UV reflectance spectroscopy probes DNA and protein changes in human breast tissues

Turning antibodies off and on again using a covalently tethered blocking peptide

In their natural form, antibodies are always in an "on-state" and are capable of binding to their targets. This leads to undesirable interactions in a wide range of therapeutic, analytical, and synthetic applications. Modulating binding kinetics of antibodies to turn them from an "off-state" to an "on-state" with temporal and spatial control can address this. Here we demonstrate a method to modulate binding activity of antibodies in a predictable and reproducible way. We designed a blocking construct that uses both covalent and non-covalent interactions with the antibody. The construct consisted of a Protein L protein attached to a flexible linker ending in a blocking-peptide designed to interact with the antibody binding site. A mutant Protein L was developed to enable photo-triggered covalent crosslinking to the antibody at a specific location. The covalent bond anchored the linker and blocking peptide to the antibody light chain keeping the blocking peptide close to the antibody binding site. This effectively put the antibody into an "off-state". We demonstrate that protease-cleavable and photocleavable moieties in the tether enable controlled antibody activation to the "on-state" for anti-FLAG and cetuximab antibodies. Protein L can bind a range of antibodies used therapeutically and in research for wide applicability.

Diffusion Reflection Measurements of Antibodies Conjugated to Gold Nanoparticles as a Method to Identify Cutaneous Squamous Cell Carcinoma Borders

Diffusion reflectance spectroscopy measurements targeted with gold nanoparticles (GNPs) can identify residual cutaneous squamous cell carcinoma (SCC) in excision borders. Human SCC specimens were stained with hematoxylin and eosin to identify tumor borders, and reflected onto an unstained deparaffinized section. Diffusion reflection of three sites (normal and SCC) were measured before and after GNPs targeting. Hyperspectral imaging showed a mean of 2.5 sites with tumor per specimen and 1.2 tumor-free (p < 0.05, t-test). GNPs were detected in 25/30 tumor sites (sensitivity 83.3%, false-negative rate 16.6%) and 12/30 non-tumor sites (specificity 60%, false-positive rate 40%). This study verifies the use of nanotechnology in identifying SCC tumor margins. Diffusion reflection scanning has high sensitivity for detecting the residual tumor.

Drug Delivery Nanoparticles with Locally Tunable Toxicity Made Entirely from a Light-Activatable Prodrug of Doxorubicin

PURPOSE

A major challenge facing nanoparticle-based delivery of chemotherapy agents is the natural and unavoidable accumulation of these particles in healthy tissue resulting in local toxicity and dose-limiting side effects. To address this issue, we have designed and characterized a new prodrug nanoparticle with controllable toxicity allowing a locally-delivered light trigger to convert the payload of the particle from a low to a high toxicity state.

METHODS

The nanoparticles are created entirely from light-activatable prodrug molecules using a nanoprecipitation process. The prodrug is a conjugate of doxorubicin and photocleavable biotin (DOX-PCB).

RESULTS

These DOX-PCB nanoparticles are 30 times less toxic to cells than doxorubicin, but can be activated to release pure therapeutic doxorubicin when exposed to 365 nm light. These nanoparticles have an average diameter of around 100 nm and achieve the maximum possible prodrug loading capacity since no support structure or coating is required to prevent loss of prodrug from the nanoparticle.

CONCLUSIONS

These light activatable nanoparticles demonstrate tunable toxicity and can be used to facilitate future therapy development whereby light delivered specifically to the tumor tissue would locally convert the nanoparticles to doxorubicin while leaving nanoparticles accumulated in healthy tissue in the less toxic prodrug form.

