Future of personalized medicine in oncology: a systems biology approach

Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review

Background

The field of precision medicine endeavors to transform the healthcare industry by advancing individualised strategies for diagnosis, treatment modalities, and predictive assessments. This is achieved by utilizing extensive multidimensional biological datasets encompassing diverse components, such as an individual's genetic makeup, functional attributes, and environmental influences. Artificial intelligence (AI) systems, namely machine learning (ML) and deep learning (DL), have exhibited remarkable efficacy in predicting the potential occurrence of specific cancers and cardiovascular diseases (CVD).

Methods

We conducted a comprehensive scoping review guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Our search strategy involved combining key terms related to CVD and AI using the Boolean operator AND. In August 2023, we conducted an extensive search across reputable scholarly databases including Google Scholar, PubMed, IEEE Xplore, ScienceDirect, Web of Science, and arXiv to gather relevant academic literature on personalised medicine for CVD. Subsequently, in January 2024, we extended our search to include internet search engines such as Google and various CVD websites. These searches were further updated in March 2024. Additionally, we reviewed the reference lists of the final selected research articles to identify any additional relevant literature.

Findings

A total of 2307 records were identified during the process of conducting the study, consisting of 564 entries from external sites like arXiv and 1743 records found through database searching. After 430 duplicate articles were eliminated, 1877 items that remained were screened for relevancy. In this stage, 1241 articles remained for additional review after 158 irrelevant articles and 478 articles with insufficient data were removed. 355 articles were eliminated for being inaccessible, 726 for being written in a language other than English, and 281 for not having undergone peer review. Consequently, 121 studies were deemed suitable for inclusion in the qualitative synthesis. At the intersection of CVD, AI, and precision medicine, we found important scientific findings in our scoping review. Intricate pattern extraction from large, complicated genetic datasets is a skill that AI algorithms excel at, allowing for accurate disease diagnosis and CVD risk prediction. Furthermore, these investigations have uncovered unique genetic biomarkers linked to CVD, providing insight into the workings of the disease and possible treatment avenues. The construction of more precise predictive models and personalised treatment plans based on the genetic profiles of individual patients has been made possible by the revolutionary advancement of CVD risk assessment through the integration of AI and genomics.

Interpretation

The systematic methodology employed ensured the thorough examination of available literature and the inclusion of relevant studies, contributing to the robustness and reliability of the study's findings. Our analysis stresses a crucial point in terms of the adaptability and versatility of AI solutions. AI algorithms designed in non-CVD domains such as in oncology, often include ideas and tactics that might be modified to address cardiovascular problems.

Funding

No funding received.

A nationwide survey of the current status of radiation oncology teaching in Spanish medical schools

Background

The present study was designed to collect information on the current status of radiation oncology (RO) teaching in undergraduate medical schools in Spain.

Materials and methods

A cross-sectional survey was conducted with the support of the Spanish Society of Radiation Oncology (SEOR). An anonymous questionnaire was sent in two waves, one month apart, between January and June 2022, to all Medical Schools and affiliated Institutions having radiotherapy departments throughout the country. Data on load, curricular location of OR, the academic course (or courses) in which the subject of OR was taught, and teachers position were recorded.

Results

Responses were obtained from 26 of the 46 available Medical Schools (response rate 56.5%). The average number of theoretical classes was 13 (0-30), seminars: 4.5 (0-12) and hours of practical training 17 (0-60). The scientific content of RO was covered very evenly. Medical physics and radiobiology were taught with different extension in 24 medical schools (92.3%). Information on technological equipment, brachytherapy, indications, and clinical results was provided in all but one medical school. In 13 medical schools (50.0%) the contents of RO were taught in more than one course, but the distribution of RO teaching during the six years of undergraduate training was quite dispersed. The teaching staff included 4 full professors, 8 tenured professors, and 68 clinical associate professors. The average number of associate professors per medical school was 2.2. Also, the average number of full professors and tenured lecturers was 0.42 per medical school, although there were none in 16 centers.

Conclusions

The overall teaching content of RO in Spanish medical schools seems appropriate but actions to improve the heterogeneity in the curricular location of RO and the shortage of teachers should be implemented.

Delineating intra-tumoral heterogeneity and tumor evolution in breast cancer using precision-based approaches

The burden of breast cancer continues to increase worldwide as it remains the most diagnosed tumor in females and the second leading cause of cancer-related deaths. Breast cancer is a heterogeneous disease characterized by different subtypes which are driven by aberrations in key genes such as BRCA1 and BRCA2, and hormone receptors. However, even within each subtype, heterogeneity that is driven by underlying evolutionary mechanisms is suggested to underlie poor response to therapy, variance in disease progression, recurrence, and relapse. Intratumoral heterogeneity highlights that the evolvability of tumor cells depends on interactions with cells of the tumor microenvironment. The complexity of the tumor microenvironment is being unraveled by recent advances in screening technologies such as high throughput sequencing; however, there remain challenges that impede the practical use of these approaches, considering the underlying biology of the tumor microenvironment and the impact of selective pressures on the evolvability of tumor cells. In this review, we will highlight the advances made thus far in defining the molecular heterogeneity in breast cancer and the implications thereof in diagnosis, the design and application of targeted therapies for improved clinical outcomes. We describe the different precision-based approaches to diagnosis and treatment and their prospects. We further propose that effective cancer diagnosis and treatment are dependent on unpacking the tumor microenvironment and its role in driving intratumoral heterogeneity. Underwriting such heterogeneity are Darwinian concepts of natural selection that we suggest need to be taken into account to ensure evolutionarily informed therapeutic decisions.

