High-risk and selected benign breast lesions diagnosed on core needle biopsy: Evidence for and against immediate surgical excision.

The vast majority of image-detected breast abnormalities are diagnosed by percutaneous core needle biopsy (CNB) in contemporary practice. For frankly malignant lesions diagnosed by CNB, the standard practice of excision and multimodality therapy have been well-defined. However, for high-risk and selected benign lesions diagnosed by CNB, there is less consensus on optimal patient management and the need for immediate surgical excision. Here we outline the arguments for and against the practice of routine surgical excision of commonly encountered high-risk and selected benign breast lesions diagnosed by CNB. The entities reviewed include atypical ductal hyperplasia, atypical lobular hyperplasia, lobular carcinoma in situ, intraductal papillomas, and radial scars. The data in the peer-reviewed literature confirm the benefits of a patient-centered, multidisciplinary approach that moves away from the reflexive "yes" or "no" for routine excision for a given pathologic diagnosis.

Clinicopathologic and immunohistochemical study of breast angiosarcoma.

Breast angiosarcoma (AS) is a rare malignancy which can be classified into primary or secondary as a result of breast cancer therapy. On histology, breast AS has a wide spectrum of morphologic presentations, and its diagnosis can be challenging based on morphologic evaluation alone. Here, we studied 10 cases of breast AS diagnosed at our institution during a 20-year period, in which 7 cases were radiation-associated AS (RA-AS) and 3 cases were primary AS (P-AS). The average latency between radiotherapy and RA-AS was 8.1 years. RA-AS mostly occurred in breast skin, while all P-AS involved breast parenchyma. All 10 AS cases were high grade, including 4 RA-AS cases demonstrating epithelioid morphology. Histologic morphologies of AS varied from confluent growth of atypical spindle or epithelioid cells to scattered marked pleomorphic cells. Some cases appeared deceptively bland or low grade, but the presence of areas of haemorrhage ('blood lake') or necrosis upgraded them to high grade lesions. Additionally, some epithelioid RA-AS cases with lymphatic differentiation (D2-40 positive) showed pseudopapillary morphology characterized by discohesive cells sloughing off at periphery of vascular cores, resembling papillary breast carcinoma. P-AS did not show prominent vesicular nuclei and/or conspicuous nucleoli, which were features observed in RA-AS. C-MYC immunostain results showed P-AS was completely negative or focal weakly positive in hypercellular areas. In comparison, RA-AS were consistently positive for c-MYC. Epithelioid RA-AS with lymphatic differentiation tended to show stronger and/or more diffuse c-MYC positivity than other AS cases. CD31 and ERG immunostains showed positivity in all cases, while CD34 were negative in some cases with lymphatic differentiation. This study offers a detailed morphologic and immunohistochemical assessment of a rare tumor of the breast that is important to recognize. Common differential diagnosis for breast AS, including post-radiation atypical vascular proliferation (AVP), are also reviewed and discussed.

Clinicopathological and radiological characterization of myofibroblastoma of breast: A single institutional case review.

Myofibroblastoma is a rare type of benign myofibroblastic neoplasm in the breast. It is clinically presented as a well-circumscribed mass, usually small in size (usually less than 4.0 cm), and can mostly be cured by local excision. Rare cases of giant myofibroblastoma greater than 10 cm have been reported, but also follow a benign clinical course. Histologically, breast myofibroblastoma is featured by bland fascicles of spindle cells intermixed with thick hyalinized collagen bundles. Mast cells are frequently found within the stroma. However, a wide spectrum of morphological variants can occur in myofibroblatoma, making its diagnosis challenging sometimes. Differential diagnosis of myofibroblastoma with other spindle cell lesions in the breast, either benign or malignant, is also important in practice. In this study, we collected 15 cases of breast myofibroblastoma diagnosed in our institution during a 20 year period. The sizes of these cases range from 0.4 cm to 35.2 cm (mean is 3.7 cm). To our knowledge, the case of giant breast myofibroblastoma we presented here is the largest one reported to date. The histological examination of the cases show great morphological variations. Besides the classical type, features of cellular, collagenized, palisading, epithelioid, myxoid, myoid, solitary fibrous tumor-like are also identified in the case series. Immunohistochemical staining patterns as well as clinical features of the cases are also summarized and compared. All cases in this study show no recurrence on follow-up. In addition, cases that are important differential diagnosis for breast myofibroblastoma are also studied. Their key histological characteristics are compared with myofibroblastoma, and their immunohistochemical and molecular features are discussed.

