Mouse Models of Breast Cancer: Platforms for Discovering Precision Imaging Diagnostics and Future Cancer Medicine

Progression from ductal carcinoma in situ to invasive breast cancer: molecular features and clinical significance

Ductal carcinoma in situ (DCIS) represents pre-invasive breast carcinoma. In untreated cases, 25-60% DCIS progress to invasive ductal carcinoma (IDC). The challenge lies in distinguishing between non-progressive and progressive DCIS, often resulting in over- or under-treatment in many cases. With increasing screen-detected DCIS in these years, the nature of DCIS has aroused worldwide attention. A deeper understanding of the biological nature of DCIS and the molecular journey of the DCIS-IDC transition is crucial for more effective clinical management. Here, we reviewed the key signaling pathways in breast cancer that may contribute to DCIS initiation and progression. We also explored the molecular features of DCIS and IDC, shedding light on the progression of DCIS through both inherent changes within tumor cells and alterations in the tumor microenvironment. In addition, valuable research tools utilized in studying DCIS including preclinical models and newer advanced technologies such as single-cell sequencing, spatial transcriptomics and artificial intelligence, have been systematically summarized. Further, we thoroughly discussed the clinical advancements in DCIS and IDC, including prognostic biomarkers and clinical managements, with the aim of facilitating more personalized treatment strategies in the future. Research on DCIS has already yielded significant insights into breast carcinogenesis and will continue to pave the way for practical clinical applications.

Mechanistic Investigation of Thiazole-Based Pyruvate Kinase M2 Inhibitor Causing Tumor Regression in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is a deadly breast cancer with a poor prognosis. Pyruvate kinase M2 (PKM2), a key rate-limiting enzyme in glycolysis, is abnormally highly expressed in TNBC. Overexpressed PKM2 amplifies glucose uptake, enhances lactate production, and suppresses autophagy, thereby expediting the progression of oncogenic processes. A high mortality rate demands novel chemotherapeutic regimens at once. Herein, we report the rational development of an imidazopyridine-based thiazole derivative 7d as an anticancer agent inhibiting PKM2. Nanomolar range PKM2 inhibitors with favorable drug-like properties emerged through enzyme assays. Experiments on two-dimensional (2D)/three-dimensional (3D) cell cultures, lactate release assay, surface plasmon resonance (SPR), and quantitative real-time polymerase chain reaction (qRT-PCR) validated 7d preclinically. In vivo, 7d outperformed lapatinib in tumor regression. This investigation introduces a lead-based approach characterized by its clear-cut chemistry and robust efficacy in designing an exceptionally potent inhibitor targeting PKM2, with a focus on combating TNBC.

The progressive trend of modeling and drug screening systems of breast cancer bone metastasis

Bone metastasis is considered as a considerable challenge for breast cancer patients. Various in vitro and in vivo models have been developed to examine this occurrence. In vitro models are employed to simulate the intricate tumor microenvironment, investigate the interplay between cells and their adjacent microenvironment, and evaluate the effectiveness of therapeutic interventions for tumors. The endeavor to replicate the latency period of bone metastasis in animal models has presented a challenge, primarily due to the necessity of primary tumor removal and the presence of multiple potential metastatic sites.The utilization of novel bone metastasis models, including three-dimensional (3D) models, has been proposed as a promising approach to overcome the constraints associated with conventional 2D and animal models. However, existing 3D models are limited by various factors, such as irregular cellular proliferation, autofluorescence, and changes in genetic and epigenetic expression. The imperative for the advancement of future applications of 3D models lies in their standardization and automation. The utilization of artificial intelligence exhibits the capability to predict cellular behavior through the examination of substrate materials' chemical composition, geometry, and mechanical performance. The implementation of these algorithms possesses the capability to predict the progression and proliferation of cancer. This paper reviewed the mechanisms of bone metastasis following primary breast cancer. Current models of breast cancer bone metastasis, along with their challenges, as well as the future perspectives of using these models for translational drug development, were discussed.

The adverse outcome pathway for breast cancer: a knowledge management framework bridging biomedicine and toxicology

Breast cancer is the most common cancer worldwide, with an estimated 2.3 million new cases diagnosed every year. Effective measures for cancer prevention and cancer therapy require a detailed understanding of the individual key disease mechanisms involved and their interactions at the molecular, cellular, tissue, organ, and organism level. In that regard, the rapid progress of biomedical and toxicological research in recent years now allows the pursuit of new approaches based on non-animal methods that provide greater mechanistic insight than traditional animal models and therefore facilitate the development of Adverse Outcome Pathways (AOPs) for human diseases. We performed a systematic review of the current state of published knowledge with regard to breast cancer to identify relevant key mechanisms for inclusion into breast cancer AOPs, i.e. decreased cell stiffness and decreased cell adhesion, and to concurrently map non-animal methods addressing these key events. We conclude that the broader sharing of expertise and methods between biomedical research and toxicology enabled by the AOP knowledge management framework can help to coordinate global research efforts and accelerate the transition to advanced non-animal methods, which, when combined into powerful method batteries, closely mimic human physiology and disease states without the need for animal testing.

