Mammary collagen is under reproductive control with implications for breast cancer.

Mammographically-detected breast density impacts breast cancer risk and progression, and fibrillar collagen is a key component of breast density. However, physiologic factors influencing collagen production in the breast are poorly understood. In female rats, we analyzed gene expression of the most abundantly expressed mammary collagens and collagen-associated proteins across a pregnancy, lactation, and weaning cycle. We identified a triphasic pattern of collagen gene regulation and evidence for reproductive state-dependent composition. An initial phase of collagen deposition occurred during pregnancy, followed by an active phase of collagen suppression during lactation. The third phase of collagen regulation occurred during weaning-induced mammary gland involution, which was characterized by increased collagen deposition. Concomitant changes in collagen protein abundance were confirmed by Masson's trichrome staining, second harmonic generation (SHG) imaging, and mass spectrometry. We observed similar reproductive-state dependent collagen patterns in human breast tissue obtained from premenopausal women. SHG analysis also revealed structural variation in collagen across a reproductive cycle, with higher packing density and more collagen fibers arranged perpendicular to the mammary epithelium in the involuting rat mammary gland compared to nulliparous and lactating glands. Involution was also characterized by high expression of the collagen cross-linking enzyme lysyl oxidase, which was associated with increased levels of cross-linked collagen. Breast cancer relevance is suggested, as we found that breast cancer diagnosed in recently postpartum women displayed gene expression signatures consistent with increased collagen deposition and crosslinking compared to breast cancers diagnosed in age-matched nulliparous women. Using publicly available data sets, we found this involution-like, collagen gene signature correlated with poor progression-free survival in breast cancer patients overall and in younger women. In sum, these findings of physiologic collagen regulation in the normal mammary gland may provide insight into normal breast function, the etiology of breast density, and inform breast cancer risk and outcomes.

Mammary collagen architecture and its association with mammographic density and lesion severity among women undergoing image-guided breast biopsy.

BACKGROUND: Elevated mammographic breast density is a strong breast cancer risk factor with poorly understood etiology. Increased deposition of collagen, one of the main fibrous proteins present in breast stroma, has been associated with increased mammographic density. Collagen fiber architecture has been linked to poor outcomes in breast cancer. However, relationships of quantitative collagen fiber features assessed in diagnostic biopsies with mammographic density and lesion severity are not well-established. METHODS: Clinically indicated breast biopsies from 65 in situ or invasive breast cancer cases and 73 frequency matched-controls with a benign biopsy result were used to measure collagen fiber features (length, straightness, width, alignment, orientation and density (fibers/µm2)) using second harmonic generation microscopy in up to three regions of interest (ROIs) per biopsy: normal, benign breast disease, and cancer. Local and global mammographic density volumes were quantified in the ipsilateral breast in pre-biopsy full-field digital mammograms. Associations of fibrillar collagen features with mammographic density and severity of biopsy diagnosis were evaluated using generalized estimating equation models with an independent correlation structure to account for multiple ROIs within each biopsy section. RESULTS: Collagen fiber density was positively associated with the proportion of stroma on the biopsy slide (p < 0.001) and with local percent mammographic density volume at both the biopsy target (p = 0.035) and within a 2 mm perilesional ring (p = 0.02), but not with global mammographic density measures. As severity of the breast biopsy diagnosis increased at the ROI level, collagen fibers tended to be less dense, shorter, straighter, thinner, and more aligned with one another (p < 0.05). CONCLUSIONS: Collagen fiber density was positively associated with local, but not global, mammographic density, suggesting that collagen microarchitecture may not translate into macroscopic mammographic features. However, collagen fiber features may be markers of cancer risk and/or progression among women referred for biopsy based on abnormal breast imaging.

Real-time polarization microscopy of fibrillar collagen in histopathology.

