Collagen Signatures Research Articles

Intratumoralcollagen signatures predict clinical outcomes in feline mammary carcinoma.

Breast cancer is the most common cause of cancer-related deaths in women worldwide. Identification of reliable prognostic indicators and therapeutic targets is critical for improving patient outcome. Cancer in companion animals often strongly resembles human cancers and a comparative approach to identify prognostic markers can improve clinical care across species. Feline mammary tumors (FMT) serve as models for extremely aggressive triple negative breast cancer (TNBC) in humans, with high rates of local and distant recurrence after resection. Despite the aggressive clinical behavior of most FMT, current prognostic indicators are insufficient for accurately predicting outcome, similar to human patients. Given significant heterogeneity of mammary tumors, there has been a recent focus on identification of universal tumor-permissive stromal features that can predict biologic behavior and provide therapeutic targets to improve outcome. As in human and canine patients, collagen signatures appear to play a key role in directing mammary tumor behavior in feline patients. We find that patients bearing FMTs with denser collagen, as well as longer, thicker and straighter fibers and less identifiable tumor-stromal boundaries had poorer outcomes, independent of the clinical variables grade and surgical margins. Most importantly, including the collagen parameters increased the predictive power of the clinical model. Thus, our data suggest that similarities with respect to the stromal microenvironment between species may allow this model to predict outcome and develop novel therapeutic targets within the tumor stroma that would benefit both veterinary and human patients with aggressive mammary tumors.

Ascites-induced compression alters the peritoneal microenvironment and promotes metastatic success in ovarian cancer

The majority of women with recurrent ovarian cancer (OvCa) develop malignant ascites with volumes that can reach > 2 L. The resulting elevation in intraperitoneal pressure (IPP), from normal values of 5 mmHg to as high as 22 mmHg, causes striking changes in the loading environment in the peritoneal cavity. The effect of ascites-induced changes in IPP on OvCa progression is largely unknown. Herein we model the functional consequences of ascites-induced compression on ovarian tumor cells and components of the peritoneal microenvironment using a panel of in vitro, ex vivo and in vivo assays. Results show that OvCa cell adhesion to the peritoneum was increased under compression. Moreover, compressive loads stimulated remodeling of peritoneal mesothelial cell surface ultrastructure via induction of tunneling nanotubes (TNT). TNT-mediated interaction between peritoneal mesothelial cells and OvCa cells was enhanced under compression and was accompanied by transport of mitochondria from mesothelial cells to OvCa cells. Additionally, peritoneal collagen fibers adopted a more linear anisotropic alignment under compression, a collagen signature commonly correlated with enhanced invasion in solid tumors. Collectively, these findings elucidate a new role for ascites-induced compression in promoting metastatic OvCa progression.

Regulation of migration of chemotactic tumor cells by the spatial distribution of collagen fiber orientation.

Collagen fibers, an important component of the extracellular matrix (ECM), can both inhibit and promote cellular migration. In vitro studies have revealed that the fibers' orientations are crucial to cellular invasion, while in vivo investigations have led to the development of tumor-associated collagen signatures (TACS) as an important prognostic factor. Studying biophysical regulation of cell invasion and the effect of the fibers' orientation not only deepens our understanding of the phenomenon, but also helps classify the TACSs precisely, which is currently lacking. We present a stochastic model for random or chemotactic migration of cells in fibrous ECM, and study the role of the various factors in it. The model provides a framework for quantitative classification of the TACSs, and reproduces quantitatively recent experimental data for cell motility. It also indicates that the spatial distribution of the fibers' orientations and extended correlations between them, hitherto ignored, as well as dynamics of cellular motion all contribute to regulation of the cells' invasion length, which represents a measure of metastatic risk. Although the fibers' orientations trivially affect randomly moving cells, their effect on chemotactic cells is completely nontrivial and unexplored, which we study in this paper.

