Current State of Breast Cancer Diagnosis, Treatment, and Theranostics

Breast cancer is one of the leading causes of cancer-related morbidity and mortality in women worldwide. Early diagnosis and effective treatment of all types of cancers are crucial for a positive prognosis. Patients with small tumor sizes at the time of their diagnosis have a significantly higher survival rate and a significantly reduced probability of the cancer being fatal. Therefore, many novel technologies are being developed for early detection of primary tumors, as well as distant metastases and recurrent disease, for effective breast cancer management. Theranostics has emerged as a new paradigm for the simultaneous diagnosis, imaging, and treatment of cancers. It has the potential to provide timely and improved patient care via personalized therapy. In nanotheranostics, cell-specific targeting moieties, imaging agents, and therapeutic agents can be embedded within a single formulation for effective treatment. In this review, we will highlight the different diagnosis techniques and treatment strategies for breast cancer management and explore recent advances in breast cancer theranostics. Our main focus will be to summarize recent trends and technologies in breast cancer diagnosis and treatment as reported in recent research papers and patents and discuss future perspectives for effective breast cancer therapy

A translational bioinformatic approach in identifying and validating an interaction between Vitamin A and CYP19A1

INTRODUCTION: One major challenge in personalized medicine research is to identify the environmental factors that can alter drug response, and to investigate their molecular mechanisms. These environmental factors include co-medications, food, and nutrition or dietary supplements. The increasing use of dietary supplements and their potential interactions with cytochrome P450 (CYP450) enzymes is a highly significant personalized medicine research domain, because most of the drugs on the market are metabolized through CYP450 enzymes. METHODS: Initial bioinformatics analysis revealed a number of regulators of CYP450 enzymes from a human liver bank gene expression quantitative loci data set. Then, a compound-gene network was constructed from the curated literature data. This network consisted of compounds that interact with either CYPs and/or their regulators that influence either their gene expression or activity. We further evaluated this finding in three different cell lines: JEG3, HeLa, and LNCaP cells. RESULTS: From a total of 868 interactions we were able to identify an interesting interaction between retinoic acid (i.e. Vitamin A) and the aromatase gene (i.e. CYP19A1). Our experimental results showed that retinoic acid at physiological concentration significantly influenced CYP19A1 gene expressions. CONCLUSIONS: These results suggest that the presence of retinoic acid may alter the efficacy of agents used to suppress aromatase expression

Personalized cancer therapy prioritization based on driver alteration co-occurrence patterns

Altres ajuts: L.M. is a recipient of an FPI fellowship. P.A. acknowledges the support of the Spanish Ministerio de Economía y Competitividad, the European Research Council (SysPharmAD: 614944), and the Generalitat de Catalunya. V.S. is a recipient of a Miguel Servet grant from ISCIII and receives funds from AGAUR (). The PDX program is supported by a GHD-Pink (FERO foundation) grant to V.S., A.G.-O. and M.P. received a FI-AGAUR and a Juan de la Cierva (MJCI-2015-25412) fellowship, respectively. M.S., P.R., and S.C. acknowledge the support of the NIH grants P30 CA008748, RO1CA190642, and the Breast Cancer Research Foundation. Additionally, P.R. receives funds from the Breast Cancer Alliance.Identification of actionable genomic vulnerabilities is key to precision oncology. Utilizing a large-scale drug screening in patient-derived xenografts, we uncover driver gene alteration connections, derive driver co-occurrence (DCO) networks, and relate these to drug sensitivity. Our collection of 53 drug-response predictors attains an average balanced accuracy of 58% in a cross-validation setting, rising to 66% for a subset of high-confidence predictions. We experimentally validated 12 out of 14 predictions in mice and adapted our strategy to obtain drug-response models from patients' progression-free survival data. Our strategy reveals links between oncogenic alterations, increasing the clinical impact of genomic profiling

Accurate Estrogen Receptor Quantification in Patients with Negative and Low-Positive Estrogen-Receptor-Expressing Breast Tumors: Sub-Analyses of Data from Two Clinical Studies

<p>Full copyright for enhanced digital features is owned by the authors.</p><p> </p><p><strong>Article full text</strong></p><p><br> The full text of this article can be found <a href="https://link.springer.com/article/10.1007/s12325-019-0896-0"><b>here</b>.</a> <br> <br> <strong>Provide enhanced digital features for this article</strong><br> If you are an author of this publication and would like to provide additional enhanced digital features for your article then please contact <u>[email protected]</u>.<br> <br> The journal offers a range of additional features designed to increase visibility and readership. All features will be thoroughly peer reviewed to ensure the content is of the highest scientific standard and all features are marked as ‘peer reviewed’ to ensure readers are aware that the content has been reviewed to the same level as the articles they are being presented alongside. Moreover, all sponsorship and disclosure information is included to provide complete transparency and adherence to good publication practices. This ensures that however the content is reached the reader has a full understanding of its origin. No fees are charged for hosting additional open access content.<br> <br> Other enhanced features include, but are not limited to:<br> • Slide decks<br> • Videos and animations<br> • Audio abstracts<br> • Audio slides</p> <p> </p