Integrative multi-omic sequencing reveals the MMTV-Myc mouse model mimics human breast cancer heterogeneity

BACKGROUND

Breast cancer is a complex and heterogeneous disease with distinct subtypes and molecular profiles corresponding to different clinical outcomes. Mouse models of breast cancer are widely used, but their relevance in capturing the heterogeneity of human disease is unclear. Previous studies have shown the heterogeneity at the gene expression level for the MMTV-Myc model, but have only speculated on the underlying genetics.

METHODS

Tumors from the microacinar, squamous, and EMT histological subtypes of the MMTV-Myc mouse model of breast cancer underwent whole genome sequencing. The genomic data obtained were then integrated with previously obtained matched sample gene expression data and extended to additional samples of each histological subtype, totaling 42 gene expression samples. High correlation was observed between genetic copy number events and resulting gene expression by both Spearman's rank correlation coefficient and the Kendall rank correlation coefficient. These same genetic events are conserved in humans and are indicative of poor overall survival by Kaplan-Meier analysis. A supervised machine learning algorithm trained on METABRIC gene expression data was used to predict the analogous human breast cancer intrinsic subtype from mouse gene expression data.

RESULTS

Herein, we examine three common histological subtypes of the MMTV-Myc model through whole genome sequencing and have integrated these results with gene expression data. Significantly, key genomic alterations driving cell signaling pathways were well conserved within histological subtypes. Genomic changes included frequent, co-occurring mutations in KIT and RARA in the microacinar histological subtype as well as SCRIB mutations in the EMT subtype. EMT tumors additionally displayed strong KRAS activation signatures downstream of genetic activating events primarily ascribed to KRAS activating mutations, but also FGFR2 amplification. Analogous genetic events in human breast cancer showed stark decreases in overall survival. In further analyzing transcriptional heterogeneity of the MMTV-Myc model, we report a supervised machine learning model that classifies MMTV-Myc histological subtypes and other mouse models as being representative of different human intrinsic breast cancer subtypes.

CONCLUSIONS

We conclude the well-established MMTV-Myc mouse model presents further opportunities for investigation of human breast cancer heterogeneity.

Comparative analysis of the molecular subtype landscape in canine and human mammary gland tumors

Breast cancers in humans belong to one of several intrinsic molecular subtypes each with different tumor biology and different clinical impact. Mammary gland tumors in dogs are proposed as a relevant comparative model for human breast cancer; however, it is still unclear whether the intrinsic molecular subtypes have the same significance in dogs and humans. Using publicly available data, we analyzed gene expression and whole-exome sequencing data from 158 canine mammary gland tumors. We performed molecular subtyping using the PAM50 method followed by subtype-specific comparisons of gene expression characteristics, mutation patterns and copy number profiles between canine tumors and human breast tumors from The Cancer Genome Atlas (TCGA) breast cancer cohort (n = 1097). We found that luminal A canine tumors greatly resemble luminal A human tumors both in gene expression characteristics, mutations and copy number profiles. Also, the basal-like canine and human tumors were relatively similar, with low expression of luminal epithelial markers and high expression of genes involved in cell proliferation. There were, however, distinct differences in immune-related gene expression patterns in basal-like tumors between the two species. Characteristic HER2-enriched and luminal B subtypes were not present in the canine cohort, and we found no tumors with high-level ERBB2 amplifications. Benign and malignant canine tumors displayed similar PAM50 subtype characteristics. Our findings indicate that deeper understanding of the different molecular subtypes in canine mammary gland tumors will further improve the value of canines as comparative models for human breast cancer.

Novel Biological and Molecular Characterization in Radiopharmaceutical Preclinical Design

In this study, the potential of a digital autoradiography system equipped with a super resolution screen has been evaluated to investigate the biodistribution of a 18F-PSMA inhibitor in a prostate cancer mouse model. Twelve double xenograft NOD/SCID mice (LNCAP and PC3 tumours) were divided into three groups according to post-injection time points of an 18F-PSMA inhibitor. Groups of 4 mice were used to evaluate the biodistribution of the radiopharmaceutical after 30-, 60- and 120-min post-injection. Data here reported demonstrated that the digital autoradiography system is suitable to analyse the biodistribution of an 18F-PSMA inhibitor in both whole small-animal bodies and in single organs. The exposure of both whole mouse bodies and organs on the super resolution screen surface allowed the radioactivity of the PSMA inhibitor distributed in the tissues to be detected and quantified. Data obtained by using a digital autoradiography system were in line with the values detected by the activity calibrator. In addition, the image obtained from the super resolution screen allowed a perfect overlap with the tumour images achieved under the optical microscope. In conclusion, biodistribution studies performed by the autoradiography system allow the microscopical modifications induced by therapeutic radiopharmaceuticals to be studied by comparing the molecular imaging and histopathological data at the sub-cellular level.

