Personalised medicine for cancer: from drug development into clinical practice

Application of nanotechnology in breast cancer screening under obstetrics and gynecology through the use of CNN and ANFIS

Breast cancer is the leading reason of death among women aged 35 to 54. Breast cancer diagnosis still presents significant challenges, and preventing the disease's most severe symptoms requires early detection. The role of nanotechnology in the tumor-treatment has recently attracted a lot of interest. In cancer therapies, nanotechnology plays a major role in the medication distribution process. Nanoparticles have the ability to target tumors. Nanoparticles are favorable and maybe preferable for usage in tumor detection and imaging due to their incredibly small size. Quantum dots, semiconductor crystals with increased labeling and imaging capabilities for cancer cells, are one of the particles that have received the most research attention. The design of the research is cross-sectional and descriptive. From April through September of 2020, data were gathered at the State Hospital. All pregnant women who came to the hospital throughout the first and second trimesters of the research's data collection were included in the study population. 100 pregnant women between the ages of 20 and 40 who had not yet had a mammogram comprised the research sample. 1100 digitized mammography images are included in the dataset, which was obtained from a hospital. Convolutional neural networks (CNN) were used to scan all images, and breast masses and mass comparisons were made using the malignant-benign categorization. The adaptive neuro-fuzzy inference system (ANFIS) then examined all of the data obtained by CNN in order to identify breast cancer early using inputs based on the nine different inputs. The precision of the mechanism used in this technique to determine the ideal radius value is significantly impacted by the radius value. Nine variables that define breast cancer indicators were utilized as inputs to the ANFIS classifier, which was then used to identify breast cancer. The parameters were given the necessary fuzzy functions, and the combined dataset was applied to train the method. Testing was initially performed by 30% of dataset that was later done with the real data obtained from the hospital. The accuracy of the results for 30% data was 84% (specificity =72.7%, sensitivity =86.7%) and the results for the real data was 89.8% (sensitivity =82.3%, specificity =75.9%), respectively.

Prostate Cancer Review: Genetics, Diagnosis, Treatment Options, and Alternative Approaches

Simple Summary

Prostate cancer affects men of all racial and ethnic groups and leads to higher rates of mortality in those belonging to a lower socioeconomic status due to late detection of the disease. There is growing evidence that suggests the contribution of an individual’s genetic profile to prostate cancer. Currently used prostate cancer treatments have serious adverse effects; therefore, new research is focusing on alternative treatment options such as the use of genetic biomarkers for targeted gene therapy, nanotechnology for controlled targeted treatment, and further exploring medicinal plants for new anticancer agents. In this review, we describe the recent advances in prostate cancer research.

Abstract

Prostate cancer is one of the malignancies that affects men and significantly contributes to increased mortality rates in men globally. Patients affected with prostate cancer present with either a localized or advanced disease. In this review, we aim to provide a holistic overview of prostate cancer, including the diagnosis of the disease, mutations leading to the onset and progression of the disease, and treatment options. Prostate cancer diagnoses include a digital rectal examination, prostate-specific antigen analysis, and prostate biopsies. Mutations in certain genes are linked to the onset, progression, and metastasis of the cancer. Treatment for localized prostate cancer encompasses active surveillance, ablative radiotherapy, and radical prostatectomy. Men who relapse or present metastatic prostate cancer receive androgen deprivation therapy (ADT), salvage radiotherapy, and chemotherapy. Currently, available treatment options are more effective when used as combination therapy; however, despite available treatment options, prostate cancer remains to be incurable. There has been ongoing research on finding and identifying other treatment approaches such as the use of traditional medicine, the application of nanotechnologies, and gene therapy to combat prostate cancer, drug resistance, as well as to reduce the adverse effects that come with current treatment options. In this article, we summarize the genes involved in prostate cancer, available treatment options, and current research on alternative treatment options.

Co-delivery of doxorubicin and SIS3 by folate-targeted polymeric micelles for overcoming tumor multidrug resistance

Multidrug resistance (MDR) is considered as a critical limiting factor for the successful chemotherapy, which is mainly characterized by the overexpression of ATP-binding cassette (ABC) transporter ABCB1 or ABCG2. In this study, folate-targeted polymeric micellar carrier was successfully constructed to co-delivery of doxorubicin (DOX) and SIS3 (FA/DOX/SIS3 micelles), a specific Smad3 inhibitor which sensitizes ABCB1- and ABCG2-overexpressing cancer cells to chemotherapeutic agents. The ratio of DOX to SIS3 in polymeric micelles was determined based on the anti-tumor activity against resistant breast cells. In addition, FA/DOX/SIS3 micelles exhibited a much longer circulation time in blood and were preferentially accumulated in resistant tumor tissue. Pharmacodynamic studies showed that FA/DOX/SIS3 micelles possessed superior anti-tumor activity than other DOX-based treatments. Overall, FA/DOX/SIS3 micelles are a promising formulation for the synergistic treatment of drug-resistant tumor.

Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey

Artificial Intelligence is providing astonishing results, with medicine being one of its favourite playgrounds. Machine Learning and, in particular, Deep Neural Networks are behind this revolution. Among the most challenging targets of interest in medicine are cancer diagnosis and therapies but, to start this revolution, software tools need to be adapted to cover the new requirements. In this sense, learning tools are becoming a commodity but, to be able to assist doctors on a daily basis, it is essential to fully understand how models can be interpreted. In this survey, we analyse current machine learning models and other in-silico tools as applied to medicine-specifically, to cancer research-and we discuss their interpretability, performance and the input data they are fed with. Artificial neural networks (ANN), logistic regression (LR) and support vector machines (SVM) have been observed to be the preferred models. In addition, convolutional neural networks (CNNs), supported by the rapid development of graphic processing units (GPUs) and high-performance computing (HPC) infrastructures, are gaining importance when image processing is feasible. However, the interpretability of machine learning predictions so that doctors can understand them, trust them and gain useful insights for the clinical practice is still rarely considered, which is a factor that needs to be improved to enhance doctors' predictive capacity and achieve individualised therapies in the near future.

Imaging of glucose metabolism by 13C-MRI distinguishes pancreatic cancer subtypes in mice

Metabolic differences among and within tumors can be an important determinant in cancer treatment outcome. However, methods for determining these differences non-invasively in vivo is lacking. Using pancreatic ductal adenocarcinoma as a model, we demonstrate that tumor xenografts with a similar genetic background can be distinguished by their differing rates of the metabolism of 13C labeled glucose tracers, which can be imaged without hyperpolarization by using newly developed techniques for noise suppression. Using this method, cancer subtypes that appeared to have similar metabolic profiles based on steady state metabolic measurement can be distinguished from each other. The metabolic maps from 13C-glucose imaging localized lactate production and overall glucose metabolism to different regions of some tumors. Such tumor heterogeneity would not be not detectable in FDG-PET.

Non-conventional dosing of oral anticancer agents in oncology and malignant haematology: a systematic review protocol

BACKGROUND

Recent advances in cancer therapeutics have resulted in significantly improved overall survival and progression-free survival for patients. Targeted oral systemic anticancer therapies (SACT) offer a range of treatment approaches that differ from traditional cytotoxic chemotherapy: non-cytotoxic oral SACT target malignant disease continuously, have less broad and more favourable safety profiles, which can improve patients' quality of life (QoL). Toxicities associated with daily oral SACT administration can, however, result in non-adherence and a reduced QoL. Non-conventional dosing of oral SACT, where unlicensed doses/schedules of drugs are prescribed, is one approach increasingly adopted by clinicians to reduce toxicities and subsequent non-adherence and to improve QoL. Guidance governing this practice is, however, limited. This systematic review aims to identify evidence about prescribing practices of, and outcomes from, non-conventional dosing of oral SACT in oncology and malignant haematology.

METHODS

A search using the following electronic databases will be conducted: Ovid MEDLINE, Ovid EMBASE, Cumulative Index to Nursing and Allied Health Literature (CINAHL) and Cochrane Registry of Controlled Trials. Studies will be selected based on predefined inclusion/exclusion criteria. Critical appraisal will be conducted to identify potential biases, strengths and limitations of included studies. Extracted data will be tabulated to sort and summarise key findings. An initial literature search indicated that studies reporting non-standard dosing of oral SACT intervention studies are diverse and heterogeneous in study design. Extracted data will, therefore, be tabulated, and together with a narrative synthesis of integrated key findings, will be presented and discussed in reference to the strengths and weaknesses of the evidence base. If sufficient stratified data is available (e.g. age group, tumour type, disease stage) or intervention (drug, dosing schedule), sub-group analysis will be conducted to inform prescribing practice.

DISCUSSION

This review will identify relevant literature on the topic to inform prescribers working in oncology and malignant haematology. It will also analyse any evidence of the following outcomes: toxicity, treatment adherence and/or QoL outcomes for patients receiving non-standard doses of oral SACT. Limitations in the evidence base may arise from variability in both the type and quality of studies reviewed.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42017076195 .

DBNorm: normalizing high-density oligonucleotide microarray data based on distributions

Background

Data from patients with rare diseases is often produced using different platforms and probe sets because patients are widely distributed in space and time. Aggregating such data requires a method of normalization that makes patient records comparable.

Results

This paper proposed DBNorm, implemented as an R package, is an algorithm that normalizes arbitrarily distributed data to a common, comparable form. Specifically, DBNorm merges data distributions by fitting functions to each of them, and using the probability of each element drawn from the fitted distribution to merge it into a global distribution. DBNorm contains state-of-the-art fitting functions including Polynomial, Fourier and Gaussian distributions, and also allows users to define their own fitting functions if required.

Conclusions

The performance of DBNorm is compared with z-score, average difference, quantile normalization and ComBat on a set of datasets, including several that are publically available. The performance of these normalization methods are compared using statistics, visualization, and classification when class labels are known based on a number of self-generated and public microarray datasets. The experimental results show that DBNorm achieves better normalization results than conventional methods. Finally, the approach has the potential to be applicable outside bioinformatics analysis.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1912-5) contains supplementary material, which is available to authorized users.

