Premenopausal breast cancer: potential clinical utility of a multi-omics based machine learning approach for patient stratification

The diagnostic value of BI-RADS grade 3 to 5 for breast masses is correlated with the expression of estrogen receptor, progesterone receptor, human epidermal growth factor receptor-2

BACKGROUND

The breast imaging-reporting and data system (BI-RADS) grading has a great advantage in diagnosing breast diseases, but with some limitations.

AIMS

The study analyzed the value of ultrasound-guided core needle biopsy (CNB) in diagnosing BI-RADS grades 3, 4, and 5 breast cancer.

METHODS

Breast cancer patients at BI-RADS grades 3 to 5 received breast ultrasonography, ultrasound-guided CNB and immunohistochemical examination. Receiver operating characteristic (ROC) curve was made to test diagnostic efficiency of regression model.

RESULTS

Calcification was positively correlated with expression of estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor (HER)-2. The areas of 4 ROC curves were 0.752, 0.805, 0.758, and 0.847, and the 95%CI was 0.660 to 0.844, 0.723 to 0.887, 0.667 to 0.849, and 0.776 to 0.918, respectively. BI-RADS grades 3 to 5 were positively correlated with expression of ER, PR and human epidermal growth factor receptor-2 (HER-2). Statistical significance existed between grade 5 and expression of ER, PR and HER-2, and between grade 4 and expression of HER-2.

CONCLUSIONS

The study demonstrates that BI-RADS can be used as an effective evaluation method in the diagnosis of breast diseases before invasive operation, and it has higher diagnostic accuracy if combined with pathological examinations.

Therapy-resistant breast cancer in focus: Clinically relevant mitigation by flavonoids targeting cancer stem cells

Significant limitations of the reactive medical approach in breast cancer management are clearly reflected by alarming statistics recorded worldwide. According to the WHO updates, breast malignancies become the leading cancer type. Further, the portion of premenopausal breast cancer cases is permanently increasing and demonstrates particularly aggressive patterns and poor outcomes exemplified by young patients with triple-negative breast cancer that lacks targeted therapy. Accumulating studies suggest the crucial role of stem cells in tumour biology, high metastatic activity, and therapy resistance of aggressive breast cancer. Therefore, targeting breast cancer stem cells is a promising treatment approach in secondary and tertiary breast cancer care. To this end, naturally occurring substances demonstrate high potential to target cancer stem cells which, however, require in-depth analysis to identify effective anti-cancer agents for cost-effective breast cancer management. The current article highlights the properties of flavonoids particularly relevant for targeting breast cancer stem cells to mitigate therapy resistance. The proposed approach is conformed with the principles of 3P medicine by applying predictive diagnostics, patient stratification and treatments tailored to the individualised patient profile. Expected impacts are very high, namely, to overcome limitations of reactive medical services improving individual outcomes and the healthcare economy in breast cancer management. Relevant clinical applications are exemplified in the paper.

DNA methylation accelerated age as captured by epigenetic clocks influences breast cancer risk

Introduction

Breast cancer continues to be the leading form of cancer among women in the United States. Additionally, disparities across the breast cancer continuum continue to increase for women of historically marginalized populations. The mechanism driving these trends are unclear, however, accelerated biological age may provide key insights into better understanding these disease patterns. Accelerated age measured by DNA methylation using epigenetic clocks is to date the most robust method for estimating accelerated age. Here we synthesize the existing evidence on epigenetic clocks measurement of DNA methylation based accelerated age and breast cancer outcomes.

Methods

Our database searches were conducted from January 2022 to April 2022 and yielded a total of 2,908 articles for consideration. We implemented methods derived from guidance of the PROSPERO Scoping Review Protocol to assess articles in the PubMed database on epigenetic clocks and breast cancer risk.

Results

Five articles were deemed appropriate for inclusion in this review. Ten epigenetic clocks were used across the five articles demonstrating statistically significant results for breast cancer risk. DNA methylation accelerated age varied by sample type. The studies did not consider social factors or epidemiological risk factors. The studies lacked representation of ancestrally diverse populations.

Discussion

DNA methylation based accelerated age as captured by epigenetic clocks has a statistically significant associative relationship with breast cancer risk, however, important social factors that contribute to patterns of methylation were not comprehensively considered in the available literature. More research is needed on DNA methylation based accelerated age across the lifespan including during menopausal transition and in diverse populations. This review demonstrates that DNA methylation accelerated age may provide key insights for tackling increasing rates of U.S. breast cancer incidence and overall disease disparities experienced by women from minoritized backgrounds.

Ischemic stroke of unclear aetiology: a case-by-case analysis and call for a multi-professional predictive, preventive and personalised approach

Due to the reactive medical approach applied to disease management, stroke has reached an epidemic scale worldwide. In 2019, the global stroke prevalence was 101.5 million people, wherefrom 77.2 million (about 76%) suffered from ischemic stroke; 20.7 and 8.4 million suffered from intracerebral and subarachnoid haemorrhage, respectively. Globally in the year 2019 — 3.3, 2.9 and 0.4 million individuals died of ischemic stroke, intracerebral and subarachnoid haemorrhage, respectively. During the last three decades, the absolute number of cases increased substantially. The current prevalence of stroke is 110 million patients worldwide with more than 60% below the age of 70 years. Prognoses by the World Stroke Organisation are pessimistic: globally, it is predicted that 1 in 4 adults over the age of 25 will suffer stroke in their lifetime. Although age is the best known contributing factor, over 16% of all strokes occur in teenagers and young adults aged 15–49 years and the incidence trend in this population is increasing. The corresponding socio-economic burden of stroke, which is the leading cause of disability, is enormous. Global costs of stroke are estimated at 721 billion US dollars, which is 0.66% of the global GDP.

Clinically manifested strokes are only the “tip of the iceberg”: it is estimated that the total number of stroke patients is about 14 times greater than the currently applied reactive medical approach is capable to identify and manage. Specifically, lacunar stroke (LS), which is characteristic for silent brain infarction, represents up to 30% of all ischemic strokes. Silent LS, which is diagnosed mainly by routine health check-up and autopsy in individuals without stroke history, has a reported prevalence of silent brain infarction up to 55% in the investigated populations. To this end, silent brain infarction is an independent predictor of ischemic stroke. Further, small vessel disease and silent lacunar brain infarction are considered strong contributors to cognitive impairments, dementia, depression and suicide, amongst others in the general population. In sub-populations such as diabetes mellitus type 2, proliferative diabetic retinopathy is an independent predictor of ischemic stroke.

According to various statistical sources, cryptogenic strokes account for 15 to 40% of the entire stroke incidence. The question to consider here is, whether a cryptogenic stroke is fully referable to unidentifiable aetiology or rather to underestimated risks. Considering the latter, translational research might be of great clinical utility to realise innovative predictive and preventive approaches, potentially benefiting high risk individuals and society at large.

In this position paper, the consortium has combined multi-professional expertise to provide clear statements towards the paradigm change from reactive to predictive, preventive and personalised medicine in stroke management, the crucial elements of which are:

Consolidation of multi-disciplinary expertise including family medicine, predictive and in-depth diagnostics followed by the targeted primary and secondary (e.g. treated cancer) prevention of silent brain infarction

Application of the health risk assessment focused on sub-optimal health conditions to effectively prevent health-to-disease transition

Application of AI in medicine, machine learning and treatment algorithms tailored to robust biomarker patterns

Application of innovative screening programmes which adequately consider the needs of young populations

Stroke is a severe brain disease which has reached an epidemic scale worldwide: in 2019, the global stroke prevalence was 101.5 million people. The World Stroke Organisation predicted that globally, 1 in 4 adults over the age of 25 will get a stroke in their lifetime. Not only old people but also teenagers and young adults are affected. Current global costs of stroke are estimated at 721 billion US dollars. Due to undiagnosed so-called “silent” brain infarction, the number of affected individuals is about 14 times greater in the population than clinically recorded. If it remains untreated, silent brain infarction may cause many severe and fatal disorders such as dementia, depression and even suicide. In this position paper, the consortium describes how the rudimental approach to treating severely diseased people could be replaced by an innovative predictive and preventive one to protect people against the health-to-disease transition.

