Development of Raman spectral markers to assess metastatic bone in breast cancer

Multimodal analysis and comparison of stoichiometric and structural characteristics of parosteal and conventional osteosarcoma with massive sclerosis in human bone

Osteosarcoma (OS) is the most common malignant primary bone tumor in humans and occurs in various subtypes. Tumor formation happens through malignant osteoblasts producing immature bone. In the present paper we studied two different subtypes of osteosarcoma, from one individual with conventional OS with massive sclerosis and one individual with parosteal OS, based on a multimodal approach including small angle x-ray scattering (SAXS), wide angle x-ray diffraction (WAXS), backscattered electron imaging (BEI) and Raman spectroscopy. It was found that both tumors showed reduced mineral particle sizes and degree of orientation of the collagen-mineral composite in the affected areas, alongside with a decreased crystallinity. Distinct differences between the tumor material from the two individuals were found in the degree of mineralization. Further differences were observed in the carbonate to phosphate ratio, which is related to the degree of carbonate substitution in bone mineral and indicative of the turnover rate. The contraction of the c-axis of the bone mineral crystals proved to be a further, very sensitive parameter, potentially indicative of malignancy.

Quantitative Chemical Imaging of Bone Tissue for Intraoperative and Diagnostic Applications

Bone is difficult to image using traditional histopathological methods, leading to challenges in intraoperative pathological evaluation that is critical in guiding surgical treatment, particularly in orthopedic oncology. In this study, we demonstrate that a multimodal quantitative imaging approach that combines stimulated Raman scattering (SRS) microscopy, two-photon fluorescence (TPF) microscopy, and second-harmonic generation (SHG) microscopy can provide useful diagnostic information regarding intact bone tissue fragments from surgical excision or biopsy specimens. We imaged bone samples from 17 patient cases and performed quantitative chemical and morphological analyses of both mineral and organic components of bone. Our main findings show that carbonate content combined with morphometric analysis of bone organic matrix can separate several major classes of bone cancer-associated diagnostic categories with an average accuracy of 92%. This proof-of-principle study demonstrates that quantitative multimodal imaging and machine learning-based analysis of bony tissue can provide crucial diagnostic information for guiding clinical decisions in orthopedic oncology. Moreover, the general methodology of morphological and chemical imaging combined with machine learning can be readily extended to other tissue types for tissue diagnosis in intraoperative and other clinical settings.

Classification of Soft Tissue Sarcoma Specimens with Raman Spectroscopy as Smart Sensing Technology

Intraoperative confirmation of negative resection margins is an essential component of soft tissue sarcoma surgery. Frozen section examination of samples from the resection bed after excision of sarcomas is the gold standard for intraoperative assessment of margin status. However, it takes time to complete histologic examination of these samples, and the technique does not provide real-time diagnosis in the operating room (OR), which delays completion of the operation. This paper presents a study and development of sensing technology using Raman spectroscopy that could be used for detection and classification of the tumor after resection with negative sarcoma margins in real time. We acquired Raman spectra from samples of sarcoma and surrounding benign muscle, fat, and dermis during surgery and developed (i) a quantitative method (QM) and (ii) a machine learning method (MLM) to assess the spectral patterns and determine if they could accurately identify these tissue types when compared to findings in adjacent H&E-stained frozen sections. High classification accuracy (>85%) was achieved with both methods, indicating that these four types of tissue can be identified using the analytical methodology. A hand-held Raman probe could be employed to further develop the methodology to obtain spectra in the OR to provide real-time in vivo capability for the assessment of sarcoma resection margin status.

Correlation and Difference Between Raman Spectral Characteristic and Feature Evaluation for Leukocytes and Tumor Cells

Tumor detection supported by Raman spectroscopy is becoming increasingly popular, yet the relevance of spectral variation and feature selection retains unclear. Here we determined the correlation and difference between spectral characteristic and feature evaluation for leukocytes and tumor cells. Some peaks were found to show noticeable spectral differences, and their intensity distributions were investigated, finding using log-normal distribution to describe Raman intensity pattern may be more appropriate. Further the importance of all Raman features was calculated, where some other peak features occupied the top status. By surveying the intensity variation and feature evaluation for those peaks, we concluded the peak with the highest importance does not correspond to the peak location with the most noticeable intensity difference in spectra. Moreover, the peak intensity ratio of I₁₅₁₇/I₇₁₉ associated with protein to nucleic acid level presented the maximum separation, thus, it can be recognized as a special indicator to develop an alternative cancer detection. It is inspiring to introduce advanced statistical models into bio-spectroscopic fields but those intrinsic spectral variations rather than classification performance should be valued. Our explorations can provide possibilities to reveal the essences within tumor carcinogenesis based on Raman spectroscopy, further overwhelming the obstacles during the translation into clinical applications.