Quantitative nuclear histomorphometric features are predictive of Oncotype DX risk categories in ductal carcinoma in situ: preliminary findings.

BACKGROUND: Oncotype DX (ODx) is a 12-gene assay assessing the recurrence risk (high, intermediate, and low) of ductal carcinoma in situ (pre-invasive breast cancer), which guides clinicians regarding prescription of radiotherapy. However, ODx is expensive, time-consuming, and tissue-destructive. In addition, the actual prognostic meaning for the intermediate ODx risk category remains unclear. METHODS: In this work, we evaluated the ability of quantitative nuclear histomorphometric features extracted from hematoxylin and eosin-stained slide images of 62 ductal carcinoma in situ (DCIS) patients to distinguish between the corresponding ODx risk categories. The prognostic value of the identified image signature was further evaluated on an independent validation set of 30 DCIS patients in its ability to distinguish those DCIS patients who progressed to invasive carcinoma versus those who did not. Following nuclear segmentation and feature extraction, feature ranking strategies were employed to identify the most discriminating features between individual ODx risk categories. The selected features were then combined with machine learning classifiers to establish models to predict ODx risk categories. The model performance was evaluated using the average area under the receiver operating characteristic curve (AUC) using cross validation. In addition, an unsupervised clustering approach was also implemented to evaluate the ability of nuclear histomorphometric features to discriminate between the ODx risk categories. RESULTS: Features relating to spatial distribution, orientation disorder, and texture of nuclei were identified as most discriminating between the high ODx and the intermediate, low ODx risk categories. Additionally, the AUC of the most discriminating set of features for the different classification tasks was as follows: (1) high vs low ODx (0.68), (2) high vs. intermediate ODx (0.67), (3) intermediate vs. low ODx (0.57), (4) high and intermediate vs. low ODx (0.63), (5) high vs. low and intermediate ODx (0.66). Additionally, the unsupervised clustering resulted in intermediate ODx risk category patients being co-clustered with low ODx patients compared to high ODx. CONCLUSION: Our results appear to suggest that nuclear histomorphometric features can distinguish high from low and intermediate ODx risk category patients. Additionally, our findings suggest that histomorphometric features for intermediate ODx were more similar to low ODx compared to high ODx risk category.

Systemic Delivery of Tumor-Targeting siRNA Nanoparticles against an Oncogenic LncRNA Facilitates Effective Triple-Negative Breast Cancer Therapy.

Long noncoding RNAs (lncRNAs), by virtue of their versatility and multilevel gene regulation, have emerged as attractive pharmacological targets for treating heterogeneous and complex malignancies like triple-negative breast cancer (TNBC). Despite multiple studies on lncRNA functions in tumor pathology, systemic targeting of these "undruggable" macromolecules with conventional approaches remains a challenge. Here, we demonstrate effective TNBC therapy by nanoparticle-mediated RNAi of the oncogenic lncRNA DANCR, which is significantly overexpressed in TNBC. Tumor-targeting RGD-PEG-ECO/siDANCR nanoparticles were formulated via self-assembly of multifunctional amino lipid ECO, cyclic RGD peptide-PEG, and siDANCR for systemic delivery. MDA-MB-231 and BT549 cells treated with the therapeutic RGD-PEG-ECO/siDANCR nanoparticles exhibited 80-90% knockdown in the expression of DANCR for up to 7 days, indicating efficient intracellular siRNA delivery and sustained target silencing. The RGD-PEG-ECO/siDANCR nanoparticles mediated excellent in vitro therapeutic efficacy, reflected by significant reduction in the invasion, migration, survival, tumor spheroid formation, and proliferation of the TNBC cell lines. At the molecular level, functional ablation of DANCR dynamically impacted the oncogenic nexus by downregulating PRC2-mediated H3K27-trimethylation and Wnt/EMT signaling, and altering the phosphorylation profiles of several kinases in the TNBC cells. Furthermore, systemic administration of the RGD-PEG-ECO/siDANCR nanoparticles at a dose of 1 mg/kg siRNA in nude mice bearing TNBC xenografts resulted in robust suppression of TNBC progression with no overt toxic side-effects, underscoring the efficacy and safety of the nanoparticle therapy. These results demonstrate that nanoparticle-mediated modulation of onco-lncRNAs and their molecular targets is a promising approach for developing curative therapies for TNBC and other cancers.

