping by FTIR revealed that variations in these lipid classes could also be detected at a single cell level, however further investigation into this approach would be needed to generate large enough data sets for quantitation. Visualisation by fluorescence microscopy showed striking variations that could be observed in lipid staining patterns between cell lines allowing visual separation of cell lines. In particular, polar lipid staining by a fluorescent marker was observed to increase significantly in prostate cancer lines cells, when compared to PNT1a cells, which was consistent with lipid quantitation by LC-ESI-MS/MS and FTIR spectroscopy. Thus, multiple technologies can be employed to either quantify or visualise changes in lipid composition, and moreover specific lipid profiles could be used to detect and phenotype prostate cancer cells.

Label-Free Prostate Cancer Detection by Characterization of Extracellular Vesicles Using Raman Spectroscopy

Mammalian cells release extracellular vesicles (EVs) into their microenvironment that travel the entire body along the stream of bodily fluids. EVs contain a wide range of biomolecules. The transported cargo varies depending on the EV origin. Knowledge of the origin and chemical composition of EVs can potentially be used as a biomarker to detect, stage, and monitor diseases. In this paper, we demonstrate the potential of EVs as a prostate cancer biomarker. A Raman optical tweezer was employed to obtain Raman signatures from four types of EV samples, which were red blood cell- and platelet-derived EVs of healthy donors and the prostate cancer cell lines- (PC3 and LNCaP) derived EVs. EVs’ Raman spectra could be clearly separated/classified into distinct groups using principal component analysis (PCA) which permits the discrimination of the investigated EV subtypes. These findings may provide new methodology to detect and monitor early stage cancer.

Combining high wavenumber and fingerprint Raman spectroscopy for the detection of prostate cancer during radical prostatectomy

For prostate cancer (PCa) patients, radical prostatectomy (complete removal of the prostate) is the only curative surgical option. To date, there is no clinical technique allowing for real-time assessment of surgical margins to minimize the extent of residual cancer. Here, we present a tissue interrogation technique using a dual excitation wavelength Raman spectroscopy system capable of sequentially acquiring fingerprint (FP) and high wavenumber (HWN) Raman spectra. Results demonstrate the ability of the system to detect PCa in post-prostatectomy specimens. In total, 477 Raman spectra were collected from 18 human prostate slices. Each area measured with Raman spectroscopy was characterized as either normal or cancer based on histopathological analyses, and each spectrum was classified based on supervised learning using support vector machines (SVMs). Based on receiver operating characteristic (ROC) analysis, FP (area under the curve [AUC] = 0.89) had slightly superior cancer detection capabilities compared with HWN (AUC = 0.86). Optimal performance resulted from combining the spectral information from FP and HWN (AUC = 0.91), suggesting that the use of these two spectral regions may provide complementary molecular information for PCa detection. The use of leave-one-(spectrum)-out (LOO) or leave-one-patient-out (LOPO) cross-validation produced similar classification results when combining FP with HWN. Our findings suggest that the application of machine learning using multiple data points from the same patient does not result in biases necessarily impacting the reliability of the classification models.

Upcoming imaging concepts and their impact on treatment planning and treatment response in radiation oncology

For 2018, the American Cancer Society estimated that there would be approximately 1.7 million new diagnoses of cancer and about 609,640 cancer-related deaths in the United States. By 2030 these numbers are anticipated to exceed a staggering 21 million annual diagnoses and 13 million cancer-related deaths. The three primary therapeutic modalities for cancer treatments are surgery, chemotherapy, and radiation therapy. Individually or in combination, these treatment modalities have provided and continue to provide curative and palliative care to the myriad victims of cancer.

Today, CT-based treatment planning is the primary means through which conventional photon radiation therapy is planned. Although CT remains the primary treatment planning modality, the field of radiation oncology is moving beyond the sole use of CT scans to define treatment targets and organs at risk. Complementary tissue scans, such as magnetic resonance imaging (MRI) and positron electron emission (PET) scans, have all improved a physician’s ability to more specifically identify target tissues, and in some cases, international guidelines have even been issued. Moreover, efforts to combine PET and MR to define solid tumors for radiotherapy planning and treatment evaluation are also gaining traction.

Keeping these advances in mind, we present brief overviews of other up-and-coming key imaging concepts that appear promising for initial treatment target definition or treatment response from radiation therapy.