Hi-C profiling of cancer spheroids identifies 3D-growth-specific chromatin interactions in breast cancer endocrine resistance

BACKGROUND

Organoids or spheroids have emerged as a physiologically relevant in vitro preclinical model to study patient-specific diseases. A recent study used spheroids of MCF10 cells to model breast cancer progression and identified targetable alterations more similar to those in vivo. Thus, it is practical and essential to explore and characterize the spheroids of the commonly used human breast cancer (BC) cells.

METHODS

In this study, we conducted Hi-C analyses in three-dimensional (3D) spheroids of MCF10A, MCF7 and MCF7TR cells and compared TADs and looping genes with those in 2D monolayers. Furthermore, we performed in silico functional analysis on 3D-growth-specific looping genes and to compare patient outcomes with or without endocrinal therapy. Finally, we performed 3C/RT-qPCR validations in 3D spheroids and 3D-FISH confirmations in organoids of breast cancer patient tissues.

RESULTS

We found that chromatin structures have experienced drastic changes during the 3D culture growth of BC cells although there is not much change in the quantity of chromatin domains. We also observed that the strengths of looping genes were statistically different between 2D monolayers and 3D spheroids. We further identified novel 3D growth-specific looping genes within Hippo relevant pathways, of which two genes showed potential prognostic values in measuring the outcome of the endocrine treatment. We finally confirmed a few selected genes in Hippo relevant pathways with enhanced looping in organoids of breast cancer patient tissues.

CONCLUSIONS

Hence, our work has provided significant insights into our understanding of 3D-growth-specific chromatin architecture in tamoxifen-resistant breast cancer. Our analyses suggest that the strengthened looping-mediated Hippo relevant pathways may contribute to endocrine therapy resistance in breast cancer patients.

The past, present, and future of breast cancer models for nanomedicine development

Even given recent advances in nanomedicine development of breast cancer treatment in recent years and promising results in pre-clinical models, cancer nanomedicines often fail at the clinical trial stage. Limitations of conventional in vitro models include the lack of representation of the stromal population, the absence of a three-dimensional (3D) structure, and a poor representation of inter-tumor and intra-tumor heterogeneity. Herein, we review those cell culture strategies that aim to overcome these limitations, including cell co-cultures, advanced 3D cell cultures, patient-derived cells, bioprinting, and microfluidics systems. The in vivo evaluation of nanomedicines must consider critical parameters that include the enhanced permeability and retention effect, the host's immune status, and the site of tumor implantation. Here, we critically discuss the advantages and limitations of current in vivo models and report how the improved selection and application of breast cancer models can improve the clinical translation of nanomedicines.

Intraductal Injection of Lentivirus Vectors for Stably Introducing Genes into Rat Mammary Epithelial Cells in Vivo

Various retroviral and lentiviral vectors have been used for up-the-teat intraductal injection to deliver markers, oncogenes, and other genes into mammary epithelial cells in mice. These methods along with the large number of genetically engineered mouse lines have greatly helped us learn normal breast development and tumorigenesis. Rats are also valuable models for studying human breast development and cancer. However, genetically engineered rats are still uncommon, and previous reports of intraductal injection of retroviral vectors into rats appear to be inefficient in generating mammary tumors. Here, we report, and describe the method for, stably introducing marker genes and oncogenes into mammary glands in rats using intraductal injection of commonly used lentiviral vectors. This method can infect mammary epithelial cells efficiently, and the infected cells can initiate tumorigenesis, including estrogen receptor-positive and hormone-dependent tumors, which are the most common subtype of human breast cancer but are yet still difficult to model in mice. This technique provides another tool for studying formation, prevention, and treatment of breast cancer, especially estrogen receptor-positive breast cancer.

Patient-Derived Xenografts as an Innovative Surrogate Tumor Model for the Investigation of Health Disparities in Triple Negative Breast Cancer

Despite a decline in overall incidence rates for cancer in the past decade, due in part to impressive advancements in both diagnosis and treatment, breast cancer (BC) remains the leading cause of cancer-related deaths in women. BC alone accounts for ∼30% of all new cancer diagnoses in women worldwide. Triple-negative BC (TNBC), defined as having no expression of the estrogen or progesterone receptors and no amplification of the HER2 receptor, is a subtype of BC that does not benefit from the use of estrogen receptor-targeting or HER2-targeting therapies. Differences in socioeconomic factors and cell intrinsic and extrinsic characteristics have been demonstrated in Black and White TNBC patient tumors. The emergence of patient-derived xenograft (PDX) models as a surrogate, translational, and functional representation of the patient with TNBC has led to the advances in drug discovery and testing of novel targeted approaches and combination therapies. However, current established TNBC PDX models fail to represent the diverse patient population and, most importantly, the specific ethnic patient populations that have higher rates of incidence and mortality. The primary aim of this review is to emphasize the importance of using clinically relevant translatable tumor models that reflect TNBC human tumor biology and heterogeneity in high-risk patient populations. The focus is to highlight the complexity of BC as it specifically relates to the management of TNBC in Black women. We discuss the importance of utilizing PDX models to study the extracellular matrix (ECM), and the distinct differences in ECM composition and biophysical properties in Black and White women. Finally, we demonstrate the crucial importance of PDX models toward novel drug discovery in this patient population.