Nuclear shape and orientation features from H&E images predict survival in early-stage estrogen receptor-positive breast cancers.

Early-stage estrogen receptor-positive (ER+) breast cancer (BCa) is the most common type of BCa in the United States. One critical question with these tumors is identifying which patients will receive added benefit from adjuvant chemotherapy. Nuclear pleomorphism (variance in nuclear shape and morphology) is an important constituent of breast grading schemes, and in ER+ cases, the grade is highly correlated with disease outcome. This study aimed to investigate whether quantitative computer-extracted image features of nuclear shape and orientation on digitized images of hematoxylin-stained and eosin-stained tissue of lymph node-negative (LN-), ER+ BCa could help stratify patients into discrete (<10 years short-term vs. >10 years long-term survival) outcome groups independent of standard clinical and pathological parameters. We considered a tissue microarray (TMA) cohort of 276 ER+, LN- patients comprising 150 patients with long-term and 126 patients with short-term overall survival, wherein 177 randomly chosen cases formed the modeling set, and 99 remaining cases the test set. Segmentation of individual nuclei was performed using multiresolution watershed; subsequently, 615 features relating to nuclear shape/texture and orientation disorder were extracted from each TMA spot. The Wilcoxon's rank-sum test identified the 15 most prognostic quantitative histomorphometric features within the modeling set. These features were then subsequently combined via a linear discriminant analysis classifier and evaluated on the test set to assign a probability of long-term vs. short-term disease-specific survival. In univariate survival analysis, patients identified by the image classifier as high risk had significantly poorer survival outcome: hazard ratio (95% confident interval) = 2.91(1.23-6.92), p = 0.02786. Multivariate analysis controlling for T-stage, histology grade, and nuclear grade showed the classifier to be independently predictive of poorer survival: hazard ratio (95% confident interval) = 3.17(0.33-30.46), p = 0.01039. Our results suggest that quantitative histomorphometric features of nuclear shape and orientation are strongly and independently predictive of patient survival in ER+, LN- BCa.

Optimizing sparse sequencing of single cells for highly multiplex copy number profiling.

Genome-wide analysis at the level of single cells has recently emerged as a powerful tool to dissect genome heterogeneity in cancer, neurobiology, and development. To be truly transformative, single-cell approaches must affordably accommodate large numbers of single cells. This is feasible in the case of copy number variation (CNV), because CNV determination requires only sparse sequence coverage. We have used a combination of bioinformatic and molecular approaches to optimize single-cell DNA amplification and library preparation for highly multiplexed sequencing, yielding a method that can produce genome-wide CNV profiles of up to a hundred individual cells on a single lane of an Illumina HiSeq instrument. We apply the method to human cancer cell lines and biopsied cancer tissue, thereby illustrating its efficiency, reproducibility, and power to reveal underlying genetic heterogeneity and clonal phylogeny. The capacity of the method to facilitate the rapid profiling of hundreds to thousands of single-cell genomes represents a key step in making single-cell profiling an easily accessible tool for studying cell lineage.

Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer.