[Research Progress of Raman Spectroscopy in the Diagnosis of Early Lung Cancer]

Lung cancer (LC) is the most common cancer and the leading cause of cancer-related death worldwide. The 5-year survival rate for LC remains low at 18% and is 5% for patients with metastatic disease, while the 5-year overall survival rate of patients with stage I NSCLC can reach 77.9%, hence early diagnosis and treatment of LC is the key to improve the prognosis. As a non-invasive detection technique, Raman spectroscopy can realize the non-destructive detection of the differences in molecular level structure between cancerous tissues and normal tissues, which can be used for the early diagnosis of lung cancer. The aim of this review is to summarize the progress of Raman spectroscopycombined with different tissue or body fluid samplesin the diagnosis of early LC.
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Label-free Molecular Imaging and Analysis by Raman Spectroscopy

Raman scattering of a cell conveys the intrinsic information inherent to chemical structures of biomolecules. The spectroscopy of Raman scattering, or Raman spectroscopy, allows label-free and quantitative molecular sensing of a biological sample in situ without disruption. For the last five decades Raman spectroscopy has been widely utilized in biological research fields. However, it is just within the latest decade that molecular imaging and discrimination of living cells and tissues have become practically available. Here we overview recent progress in Raman spectroscopy and its application to life sciences. We discuss imaging of functional molecules in living cells and tissues; e.g., cancer cells and ischemic or infarcted hearts, together with a number of studies in the biomedical fields. We further explore comprehensive understandings of a complex spectrum by multivariate analysis for, e.g., accurate peripheral nerve detection, and characterization of the histological differences in the healing process of myocardial infarct. Although limitations still remain, e.g., weakness of the scattering intensity and practical difficulty in comprehensive molecular analysis, continuous progress in related technologies will allow wider use of Raman spectroscopy for biomedical applications.

Discrimination of malignant and normal kidney tissue with short wave infrared dispersive Raman spectroscopy

Renal mass biopsy is still controversial due to imperfect accuracy. Raman spectroscopy (RS) demonstrated promise as an in vivo real-time, nondestructive diagnostic tool in many malignancies. Short wave infrared (SWIR) RS has the potential to improve on previous RS systems for renal mass diagnosis. The aim of this study is to evaluate a SWIR RS system in differentiating normal and malignant renal samples. Measurements were acquired using a benchtop RS system with excitation wavelength at 1064 nm and an InGaAs array detector. Processed spectra were classified with a Bayesian machine learning algorithm, sparse multinomial logistic regression. Sensitivity and receiver operating characteristic curve analyses evaluated the classifier accuracy. Accuracy of the classifier was 92.5% with sensitivity and specificity of 95.8% and 88.8%, respectively. For posterior probability of malignant class assignment, the area under the ROC curve is 0.94 (95% confidence interval: 0.89-0.99, P < .001). SWIR RS accurately differentiated normal and malignant kidney tumors. RS has the potential to be used as a diagnostic tool in kidney cancer.

Near-infrared Raman spectroscopy for estimating biochemical changes associated with different pathological conditions of cervix

The molecular level changes associated with oncogenesis precede the morphological changes in cells and tissues. Hence molecular level diagnosis would promote early diagnosis of the disease. Raman spectroscopy is capable of providing specific spectral signature of various biomolecules present in the cells and tissues under various pathological conditions. The aim of this work is to develop a non-linear multi-class statistical methodology for discrimination of normal, neoplastic and malignant cells/tissues. The tissues were classified as normal, pre-malignant and malignant by employing Principal Component Analysis followed by Artificial Neural Network (PC-ANN). The overall accuracy achieved was 99%. Further, to get an insight into the quantitative biochemical composition of the normal, neoplastic and malignant tissues, a linear combination of the major biochemicals by non-negative least squares technique was fit to the measured Raman spectra of the tissues. This technique confirms the changes in the major biomolecules such as lipids, nucleic acids, actin, glycogen and collagen associated with the different pathological conditions. To study the efficacy of this technique in comparison with histopathology, we have utilized Principal Component followed by Linear Discriminant Analysis (PC-LDA) to discriminate the well differentiated, moderately differentiated and poorly differentiated squamous cell carcinoma with an accuracy of 94.0%. And the results demonstrated that Raman spectroscopy has the potential to complement the good old technique of histopathology.