New optical sensing technique of tissue viability and blood flow based on nanophotonic iterative multi-plane reflectance measurements

Physiological substances pose a challenge for researchers since their optical properties change constantly according to their physiological state. Examination of those substances noninvasively can be achieved by different optical methods with high sensitivity. Our research suggests the application of a novel noninvasive nanophotonics technique, ie, iterative multi-plane optical property extraction (IMOPE) based on reflectance measurements, for tissue viability examination and gold nanorods (GNRs) and blood flow detection. The IMOPE model combines an experimental setup designed for recording light intensity images with the multi-plane iterative Gerchberg-Saxton algorithm for reconstructing the reemitted light phase and calculating its standard deviation (STD). Changes in tissue composition affect its optical properties which results in changes in the light phase that can be measured by its STD. We have demonstrated this new concept of correlating the light phase STD and the optical properties of a substance, using transmission measurements only. This paper presents, for the first time, reflectance based IMOPE tissue viability examination, producing a decrease in the computed STD for older tissues, as well as investigating their organic material absorption capability. Finally, differentiation of the femoral vein from adjacent tissues using GNRs and the detection of their presence within blood circulation and tissues are also presented with high sensitivity (better than computed tomography) to low quantities of GNRs (<3 mg).

Localized in vivo activation of a photoactivatable doxorubicin prodrug in deep tumor tissue

Sparing sensitive healthy tissue from chemotherapy exposure is a critical challenge in the treatment of cancer. The work described here demonstrates the localized in vivo photoactivation of a new chemotherapy prodrug of doxorubicin (DOX). The DOX prodrug (DOX-PCB) was 200 times less toxic than DOX and was designed to release pure DOX when exposed to 365 nm light. This wavelength was chosen because it had good tissue penetration through a 1 cm diameter tumor, but had very low skin penetration, due to melanin absorption, preventing uncontrolled activation from outside sources. The light was delivered specifically to the tumor tissue using a specialized fiber-optic LED system. Pharmacokinetic studies showed that DOX-PCB had an α circulation half-life of 10 min which was comparable to that of DOX at 20 min. DOX-PCB demonstrated resistance to metabolic cleavage ensuring that exposure to 365 nm light was the main mode of in vivo activation. Tissue extractions from tumors exposed to 365 nm light in vivo showed the presence of DOX-PCB as well as activated DOX. The exposed tumors had six times more DOX concentration than nearby unexposed control tumors. This in vivo proof of concept demonstrates the first preferential activation of a photocleavable prodrug in deep tumor tissue.

Intercoupling surface plasmon resonance and diffusion reflection measurements for real-time cancer detection

Spatial diffusion reflection (DR) measurements of gold nanorods (GNR) were recently suggested as a simple and highly sensitive non-invasive and non-ionizing method for real-time cancer detection. In this paper we demonstrate that wavelength dependent DR measurements enable the spectral red-shift observation of highly concentrated GNR. By conjugating targeting moieties to the GNR, large density of GNR can specifically home onto cancer cells. The inter-particle plasmon resonance pattern of the highly concentrated GNR leads to an extension and a red-shift (Δλ) in the absorption spectrum of the concentrated GNR. Dark-field microscopy was used in order to measure the expected Δλ in different GNR concentrations in vitro. Double-wavelength DR measurements of tissue-like phantoms and tumor bearing mice containing different GNR concentrations are presented. We show that the DR profile of the highly concentrated GNR directly correlate with the spectral extension and red-shift. This presented work suggests that wavelength dependent DR method can serve as a promising tool for real-time superficial tumor detection.

Stokes shift spectroscopy pilot study for cancerous and normal prostate tissues

Stokes shift spectroscopy (S3) is an emerging approach toward cancer detection. The goal of this paper is to evaluate the diagnostic potential of the S3 technique for the detection and characterization of normal and cancerous prostate tissues. Pairs of cancerous and normal prostate tissue samples were taken from each of eight patients. Stokes shift spectra were measured by simultaneously scanning both the excitation and emission wavelengths while keeping a fixed wavelength interval Δλ=20  nm between them. The salient features of this technique are the highly resolved emission peaks and significant spectral differences between the normal and cancerous prostate tissues, as observed in the wavelength region of 250 to 600 nm. The Stokes shift spectra of cancerous and normal prostate tissues revealed distinct peaks around 300, 345, 440, and 510 nm, which are attributed to tryptophan, collagen, NADH, and flavin, respectively. To quantify the spectral differences between the normal and cancerous prostate tissues, two spectral ratios were computed. The findings revealed that both ratio parameters R1=I297/I345 and R2=I307/I345 were excellent diagnostic ratio parameters giving 100% specificity and 100% sensitivity for distinguishing cancerous tissue from the normal tissue. Our results demonstrate that S3 is a sensitive and specific technique for detecting cancerous prostate tissue.