Translational Bioinformatics for Human Reproductive Biology Research: Examples, Opportunities and Challenges for a Future Reproductive Medicine

Since 1978, with the first IVF (in vitro fertilization) baby birth in Manchester (England), more than eight million IVF babies have been born throughout the world, and many new techniques and discoveries have emerged in reproductive medicine. To summarize the modern technology and progress in reproductive medicine, all scientific papers related to reproductive medicine, especially papers related to reproductive translational medicine, were fully searched, manually curated and reviewed. Results indicated whether male reproductive medicine or female reproductive medicine all have made significant progress, and their markers have experienced the progress from karyotype analysis to single-cell omics. However, due to the lack of comprehensive databases, especially databases collecting risk exposures, disease markers and models, prevention drugs and effective treatment methods, the application of the latest precision medicine technologies and methods in reproductive medicine is limited.

The emerging roles of NGS in clinical oncology and personalized medicine

Next-generation sequencing (NGS) has been increasingly popular in genomics studies over the last decade, as new sequencing technology has been created and improved. Recently, NGS started to be used in clinical oncology to improve cancer therapy through diverse modalities ranging from finding novel and rare cancer mutations, discovering cancer mutation carriers to reaching specific therapeutic approaches known as personalized medicine (PM). PM has the potential to minimize medical expenses by shifting the current traditional medical approach of treating cancer and other diseases to an individualized preventive and predictive approach. Currently, NGS can speed up in the early diagnosis of diseases and discover pharmacogenetic markers that help in personalizing therapies. Despite the tremendous growth in our understanding of genetics, NGS holds the added advantage of providing more comprehensive picture of cancer landscape and uncovering cancer development pathways. In this review, we provided a complete overview of potential NGS applications in scientific and clinical oncology, with a particular emphasis on pharmacogenomics in the direction of precision medicine treatment options.

Host-mycobiome metabolic interactions in health and disease

Fungal communities (mycobiome) have an important role in sustaining the resilience of complex microbial communities and maintenance of homeostasis. The mycobiome remains relatively unexplored compared to the bacteriome despite increasing evidence highlighting their contribution to host-microbiome interactions in health and disease. Despite being a small proportion of the total species, fungi constitute a large proportion of the biomass within the human microbiome and thus serve as a potential target for metabolic reprogramming in pathogenesis and disease mechanism. Metabolites produced by fungi shape host niches, induce immune tolerance and changes in their levels prelude changes associated with metabolic diseases and cancer. Given the complexity of microbial interactions, studying the metabolic interplay of the mycobiome with both host and microbiome is a demanding but crucial task. However, genome-scale modelling and synthetic biology can provide an integrative platform that allows elucidation of the multifaceted interactions between mycobiome, microbiome and host. The inferences gained from understanding mycobiome interplay with other organisms can delineate the key role of the mycobiome in pathophysiology and reveal its role in human disease.

Oral Cancer: Genetics and the Role of Precision Medicine

Oral squamous cell carcinoma (OSCC) is one of the leading cancers in the world. OSCC patients are managed with surgery and/or chemoradiation. Prognoses and survival rates are dismal, however, and have not improved for more than 20 years. Recently, the concept of precision medicine was introduced, and the introduction of targeted therapeutics demonstrated promising outcomes. This article reviews the current understanding of initiation, progression, and metastasis of OSCC from both genetic and epigenetic perspectives. In addition, the applications and integration of omics technologies in biomarker discovery and drug development for treating OSCC are reviewed.

Purpurin, a anthraquinone induces ROS-mediated A549 lung cancer cell apoptosis via inhibition of PI3K/AKT and proliferation

OBJECTIVES

In this study, we sought to evaluate purpurin, a natural biomedicine and a potential inhibitor in decreasing the growth rate of lung cancer cells by modulating the role of PI3K/AKT signalling-associated proliferation and apoptosis.

METHODS

A549 cells were treated with purpurin (30 μM) for 24 and 48 h incubation, respectively, and it has been analysed for cytotoxicity, ROS-mediated apoptotic staining. Moreover, purpurin-mediated lipid peroxidation and GSH were measured by biochemical estimation. Furthermore, PI3K/AKT signalling-mediated cell proliferation and apoptotic gene expression done were by western blot.

KEY FINDINGS

In this study, we observed that purpurin could effectively kill A549 cancer cell lines and leads to cell death, thus conforming increased cytotoxicity, production of ROS-mediated enhancement of lipid peroxidation, nuclear fragmentation and apoptosis. Moreover, the GSH content of A549 cell lines was also diminished after treatment with purpurin. This study demonstrates that purpurin inhibits the phosphorylated PI3K/AKT molecules mediated cyclin-D1 and PCNA, thereby inducing apoptosis by observing increased proapoptotic mediators Bax, cleaved PARP, cytochrome-c, caspase-9 and caspase-3; and decreased Bcl-2 expression in the lung cancer cell lines.

CONCLUSION

This result concluded that purpurin eliminates the A549 lung cancer cells by blocking the PI3K/AKT pathway thereby inducing apoptosis.