Multi-omics assessment of dilated cardiomyopathy using non-negative matrix factorization

Dilated cardiomyopathy (DCM), a myocardial disease, is heterogeneous and often results in heart failure and sudden cardiac death. Unavailability of cardiac tissue has hindered the comprehensive exploration of gene regulatory networks and nodal players in DCM. In this study, we carried out integrated analysis of transcriptome and methylome data using non-negative matrix factorization from a cohort of DCM patients to uncover underlying latent factors and covarying features between whole-transcriptome and epigenome omics datasets from tissue biopsies of living patients. DNA methylation data from Infinium HM450 and mRNA Illumina sequencing of n = 33 DCM and n = 24 control probands were filtered, analyzed and used as input for matrix factorization using R NMF package. Mann-Whitney U test showed 4 out of 5 latent factors are significantly different between DCM and control probands (P<0.05). Characterization of top 10% features driving each latent factor showed a significant enrichment of biological processes known to be involved in DCM pathogenesis, including immune response (P = 3.97E-21), nucleic acid binding (P = 1.42E-18), extracellular matrix (P = 9.23E-14) and myofibrillar structure (P = 8.46E-12). Correlation network analysis revealed interaction of important sarcomeric genes like Nebulin, Tropomyosin alpha-3 and ERC-protein 2 with CpG methylation of ATPase Phospholipid Transporting 11A0, Solute Carrier Family 12 Member 7 and Leucine Rich Repeat Containing 14B, all with significant P values associated with correlation coefficients >0.7. Using matrix factorization, multi-omics data derived from human tissue samples can be integrated and novel interactions can be identified. Hypothesis generating nature of such analysis could help to better understand the pathophysiology of complex traits such as DCM.

Anti-breast cancer effects of phytochemicals: primary, secondary, and tertiary care

Breast cancer incidence is actually the highest one among all cancers. Overall breast cancer management is associated with challenges considering risk assessment and predictive diagnostics, targeted prevention of metastatic disease, appropriate treatment options, and cost-effectiveness of approaches applied. Accumulated research evidence indicates promising anti-cancer effects of phytochemicals protecting cells against malignant transformation, inhibiting carcinogenesis and metastatic spread, supporting immune system and increasing effectiveness of conventional anti-cancer therapies, among others. Molecular and sub-/cellular mechanisms are highly complex affecting several pathways considered potent targets for advanced diagnostics and cost-effective treatments. Demonstrated anti-cancer affects, therefore, are clinically relevant for improving individual outcomes and might be applicable to the primary (protection against initial cancer development), secondary (protection against potential metastatic disease development), and tertiary (towards cascading complications) care. However, a detailed data analysis is essential to adapt treatment algorithms to individuals’ and patients’ needs. Consequently, advanced concepts of patient stratification, predictive diagnostics, targeted prevention, and treatments tailored to the individualized patient profile are instrumental for the cost-effective application of natural anti-cancer substances to improve overall breast cancer management benefiting affected individuals and the society at large.

Development and Validation of a New Multiparametric Random Survival Forest Predictive Model for Breast Cancer Recurrence with a Potential Benefit to Individual Outcomes

Purpose

Breast cancer (BC) is a multi-factorial disease. Its individual prognosis varies; thus, individualized patient profiling is instrumental to improving BC management and individual outcomes. An economical, multiparametric, and practical model to predict BC recurrence is needed.

Patients and Methods

We retrospectively investigated the clinical data of BC patients treated at the Third Affiliated Hospital of Sun Yat-sen University and Liuzhou Women and Children’s Medical Center from January 2013 to December 2020. Random forest-recursive feature elimination (run by R caret package) was used to determine the best variable set, and the random survival forest method was used to develop a predictive model for BC recurrence.

Results

The training and validations sets included 623 and 151 patients, respectively. We selected 14 variables, the pathological (TNM) stage, gamma-glutamyl transpeptidase, total cholesterol, Ki-67, lymphocyte count, low-density lipoprotein, age, apolipoprotein B, high-density lipoprotein, globulin, neutrophil count to lymphocyte count ratio, alanine aminotransferase, triglyceride, and albumin to globulin ratio, using random survival forest (RSF)-recursive feature elimination. We developed a recurrence prediction model using RSF. Using area under the receiver operating characteristic curve and Kaplan–Meier survival analyses, the model performance was determined to be accurate. C-indexes were 0.997 and 0.936 for the training and validation sets, respectively.

Conclusion

The model could accurately predict BC recurrence. It aids clinicians in identifying high-risk patients and making treatment decisions for Breast cancer patients in China. This new multiparametric RSF model is instrumental for breast cancer recurrence prediction and potentially improves individual outcomes.

Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer

Cancer is defined as a large group of diseases that is associated with abnormal cell growth, uncontrollable cell division, and may tend to impinge on other tissues of the body by different mechanisms through metastasis. What makes cancer so important is that the cancer incidence rate is growing worldwide which can have major health, economic, and even social impacts on both patients and the governments. Thereby, the early cancer prognosis, diagnosis, and treatment can play a crucial role at the front line of combating cancer. The onset and progression of cancer can occur under the influence of complicated mechanisms and some alterations in the level of genome, proteome, transcriptome, metabolome etc. Consequently, the advent of omics science and its broad research branches (such as genomics, proteomics, transcriptomics, metabolomics, and so forth) as revolutionary biological approaches have opened new doors to the comprehensive perception of the cancer landscape. Due to the complexities of the formation and development of cancer, the study of mechanisms underlying cancer has gone beyond just one field of the omics arena. Therefore, making a connection between the resultant data from different branches of omics science and examining them in a multi-omics field can pave the way for facilitating the discovery of novel prognostic, diagnostic, and therapeutic approaches. As the volume and complexity of data from the omics studies in cancer are increasing dramatically, the use of leading-edge technologies such as machine learning can have a promising role in the assessments of cancer research resultant data. Machine learning is categorized as a subset of artificial intelligence which aims to data parsing, classification, and data pattern identification by applying statistical methods and algorithms. This acquired knowledge subsequently allows computers to learn and improve accurate predictions through experiences from data processing. In this context, the application of machine learning, as a novel computational technology offers new opportunities for achieving in-depth knowledge of cancer by analysis of resultant data from multi-omics studies. Therefore, it can be concluded that the use of artificial intelligence technologies such as machine learning can have revolutionary roles in the fight against cancer.

Central Nervous System Metastases from Triple-Negative Breast Cancer: Current Treatments and Future Prospective

It is estimated that approximately one-third of patients with triple-negative breast cancer (TNBC) will develop brain metastases. The prognosis for patients with breast cancer brain metastasis has improved in the recent past, especially for hormone receptor and human epidermal growth factor receptor 2 (HER) positive subtypes. However, the overall survival rate for patients with triple-negative subtype remains poor. The development of newer treatment options, including antibody-drug conjugates such as Sacituzumab govitecan, is particularly encouraging. This article reviews the clinical outcomes, challenges, and current approach to the treatment of brain metastasis in TNBC. We have also briefly discussed newer treatment options and ongoing clinical trials. The development of brain metastasis significantly decreases the quality of life of patients with TNBC, and newer treatment strategies and therapeutics are the need of the hour for this disease subgroup.