Alzheimer's Disease, and Breast and Prostate Cancer Research: Translational Failures and the Importance to Monitor Outputs and Impact of Funded Research

Dementia and cancer are becoming increasingly prevalent in Western countries. In the last two decades, research focused on Alzheimer's disease (AD) and cancer, in particular, breast cancer (BC) and prostate cancer (PC), has been substantially funded both in Europe and worldwide. While scientific research outcomes have contributed to increase our understanding of the disease etiopathology, still the prevalence of these chronic degenerative conditions remains very high across the globe. By definition, no model is perfect. In particular, animal models of AD, BC, and PC have been and still are traditionally used in basic/fundamental, translational, and preclinical research to study human disease mechanisms, identify new therapeutic targets, and develop new drugs. However, animals do not adequately model some essential features of human disease; therefore, they are often unable to pave the way to the development of drugs effective in human patients. The rise of new technological tools and models in life science, and the increasing need for multidisciplinary approaches have encouraged many interdisciplinary research initiatives. With considerable funds being invested in biomedical research, it is becoming pivotal to define and apply indicators to monitor the contribution to innovation and impact of funded research. Here, we discuss some of the issues underlying translational failure in AD, BC, and PC research, and describe how indicators could be applied to retrospectively measure outputs and impact of funded biomedical research.

Experimental Breast Cancer Models: Preclinical Imaging Perspective

BACKGROUND

Breast cancer is the leading cause of cancer in women. 13% of breast cancer patients are at a distant stage and mortality is due to metastases rather than primary disease. The unique genetic structure and natural process of breast cancer make it a very suitable area for targeted therapies. Experimental tumor models are validated methods to examine the pathogenesis of cancer, the onset of the neoplastic process and progression.

OBJECTIVE

This study aims to review the current literature on experimental breast cancer models and to bring a new perspective to the use of these models in teranostic preclinical studies in terms of the imaging.

METHODS

Search for relevant literature from academic databases using keywords (Breast cancer, theranostic, preclinical imaging, tumor models, animal study, and tailored therapy) was conducted. The full text of the articles was reached and reviewed. Current scientific data has been reevaluated and compiled according to subtitles.

RESULTS AND CONCLUSION

The development of animal models for breast cancer research has been done in the last century. Imaging methods used in breast cancer are used for tumor localization, quantification of tumor mass, imaging of genes and proteins, evaluation of tumor microenvironment, evaluation of tumor cell proliferation and metabolism and treatment response evaluation. Since human breast cancer is a heterogeneous group of diseases in terms of genetics and phenotype; it is not possible for a single model to adequately address all aspects of breast cancer biology. Considering that each model has advantages and disadvantages, the most suitable model should be chosen to verify the thesis of the study.

Breast cancer models: Engineering the tumor microenvironment

The mechanisms behind cancer initiation and progression are not clear. Therefore, development of clinically relevant models to study cancer biology and drug response in tumors is essential. In vivo models are very valuable tools for studying cancer biology and for testing drugs; however, they often suffer from not accurately representing the clinical scenario because they lack either human cells or a functional immune system. On the other hand, two-dimensional (2D) in vitro models lack the three-dimensional (3D) network of cells and extracellular matrix (ECM) and thus do not represent the tumor microenvironment (TME). As an alternative approach, 3D models have started to gain more attention, as such models offer a platform with the ability to study cell-cell and cell-material interactions parametrically, and possibly include all the components present in the TME. Here, we first give an overview of the breast cancer TME, and then discuss the current state of the pre-clinical breast cancer models, with a focus on the engineered 3D tissue models. We also highlight two engineering approaches that we think are promising in constructing models representative of human tumors: 3D printing and microfluidics. In addition to giving basic information about the TME in the breast tissue, this review article presents the state-of-the-art tissue engineered breast cancer models. STATEMENT OF SIGNIFICANCE: Involvement of biomaterials and tissue engineering fields in cancer research enables realistic mimicry of the cell-cell and cell-extracellular matrix (ECM) interactions in the tumor microenvironment (TME), and thus creation of better models that reflect the tumor response against drugs. Engineering the 3D in vitro models also requires a good understanding of the TME. Here, an overview of the breast cancer TME is given, and the current state of the pre-clinical breast cancer models, with a focus on the engineered 3D tissue models is discussed. This review article is useful not only for biomaterials scientists aiming to engineer 3D in vitro TME models, but also for cancer researchers willing to use these models for studying cancer biology and drug testing.

Breast cancer pathogenesis is linked to the intra-tumoral estrogen sulfotransferase (hSULT1E1) expressions regulated by cellular redox dependent Nrf-2/NFκβ interplay

BACKGROUND

Estrogen sulfotransferase catalyzes conjugation of sulfuryl-group to estradiol/estrone and regulates E2 availability/activity via estrogen-receptor or non-receptor mediated pathways. Sulfoconjugated estrogen fails to bind estrogen-receptor (ER). High estrogen is a known carcinogen in postmenopausal women. Reports reveal a potential redox-regulation of hSULT1E1/E2-signalling. Further, oxidatively-regulated nuclear-receptor-factor 2 (Nrf2) and NFκβ in relation to hSULT1E1/E2 could be therapeutic-target via cellular redox-modification.