A Systems Approach to Brain Tumor Treatment

Brain tumors are among the most lethal tumors. Glioblastoma, the most frequent primary brain tumor in adults, has a median survival time of approximately 15 months after diagnosis or a five-year survival rate of 10%; the recurrence rate is nearly 90%. Unfortunately, this prognosis has not improved for several decades. The lack of progress in the treatment of brain tumors has been attributed to their high rate of primary therapy resistance. Challenges such as pronounced inter-patient variability, intratumoral heterogeneity, and drug delivery across the blood-brain barrier hinder progress. A comprehensive, multiscale understanding of the disease, from the molecular to the whole tumor level, is needed to address the intratumor heterogeneity resulting from the coexistence of a diversity of neoplastic and non-neoplastic cell types in the tumor tissue. By contrast, inter-patient variability must be addressed by subtyping brain tumors to stratify patients and identify the best-matched drug(s) and therapies for a particular patient or cohort of patients. Accomplishing these diverse tasks will require a new framework, one involving a systems perspective in assessing the immense complexity of brain tumors. This would in turn entail a shift in how clinical medicine interfaces with the rapidly advancing high-throughput (HTP) technologies that have enabled the omics-scale profiling of molecular features of brain tumors from the single-cell to the tissue level. However, several gaps must be closed before such a framework can fulfill the promise of precision and personalized medicine for brain tumors. Ultimately, the goal is to integrate seamlessly multiscale systems analyses of patient tumors and clinical medicine. Accomplishing this goal would facilitate the rational design of therapeutic strategies matched to the characteristics of patients and their tumors. Here, we discuss some of the technologies, methodologies, and computational tools that will facilitate the realization of this vision to practice.

Drug Sensitivity and Drug Repurposing Platform for Cancer Precision Medicine

One of the critical Global challenges in achieving the UN Sustainable Development Goals 3 Good Health and Well Being is optimizing drug discovery and translational research for unmet medical need in both communicable and non-communicable diseases. Recently, the WHO reports there has been a shift from communicable diseases to non-communicable diseases with respect to being the leading cause of death globally and particularly in low- and middle-income countries such as South Africa. Hence, there is current drive to establish functional precision medicine program that addresses the unmet medical need using high throughput drug sensitivity and drug repurposing platform. Here, this paper serves as a perspective to showcase the recent development in high throughput drug sensitivity screening platform for the cancer precision medicine. We also elaborate on the benefit and applications of high-throughput drug screening platform for Precision Medicine. From a future perspective, this paper aims to showcase the possibility to integrate existing high-throughput drug sensitivity screening platform with the newly developed platform technologies such as microfluidics based single cell drug screening.

Cellular State Transformations Using Deep Learning for Precision Medicine Applications

We introduce the Transcriptome State Perturbation Generator (TSPG) as a novel deep-learning method to identify changes in genomic expression that occur between tissue states using generative adversarial networks. TSPG learns the transcriptome perturbations from RNA-sequencing data required to shift from a source to a target class. We apply TSPG as an effective method of detecting biologically relevant alternate expression patterns between normal and tumor human tissue samples. We demonstrate that the application of TSPG to expression data obtained from a biopsy sample of a patient's kidney cancer can identify patient-specific differentially expressed genes between their individual tumor sample and a target class of healthy kidney gene expression. By utilizing TSPG in a precision medicine application in which the patient sample is not replicated (i.e., n=1), we present a novel technique of determining significant transcriptional aberrations that can be used to help identify potential targeted therapies.

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We present the Transcriptome State Perturbation Generator (TSPG) application

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We apply TSPG to The Cancer Genome Atlas data to perturb gene expression states

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TSPG was used to learn patient-specific (n = 1) gene expression tumor alterations

Deep learning has shown tremendous success in image and natural language processing; however, attempts to apply the tools of machine learning to better understanding biological systems are still in the stage of early adoption. We propose a novel deep-learning tool that can be used to process samples of RNA-sequencing data. By applying the Transcriptome State Perturbation Generator to human samples, we show that deep learning derives insight into the gene expression shifts required for transition between two biological conditions (e.g., normal versus tumor). RNA-sequencing data derived from a single patient's tumor were analyzed using this tool to determine gene expression aberrations specific to that patient's tumor. As medicine shifts from cohort-based population studies to individual-based precision treatments, our example demonstrates that deep learning is a powerful ally in the quest to understand how complex biological systems have shifted for a single patient.

Medical applications of clustered regularly interspaced short palindromic repeats (CRISPR/Cas) tool: A comprehensive overview

Since the discovery of the double helix and the introduction of genetic engineering, the possibility to develop new strategies to manipulate the genome has fascinated scientists around the world. Currently scientists have the knowledge andabilitytoedit the genomes. Several methodologies of gene editing have been established, all of them working like "scissor", creating double strand breaks at specific spots. The introduction of a new technology, which was adapted from the clustered regularly interspaced short palindromic repeats (CRISPR)/Cas bacterial immune system, has revolutionized the genetic therapy field, as it allows a much more precise editing of gene than the previously described tools and, therefore, to prevent and treat disease in humans. This review aims to revisit the genome editing history that led to the rediscovery of the CRISPR/Cas technology and to explore the technical aspects, applications and perspectives of this fascinating, powerful, precise, simpler and cheaper technology in different fields.

Characterization of a Complex Mixture of Immunomodulator Peptides Obtained from Autologous Urine

A complex mixture of peptides plays a key role in the regulation of the immune system; different sources as raw materials mainly from animals and vegetables have been reported to provide these extracts. The batch-to-batch product consistency depends on in-process controls established. However, when an immunomodulator is a customized product obtained from the same volunteer who will receive the product to personalize the treatment, the criteria to establish the consistency between volunteers are different. In this sense, it is expected to have the same molecular weight range although the profile of peptide abundance is different. Here, we characterized the peptide profile of three extracts of an immunomodulator obtained from the urine of different volunteers suffering from three different diseases (i.e., allergic rhinitis, rheumatoid arthritis, and chronic rhinopharyngitis), using size exclusion chromatography (SEC) and mass spectrometry (MS). The peptides contained in the immunomodulators were stable after six months, stored in a refrigerator. Our results showed a chromatographic profile with the same range of low molecular weight (less than 17 kDa) in all analyzed samples by SEC; these results were also confirmed by MS showing an exact mass spectrum from 3 to 13 kDa. The fact that the peptide profiles were conserved during a six-month period at refrigeration conditions (2 to 8°C) maintaining the quality and stability of the immunomodulator supports the notion that it might be an alternative in the treatment of chronic hypersensibility disorders.