Parity predisposes breasts to the oncogenic action of PAPP-A and activation of the collagen receptor DDR2

BackgroundWomen who had children at a young age (less than 25) show a reduced overall risk of breast cancer. However, epidemiological studies showed that for all other women, pregnancy increases the risk of breast cancer and the risk remains higher for decades. Further, even in women who had children at a young age, there is a transient increase risk that peaks 6 years after pregnancy. Women diagnosed with breast cancer following pregnancy show a higher rate of metastasis. Yet, the factors that increase the predisposition of post-partum breasts to more aggressive cancers remain unknown. Pregnancy-associated plasma protein A (PAPP-A) is a secreted protease that is overexpressed in more than 70% of breast cancers. However, PAPP-A is a collagen-dependent oncogene. We initiated this study to test the effect of PAPP-A on the predisposition of post-partum breasts.MethodsWe used PAPP-A mouse models for the analysis of its effect on virgin, involuting, or post-partum mammary glands. We performed second-harmonic generation microscopy for the analysis of collagen, defined tumor-associated collagen signature (TACS), the rate of mammary tumors, and the status of the collagen-DDR2-Snail axis of metastasis. We knockdown DDR2 by CRISPR and performed invasion assays. A transcriptomic approach was used to define a PAPP-A and parity-dependent genetic signature and assess its correlation with breast cancer recurrence in humans.ResultsWe confirmed that post-partum mammary glands have a higher level of collagen than virgin glands and that this collagen is characterized by an anti-proliferative architecture. However, PAPP-A converts the anti-proliferative post-partum collagen into pro-tumorigenic collagen. We show that PAPP-A activates the collagen receptor DDR2 and metastasis. Further, deletion of DDR2 by CRISPR abolished the effect of PAPP-A on invasion. We defined a PAPP-A-driven genetic signature that identifies patients at higher risk of metastasis.ConclusionsThese results support the notion that information about pregnancy may be critical in the prognosis of breast cancer as passage through a single pregnancy predisposes to the oncogenic action of PAPP-A. Our data indicate that history of pregnancy combined with the expression of PAPP-A-driven genetic signature may be useful to identify patients at higher risk of metastatic disease.

Gliosarcoma Is Driven by Alterations in PI3K/Akt, RAS/MAPK Pathways and Characterized by Collagen Gene Expression Signature.

Gliosarcoma is a very rare brain tumor reported to be a variant of glioblastoma (GBM), IDH-wildtype. While differences in molecular and histological features between gliosarcoma and GBM were reported, detailed information on the genetic background of this tumor is lacking. We intend to fill in this knowledge gap by the complex analysis of somatic mutations, indels, copy number variations, translocations and gene expression patterns in gliosarcomas. Using next generation sequencing, we determined somatic mutations, copy number variations (CNVs) and translocations in 10 gliosarcomas. Six tumors have been further subjected to RNA sequencing analysis and gene expression patterns have been compared to those of GBMs. We demonstrate that gliosarcoma bears somatic alterations in gene coding for PI3K/Akt (PTEN, PI3K) and RAS/MAPK (NF1, BRAF) signaling pathways that are crucial for tumor growth. Interestingly, the frequency of PTEN alterations in gliosarcomas was much higher than in GBMs. Aberrations of PTEN were the most frequent and occurred in 70% of samples. We identified genes differentially expressed in gliosarcoma compared to GBM (including collagen signature) and confirmed a difference in the protein level by immunohistochemistry. We found several novel translocations (including translocations in the RABGEF1 gene) creating potentially unfavorable combinations. Collected results on genetic alterations and transcriptomic profiles offer new insights into gliosarcoma pathobiology, highlight differences in gliosarcoma and GBM genetic backgrounds and point out to distinct molecular cues for targeted treatment.

Association of the Collagen Signature in the Tumor Microenvironment With Lymph Node Metastasis in Early Gastric Cancer

