Abigail S. Haka

In vivo Margin Assessment during Partial Mastectomy Breast Surgery Using Raman Spectroscopy

We present the first demonstration of in vivo collection of Raman spectra of breast tissue. Raman spectroscopy, which analyzes molecular vibrations, is a promising new technique for the diagnosis of breast cancer. We have collected 31 Raman spectra from nine patients undergoing partial mastectomy procedures to show the feasibility of in vivo Raman spectroscopy for intraoperative margin assessment. The data was fit with an established model, resulting in spectral-based tissue characterization in only 1 second. Application of our previously developed diagnostic algorithm resulted in perfect sensitivity and specificity for distinguishing cancerous from normal and benign tissues in our small data set. Significantly, we have detected a grossly invisible cancer that, upon pathologic review, required the patient to undergo a second surgical procedure. Had Raman spectroscopy been used in a real-time fashion to guide tissue excision during the procedure, the additional reexcision surgery might have been avoided. These preliminary findings suggest that Raman spectroscopy has the potential to lessen the need for reexcision surgeries resulting from positive margins and thereby reduce the recurrence rate of breast cancer following partial mastectomy surgeries.

Diagnosing breast cancer using Raman spectroscopy: prospective analysis

We present the first prospective test of Raman spectroscopy in diagnosing normal, benign, and malignant human breast tissues. Prospective testing of spectral diagnostic algorithms allows clinicians to accurately assess the diagnostic information contained in, and any bias of, the spectroscopic measurement. In previous work, we developed an accurate, internally validated algorithm for breast cancer diagnosis based on analysis of Raman spectra acquired from fresh-frozen in vitro tissue samples. We currently evaluate the performance of this algorithm prospectively on a large ex vivo clinical data set that closely mimics the in vivo environment. Spectroscopic data were collected from freshly excised surgical specimens, and 129 tissue sites from 21 patients were examined. Prospective application of the algorithm to the clinical data set resulted in a sensitivity of 83%, a specificity of 93%, a positive predictive value of 36%, and a negative predictive value of 99% for distinguishing cancerous from normal and benign tissues. The performance of the algorithm in different patient populations is discussed. Sources of bias in the in vitro calibration and ex vivo prospective data sets, including disease prevalence and disease spectrum, are examined and analytical methods for comparison provided.

Diagnosing breast cancer using diffuse reflectance spectroscopy and intrinsic fluorescence spectroscopy

Using diffuse reflectance spectroscopy and intrinsic fluorescence spectroscopy, we have developed an algorithm that successfully classifies normal breast tissue, fibrocystic change, fibroadenoma, and infiltrating ductal carcinoma in terms of physically meaningful parameters. We acquire 202 spectra from 104 sites in freshly excised breast biopsies from 17 patients within 30 min of surgical excision. The broadband diffuse reflectance and fluorescence spectra are collected via a portable clinical spectrometer and specially designed optical fiber probe. The diffuse reflectance spectra are fit using modified diffusion theory to extract absorption and scattering tissue parameters. Intrinsic fluorescence spectra are extracted from the combined fluorescence and diffuse reflectance spectra and analyzed using multivariate curve resolution. Spectroscopy results are compared to pathology diagnoses, and diagnostic algorithms are developed based on parameters obtained via logistic regression with cross-validation. The sensitivity, specificity, positive predictive value, negative predictive value, and overall diagnostic accuracy (total efficiency) of the algorithm are 100, 96, 69, 100, and 91%, respectively. All invasive breast cancer specimens are correctly diagnosed. The combination of diffuse reflectance spectroscopy and intrinsic fluorescence spectroscopy yields promising results for discrimination of breast cancer from benign breast lesions and warrants a prospective clinical study.

In vivo Raman spectral pathology of human atherosclerosis and vulnerable plaque.