Ultrasensitive detection of nucleic acids using deformed graphene channel field effect biosensors

Field-effect transistor (FET)-based biosensors allow label-free detection of biomolecules by measuring their intrinsic charges. The detection limit of these sensors is determined by the Debye screening of the charges from counter ions in solutions. Here, we use FETs with a deformed monolayer graphene channel for the detection of nucleic acids. These devices with even millimeter scale channels show an ultra-high sensitivity detection in buffer and human serum sample down to 600 zM and 20 aM, respectively, which are ∼18 and ∼600 nucleic acid molecules. Computational simulations reveal that the nanoscale deformations can form 'electrical hot spots' in the sensing channel which reduce the charge screening at the concave regions. Moreover, the deformed graphene could exhibit a band-gap, allowing an exponential change in the source-drain current from small numbers of charges. Collectively, these phenomena allow for ultrasensitive electronic biomolecular detection in millimeter scale structures.

Ultrasensitive detection of nucleic acids using deformed graphene channel field effect biosensors

Field-effect transistor (FET)-based biosensors allow label-free detection of biomolecules by measuring their intrinsic charges. The detection limit of these sensors is determined by the Debye screening of the charges from counter ions in solutions. Here, we use FETs with a deformed monolayer graphene channel for the detection of nucleic acids. These devices with even millimeter scale channels show an ultra-high sensitivity detection in buffer and human serum sample down to 600 zM and 20 aM, respectively, which are ∼18 and ∼600 nucleic acid molecules. Computational simulations reveal that the nanoscale deformations can form 'electrical hot spots' in the sensing channel which reduce the charge screening at the concave regions. Moreover, the deformed graphene could exhibit a band-gap, allowing an exponential change in the source-drain current from small numbers of charges. Collectively, these phenomena allow for ultrasensitive electronic biomolecular detection in millimeter scale structures.

Deep Learning in Personalization of Cardiovascular Stents

Deep learning (DL) application has demonstrated its enormous potential in accomplishing biomedical tasks, such as vessel segmentation, brain visualization, and speech recognition. This review article has mainly covered recent advances in the principles of DL algorithms, existing DL software, and designing strategies of DL models. Latest progresses in cardiovascular devices, especially DL-based cardiovascular stent used for angioplasty, differential and advanced diagnostic means, and the treatment outcomes involved with coronary artery disease (CAD), are discussed. Also presented is DL-based discovery of new materials and future medical technologies that will facilitate the development of tailored and personalized treatment strategies by identifying and forecasting individual impending risks of cardiovascular diseases.

Laser-driven radiation: Biomarkers for molecular imaging of high dose-rate effects

Recently developed short‐pulsed laser sources garner high dose‐rate beams such as energetic ions and electrons, x rays, and gamma rays. The biological effects of laser‐generated ion beams observed in recent studies are different from those triggered by radiation generated using classical accelerators or sources, and this difference can be used to develop new strategies for cancer radiotherapy. High‐power lasers can now deliver particles in doses of up to several Gy within nanoseconds. The fast interaction of laser‐generated particles with cells alters cell viability via distinct molecular pathways compared to traditional, prolonged radiation exposure. The emerging consensus of recent literature is that the differences are due to the timescales on which reactive molecules are generated and persist, in various forms. Suitable molecular markers have to be adopted to monitor radiation effects, addressing relevant endogenous molecules that are accessible for investigation by noninvasive procedures and enable translation to clinical imaging. High sensitivity has to be attained for imaging molecular biomarkers in cells and in vivo to follow radiation‐induced functional changes. Signal‐enhanced MRI biomarkers enriched with stable magnetic nuclear isotopes can be used to monitor radiation effects, as demonstrated recently by the use of dynamic nuclear polarization (DNP) for biomolecular observations in vivo. In this context, nanoparticles can also be used as radiation enhancers or biomarker carriers. The radiobiology‐relevant features of high dose‐rate secondary radiation generated using high‐power lasers and the importance of noninvasive biomarkers for real‐time monitoring the biological effects of radiation early on during radiation pulse sequences are discussed.

Computational algorithms for in silico profiling of activating mutations in cancer

Methods to catalog and computationally assess the mutational landscape of proteins in human cancers are desirable. One approach is to adapt evolutionary or data-driven methods developed for predicting whether a single-nucleotide polymorphism (SNP) is deleterious to protein structure and function. In cases where understanding the mechanism of protein activation and regulation is desired, an alternative approach is to employ structure-based computational approaches to predict the effects of point mutations. Through a case study of mutations in kinase domains of three proteins, namely, the anaplastic lymphoma kinase (ALK) in pediatric neuroblastoma patients, serine/threonine-protein kinase B-Raf (BRAF) in melanoma patients, and erythroblastic oncogene B 2 (ErbB2 or HER2) in breast cancer patients, we compare the two approaches above. We find that the structure-based method is most appropriate for developing a binary classification of several different mutations, especially infrequently occurring ones, concerning the activation status of the given target protein. This approach is especially useful if the effects of mutations on the interactions of inhibitors with the target proteins are being sought. However, many patients will present with mutations spread across different target proteins, making structure-based models computationally demanding to implement and execute. In this situation, data-driven methods-including those based on machine learning techniques and evolutionary methods-are most appropriate for recognizing and illuminate mutational patterns. We show, however, that, in the present status of the field, the two methods have very different accuracies and confidence values, and hence, the optimal choice of their deployment is context-dependent.