Human disease biomarker panels through systems biology

As more uses for biomarkers are sought after for an increasing number of disease targets, single-target biomarkers are slowly giving way for biomarker panels. These panels incorporate various sources of biomolecular and clinical data to guarantee a higher robustness and power of separation for a clinical test. Multifactorial diseases such as psychiatric disorders show great potential for clinical use, assisting medical professionals during the analysis of risk and predisposition, disease diagnosis and prognosis, and treatment applicability and efficacy. More specific tests are also being developed to assist in ruling out, distinguishing between, and confirming suspicions of multifactorial diseases, as well as to predict which therapy option may be the best option for a given patient's biochemical profile. As more complex datasets are entering the field, involving multi-omic approaches, systems biology has stepped in to facilitate the discovery and validation steps during biomarker panel generation. Filtering biomolecules and clinical data, pre-validating and cross-validating potential biomarkers, generating final biomarker panels, and testing the robustness and applicability of those panels are all beginning to rely on machine learning and systems biology and research in this area will only benefit from advances in these approaches.

Detection of Minimal Residual Disease in the Peripheral Blood of Breast Cancer Patients, with a Multi Marker (MGB-1, MGB-2, CK-19 and NY-BR-1) Assay

Purpose

Minimal residual disease (MRD) refers to micrometastases that are undetectable by conventional means and is a potential source of disease relapse. This study aimed to detect the presence of breast cancer (BC) biomarkers (MGB-1, MGB-2, CK-19, NY-BR-1) using real-time polymerase chain reaction (RT-PCR) in peripheral blood mononuclear cells (PBMC) of BC patients and the impact of a positive assay on clinical outcome.

Patients and Methods

Patients in the analysis included females >18 years of age with biopsy-proven carcinoma of the breast. A 10 mL sample of venous blood was obtained from 10 healthy controls and 25 breast cancer patients. Comparisons of peripheral blood markers were made with clinicopathological variables.

Results

High-quality RNA was extracted from all samples with a mean RNA concentration of 224.8±155.3 ng/µL. Each of the molecular markers examined was highly expressed in the primary breast tumors (n = 3, positive controls) with none of the markers detected in healthy negative controls. The NY-BR-1 marker was expressed in one (4%) patient with metastatic disease with no MGB-1 and MGB-2 detected in any sample derived from the study patients. The CK-19 marker was detected in 16 (64%) of the BC cases. No correlation was found between CK-19 expression and tumor stage (P = 0.07) or nodal status (P = 0.32). No correlation was identified in the BC patients between CK-19 expression and receptor status in the BC primary tumor.

Conclusion

This study showed high expression of all 4 markers NY-BR-1, MGB-1, MGB-2 and CK-19 in the PBMCs derived from breast cancer patients. CK-19 was detected in 64% of the stage I–III cases operated with curative intent, the only recurrent events occurring in the CK-19-positive cases. Our data confirm the need to enhance techniques for detection of MRD, which may better predict patients at risk for relapse.

Caution, "normal" BMI: health risks associated with potentially masked individual underweight-EPMA Position Paper 2021

An increasing interest in a healthy lifestyle raises questions about optimal body weight. Evidently, it should be clearly discriminated between the standardised "normal" body weight and individually optimal weight. To this end, the basic principle of personalised medicine "one size does not fit all" has to be applied. Contextually, "normal" but e.g. borderline body mass index might be optimal for one person but apparently suboptimal for another one strongly depending on the individual genetic predisposition, geographic origin, cultural and nutritional habits and relevant lifestyle parameters-all included into comprehensive individual patient profile. Even if only slightly deviant, both overweight and underweight are acknowledged risk factors for a shifted metabolism which, if being not optimised, may strongly contribute to the development and progression of severe pathologies. Development of innovative screening programmes is essential to promote population health by application of health risks assessment, individualised patient profiling and multi-parametric analysis, further used for cost-effective targeted prevention and treatments tailored to the person. The following healthcare areas are considered to be potentially strongly benefiting from the above proposed measures: suboptimal health conditions, sports medicine, stress overload and associated complications, planned pregnancies, periodontal health and dentistry, sleep medicine, eye health and disorders, inflammatory disorders, healing and pain management, metabolic disorders, cardiovascular disease, cancers, psychiatric and neurologic disorders, stroke of known and unknown aetiology, improved individual and population outcomes under pandemic conditions such as COVID-19. In a long-term way, a significantly improved healthcare economy is one of benefits of the proposed paradigm shift from reactive to Predictive, Preventive and Personalised Medicine (PPPM/3PM). A tight collaboration between all stakeholders including scientific community, healthcare givers, patient organisations, policy-makers and educators is essential for the smooth implementation of 3PM concepts in daily practice.

Exploring Nanoemulsions for Prostate Cancer Therapy

Prostate carcinoma is typical cancer. It is the second most common cancer globally. The estimated new cases in 2020 was 191 930 and estimated deaths was 33 330. Age, family history, & genetic factors are major factors that drive prostate cancer. Although, for treating metastatic disease, the major therapies available are radiation,bisphosphonate, and palliative chemotherapy. But the major drawback is therapy is disease-driven and later becomes metastatic and requires treatment. The ability to revolutionize cancer treatment by major targeting vehicles via the exploration of nanoemulsion suggests a potential for cancer treatment. The unique property of a biphasic liquid dosage form called nanoemulsion to reach leaky tumor vasculature is due to its nano-meter oil-droplet size of 20-200 nm. Recent reporting on nanoemulsions disclose their embracing and lay alternative for re-purposing herbal and synthetic drugs and their combination especially for targeting prostate cancer formulating an obtainable nanomedicine. So, this article emphasizes the use of nanoemulsions incorporating therapeutic agents for successful and targeted delivery for prostate cancer.

SurvCNN: A Discrete Time-to-Event Cancer Survival Estimation Framework Using Image Representations of Omics Data

The utility of multi-omics in personalized therapy and cancer survival analysis has been debated and demonstrated extensively in the recent past. Most of the current methods still suffer from data constraints such as high-dimensionality, unexplained interdependence, and subpar integration methods. Here, we propose SurvCNN, an alternative approach to process multi-omics data with robust computer vision architectures, to predict cancer prognosis for Lung Adenocarcinoma patients. Numerical multi-omics data were transformed into their image representations and fed into a Convolutional Neural network with a discrete-time model to predict survival probabilities. The framework also dichotomized patients into risk subgroups based on their survival probabilities over time. SurvCNN was evaluated on multiple performance metrics and outperformed existing methods with a high degree of confidence. Moreover, comprehensive insights into the relative performance of various combinations of omics datasets were probed. Critical biological processes, pathways and cell types identified from downstream processing of differentially expressed genes suggested that the framework could elucidate elements detrimental to a patient's survival. Such integrative models with high predictive power would have a significant impact and utility in precision oncology.