Over the past two decades, fibrillar collagen reorganization parameters such as the amount of collagen deposition, fiber angle and alignment have been widely explored in numerous studies. These parameters are now widely accepted as stromal biomarkers and linked to disease progression and survival time in several cancer types. Despite all these advances, there has not been a significant effort to make it possible for clinicians to explore these biomarkers without adding steps to the clinical workflow or by requiring high-cost imaging systems. In this paper, we evaluate previously described polychromatic polarization microscope (PPM) to visualize collagen fibers with an optically generated color representation of fiber orientation and alignment when inspecting the sample by a regular microscope with minor modifications. This system does not require stained slides, but is compatible with histological stains such as H&E. Consequently, it can be easily accommodated as part of regular pathology review of tissue slides, while providing clinically useful insight into stromal composition.

Collagen Organization in Relation to Ductal Carcinoma In Situ Pathology and Outcomes.

BACKGROUND: There is widespread interest in discriminating indolent from aggressive ductal carcinoma in situ (DCIS). We sought to evaluate collagen organization in the DCIS tumor microenvironment in relation to pathologic characteristics and patient outcomes. METHODS: We retrieved fixed tissue specimens for 90 DCIS cases within the population-based Vermont DCIS Cohort. We imaged collagen fibers within 75 µm of the tumor/stromal boundary on hematoxylin and eosin-stained slides using multiphoton microscopy with second-harmonic generation. Automated software quantified collagen fiber length, width, straightness, density, alignment, and angle to the tumor/stroma boundary. Factor analysis identified linear combinations of collagen fiber features representing composite attributes of collagen organization. RESULTS: Multiple collagen features were associated with DCIS grade, necrosis pattern, or periductal fibrosis (P < 0.05). After adjusting for treatments and nuclear grade, risk of recurrence (defined as any second breast cancer diagnosis) was lower among cases with greater collagen fiber width [hazard ratio (HR), 0.57 per one standard deviation increase; 95% confidence interval (CI), 0.39-0.84] and fiber density (HR, 0.60; 95% CI, 0.42-0.85), whereas risk was elevated among DCIS cases with higher fiber straightness (HR, 1.47; 95% CI, 1.05-2.06) and distance to the nearest two fibers (HR, 1.47; 95% CI, 1.06-2.02). Fiber length, alignment, and fiber angle were not associated with recurrence (P > 0.05). Five composite factors were identified, accounting for 72.4% of the total variability among fibers; three were inversely associated with recurrence (HRs ranging from 0.60 to 0.67; P ≤ 0.01). CONCLUSIONS: Multiple aspects of collagen organization around DCIS lesions are associated with recurrence risk. IMPACT: Collagen organization should be considered in the development of prognostic DCIS biomarker signatures.

Non-disruptive collagen characterization in clinical histopathology using cross-modality image synthesis.

The importance of fibrillar collagen topology and organization in disease progression and prognostication in different types of cancer has been characterized extensively in many research studies. These explorations have either used specialized imaging approaches, such as specific stains (e.g., picrosirius red), or advanced and costly imaging modalities (e.g., second harmonic generation imaging (SHG)) that are not currently in the clinical workflow. To facilitate the analysis of stromal biomarkers in clinical workflows, it would be ideal to have technical approaches that can characterize fibrillar collagen on standard H&E stained slides produced during routine diagnostic work. Here, we present a machine learning-based stromal collagen image synthesis algorithm that can be incorporated into existing H&E-based histopathology workflow. Specifically, this solution applies a convolutional neural network (CNN) directly onto clinically standard H&E bright field images to extract information about collagen fiber arrangement and alignment, without requiring additional specialized imaging stains, systems or equipment.

Fibrillar Collagen Quantification With Curvelet Transform Based Computational Methods.