BACKGROUND: Gene-expression companion diagnostic tests, such as the Oncotype DX test, assess the risk of early stage Estrogen receptor (ER) positive (+) breast cancers, and guide clinicians in the decision of whether or not to use chemotherapy. However, these tests are typically expensive, time consuming, and tissue-destructive. METHODS: In this paper, we evaluate the ability of computer-extracted nuclear morphology features from routine hematoxylin and eosin (H&E) stained images of 178 early stage ER+ breast cancer patients to predict corresponding risk categories derived using the Oncotype DX test. A total of 216 features corresponding to the nuclear shape and architecture categories from each of the pathologic images were extracted and four feature selection schemes: Ranksum, Principal Component Analysis with Variable Importance on Projection (PCA-VIP), Maximum-Relevance, Minimum Redundancy Mutual Information Difference (MRMR MID), and Maximum-Relevance, Minimum Redundancy - Mutual Information Quotient (MRMR MIQ), were employed to identify the most discriminating features. These features were employed to train 4 machine learning classifiers: Random Forest, Neural Network, Support Vector Machine, and Linear Discriminant Analysis, via 3-fold cross validation. RESULTS: The four sets of risk categories, and the top Area Under the receiver operating characteristic Curve (AUC) machine classifier performances were: 1) Low ODx and Low mBR grade vs. High ODx and High mBR grade (Low-Low vs. High-High) (AUC = 0.83), 2) Low ODx vs. High ODx (AUC = 0.72), 3) Low ODx vs. Intermediate and High ODx (AUC = 0.58), and 4) Low and Intermediate ODx vs. High ODx (AUC = 0.65). Trained models were tested independent validation set of 53 cases which comprised of Low and High ODx risk, and demonstrated per-patient accuracies ranging from 75 to 86%. CONCLUSION: Our results suggest that computerized image analysis of digitized H&E pathology images of early stage ER+ breast cancer might be able predict the corresponding Oncotype DX risk categories.

Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI.

BACKGROUND: In this study, we evaluated the ability of radiomic textural analysis of intratumoral and peritumoral regions on pretreatment breast cancer dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict pathological complete response (pCR) to neoadjuvant chemotherapy (NAC). METHODS: A total of 117 patients who had received NAC were retrospectively analyzed. Within the intratumoral and peritumoral regions of T1-weighted contrast-enhanced MRI scans, a total of 99 radiomic textural features were computed at multiple phases. Feature selection was used to identify a set of top pCR-associated features from within a training set (n = 78), which were then used to train multiple machine learning classifiers to predict the likelihood of pCR for a given patient. Classifiers were then independently tested on 39 patients. Experiments were repeated separately among hormone receptor-positive and human epidermal growth factor receptor 2-negative (HR+, HER2-) and triple-negative or HER2+ (TN/HER2+) tumors via threefold cross-validation to determine whether receptor status-specific analysis could improve classification performance. RESULTS: Among all patients, a combined intratumoral and peritumoral radiomic feature set yielded a maximum AUC of 0.78 ± 0.030 within the training set and 0.74 within the independent testing set using a diagonal linear discriminant analysis (DLDA) classifier. Receptor status-specific feature discovery and classification enabled improved prediction of pCR, yielding maximum AUCs of 0.83 ± 0.025 within the HR+, HER2- group using DLDA and 0.93 ± 0.018 within the TN/HER2+ group using a naive Bayes classifier. In HR+, HER2- breast cancers, non-pCR was characterized by elevated peritumoral heterogeneity during initial contrast enhancement. However, TN/HER2+ tumors were best characterized by a speckled enhancement pattern within the peritumoral region of nonresponders. Radiomic features were found to strongly predict pCR independent of choice of classifier, suggesting their robustness as response predictors. CONCLUSIONS: Through a combined intratumoral and peritumoral radiomics approach, we could successfully predict pCR to NAC from pretreatment breast DCE-MRI, both with and without a priori knowledge of receptor status. Further, our findings suggest that the radiomic features most predictive of response vary across different receptor subtypes.

A deep learning based strategy for identifying and associating mitotic activity with gene expression derived risk categories in estrogen receptor positive breast cancers.