Lymph node status is the primary determinant in treatment decision making in early gastric cancer (EGC). Current evaluation methods are not adequate for estimating lymph node metastasis (LNM) in EGC. To develop and validate a prediction model based on a fully quantitative collagen signature in the tumor microenvironment to estimate the individual risk of LNM in EGC. This retrospective study was conducted from August 1, 2016, to May 10, 2018, at 2 medical centers in China (Nanfang Hospital and Fujian Provincial Hospital). Participants included a primary cohort (n = 232) of consecutive patients with histologically confirmed gastric cancer who underwent radical gastrectomy and received a T1 gastric cancer diagnosis from January 1, 2008, to December 31, 2012. Patients with neoadjuvant radiotherapy, chemotherapy, or chemoradiotherapy were excluded. An additional consecutive cohort (n = 143) who received the same diagnosis from January 1, 2011, to December 31, 2013, was enrolled to provide validation. Baseline clinicopathologic data of each patient were collected. Collagen features were extracted in specimens using multiphoton imaging, and the collagen signature was constructed. An LNM prediction model based on the collagen signature was developed and was internally and externally validated. The area under the receiver operating characteristic curve (AUROC) of the prediction model and decision curve were analyzed for estimating LNM. In total, 375 patients were included. The primary cohort comprised 232 consecutive patients, in whom the LNM rate was 16.4% (n = 38; 25 men [65.8%] with a mean [SD] age of 57.82 [10.17] years). The validation cohort consisted of 143 consecutive patients, in whom the LNM rate was 20.9% (n = 30; 20 men [66.7%] with a mean [SD] age of 54.10 [13.19] years). The collagen signature was statistically significantly associated with LNM (odds ratio, 5.470; 95% CI, 3.315-9.026; P < .001). Multivariate analysis revealed that the depth of tumor invasion, tumor differentiation, and the collagen signature were independent predictors of LNM. These 3 predictors were incorporated into the new prediction model, and a nomogram was established. The model showed good discrimination in the primary cohort (AUROC, 0.955; 95% CI, 0.919-0.991) and validation cohort (AUROC, 0.938; 95% CI, 0.897-0.981). An optimal cutoff value was selected in the primary cohort, which had a sensitivity of 86.8%, a specificity of 93.3%, an accuracy of 92.2%, a positive predictive value of 71.7%, and a negative predictive value of 97.3%. The validation cohort had a sensitivity of 90.0%, a specificity of 90.3%, an accuracy of 90.2%, a positive predictive value of 71.1%, and a negative predictive value of 97.1%. Among the 375 patients, a sensitivity of 87.3%, a specificity of 92.1%, an accuracy of 91.2%, a positive predictive value of 72.1%, and a negative predictive value of 96.9% were found. This study's findings suggest that the collagen signature in the tumor microenvironment is an independent indicator of LNM in EGC, and the prediction model based on this collagen signature may be useful in treatment decision making for patients with EGC.

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.

Abstract P5-02-02: Second harmonic generation in combination with nuclear morphometry in the evaluation of DCIS

Abstract Purpose/Objective: Collagen is a major extracellular matrix (ECM) constituent in normal breast and is extensively remodeled in breast carcinoma. Therefore, features of remodeled collagen in the stroma adjacent to ductal carcinoma in situ (DCIS) could indicate cancer progression. The major objective of this study is to identify potential tumor-associated collagen signatures unique to DCIS that will allow us to predict progression based on the collagen texture and nuclear morphology. In this present study, we develop two image analysis pipelines (SHG Texture Extraction and H&amp;E Nuclear Morphology Extractor) to quantify 1) stromal changes, 2) collagen signatures and 3) nuclear morphology from normal breast to DCIS in order to predict local breast cancer recurrence. Method: We used second harmonic generation (SHG) images and H&amp;E to analyze collagen features and to study nuclear morphology using a data set of 336 patients (from which 310 normal and 327 DCIS regions were imaged). The 336 patients were a subset of patients with pure DCIS taken from a case-control study. Clinical-pathologic factors were associated with risk of subsequent ipsilateral cancer (DCIS or invasive). The SHG framework consisted of collagen segmentation using 1) adaptive thresholding and 2) morphological operations. The H&amp;E framework consisted of nuclear segmentation using adaptive thresholding and a maker-controlled watershed algorithm; and nuclear feature extractions including intensity, texture and morphology. Overall, the SHG framework segments collagen regions and computes textural features specifically at collagen regions. Furthermore, the H&amp;E framework segments nuclei and computes nuclei morphology and textural features. These features were used in L1-regularized logistic regression to construct classification models to discriminate normal vs DCIS regions; and to distinguish regions from DCIS patients with vs. without local recurrences. Results: In first experiment, we performed L1-regularized logistic regression to construct a classification model to discriminate normal vs DCIS regions. Our results suggest that using only SHG collagen features, this logistic model selected 19 significant features to build a classification model that achieved area under curve (AUC) 90% and accuracy 83% using 5-Fold cross validation. When H&amp;E nuclei features are used, the logistic model selected 88 significant features and achieved AUC 91% and accuracy 86%. By combined both SHG and H&amp;E features, the model achieved classification AUC 93% and accuracy 88%. By using L1-regularized logistic model with combined significant SHG and H&amp;E features, we achieved AUC 59% with an accuracy of 61% for DCIS and recurrent DCIS regions. Conclusions: Our study suggests that SHG and nuclear morphology features extracted from H&amp;E can improve the classification of normal and DCIS regions. Overall, these results suggest that second harmonic generation and H&amp;E nuclear morphology analysis could aid in the assessment of prognosis and risk of progression to invasive breast cancer. Citation Format: Park CC, Irshad H, Ziaee S, Martin-Tuite P, Habel L, Weaver VM, Schnitt SJ, Beck AH. Second harmonic generation in combination with nuclear morphometry in the evaluation of DCIS [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P5-02-02.