Quantification of fibrillar collagen organization has given new insight into the possible role of collagen topology in many diseases and has also identified candidate image-based bio-markers in breast cancer and pancreatic cancer. We have been developing collagen quantification tools based on the curvelet transform (CT) algorithm and have demonstrated this to be a powerful multiscale image representation method due to its unique features in collagen image denoising and fiber edge enhancement. In this paper, we present our CT-based collagen quantification software platform with a focus on new features and also giving a detailed description of curvelet-based fiber representation. These new features include C++-based code optimization for fast individual fiber tracking, Java-based synthetic fiber generator module for method validation, automatic tumor boundary generation for fiber relative quantification, parallel computing for large-scale batch mode processing, region-of-interest analysis for user-specified quantification, and pre- and post-processing modules for individual fiber visualization. We present a validation of the tracking of individual fibers and fiber orientations by using synthesized fibers generated by the synthetic fiber generator. In addition, we provide a comparison of the fiber orientation calculation on pancreatic tissue images between our tool and three other quantitative approaches. Lastly, we demonstrate the use of our software tool for the automatic tumor boundary creation and the relative alignment quantification of collagen fibers in human breast cancer pathology images, as well as the alignment quantification of in vivo mouse xenograft breast cancer images.

Meflin-Positive Cancer-Associated Fibroblasts Inhibit Pancreatic Carcinogenesis.

Cancer-associated fibroblasts (CAF) constitute a major component of the tumor microenvironment. Recent observations in genetically engineered mouse models and clinical studies have suggested that there may exist at least two functionally different populations of CAFs, that is, cancer-promoting CAFs (pCAF) and cancer-restraining CAFs (rCAF). Although various pCAF markers have been identified, the identity of rCAFs remains unknown because of the lack of rCAF-specific marker(s). In this study, we found that Meflin, a glycosylphosphatidylinositol-anchored protein that is a marker of mesenchymal stromal/stem cells and maintains their undifferentiated state, is expressed by pancreatic stellate cells that are a source of CAFs in pancreatic ductal adenocarcinoma (PDAC). In situ hybridization analysis of 71 human PDAC tissues revealed that the infiltration of Meflin-positive CAFs correlated with favorable patient outcome. Consistent herewith, Meflin deficiency led to significant tumor progression with poorly differentiated histology in a PDAC mouse model. Similarly, genetic ablation of Meflin-positive CAFs resulted in poor differentiation of tumors in a syngeneic transplantation model. Conversely, delivery of a Meflin-expressing lentivirus into the tumor stroma or overexpression of Meflin in CAFs suppressed the growth of xenograft tumors. Lineage tracing revealed that Meflin-positive cells gave rise to α-smooth muscle actin-positive CAFs that are positive or negative for Meflin, suggesting a mechanism for generating CAF heterogeneity. Meflin deficiency or low expression resulted in straightened stromal collagen fibers, which represent a signature for aggressive tumors, in mouse or human PDAC tissues, respectively. Together, the data suggest that Meflin is a marker of rCAFs that suppress PDAC progression. SIGNIFICANCE: Meflin marks and functionally contributes to a subset of cancer-associated fibroblasts that exert antitumoral effects.Graphical Abstract: http://cancerres.aacrjournals.org/content/canres/79/20/5367/F1.large.jpg.

Mammographic density: intersection of advocacy, science, and clinical practice.

Purpose: Here we aim to review the association between mammographic density, collagen structure and breast cancer risk. Findings: While mammographic density is a strong predictor of breast cancer risk in populations, studies by Boyd show that mammographic density does not predict breast cancer risk in individuals. Mammographic density is affected by age, parity, menopausal status, race/ethnicity, and body mass index (BMI).New studies normalize mammographic density to BMI may provide a more accurate way to compare mammographic density in women of diverse race and ethnicity. Preclinical and tissue-based studies have investigated the role collagen composition and structure in predicting breast cancer risk. There is emerging evidence that collagen structure may activate signaling pathways associated with aggressive breast cancer biology. Summary: Measurement of film mammographic density does not adequately capture the complex signaling that occurs in women with at-risk collagen. New ways to measure at-risk collagen potentially can provide a more accurate view of risk.

Targeted matrisome analysis identifies thrombospondin-2 and tenascin-C in aligned collagen stroma from invasive breast carcinoma.