Flavonoids as an effective sensitizer for anti-cancer therapy: insights into multi-faceted mechanisms and applicability towards individualized patient profiles

Cost-efficacy of currently applied treatments is an issue in overall cancer management challenging healthcare and causing tremendous economic burden to societies around the world. Consequently, complex treatment models presenting concepts of predictive diagnostics followed by targeted prevention and treatments tailored to the personal patient profiles earn global appreciation as benefiting the patient, healthcare economy, and the society at large. In this context, application of flavonoids as a spectrum of compounds and their nano-technologically created derivatives is extensively under consideration, due to their multi-faceted anti-cancer effects applicable to the overall cost-effective cancer management, primary, secondary, and even tertiary prevention. This article analyzes most recently updated data focused on the potent capacity of flavonoids to promote anti-cancer therapeutic effects and interprets all the collected research achievements in the frame-work of predictive, preventive, and personalized (3P) medicine. Main pillars considered are: - Predictable anti-neoplastic, immune-modulating, drug-sensitizing effects; - Targeted molecular pathways to improve therapeutic outcomes by increasing sensitivity of cancer cells and reversing their resistance towards currently applied therapeutic modalities.

Artificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer

Cancer is the manifestation of abnormalities of different physiological processes involving genes, DNAs, RNAs, proteins, and other biomolecules whose profiles are reflected in different omics data types. As these bio-entities are very much correlated, integrative analysis of different types of omics data, multi-omics data, is required to understanding the disease from the tumorigenesis to the disease progression. Artificial intelligence (AI), specifically machine learning algorithms, has the ability to make decisive interpretation of "big"-sized complex data and, hence, appears as the most effective tool for the analysis and understanding of multi-omics data for patient-specific observations. In this review, we have discussed about the recent outcomes of employing AI in multi-omics data analysis of different types of cancer. Based on the research trends and significance in patient treatment, we have primarily focused on the AI-based analysis for determining cancer subtypes, disease prognosis, and therapeutic targets. We have also discussed about AI analysis of some non-canonical types of omics data as they have the capability of playing the determiner role in cancer patient care. Additionally, we have briefly discussed about the data repositories because of their pivotal role in multi-omics data storing, processing, and analysis.

ANLN Directly Interacts with RhoA to Promote Doxorubicin Resistance in Breast Cancer Cells

Background

Chemotherapy resistance is the leading cause of cancer treatment failure. This research was conducted to explore a potential link between actin-binding protein anillin (ANLN) and doxorubicin resistance in breast cancer.

Materials and Methods

We compared ANLN expression and 50% inhibition concentration (IC50) of doxorubicin in human breast cancer cells (MDA-MB-231) and human breast cancer cells with doxorubicin resistance (MDA-MB-231/ADM). Co-immunoprecipitation was used to investigate the interaction between ANLN and RhoA. The cell viability, apoptosis, gene and protein expression were estimated by MTT, flow cytometry, quantitative real-time PCR and western blot.

Results

The doxorubicin resistance in MDA-MB-231/ADM cells (IC50 = 19.40 ± 1.16 μg/mL) was significantly higher than that in MDA-MB-231 cells (IC50 = 1.65 ± 0.23 μg/mL). ANLN was up-regulated in MDA-MB-231/ADM cells compared to MDA-MB-231 cells. Furthermore, ANLN overexpression promoted cell viability and inhibited apoptosis of MDA-MB-231 cells. The gene and protein expression of multidrug resistance (MDR1) and cancer resistance protein (BCRP) were enhanced by ANLN overexpression in MDA-MB-231 cells. ANLN silencing suppressed cell viability and the expression of MDR1 and BCRP and facilitated apoptosis in MDA-MB-231/ADM cells. Moreover, ANLN promoted RhoA activation by interacting with RhoA. ANLN up-regulation enhanced cell viability and the expression of MDR1 and BCRP and decreased apoptosis of MDA-MB-231 cells. The influence conferred by ANLN overexpression was effectively abolished by C3 transferase.

Conclusion

This work revealed that ANLN promoted doxorubicin resistance in breast cancer cells by activating RhoA. Thus, our study suggests a novel target for breast cancer treatment.

Biobanks in the era of big data: objectives, challenges, perspectives, and innovations for predictive, preventive, and personalised medicine

Biobanking is entering the new era-era of big data. New technologies, techniques, and knowledge opened the potential of the whole domain of biobanking. Biobanks collect, analyse, store, and share the samples and associated data. Both samples and especially associated data are growing enormously, and new innovative approaches are required to handle samples and to utilize the potential of biobanking data. The data reached the quantity and quality of big data, and the scientists are facing the questions how to use them more efficiently, both retrospectively and prospectively with the aim to discover new preventive methods, optimize treatment, and follow up and to optimize healthcare processes. Biobanking in the era of big data contribute to the development of predictive, preventive, and personalised medicine, for every patient providing the right treatment at the right time. Biobanking in the era of big data contributes to the paradigm shift towards personalising of healthcare.

MASS SPECTROMETRY-BASED MITOCHONDRIAL PROTEOMICS IN HUMAN OVARIAN CANCERS

The prominent characteristics of mitochondria are highly dynamic and regulatory, which have crucial roles in cell metabolism, biosynthetic, senescence, apoptosis, and signaling pathways. Mitochondrial dysfunction might lead to multiple serious diseases, including cancer. Therefore, identification of mitochondrial proteins in cancer could provide a global view of tumorigenesis and progression. Mass spectrometry-based quantitative mitochondrial proteomics fulfils this task by enabling systems-wide, accurate, and quantitative analysis of mitochondrial protein abundance, and mitochondrial protein posttranslational modifications (PTMs). Multiple quantitative proteomics techniques, including isotope-coded affinity tag, stable isotope labeling with amino acids in cell culture, isobaric tags for relative and absolute quantification, tandem mass tags, and label-free quantification, in combination with different PTM-peptide enrichment methods such as TiO₂ enrichment of tryptic phosphopeptides and antibody enrichment of other PTM-peptides, increase flexibility for researchers to study mitochondrial proteomes. This article reviews isolation and purification of mitochondria, quantitative mitochondrial proteomics, quantitative mitochondrial phosphoproteomics, mitochondrial protein-involved signaling pathway networks, mitochondrial phosphoprotein-involved signaling pathway networks, integration of mitochondrial proteomic and phosphoproteomic data with whole tissue proteomic and transcriptomic data and clinical information in ovarian cancers (OC) to in-depth understand its molecular mechanisms, and discover effective mitochondrial biomarkers and therapeutic targets for predictive, preventive, and personalized treatment of OC. This proof-of-principle model about OC mitochondrial proteomics is easily implementable to other cancer types. © 2020 John Wiley & Sons Ltd. Mass Spec Rev.

Optimal multiparametric set-up modelled for best survival outcomes in palliative treatment of liver malignancies: unsupervised machine learning and 3 PM recommendations

Over the last decade, a rapid rise in deaths due to liver disease has been observed especially amongst young people. Nowadays liver disease accounts for approximately 2 million deaths per year worldwide: 1 million due to complications of cirrhosis and 1 million due to viral hepatitis and hepatocellular carcinoma. Besides primary liver malignancies, almost all solid tumours are capable to spread metastases to the liver, in particular, gastrointestinal cancers, breast and genitourinary cancers, lung cancer, melanomas and sarcomas. A big portion of liver malignancies undergo palliative care. To this end, the paradigm of the palliative care in the liver cancer management is evolving from "just end of the life" care to careful evaluation of all aspects relevant for the survivorship. In the presented study, an evidence-based approach has been taken to target molecular pathways and subcellular components for modelling most optimal conditions with the longest survival rates for patients diagnosed with advanced liver malignancies who underwent palliative treatments. We developed an unsupervised machine learning (UML) approach to robustly identify patient subgroups based on estimated survival curves for each individual patient and each individual potential biomarker. UML using consensus hierarchical clustering of biomarker derived risk profiles resulted into 3 stable patient subgroups. There were no significant differences in age, gender, therapy, diagnosis or comorbidities across clusters. Survival times across clusters differed significantly. Furthermore, several of the biomarkers demonstrated highly significant pairwise differences between clusters after correction for multiple testing, namely, "comet assay" patterns of classes I, III, IV and expression rates of calgranulin A (S100), SOD2 and profilin-all measured ex vivo in circulating leucocytes. Considering worst, intermediate and best survival curves with regard to identified clusters and corresponding patterns of parameters measured, clear differences were found for "comet assay" and S100 expression patterns. In conclusion, multi-faceted cancer control within the palliative care of liver malignancies is crucial for improved disease outcomes including individualised patient profiling, predictive models and implementation of corresponding cost-effective risks mitigating measures detailed in the paper. The "proof-of-principle" model is presented.