METHODS

Here, oxidative stress-regulated SULT1E1-expression was analyzed in human breast carcinoma-tissues and in rat xenografted with human breast-tumor. Tumor and its surrounding tissues were obtained from the district-hospital. Intracellular redox-environment of tumors was screened with some in vitro studies. RT-PCR and western blotting was done for SULT1E1 expression. Immunohistochemistry was performed to analyze SULT1E1/Nrf2/NFκβ localization. Tissue-histoarchitecture/DNA-stability (comet assay) studies were done.

RESULTS

Oxidative-stress induces SULT1E1 via Nrf2/NFκβ cooperatively in tumor-pathogenesis to maintain the required proliferative-state under enriched E2-environment. Higher malondialdehyde/non-protein-soluble-thiol with increased superoxide-dismutase/glutathione-peroxidase/catalase activities was noticed. SULT1E1 expression and E2-level were increased in tumor-tissue compared to their corresponding surrounding-tissues.

CONCLUSIONS

It may be concluded that tumors maintain a sustainable oxidative-stress through impaired antioxidants as compared to the surrounding. Liver-tissues from xenografted rat manifested similar E2/antioxidant dysregulations favoring pre-tumorogenic environment.

Expression of Diverse Angiogenesis Factor in Different Stages of the 4T1 Tumor as a Mouse Model of Triple-Negative Breast Cancer

Purpose: Triple-negative breast cancer (TNBC) is specified by high vascularity and repetitious metastasis. Although several studies have indicated that angiogenesis has an important role in invasive breast cancer, a suitable model of TNBC that can show the exact onset of angiogenesis factors still needs to be developed. The purpose of this study is to determine the expression level of angiogenesis factors in different clinical stages of the 4T1 tumor as TNBC mouse model.

Methods: Twenty mice were injected by the 4T1 cell line, and four mice selected as healthy controls. Following by tumor induction, the mice were randomly put into four groups, each contains four mice. Once the tumor volume reached to the early stage (<100 mm³), intermediate stage (100-300 mm³), advanced stage (300-500 mm³), and end stage (>500 mm³), they were removed by surgery. Then, the expression levels of Hif1α, VEGFR1, and VEGFR2 genes, as well as tumor markers of VEGF, bFGF and CD31, were evaluated by qPCR and immunohistochemistry (IHC) respectively. The statistical analysis was done by SPSS version 16.

Results: TNBC tumors were confirmed and multi-foci metastasis in the lung were seen. The mRNA and protein expression levels of the angiogenesis factors increased in the early stage and as the tumor grew, their expression level enhanced dramatically.

Conclusion: The 4T1 syngeneic mouse tumor may serve as an appropriate TNBC model for further investigation of the angiogenesis and therapies. Moreover, angiogenesis factors are induced before the advanced stage, and anti-angiogenesis therapy is necessary to be considered at the first line of treatment in TBNC.

Metabolomic studies of breast cancer in murine models: A review

BACKGROUND

Metabolomic strategies have been extensively used to search for biomarkers of disease, including cancer, in biological complex mixtures such as cells, tissues and biofluids. In breast cancer research, murine models are of great value and metabolomics has been increasingly applied to characterize tumor or organ tissues, or biofluids, for instance to follow-up metabolism during cancer progression or response to specific therapies.

SCOPE OF REVIEW

This review briefly introduces the different murine models used in breast cancer research and proceeds to present the metabolomic studies reported so far to describe the deviant metabolic behavior associated to breast cancer, in each type of model: xenografts (cell- or patient-derived), spontaneous (naturally-occurring or genetically engineered) and carcinogen-induced. The type of sample and strategies followed are identified, as well as the main findings from of study.

MAJOR CONCLUSIONS

Metabolomics has gradually become relevant in characterizing murine models of breast cancer, using either Nuclear Magnetic Resonance (NMR) or Mass Spectromety (MS). Both tissue and biofluids are matrixes of interest in this context, although in some type of models, reports have focused primarily on the former. The aims of tissue studies have comprised the search for mechanistic knowledge of carcinogenesis, metastasis development and response/resistance to therapies. Biofluid metabolomics has mainly aimed at finding non-invasive biomarkers for early breast cancer detection or prognosis determination.

GENERAL SIGNIFICANCE

Metabolomics provides exquisite detail on murine tumor and systemic metabolism of breast cancer. This knowledge paves the way for the discovery of new biomarkers, potentially translatable to in vivo non-invasive patient follow-up.

Establishment of chemosensitivity tests in triple-negative and BRCA-mutated breast cancer patient-derived xenograft models

Purpose

A patient-derived xenograft (PDX) model is an in vivo animal model which provides biological and genomic profiles similar to a primary tumor. The characterization of factors that influence the establishment of PDX is crucial. Furthermore, PDX models can provide a platform for chemosensitivity tests to evaluate the effectiveness of a target agent before applying it in clinical trials.