Comparing deep belief networks with support vector machines for classifying gene expression data from complex disorders

Genomics data provide great opportunities for translational research and the clinical practice, for example, for predicting disease stages. However, the classification of such data is a challenging task due to their high dimensionality, noise, and heterogeneity. In recent years, deep learning classifiers generated much interest, but due to their complexity, so far, little is known about the utility of this method for genomics. In this paper, we address this problem by studying a computational diagnostics task by classification of breast cancer and inflammatory bowel disease patients based on high‐dimensional gene expression data. We provide a comprehensive analysis of the classification performance of deep belief networks (DBNs) in dependence on its multiple model parameters and in comparison with support vector machines (SVMs). Furthermore, we investigate combined classifiers that integrate DBNs with SVMs. Such a classifier utilizes a DBN as representation learner forming the input for a SVM. Overall, our results provide guidelines for the complex usage of DBN for classifying gene expression data from complex diseases.

Patterns of DNA methylation as an indicator of biological aging: State of the science and future directions in precision health promotion

BACKGROUND

A rapidly expanding literature suggests that individuals of the same chronological age show significant variation in biological age.

PURPOSE

The purpose of this article is to review the literature surrounding epigenetic age as estimated by DNA methylation, involving the addition or removal of methyl groups to DNA that can alter gene expression without changing the DNA sequence.

METHODS

This state of the science literature review summarizes current approaches in epigenetic age determination and applications of aging algorithms.

FINDINGS

A number of algorithms estimate epigenetic age using DNA methylation markers, primarily among adults. Algorithm application has focused on determining predictive value for risk of disease and death and identifying antecedents to age acceleration. Several studies have incorporated epigenetic age to evaluate intervention effectiveness.

DISCUSSION

As the research community continues to refine aging algorithms, there may be opportunity to promote health from a precision health perspective.

Preoperative MRI-radiomics features improve prediction of survival in glioblastoma patients over MGMT methylation status alone

BACKGROUND

Glioblastoma (GBM) is the most common malignant central nervous system tumor, and MGMT promoter hypermethylation in this tumor has been shown to be associated with better prognosis. We evaluated the capacity of radiomics features to add complementary information to MGMT status, to improve the ability to predict prognosis.

METHODS

159 patients with untreated GBM were included in this study and divided into training and independent test sets. 286 radiomics features were extracted from the magnetic resonance images acquired prior to any treatments. A least absolute shrinkage selection operator (LASSO) selection followed by Kaplan-Meier analysis was used to determine the prognostic value of radiomics features to predict overall survival (OS). The combination of MGMT status with radiomics was also investigated and all results were validated on the independent test set.

RESULTS

LASSO analysis identified 8 out of the 286 radiomic features to be relevant which were then used for determining association to OS. One feature (edge descriptor) remained significant on the external validation cohort after multiple testing (p=0.04) and the combination with MGMT identified a group of patients with the best prognosis with a survival probability of 0.61 after 43 months (p=0.0005).

CONCLUSION

Our results suggest that combining radiomics with MGMT is more accurate in stratifying patients into groups of different survival risks when compared to with using these predictors in isolation. We identified two subgroups within patients who have methylated MGMT: one with a similar survival to unmethylated MGMT patients and the other with a significantly longer OS.

Data assimilation and multisource decision-making in systems biology based on unobtrusive Internet-of-Things devices

Biological and medical diagnoses depend on high-quality measurements. A wearable device based on Internet of Things (IoT) must be unobtrusive to the human body to encourage users to accept continuous monitoring. However, unobtrusive IoT devices are usually of low quality and unreliable because of the limitation of technology progress that has slowed down at high peak. Therefore, advanced inference techniques must be developed to address the limitations of IoT devices. This review proposes that IoT technology in biological and medical applications should be based on a new data assimilation process that fuses multiple data scales from several sources to provide diagnoses. Moreover, the required technologies are ready to support the desired disease diagnosis levels, such as hypothesis test, multiple evidence fusion, machine learning, data assimilation, and systems biology. Furthermore, cross-disciplinary integration has emerged with advancements in IoT. For example, the multiscale modeling of systems biology from proteins and cells to organs integrates current developments in biology, medicine, mathematics, engineering, artificial intelligence, and semiconductor technologies. Based on the monitoring objectives of IoT devices, researchers have gradually developed ambulant, wearable, noninvasive, unobtrusive, low-cost, and pervasive monitoring devices with data assimilation methods that can overcome the limitations of devices in terms of quality measurement. In the future, the novel features of data assimilation in systems biology and ubiquitous sensory development can describe patients' physical conditions based on few but long-term measurements.

A Machine Learning Perspective on Personalized Medicine: An Automized, Comprehensive Knowledge Base with Ontology for Pattern Recognition

Personalized or precision medicine is a new paradigm that holds great promise for individualized patient diagnosis, treatment, and care. However, personalized medicine has only been described on an informal level rather than through rigorous practical guidelines and statistical protocols that would allow its robust practical realization for implementation in day-to-day clinical practice. In this paper, we discuss three key factors, which we consider dimensions that effect the experimental design for personalized medicine: (I) phenotype categories; (II) population size; and (III) statistical analysis. This formalization allows us to define personalized medicine from a machine learning perspective, as an automized, comprehensive knowledge base with an ontology that performs pattern recognition of patient profiles.

Oral Cancer: Genetics and the Role of Precision Medicine

Oral squamous cell carcinoma (OSCC) is one of the leading cancers in the world. OSCC patients are managed with surgery and/or chemoradiation. Prognoses and survival rates are dismal, however, and have not improved for more than 20 years. Recently, the concept of precision medicine was introduced, and the introduction of targeted therapeutics demonstrated promising outcomes. This article reviews the current understanding of initiation, progression, and metastasis of OSCC from both genetic and epigenetic perspectives. In addition, the applications and integration of omics technologies in biomarker discovery and drug development for treating OSCC are reviewed.