The effects of metastatic lesion on the structural determinants of bone: Current clinical and experimental approaches

Metastatic bone disease is incurable with an associated increase in skeletal-related events, particularly a 17-50% risk of pathologic fractures. Current surgical and oncological treatments are palliative, do not reduce overall mortality, and therefore optimal management of adults at risk of pathologic fractures presents an unmet medical need. Plain radiography lacks specificity and may result in unnecessary prophylactic fixation. Radionuclide imaging techniques primarily supply information on the metabolic activity of the tumor or the bone itself. Magnetic resonance imaging and computed tomography provide excellent anatomical and structural information but do not quantitatively assess bone matrix. Research has now shifted to developing unbiased data-driven tools that can predict risk of impending fractures and guide individualized treatment decisions. This review discusses the state-of-the-art in clinical and experimental approaches for prediction of pathologic fractures with bone metastases. Alterations in bone matrix quality are associated with an age-related increase in skeletal fragility but the impact of metastases on the intrinsic material properties of bone is unclear. Engineering-based analyses are non-invasive with the capability to evaluate oncological treatments and predict failure due to the progression of metastasis. The combination of these approaches may improve our understanding of the underlying deterioration in mechanical performance.

Fluorescence imaging and Raman spectroscopy applied for the accurate diagnosis of breast cancer with deep learning algorithms

Deep learning is usually combined with a single detection technique in the field of disease diagnosis. This study focused on simultaneously combining deep learning with multiple detection technologies, fluorescence imaging and Raman spectroscopy, for breast cancer diagnosis. A number of fluorescence images and Raman spectra were collected from breast tissue sections of 14 patients. Pseudo-color enhancement algorithm and a convolutional neural network were applied to the fluorescence image processing, so that the discriminant accuracy of test sets, 88.61%, was obtained. Two different BP-neural networks were applied to the Raman spectra that mainly comprised collagen and lipid, so that the discriminant accuracy of 95.33% and 98.67% of test sets were gotten, respectively. Then the discriminant results of fluorescence images and Raman spectra were counted and arranged into a characteristic variable matrix to predict the breast tissue samples with partial least squares (PLS) algorithm. As a result, the predictions of all samples are correct, with minor error of predictive value. This study proves that deep learning algorithms can be applied into multiple diagnostic optics/spectroscopy techniques simultaneously to improve the accuracy in disease diagnosis.

Contribution of Raman Spectroscopy to Diagnosis and Grading of Chondrogenic Tumors

In the last decade, Raman Spectroscopy has demonstrated to be a label-free and non-destructive optical spectroscopy able to improve diagnostic accuracy in cancer diagnosis. This is because Raman spectroscopic measurements can reveal a deep molecular understanding of the biochemical changes in cancer tissues in comparison with non-cancer tissues. In this pilot study, we apply Raman spectroscopy imaging to the diagnosis and grading of chondrogenic tumors, including enchondroma and chondrosarcomas of increasing histologic grades. The investigation included the analysis of areas of 50×50 μm² to approximately 200×200 μm², respectively. Multivariate statistical analysis, based on unsupervised (Principal Analysis Components) and supervised (Linear Discriminant Analysis) methods, differentiated between the various tumor samples, between cells and extracellular matrix, and between collagen and non-collagenous components. The results dealt out basic biochemical information on tumor progression giving the possibility to grade with certainty the malignant cartilaginous tumors under investigation. The basic processes revealed by Raman Spectroscopy are the progressive degrading of collagen type-II components, the formation of calcifications and the cell proliferation in tissues ranging from enchondroma to chondrosarcomas. This study highlights that Raman spectroscopy is particularly effective when cartilaginous tumors need to be subjected to histopathological analysis.