The rupture of vulnerable atherosclerotic plaque accounts for the majority of clinically significant acute cardiovascular events. Because stability of these culprit lesions is directly related to chemical and morphological composition, Raman spectroscopy may be a useful technique for their study. Recent developments in optical fiber probe technology have allowed for the real-time in vivo Raman spectroscopic characterization of human atherosclerotic plaque demonstrated in this work. We spectroscopically examine 74 sites during carotid endarterectomy and femoral artery bypass surgeries. Of these, 34 are surgically biopsied and examined histologically. Excellent signal-to-noise ratio spectra are obtained in only 1 s and fit with an established model, demonstrating accurate tissue characterization. We also report the first evidence that Raman spectroscopy has the potential to identify vulnerable plaque, achieving a sensitivity and specificity of 79 and 85%, respectively. These initial findings indicate that Raman spectroscopy has the potential to be a clinically relevant diagnostic tool for studying cardiovascular disease.

Real-time Raman system for in vivo disease diagnosis

Raman spectroscopy has been well established as a powerful in vitro method for studying biological tissue and diagnosing disease. The recent development of efficient, high-throughput, low-background optical fiber Raman probes provides, for the first time, the opportunity to obtain real-time performance in the clinic. We present an instrument for in vivo tissue analysis which is capable of collecting and processing Raman spectra in less than 2 s. This is the first demonstration that data acquisition, analysis, and diagnostics can be performed in clinically relevant times. The instrument is designed to work with the new Raman probes and includes custom written LabVIEW and Matlab programs to provide accurate spectral calibration, analysis, and diagnosis along with important safety features related to laser exposure. The real-time capabilities of the system were demonstrated in vivo during femoral bypass and breast lumpectomy surgeries. Such a system will greatly facilitate the adoption of Raman spectroscopy into clinical research and practice.

Model‐based biological Raman spectral imaging

Raman spectral imaging is a powerful tool for determining chemical information in a biological specimen. The challenge is to condense the large amount of spectral information into an easily visualized form with high information content. Researchers have applied a range of techniques, from peak‐height ratios to sophisticated models, to produce interpretable Raman images. The purpose of this article is to review some of the more common imaging approaches, in particular principal components analysis, multivariate curve resolution, and Euclidean distance, as well as to present a new technique, morphological modeling. How to best extract meaningful chemical information using each imaging approach will be discussed and examples of images produced with each will be shown. J. Cell. Biochem. Suppl. 39: 125–137, 2002.

Classification of Fourier Transform Infrared Microscopic Imaging Data of Human Breast Cells by Cluster Analysis and Artificial Neural Networks

Cluster analysis and artificial neural networks (ANNs) are applied to the automated assessment of disease state in Fourier transform infrared microscopic imaging measurements of normal and carcinomatous immortalized human breast cell lines. K-means clustering is used to implement an automated algorithm for the assignment of pixels in the image to cell and non-cell categories. Cell pixels are subsequently classified into carcinoma and normal categories through the use of a feed-forward ANN computed with the Broyden–Fletcher–Goldfarb–Shanno training algorithm. Inputs to the ANN consist of principal component scores computed from Fourier filtered absorbance data. A grid search optimization procedure is used to identify the optimal network architecture and filter frequency response. Data from three images corresponding to normal cells, carcinoma cells, and a mixture of normal and carcinoma cells are used to build and test the classification methodology. A successful classifier is developed through this work, although differences in the spectral backgrounds between the three images are observed to complicate the classification problem. The robustness of the final classifier is improved through the use of a rejection threshold procedure to prevent classification of outlying pixels.

Sphingosine kinases are not required for inflammatory responses in macrophages.