Self-Calibrating On-Chip Localized Surface Plasmon Resonance Sensing for Quantitative and Multiplexed Detection of Cancer Markers in Human Serum

The need for point-of-care devices able to detect diseases early and monitor their status, out of a lab environment, has stimulated the development of compact biosensing configurations. Whereas localized surface plasmon resonance (LSPR) sensing integrated into a state-of-the-art microfluidic chip stands as a promising approach to meet this demand, its implementation into an operating sensing platform capable of quantitatively detecting a set of molecular biomarkers in an unknown biological sample is only in its infancy. Here, we present an on-chip LSPR sensor capable of performing automatic, quantitative, and multiplexed screening of biomarkers. We demonstrate its versatility by programming it to detect and quantify in human serum four relevant human serum protein markers associated with breast cancer.

Cancer driver gene discovery through an integrative genomics approach in a non-parametric Bayesian framework

Motivation

Comprehensive catalogue of genes that drive tumor initiation and progression in cancer is key to advancing diagnostics, therapeutics and treatment. Given the complexity of cancer, the catalogue is far from complete yet. Increasing evidence shows that driver genes exhibit consistent aberration patterns across multiple-omics in tumors. In this study, we aim to leverage complementary information encoded in each of the omics data to identify novel driver genes through an integrative framework. Specifically, we integrated mutations, gene expression, DNA copy numbers, DNA methylation and protein abundance, all available in The Cancer Genome Atlas (TCGA) and developed iDriver, a non-parametric Bayesian framework based on multivariate statistical modeling to identify driver genes in an unsupervised fashion. iDriver captures the inherent clusters of gene aberrations and constructs the background distribution that is used to assess and calibrate the confidence of driver genes identified through multi-dimensional genomic data.

Results

We applied the method to 4 cancer types in TCGA and identified candidate driver genes that are highly enriched with known drivers. (e.g.: P < 3.40 × 10 -36 for breast cancer). We are particularly interested in novel genes and observed multiple lines of supporting evidence. Using systematic evaluation from multiple independent aspects, we identified 45 candidate driver genes that were not previously known across these 4 cancer types. The finding has important implications that integrating additional genomic data with multivariate statistics can help identify cancer drivers and guide the next stage of cancer genomics research.

Availability and Implementation

The C ++ source code is freely available at https://medschool.vanderbilt.edu/cgg/ .

Contacts

hai.yang@vanderbilt.edu or bingshan.li@Vanderbilt.Edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Research and Discovery Science and the Future of Dental Education and Practice

Dental graduates of 2040 will face new and complex challenges. If they are to meet these challenges, dental schools must develop a research and discovery mission that will equip graduates with the new knowledge required to function in a modern health care environment. The dental practitioner of 2040 will place greater emphasis on risk assessment, disease prevention, and health maintenance; and the emerging discipline of precision medicine and systems biology will revolutionize disease diagnosis and reveal new targeted therapies. The dental graduate of 2040 will be expected to function effectively in a collaborative, learning health care system and to understand the impact of health care policy on local, national, and global communities. Emerging scientific fields such as big data analytics, stem cell biology, tissue engineering, and advanced biomimetics will impact dental practice. Despite all the warning signs indicating how the changing scientific and heath care landscape will dramatically alter dental education and dental practice, dental schools have yet to reconsider their research and educational priorities and clinical practice objectives. Until dental schools and the practicing community come to grips with these challenges, this persistent attitude of complacency will likely be at the dental profession's peril. This article was written as part of the project "Advancing Dental Education in the 21^st Century."

Exploitation of Gene Expression and Cancer Biomarkers in Paving the Path to Era of Personalized Medicine

Cancer therapy agents have been used extensively as cytotoxic drugs against tissue or organ of a specific type of cancer. With the better understanding of molecular mechanisms underlying carcinogenesis and cellular events during cancer progression and metastasis, it is now possible to use targeted therapy for these molecular events. Targeted therapy is able to identify cancer patients with dissimilar genetic defects at cellular level for the same cancer type and consequently requires individualized approach for treatment. Cancer therapy begins to shift steadily from the traditional approach of “one regimen for all patients” to a more individualized approach, through which each patient will be treated specifically according to their specific genetic defects. Personalized medicine accordingly requires identification of indicators or markers that guide in the decision making of such therapy to the chosen patients for more effective therapy. Cancer biomarkers are frequently used in clinical practice for diagnosis and prognosis, as well as identification of responsive patients and prediction of treatment response of cancer patient. The rapid breakthrough and development of microarray and sequencing technologies is probably the main tool for paving the way toward “individualized biomarker-driven cancer therapy” or “personalized medicine”. In this review, we aim to provide an updated knowledge and overview of the current landscape of cancer biomarkers and their role in personalized medicine, emphasizing the impact of genomics on the implementation of new potential targeted therapies and development of novel cancer biomarkers in improving the outcome of cancer therapy.

Paradigm Shift From Halstedian Radical Mastectomy to Personalized Medicine

Breast cancer management changed from radical mastectomy to precision medicine in a period longer than a century. The aims of these changes were to refrain from overdiagnoses and overtreatments as well as their harmful side effects and extra costs. Breast cancer is a heterogeneous disease and characterized by many morphological, clinical and molecular features. We now increasingly realise that a one-size-fits-all strategy does not apply to all breast cancer patients. Personalized medicine may be used for breast cancer screening, diagnosis and treatment. Individualized screening can decrease the number of unnecessary mammograms, additional radiologic studies, breast biopsies and false positivity rates. However, additional 15 to 20 years are necessary to reach the results of prospective randomized trials comparing low-risk and normal-risk women. We also should wait for outcomes of risk-based screening trials. The rates of overtreatment in patients with early-stage breast cancer have reached 40% in many studies. Personalized treatment has succeeded in reducing it substantially by using tumour genetic profiling and tumour receptors in early breast cancer patients. However, it has its limits and it is impossible to generalize it to all patients. New biomarkers and molecular classifications have also led to the development of novel therapies and treatment strategies. And, they can contribute to a more personalized management of breast cancer patients.