Endoscopic imaging using surface-enhanced Raman scattering

In this review, we assessed endoscopic imaging using surface-enhanced Raman scattering (SERS). As white-light endoscopy, the current standard for gastrointestinal endoscopy, is limited to morphology, Raman endoscopy using surface-enhanced Raman scattering nanoparticles (SERS endoscopy) was introduced as one of the novel functional modalities. SERS endoscopy has multiplex capability and high sensitivity with low autofluorescence and photobleaching. As a result, multiple molecular characteristics of the lesion can be accurately evaluated in real time while performing endoscopy using SERS probes and appropriate instrumentation. Especially, recently developed dual modality of fluorescence and SERS endoscopy offers easy localization with identification of multiple target molecules. For clinical use of SERS endoscopy in the future, problems of limited field of view and cytotoxicity should be addressed by fusion imaging, topical administration, and non-toxic coating of nanoparticles. We expect SERS endoscopic imaging would be an essential endoscopic technique for diagnosis of cancerous lesions, assessment of resection margins and evaluation of therapeutic responses.

Intraoperative Raman Spectroscopy

Surgical excision of brain tumors provides a means of cytoreduction and diagnosis while minimizing neurologic deficit and improving overall survival. Despite advances in functional and three-dimensional stereotactic navigation and intraoperative MRI, delineating tissue in real time with physiologic confirmation is challenging. Raman spectroscopy has potential to be an important modality in the intraoperative evaluation of tissue during surgical resection. In vitro experimental studies have shown that this technique can be used to differentiate normal brain tissue from tissue with infiltrating cancer cells and dense cancerous masses with high specificity, indicating the feasibility of this method for in vivo application.

Surface-enhanced Raman spectroscopy of serum accurately detects prostate cancer in patients with prostate-specific antigen levels of 4-10 ng/mL

The surface-enhanced Raman spectroscopy (SERS) of blood serum was investigated to differentiate between prostate cancer (PCa) and benign prostatic hyperplasia (BPH) in males with a prostate-specific antigen level of 4-10 ng/mL, so as to reduce unnecessary biopsies. A total of 240 SERS spectra from blood serum were acquired from 40 PCa subjects and 40 BPH subjects who had all received prostate biopsies and were given a pathological diagnosis. Multivariate statistical techniques, including principal component analysis (PCA) and linear discriminant analysis (LDA) diagnostic algorithms, were used to analyze the spectra data of serum from patients in control (CTR), PCa and BPH groups; results offered a sensitivity of 97.5%, a specificity of 100.0%, a precision of 100.0% and an accuracy of 99.2% for CTR; a sensitivity of 90.0%, a specificity of 97.5%, a precision of 94.7% and an accuracy of 98.3% for BPH; a sensitivity of 95.0%, a specificity of 93.8%, a precision of 88.4% and an accuracy of 94.2% for PCa. Similarly, this technique can significantly differentiate low- and high-risk PCa with an accuracy of 92.3%, a specificity of 95% and a sensitivity of 89.5%. The results suggest that analyzing blood serum using SERS combined with PCA-LDA diagnostic algorithms is a promising clinical tool for PCa diagnosis and assessment.

Biomedical optical spectroscopy for the early diagnosis of gastrointestinal neoplasms

Gastrointestinal cancer is a leading contributor to cancer-related morbidity and mortality worldwide. Early diagnosis currently plays a key role in the prognosis of patients with gastrointestinal cancer. Despite the advances in endoscopy over the last decades, missing lesions, undersampling and incorrect sampling in biopsies, as well as invasion still result in a poor diagnostic rate of early gastrointestinal cancers. Accordingly, there is a pressing need to develop non-invasive methods for the early detection of gastrointestinal cancers. Biomedical optical spectroscopy, including infrared spectroscopy, Raman spectroscopy, diffuse scattering spectroscopy and autofluorescence, is capable of providing structural and chemical information about biological specimens with the advantages of non-destruction, non-invasion and reagent-free and waste-free analysis and has thus been widely investigated for the diagnosis of oesophageal, gastric and colorectal cancers. This review will introduce the advances of biomedical optical spectroscopy techniques, highlight their applications for the early detection of gastrointestinal cancers and discuss their limitations.