MULTIOMIC PATTERNS IN BODY FLUIDS: TECHNOLOGICAL CHALLENGE WITH A GREAT POTENTIAL TO IMPLEMENT THE ADVANCED PARADIGM OF 3P MEDICINE

Liquid biopsy (LB) is defined as a sample of any of body fluids (blood, saliva, tear fluid, urine, sweat, amniotic, cerebrospinal and pleural fluids, cervicovaginal secretion, and wound efflux, amongst others), which can be ex vivo analysed to detect and quantity the target(s) of interest. LB represents diagnostic approach relevant for organ-specific changes and systemic health conditions including both manifested diseases and their prestages such as suboptimal health. Further, experts emphasise that DNA-based analysis alone does not provide sufficient information for optimal diagnostics and effective treatments. Consequently, of great scientific and clinical utility are molecular patterns detected by hybrid technologies such as metabolomic tools and molecular imaging. Future proposed strategies utilise multiomic pillars (generally genome, tanscriptome, proteome, metabolome, epigenome, radiome, and microbiome), system-biological approach, and multivariable algorithms for diagnostic, prognostic, and therapeutic purposes. Current article analyses pros and cons of the mass spectrometry-based technologies, provides eminent examples of a success story "from discovery to clinical application," and demonstrates a "road-map" for the technology-driven paradigm change from reactive to predictive, preventive and personalised medical services as the medicine of the future benefiting the patient and healthcare at large. © 2019 The Authors. Mass Spectrometry Reviews published by John Wiley & Sons Ltd. Mass Spec Rev.

Half-dose fulvestrant plus anastrozole as a first-line treatment for hormone receptor-positive metastatic breast cancer: a cost-effectiveness analysis

OBJECTIVE

The S0226 trial demonstrated that the combination of half-dose fulvestrant (FUL) and anastrozole (ANA) (F&A) caused a significant improvement in overall survival (OS) versus ANA monotherapy for first-line treatment of postmenopausal women with hormone receptor-positive metastatic breast cancer (PMW-MBC (HR+)). The objective of this study was to evaluate the cost-effectiveness of F&A in the first-line treatment for PMW-MBC (HR+) in China.

DESIGN

We constructed a Markov model over a life-time horizon. The clinical outcomes and utility data were obtained from published literature. Cost data were obtained from official Chinese websites. Sensitivity analyses were performed to test result uncertainty.

SETTING

Chinese healthcare system perspective.

POPULATION

A hypothetical cohort of adult patients presenting with PMW-MBC (HR+).

INTERVENTIONS

F&A compared with full-dose FUL and ANAmonotherapy.

MAIN OUTCOME MEASURES

The main outcome of this study was the incremental cost-effectiveness ratio (ICER) and quality-adjusted life-years (QALY).

RESULTS

ANA was estimated to have the lowest cost and minimum life-years. The ICER of F&A versus ANA was US$15 665.891/QALY with incremental cost and QALY of US$12 401.120 and 0.792, respectively, which was less than the willingness-to-pay of US$29 383/QALY. Compared with F&A, FUL yielded a higher cost and a shorter lifetime; hence, it was identified as a dominated strategy. The univariate sensitivity analysis indicated the price of FUL was the most influential factor in our study. The probability that F&A was cost-effective at a threshold of US$29 383/QALY in China was 86.5%.

CONCLUSION

F&A is a cost-effective alternative to FUL and ANA monotherapy for the first-line treatment of PMW-MBC (HR+) in China. F&A is a promising first-line treatment for PMW-MBC (HR+), and more research is needed to evaluate the economy of using F&A in other countries.

Carotenoids in Cancer Apoptosis-The Road from Bench to Bedside and Back

An incidence and mortality of cancer are rapidly growing worldwide, especially due to heterogeneous character of the disease that is associated with irreversible impairment of cellular homeostasis and function. Targeting apoptosis, one of cancer hallmarks, represents a potent cancer treatment strategy. Carotenoids are phytochemicals represented by carotenes, xanthophylls, and derived compounds such as apocarotenoids that demonstrate a broad spectrum of anti-cancer effects involving pro-apoptotic signaling through extrinsic and intrinsic pathways. As demonstrated in preclinical oncology research, the apoptotic modulation is performed at post-genomic levels. Further, carotenoids demonstrate additive/synergistic action in combination with conventional oncostatic agents. In addition, a sensitization of tumor cells to anti-cancer conventional treatment can be achieved by carotenoids. The disadvantage of anti-cancer application of carotenoids is associated with their low solubility and, therefore, poor bioavailability. However, this deficiency can be improved by using nanotechnological approaches, solid dispersions, microemulsions or biofortification that significantly increase the anti-cancer and pro-apoptotic efficacy of carotenoids. Only limited number of studies dealing with apoptotic potential of carotenoids has been published in clinical sphere. Pro-apoptotic effects of carotenoids should be beneficial for individuals at high risk of cancer development. The article considers the utility of carotenoids in the framework of 3P medicine.

Liquid Biopsy is Instrumental for 3PM Dimensional Solutions in Cancer Management

One in every four deaths is due to cancer in Europe. In view of its increasing incidence, cancer became the leading cause of death and disease burden in Denmark, France, the Netherlands, and the UK. Without essential improvements in cancer prevention, an additional 775,000 cases of annual incidence have been prognosed until 2040. Between 1995 and 2018, the direct costs of cancer doubled from EUR 52 billion to EUR 103 billion in Europe, and per capita health spending on cancer increased by 86% from EUR 105 to EUR 195 in general, whereby Austria, Germany, Switzerland, Benelux, and France spend the most on cancer care compared to other European countries. In view of the consequent severe socio-economic burden on society, the paradigm change from a reactive to a predictive, preventive, and personalized medical approach in the overall cancer management is essential. Concepts of predictive, preventive, and personalized medicine (3PM) demonstrate a great potential to revise the above presented trends and to implement cost-effective healthcare that benefits the patient and society as a whole. At any stage, application of early and predictive diagnostics, targeted prevention, and personalization of medical services are basic pillars making 3PM particularly attractive for the patients as well as ethical and cost-effective healthcare. Optimal 3PM approach requires novel instruments such as well-designed liquid biopsy application. This review article highlights current achievements and details liquid biopsy approaches specifically in cancer management. 3PM-relevant expert recommendations are provided.