The treatment and management of early stage estrogen receptor positive (ER+) breast cancer is hindered by the difficulty in identifying patients who require adjuvant chemotherapy in contrast to those that will respond to hormonal therapy. To distinguish between the more and less aggressive breast tumors, which is a fundamental criterion for the selection of an appropriate treatment plan, Oncotype DX (ODX) and other gene expression tests are typically employed. While informative, these gene expression tests are expensive, tissue destructive, and require specialized facilities. Bloom-Richardson (BR) grade, the common scheme employed in breast cancer grading, has been shown to be correlated with the Oncotype DX risk score. Unfortunately, studies have also shown that the BR grade determined experiences notable inter-observer variability. One of the constituent categories in BR grading is the mitotic index. The goal of this study was to develop a deep learning (DL) classifier to identify mitotic figures from whole slides images of ER+ breast cancer, the hypothesis being that the number of mitoses identified by the DL classifier would correlate with the corresponding Oncotype DX risk categories. The mitosis detector yielded an average F-score of 0.556 in the AMIDA mitosis dataset using a 6-fold validation setup. For a cohort of 174 whole slide images with early stage ER+ breast cancer for which the corresponding Oncotype DX score was available, the distributions of the number of mitoses identified by the DL classifier was found to be significantly different between the high vs low Oncotype DX risk groups (P < 0.01). Comparisons of other risk groups, using both ODX score and histological grade, were also found to present significantly different automated mitoses distributions. Additionally, a support vector machine classifier trained to separate low/high Oncotype DX risk categories using the mitotic count determined by the DL classifier yielded a 83.19% classification accuracy. © 2017 International Society for Advancement of Cytometry.

UbcH7 regulates 53BP1 stability and DSB repair.

DNA double-strand break (DSB) repair is not only key to genome stability but is also an important anticancer target. Through an shRNA library-based screening, we identified ubiquitin-conjugating enzyme H7 (UbcH7, also known as Ube2L3), a ubiquitin E2 enzyme, as a critical player in DSB repair. UbcH7 regulates both the steady-state and replicative stress-induced ubiquitination and proteasome-dependent degradation of the tumor suppressor p53-binding protein 1 (53BP1). Phosphorylation of 53BP1 at the N terminus is involved in the replicative stress-induced 53BP1 degradation. Depletion of UbcH7 stabilizes 53BP1, leading to inhibition of DSB end resection. Therefore, UbcH7-depleted cells display increased nonhomologous end-joining and reduced homologous recombination for DSB repair. Accordingly, UbcH7-depleted cells are sensitive to DNA damage likely because they mainly used the error-prone nonhomologous end-joining pathway to repair DSBs. Our studies reveal a novel layer of regulation of the DSB repair choice and propose an innovative approach to enhance the effect of radiotherapy or chemotherapy through stabilizing 53BP1.

Brief-exposure to preoperative bevacizumab reveals a TGF-ß signature predictive of response in HER2-negative breast cancers.

To best define biomarkers of response, and to shed insight on mechanism of action of certain clinically important agents for early breast cancer, we used a brief-exposure paradigm in the preoperative setting to study transcriptional changes in patient tumors that occur with one dose of therapy prior to combination chemotherapy. Tumor biopsies from breast cancer patients enrolled in two preoperative clinical trials were obtained at baseline and after one dose of bevacizumab (HER2-negative), trastuzumab (HER2-positive) or nab-paclitaxel, followed by treatment with combination chemo-biologic therapy. RNA-Sequencing based PAM50 subtyping at baseline of 46 HER2-negative patients revealed a strong association between the basal-like subtype and pathologic complete response (pCR) to chemotherapy plus bevacizumab (p ≤ 0.0027), but did not provide sufficient specificity to predict response. However, a single dose of bevacizumab resulted in down-regulation of a well-characterized TGF-ß activity signature in every single breast tumor that achieved pCR (p ≤ 0.004). The TGF-ß signature was confirmed to be a tumor-specific read-out of the canonical TGF-ß pathway using pSMAD2 (p ≤ 0.04), with predictive power unique to brief-exposure to bevacizumab (p ≤ 0.016), but not trastuzumab or nab-paclitaxel. Down-regulation of TGF-ß activity was associated with reduction in tumor hypoxia by transcription and protein levels, suggesting therapy-induced disruption of an autocrine-loop between tumor stroma and malignant cells. Modulation of the TGF-ß pathway upon brief-exposure to bevacizumab may provide an early functional readout of pCR to preoperative anti-angiogenic therapy in HER2-negative breast cancer, thus providing additional avenues for exploration in both preclinical and clinical settings with these agents.

