Intuitive repositioning of an anti-depressant drug in combination with tivozanib: precision medicine for breast cancer therapy

Despite the existing therapies and lack of receptors such as HER-2, estrogen receptor and progesterone receptor, triple-negative breast cancer is one of the most aggressive subtypes of breast cancer. TNBCs are known for their highly aggressive metastatic behavior and typically migrate to brain and bone for secondary site propagation. Many diseases share similar molecular pathology exposing new avenues in molecular signaling for engendering innovative therapies. Generation of newer therapies and novel drugs are time consuming associated with very high resources. In order to provide personalized or precision medicine, drug repositioning will contribute in a cost-effective manner. In our study, we have repurposed and used a neoteric combination of two drug molecules namely, fluvoxamine and tivozanib, to target triple-negative breast cancer growth and progression. Our combination regime significantly targets two diverse but significant pathways in TNBCs. Subsequent analysis on migratory, invasive, and angiogenic properties showed the significance of our repurposed drug combination. Molecular array data resulted in identifying the specific and key players participating in cancer progression when the drug combination was used. The innovative combination of fluvoxamine and tivozanib reiterates the use of drug repositioning for precision medicine and subsequent companion diagnostic development.

     

Adjuvant Trastuzumab in HER2-Positive Early Breast Cancer by Age and Hormone Receptor Status: A Cost-Utility Analysis

BackgroundThe anti–human epidermal growth factor receptor 2 (HER2) monoclonal antibody trastuzumab improves outcomes in patients with node-positive HER2+ early breast cancer. Given trastuzumab’s high cost, we aimed to estimate its cost-effectiveness by heterogeneity in age and estrogen receptor (ER) and progesterone receptor (PR) status, which has previously been unexplored, to assist prioritisation.ConclusionsThis study highlights how cost-effectiveness can vary greatly by heterogeneity in age and hormone receptor subtype. Resource allocation and licensing of subsidised therapies such as trastuzumab should consider demographic and clinical heterogeneity; there is currently a profound disconnect between how funding decisions are made (largely agnostic to heterogeneity) and the principles of personalised medicine.     

An Integrative Approach to Precision Cancer Medicine Using Patient-Derived Xenografts

Cancer is a heterogeneous disease caused by diverse genomic alterations in oncogenes and tumor suppressor genes. Despite recent advances in high-throughput sequencing technologies and development of targeted therapies, novel cancer drug development is limited due to the high attrition rate from clinical studies. Patient-derived xenografts (PDX), which are established by the transfer of patient tumors into immunodeficient mice, serve as a platform for co-clinical trials by enabling the integration of clinical data, genomic profiles, and drug responsiveness data to determine precisely targeted therapies. PDX models retain many of the key characteristics of patients’ tumors including histology, genomic signature, cellular heterogeneity, and drug responsiveness. These models can also be applied to the development of biomarkers for drug responsiveness and personalized drug selection. This review summarizes our current knowledge of this field, including methodologic aspects, applications in drug development, challenges and limitations, and utilization for precision cancer medicine.     

Gene mutation‐based and specific therapies in precision medicine

Precision medicine has been initiated and gains more and more attention from preclinical and clinical scientists. A number of key elements or critical parts in precision medicine have been described and emphasized to establish a systems understanding of precision medicine. The principle of precision medicine is to treat patients on the basis of genetic alterations after gene mutations are identified, although questions and challenges still remain before clinical application. Therapeutic strategies of precision medicine should be considered according to gene mutation, after biological and functional mechanisms of mutated gene expression or epigenetics, or the correspondent protein, are clearly validated. It is time to explore and develop a strategy to target and correct mutated genes by direct elimination, restoration, correction or repair of mutated sequences/genes. Nevertheless, there are still numerous challenges to integrating widespread genomic testing into individual cancer therapies and into decision making for one or another treatment. There are wide‐ranging and complex issues to be solved before precision medicine becomes clinical reality. Thus, the precision medicine can be considered as an extension and part of clinical and translational medicine, a new alternative of clinical therapies and strategies, and have an important impact on disease cures and patient prognoses.     