Identifying critical steps towards improved access to innovation in cancer care: a European CanCer Organisation position paper

In recent decades cancer care has seen improvements in the speed and accuracy of diagnostic procedures; the effectiveness of surgery, radiation therapy and medical treatments; the power of information technology; and the development of multidisciplinary, specialist-led approaches to care. Such innovations are essential if we are to continue improving the lives of cancer patients across Europe despite financial pressures on our healthcare systems. Investment in innovation must be balanced with the need to ensure the sustainability of healthcare budgets, and all health professionals have a responsibility to help achieve this balance. It requires scrutiny of the way care is delivered; we must be ready to discontinue practices or interventions that are inefficient, and prioritise innovations that may deliver the best outcomes possible for patients within the limits of available resources. Decisions on innovations should take into account their long-term impact on patient outcomes and costs, not just their immediate costs. Adopting a culture of innovation requires a multidisciplinary team approach, with the patient at the centre and an integral part of the team. It must take a whole-system and whole-patient perspective on cancer care and be guided by high-quality real-world data, including outcomes relevant to the patient and actual costs of care; this accurately reflects the impact of any innovation in clinical practice. The European CanCer Organisation is committed to working with its member societies, patient organisations and the cancer community at large to find sustainable ways to identify and integrate the most meaningful innovations into all aspects of cancer care.

Using complex networks for refining survival prognosis in prostate cancer patient

Complex network theory has been used, during the last decade, to understand the structures behind complex biological problems, yielding new knowledge in a large number of situations. Nevertheless, such knowledge has remained mostly qualitative. In this contribution, I show how information extracted from a network representation can be used in a quantitative way, to improve the score of a classification task. As a test bed, I consider a dataset corresponding to patients suffering from prostate cancer, and the task of successfully prognosing their survival. When information from a complex network representation is added on top of a simple classification model, the error is reduced from 27.9% to 23.8%. This confirms that network theory can be used to synthesize information that may not readily be accessible by standard data mining algorithms.

Toward precision medicine of breast cancer

In this review, we report on breast cancer's molecular features and on how high throughput technologies are helping in understanding the dynamics of tumorigenesis and cancer progression with the aim of developing precision medicine methods. We first address the current state of the art in breast cancer therapies and challenges in order to progress towards its cure. Then, we show how the interaction of high-throughput technologies with in silico modeling has led to set up useful inferences for promising strategies of target-specific therapies with low secondary effect incidence for patients. Finally, we discuss the challenge of pharmacogenetics in the clinical practice of cancer therapy. All these issues are explored within the context of precision medicine.

Challenges and Opportunities for Exploring Patient-Level Data

The proper exploration of patient-level data will pave the way towards personalised medicine. To better assess the state of the art in this field we identify the challenges and uncover the opportunities for the exploration of patient-level data through the review of well-known initiatives and projects focusing on the exploration of patient-level data. These cover a broad array of topics, from genomics to patient registries up to rare diseases research, among others. For each, we identified basic goals, involved partners, defined strategies and key technological and scientific outcomes, establishing the foundation for our analysis framework with four pillars: control, sustainability, technology, and science. Substantial research outcomes have been produced towards the exploration of patient-level data. The potential behind these data will be essential to realise the personalised medicine premise in upcoming years. Hence, relevant stakeholders continually push forward new developments in this domain, bringing novel opportunities that are ripe for exploration. Despite last decade's translational research advances, personalised medicine is still far from being a reality. Patients' data underlying potential goes beyond daily clinical practice. There are miscellaneous challenges and opportunities open for the exploration of these data by academia and business stakeholders.

Design and evaluation of radiolabeled tracers for tumor imaging

The growing understanding of tumor biology and the identification of tumor-specific genetic and molecular alterations, such as the overexpression of membrane receptors and other proteins, allows for personalization of patient management using targeted therapies. However, this puts stringent demands on the diagnostic tools used to identify patients who are likely to respond to a particular treatment. Radionuclide molecular imaging is a promising noninvasive method to visualize and characterize the expression of such targets. A number of different proteins, from full-length antibodies and their derivatives to small scaffold proteins and peptide receptor-ligands, have been applied to molecular imaging, each demonstrating strengths and weaknesses. Here, we discuss the concept of molecular targeting and, in particular, molecular imaging of cancer-associated targets. Additionally, we describe important biotechnological considerations and desired features when designing and developing tracers for radionuclide molecular imaging.

Miniaturization applied to analysis of nucleic acids in heterogeneous tissues

Despite huge efforts in sample analysis, the measurement of marker nucleic acids within tissues remains largely nonquantitative. Gene analyses have benefited from sensitivity gains through in vitro gene amplification, including PCR. However, whilst these processes are intrinsically suited to highly reproducible, accurate and precise gene measurement, the term semiquantitative analysis is still commonly used, suggesting that other fundamental limitations preclude a generic quantitative basis to gene analysis. The most poorly defined aspect of gene analysis relates to the sample itself. The amount of cells and, particularly, cell subtype composition are rarely annotated before analysis; indeed, they are often extrapolated after analysis. To advance our understanding of pathogenesis, assay formats will benefit from resembling the dimensions of the cell, to assist in the analysis of cellular components of tissue complexes. This review is partly a perspective on how current miniaturization technologies, in association with molecular biology, microfluidics and surface chemistries, may evolve from the parts of a paradigm to enable the unambiguous quantitative analysis of complex biologic matter.