Rho GTPases in Gynecologic Cancers: In-Depth Analysis toward the Paradigm Change from Reactive to Predictive, Preventive, and Personalized Medical Approach Benefiting the Patient and Healthcare

Rho guanosine triphospatases (GTPases) resemble a conserved family of GTP-binding proteins regulating actin cytoskeleton dynamics and several signaling pathways central for the cell. Rho GTPases create a so-called Ras-superfamily of GTPases subdivided into subgroups comprising at least 20 members. Rho GTPases play a key regulatory role in gene expression, cell cycle control and proliferation, epithelial cell polarity, cell migration, survival, and apoptosis, among others. They also have tissue-related functions including angiogenesis being involved in inflammatory and wound healing processes. Contextually, any abnormality in the Rho GTPase function may result in severe consequences at molecular, cellular, and tissue levels. Rho GTPases also play a key role in tumorigenesis and metastatic disease. Corresponding mechanisms include a number of targets such as kinases and scaffold/adaptor-like proteins initiating GTPases-related signaling cascades. The accumulated evidence demonstrates the oncogenic relevance of Rho GTPases for several solid malignancies including breast, liver, bladder, melanoma, testicular, lung, central nervous system (CNS), head and neck, cervical, and ovarian cancers. Furthermore, Rho GTPases play a crucial role in the development of radio- and chemoresistance e.g. under cisplatin-based cancer treatment. This article provides an in-depth overview on the role of Rho GTPases in gynecological cancers, highlights relevant signaling pathways and pathomechanisms, and sheds light on their involvement in tumor progression, metastatic spread, and radio/chemo resistance. In addition, insights into a spectrum of novel biomarkers and innovative approaches based on the paradigm shift from reactive to predictive, preventive, and personalized medicine are provided.

Aberrantly Methylated cfDNA in Body Fluids as a Promising Diagnostic Tool for Early Detection of Breast Cancer

Breast malignancies are the leading type of cancer among women. Its prevention and early detection, particularly in young women, remains challenging. To this end, cell-free DNA (cfDNA) detected in body fluids demonstrates great potential for early detection of tissue transformation and altered molecular setup, such as epigenetic profiles. Aberrantly methylated cfDNA in body fluids could therefore serve as a potential diagnostic and prognostic tool in breast cancer management. Abnormal methylation may lead to both an activation of oncogenes via hypomethylation and an inactivation of tumor suppressor genes by hypermethylation. We update the state of the art in the area of aberrant cfDNA methylation analyses as a diagnostic and prognostic tool in breast cancer, report on the main technological challenges, and provide an outlook for advancing the overall management of breast malignancies based on cfDNA as a target for diagnosis and tailored therapies.

Implementation of artificial intelligence and non-contact infrared thermography for prediction and personalized automatic identification of different stages of cellulite

Background

Cellulite is a common physiological condition of dermis, epidermis, and subcutaneous tissues experienced by 85 to 98% of the post-pubertal females in developed countries. Infrared (IR) thermography combined with artificial intelligence (AI)-based automated image processing can detect both early and advanced cellulite stages and open up the possibility of reliable diagnosis. Although the cellulite lesions may have various levels of severity, the quality of life of every woman, both in the physical and emotional sphere, is always an individual concern and therefore requires patient-oriented approach.

Objectives

The purpose of this work was to elaborate an objective, fast, and cost-effective method for automatic identification of different stages of cellulite based on IR imaging that may be used for prescreening and personalization of the therapy.

Materials and methods

In this study, we use custom-developed image preprocessing algorithms to automatically select cellulite regions and combine a total of 9 feature extraction methods with 9 different classification algorithms to determine the efficacy of cellulite stage recognition based on thermographic images taken from 212 female volunteers aged between 19 and 22.

Results

A combination of histogram of oriented gradients (HOG) and artificial neural network (ANN) enables determination of all stages of cellulite with an average accuracy higher than 80%. For primary stages of cellulite, the average accuracy achieved was more than 90%.

Conclusions

The implementation of computer-aided, automatic identification of cellulite severity using infrared imaging is feasible for reliable diagnosis. Such a combination can be used for early diagnosis, as well as monitoring of cellulite progress or therapeutic outcomes in an objective way. IR thermography coupled to AI sets the vision towards their use as an effective tool for complex assessment of cellulite pathogenesis and stratification, which are critical in the implementation of IR thermographic imaging in predictive, preventive, and personalized medicine (PPPM).

Electronic supplementary material

The online version of this article (10.1007/s13167-020-00199-x) contains supplementary material, which is available to authorized users.

Dietary phytochemicals as the potential protectors against carcinogenesis and their role in cancer chemoprevention

Health-threatening consequences of carcinogen exposure are mediated via occurrence of electrophiles or reactive oxygen species. As a result, the accumulation of biomolecular damage leads to the cancer initiation, promotion or progression. Accordingly, there is an association between lifestyle factors including inappropriate diet or carcinogen formation during food processing, mainstream, second or third-hand tobacco smoke and other environmental or occupational carcinogens and malignant transformation. Nevertheless, increasing evidence supports the protective effects of naturally occurring phytochemicals against carcinogen exposure as well as carcinogenesis in general. Isolated phytochemicals or their mixtures present in the whole plant food demonstrate efficacy against malignancy induced by carcinogens widely spread in our environment. Phytochemicals also minimize the generation of carcinogenic substances during the processing of meat and meat products. Based on numerous data, selected phytochemicals or plant foods should be highly recommended to become a stable and regular part of the diet as the protectors against carcinogenesis.

Comparing Quality of Life in Breast Cancer Patients Who Underwent Mastectomy Versus Breast-Conserving Surgery: A Meta-Analysis

: The purpose of our study was to carry out a meta-analysis of current literature to determine whether total mastectomy and breast-conserving surgery induce different outcomes in quality of life, based on the breast-cancer-specific module of the European Organizaation for Research and Treatment of Cancer core questionnaire (EORTC QLQ-BR23) used postoperatively. A systematic literature search of PubMed and EMBASE was conducted. Observational clinical studies that compared the quality of life in different surgery groups and presented empirical findings were selected. Six studies met the inclusion criteria. Breast-conserving surgery has statistically significant better outcomes than mastectomy in three of the eight outcomes measured in the EORTC QLQ-BR23, namely body image (standard mean difference, SMD = 1.742, 95% CI 0.579-2.905, p = 0.003), future perspective (SMD = 0.606, 95% CI 0.075-1.138, p = 0.025) and systemic therapy side effects (SMD = -0.641, 95% CI 0.101-1.181, p = 0.020). Our study highlighted that breast-conserving surgery was preferred over mastectomy because breast-conserving surgery leads to better outcomes in body image, future perspectives and less systemic side effects.

Fluctuations of Histone Chemical Modifications in Breast, Prostate, and Colorectal Cancer: An Implication of Phytochemicals as Defenders of Chromatin Equilibrium

Natural substances of plant origin exert health beneficiary efficacy due to the content of various phytochemicals. Significant anticancer abilities of natural compounds are mediated via various processes such as regulation of a cell's epigenome. The potential antineoplastic activity of plant natural substances mediated by their action on posttranslational histone modifications (PHMs) is currently a highly evaluated area of cancer research. PHMs play an important role in maintaining chromatin structure and regulating gene expression. Aberrations in PHMs are directly linked to the process of carcinogenesis in cancer such as breast (BC), prostate (PC), and colorectal (CRC) cancer, common malignant diseases in terms of incidence and mortality among both men and women. This review summarizes the effects of plant phytochemicals (isolated or mixtures) on cancer-associated PHMs (mainly modulation of acetylation and methylation) resulting in alterations of chromatin structure that are related to the regulation of transcription activity of specific oncogenes, which are crucial in the development of BC, PC, and CRC. Significant effectiveness of natural compounds in the modulation of aberrant PHMs were confirmed by a number of in vitro or in vivo studies in preclinical cancer research. However, evidence concerning PHMs-modulating abilities of plant-based natural substances in clinical trials is insufficient.