Increasing evidence demonstrates an important role for the extracellular matrix (ECM) in breast cancer progression. Collagen type I, a core constituent of the fibrous ECM, undergoes a significant set of changes that accompany tumor progression, termed Tumor Associated Collagen Signatures (TACS). Late stages of this progression are characterized by the presence of bundled, straight collagen (TACS-2) that become oriented perpendicular to the tumor-stromal boundary (TACS-3). Importantly, the presence of TACS-3 collagen is an independent predictor of poor patient outcome. At present, it remains unclear whether reorganization of the collagen matrix is the consequence of mechanical or compositional tissue remodeling. Here, we identify compositional changes in ECM correlating to collagen fiber reorganization from nineteen normal and invasive ductal carcinoma (IDC) patient biopsies using matrisome-targeted proteomics. Twenty-seven ECM proteins were significantly altered in IDC samples compared to normal tissue. Further, a set of nineteen matrisome proteins positively correlate and five proteins inversely correlate with IDC tissues containing straightened collagen fibers. Tenascin-C and thrombospondin-2 significantly co-localized with aligned collagen fibers in IDC tissues. This study highlights the compositional change in matrisome proteins accompanying collagen re-organization during breast cancer progression and provides candidate proteins for investigation into cellular and structural influences on collagen alignment.

The Presence of Cyclooxygenase 2, Tumor-Associated Macrophages, and Collagen Alignment as Prognostic Markers for Invasive Breast Carcinoma Patients.

Inflammation, and the organization of collagen in the breast tumor microenvironment, is an important mediator of breast tumor progression. However, a direct link between markers of inflammation, collagen organization, and patient outcome has yet to be established. A tumor microarray of 371 invasive breast carcinoma biopsy specimens was analyzed for expression of inflammatory markers, including cyclooxygenase 2 (COX-2), macrophages, and several collagen features in the tumor nest (TN) or the tumor-associated stroma (TS). The tumor microarray cohort included females, aged 18 to 80 years, with a median follow-up of 8.4 years. High expression of COX-2 (TN), CD68 (TS), and CD163 (TN and TS) predicted worse patient overall survival (OS). This notion was strengthened by the finding from the multivariate analysis that high numbers of CD163+ macrophages in the TS is an independent prognostic factor. Overall collagen deposition was associated with high stromal expression of COX-2 and CD163; however, total collagen deposition was not a predictor for OS. Conversely, local collagen density, alignment and perpendicular alignment to the tumor boundary (tumor-associated collagen signature-3) were predictors of OS. These results suggest that in invasive carcinoma, the localization of inflammatory cells and aligned collagen orientation predict poor patient survival. Additional clinical studies may help validate whether therapy with selective COX-2 inhibitors alters expression of CD68 and CD163 inflammatory markers.

Collagen Alignment as a Predictor of Recurrence after Ductal Carcinoma In Situ.

Background: Collagen fibers surrounding breast ducts may influence breast cancer progression. Syndecan-1 interacts with constituents in the extracellular matrix, including collagen fibers, and may contribute to cancer cell migration. Thus, the orientation of collagen fibers surrounding ductal carcinoma in situ (DCIS) lesions and stromal syndecan-1 expression may predict recurrence.Methods: We evaluated collagen fiber alignment and syndecan-1 expression in 227 women diagnosed with DCIS in 1995 to 2006 followed through 2014 (median, 14.5 years; range, 0.7-17.6). Stromal collagen alignment was evaluated from diagnostic tissue slides using second harmonic generation microscopy and fiber analysis software. Univariate analysis was conducted using χ2 tests and ANOVA. The association between collagen alignment z-scores, syndecan-1 staining intensity, and time to recurrence was evaluated using HRs and 95% confidence intervals (CIs).Results: Greater fiber angles surrounding DCIS lesions, but not syndecan-1 staining intensity, were related to positive HER2 (P = 0.002) status, comedo necrosis (P = 0.03), and negative estrogen receptor (P = 0.002) and progesterone receptor (P = 0.02) status. Fiber angle distributions surrounding lesions included more angles closer to 90 degrees than normal ducts (P = 0.06). Collagen alignment z-scores for DCIS lesions were positively related to recurrence (HR = 1.25; 95% CI, 0.84-1.87 for an interquartile range increase in average fiber angles).Conclusions: Although collagen alignment and stromal syndecan-1 expression did not predict recurrence, collagen fibers perpendicular to the duct perimeter were more frequent in DCIS lesions with features typical of poor prognosis.Impact: Follow-up studies are warranted to examine whether additional features of the collagen matrix may more strongly predict patient outcomes. Cancer Epidemiol Biomarkers Prev; 27(2); 138-45. ©2017 AACR.