Targeted isolation and analysis of single tumor cells with aptamer-encoded microwell array on microfluidic device

Microfluidic-based single cells analysis has been of great interest in recent years, promising disease diagnosis and personalized medicine. Current technologies are challenging in bioselectively isolating specific single cells from complex matrices. Herein, a novel microfluidic platform integrated with cell-recognizable aptamer-encoded microwells was specifically developed to isolate single tumor cells with satisfied single-cell occupancy and unique bioselectivity. In this work, the designed microwell-structures enable us to encourage strong 3D local topographic interactions of the target cell surface with biomolecules and regulate the single-cell resolution. Under the optimized size of microwells, the single-cell occupancy was significantly enhanced from 0.5% to 88.2% through the introduction of the aptamer. Analysis of the target cells was directly performed in short time periods (<5.0 min) with small volumes (4.5 μL). Importantly, such an aptamer-enabled microfluidic device shows an excellent selectivity for target single cells isolation compared with three control cells. Subsequently, targeted isolation and analysis of single tumor cells were demonstrated by using artificial complex cell samples at simulated conditions, and various cellular carboxylesterases were studied by time-course measurements of cellular fluorescence kinetics at individual-cell level. Thus, our technique will open up a new opportunity in single-cell level-based disease diagnosis and personalize medicine screening.

Nanotheranostics for personalized medicine

The application of nanotechnology in the biomedical field, known as nanomedicine, has gained much interest in the recent past, as versatile strategy for selective drug delivery and diagnostic purposes. The already encouraging results obtained with monofunctional nanomedicines have directed the efforts of the scientists towards the creation of "nanotheranostics" (i.e. theranostic nanomedicines) which integrate imaging and therapeutic functions in a single platform. Nanotheranostics hold great promises because they combine the simultaneous non-invasive diagnosis and treatment of diseases with the exciting possibility to monitor in real time drug release and distribution, thus predicting and validating the effectiveness of the therapy. Due to these features nanotheranostics are extremely attractive for optimizing treatment outcomes in cancer and other severe diseases. The following step is the attempt to use nanotheranostics for performing a real personalized medicine which will tailor optimized treatment to each patient, taking into account the individual variability. Clinical application of nanotheranostics would enable earlier detection and treatment of diseases and earlier assessment of the response, thus allowing screening for patients which would potentially respond to therapy and have higher possibilities of a favorable outcome. This concept makes nanotheranostics extremely appealing to elaborate personalized therapeutic protocols for achieving the maximal benefit along with a high safety profile. Among the several systems developed up to now, this review focuses on the nanotheranostics which, due to the promising results, show the highest potential of translation to clinical applications and may transform into concrete practice the concept of personalized nanomedicine.

A metabonomic approach to chemosensitivity prediction of cisplatin plus 5-fluorouracil in a human xenograft model of gastric cancer

The prediction of chemosensitivity is a challenging problem in the management of cancer. In the present study, a metabonomic approach was proposed to assess the feasibility of chemosensitivity prediction in a human xenograft model of gastric cancer. BALB/c-nu/nu mice were transplanted with MKN-45 cell line to establish the xenograft model. The mice were then randomized into treatment group (cisplatin and 5-fluorouracil) and control group (0.9% sodium chloride), and their plasma were collected before treatment. Metabolic profiles of all plasma samples were acquired by using high-performance liquid chromatography coupled with a quadrupole time-of-flight mass spectrometer (HPLC/Q-TOF-MS). Based on the data of metabolic profiles and k-Nearest Neighbor algorithm, a prediction model for chemosensitivity was developed and an average accuracy of 90.4% was achieved. In addition, a series of endogenous metabolites, including 1-acyl-lysophosphatidycholines, polyunsaturated fatty acids and their derivatives, were determined as potential indicators of chemosensitivity. In conclusion, our results suggest that the proposed metabonomic approach allows effective chemosensitivity prediction in human xenograft model of gastric cancer. The approach presents a new concept in the chemosensitivtiy prediction of cancer and is expected to be developed as a powerful tool in the personalized cancer therapy.

Optimizing high dimensional gene expression studies for immune response following smallpox vaccination using Taqman® low density immune arrays

INTRODUCTION

We sought to determine the time and vaccinia virus dose combination that would maximize the number of acute immune response changes in response to vaccinia stimulation in preparation for a large gene expression microarray experiment.

METHODS

PBMCs from ten subjects were exposed to five vaccinia virus doses for three lengths of time. Gene expression was measured for 90 immune response genes via Taqman® Low Density Immune Arrays. Expression data were normalized via model-based non-linear normalization. Linear mixed effects model results were used to standardize changes across genes and determine the time/multiplicity of infection (MOI) combination with the largest number of changes.

RESULTS

The greatest number of changes occurred with a MOI of 5.0 and exposure time of 48 h. Further inspection revealed that most changes had occurred earlier and faded at this combination. The second highest number of changes was found at a MOI of 0.5 PFU/cell and time of 18 h.

CONCLUSIONS

We conclude a time of 18 h with a MOI of 0.5 PFU/cell is the optimal time/MOI combination for the full scale gene expression study. The strategy described herein is a general and resource efficient way to make critical decisions regarding experimental parameters for studies utilizing expensive assays that interrogate a large number of variables.