Methods

We implanted 83 cases of breast cancer into NOD.Cg-Prkdcscid Il2rgtm1Sug/Jic mice, to develop PDX models. Clinicopathological factors of primary tumors were reviewed to identify the factors affecting engraftment success rates. After the establishment of PDX models, we performed olaparib and carboplatin chemosensitivity tests. We used PDX models from triple-negative breast cancer (TNBC) with neoadjuvant chemotherapy and/or germline BRCA1 mutations in chemosensitivity tests.

Results

The univariate analyses (p<0.05) showed factors which were significantly associated with successful engraftment of PDX models include poor histologic grade, presence of BRCA mutation, aggressive diseases, and death. Factors which were independently associated with successful engraftment of PDX models on multivariate analyses include poor histologic grade and aggressive diseases status. In chemosensitivity tests, a PDX model with the BRCA1 L1780P mutation showed partial response to olaparib and complete response to carboplatin.

Conclusions

Successful engraftment of PDX models was significantly associated with aggressive diseases. Patients who have aggressive diseases status, large tumors, and poor histologic grade are ideal candidates for developing successful PDX models. Chemosensitivity tests using the PDX models provide additional information about alternative treatment strategies for residual TNBC after neoadjuvant chemotherapy.

Characterization of triple-negative breast cancer preclinical models provides functional evidence of metastatic progression

Triple-negative breast cancer (TNBC), an aggressive, metastatic and recurrent breast cancer (BC) subtype, currently suffers from a lack of adequately described spontaneously metastatic preclinical models that faithfully reproduce the clinical scenario. We describe two preclinical spontaneously metastatic TNBC orthotopic murine models for the development of advanced therapeutics: an immunodeficient human MDA-MB-231-Luc model and an immunocompetent mouse 4T1 model. Furthermore, we provide a broad range of multifactorial analysis for both models that could provide relevant information for the development of new therapies and diagnostic tools. Our comparisons uncovered differential growth rates, stromal arrangements and metabolic profiles in primary tumors, and the presence of cancer-associated adipocyte infiltration in the MDA-MB-231-Luc model. Histopathological studies highlighted the more rapid metastatic spread to the lungs in the 4T1 model following a lymphatic route, while we observed both homogeneous (MDA-MB-231-Luc) and heterogeneous (4T1) metastatic spread to axillary lymph nodes. We encountered unique metabolomic signatures in each model, including crucial amino acids and cell membrane components. Hematological analysis demonstrated severe leukemoid and lymphoid reactions in the 4T1 model with the partial reestablishment of immune responses in the immunocompromised MDA-MB-231-Luc model. Additionally, we discovered β-immunoglobulinemia and increased basal levels of G-CSF correlating with a metastatic switch, with G-CSF also promoting extramedullary hematopoiesis (both models) and causing hepatosplenomegaly (4T1 model). Overall, we believe that the characterization of these preclinical models will foster the development of advanced therapeutic strategies for TNBC treatment, especially for the treatment of patients presenting both, primary tumors and metastatic spread.

Preclinical Molecular Imaging for Precision Medicine in Breast Cancer Mouse Models

Precision and personalized medicine is gaining importance in modern clinical medicine, as it aims to improve diagnostic precision and to reduce consequent therapeutic failures. In this regard, prior to use in human trials, animal models can help evaluate novel imaging approaches and therapeutic strategies and can help discover new biomarkers. Breast cancer is the most common malignancy in women worldwide, accounting for 25% of cases of all cancers and is responsible for approximately 500,000 deaths per year. Thus, it is important to identify accurate biomarkers for precise stratification of affected patients and for early detection of responsiveness to the selected therapeutic protocol. This review aims to summarize the latest advancements in preclinical molecular imaging in breast cancer mouse models. Positron emission tomography (PET) imaging remains one of the most common preclinical techniques used to evaluate biomarker expression in vivo, whereas magnetic resonance imaging (MRI), particularly diffusion-weighted (DW) sequences, has been demonstrated as capable of distinguishing responders from nonresponders for both conventional and innovative chemo- and immune-therapies with high sensitivity and in a noninvasive manner. The ability to customize therapies is desirable, as this will enable early detection of diseases and tailoring of treatments to individual patient profiles. Animal models remain irreplaceable in the effort to understand the molecular mechanisms and patterns of oncologic diseases.

Investigating circulating tumor cells and distant metastases in patient-derived orthotopic xenograft models of triple-negative breast cancer

BACKGROUND

Circulating tumor cells (CTCs) represent a temporal "snapshot" of a patient's cancer and changes that occur during disease evolution. There is an extensive literature studying CTCs in breast cancer patients, and particularly in those with metastatic disease. In parallel, there is an increasing use of patient-derived models in preclinical investigations of human cancers. Yet studies are still limited demonstrating CTC shedding and metastasis formation in patient-derived models of breast cancer.