A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images.

Epithelial (EP) and stromal (ST) are two types of tissues in histological images. Automated segmentation or classification of EP and ST tissues is important when developing computerized system for analyzing the tumor microenvironment. In this paper, a Deep Convolutional Neural Networks (DCNN) based feature learning is presented to automatically segment or classify EP and ST regions from digitized tumor tissue microarrays (TMAs). Current approaches are based on handcraft feature representation, such as color, texture, and Local Binary Patterns (LBP) in classifying two regions. Compared to handcrafted feature based approaches, which involve task dependent representation, DCNN is an end-to-end feature extractor that may be directly learned from the raw pixel intensity value of EP and ST tissues in a data driven fashion. These high-level features contribute to the construction of a supervised classifier for discriminating the two types of tissues. In this work we compare DCNN based models with three handcraft feature extraction based approaches on two different datasets which consist of 157 Hematoxylin and Eosin (H&E) stained images of breast cancer and 1376 immunohistological (IHC) stained images of colorectal cancer, respectively. The DCNN based feature learning approach was shown to have a F1 classification score of 85%, 89%, and 100%, accuracy (ACC) of 84%, 88%, and 100%, and Matthews Correlation Coefficient (MCC) of 86%, 77%, and 100% on two H&E stained (NKI and VGH) and IHC stained data, respectively. Our DNN based approach was shown to outperform three handcraft feature extraction based approaches in terms of the classification of EP and ST regions.

Defining the cellular precursors to human breast cancer.

Human breast cancers are broadly classified based on their gene-expression profiles into luminal- and basal-type tumors. These two major tumor subtypes express markers corresponding to the major differentiation states of epithelial cells in the breast: luminal (EpCAM(+)) and basal/myoepithelial (CD10(+)). However, there are also rare types of breast cancers, such as metaplastic carcinomas, where tumor cells exhibit features of alternate cell types that no longer resemble breast epithelium. Until now, it has been difficult to identify the cell type(s) in the human breast that gives rise to these various forms of breast cancer. Here we report that transformation of EpCAM(+) epithelial cells results in the formation of common forms of human breast cancer, including estrogen receptor-positive and estrogen receptor-negative tumors with luminal and basal-like characteristics, respectively, whereas transformation of CD10(+) cells results in the development of rare metaplastic tumors reminiscent of the claudin-low subtype. We also demonstrate the existence of CD10(+) breast cells with metaplastic traits that can give rise to skin and epidermal tissues. Furthermore, we show that the development of metaplastic breast cancer is attributable, in part, to the transformation of these metaplastic breast epithelial cells. These findings identify normal cellular precursors to human breast cancers and reveal the existence of a population of cells with epidermal progenitor activity within adult human breast tissues.

Anatomical localization of progenitor cells in human breast tissue reveals enrichment of uncommitted cells within immature lobules.

INTRODUCTION: Lineage tracing studies in mice have revealed the localization and existence of lineage-restricted mammary epithelial progenitor cells that functionally contribute to expansive growth during puberty and differentiation during pregnancy. However, extensive anatomical differences between mouse and human mammary tissues preclude the direct translation of rodent findings to the human breast. Therefore, here we characterize the mammary progenitor cell hierarchy and identify the anatomic location of progenitor cells within human breast tissues. METHODS: Mammary epithelial cells (MECs) were isolated from disease-free reduction mammoplasty tissues and assayed for stem/progenitor activity in vitro and in vivo. MECs were sorted and evaluated for growth on collagen and expression of lineages markers. Breast lobules were microdissected and individually characterized based on lineage markers and steroid receptor expression to identify the anatomic location of progenitor cells. Spanning-tree progression analysis of density-normalized events (SPADE) was used to identify the cellular hierarchy of MECs within lobules from high-dimensional cytometry data. RESULTS: Integrating multiple assays for progenitor activity, we identified the presence of luminal alveolar and basal ductal progenitors. Further, we show that Type I lobules of the human breast were the least mature, demonstrating an unrestricted pattern of expression of luminal and basal lineage markers. Consistent with this, SPADE analysis revealed that immature lobules were enriched for basal progenitor cells, while mature lobules consisted of increased hierarchal complexity of cells within the luminal lineages. CONCLUSIONS: These results reveal underlying differences in the human breast epithelial hierarchy and suggest that with increasing glandular maturity, the epithelial hierarchy also becomes more complex.