Tumor mechanics and metabolic dysfunction.

Desmosplasia is a characteristic of most solid tumors and leads to fibrosis through abnormal extracellular matrix (ECM) deposition, remodeling, and posttranslational modifications. The resulting stiff tumor stroma not only compromises vascular integrity to induce hypoxia and impede drug delivery, but also promotes aggressiveness by potentiating the activity of key growth, invasion, and survival pathways. Intriguingly, many of the protumorigenic signaling pathways that are mechanically activated by ECM stiffness also promote glucose uptake and aerobic glycolysis, and an altered metabolism is a recognized hallmark of cancer. Indeed, emerging evidence suggests that metabolic alterations and an abnormal ECM may cooperatively drive cancer cell aggression and treatment resistance. Accordingly, improved methods to monitor tissue mechanics and metabolism promise to improve diagnostics and treatments to ameliorate ECM stiffening and elevated mechanosignaling may improve patient outcome. Here we discuss the interplay between ECM mechanics and metabolism in tumor biology and suggest that monitoring these processes and targeting their regulatory pathways may improve diagnostics, therapy, and the prevention of malignant transformation.

Automated quantification of aligned collagen for human breast carcinoma prognosis.

BACKGROUND: Mortality in cancer patients is directly attributable to the ability of cancer cells to metastasize to distant sites from the primary tumor. This migration of tumor cells begins with a remodeling of the local tumor microenvironment, including changes to the extracellular matrix and the recruitment of stromal cells, both of which facilitate invasion of tumor cells into the bloodstream. In breast cancer, it has been proposed that the alignment of collagen fibers surrounding tumor epithelial cells can serve as a quantitative image-based biomarker for survival of invasive ductal carcinoma patients. Specific types of collagen alignment have been identified for their prognostic value and now these tumor associated collagen signatures (TACS) are central to several clinical specimen imaging trials. Here, we implement the semi-automated acquisition and analysis of this TACS candidate biomarker and demonstrate a protocol that will allow consistent scoring to be performed throughout large patient cohorts. METHODS: Using large field of view high resolution microscopy techniques, image processing and supervised learning methods, we are able to quantify and score features of collagen fiber alignment with respect to adjacent tumor-stromal boundaries. RESULTS: Our semi-automated technique produced scores that have statistically significant correlation with scores generated by a panel of three human observers. In addition, our system generated classification scores that accurately predicted survival in a cohort of 196 breast cancer patients. Feature rank analysis reveals that TACS positive fibers are more well-aligned with each other, are of generally lower density, and terminate within or near groups of epithelial cells at larger angles of interaction. CONCLUSION: These results demonstrate the utility of a supervised learning protocol for streamlining the analysis of collagen alignment with respect to tumor stromal boundaries.

Computational segmentation of collagen fibers from second-harmonic generation images of breast cancer.