Bioinformatic-driven search for metabolic biomarkers in disease

The search and validation of novel disease biomarkers requires the complementary power of professional study planning and execution, modern profiling technologies and related bioinformatics tools for data analysis and interpretation. Biomarkers have considerable impact on the care of patients and are urgently needed for advancing diagnostics, prognostics and treatment of disease. This survey article highlights emerging bioinformatics methods for biomarker discovery in clinical metabolomics, focusing on the problem of data preprocessing and consolidation, the data-driven search, verification, prioritization and biological interpretation of putative metabolic candidate biomarkers in disease. In particular, data mining tools suitable for the application to omic data gathered from most frequently-used type of experimental designs, such as case-control or longitudinal biomarker cohort studies, are reviewed and case examples of selected discovery steps are delineated in more detail. This review demonstrates that clinical bioinformatics has evolved into an essential element of biomarker discovery, translating new innovations and successes in profiling technologies and bioinformatics to clinical application.

Ron kinase transphosphorylation sustains MET oncogene addiction

Receptors for the scatter factors HGF and MSP that are encoded by the MET and RON oncogenes are key players in invasive growth. Receptor cross-talk between Met and Ron occurs. Amplification of the MET oncogene results in kinase activation, deregulated expression of an invasive growth phenotype, and addiction to MET oncogene signaling (i.e., dependency on sustained Met signaling for survival and proliferation). Here we show that cancer cells addicted to MET also display constitutive activation of the Ron kinase. In human cancer cell lines coexpressing the 2 oncogenes, Ron is specifically transphosphorylated by activated Met. In contrast, Ron phosphorylation is not triggered in cells harboring constitutively active kinase receptors other than Met, including Egfr or Her2. Furthermore, Ron phosphorylation is suppressed by Met-specific kinase inhibitors (PHA-665752 or JNJ-38877605). Last, Ron phosphorylation is quenched by reducing cell surface expression of Met proteins by antibody-induced shedding. In MET-addicted cancer cells, short hairpin RNA-mediated silencing of RON expression resulted in decreased proliferation and clonogenic activity in vitro and tumorigenicity in vivo. Our findings establish that oncogene addiction to MET involves Ron transactivation, pointing to Ron kinase as a target for combinatorial cancer therapy.

Anti-tumor activity of liposomal glucocorticoids: The relevance of liposome-mediated drug delivery, intratumoral localization and systemic activity

Tumor-associated inflammation has been recognized as an important tumor growth propagator and, therefore, represents an attractive target for anti-cancer therapy. In the current study, inspired by recent findings on the anti-tumor activity of liposomal glucocorticoids, we introduce paramagnetic and fluorescent liposomes, encapsulating prednisolone phosphate (PLP), to evaluate the local delivery of liposomal glucocorticoids to the tumor and its importance for the therapeutic response. The new multifunctional liposomes (Gd-PLP-L) (120nm diameter, 5.8mg PLP/60μmol lipid, bioexponential blood-clearance kinetics (T(1/2α)=2.4±0.5h, T(1/2β)=42.0±12.4h), drug leakage of 15%/72h (in vitro)), containing 25mol% Gd-DTPA-lipid and 0.1mol% of rhodamine-lipid, were tested in B16F10 melanoma subcutaneously inoculated in C57BL/6 mice, and compared to the original PLP formulation (PLP-L). A single dose of Gd-PLP-L (20mgPLP/kg/week, i.v.) was found to significantly inhibit tumor growth compared to non-treated mice (P<0.05), similarly to PLP-L. The accumulation efficacy of the liposomal agent in the tumor was assessed with MRI, using the increase in the longitudinal relaxation rate (ΔR(1)) as a marker. Interestingly, large inter-tumor differences in ΔR(1) (0.009-0.063s(-1), 24h post-administration), corresponding to highly variable intratumoral Gd-PLP-L levels, did not correlate to the effectiveness of tumor growth inhibition. Uptake of liposomes by tumor-associated macrophages (TAM), determined by ex-vivo fluorescence microscopy, was limited to only 5% of the TAM population. Furthermore, the therapy did not lead to TAM depletion. Importantly, a 90% drop in white blood cell count both after Gd-PLP-L and PLP-L administration was observed. This depletion may reduce tumor infiltration of monocytes, which stimulate angiogenesis, and, thus, possibly co-contributes to the anti-tumor effects. In conclusion, MRI provides a powerful instrument to monitor the delivery of liposomal therapeutics to tumors and guided us to reveal that the activity of liposomal glucocorticoids is not limited to the tumor site only.

Designing multifunctional quantum dots for bioimaging, detection, and drug delivery

The emerging field of bionanotechnology aims at revolutionizing biomedical research and clinical practice via introduction of nanoparticle-based tools, expanding capabilities of existing investigative, diagnostic, and therapeutic techniques as well as creating novel instruments and approaches for addressing challenges faced by medicine. Quantum dots (QDs), semiconductor nanoparticles with unique photo-physical properties, have become one of the dominant classes of imaging probes as well as universal platforms for engineering of multifunctional nanodevices. Possessing versatile surface chemistry and superior optical features, QDs have found initial use in a variety of in vitro and in vivo applications. However, careful engineering of QD probes guided by application-specific design criteria is becoming increasingly important for successful transition of this technology from proof-of-concept studies towards real-life clinical applications. This review outlines the major design principles and criteria, from general ones to application-specific, governing the engineering of novel QD probes satisfying the increasing demands and requirements of nanomedicine and discusses the future directions of QD-focused bionanotechnology research (critical review, 201 references).