METHODS

We used seven patient-derived orthotopic xenograft (PDOX) models generated from triple-negative breast cancer (TNBC) patients to study CTCs and distant metastases. Tumor fragments from PDOX tissue from each of the seven models were implanted into 57 NOD scid gamma (NSG) mice, and tumor growth and volume were monitored. Human CTC capture from mouse blood was first optimized on the marker-agnostic Vortex CTC isolation platform, and whole blood was processed from 37 PDOX tumor-bearing mice.

RESULTS

Staining and imaging revealed the presence of CTCs in 32/37 (86%). The total number of CTCs varied between different PDOX tumor models and between individual mice bearing the same PDOX tumors. CTCs were heterogeneous and showed cytokeratin (CK) positive, vimentin (VIM) positive, and mixed CK/VIM phenotypes. Metastases were detected in the lung (20/57, 35%), liver (7/57, 12%), and brain (1/57, less than 2%). The seven different PDOX tumor models displayed varying degrees of metastatic potential, including one TNBC PDOX tumor model that failed to generate any detectable metastases (0/8 mice) despite having CTCs present in the blood of 5/5 tested, suggesting that CTCs from this particular PDOX tumor model may typify metastatic inefficiency.

CONCLUSION

PDOX tumor models that shed CTCs and develop distant metastases represent an important tool for investigating TNBC.

Effects of isoflavones on breast tissue and the thyroid hormone system in humans: a comprehensive safety evaluation

Isoflavones are secondary plant constituents of certain foods and feeds such as soy, linseeds, and red clover. Furthermore, isoflavone-containing preparations are marketed as food supplements and so-called dietary food for special medical purposes to alleviate health complaints of peri- and postmenopausal women. Based on the bioactivity of isoflavones, especially their hormonal properties, there is an ongoing discussion regarding their potential adverse effects on human health. This review evaluates and summarises the evidence from interventional and observational studies addressing potential unintended effects of isoflavones on the female breast in healthy women as well as in breast cancer patients and on the thyroid hormone system. In addition, evidence from animal and in vitro studies considered relevant in this context was taken into account along with their strengths and limitations. Key factors influencing the biological effects of isoflavones, e.g., bioavailability, plasma and tissue concentrations, metabolism, temporality (pre- vs. postmenopausal women), and duration of isoflavone exposure, were also addressed. Final conclusions on the safety of isoflavones are guided by the aim of precautionary consumer protection.

MIND model for triple-negative breast cancer in syngeneic mice for quick and sequential progression analysis of lung metastasis

Mouse models of breast cancer with specific molecular subtypes (e.g., ER or HER2 positive) in an immunocompetent or an immunocompromised environment significantly contribute to our understanding of cancer biology, despite some limitations, and they give insight into targeted therapies. However, an ideal triple-negative breast cancer (TNBC) mouse model is lacking. What has been missing in the TNBC mouse model is a sequential progression of the disease in an essential native microenvironment. This notion inspired us to develop a TNBC-model in syngeneic mice using a mammary intraductal (MIND) method. To achieve this goal, Mvt-1and 4T1 TNBC mouse cell lines were injected into the mammary ducts via nipples of FVB/N mice and BALB/c wild-type immunocompetent mice, respectively. We established that the TNBC-MIND model in syngeneic mice could epitomize all breast cancer progression stages and metastasis into the lungs via lymphatic or hematogenous dissemination within four weeks. Collectively, the syngeneic mouse-TNBC-MIND model may serve as a unique platform for further investigation of the underlying mechanisms of TNBC growth and therapies.

Identification of Differentially Expressed Genes between Original Breast Cancer and Xenograft Using Machine Learning Algorithms

Breast cancer is one of the most common malignancies in women. Patient-derived tumor xenograft (PDX) model is a cutting-edge approach for drug research on breast cancer. However, PDX still exhibits differences from original human tumors, thereby challenging the molecular understanding of tumorigenesis. In particular, gene expression changes after tissues are transplanted from human to mouse model. In this study, we propose a novel computational method by incorporating several machine learning algorithms, including Monte Carlo feature selection (MCFS), random forest (RF), and rough set-based rule learning, to identify genes with significant expression differences between PDX and original human tumors. First, 831 breast tumors, including 657 PDX and 174 human tumors, were collected. Based on MCFS and RF, 32 genes were then identified to be informative for the prediction of PDX and human tumors and can be used to construct a prediction model. The prediction model exhibits a Matthews coefficient correlation value of 0.777. Seven interpretable interactions within the informative gene were detected based on the rough set-based rule learning. Furthermore, the seven interpretable interactions can be well supported by previous experimental studies. Our study not only presents a method for identifying informative genes with differential expression but also provides insights into the mechanism through which gene expression changes after being transplanted from human tumor into mouse model. This work would be helpful for research and drug development for breast cancer.

A novel patient-derived xenograft model for claudin-low triple-negative breast cancer

BACKGROUND

Triple-negative breast cancer (TNBC) subtypes are clinically aggressive and cannot be treated with targeted therapeutics commonly used in other breast cancer subtypes. The claudin-low (CL) molecular subtype of TNBC has high rates of metastases, chemoresistance and recurrence. There exists an urgent need to identify novel therapeutic targets in TNBC; however, existing models utilized in target discovery research are limited. Patient-derived xenograft (PDX) models have emerged as superior models for target discovery experiments because they recapitulate features of patient tumors that are limited by cell-line derived xenograft methods.