Assessment of Renal Osteodystrophy via Computational Analysis of Label-free Raman Detection of Multiple Biomarkers

Accurate clinical evaluation of renal osteodystrophy (ROD) is currently accomplished using invasive in vivo transiliac bone biopsy, followed by in vitro histomorphometry. In this study, we demonstrate that an alternative method for ROD assessment is through a fast, label-free Raman recording of multiple biomarkers combined with computational analysis for predicting the minimally required number of spectra for sample classification at defined accuracies. Four clinically relevant biomarkers: the mineral-to-matrix ratio, the carbonate-to-matrix ratio, phenylalanine, and calcium contents were experimentally determined and simultaneously considered as input to a linear discriminant analysis (LDA). Additionally, sample evaluation was performed with a linear support vector machine (LSVM) algorithm, with a 300 variable input. The computed probabilities based on a single spectrum were only marginally different (~80% from LDA and ~87% from LSVM), both providing an unacceptable classification power for a correct sample assignment. However, the Type I and Type II assignment errors confirm that a relatively small number of independent spectra (7 spectra for Type I and 5 spectra for Type II) is necessary for a p < 0.05 error probability. This low number of spectra supports the practicality of future in vivo Raman translation for a fast and accurate ROD detection in clinical settings.

Raman Spectroscopic and Microscopic Analysis for Monitoring Renal Osteodystrophy Signatures

Defining the pathogenesis of renal osteodystrophy (ROD) and its treatment efficacy are difficult, since many factors potentially affect bone quality. In this study, confocal Raman microscopy and parallel statistical analysis were used to identify differences in bone composition between healthy and ROD bone tissues through direct visualization of three main compositional parametric ratios, namely, calcium content, mineral-to-matrix, and carbonate-to-matrix. Besides the substantially lower values found in ROD specimens for these representative ratios, an obvious accumulation of phenylalanine is Raman spectroscopically observed for the first time in ROD samples and reported here. Thus, elevated phenylalanine could also be considered as an indicator of the disease. Since the image results are based on tens of thousands of spectra per sample, not only are the average ratios statistically significantly different for normal and ROD bone, but the method is clearly powerful in distinguishing between the two types of samples. Furthermore, the statistical outcomes demonstrate that only a relatively small number of spectra need to be recorded in order to classify the samples. This work thus opens the possibility of future development of in vivo Raman sensors for assessment of bone structure, remodeling, and mineralization, where different biomarkers are simultaneously detected with unprecedented accuracy.

Label-free Raman spectroscopy provides early determination and precise localization of breast cancer-colonized bone alterations

Raman spectral markers offer new routes to recognition of biomolecular alterations at sites of nascent and progressing metastatic cancer in bone.

Breast neoplasms frequently colonize bone and induce development of osteolytic bone lesions by disrupting the homeostasis of the bone microenvironment. This degenerative process can lead to bone pain and pathological bone fracture, a major cause of cancer morbidity and diminished quality of life, which is exacerbated by our limited ability to monitor early metastatic disease in bone and assess fracture risk. Spurred by its label-free, real-time nature and its exquisite molecular specificity, we employed spontaneous Raman spectroscopy to assess and quantify early metastasis driven biochemical alterations to bone composition. As early as two weeks after intracardiac inoculations of MDA-MB-435 breast cancer cells in NOD-SCID mice, Raman spectroscopic measurements in the femur and spine revealed consistent changes in carbonate substitution, overall mineralization as well as crystallinity increase in tumor-bearing bones when compared with their normal counterparts. Our observations reveal the possibility of early stage detection of biochemical changes in the tumor-bearing bones – significantly before morphological variations are captured through radiographic diagnosis. This study paves the way for a better molecular understanding of altered bone remodeling in such metastatic niches, and for further clinical studies with the goal of establishing a non-invasive tool for early metastasis detection and prediction of pathological fracture risk in breast cancer.