HER2 targeting as a two-sided strategy for breast cancer diagnosis and treatment: Outlook and recent implications in nanomedical approaches

At present, mammary carcinoma is the second most common type of malignant tumor in adult women after lung cancer, as more than one million women are diagnosed with breast cancer every year. Despite advances in diagnosis and treatment, which have resulted in a decrease in mortality in recent decades, breast cancer remains a major public health problem. One of the most significant unresolved clinical and scientific problems is the occurrence of resistance to clinical treatments and their toxicity (and how to predict, prevent and overcome them). However, the heterogeneity of human breast cancer in terms of genetic features, molecular profiles and clinical behavior represents a constraint obstructing the discovery of a solution to the disease. It is currently considered that the chances of success of therapy may increase if the tumor cells are selectively removed before they can evolve to their mature stages up to metastases production. Therefore, novel and more sensitive diagnostic tools are being developed, with the aim of improving the early and noninvasive detection of rising malignancies and the accuracy of tumor tissue localization. Meanwhile, there is an emerging use of targeted therapies in oncology, depending on the expression of specific proteins or genes present in tumor cells. Among the molecular targets considered for the treatment of breast cancer cells so far, we chose to focus on examples involving overexpression and/or gene amplification of "Human Epidermal growth factor Receptor 2" (HER2) protein. In current studies, various types of nanoparticles conjugated with the anti-HER2 monoclonal antibody, the so-called "trastuzumab", are investigated extensively due to promising results in biological and preclinical applications aimed at improving the treatment of breast cancer. In this paper, we present a critical review of the preparation and use of different kinds of trastuzumab-functionalized nanoparticles, with an emphasis on the therapeutic and diagnostic (theranostic) potential of this generation of hybrid nanoparticles, exploiting the multifaceted mechanisms of action of trastuzumab against malignant cells.

Multifunctional Quantum Dots for Personalized Medicine

Successes in biomedical research and state-of-the-art medicine have undoubtedly improved the quality of life. However, a number of diseases, such as cancer, immunodeficiencies, and neurological disorders, still evade conventional diagnostic and therapeutic approaches. A transformation towards personalized medicine may help to combat these diseases. For this, identification of disease molecular fingerprints and their association with prognosis and targeted therapy must become available. Quantum dots (QDs), semiconductor nanocrystals with unique photo-physical properties, represent a novel class of fluorescence probes to address many of the needs of personalized medicine. This review outlines the properties of QDs that make them a suitable platform for advancing personalized medicine, examines several proof-of-concept studies showing utility of QDs for clinically relevant applications, and discusses current challenges in introducing QDs into clinical practice.

The multiplex bead array approach to identifying serum biomarkers associated with breast cancer

Introduction

Breast cancer is the most common type of cancer seen in women in western countries. Thus, diagnostic modalities sensitive to early-stage breast cancer are needed. Antibody-based array platforms of a data-driven type, which are expected to facilitate more rapid and sensitive detection of novel biomarkers, have emerged as a direct, rapid means for profiling cancer-specific signatures using small samples. In line with this concept, our group constructed an antibody bead array panel for 35 analytes that were selected during the discovery step. This study was aimed at testing the performance of this 35-plex array panel in profiling signatures specific for primary non-metastatic breast cancer and validating its diagnostic utility in this independent population.

Methods

Thirty-five analytes were selected from more than 50 markers through screening steps using a serum bank consisting of 4,500 samples from various types of cancer. An antibody-bead array of 35 markers was constructed using the Luminex™ bead array platform. A study population consisting of 98 breast cancer patients and 96 normal subjects was analysed using this panel. Multivariate classification algorithms were used to find discriminating biomarkers and validated with another independent population of 90 breast cancer and 79 healthy controls.

Results

Serum concentrations of epidermal growth factor, soluble CD40-ligand and proapolipoprotein A1 were increased in breast cancer patients. High-molecular-weight-kininogen, apolipoprotein A1, soluble vascular cell adhesion molecule-1, plasminogen activator inhibitor-1, vitamin-D binding protein and vitronectin were decreased in the cancer group. Multivariate classification algorithms distinguished breast cancer patients from the normal population with high accuracy (91.8% with random forest, 91.5% with support vector machine, 87.6% with linear discriminant analysis). Combinatorial markers also detected breast cancer at an early stage with greater sensitivity.

Conclusions

The current study demonstrated the usefulness of the antibody-bead array approach in finding signatures specific for primary non-metastatic breast cancer and illustrated the potential for early, high sensitivity detection of breast cancer. Further validation is required before array-based technology is used routinely for early detection of breast cancer.