Human breast cancer decellularized scaffolds promote epithelial-to-mesenchymal transitions and stemness of breast cancer cells in vitro

Breast cancer, with unsatisfactory survival rates, is the leading cause of cancer-related death in women worldwide. Recent advances in the genetic basis of breast cancer have benefitted the development of gene-based medicines and therapies. Tissue engineering technologies, including tissue decellularizations and reconstructions, are potential therapeutic alternatives for cancer research and tissue regeneration. In our study, human breast cancer biopsies were decellularized by a detergent technique, with sodium lauryl ether sulfate (SLES) solution, for the first time. And the decellularization process was optimized to maximally maintain tissue microarchitectures and extracellular matrix (ECM) components with minimal DNA compounds preserved. Histology analysis and DNA quantification results confirmed the decellularization effect with maximal genetic compounds removal. Quantification, immunofluorescence, and histology analyses demonstrated better preservation of ECM components in 0.5% SLES-treated scaffolds. Scaffolds seeded with MCF-7 cells demonstrated the process of cell recellularization in vitro, with increased cell migration, proliferation, and epithelial-to-mesenchymal transition (EMT) process. When treated with 5-fluorouracil, the expressions of stem cell markers, including Oct4, Sox2, and CD49F, were maximally maintained in the recellularized scaffold with decreased apoptosis rates compared with monolayer cells. These results showed that the decellularized breast scaffold model with SLES treatments would help to simulate the pathogenesis of breast cancer in vitro. And we hope that this model could further accelerate the development of effective therapies for breast cancer and benefit drug screenings.     

NHERF1 Between Promises and Hopes: Overview on Cancer and Prospective Openings

Na⁺/H⁺ exchanger regulatory factor 1 (NHERF1) is a scaffold protein, with two tandem PDZ domains and a carboxyl-terminal ezrin-binding (EB) region. This particular sticky structure is responsible for its interaction with different molecules to form multi-complexes that have a pivotal role in a lot of diseases. In particular, its involvement during carcinogenesis and cancer progression has been deeply analyzed in different tumors. The role of NHERF1 is not unique in cancer; its activity is connected to its subcellular localization. The literature data suggest that NHERF1 could be a new prognostic/predictive biomarker from breast cancer to hematological cancers. Furthermore, the high potential of this molecule as therapeutical target in different carcinomas is a new challenge for precision medicine. These evidences are part of a future view to improving patient clinical management, which should allow different tumor phenotypes to be treated with tailored therapies. This article reviews the biology of NHERF1, its engagement in different signal pathways and its involvement in different cancers, with a specific focus on breast cancer. It also considers NHERF1 potential role during inflammation related to most human cancers, designating new perspectives in the study of this “Janus-like” protein.     

Deconstructing the molecular portrait of basal-like breast cancer

Gene-expression profiling has revealed several molecular subtypes of breast cancer, which differ in their pathobiology and clinical outcomes. Basal-like tumors are a newly recognized subtype of breast cancer, which express genes that are characteristic of basal epithelial cells, such as the basal cytokeratins, and are associated with poor relapse-free and overall survival. However, the genetic and epigenetic alterations that are responsible for the biologically aggressive phenotype of these estrogen receptor-negative and HER2/ErbB2-negative tumors are not well understood, thereby hindering efforts to develop targeted therapies. Here, we focus on new insights into the molecular pathogenesis of basal-like breast cancer and explore how these discoveries might impact the treatment of these poor-prognosis tumors.     

Precision Medicine with Imprecise Therapy: Computational Modeling for Chemotherapy in Breast Cancer

Medical oncology is in need of a mathematical modeling toolkit that can leverage clinically-available measurements to optimize treatment selection and schedules for patients. Just as the therapeutic choice has been optimized to match tumor genetics, the delivery of those therapeutics should be optimized based on patient-specific pharmacokinetic/pharmacodynamic properties. Under the current approach to treatment response planning and assessment, there does not exist an efficient method to consolidate biomarker changes into a holistic understanding of treatment response. While the majority of research on chemotherapies focus on cellular and genetic mechanisms of resistance, there are numerous patient-specific and tumor-specific measures that contribute to treatment response. New approaches that consolidate multimodal information into actionable data are needed. Mathematical modeling offers a solution to this problem. In this perspective, we first focus on the particular case of breast cancer to highlight how mathematical models have shaped the current approaches to treatment. Then we compare chemotherapy to radiation therapy. Finally, we identify opportunities to improve chemotherapy treatments using the model of radiation therapy. We posit that mathematical models can improve the application of anticancer therapeutics in the era of precision medicine. By highlighting a number of historical examples of the contributions of mathematical models to cancer therapy, we hope that this contribution serves to engage investigators who may not have previously considered how mathematical modeling can provide real insights into breast cancer therapy.     