Precision medicine and molecular imaging: new targeted approaches toward cancer therapeutic and diagnosis

This paper presents a review of the importance and role of precision medicine and molecular imaging technologies in cancer diagnosis with therapeutics and diagnostics purposes. Precision medicine is progressively becoming a hot topic in all disciplines related to biomedical investigation and has the capacity to become the paradigm for clinical practice. The future of medicine lies in early diagnosis and individually appropriate treatments, a concept that has been named precision medicine, i.e. delivering the right treatment to the right patient at the right time. Molecular imaging is quickly being recognized as a tool with the potential to ameliorate every aspect of cancer treatment. On the other hand, emerging high-throughput technologies such as omics techniques and systems approaches have generated a paradigm shift for biological systems in advanced life science research. In this review, we describe the precision medicine, difference between precision medicine and personalized medicine, precision medicine initiative, systems biology/medicine approaches (such as genomics, radiogenomics, transcriptomics, proteomics, and metabolomics), P4 medicine, relationship between systems biology/medicine approaches and precision medicine, and molecular imaging modalities and their utility in cancer treatment and diagnosis. Accordingly, the precision medicine and molecular imaging will enable us to accelerate and improve cancer management in future medicine.

Cancerome: A hidden informative subnetwork of the diseasome

Neoplastic disorders are a leading cause of mortality and morbidity worldwide. Studying the relationships between different cancers using high throughput-generated data may elucidate undisclosed aspects of cancer etiology, diagnosis, and treatment. Several studies have described relationships between different diseases based on genes, proteins, pathways, gene ontology, comorbidity, symptoms, and other features. In this study, we first constructed an integrated human disease network based on nine different biological aspects, including molecular, functional, and clinical features. Next, we extracted the cancerome as a cancer-related subnetwork. Further investigation of cancerome could reveal hidden mechanisms of cancer and could be useful in developing new diagnostic tests and effective new drugs.

Combined CT-guided radiofrequency ablation with systemic chemotherapy improves the survival for nasopharyngeal carcinoma with oligometastasis in liver: Propensity score matching analysis

The aim of this study was to retrospectively compare the treatment efficacy of systemic chemotherapy combined with sequential CT-guided radiofrequency ablation (Chemo-RFA) to chemotherapy alone (Chemo-only) in the management of nasopharyngeal carcinoma (NPC) with liver metastasis. Between 2003 and 2011, 328 NPC patients diagnosed with liver metastasis at Sun Yat-sen University Cancer Center were enrolled. One-to-one matched pairs between Chemo-RFA group with the Chemo-only group were generated using propensity score matching. The associations of treatment modality with overall survival (OS) and progression-free survival (PFS) were determined by Cox regression. Of the patients enrolled, 37 patients (11.8 %) received combined treatment, 291 (82.2) received chemotherapy alone. The patients in Chemo-RFA group were more frequently classified as lower number (≤3) of liver metastatic lesions (P<0.001), had lower rates of bi-lobar liver metastasis (P<0.001) and extra-hepatic metastasis (P<0.001) than patients in Chemo-only group. After propensity score matching, 37 pairs of well-matched liver metastatic NPC patients were selected from different treatment groups. The adjusted hazard ratio in OS and PFS of the choice for Chemo-RFA approach to Chemo-only was 0.53 (95%CI, 0.30-0.93) and 0.60 (95%CI, 0.36-0.97), respectively. In conclusion, combined CT-guided RFA and chemotherapy approach offer the chance of improved survival for NPC patients with oligometastasis in liver, and should be considered if the ablation is technically feasible.

A Set of miRNAs, Their Gene and Protein Targets and Stromal Genes Distinguish Early from Late Onset ER Positive Breast Cancer

Breast cancer (BC) in young adult patients (YA) has a more aggressive biological behavior and is associated with a worse prognosis than BC arising in middle aged patients (MA). We proposed that differentially expressed miRNAs could regulate genes and proteins underlying aggressive phenotypes of breast tumors in YA patients when compared to those arising in MA patients.

OBJECTIVE

Using integrated expression analyses of miRs, their mRNA and protein targets and stromal gene expression, we aimed to identify differentially expressed profiles between tumors from YA-BC and MA-BC.

METHODOLOGY AND RESULTS

Samples of ER+ invasive ductal breast carcinomas, divided into two groups: YA-BC (35 years or less) or MA-BC (50-65 years) were evaluated. Screening for BRCA1/2 status according to the BOADICEA program indicated low risk of patients being carriers of these mutations. Aggressive characteristics were more evident in YA-BC versus MA-BC. Performing qPCR, we identified eight miRs differentially expressed (miR-9, 18b, 33b, 106a, 106b, 210, 518a-3p and miR-372) between YA-BC and MA-BC tumors with high confidence statement, which were associated with aggressive clinicopathological characteristics. The expression profiles by microarray identified 602 predicted target genes associated to proliferation, cell cycle and development biological functions. Performing RPPA, 24 target proteins differed between both groups and 21 were interconnected within a network protein-protein interactions associated with proliferation, development and metabolism pathways over represented in YA-BC. Combination of eight mRNA targets or the combination of eight target proteins defined indicators able to classify individual samples into YA-BC or MA-BC groups. Fibroblast-enriched stroma expression profile analysis resulted in 308 stromal genes differentially expressed between YA-BC and MA-BC.