Background: Sphingosine kinases (Sphks) were proposed to be essential for inflammatory responses. Results: Robust inflammatory responses were seen in macrophages that lack Sphks. However, intracellular sphingolipids and autophagic vesicles were induced. Conclusion: Sphingosine kinases are not required for inflammation. Significance: Attenuation of Sphk activity may not be critical for inflammation but could lead to altered sphingolipid levels and autophagy. Sphingosine kinases (Sphks), which catalyze the formation of sphingosine 1-phosphate (S1P) from sphingosine, have been implicated as essential intracellular messengers in inflammatory responses. Specifically, intracellular Sphk1-derived S1P was reported to be required for NFκB induction during inflammatory cytokine action. To examine the role of intracellular S1P in the inflammatory response of innate immune cells, we derived murine macrophages that lack both Sphk1 and Sphk2 (MΦ Sphk dKO). Compared with WT counterparts, MΦ Sphk dKO cells showed marked suppression of intracellular S1P levels whereas sphingosine and ceramide levels were strongly up-regulated. Cellular proliferation and apoptosis were similar in MΦ Sphk dKO cells compared with WT counterparts. Treatment of WT and MΦ Sphk dKO with inflammatory mediators TNFα or Escherichia coli LPS resulted in similar NFκB activation and cytokine expression. Furthermore, LPS-induced inflammatory responses, mortality, and thioglycolate-induced macrophage recruitment to the peritoneum were indistinguishable between MΦ Sphk dKO and littermate control mice. Interestingly, autophagic markers were constitutively induced in bone marrow-derived macrophages from Sphk dKO mice. Treatment with exogenous sphingosine further enhanced intracellular sphingolipid levels and autophagosomes. Inhibition of autophagy resulted in caspase-dependent cell death. Together, these data suggest that attenuation of Sphk activity, particularly Sphk2, leads to increased intracellular sphingolipids and autophagy in macrophages.

Intrinsic Fluorescence and Diffuse Reflectance Spectroscopy Identify Superficial Foam Cells in Coronary Plaques Prone to Erosion

Objective—Foam cells perform critical functions in atherosclerosis. We hypothesize that coronary segments with superficial foam cells (SFCs) situated in a region of interest with a depth of 200 &mgr;m can be identified using intrinsic fluorescence spectroscopy (IFS) and diffuse reflectance spectroscopy (DRS). This is a key step in our ongoing program to develop a spectroscopic technique for real-time in vivo diagnosis of vulnerable atherosclerotic plaque. Methods and Results—We subjected 132 human coronary segments to in vitro IFS and DRS. We detected SFCs in 13 thick fibrous cap atheromas and 8 pathologic intimal thickening (PIT) lesions. SFCs colocalized with accumulations of smooth muscle cells and proteoglycans, including hyaluronan (P<0.001). Two spectroscopic parameters were generated from analysis of IFS at 480 nm excitation and DRS. A discriminatory algorithm using these parameters identified specimens with SFC area >40%, 20%, 10%, 5%, 2.5%, and 0% of the region of interest with 98%, 98%, 93%, 94%, 93%, and 90% accuracy, respectively. Conclusion—Our combined IFS and DRS technique accurately detects SFCs in thick fibrous cap atheromas and PIT lesions. Because SFCs are associated with histological markers of plaque erosion, our spectroscopic technique could prove useful in identifying vulnerable plaques.

Aggregated LDL in Contact With Macrophages Induces Local Increases in Free Cholesterol Levels That Regulate Local Actin Polymerization

Objective—Interaction of macrophages with aggregated matrix-anchored lipoprotein deposits is an important initial step in atherogenesis. Aggregated lipoproteins require different cellular uptake processes than those used for endocytosis of monomeric lipoproteins. In this study, we tested the hypothesis that engagement of aggregated LDL (agLDL) by macrophages could lead to local increases in free cholesterol levels and that these increases in free cholesterol regulate signals that control cellular actin. Methods and Results—AgLDL resides for prolonged periods in surface-connected compartments. Although agLDL is still extracellular, we demonstrate that an increase in free cholesterol occurs at sites of contact between agLDL and cells because of hydrolysis of agLDL-derived cholesteryl ester. This increase in free cholesterol causes enhanced actin polymerization around the agLDL. Inhibition of cholesteryl ester hydrolysis results in decreased actin polymerization. Conclusions—We describe a novel process that occurs during agLDL–macrophage interactions in which local release of free cholesterol causes local actin polymerization, promoting a pathological positive feedback loop for increased catabolism of agLDL and eventual foam cell formation.