A new method for cancer detection based on diffusion reflection measurements of targeted gold nanorods

This paper presents a new method for cancer detection based on diffusion reflection measurements. This method enables discrimination between cancerous and noncancerous tissues due to the intense light absorption of gold nanorods (GNRs), which are selectively targeted to squamous cell carcinoma head and neck cancer cells. Presented in this paper are tissue-like phantom and in vivo results that demonstrate the high sensitivity of diffusion reflection measurements to the absorption differences between the GNR-targeted cancerous tissue and normal, noncancerous tissue. This noninvasive and nonionizing optical detection method provides a highly sensitive, simple, and inexpensive tool for cancer detection.

A novel Doxorubicin prodrug with controllable photolysis activation for cancer chemotherapy

Purpose

Doxorubicin (DOX) is a very effective anticancer agent. However, in its pure form, its application is limited by significant cardiotoxic side effects. The purpose of this study was to develop a controllably activatable chemotherapy prodrug of DOX created by blocking its free amine group with a biotinylated photocleavable blocking group (PCB).

Methods

An n-hydroxy succunamide protecting group on the PCB allowed selective binding at the DOX active amine group. The PCB included an ortho-nitrophenyl group for photo cleavability and a water-soluble glycol spacer arm ending in a biotin group for enhanced membrane interaction.

Results

This novel DOX-PCB prodrug had a 200-fold decrease in cytotoxicity compared to free DOX and could release active DOX upon exposure to UV light at 350 nm. Unlike DOX, DOX-PCB stayed in the cell cytoplasm, did not enter the nucleus, and did not stain the exposed DNA during mitosis. Human liver microsome incubation with DOX-PCB indicated stability against liver metabolic breakdown.

Conclusions

The development of the DOX-PCB prodrug demonstrates the possibility of using light as a method of prodrug activation in deep internal tissues without relying on inherent physical or biochemical differences between the tumor and healthy tissue for use as the trigger.

A robust Monte Carlo model for the extraction of biological absorption and scattering in vivo

We have a toolbox to quantify tissue optical properties that is composed of specialized fiberoptic probes for UV-visible diffuse reflectance spectroscopy and a fast, scalable inverse Monte Carlo (MC) model. In this paper, we assess the robustness of the toolbox for quantifying physiologically relevant parameters from turbid tissue-like media. In particular, we consider the effects of using different instruments, fiberoptic probes, and instrument-specific settings for a wide range of optical properties. Additionally, we test the quantitative accuracy of the inverse MC model for extracting the biologically relevant parameters of hemoglobin saturation and total hemoglobin concentration. We also test the effect of double-absorber phantoms (hemoglobin and crocin to model the absorption of hemoglobin and beta carotene, respectively, in the breast) for a range of absorption and scattering properties. We include an assessment on which reference phantom serves as the best calibration standard to enable accurate extraction of the absorption and scattering properties of the target sample. We found the best reference-target phantom combinations to be ones with similar scattering levels. The results from these phantom studies provide a set of guidelines for extracting optical parameters from clinical studies.

Automated algorithm for breast tissue differentiation in optical coherence tomography

An automated algorithm for differentiating breast tissue types based on optical coherence tomography (OCT) data is presented. Eight parameters are derived from the OCT reflectivity profiles and their means and covariance matrices are calculated for each tissue type from a training set (48 samples) selected based on histological examination. A quadratic discrimination score is then used to assess the samples from a validation set. The algorithm results for a set of 89 breast tissue samples were correlated with the histological findings, yielding specificity and sensitivity of 0.88. If further perfected to work in real time and yield even higher sensitivity and specificity, this algorithm would be a valuable tool for biopsy guidance and could significantly increase procedure reliability by reducing both the number of nondiagnostic aspirates and the number of false negatives.