Breast Cancer in Young Women: Status Quo and Advanced Disease Management by a Predictive, Preventive, and Personalized Approach

Why does healthcare of breast cancer (BC) patients, especially in a young population, matter and why are innovative strategies by predictive, preventive, and personalized medicine (PPPM) strongly recommended to replace current reactive medical approach in BC management? Permanent increase in annual numbers of new BC cases with particularly quick growth of premenopausal BC patients, an absence of clearly described risk factors for those patients, as well as established screening tools and programs represent important reasons to focus on BC in young women. Moreover, "young" BC cases are frequently "asymptomatic", difficult to diagnose, and to treat effectively on time. The objective of this article is to update the knowledge on BC in young females, its unique molecular signature, newest concepts in diagnostics and therapy, and to highlight the concepts of predictive, preventive, and personalized medicine with a well-acknowledged potential to advance the overall disease management.

Development of a membrane lipid metabolism-based signature to predict overall survival for personalized medicine in ccRCC patients

Background

Clear cell renal cell carcinoma (ccRCC) is the most common type of renal cell carcinoma and is characterized by a dysregulation of changes in cellular metabolism. Altered lipid metabolism contributes to ccRCC progression and malignancy.

Method

Associations among survival potential and each gene ontology (GO) term were analyzed by univariate Cox regression. The results revealed that membrane lipid metabolism had the greatest hazard ratio (HR). Weighted gene co-expression network analysis (WGCNA) was applied to determine the key genes associated with membrane lipid metabolism. Consensus clustering was used to identify novel molecular subtypes based on the key genes. LASSO Cox regression was performed to build a membrane lipid metabolism-based signature. The random forest algorithm was applied to find the most important mutations associated with membrane lipid metabolism. Decision trees and nomograms were constructed to quantify risks for individual patients.

Result

Membrane lipid metabolism stratified ccRCC patients into high- and low-risk groups. Key genes were identified by WGCNA. Membrane lipid metabolism-based signatures exhibited higher prediction efficiency than other clinicopathological traits in both whole cohort and subgroup analyses. The random forest algorithm revealed high associations among the membrane lipid metabolism-based signature and BAP1, PBRM1 and VHL mutations. Decision trees and nomograms indicated high efficiency for risk stratification.

Conclusion

Our study might contribute to the optimization of risk stratification for survival and personalized management of ccRCC patients.

Platelet Volume Is Reduced In Metastasing Breast Cancer: Blood Profiles Reveal Significant Shifts

Background

Platelets play a crucial role in breast cancer (BC) progression and metastases. Mean platelet volume (MPV) is an indicator of platelet activation. The aim of the present study was to assess whether there is a difference in MPV between patients with metastatic BC with liver metastases and those with BC without liver metastases.

Methods

Between January 2014 and December 2017, 211 metastatic BC patients with synchronous liver metastases and 215 BC patients without metastases were retrospectively analyzed. Patients’ clinicopathological characteristics data were collected.

Results

MPV levels were reduced in patients with liver metastases compared with those in patients without liver metastases. There were significant differences in MPV levels according to liver metastases status both in premenopausal and in postmenopausal non-TNBC or non-HER2+ patients. Moreover, in postmenopausal HER2+ or TNBC patients, MPV levels were lower in patients with liver metastases compared with those in patients without liver metastases. In the group with non-liver metastasis, platelet distribution width was significantly associated with tumor N stage. In addition, the prevalence of BC liver metastases decreased as MPV quartiles increased. After adjusting for other risk factors, the odds ratios for liver metastases according to MPV quartiles were 1.000, 0.267 (0.134–0.530), 0.072 (0.034–0.152), and 0.137 (0.066–0.281), respectively.

Conclusion

MPV is reduced in BC patients with liver metastases compared with that in BC patients without metastases. Moreover, MPV is independently associated with the presence of liver metastases.

BioDog, biomarker detection for improving identification power of breast cancer histologic grade in methylomics

Aim: Breast cancer histologic grade (HG) is a well-established prognostic factor. This study aimed to select methylomic biomarkers to predict breast cancer HGs. Materials & methods: The proposed algorithm BioDog firstly used correlation bias reduction strategy to eliminate redundant features. Then incremental feature selection was applied to find the features with a high HG prediction accuracy. The sequential backward feature elimination strategy was employed to further refine the biomarkers. A comparison with existing algorithms were conducted. The HG-specific somatic mutations were investigated. Results & conclusions: BioDog achieved accuracy 0.9973 using 92 methylomic biomarkers for predicting breast cancer HGs. Many of these biomarkers were within the genes and lncRNAs associated with the HG development in breast cancer or other cancer types.

Reviewing ensemble classification methods in breast cancer

CONTEXT

Ensemble methods consist of combining more than one single technique to solve the same task. This approach was designed to overcome the weaknesses of single techniques and consolidate their strengths. Ensemble methods are now widely used to carry out prediction tasks (e.g. classification and regression) in several fields, including that of bioinformatics. Researchers have particularly begun to employ ensemble techniques to improve research into breast cancer, as this is the most frequent type of cancer and accounts for most of the deaths among women.

OBJECTIVE AND METHOD

The goal of this study is to analyse the state of the art in ensemble classification methods when applied to breast cancer as regards 9 aspects: publication venues, medical tasks tackled, empirical and research types adopted, types of ensembles proposed, single techniques used to construct the ensembles, validation framework adopted to evaluate the proposed ensembles, tools used to build the ensembles, and optimization methods used for the single techniques. This paper was undertaken as a systematic mapping study.

RESULTS

A total of 193 papers that were published from the year 2000 onwards, were selected from four online databases: IEEE Xplore, ACM digital library, Scopus and PubMed. This study found that of the six medical tasks that exist, the diagnosis medical task was that most frequently researched, and that the experiment-based empirical type and evaluation-based research type were the most dominant approaches adopted in the selected studies. The homogeneous type was that most widely used to perform the classification task. With regard to single techniques, this mapping study found that decision trees, support vector machines and artificial neural networks were those most frequently adopted to build ensemble classifiers. In the case of the evaluation framework, the Wisconsin Breast Cancer dataset was the most frequently used by researchers to perform their experiments, while the most noticeable validation method was k-fold cross-validation. Several tools are available to perform experiments related to ensemble classification methods, such as Weka and R Software. Few researchers took into account the optimisation of the single technique of which their proposed ensemble was composed, while the grid search method was that most frequently adopted to tune the parameter settings of a single classifier.

CONCLUSION

This paper reports an in-depth study of the application of ensemble methods as regards breast cancer. Our results show that there are several gaps and issues and we, therefore, provide researchers in the field of breast cancer research with recommendations. Moreover, after analysing the papers found in this systematic mapping study, we discovered that the majority report positive results concerning the accuracy of ensemble classifiers when compared to the single classifiers. In order to aggregate the evidence reported in literature, it will, therefore, be necessary to perform a systematic literature review and meta-analysis in which an in-depth analysis could be conducted so as to confirm the superiority of ensemble classifiers over the classical techniques.