Second-harmonic generation (SHG) imaging can help reveal interactions between collagen fibers and cancer cells. Quantitative analysis of SHG images of collagen fibers is challenged by the heterogeneity of collagen structures and low signal-to-noise ratio often found while imaging collagen in tissue. The role of collagen in breast cancer progression can be assessed post acquisition via enhanced computation. To facilitate this, we have implemented and evaluated four algorithms for extracting fiber information, such as number, length, and curvature, from a variety of SHG images of collagen in breast tissue. The image-processing algorithms included a Gaussian filter, SPIRAL-TV filter, Tubeness filter, and curvelet-denoising filter. Fibers are then extracted using an automated tracking algorithm called fiber extraction (FIRE). We evaluated the algorithm performance by comparing length, angle and position of the automatically extracted fibers with those of manually extracted fibers in twenty-five SHG images of breast cancer. We found that the curvelet-denoising filter followed by FIRE, a process we call CT-FIRE, outperforms the other algorithms under investigation. CT-FIRE was then successfully applied to track collagen fiber shape changes over time in an in vivo mouse model for breast cancer.

Why the stroma matters in breast cancer: insights into breast cancer patient outcomes through the examination of stromal biomarkers.

Survival and recurrence rates in breast cancer are variable for common diagnoses, and therefore the biological underpinnings of the disease that determine those outcomes are yet to be fully understood. As a result, translational medicine is one of the fastest growing arenas of study in tumor biology. With advancements in genetic and imaging techniques, archived biopsies can be examined for purposes other than diagnosis. There is a great deal of evidence that points to the stroma as the major regulator of tumor progression following the initial stages of tumor formation, and the stroma may also contribute to risk factors determining tumor formation. Therefore, aspects of stromal biology are well-suited to be a focus for studies of patient outcome, where statistical differences in survival among patients provide evidence as to whether that stromal component is a signpost for tumor progression. In this review we summarize the latest research done where breast cancer patient survival was correlated with aspects of stromal biology, which have been put into four categories: reorganization of the extracellular matrix (ECM) to promote invasion, changes in the expression of stromal cell types, changes in stromal gene expression, and changes in cell biology signaling cascades to and from the stroma.

Postpartum mammary gland involution drives progression of ductal carcinoma in situ through collagen and COX-2.

The prognosis of breast cancer in young women is influenced by reproductive history. Women diagnosed within 5 years postpartum have worse prognosis than nulliparous women or women diagnosed during pregnancy. Here we describe a mouse model of postpartum breast cancer that identifies mammary gland involution as a driving force of tumor progression. In this model, human breast cancer cells exposed to the involuting mammary microenvironment form large tumors that are characterized by abundant fibrillar collagen, high cyclooxygenase-2 (COX-2) expression and an invasive phenotype. In culture, tumor cells are invasive in a fibrillar collagen and COX-2-dependent manner. In the involuting mammary gland, inhibition of COX-2 reduces the collagen fibrillogenesis associated with involution, as well as tumor growth and tumor cell infiltration to the lung. These data support further research to determine whether women at high risk for postpartum breast cancer would benefit from treatment with nonsteroidal anti-inflammatory drugs (NSAIDs) during postpartum involution.

Aligned collagen is a prognostic signature for survival in human breast carcinoma.

Evidence for the potent influence of stromal organization and function on invasion and metastasis of breast tumors is ever growing. We have performed a rigorous examination of the relationship of a tumor-associated collagen signature-3 (TACS-3) to the long-term survival rate of human patients. TACS-3 is characterized by bundles of straightened and aligned collagen fibers that are oriented perpendicular to the tumor boundary. An evaluation of TACS-3 was performed in biopsied tissue sections from 196 patients by second harmonic generation imaging of the backscattered signal generated by collagen. Univariate analysis of a Cox proportional hazard model demonstrated that the presence of TACS-3 was associated with poor disease-specific and disease-free survival, resulting in hazard ratios between 3.0 and 3.9. Furthermore, TACS-3 was confirmed to be an independent prognostic indicator regardless of tumor grade and size, estrogen or progesterone receptor status, human epidermal growth factor receptor-2 status, node status, and tumor subtype. Interestingly, TACS-3 was positively correlated to expression of stromal syndecan-1, a receptor for several extracellular matrix proteins including collagens. Because of the strong statistical evidence for poor survival in patients with TACS, and because the assessment can be performed in routine histopathological samples imaged via second harmonic generation or using picrosirius, we propose that quantifying collagen alignment is a viable, novel paradigm for the prediction of human breast cancer survival.