Highly Accurate Detection of Cancer In Situ with Intraoperative, Label-Free, Multimodal Optical Spectroscopy

Effectiveness of surgery as a cancer treatment is reduced when all cancer cells are not detected during surgery, leading to recurrences that negatively impact survival. To maximize cancer cell detection during cancer surgery, we designed an in situ intraoperative, label-free, optical cancer detection system that combines intrinsic fluorescence spectroscopy, diffuse reflectance spectroscopy, and Raman spectroscopy. Using this multimodal optical cancer detection system, we found that brain, lung, colon, and skin cancers could be detected in situ during surgery with an accuracy, sensitivity, and specificity of 97%, 100%, and 93%, respectively. This highly sensitive optical molecular imaging approach can profoundly impact a wide range of surgical and noninvasive interventional oncology procedures by improving cancer detection capabilities, thereby reducing cancer burden and improving survival and quality of life. Cancer Res; 77(14); 3942-50. ©2017 AACR.

Probing the action of a novel anti-leukaemic drug therapy at the single cell level using modern vibrational spectroscopy techniques

Acute myeloid leukaemia (AML) is a life threatening cancer for which there is an urgent clinical need for novel therapeutic approaches. A redeployed drug combination of bezafibrate and medroxyprogesterone acetate (BaP) has shown anti-leukaemic activity in vitro and in vivo. Elucidation of the BaP mechanism of action is required in order to understand how to maximise the clinical benefit. Attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy, Synchrotron radiation FTIR (S-FTIR) and Raman microspectroscopy are powerful complementary techniques which were employed to probe the biochemical composition of two AML cell lines in the presence and absence of BaP. Analysis was performed on single living cells along with dehydrated and fixed cells to provide a large and detailed data set. A consideration of the main spectral differences in conjunction with multivariate statistical analysis reveals a significant change to the cellular lipid composition with drug treatment; furthermore, this response is not caused by cell apoptosis. No change to the DNA of either cell line was observed suggesting this combination therapy primarily targets lipid biosynthesis or effects bioactive lipids that activate specific signalling pathways.

Simultaneous Detection of EGFR and VEGF in Colorectal Cancer using Fluorescence-Raman Endoscopy

Fluorescence endomicroscopy provides quick access to molecular targets, while Raman spectroscopy allows the detection of multiple molecular targets. Using a simultaneous fluorescence-Raman endoscopic system (FRES), we herein demonstrate its potential in cancer diagnosis in an orthotopically induced colorectal cancer (CRC) xenograft model. In the model, epidermal growth factor receptor (EGFR) and vascular endothelial growth factor (VEGF) were targeted with antibody-conjugated fluorescence and surface-enhanced Raman scattering (F-SERS) dots. FRES demonstrated fast signal detection and multiplex targeting ability using fluorescence and Raman signals to detect the F-SERS dots. In addition, FRES showed a multiplex targeting ability even on a subcentimeter-sized CRC after spraying with a dose of 50 µg F-SERS dots. In conclusion, molecular characteristics of tumor cells (EGFR in cancer cell membranes) and tumor microenvironments (VEGF in the extracellular matrix) could be simultaneously investigated when performing a colonoscopy.

Lipid quantification by Raman microspectroscopy as a potential biomarker in prostate cancer

Metastatic castration-resistant prostate cancer (mCRPC) remains incurable and is one of the leading causes of cancer-related death among American men. Therefore, detection of prostate cancer (PCa) at early stages may reduce PCa-related mortality in men. We show that lipid quantification by vibrational Raman Microspectroscopy and Biomolecular Component Analysis may serve as a potential biomarker in PCa. Transcript levels of lipogenic genes including sterol regulatory element-binding protein-1 (SREBP-1) and its downstream effector fatty acid synthase (FASN), and rate-limiting enzyme acetyl CoA carboxylase (ACACA) were upregulated corresponding to both Gleason score and pathologic T stage in the PRAD TCGA cohort. Increased lipid accumulation in late-stage transgenic adenocarcinoma of mouse prostate (TRAMP) tumors compared to early-stage TRAMP and normal prostate tissues were observed. FASN along with other lipogenesis enzymes, and SREBP-1 proteins were upregulated in TRAMP tumors compared to wild-type prostatic tissues. Genetic alterations of key lipogenic genes predicted the overall patient survival using TCGA PRAD cohort. Correlation between lipid accumulation and tumor stage provides quantitative marker for PCa diagnosis. Thus, Raman spectroscopy-based lipid quantification could be a sensitive and reliable tool for PCa diagnosis and staging.

