34 research outputs found

    Endothelial-like properties of claudin-low breast cancer cells promote tumor vascular permeability and metastasis

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    The vasculature serves as the main conduit for breast tumor metastases and is a target of therapeutics in many tumor types. In this study, we aimed to determine if tumor-associated vascular properties could help to explain the differences observed in metastagenicity across the intrinsic subtypes of human breast tumors. Analysis of gene expression signatures from more than 3,000 human breast tumors found that genomic programs that measured vascular quantity, vascular proliferation, and a VEGF/Hypoxia-signature were the most highly expressed in claudin-low and basal-like tumors. The majority of the vascular gene signatures added metastasis-predictive information to immunohistochemistry-defined microvessel density scores and genomically defined-intrinsic subtype classification. Interestingly, pure claudin-low cell lines, and subsets of claudin-low-like cells within established basal-like cancer cell lines, exhibited endothelial/tube-like morphology when cultured on Matrigel. In vivo xenografts found that claudin-low tumors, but not luminal tumors, extensively perfused injected contrast agent through paracellular spaces and non-vascular tumor-lined channels. Taken together, the endothelial-like characteristics of the cancer cells, combined with both the amount and the physiologic state of the vasculature contribute to breast cancer metastatic progression. We hypothesize that the genetic signatures we have identified highlight patients that should respond most favorably to anti-vascular agents.Electronic supplementary materialThe online version of this article (doi:10.1007/s10585-013-9607-4) contains supplementary material, which is available to authorized users

    Department of Pathology, Thomas Jefferson University, Identification of conserved gene expression features between murine mammary carcinoma models and human breast tumors.

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    BACKGROUND: Although numerous mouse models of breast carcinomas have been developed, we do not know the extent to which any faithfully represent clinically significant human phenotypes. To address this need, we characterized mammary tumor gene expression profiles from 13 different murine models using DNA microarrays and compared the resulting data to those from human breast tumors. RESULTS: Unsupervised hierarchical clustering analysis showed that six models (TgWAP-Myc, TgMMTV-Neu, TgMMTV-PyMT, TgWAP-Int3, TgWAP-Tag, and TgC3(1)-Tag) yielded tumors with distinctive and homogeneous expression patterns within each strain. However, in each of four other models (TgWAP-T121, TgMMTV-Wnt1, Brca1Co/Co;TgMMTV-Cre;p53+/- and DMBA-induced), tumors with a variety of histologies and expression profiles developed. In many models, similarities to human breast tumors were recognized, including proliferation and human breast tumor subtype signatures. Significantly, tumors of several models displayed characteristics of human basal-like breast tumors, including two models with induced Brca1 deficiencies. Tumors of other murine models shared features and trended towards significance of gene enrichment with human luminal tumors; however, these murine tumors lacked expression of estrogen receptor (ER) and ER-regulated genes. TgMMTV-Neu tumors did not have a significant gene overlap with the human HER2+/ER- subtype and were more similar to human luminal tumors. CONCLUSION: Many of the defining characteristics of human subtypes were conserved among the mouse models. Although no single mouse model recapitulated all the expression features of a given human subtype, these shared expression features provide a common framework for an improved integration of murine mammary tumor models with human breast tumors

    Genomic analysis of estrogen cascade reveals histone variant H2A.Z associated with breast cancer progression

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    We demonstrate an integrated approach to the study of a transcriptional regulatory cascade involved in the progression of breast cancer and we identify a protein associated with disease progression. Using chromatin immunoprecipitation and genome tiling arrays, whole genome mapping of transcription factor-binding sites was combined with gene expression profiling to identify genes involved in the proliferative response to estrogen (E2). Using RNA interference, selected ERα and c-MYC gene targets were knocked down to identify mediators of E2-stimulated cell proliferation. Tissue microarray screening revealed that high expression of an epigenetic factor, the E2-inducible histone variant H2A.Z, is significantly associated with lymph node metastasis and decreased breast cancer survival. Detection of H2A.Z levels independently increased the prognostic power of biomarkers currently in clinical use. This integrated approach has accelerated the identification of a molecule linked to breast cancer progression, has implications for diagnostic and therapeutic interventions, and can be applied to a wide range of cancers

    Antitumor Activity of Auger Electron Emitter 111In Delivered by Modular Nanotransporter for Treatment of Bladder Cancer With EGFR Overexpression