Identification of prognostic collagen signatures and potential therapeutic stromal targets in canine mammary gland carcinoma.

Increasing evidence indicates that the tumor microenvironment plays a critical role in regulating the biologic behavior of breast cancer. In veterinary oncology, there is a need for improved prognostic markers to accurately identify dogs at risk for local and distant (metastatic) recurrence of mammary gland carcinoma and therefore would benefit from adjuvant therapy. Collagen density and fiber organization have been shown to regulate tumor progression in both mouse and human mammary tumors, with certain collagen signatures predicting poor outcomes in women with breast cancer. We hypothesized that collagen signatures in canine mammary tumor biopsies can serve as prognostic biomarkers and potential targets for treatment. We used second harmonic generation imaging to evaluate fibrillar collagen density, the presence of a tumor-stromal boundary, tumor associated collagen signatures (TACS) and individual collagen fiber characteristics (width, length and straightness) in grade I/II and grade III canine mammary tumors. Collagen density, as well as fiber width, length and straightness, were inversely correlated with patient overall survival time. Notably, grade III cases were less likely to have a tumor-stromal boundary and the lack of a boundary predicted poor outcome. Importantly, a lack of a defined tumor-stromal boundary and an increased collagen fiber width were associated with decreased survival even when tumor grade, patient stage, ovariohysterectomy status at the time of mammary tumor excision, and histologic evidence of lymphovascular invasion were considered in a multivariable model, indicating that these parameters could augment current methods to identify patients at high risk for local or metastatic progression/recurrence. Furthermore, these data, which identify for the first time, prognostic collagen biomarkers in naturally occurring mammary gland neoplasia in the dog, support the use of the dog as a translational model for tumor-stromal interactions in breast cancer.

Tumor-associated Collagen Signatures: An Insight

ABSTRACT Tumor microenvironment has a diverse capability to induce both beneficial and adverse consequences for tumorigenesis. It is a multifactorial process induced by the imbalance in the tumor cells and extracellular matrix (ECM). Collagen, the main component of ECM, is traditionally regarded as a passive barrier to resist tumor cell invasion. In recent years, collagen is marked to have its pivotal role to initiate and promote tumor progression. Remodeling of collagen has been appreciated in various benign and malignant tumors. These alterations can be identified and demonstrated as tumor-associated collagen signatures that can be demonstrated using second harmonic generation imaging. Recognition of these characteristic changes in the organization of collagen fiber may potentially serve as an early diagnostic marker in various pathological processes, such as hyperplastic, dysplastic, and cancerous tissues. This review focuses on the physiological and pathological orientation of collagen fibers in relation to epithelium that acts as an image-based biomarker. How to cite this article Pavithra V, Sowmya SV, Rao RS, Patil S, Augustine D, Haragannavar VC, Nambiar S. Tumorassociated Collagen Signatures: An Insight. World J Dent 2017;8(3):224-230.

Association of collagen architecture with glioblastoma patient survival.