Spectral-domain low coherence interferometry/optical coherence tomography system for fine needle breast biopsy guidance

A novel technology and instrumentation for fine needle aspiration (FNA) breast biopsy guidance is presented. This technology is based on spectral-domain low coherence interferometry (SD-LCI). The method, apparatus, and preliminary in vitro/in vivo results proving the viability of the method and apparatus are presented in detail. An advanced tissue classification algorithm, preliminarily tested on breast tissue specimens and a mouse model of breast cancer is presented as well. Over 80% sensitivity and specificity in differentiating all tissue types and 93% accuracy in differentiating fatty tissue from fibrous or tumor tissue was obtained with this technology and apparatus. These results suggest that SD-LCI could help for more precise needle placement during the FNA biopsy and therefore could substantially reduce the number of the nondiagnostic aspirates and improve the sensitivity and specificity of the FNA procedures.

Model based and empirical spectral analysis for the diagnosis of breast cancer

We explored the use of both empirical (Partial Least Squares, PLS) and Monte Carlo model based approaches for the analysis of fluorescence and diffuse reflectance spectra measured ex vivo from freshly excised breast tissues and for the diagnosis of breast cancer. Features extracted using both approaches, i.e. principal components (PCs) obtained from empirical analysis or tissue properties obtained from model based analysis, displayed statistically significant difference between malignant and non-malignant tissues, and can be used to discriminate breast malignancy with comparable sensitivity and specificity of up to 90%. The PC scores of a subset of PCs also displayed significant correlation with the tissue properties extracted from the model based analysis, suggesting both approaches likely probe the same sources of contrast in the tissue spectra that discriminate between malignant and non-malignant breast tissues but in different ways.

New diagnostic techniques for breast cancer detection

Breast imaging has made huge advances in the last decade, and along with newer techniques to diagnose primary breast cancer, many novel methods are being used and look promising in detecting distant metastasis, recurrent disease and assessing response to treatment. Full-field digital mammography optimizes the lesion–background contrast and gives better sensitivity, and it is possible to see through the dense tissues by altering computer windows; this may be particularly useful in younger women with dense breasts. The need for repeat imaging is reduced, with the added advantage of reduced radiation dose to patients. Computer-aided detection systems may help the radiologist in interpretation of both conventional and digital mammograms. MRI has a role in screening women at high risk for breast cancer. It also aids in cancer management by assessing response to treatment and can help in deciding appropriate surgery by providing accurate information on the extent of the tumor. Newer diagnostic techniques such as sestamibi scans, optical imaging and molecular diagnostic techniques look promising, but need more investigation into their use. Their roles will appear clearer in coming years, and they may prove to be of help in further investigating lesions that are indeterminate on standard imaging. Other upcoming techniques are contrast-enhanced mammography and tomosynthesis. These may give additional information in indeterminate lesions, and when used in screening they aid in reducing recall rates, as shown in recent studies. PET/computed tomography has a role in detecting local disease recurrence and distant metastasis in breast cancer patients.

Diagnosis of breast cancer using fluorescence and diffuse reflectance spectroscopy: a Monte-Carlo-model-based approach

We explore the use of Monte-Carlo-model-based approaches for the analysis of fluorescence and diffuse reflectance spectra measured ex vivo from breast tissues. These models are used to extract the absorption, scattering, and fluorescence properties of malignant and nonmalignant tissues and to diagnose breast cancer based on these intrinsic tissue properties. Absorption and scattering properties, including beta-carotene concentration, total hemoglobin concentration, hemoglobin saturation, and the mean reduced scattering coefficient are derived from diffuse reflectance spectra using a previously developed Monte Carlo model of diffuse reflectance. A Monte Carlo model of fluorescence described in an earlier manuscript was employed to retrieve the intrinsic fluorescence spectra. The intrinsic fluorescence spectra were decomposed into several contributing components, which we attribute to endogenous fluorophores that may present in breast tissues including collagen, NADH, and retinol/vitamin A. The model-based approaches removes any dependency on the instrument and probe geometry. The relative fluorescence contributions of individual fluorescing components, as well as beta-carotene concentration, hemoglobin saturation, and the mean reduced scattering coefficient display statistically significant differences between malignant and adipose breast tissues. The hemoglobin saturation and the reduced scattering coefficient display statistically significant differences between malignant and fibrous/benign breast tissues. A linear support vector machine classification using (1) fluorescence properties alone, (2) absorption and scattering properties alone, and (3) the combination of all tissue properties achieves comparable classification accuracies of 81 to 84% in sensitivity and 75 to 89% in specificity for discriminating malignant from nonmalignant breast tissues, suggesting each set of tissue properties are diagnostically useful for the discrimination of breast malignancy.