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    Gamma-ray emitting 111In, which is extensively used for imaging, is also a source of short-range Auger electrons (AE). While exhibiting negligible effect outside cells, these AE become highly toxic near DNA within the cell nucleus. Therefore, these radionuclides can be used as a therapeutic anticancer agent if delivered precisely into the nuclei of tumor target cells. Modular nanotransporters (MNTs) designed to provide receptor-targeted delivery of short-range therapeutic cargoes into the nuclei of target cells are perspective candidates for specific intracellular delivery of AE emitters. The objective of this study was to evaluate the in vitro and in vivo efficacy of 111In attached MNTs to kill human bladder cancer cells overexpressing epidermal growth factor receptor (EGFR). The cytotoxicity of 111In delivered by the EGFR-targeted MNT (111In-MNT) was greatly enhanced on EJ-, HT-1376-, and 5637-expressing EGFR bladder cancer cell lines compared with 111In non-targeted control. In vivo microSPECT/CT imaging and antitumor efficacy studies revealed prolonged intratumoral retention of 111In-MNT with tœ = 4.1 ± 0.5 days as well as significant dose-dependent tumor growth delay (up to 90% growth inhibition) after local infusion of 111In-MNT in EJ xenograft-bearing mice

    Apc Mutation Enhances PyMT-Induced Mammary Tumorigenesis

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    The Adenomatous Polyposis Coli (APC) tumor suppressor gene is silenced by hypermethylation or mutated in up to 70% of human breast cancers. In mouse models, Apc mutation disrupts normal mammary development and predisposes to mammary tumor formation; however, the cooperation between APC and other mutations in breast tumorigenesis has not been studied. To test the hypothesis that loss of one copy of APC promotes oncogene-mediated mammary tumorigenesis, ApcMin/+ mice were crossed with the mouse mammary tumor virus (MMTV)-Polyoma virus middle T antigen (PyMT) or MMTV-c-Neu transgenic mice. In the PyMT tumor model, the ApcMin/+ mutation significantly decreased survival and tumor latency, promoted a squamous adenocarcinoma phenotype, and enhanced tumor cell proliferation. In tumor-derived cell lines, the proliferative advantage was a result of increased FAK, Src and JNK signaling. These effects were specific to the PyMT model, as no changes were observed in MMTV-c-Neu mice carrying the ApcMin/+ mutation. Our data indicate that heterozygosity of Apc enhances tumor development in an oncogene-specific manner, providing evidence that APC-dependent pathways may be valuable therapeutic targets in breast cancer. Moreover, these preclinical model systems offer a platform for dissection of the molecular mechanisms by which APC mutation enhances breast carcinogenesis, such as altered FAK/Src/JNK signaling

    Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: a report of the international immuno‐oncology biomarker working group

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    The clinical significance of the tumor-immune interaction in breast cancer (BC) has been well established, and tumor-infiltrating lymphocytes (TILs) have emerged as a predictive and prognostic biomarker for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2 negative) breast cancer (TNBC) and HER2-positive breast cancer. How computational assessment of TILs can complement manual TIL-assessment in trial- and daily practices is currently debated and still unclear. Recent efforts to use machine learning (ML) for the automated evaluation of TILs show promising results. We review state-of-the-art approaches and identify pitfalls and challenges by studying the root cause of ML discordances in comparison to manual TILs quantification. We categorize our findings into four main topics; (i) technical slide issues, (ii) ML and image analysis aspects, (iii) data challenges, and (iv) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns, or design choices in the computational implementation. To aid the adoption of ML in TILs assessment, we provide an in-depth discussion of ML and image analysis including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial- and routine clinical management of patients with TNBC

    Therapeutic properties of a vector carrying the HSV thymidine kinase and GM-CSF genes and delivered as a complex with a cationic copolymer

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    Spatial analyses of immune cell infiltration in cancer : current methods and future directions. A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer

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    Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector-based). We then provide a compendium of spatial immune cell metrics that have been reported in the literature, summarizing prognostic associations in the context of a variety of cancers. We conclude by discussing two well-described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, and describe investigative opportunities to improve clinical utility of these spatial biomarkers. © 2023 The Pathological Society of Great Britain and Ireland.http://www.thejournalofpathology.com/hj2024ImmunologySDG-03:Good heatlh and well-bein
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