OBJECTIVE Glioblastoma (GBM) is the most malignant primary brain tumor. Collagen is present in low amounts in normal brain, but in GBMs, collagen gene expression is reportedly upregulated. However, to the authors' knowledge, direct visualization of collagen architecture has not been reported. The authors sought to perform the first direct visualization of GBM collagen architecture, identify clinically relevant collagen signatures, and link them to differential patient survival. METHODS Second-harmonic generation microscopy was used to detect collagen in a GBM patient tissue microarray. Focal and invasive GBM mouse xenografts were stained with Picrosirius red. Quantitation of collagen fibers was performed using custom software. Multivariate survival analysis was done to determine if collagen is a survival marker for patients. RESULTS In focal xenografts, collagen was observed at tumor brain boundaries. For invasive xenografts, collagen was intercalated with tumor cells. Quantitative analysis showed significant differences in collagen fibers for focal and invasive xenografts. The authors also found that GBM patients with more organized collagen had a longer median survival than those with less organized collagen. CONCLUSIONS Collagen architecture can be directly visualized and is different in focal versus invasive GBMs. The authors also demonstrate that collagen signature is associated with patient survival. These findings suggest that there are collagen differences in focal versus invasive GBMs and that collagen is a survival marker for GBM.

Identification of the earliest collagen- and plant-based coatings from Neolithic artefacts (Nahal Hemar cave, Israel).

Mortuary practices in human evolution record cognitive, social changes and technological innovations. The Neolithic Revolution in the Levant was a watershed in this domain that has long fascinated the archaeological community. Plaster modelled skulls are well known at Jericho and several other Neolithic sites, and in Nahal Hemar cave (Israel, ca. 8200 −7300 cal. BC) excavations yielded six unique human skulls covered with a black organic coating applied in a net pattern evoking a headdress. This small cave was used as storage for paraphernalia in the semi-arid area of the Judean desert and the dry conditions preserved other artefacts such as baskets coated with a similar dark substance. While previous analysis had revealed the presence of amino acids consistent with a collagen signature, in the present report, specific biomarkers were characterised using combined proteomic and lipid approaches. Basket samples yielded collagen and blood proteins of bovine origin (Bos genus) and a large sequence coverage of a plant protein charybdin (Charybdis genus). The skull residue samples were dominated by benzoate and cinnamate derivatives and triterpenes consistent with a styrax-type resin (Styrax officinalis), thus providing the earliest known evidence of an odoriferous plant resin used in combination with an animal product.

Lactation opposes pappalysin-1-driven pregnancy-associated breast cancer.

Pregnancy is associated with a transient increase in risk for breast cancer. However, the mechanism underlying pregnancy‐associated breast cancer (PABC) is poorly understood. Here, we identify the protease pappalysin‐1 (PAPP‐A) as a pregnancy‐dependent oncogene. Transgenic expression of PAPP‐A in the mouse mammary gland during pregnancy and involution promotes the deposition of collagen. We demonstrate that collagen facilitates the proteolysis of IGFBP‐4 and IGFBP‐5 by PAPP‐A, resulting in increased proliferative signaling during gestation and a delayed involution. However, while studying the effect of lactation, we found that although PAPP‐A transgenic mice lactating for an extended period of time do not develop mammary tumors, those that lactate for a short period develop mammary tumors characterized by a tumor‐associated collagen signature (TACS‐3). Mechanistically, we found that the protective effect of lactation is associated with the expression of inhibitors of PAPP‐A, STC1, and STC2. Collectively, these results identify PAPP‐A as a pregnancy‐dependent oncogene while also showing that extended lactation is protective against PAPP‐A‐mediated carcinogenesis. Our results offer the first mechanism that explains the link between breast cancer, pregnancy, and breastfeeding.