Amyloid-ß precursor protein promotes cell proliferation and motility of advanced breast cancer.

BACKGROUND: Amyloid-ß precursor protein (APP) is a highly conserved single transmembrane protein that has been linked to Alzheimer disease. Recently, the increased expression of APP in multiple types of cancers has been reported where it has significant correlation with the cancer cell proliferation. However, the function of APP in the pathogenesis of breast cancer has not previously been determined. In this study, we studied the pathological role of APP in breast cancer and revealed its potential mechanism. METHODS: The expression level of APP in multiple breast cancer cell lines was measured by Western blot analysis and the breast cancer tissue microarray was utilized to analyze the expression pattern of APP in human patient specimens. To interrogate the functional role of APP in cell growth and apoptosis, the effect of APP knockdown in MDA-MB-231 cells were analyzed. Specifically, multiple signal transduction pathways and functional alterations linked to cell survival and motility were examined in in vivo animal model as well as in vitro cell culture with the manipulation of APP expression. RESULTS: We found that the expression of APP is increased in mouse and human breast cancer cell lines, especially in the cell line possessing higher metastatic potential. Moreover, the analysis of human breast cancer tissues revealed a significant correlation between the level of APP and tumor development. Knockdown of APP (APP-kd) in breast cancer cells caused the retardation of cell growth in vitro and in vivo, with both the induction of p27(kip1) and caspase-3-mediated apoptosis. APP-kd cells also had higher sensitivity to treatment of chemotherapeutic agents, TRAIL and 5-FU. Such anti-tumorigenic effects shown in the APP-kd cells partially came from reduced pro-survival AKT activation in response to IGF-1, leading to activation of key signaling regulators for cell growth, survival, and pro-apoptotic events such as GSK3-ß and FOXO1. Notably, knock-down of APP in metastatic breast cancer cells limited cell migration and invasion ability upon stimulation of IGF-1. CONCLUSION: The present data strongly suggest that the increase of APP expression is causally linked to tumorigenicity as well as invasion of aggressive breast cancer and, therefore, the targeting of APP may be an effective therapy for breast cancer.

Cat Scratch Disease of the Breast/Axilla: Recognition of a Rare Disease and Approaches for Differential Diagnosis.

Cat scratch disease rarely presents as a breast or axillary mass mimicking carcinoma both clinically and radiologically. Diagnosing breast/axillary cat scratch disease is challenging due to its rarity and nonspecific findings. Here, we reported 2 patients with breast cat scratch disease and reviewed 14 patients with cat scratch disease involving breast/axilla from the past 30 years. It mainly affects women (median age: 48), consistently presenting as axillary lymphadenopathy, and demonstrates ipsilateral breast mass in half of patients (50%, 8/16). The breast mass was most commonly located in the upper outer quadrant (88%, 7/8), indicating the possibility of disease extension from axillary adenopathy. Around half of patients (56%, 9/16) reported cat exposure. Histologically, most patients (93%, 14/15) presented as necrotizing granulomas, with characteristic stellate-shaped necrosis in 5 patients. Although pathologic differential diagnoses between cat scratch disease and cancer are straightforward, distinguishing cat scratch disease from other granulomatous mastitis poses diagnostic challenges. Silver stains should be included in the diagnostic workup panel when highly suspecting cat scratch disease clinically. However, they were only able to highlight the causative microorganism in 54% (7/18) patients, and the gram stain was negative in all 12 tested patients. In contrast, polymerase chain reaction (PCR) for the causative microorganism was consistently positive in all 3 tested patients, while serologic test confirmed diagnosis in 85% (11/13) patients; 1 patient with negative serology showed a positive PCR result. Therefore, upfront PCR tests with or without serologic study should be considered to confirm the diagnosis of cat scratch disease in a timely manner.