Fkbp10 Deletion in Osteoblasts Leads to Qualitative Defects in Bone

Osteogenesis imperfecta (OI), also known as brittle bone disease, displays a spectrum of clinical severity from mild (OI type I) to severe early lethality (OI type II), with clinical features including low bone mass, fractures, and deformities. Mutations in the FK506 Binding Protein 10 (FKBP10), gene encoding the 65-kDa protein FKBP65, cause a recessive form of OI and Bruck syndrome, the latter being characterized by joint contractures in addition to low bone mass. We previously showed that Fkbp10 expression is limited to bone, tendon, and ligaments in postnatal tissues. Furthermore, in both patients and Fkbp10 knockout mice, collagen telopeptide hydroxylysine crosslinking is dramatically reduced. To further characterize the bone specific contributions of Fkbp10, we conditionally ablated FKBP65 in Fkbp10fl/fl mice (Mus musculus; C57BL/6) using the osteoblast-specific Col1a1 2.3-kb Cre recombinase. Using μCT, histomorphometry and quantitative backscattered electron imaging, we found minimal alterations in the quantity of bone and no differences in the degree of bone matrix mineralization in this model. However, mass spectroscopy (MS) of bone collagen demonstrated a decrease in mature, hydroxylysine-aldehyde crosslinking. Furthermore, bone of mutant mice exhibits a reduction in mineral-to-matrix ratio and in crystal size as shown by Raman spectroscopy and small-angle X-ray scattering, respectively. Importantly, abnormalities in bone quality were associated with impaired bone biomechanical strength in mutant femurs compared with those of wild-type littermates. Taken together, these data suggest that the altered collagen crosslinking through Fkbp10 ablation in osteoblasts primarily leads to a qualitative defect in the skeleton. © 2017 American Society for Bone and Mineral Research.

Correlations Between Bone Mechanical Properties and Bone Composition Parameters in Mouse Models of Dominant and Recessive Osteogenesis Imperfecta and the Response to Anti-TGF-β Treatment

Osteogenesis imperfecta (OI) is a group of genetic disorders characterized by brittle bones that are prone to fracture. Although previous studies in animal models investigated the mechanical properties and material composition of OI bone, little work has been conducted to statistically correlate these parameters to identify key compositional contributors to the impaired bone mechanical behaviors in OI. Further, although increased TGF-β signaling has been demonstrated as a contributing mechanism to the bone pathology in OI models, the relationship between mechanical properties and bone composition after anti-TGF-β treatment in OI has not been studied. Here, we performed follow-up analyses of femurs collected in an earlier study from OI mice with and without anti-TGF-β treatment from both recessive (Crtap^-/- ) and dominant (Col1a2^+/P.G610C ) OI mouse models and WT mice. Mechanical properties were determined using three-point bending tests and evaluated for statistical correlation with molecular composition in bone tissue assessed by Raman spectroscopy. Statistical regression analysis was conducted to determine significant compositional determinants of mechanical integrity. Interestingly, we found differences in the relationships between bone composition and mechanical properties and in the response to anti-TGF-β treatment. Femurs of both OI models exhibited increased brittleness, which was associated with reduced collagen content and carbonate substitution. In the Col1a2^+/P.G610C femurs, reduced hydroxyapatite crystallinity was also found to be associated with increased brittleness, and increased mineral-to-collagen ratio was correlated with increased ultimate strength, elastic modulus, and bone brittleness. In both models of OI, regression analysis demonstrated that collagen content was an important predictor of the increased brittleness. In summary, this work provides new insights into the relationships between bone composition and material properties in models of OI, identifies key bone compositional parameters that correlate with the impaired mechanical integrity of OI bone, and explores the effects of anti-TGF-β treatment on bone-quality parameters in these models. © 2016 American Society for Bone and Mineral Research.

ZIP4 silencing improves bone loss in pancreatic cancer

Metabolic bone disorders are associated with several types of human cancers. Pancreatic cancer patients usually suffer from severe nutrition deficiency, muscle wasting, and loss of bone mass. We have previously found that silencing of a zinc transporter ZIP4 prolongs the survival and reduces the severity of the cachexia in vivo. However, the role of ZIP4 in the pancreatic cancer related bone loss remains unknown. In this study we investigated the effect of ZIP4 knockdown on the bone structure, composition and mechanical properties of femurs in an orthotopic xenograft mouse model. Our data showed that silencing of ZIP4 resulted in increased bone tissue mineral density, decreased bone crystallinity and restoration of bone strength through the RANK/RANKL pathway. The results further support the impact of ZIP4 on the progression of pancreatic cancer, and suggest its potential significance as a therapeutic target for treating patients with such devastating disease and cancer related disorders.