Reduced chemotherapeutic sensitivity in high glucose condition: implication of antioxidant response

Resistance to chemotherapy represents a major obstacle to successful treatment. The generation of reactive oxygen species (ROS) has been directly linked to the cytotoxic effects of several antitumor agents, including Adriamycin (ADR), and modulation of the oxidative balance has been implicated in the development and/or regulation of resistance to chemotherapeutic drugs. We recently showed that high glucose (HG) markedly diminished the cancer cell death induced by anticancer agents such as ADR. In the present study we attempted to evaluate the mechanism that impaired the cytotoxic effect of ADR in HG. We found that, in colon cancer cells, HG attenuated ADR-induced ROS production that consequently diminished ADR-induced H2AX phosphorylation and micronuclei (MN) formation. Mechanistically, HG attenuation of ADR-induced ROS production correlated with increased antioxidant response promoted by NRF2 activity. Thus, pharmacologic inhibition of NRF2 pathway by brusatol re-established the ADR cytotoxic effect impaired by HG. Together, the data provide new insights into chemotherapeutic-resistance mechanisms in HG condition dictated by increased NRF2-induced antioxidant response and how they may be overcome in order to restore chemosensitivity and ADR-induced cell death.

DNA Methylation Status in Cancer Disease: Modulations by Plant-Derived Natural Compounds and Dietary Interventions

The modulation of the activity of DNA methyltransferases (DNMTs) represents a crucial epigenetic mechanism affecting gene expressions or DNA repair mechanisms in the cells. Aberrant modifications in the function of DNMTs are a fundamental event and part of the pathogenesis of human cancer. Phytochemicals, which are biosynthesized in plants in the form of secondary metabolites, represent an important source of biomolecules with pleiotropic effects and thus provide a wide range of possible clinical applications. It is well documented that phytochemicals demonstrate significant anticancer properties, and in this regard, rapid development within preclinical research is encouraging. Phytochemicals affect several epigenetic molecular mechanisms, including DNA methylation patterns such as the hypermethylation of tumor-suppressor genes and the global hypomethylation of oncogenes, that are specific cellular signs of cancer development and progression. This review will focus on the latest achievements in using plant-derived compounds and plant-based diets targeting epigenetic regulators and modulators of gene transcription in preclinical and clinical research in order to generate novel anticancer drugs as sensitizers for conventional therapy or compounds suitable for the chemoprevention clinical setting in at-risk individuals. In conclusion, indisputable anticancer activities of dietary phytochemicals linked with proper regulation of DNA methylation status have been described. However, precisely designed and well-controlled clinical studies are needed to confirm their beneficial epigenetic effects after long-term consumption in humans.

Why the Gold Standard Approach by Mammography Demands Extension by Multiomics? Application of Liquid Biopsy miRNA Profiles to Breast Cancer Disease Management

In the global context, the epidemic of breast cancer (BC) is evident for the early 21st century. Evidence shows that national mammography screening programs have sufficiently reduced BC related mortality. Therefore, the great utility of the mammography-based screening is not an issue. However, both false positive and false negative BC diagnosis, excessive biopsies, and irradiation linked to mammography application, as well as sub-optimal mammography-based screening, such as in the case of high-dense breast tissue in young females, altogether increase awareness among the experts regarding the limitations of mammography-based screening. Severe concerns regarding the mammography as the “golden standard” approach demanding complementary tools to cover the evident deficits led the authors to present innovative strategies, which would sufficiently improve the quality of the BC management and services to the patient. Contextually, this article provides insights into mammography deficits and current clinical data demonstrating the great potential of non-invasive diagnostic tools utilizing circulating miRNA profiles as an adjunct to conventional mammography for the population screening and personalization of BC management.

Incidence and prognostic factors of patients with synchronous liver metastases upon initial diagnosis of breast cancer: a population-based study

Background

The purpose of this study was to analyze the incidence and prognostic factors of patients with breast cancer liver metastases (BCLM) at initial diagnosis.

Methods

We utilized the Surveillance, Epidemiology, and End Results database to extract data on patients with primary invasive breast cancer from 2010 to 2014. Multivariate logistic regression was conducted to determine factors associated with the presence of liver metastases upon initial diagnosis of breast cancer. Univariate and multivariate Cox regression analyses were performed to identify the prognostic factors in these patients.

Results

In total, 3,276 patients with liver metastases were identified upon initial diagnosis of breast cancer. Patients with hormone receptor-negative (HR−), human epidermal growth factor receptor 2-positive (HER2+) breast cancer had the highest incidence (4.6% among the entire population, 46.5% among the metastatic subgroup). Age, gender, race, pathological grade, extrahepatic metastases, tumor subtype, and marital status were identified as factors associated with the presence of liver metastases upon initial diagnosis of breast cancer. The median overall survival among the entire population with BCLM was 20.0 months. Patients with HR+/HER2+ breast cancer had the longest median survival of 36.0 months. The survival analyses indicated that older age, higher pathological grade, extrahepatic metastases, triple-negative subtype, unmarried status, and uninsured status were independent prognostic factors for a poorer prognosis.

Conclusion

The study provides insight into the incidence and prognostic factors for patients with BCLM at initial diagnosis, which is important clinical information for risk evaluation and prognostic assessment.

Plant natural modulators in breast cancer prevention: status quo and future perspectives reinforced by predictive, preventive, and personalized medical approach

In contrast to the genetic component in mammary carcinogenesis, epigenetic alterations are particularly important for the development of sporadic breast cancer (BC) comprising over 90% of all BC cases worldwide. Most of the DNA methylation processes are physiological and essential for human cellular and tissue homeostasis, playing an important role in a number of key mechanisms. However, if dysregulated, DNA methylation contributes to pathological processes such as cancer development and progression. A global hypomethylation of oncogenes and hypermethylation of tumor-suppressor genes are characteristic of most cancer types. Moreover, histone chemical modifications and non-coding RNA-associated multi-gene controls are considered as the key epigenetic mechanisms governing the cellular homeostasis and differentiation states. A number of studies demonstrate dietary plant products as actively affecting the development and progression of cancer. "Nutri-epigenetics" focuses on the influence of dietary agents on epigenetic mechanisms. This approach has gained considerable attention; since in contrast to genetic alterations, epigenetic modifications are reversible affect early carcinogenesis. Currently, there is an evident lack of papers dedicated to the phytochemicals/plant extracts as complex epigenetic modulators, specifically in BC. Our paper highlights the role of plant natural compounds in targeting epigenetic alterations associated with BC development, progression, as well as its potential chemoprevention in the context of preventive medicine. Comprehensive measures are stated with a great potential to advance the overall BC management in favor of predictive, preventive, and personalized medical services and can be considered as "proof-of principle" model, for their potential application to other multifactorial diseases.

Dietary phytochemicals in breast cancer research: anticancer effects and potential utility for effective chemoprevention

Cancerous tissue transformation developing usually over years or even decades of life is a highly complex process involving strong stressors damaging DNA, chronic inflammation, comprehensive interaction between relevant molecular pathways, and cellular cross-talk within the neighboring tissues. Only the minor part of all cancer cases are caused by inborn predisposition; the absolute majority carry a sporadic character based on modifiable risk factors which play a central role in cancer prevention. Amongst most promising candidates for dietary supplements are bioactive phytochemicals demonstrating strong anticancer effects. Abundant evidence has been collected for beneficial effects of flavonoids, carotenoids, phenolic acids, and organosulfur compounds affecting a number of cancer-related pathways. Phytochemicals may positively affect processes of cell signaling, cell cycle regulation, oxidative stress response, and inflammation. They can modulate non-coding RNAs, upregulate tumor suppressive miRNAs, and downregulate oncogenic miRNAs that synergically inhibits cancer cell growth and cancer stem cell self-renewal. Potential clinical utility of the phytochemicals is discussed providing examples for chemoprevention against and therapy for human breast cancer. Expert recommendations are provided in the context of preventive medicine.