Breast MALT Lymphoma: A Clinical, Histomorphologic, and Immunophenotypic Evaluation.

Introduction. Primary breast extranodal marginal zone lymphoma of mucosa-associated lymphoid tissue (MALT lymphoma) is rare and understudied. Embryonically, mammary glands are developed as specialized skin appendages. It is possible that overlapping features exist between breast MALT lymphoma and primary cutaneous marginal zone lymphoma. Methods. We studied 5 primary and 6 secondary breast MALT lymphomas diagnosed in our institution during a 20-year period. Clinical and pathologic features of these lymphomas were analyzed and compared. Results. Most primary and secondary breast MALT lymphomas had similar clinical presentations as unilateral breast lesions without axillary lymphadenopathy. However, primary lymphomas tended to be diagnosed in older patients (median: 77 years old) than secondary lymphomas (median: 60 years old). Thyroid abnormality was a common finding in both primary (3/5) and secondary (5/6) lymphomas. Hashimoto's thyroiditis was diagnosed in one primary lymphoma. No distinct histopathologic findings were found in primary lymphomas. Features for primary cutaneous marginal zone lymphoma, including overexpression of IgG and IgG4 and high IgG4/IgG ratio, were absent in all primary but present in one secondary lymphoma with cutaneous origin. This secondary lymphoma also had expansion of CD30-positive cells. Conclusion. Primary breast MALT lymphoma does not share the distinctive features of primary cutaneous marginal zone lymphoma that set it apart from other extranodal marginal zone lymphomas. Having increased IgG- and IgG4-positive cells with a high IgG/IgG4 ratio in breast MALT lymphoma may indicate cutaneous origin. CD30 overexpression may be a feature seen in marginal zone lymphoma of cutaneous origin, which needs further studies to prove.

Evaluating Dual-Targeted ECO/siRNA Nanoparticles against an Oncogenic lncRNA for Triple Negative Breast Cancer Therapy with Magnetic Resonance Molecular Imaging.

Differentiation antagonizing noncoding RNA (DANCR) is recognized as an oncogenic long noncoding RNA (lncRNA) overexpressed in triple negative breast cancer (TNBC). We showed in a previous study that RNAi with targeted multifunctional ionizable lipid ECO/siRNA nanoparticles was effective to regulate this undruggable target for effective treatment of TNBC. In this study, we developed dual-targeted ECO/siDANCR nanoparticles by targeting a tumor extracellular matrix oncoprotein, extradomain B fibronectin (EDB-FN), and integrins overexpressed on cancer cells for enhanced delivery of siDANCR. The treatment of Hs578T TNBC cells and MCF-7 estrogen receptor-positive cells in vitro resulted in significant down-regulation of DANCR and EDB-FN and suppressed invasion and 3D spheroid formation of the cells. Magnetic resonance molecular imaging (MRMI) with an EDB-FN-targeted contrast agent, MT218, was used to noninvasively evaluate tumor response to treatment with the targeted ECO/siDANCR nanoparticles in female nude mice bearing orthotopic Hs578T and MCF-7 xenografts. MRMI with MT218 was effective to differentiate between aggressive TNBC with high DANCR and EDB-FN expression and ER+ MCF-7 tumors with low expression of the targets. MRMI showed that the dual-targeted ECO/siDANCR nanoparticles resulted in more significant inhibition of tumor growth in both models than the controls and significantly reduced EDB-FN expression in the TNBC tumors. The combination of MRMI and dual-targeted ECO/siDANCR nanoparticles is a promising approach for image-guided treatment of TNBC by regulating the onco-lncRNA.