BI-24 * COLLAGEN PLAYS A ROLE IN GLIOBLASTOMA TUMOR INVASION AND PATIENT SURVIVAL

BACKGROUND: Glioblastoma (GBM) is the most malignant brain cancer. Collagen is minimally expressed in normal brain parenchyma; however, previous reports show upregulated collagen gene expression in GBM. We perform the first direct GBM collagen visualization, identify clinically relevant collagen signatures in focal and invasive GBMs and link them to differential patient survival. METHODS: Customized second harmonic generation (SHG) multiphoton workstation with excitation wavelength tuned to 890nm and a 445± 25nm bandpass emission filter was used to detect collagen in a clinically-annotated human GBM tissue microarray (TMA) for 111 patients. Human GBM cancer stem cell lines that form focal or invasive xenografts were implanted into mice brains. Symptomatic mice with verified xenografts were euthanized, brains excised, and stained with picrosirius red. Quantitative characterization of collagen fibers in xenografts and TMA images was performed with custom software. RESULTS: Collagen was directly visualized in GBMs with SHG and picrosirius red. In focal GBM xenografts, collagen was observed at tumor-brain boundaries, appearing to encapsulate the tumors. For invasive GBM xenografts, collagen was intercalated with tumor cells. Quantitative collagen analysis showed significant differences for mean angle between fibers for focal (60.3856 ± 2.2113) and invasive (70.2529 ± 1.6211) GBM xenografts. GBM TMA specimens were segregated into two collagen groups: fibrillar and immature. Patients with fibrillar GBM collagen had a statistically significantly better median survival (69.6 weeks) compared to immature GBM collagen patients (30.4 weeks). The mean angle between fibers was significantly different for GBMs with fibrillar (84.37 ± 7.298) versus immature (113.2 ± 11.60) collagen. CONCLUSIONS: We demonstrate that collagen can be directly visualized in GBMs, and is different in focal vs. invasive tumors. Our data suggest collagen is a potential survival marker: patients with fibrillar collagen have improved survival. Therefore, we hypothesize that collagen may influence tumor cell invasion by acting as a barrier, and collagen disruption may promote tumor invasion.

SU‐E‐J‐107: Supervised Learning Model of Aligned Collagen for Human Breast Carcinoma Prognosis

Purpose:Our goal is to develop and apply a set of optical and computational tools to enable large‐scale investigations of the interaction between collagen and tumor cells.Methods:We have built a novel imaging system for automating the capture of whole‐slide second harmonic generation (SHG) images of collagen in registry with bright field (BF) images of hematoxylin and eosin stained tissue. To analyze our images, we have integrated a suite of supervised learning tools that semi‐automatically model and score collagen interactions with tumor cells via a variety of metrics, a method we call Electronic Tumor Associated Collagen Signatures (eTACS). This group of tools first segments regions of epithelial cells and collagen fibers from BF and SHG images respectively. We then associate fibers with groups of epithelial cells and finally compute features based on the angle of interaction and density of the collagen surrounding the epithelial cell clusters. These features are then processed with a support vector machine to separate cancer patients into high and low risk groups.Results:We validated our model by showing that eTACS produces classifications that have statistically significant correlation with manual classifications. In addition, our system generated classification scores that accurately predicted breast cancer patient survival in a cohort of 196 patients. Feature rank analysis revealed that TACS positive fibers are more well aligned with each other, generally lower density, and terminate within or near groups of epithelial cells.Conclusion:We are working to apply our model to predict survival in larger cohorts of breast cancer patients with a diversity of breast cancer types, predict response to treatments such as COX2 inhibitors, and to study collagen architecture changes in other cancer types. In the future, our system may be used to provide metastatic potential information to cancer patients to augment existing clinical assays.

Collagen Fiber Alignment in Relation to Prognostic Markers for Ductal Carcinoma In Situ of the Breast