Use of H2(15)O-PET and DCE-MRI to measure tumor blood flow

Positron emission tomography (PET) with H2(15)O and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provide noninvasive measurements of tumor blood flow. Both tools offer the ability to monitor the direct target of antiangiogenic treatment, and their use is increasingly being studied in trials evaluating such drugs. Antiangiogenic therapy offers great potential and, to an increasing extent, benefit for oncological patients in a variety of palliative and curative settings. Because this type of targeted therapy frequently results in consolidation of the tumor mass instead of regression, monitoring treatment response with the standard volumetric approach (Response Evaluation Criteria in Solid Tumors) leads to underestimation of the response rate. Monitoring direct targets of anticancer therapy might be superior to indirect size changes. In addition, measures of tumor blood flow contribute to a better understanding of tumor biology. This review shows that DCE-MRI and H2(15)O-PET provide reliable measures of tumor perfusion, provided that a certain level of standardization is applied. Heterogeneity in scan acquisition and data analysis complicates the interpretation of study results. Also, limitations inherent to both techniques must be considered when interpreting DCE-MRI and H2(15)O-PET results. This review focuses on the technical and physiological aspects of both techniques and aims to provide the essential information necessary to critically evaluate the use of DCE-MRI and H2(15)O-PET in an oncological setting.

Resistance in malignant tumors: can resistance assays optimize cytostatic chemotherapy?

The frequent resistance of malignant tumors to chemotherapy documents the fact that numerous patients are uselessly traumatized by highly toxic drugs. Investigations into this resistance have shown that many different mechanisms exist which can abolish the antitumor action of chemotherapeutics. So far, no safe method has been found to counteract this resistance. To date, the only way to avoid harm by an ineffective chemotherapeutic is to refrain from its use when resistance to it is detected. The present survey reports on the available assays that diagnose resistance reliably; sensitivity tests are excluded as being unreliable. Resistance assays are still not fully recognized in hospitals, despite the fact that they are able to optimize chemotherapy by eliminating treatment that is ineffective and merely harmful. Since the number of chemotherapeutics will increase enormously in the future, pharmacological cancer treatment cannot dispense with resistance assays.

Serum proteome profiling of metastatic breast cancer using recombinant antibody microarrays

The driving force behind oncoproteomics is to identify biomarker signatures associated with a particular malignancy. Here, we have for the first time used large-scale recombinant scFv antibody microarrays in an attempt to classify metastatic breast cancer versus healthy controls, based on differential protein expression profiling of whole serum samples. Using this multiplexed and miniaturised assay set-up providing pM range sensitivities, breast cancer could be classified with a specificity and sensitivity of 85% based on 129 serum analytes. However, by adopting a condensed 11 analyte biomarker signature, composed of nine non-redundant serum proteins, we were able to distinguish cancer versus healthy serum proteomes with a 95% sensitivity and specificity, respectively. When a subgroup of patients, not receiving anti-inflammatory drugs, was analysed, a novel eight analyte biomarker signature with a further improved predictive power was indicated. In a longer perspective, antibody microarray analysis could provide a tool for the development of improved diagnostics and intensified biomarker discovery for breast cancer patients.

Growth of the Asian health-care market: global implications for the pharmaceutical industry

The global economy is being transformed by an explosion of information unleashed by the internet, the digital revolution, communications and increased international mobility. This transformation is manifesting in many ways, including rapid development of countries such as China, commoditization of public services, mobilization of workforces, shifting of market control from suppliers to consumers, interlinked rises in product demand and customer expectations, and problems regulating international business competition. As Asia is home to half of the world's population, and offers both a large relatively low-cost workforce in some countries and a potentially huge retail market, this region could be central to the future of the global economy. Like other industries, the pharmaceutical industry faces a new array of Asia-specific opportunities and challenges. Success in meeting these challenges will go to those pharmaceutical companies that best understand the unique strengths and constraints of Asia's diverse cultures, talents and markets.

Contribution of oncoproteomics to cancer biomarker discovery

Oncoproteomics is the study of proteins and their interactions in a cancer cell by proteomic technologies. Proteomic research first came to the fore with the introduction of two-dimensional gel electrophoresis. At the turn of the century, proteomics has been increasingly applied to cancer research with the wide-spread introduction of mass spectrometry and proteinchip. There is an intense interest in applying proteomics to foster an improved understanding of cancer pathogenesis, develop new tumor biomarkers for diagnosis, and early detection using proteomic portrait of samples. Oncoproteomics has the potential to revolutionize clinical practice, including cancer diagnosis and screening based on proteomic platforms as a complement to histopathology, individualized selection of therapeutic combinations that target the entire cancer-specific protein network, real-time assessment of therapeutic efficacy and toxicity, and rational modulation of therapy based on changes in the cancer protein network associated with prognosis and drug resistance. Besides, oncoproteomics is also applied to the discovery of new therapeutic targets and to the study of drug effects. In pace with the successful completion of the Human Genome Project, the wave of proteomics has raised the curtain on the postgenome era. The study of oncoproteomics provides mankind with a better understanding of neoplasia. In this article, the discovery of cancer biomarkers in recent years is reviewed. The challenges ahead and perspectives of oncoproteomics for biomarkers development are also addressed. With a wealth of information that can be applied to a broad spectrum of biomarker research projects, this review serves as a reference for biomarker researchers, scientists working in proteomics and bioinformatics, oncologists, pharmaceutical scientists, biochemists, biologists, and chemists.