Noninvasive glucose sensing by transcutaneous Raman spectroscopy

We present the development of a transcutaneous Raman spectroscopy system and analysis algorithm for noninvasive glucose sensing. The instrument and algorithm were tested in a preclinical study in which a dog model was used. To achieve a robust glucose test system, the blood levels were clamped for periods of up to 45 min. Glucose clamping and rise/fall patterns have been achieved by injecting glucose and insulin into the ear veins of the dog. Venous blood samples were drawn every 5 min and a plasma glucose concentration was obtained and used to maintain the clamps, to build the calibration model, and to evaluate the performance of the system. We evaluated the utility of the simultaneously acquired Raman spectra to be used to determine the plasma glucose values during the 8-h experiment. We obtained prediction errors in the range of ~1.5-2  mM. These were in-line with a best-case theoretical estimate considering the limitations of the signal-to-noise ratio estimates. As expected, the transition regions of the clamp study produced larger predictive errors than the stable regions. This is related to the divergence of the interstitial fluid (ISF) and plasma glucose values during those periods. Two key contributors to error beside the ISF/plasma difference were photobleaching and detector drift. The study demonstrated the potential of Raman spectroscopy in noninvasive applications and provides areas where the technology can be improved in future studies.

Raman spectroscopy for grading of live osteosarcoma cells

Introduction

Osteosarcoma is the most common primary malignant bone tumor, and the grading of osteosarcoma cells relies on traditional histopathology and molecular biology methods, which require RNA extraction, protein isolation and immunohistological staining. All these methods require cell isolation, lysis or fixation, which is time-consuming and requires certain amount of tumor specimen. In this study, we report the use of Raman spectroscopy for grading of malignant osteosarcoma cells.

Methods

We demonstrate that, based on the detection of differential production of mineral species, Raman spectroscopy can be used as a live cell analyzer to accurately assess the grades of osteosarcoma cells by evaluating their mineralization levels. Mineralization level was assessed by measuring amount of hydroxyapatite (HA), which is highly expressed in mature osteoblasts, but not in poorly differentiated osteosarcoma cell or mesenchymal stem cells, the putative cell-of-origin of osteosarcoma.

Results

We found that under Raman spectroscopy, the level of HA production was high in MG-63 cells, which are low-grade. Moreover, hydroxyapatite production was low in high-grade osteosarcoma cells such as 143B and SaOS2 cells (p < 0.05). Matrix metalloproteinase MMP2, MMP9 were highly expressed in SaOS2, 143B and MSCs and decreased in human fetal osteoblast (FOB) and MG-63 cells as expected (p < 0.05). These results may highlight the inverse correlation between HA level and prognosis of osteosarcoma.

Conclusions

The use of Raman spectroscopy for the measurement of HA production by the protocol reported in this study may serve as a useful tool to rapidly and accurately assess the degree of malignancy in osteosarcoma cells in a label-free manner. Such application may shorten the period of pathological diagnosis and may benefit patients who are inflicted with osteosarcoma.

Vibrational spectroscopic imaging for the evaluation of matrix and mineral chemistry

Metabolic bone diseases manifesting fragility fractures (such as osteoporosis) are routinely diagnosed based on bone mineral density (BMD) measurements, and the effect of various therapies also evaluated based on the same outcome. Although useful, it is well recognized that this metric does not fully account for either fracture incidence or the effect of various therapies on fracture incidence, thus, the emergence of bone quality as a contributing factor in the determination of bone strength. Infrared and Raman vibrational spectroscopic techniques are particularly well-suited for the determination of bone quality as they provide quantitative and qualitative information of the mineral and organic matrix bone components, simultaneously. Through the use of microspectroscopic techniques, this information is available in a spatially resolved manner, thus, the outcomes may be easily correlated with outcomes from techniques such as histology, histomorphometry, and nanoindentation, linking metabolic status with material properties.