METHODS

We utilize immunohistochemistry, qRT-PCR and Western Blot to visualize tumor architecture, cellular composition, genomic and protein expressions of a new CL-TNBC PDX model (TU-BcX-2O0). We utilize tissue decellularization techniques to examine extracellular matrix composition of TU-BcX-2O0.

RESULTS

Our laboratory successfully established a TNBC PDX tumor, TU-BCX-2O0, which represents a CL-TNBC subtype and maintains this phenotype throughout subsequent passaging. We dissected TU-BCx-2O0 to examine aspects of this complex tumor that can be targeted by developing therapeutics, including the whole and intact breast tumor, specific cell populations within the tumor, and the extracellular matrix.

CONCLUSIONS

Here, we characterize a claudin-low TNBC patient-derived xenograft model that can be utilized for therapeutic research studies.

Mechanistic modeling quantifies the influence of tumor growth kinetics on the response to anti-angiogenic treatment

Tumors exploit angiogenesis, the formation of new blood vessels from pre-existing vasculature, in order to obtain nutrients required for continued growth and proliferation. Targeting factors that regulate angiogenesis, including the potent promoter vascular endothelial growth factor (VEGF), is therefore an attractive strategy for inhibiting tumor growth. Computational modeling can be used to identify tumor-specific properties that influence the response to anti-angiogenic strategies. Here, we build on our previous systems biology model of VEGF transport and kinetics in tumor-bearing mice to include a tumor compartment whose volume depends on the “angiogenic signal” produced when VEGF binds to its receptors on tumor endothelial cells. We trained and validated the model using published in vivo measurements of xenograft tumor volume, producing a model that accurately predicts the tumor’s response to anti-angiogenic treatment. We applied the model to investigate how tumor growth kinetics influence the response to anti-angiogenic treatment targeting VEGF. Based on multivariate regression analysis, we found that certain intrinsic kinetic parameters that characterize the growth of tumors could successfully predict response to anti-VEGF treatment, the reduction in tumor volume. Lastly, we use the trained model to predict the response to anti-VEGF therapy for tumors expressing different levels of VEGF receptors. The model predicts that certain tumors are more sensitive to treatment than others, and the response to treatment shows a nonlinear dependence on the VEGF receptor expression. Overall, this model is a useful tool for predicting how tumors will respond to anti-VEGF treatment, and it complements pre-clinical in vivo mouse studies.

One hallmark of cancer is angiogenesis, the formation of new blood capillaries from pre-existing vessels. Angiogenesis promotes tumor growth by enabling the tumor to obtain oxygen and nutrients from the surrounding microenvironment. Cancer drugs that inhibit angiogenesis ("anti-angiogenic therapies") have focused on inhibiting proteins that promote the growth of new blood vessels. The response to anti-angiogenic therapy is highly variable, and some tumors do not respond at all. Therefore, identifying a biomarker that predicts how specific tumors will respond would be extremely valuable. This work uses a computational model of tumor-bearing mice to investigate the response to anti-angiogenic treatment that targets the potent promoter of angiogenesis, vascular endothelial growth factor (VEGF), and how the response is influenced by tumor growth kinetics. We show that certain properties of tumor growth can be used to predict how much the tumor volume will be reduced upon administration of an anti-VEGF drug. This work identifies tumor growth parameters that may be reliable biomarkers for predicting how tumors will respond to anti-VEGF therapy. Our computational model generates novel, testable hypotheses and nicely complements pre-clinical studies of anti-angiogenic therapeutics.

[Isoflavone-containing dietary supplements]

Isoflavones (IFs) from soy and other legumes have weak estrogenic properties. Isolated IFs are available as dietary supplements and advertised to alleviate symptoms of menopause. The present chapter provides an overview of the occurrence, the chemical structure of IFs and their metabolites, the market situation and reviews the current evidence on the efficacy and safety of IF-containing dietary supplements.The biological effectiveness of IFs is attributable to the activation of the estrogen receptor (ER). Studies on the influence of IFs on endogenous estrogen levels in women show inconsistent results. So far, the European Food Safety Authority (EFSA) has rejected all submitted health claims for IFs due to insufficient scientific evidence for any of the postulated health effects. Based on the results of their recent risk assessment, the EFSA concluded that the available human studies did not support the hypothesis of adverse effects of isolated IFs on the human mammary gland, uterus or thyroid in healthy postmenopausal women. However, the assessment does not allow a general statement on the safety of IF-containing dietary supplements. Studies in animal models are often not comparable with the complex interactions in humans due to differences in the metabolism of IFs, in the developmental stage at time of consumption and in the temporarily restricted uptake of IFs during certain stages of life.

CONCLUSION

So far, for none of the advertised functions is unequivocal scientific evidence available. On the basis of available data, potential unwanted side effects cannot be fully excluded. This holds particularly true for women with undiagnosed diseases, especially for those with undetected precancerous lesions in the mammary gland.