Nucleic acid-based marker approaches to urologic cancers

There are numerous molecular modifications known to occur in cancer. New nucleic acid-based biomarkers provide a unique approach to patient management in urologic oncology. Malignant transformation of a normal cell requires a series of epigenetic and genetic changes or "hits." Epigenetics produced by deoxyribonucleic acid methylation, adding a methyl group to the fifth position of cytosine within CpG dinucleotides, are important players in deoxyribonucleic acid repair, genome instability, and regulation of chromatin structure. Genetic alterations in cancer can include mutations, chromosome deletions, insertions, amplifications, and translocations. In addition, the modifications of telomeres are critical to the maintenance of chromatin structure, transcription, and cell function in cancer. We review only nucleic acid-based molecular biomarkers in urologic oncology that can assist the clinician in establishing the diagnosis of disease, or that can predict the behavior of the disease or the patient's survival.

Development and characterization of mouse hybridomas

Cell fusion protocols that were developed by Kohler and Milstein in the mid-1970s and aimed at producing and characterization of mouse monoclonal antibodies (MAbs) remain the gold standard of hybridoma development. Despite tremendous progress in using MAbs in multiple research, diagnostic, and therapeutic areas, major experimental flaws in designing and carrying out hybridoma experimentation often result in the production of hybridomas exhibiting poor growth parameters and secreting low-specificity and low-affinity antibodies. This methodology chapter is built around the conventional hybridoma protocol, with a special emphasis on tissue culture and biochemical techniques aimed at producing truly monospecific and highly active mouse MAbs.

Technology insight: pharmacoproteomics for cancer--promises of patient-tailored medicine using protein microarrays

Patient-tailored medicine can be defined as the selection of specific therapeutics to treat disease in a particular individual based on genetic, genomic or proteomic information. While individualized treatments have been used in medicine for years, advances in cancer treatment have now generated a need to more precisely define and identify those patients who will derive the most benefit from new-targeted agents. Cellular signaling pathways are a protein-based network, and the intended drug effect is to disrupt aberrant protein phosphorylation-based enzymatic activity and epigenetic phenomena. Pharmacoproteomics, or the tailoring of therapy based on proteomic knowledge, will begin to take a central role in this process. A new type of protein array platform, the reverse-phase protein microarray, shows potential for providing detailed information about the state of the cellular 'circuitry' from small samples such as patient biopsy specimens. Measurements of hundreds of specific phosphorylated proteins that span large classes of important signaling pathways can be obtained at once from only a few thousand cells. Clinical implementation of these new proteomic tools to aid the clinical, medical and surgical oncologist in making decisions about patient care will now require thoughtful communication between practicing clinicians and research scientists.

Emerging use of nanoparticles in diagnosis and treatment of breast cancer

The biological application of nanoparticles is a rapidly developing area of nanotechnology that raises new possibilities in the diagnosis and treatment of human cancers. In cancer diagnostics, fluorescent nanoparticles can be used for multiplex simultaneous profiling of tumour biomarkers and for detection of multiple genes and matrix RNA with fluorescent in-situ hybridisation. In breast cancer, three crucial biomarkers can be detected and accurately quantified in single tumour sections by use of nanoparticles conjugated to antibodies. In the near future, the use of conjugated nanoparticles will allow at least ten cancer-related proteins to be detected on tiny tumour sections, providing a new method of analysing the proteome of an individual tumour. Supermagnetic nanoparticles have exciting possibilities as contrast agents for cancer detection in vivo, and for monitoring the response to treatment. Several chemotherapy agents are available as nanoparticle formulations, and have at least equivalent efficacy and fewer toxic effects compared with conventional formulations. Ultimately, the use of nanoparticles will allow simultaneous tumour targeting and drug delivery in a unique manner. In this review, we give an overview of the use of clinically applicable nanoparticles in oncology, with particular focus on the diagnosis and treatment of breast cancer.

Antibody microarray-based oncoproteomics

The driving force behind oncoproteomics is the belief that certain protein signatures or patterns exist that are associated with a particular malignancy. If so, the correlation of clinical parameters with defined protein expression patterns would allow us to predict disease progression and perhaps even postulate improved therapeutic modalities. The technological challenges to achieve these goals are significant, as the human proteome is not defined. No general methodological approach exists today, and human cancer can, furthermore, be divided into several disease subgroups. One potential solution to finding cancer-associated protein signatures is the emerging technology of affinity proteomics. This approach addresses some of the shortcomings of traditional proteomics and combines it with the power of microarrays. The present review focuses on the role of antibody microarrays in oncoproteomics and its potential to provide a truly proteome-wide analytical approach.

Role of nanobiotechnology in developing personalized medicine for cancer

Personalized medicine simply means the prescription of specific therapeutics best suited for an individual. Personalization of cancer therapies is based on a better understanding of the disease at the molecular level. Nanotechnology will play an important role in this area. Nanobiotechnology is being used to refine discovery of biomarkers, molecular diagnostics, drug discovery and drug delivery, which are important basic components of personalized medicine and are applicable to management of cancer as well. Examples are given of the application of quantum dots, gold nanoparticles, and molecular imaging in diagnostics and combination with therapeutics -- another important feature of personalized medicine. Personalized medicine is beginning to be recognized and is expected to become a part of medical practice within the next decade. Personalized management of cancer, facilitated by nanobiotechnology, is expected to enable early detection of cancer, more effective and less toxic treatment increasing the chances of cure.