Diagnosis of breast cancer using diffuse reflectance spectroscopy: Comparison of a Monte Carlo versus partial least squares analysis based feature extraction technique

BACKGROUND AND OBJECTIVE

We explored the use of diffuse reflectance spectroscopy in the ultraviolet-visible (UV-VIS) spectrum for the diagnosis of breast cancer. A physical model (Monte Carlo inverse model) and an empirical model (partial least squares analysis) based approach, were compared for extracting diagnostic features from the diffuse reflectance spectra.

STUDY DESIGN/METHODS

The physical model and the empirical model were employed to extract features from diffuse reflectance spectra measured from freshly excised breast tissues. A subset of extracted features obtained using each method showed statistically significant differences between malignant and non-malignant breast tissues. These features were separately input to a support vector machine (SVM) algorithm to classify each tissue sample as malignant or non-malignant.

RESULTS AND CONCLUSIONS

The features extracted from the Monte Carlo based analysis were hemoglobin saturation, total hemoglobin concentration, beta-carotene concentration and the mean (wavelength averaged) reduced scattering coefficient. Beta-carotene concentration was positively correlated and the mean reduced scattering coefficient was negatively correlated with percent adipose tissue content in normal breast tissues. In addition, there was a statistically significant decrease in the beta-carotene concentration and hemoglobin saturation, and a statistically significant increase in the mean reduced scattering coefficient in malignant tissues compared to non-malignant tissues. The features extracted from the partial least squares analysis were a set of principal components. A subset of principal components showed that the diffuse reflectance spectra of malignant breast tissues displayed an increased intensity over wavelength range of 440-510 nm and a decreased intensity over wavelength range of 510-600 nm, relative to that of non-malignant breast tissues. The diagnostic performance of the classification algorithms based on both feature extraction techniques yielded similar sensitivities and specificities of approximately 80% for discriminating between malignant and non-malignant breast tissues. While both methods yielded similar classification accuracies, the model based approach provided insight into the physiological and structural features that discriminate between malignant and non-malignant breast tissues.

Advances in optical spectroscopy and imaging of breast lesions

A review is presented of recent advances in optical imaging and spectroscopy and the use of light for addressing breast cancer issues. Spectroscopic techniques offer the means to characterize tissue components and obtain functional information in real time. Three-dimensional optical imaging of the breast using various illumination and signal collection schemes in combination with image reconstruction algorithms may provide a new tool for cancer detection and treatment monitoring.

Monte Carlo-based inverse model for calculating tissue optical properties. Part II: Application to breast cancer diagnosis

The Monte Carlo-based inverse model of diffuse reflectance described in part I of this pair of companion papers was applied to the diffuse reflectance spectra of a set of 17 malignant and 24 normal-benign ex vivo human breast tissue samples. This model allows extraction of physically meaningful tissue parameters, which include the concentration of absorbers and the size and density of scatterers present in tissue. It was assumed that intrinsic absorption could be attributed to oxygenated and deoxygenated hemoglobin and beta-carotene, that scattering could be modeled by spheres of a uniform size distribution, and that the refractive indices of the spheres and the surrounding medium are known. The tissue diffuse reflectance spectra were evaluated over a wavelength range of 400-600 nm. The extracted parameters that showed the statistically most significant differences between malignant and nonmalignant breast tissues were hemoglobin saturation and the mean reduced scattering coefficient. Malignant tissues showed decreased hemoglobin saturation and an increased mean reduced scattering coefficient compared with nonmalignant tissues. A support vector machine classification algorithm was then used to classify a sample as malignant or nonmalignant based on these two extracted parameters and produced a cross-validated sensitivity and specificity of 82% and 92%, respectively.