Cancer proteomics

Conclusion The future of cancer diagnostics will be based on a panel of proteomic biomarkers. They could be used to detect cancer at an early stage, to predict and to direct therapies. Enzymes and related proteins are important biological molecules, which could serve as cancer biomarkers. These biomarkers could be intact or fragments of proteins. The challenge is to be able to find and validate these potential biomarkers as clinical diagnostics. With the advances in proteomic technologies, we are closer than ever to find these “new” enzyme molecules or fragments. The translation of newly discovered biomarkers could provide an opportunity to revolutionize the era of personalized medicine.     

The triad of success in personalised medicine: pharmacogenomics, biotechnology and regulatory issues from a Central European perspective

The population of the world has recently passed the 7 billion milestone and as the cost of human genome sequencing is rapidly declining, sequence data of billions of people should be accessible much sooner than anyone would have predicted 10years ago. This will form the basis of personalised medicine. However it is still not clear, even in principle, whether these data, combined with data of the expression of one's genome in various cells and tissues relevant to different diseases, could be used effectively in clinical medicine and healthcare, or in predicting responses to different therapies. Therefore this is an important issue which needs to be addressed before more resources are wasted on less than informative studies and surveys simply because technologies exist. As a typical example, we have selected and summarise here key studies from the biomedical literature that focus on gene expression profiling of the response to biologic therapies in peripheral blood and biopsy samples in autoimmune diseases such as rheumatoid arthritis, spondylarthropathy, inflammatory bowel diseases and psoriasis. We also present the state of the biotechnology market from a European perspective, discuss how spin-offs leverage the power of genomic technologies and describe how they might contribute to personalised medicine. As ethical, legal and social issues are essential in the area of genomics, we analysed these aspects and present here the European situation with a special focus on Hungary. We propose that the synergy of these three issues: pharmacogenomics, biotechnology and regulatory issues should be considered a triad necessary to succeed in personalised medicine.     

Molecular targets for breast cancer therapy and prevention

The recent completion of the human genome sequence has raised great hopes for the discovery of new breast cancer therapies based on newly-discovered genes linked to breast cancer development and progression. Here we describe breast cancer therapies that have emerged from gene-based scientific efforts over the past 20 years and that are now approved for clinical testing or treatment.     

Using Functional Genetics to Understand Breast Cancer Biology

Genetic screens were for long the prerogative of those that studied model organisms. The discovery in 2001 that gene silencing through RNA interference (RNAi) can also be brought about in mammalian cells paved the way for large scale loss-of-function genetic screens in higher organisms. In this article, we describe how functional genetic studies can help us understand the biology of breast cancer, how it can be used to identify novel targets for breast cancer therapy, and how it can help in the identification of those patients that are most likely to respond to a given therapy.Much remains to be learned regarding the function of mammalian genes. Only some quarter of all human genes have well-described functions. It is likely that quite a few of these currently unannotated genes will turn out to play key parts in cancer biology. For example, a 70-gene gene signature that can discriminate breast tumors of good and poor prognosis contained some 20 genes of currently unknown function (van ‘t Veer et al. 2002). The fact that these genes of unknown function foretell breast cancer prognosis hints at a role for at least some of these genes in breast cancer biology. The unbiased search for genes that contribute to breast cancer development is therefore likely to yield a rich harvest of new insights. RNA interference allows us to suppress genes systematically on a large scale and study the effects of gene suppression on specific cellular processes or signaling pathways. Consequently, RNA interference-based genetic screens have the potential to deepen our understanding of the molecular events that cause breast cancer, to find novel targets for therapy and to find biomarkers of drug responsiveness. In this article, we will describe the technologies available to perform both gain-of-function and loss-of-function genetic screens and will illustrate how such functional genetic screens have been used in the recent past to study a variety of outstanding questions in the biology of breast cancer.     