Evaluation of expressed prostatic secretion and serum using surface-enhanced Raman spectroscopy for the noninvasive detection of prostate cancer, a preliminary study

Surface-enhanced Raman spectroscopy (SERS) involving expressed prostatic secretion (EPS) and serum was investigated; the objective was to determine if this approach could distinguish prostate cancer from benign prostatic hyperplasia. A total of 120 SERS spectra for EPS and 96 spectra for serum were gathered from patients within a prospective contemporary biopsy cohort. Significant differences in spectra between prostate cancer and benign prostatic hyperplasia were tentatively assigned to component changes in EPS and serum samples. Principal component analysis and linear discriminate analysis were utilized to evaluate the spectral data for EPS and serum, to build diagnostic algorithms. The leave-one-out cross-validation method was used to validate the diagnostic algorithms; it revealed diagnostic sensitivities of 75% and 60%, specificities of 75% and 76.5%, and accuracies of 75% and 68% for EPS and serum, respectively. The results suggest that EPS and serum SERS analysis could be a potential tool for prostate cancer detection.

Shining light on neurosurgery diagnostics using Raman spectroscopy

Surgical excision of brain tumors provides a means of cytoreduction and diagnosis while minimizing neurologic deficit and improving overall survival. Despite advances in functional and three-dimensional stereotactic navigation and intraoperative magnetic resonance imaging, delineating tissue in real time with physiological confirmation is challenging. Raman spectroscopy is a promising investigative and diagnostic tool for neurosurgery, which provides rapid, non-destructive molecular characterization in vivo or in vitro for biopsy, margin assessment, or laboratory uses. The Raman Effect occurs when light temporarily changes a bond's polarizability, causing change in the vibrational frequency, with a corresponding change in energy/wavelength of the scattered photon. The recorded inelastic scattering results in a "fingerprint" or Raman spectrum of the constituent under investigation. The amount, location, and intensity of peaks in the fingerprint vary based on the amount of vibrational bonds in a molecule and their ensemble interactions with each other. Distinct differences between various pathologic conditions are shown as different intensities of the same peak, or shifting of a peak based on the binding conformation. Raman spectroscopy has potential for integration into clinical practice, particularly in distinguishing normal and diseased tissue as an adjunct to standard pathologic diagnosis. Further, development of fiber-optic Raman probes that fit through the instrument port of a standard endoscope now allows researchers and clinicians to utilize spectroscopic information for evaluation of in vivo tissue. This review highlights the need for such an instrument, summarizes neurosurgical Raman work performed to date, and discusses the future applications of neurosurgical Raman spectroscopy.

Surface-Enhanced Raman Spectroscopy Biosensing: In Vivo Diagnostics and Multimodal Imaging

This perspective presents recent developments in the application of surface-enhanced Raman spectroscopy (SERS) to biosensing, with a focus on in vivo diagnostics. We describe the concepts and methodologies developed to date and the target analytes that can be detected. We also discuss how SERS has evolved from a "point-and-shoot" stand-alone technique in an analytical chemistry laboratory to an integrated quantitative analytical tool for multimodal imaging diagnostics. Finally, we offer a guide to the future of SERS in the context of clinical diagnostics.

Role of Raman spectroscopy and surface enhanced Raman spectroscopy in colorectal cancer

Colorectal cancer (CRC) is the fourth most common cancer in the United Kingdom and is the second largest cause of cancer related death in the United Kingdom after lung cancer. Currently in the United Kingdom there is not a diagnostic test that has sufficient differentiation between patients with cancer and those without cancer so the current referral system relies on symptomatic presentation in a primary care setting. Raman spectroscopy and surface enhanced Raman spectroscopy (SERS) are forms of vibrational spectroscopy that offer a non-destructive method to gain molecular information about biological samples. The techniques offer a wide range of applications from in vivo or in vitro diagnostics using endoscopic probes, to the use of micro-spectrometers for analysis of biofluids. The techniques have the potential to detect molecular changes prior to any morphological changes occurring in the tissue and therefore could offer many possibilities to aid the detection of CRC. The purpose of this review is to look at the current state of diagnostic technology in the United Kingdom. The development of Raman spectroscopy and SERS in clinical applications relation for CRC will then be discussed. Finally, future areas of research of Raman/SERS as a clinical tool for the diagnosis of CRC are also discussed.