EPMA position paper in cancer:current overview and future perspectives

At present, a radical shift in cancer treatment is occurring in terms of predictive, preventive, and personalized medicine (PPPM). Individual patients will participate in more aspects of their healthcare. During the development of PPPM, many rapid, specific, and sensitive new methods for earlier detection of cancer will result in more efficient management of the patient and hence a better quality of life. Coordination of the various activities among different healthcare professionals in primary, secondary, and tertiary care requires well-defined competencies, implementation of training and educational programs, sharing of data, and harmonized guidelines. In this position paper, the current knowledge to understand cancer predisposition and risk factors, the cellular biology of cancer, predictive markers and treatment outcome, the improvement in technologies in screening and diagnosis, and provision of better drug development solutions are discussed in the context of a better implementation of personalized medicine. Recognition of the major risk factors for cancer initiation is the key for preventive strategies (EPMA J. 4(1):6, 2013). Of interest, cancer predisposing syndromes in particular the monogenic subtypes that lead to cancer progression are well defined and one should focus on implementation strategies to identify individuals at risk to allow preventive measures and early screening/diagnosis. Implementation of such measures is disturbed by improper use of the data, with breach of data protection as one of the risks to be heavily controlled. Population screening requires in depth cost-benefit analysis to justify healthcare costs, and the parameters screened should provide information that allow an actionable and deliverable solution, for better healthcare provision

Recent Advances in the Neoadjuvant Treatment of Breast Cancer

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Designing Epigenome Editing Tools to Understand the Functional Role of DNA Methylation Changes in Cancer

DNA methylation is known to silence gene expression in the context of imprinting, X-chromosome inactivation, and retrotransposon silencing. However, the role of DNA methylation in silencing gene expression outside of these contexts is not fully understood. This is especially true in diseases such as cancer, where normal DNA methylation patterns are significantly altered. In breast cancer as well as nearly all cancer types, most of the genome loses DNA methylation while small regions of the genome gain methylation. DNA methylation generally correlates with decreased gene expression when present at a gene promoter. Therefore, these regions of hypo- and hyper-methylation may contribute to cancer development and progression by activating oncogenes or silencing tumor suppressor genes. My work focuses on building tools to study the functional role of DNA methylation changes and exploring how methylation changes at a gene promoter promote resistance to treatment in breast cancer. About 75% of breast cancers depend on estrogen signaling through the estrogen receptor (ERα). These tumors are effectively treated by aromatase inhibitors (AI) that prevent estrogen production. However, almost all advanced cases of ERα positive breast cancer develop resistance to AI therapy. I therefore sought to identify methylation changes that promote this resistance. I studied UCA1 and PTGER4, two genes identified by a screen for negatively correlated methylation and expression changes in a cell line model of AI resistance. UCA1 is a long non-coding RNA that promotes growth and metastasis in bladder cancer. PTGER4 encodes the prostaglandin E2 receptor 4 (EP4), which supports the progression of multiple cancer types by altering cell signaling. While my experiments did not indicate that UCA1 has a strong role in AI resistance, I found that hypomethylation of the PTGER4 promoter correlates with increased expression and EP4 signaling. My data further suggest that the downstream effector of EP4 signaling, CARM1, promotes endocrine therapy resistance by increasing the ligand-independent transcription activity of ERα. The effects of local DNA methylation changes are most often identified by correlating the methylation and expression levels from two samples. To show that methylation causes the expression change, these studies rely on non-specific tools that demethylate the whole genome: DNA methyltransferase (DNMT) inhibitors or by DNMT knockout/knockdown. To build a tool capable of inducing site-specific DNA methylation changes, I fused the human DNMT3A catalytic domain to the RNA-guided nuclease Cas9 (the Cas9 is nuclease dead). I used this tool to induce up to 53% DNA methylation on individual cytosines within 50 bp of the target site. When multiple sites within the CDKN2A or ARF promoters were targeted, the induced DNA methylation decreased the expression of the targeted gene. To determine the optimal DNMT catalytic domain to use in this system, I created alternative DNMT fusions that included human DNMT1, a fusion of mouse Dnmt3a to mouse Dnmt3L, human DNMT3B, and the bacterial methyltransferase M.SssI. While the Dnmt3a-Dnmt3L fusion increased methylation relative to DNMT3A alone, it also induced more off-target methylation. The continued development of targeted DNA methylation technologies will increase our ability to identify functional methylation changes in tumors. As a result, we will learn the specific ways that methylation-induced gene expression changes contribute to cancer