The tumor macroenvironment and systemic regulation of breast cancer progression

Breast cancer is the most common malignancy among women worldwide and is the most common cause of death for women between 35 and 50 years of age. Women with breast cancer are at risk of developing metastases for their entire lifetime and, despite local and systemic therapies, approximately 30% of breast cancer patients will relapse (Jemal et al., 2010). Nearly all breast cancer related deaths are due to metastatic disease, even though metastasis is considered to be an inefficient process. In some cases, tumor cells disseminate from primary sites at an early stage, but remain indolent for protracted periods of time before becoming overt, life-threatening tumors. Little is known about the mechanisms that cause these indolent tumors to grow into malignant disease. Because of this gap in our understanding, we are unable to predict which breast cancer patients are likely to experience disease relapse or develop metastases years after treatment of their primary tumor. A better understanding of the mechanisms and signals involved in the exit of tumor cells from dormancy would not only allow for more accurate selection of patients that would benefit from systemic therapy, but could also lead to the development of more targeted therapies to inhibit the signals that promote disease progression. In this review, we address the systemic, or "macroenvironmental", contribution to tumor initiation and progression and what is known about how a pro-tumorigenic systemic environment is established.     

Network controllability-based algorithm to target personalized driver genes for discovering combinatorial drugs of individual patients

Multiple driver genes in individual patient samples may cause resistance to individual drugs in precision medicine. However, current computational methods have not studied how to fill the gap between personalized driver gene identification and combinatorial drug discovery for individual patients. Here, we developed a novel structural network controllability-based personalized driver genes and combinatorial drug identification algorithm (CPGD), aiming to identify combinatorial drugs for an individual patient by targeting personalized driver genes from network controllability perspective. On two benchmark disease datasets (i.e. breast cancer and lung cancer datasets), performance of CPGD is superior to that of other state-of-the-art driver gene-focus methods in terms of discovery rate among prior-known clinical efficacious combinatorial drugs. Especially on breast cancer dataset, CPGD evaluated synergistic effect of pairwise drug combinations by measuring synergistic effect of their corresponding personalized driver gene modules, which are affected by a given targeting personalized driver gene set of drugs. The results showed that CPGD performs better than existing synergistic combinatorial strategies in identifying clinical efficacious paired combinatorial drugs. Furthermore, CPGD enhanced cancer subtyping by computationally providing personalized side effect signatures for individual patients. In addition, CPGD identified 90 drug combinations candidates from SARS-COV2 dataset as potential drug repurposing candidates for recently spreading COVID-19.     

The DEK Oncogene Is a Target of Steroid Hormone Receptor Signaling in Breast Cancer

Expression of estrogen and progesterone hormone receptors indicates a favorable prognosis due to the successful use of hormonal therapies such as tamoxifen and aromatase inhibitors. Unfortunately, 15–20% of patients will experience breast cancer recurrence despite continued use of tamoxifen. Drug resistance to hormonal therapies is of great clinical concern so it is imperative to identify novel molecular factors that contribute to tumorigenesis in hormone receptor positive cancers and/or mediate drug sensitivity. The hope is that targeted therapies, in combination with hormonal therapies, will improve survival and prevent recurrence. We have previously shown that the DEK oncogene, which is a chromatin remodeling protein, supports breast cancer cell proliferation, invasion and the maintenance of the breast cancer stem cell population. In this report, we demonstrate that DEK expression is associated with positive hormone receptor status in primary breast cancers and is up-regulated in vitro following exposure to the hormones estrogen, progesterone, and androgen. Chromatin immunoprecipitation experiments identify DEK as a novel estrogen receptor α (ERα) target gene whose expression promotes estrogen-induced proliferation. Finally, we report for the first time that DEK depletion enhances tamoxifen-induced cell death in ER+ breast cancer cell lines. Together, our data suggest that DEK promotes the pathogenesis of ER+ breast cancer and that the targeted inhibition of DEK may enhance the efficacy of conventional hormone therapies.     