Nonlinear optical microscopy and computational analysis of intrinsic signatures in breast cancer.

Recently, new non-invasive imaging methods have been developed and applied to cellular and animal mammary models that have enabled breast cancer researchers to track key players and events in mammary metastasis. Noninvasive nonlinear optical methods such as multiphoton laser scanning microscopy (MPLSM), Fluorescence Lifetime Microscopy (FLIM) and second harmonic generation (SHG) imaging provide an unrivaled ability for obtaining high-resolution images from deep within tissue that can be exploited in the quest to understand breast cancer progression. These optical methods can add greatly to our knowledge of cancer progression by allowing key processes to be non-invasively imaged such as metabolism (on the basis of free and bound NADH detection via FLIM) and interactions with the extracellular matrix (SHG imaging of collagen). In this short application note we present a survey of our latest optical and computational efforts to study intrinsic fluorescence in breast cancer models. In particular we present the latest development in our SLIM Plotter application, an open source visualization program for interactive visualization and inspection of combined spectral lifetime (SLIM) data.

Fluorescence lifetime imaging of endogenous fluorophores in histopathology sections reveals differences between normal and tumor epithelium in carcinoma in situ of the breast.

The classical examination of histology slides from a mouse model of breast cancer has been extended in this study to incorporate modern multiphoton excitation and photon-counting techniques. The advantage of such approaches is quantification of potential diagnostic parameters from the fluorescence emission signal, whereby the traditional descriptive staging process is complemented by measurements of fluorescence intensity, lifetime, and spectra. We explored whether the clinical "gold standard" of eosin and hematoxylin stained histology slides would provide optical biomarker signatures of diagnostic value. Alternatively, we examined unstained slides for changes in intensity and/or fluorescence lifetime of relevant endogenous fluorophores. Although eosin provided a strong emission signal and had distinct spectra and lifetime, we found that it was not useful as a fluorescent biological marker, particularly when combined with hematoxylin. Instead, we found that the properties of the fluorescence from the endogenous fluorophores NADH and FAD were indicative of the pathological state of the tissue. Comparing regions of carcinoma in situ to adjacent histologically normal regions, we found that tumor cells produced higher intensity and had a longer fluorescence lifetime. By imaging at 780 nm and 890 nm excitation, we were able to differentiate the fluorescence of FAD from NADH by separating the emission spectra. The shift to a longer lifetime in tumor cells was independent of the free or bound state of FAD and NADH, and of the excitation wavelength. Most forms of cancer have altered metabolism and redox ratios; here we present a method that has potential for early detection of these changes, which are preserved in fixed tissue samples such as classic histopathology slides.

Nonlinear optical imaging of cellular processes in breast cancer.

Nonlinear optical imaging techniques such as multiphoton and second harmonic generation (SHG) microscopy used in conjunction with novel signal analysis techniques such as spectroscopic and fluorescence excited state lifetime detection have begun to be used widely for biological studies. This is largely due to their promise to noninvasively monitor the intracellular processes of a cell together with the cell's interaction with its microenvironment. Compared to other optical methods these modalities provide superior depth penetration and viability and have the additional advantage in that they are compatible technologies that can be applied simultaneously. Therefore, application of these nonlinear optical approaches to the study of breast cancer holds particular promise as these techniques can be used to image exogeneous fluorophores such as green fluorescent protein as well as intrinsic signals such as SHG from collagen and endogenous fluorescence from nicotinamide adenine dinucleotide or flavin adenine dinucleotide. In this article the application of multiphoton excitation, SHG, and fluorescence lifetime imaging microscopy to relevant issues regarding the tumor-stromal interaction, cellular metabolism, and cell signaling in breast cancer is described. Furthermore, the ability to record and monitor the intrinsic fluorescence and SHG signals provides a unique tool for researchers to understand key events in cancer progression in its natural context.