Abstract Almost 20% of new diagnoses of breast cancer are ductal carcinoma in situ (DCIS). DCIS increases risk for invasive breast cancer, and new prognostic markers of disease-free survival are needed to complement the few that are known. Limited studies have investigated the prognostic value of the tumor microenvironment, which is increasingly recognized as potentially playing a key role in tumor invasion and progression. Collagen is the most abundant component of the stroma surrounding the breast ducts, and laboratory studies suggest that collagen alignment facilitates cancer cell invasion and migration. Here we examined whether collagen alignment patterns in human DCIS specimens correlate with known prognostic markers. We evaluated collagen fiber alignment in 235 Wisconsin women diagnosed with DCIS in 1997–2000 who participated in a clinicopathologic cohort study. Fiber alignment was evaluated from routine H&amp;E tissue slides prepared at the time of diagnosis using second harmonic generation (SHG) microscopy, a label-free multiphoton laser scanning technique that selectively images collagen. Images were evaluated for the presence of collagen fibers arranged in a radial pattern (approximately 75- to 90-degree angles) with respect to the DCIS lesion/stroma boundary, a phenotype termed TACS (Tumor Associated Collagen Signature). Chi-square tests were used to compare presence or absence of TACS with prognostic factors from a central review of pathology reports and slides, and analysis of tissue for ER, PR, Ki-67, and HER2. Overall, slides for about half (49.8%) of DCIS cases showed the TACS phenotype. TACS was not associated with age of the cases (P = 0.29), size of the lesion (P = 0.76), grade (P = 0.61), or margin size (P = 0.77). However, results suggested that TACS was more common (63.1%) among DCIS cases detected symptomatically than by mammography (47.3%, P = 0.09). TACS was more common among cases that were ER-negative (P = 0.002), PR-negative (P = 0.02), Ki-67 positive (P = 0.06), and HER2-positive (P = 0.002). This study underscores the relevance of the tumor microenvironment, in particular the arrangement of the collagen fiber matrix. On-going analysis will examine disease-free survival among DCIS cases according to patterns of collagen fiber alignment.

Nonlinear optical microscopy signal processing strategies in cancer.

This work reviews the most relevant present-day processing methods used to improve the accuracy of multimodal nonlinear images in the detection of epithelial cancer and the supporting stroma. Special emphasis has been placed on methods of non linear optical (NLO) microscopy image processing such as: second harmonic to autofluorescence ageing index of dermis (SAAID), tumor-associated collagen signatures (TACS), fast Fourier transform (FFT) analysis, and gray level co-occurrence matrix (GLCM)-based methods. These strategies are presented as a set of potential valuable diagnostic tools for early cancer detection. It may be proposed that the combination of NLO microscopy and informatics based image analysis approaches described in this review (all carried out on free software) may represent a powerful tool to investigate collagen organization and remodeling of extracellular matrix in carcinogenesis processes.

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.

Abstract A35: Aligned collagen is a prognostic signature for survival in human breast carcinoma

Abstract Evidence for the potent influence of stromal organization and function on invasion and metastasis of breast tumors is ever growing. Here we have performed a rigorous examination of the relationship of a tumor-associated collagen signature (TACS-3), to the long term survival rate of human patients diagnosed with invasive breast cancer. 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 (SHG) 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 3.0-3.9. Furthermore, TACS-3 was confirmed to be an independent prognostic indicator regardless of tumor grade and size, ER or PR status, HER-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. Ongoing research is currently investigating both the biochemical signaling pathways that give rise to the TACS-3 phenotype as well as the extent of the influence the mechanical properties of an aligned matrix have on invasion. Because of the strong statistical evidence for poor survival in patients with TACS, and since the assessment can be performed in routine histopathologic samples imaged via SHG or using picrosirius, we propose that quantifying collagen alignment is a viable, novel paradigm for the prediction of human breast cancer survival. Citation Format: Matthew W. Conklin, Jens Eickhoff, Kristin Riching, Carolyn Pehlke, Kevin Eliceiri, Paolo Provenzano, Andreas Friedl, Patricia J. Keely. Aligned collagen is a prognostic signature for survival in human breast carcinoma. [abstract]. In: Proceedings of the AACR Special Conference on Tumor Invasion and Metastasis; Jan 20-23, 2013; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2013;73(3 Suppl):Abstract nr A35.

Aspects of Collagen Changes in Breast Cancer

It is widely acknowledged that the Extracellular Matrix (ECM) of connective tissues plays active roles in numerous biological processes such as cell differentiation, life/death promotion and carcinogenesis. Breast cancer is commonly associated to drastic massive alterations of ECM architecture and composition, especially for what concerns its collagenous component. This mini-review is not aimed to give a comprehensive overview of this complex biological event, but to pick up selected early and recent studies on peculiar features of this field and recapitulate specific aspects related to collagen changes in the affected stroma, such as the re-appearance of OF/LB collagen the accumulation of type V collagen and the identification of tumour-associated collagen signatures at structural and gene expression levels. In addition, the effects of OF/LB and type V collagens on the phenotype of cultured breast cancer cells, as well as recent data on the active role played on neoplastic cells by collagen fragments originating from prominent tissue turn-over are summarized and discussed.