Re-purposing cancer therapeutics for breast cancer immunotherapy

After decades of work to develop immune-based therapies for cancer, the first drugs designed specifically to engage the host anti-tumor immune response for therapeutic benefit were recently approved for clinical use. Sipuleucel-T, a vaccine for advanced prostate cancer, and ipilimumab, a monoclonal antibody that mitigates the negative impact of cytotoxic T lymphocyte antigen-4 signaling on tumor immunity, provide a modest clinical benefit in some patients. The arrival of these drugs in the clinic is a significant advance that we can capitalize on for even better clinical outcomes. The strategic and scientifically rational integration of vaccines and other direct immunomodulators with standard cancer therapeutics should lead to therapeutic synergy and high rates of tumor rejection. This review focuses on the use of cyclophosphamide, doxorubicin, and HER-2-specific monoclonal antibodies to dissect mechanisms of immune tolerance relevant to breast cancer patients and illustrates how appropriate preclinical models can powerfully inform clinical translation. The immune-modulating activity of targeted, pathway-specific, small molecule therapeutics is also discussed. Fully understanding how cancer drugs impact the immune system should lead to the ultimate personalized cancer medicine: effective combinatorial immunotherapy strategies that simultaneously target signaling pathways essential for tumor growth and progression, and systematically break multiple, distinct immune tolerance pathways to maximize tumor rejection and effect cure.     

On being “actionable”: clinical sequencing and the emerging contours of a regime of genomic medicine in oncology

This article explores commercial, academic, and national initiatives aimed at using sequencing technologies to generate “actionable” genomic results that can be applied to the clinical management of oncology patients. We argue that the term “actionable” is not merely a buzzword, but signals the emergence of a distinctive sociotechnical regime of genomic medicine in oncology. Unlike other regimes of genomic medicine that are organized around assessing and managing inherited risk for developing cancer (e.g. BRCA testing), actionable regimes aim to generate predictive relationships between genetic information and drug therapies, thereby generating new kinds of clinical actions. We explore how these genomic results are made actionable by articulating them with existing clinical routines, clinical trials, regulatory regimes, and health care systems; and in turn, how clinical sequencing programs have begun to reconfigure knowledge and practices in oncology. Actionability regimes confirm the emergence of bio-clinical decision-making in oncology, whereby the articulation of molecular hypotheses and experimental therapeutics become central to patient care.     

Identification of specific reachable molecular targets in human breast cancer using a versatile ex vivo proteomic method

Targeting of tumoral tissues is one of the most promising approaches to improve both the efficacy and safety of anticancer treatments. The identification of valid targets, including proteins specifically and abundantly expressed in cancer lesions, is of utmost importance. Despite state-of-the-art technologies, the discovery of cancer-associated target proteins still faces the limitation, in human tissues, of antigen accessibility to suitable high-affinity ligands such as human mAb bound to bioactive molecules. Terminal perfusion of tumor-bearing mice or ex vivo perfusion of human cancer-bearing organs with a reactive biotin ester solution has successfully led to the identification of novel accessible biomarkers. This methodology is however restricted to perfusable organs, and excludes most of the tissues of interest to targeted therapies, e.g. primary breast cancer and metastases. Herein, we report on the development of a new chemical proteomic method that bypasses the perfusion step and thus offers the potential to identify accessible molecular targets in virtually all types of animal and human tissues. We have validated our new procedure by identifying biomarkers selectively expressed in human breast carcinoma. Overall, this powerful technology may lay the ground not only for custom-made therapies in cancer, but also for the development of therapies that need to be selectively delivered in a specific tissue.     

Subgroup-Based Adaptive (SUBA) Designs for Multi-arm Biomarker Trials

Targeted therapies based on biomarker profiling are becoming a mainstream direction of cancer research and treatment. Depending on the expression of specific prognostic biomarkers, targeted therapies assign different cancer drugs to subgroups of patients even if they are diagnosed with the same type of cancer by traditional means, such as tumor location. For example, Herceptin is only indicated for the subgroup of patients with HER2+ breast cancer, but not other types of breast cancer. However, subgroups like HER2+ breast cancer with effective targeted therapies are rare, and most cancer drugs are still being applied to large patient populations that include many patients who might not respond or benefit. Also, the response to targeted agents in humans is usually unpredictable. To address these issues, we propose subgroup-based adaptive (SUBA), designs that simultaneously search for prognostic subgroups and allocate patients adaptively to the best subgroup-specific treatments throughout the course of the trial. The main features of SUBA include the continuous reclassification of patient subgroups based on a random partition model and the adaptive allocation of patients to the best treatment arm based on posterior predictive probabilities. We compare the SUBA design with three alternative designs including equal randomization, outcome-adaptive randomization, and a design based on a probit regression. In simulation studies, we find that SUBA compares favorably against the alternatives.