Use of a multiseparation fiber optic probe for the optical diagnosis of breast cancer

We explore the effects of the illumination and collection geometry on optical spectroscopic diagnosis of breast cancer. Fluorescence and diffuse reflectance spectroscopy in the UV-visible spectral range are made with a multiseparation probe at three illumination-collection separations of 735, 980, and 1225 microm, respectively, from 13 malignant and 34 nonmalignant breast tissues. Statistical analysis is carried out on two types of data inputs: (1) the fluorescence and diffuse reflectance spectra recorded at each of the three illumination-collection separations and (2) the integrated fluorescence (at each excitation wavelength) or diffuse reflectance over the entire spectrum at all three illumination-collection separations. The results show that using the integrated fluorescence intensities recorded at a single excitation wavelength at all three illumination-collection separations can discriminate malignant from nonmalignant breast tissues with similar classification accuracy to that using spectral data measured at several excitation wavelengths with a single illumination-collection separation. These findings have significant implications with respect to the design of an optical system for breast cancer diagnosis. Examining the intensity attenuation at a single wavelength rather than spectral intensities at multiple wavelengths can significantly reduce the measurement and data processing time in a clinical setting as well as the cost and complexity of the optical system.

Autofluorescence and diffuse reflectance properties of malignant and benign breast tissues

BACKGROUND

Fluorescence spectroscopy is an evolving technology that can rapidly differentiate between benign and malignant tissues. These differences are thought to be due to endogenous fluorophores, including nicotinamide adenine dinucleotide, flavin adenine dinucleotide, and tryptophan, and absorbers such as beta-carotene and hemoglobin. We hypothesized that a statistically significant difference would be demonstrated between benign and malignant breast tissues on the basis of their unique fluorescence and reflectance properties.

METHODS

Optical measurements were performed on 56 samples of tumor or benign breast tissue. Autofluorescence spectra were measured at excitation wavelengths ranging from 300 to 460 nm, and diffuse reflectance was measured between 300 and 600 nm. Principal component analysis to dimensionally reduce the spectral data and a Wilcoxon ranked sum test were used to determine which wavelengths showed statistically significant differences. A support vector machine algorithm compared classification results with the histological diagnosis (gold standard).

RESULTS

Several excitation wavelengths and diffuse reflectance spectra showed significant differences between tumor and benign tissues. By using the support vector machine algorithm to incorporate relevant spectral differences, a sensitivity of 70.0% and specificity of 91.7% were achieved.

CONCLUSIONS

A statistically significant difference was demonstrated in the diffuse reflectance and fluorescence emission spectra of benign and malignant breast tissue. These differences could be exploited in the development of adjuncts to diagnostic and surgical procedures.

Comparison of multiexcitation fluorescence and diffuse reflectance spectroscopy for the diagnosis of breast cancer (march 2003)

Nonmalignant (n = 36) and malignant (n = 20) tissue samples were obtained from breast cancer and breast reduction surgeries. These tissues were characterized using multiple excitation wavelength fluorescence spectroscopy and diffuse reflectance spectroscopy in the ultraviolet-visible wavelength range, immediately after excision. Spectra were then analyzed using principal component analysis (PCA) as a data reduction technique. PCA was performed on each fluorescence spectrum, as well as on the diffuse reflectance spectrum individually, to establish a set of principal components for each spectrum. A Wilcoxon rank-sum test was used to determine which principal components show statistically significant differences between malignant and nonmalignant tissues. Finally, a support vector machine (SVM) algorithm was utilized to classify the samples based on the diagnostically useful principal components. Cross-validation of this nonparametric algorithm was carried out to determine its classification accuracy in an unbiased manner. Multiexcitation fluorescence spectroscopy was successful in discriminating malignant and nonmalignant tissues, with a sensitivity and specificity of 70% and 92%, respectively. The sensitivity (30%) and specificity (78%) of diffuse reflectance spectroscopy alone was significantly lower. Combining fluorescence and diffuse reflectance spectra did not improve the classification accuracy of an algorithm based on fluorescence spectra alone. The fluorescence excitation-emission wavelengths identified as being diagnostic from the PCA-SVM algorithm suggest that the important fluorophores for breast cancer diagnosis are most likely tryptophan, NAD(P)H and flavoproteins.