CONCLUSION

We defined a set of differentially expressed miRNAs, their mRNAs and protein targets and stromal genes that distinguish early onset from late onset ER positive breast cancers which may be involved with tumor aggressiveness of YA-BC.

Transcriptomic analysis identified up-regulation of a solute carrier transporter and UDP glucuronosyltransferases in dogs with aggressive cutaneous mast cell tumours

Gene expression analyses have been recently used in cancer research to identify genes associated with tumorigenesis and potential prognostic markers or therapeutic targets. In the present study, the transcriptome of dogs that had died because of mast cell tumours (MCTs) was characterised to identify a fingerprint having significant influence on prognosis determination and treatment selection. A dataset (GSE50433) obtained using a commercial canine DNA microarray platform was used. The transcriptome of seven biopsies obtained from dogs with histologically confirmed, surgically removed MCTs, treated with chemotherapy, and dead for MCT-related causes, was compared with the transcriptional portrait of 40 samples obtained from dogs with histologically confirmed, surgically removed MCTs and that were still alive at the end of the follow-up period. Among the differentially expressed genes (DEGs), eight transcripts were validated by quantitative real time PCR and their mRNA levels were measured in a cohort of 22 additional MCTs. Statistical analysis identified 375 DEGs (fold change 2, false discovery rate 5%). The functional annotation analysis indicated that the DEGs were associated with drug metabolism and cell cycle pathways. Particularly, members of solute carrier transporter (SLC) and UDP glucuronosyltransferase (UGT) gene families were identified as dysregulated. Principal component analysis (PCA) of the 22 additional MCTs identified the separate cluster dogs dead for MCT-related causes. SLCs and UGTs have been recently recognised in human cancer as important key factors in tumour progression and chemo-resistance. An in-depth analysis of their roles in aggressive canine MCT is warranted in future studies.

"RADIOTRANSCRIPTOMICS": A synergy of imaging and transcriptomics in clinical assessment

Recent advances in quantitative imaging and "omics" technology have generated a wealth of mineable biological "big data". With the push towards a P4 "predictive, preventive, personalized, and participatory" approach to medicine, researchers began integrating complementary tools to further tune existing diagnostic and therapeutic models. The field of radiogenomics has long pioneered such multidisciplinary investigations in neuroscience and oncology, correlating genotypic and phenotypic signatures to study structural and functional changes in relation to altered molecular behavior. Given the innate dynamic nature of complex disorders and the role of environmental and epigenetic factors in pathogenesis, the transcriptome can further elucidate serial modifications undetected at the genome level. We therefore propose "radiotranscriptomics" as a new member of the P4 medicine initiative, combining transcriptome information, including gene expression and isoform variation, and quantitative image annotations.

Companion Diagnostics and Molecular Imaging

Companion diagnostics (CDx) is a positive attempt in the direction of improving the drug development process, especially in the field of oncology, with the advent of newer targeted therapies. It helps the oncologist in deciding the choice of treatment for the individual patient. The role of CDx assays has attracted the attention of regulators, and especially the US Food and Drug Administration developed regulatory strategies for CDx and the drug-diagnostic codevelopment project. For an increasing number of cancer patients, the treatment selection will depend on the result generated by a CDx assay, and consequently this type of assay has become critical for the care and safety of the patients. In addition to the assay-based approach, molecular imaging with its ability to image at the genetic and receptor level has made foray into the field of drug development and personalized medicine. We shall review these aspects of CDx, with special focus on molecular imaging and the upcoming concept of Theranostics.

Conditional robustness analysis for fragility discovery and target identification in biochemical networks and in cancer systems biology

Background

The study of cancer therapy is a key issue in the field of oncology research and the development of target therapies is one of the main problems currently under investigation. This is particularly relevant in different types of tumor where traditional chemotherapy approaches often fail, such as lung cancer.

Results

We started from the general definition of robustness introduced by Kitano and applied it to the analysis of dynamical biochemical networks, proposing a new algorithm based on moment independent analysis of input/output uncertainty. The framework utilizes novel computational methods which enable evaluating the model fragility with respect to quantitative performance measures and parameters such as reaction rate constants and initial conditions. The algorithm generates a small subset of parameters that can be used to act on complex networks and to obtain the desired behaviors. We have applied the proposed framework to the EGFR-IGF1R signal transduction network, a crucial pathway in lung cancer, as an example of Cancer Systems Biology application in drug discovery. Furthermore, we have tested our framework on a pulse generator network as an example of Synthetic Biology application, thus proving the suitability of our methodology to the characterization of the input/output synthetic circuits.

Conclusions

The achieved results are of immediate practical application in computational biology, and while we demonstrate their use in two specific examples, they can in fact be used to study a wider class of biological systems.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-015-0216-5) contains supplementary material, which is available to authorized users.

New Functional Signatures for Understanding Melanoma Biology from Tumor Cell Lineage-Specific Analysis

Molecular signatures specific to particular tumor types are required to design treatments for resistant tumors. However, it remains unclear whether tumors and corresponding cell lines used for drug development share such signatures. We developed similarity core analysis (SCA), a universal and unsupervised computational framework for extracting core molecular features common to tumors and cell lines. We applied SCA to mRNA/miRNA expression data from various sources, comparing melanoma cell lines and metastases. The signature obtained was associated with phenotypic characteristics in vitro, and the core genes CAPN3 and TRIM63 were implicated in melanoma cell migration/invasion. About 90% of the melanoma signature genes belong to an intrinsic network of transcription factors governing neural development (TFAP2A, DLX2, ALX1, MITF, PAX3, SOX10, LEF1, and GAS7) and miRNAs (211-5p, 221-3p, and 10a-5p). The SCA signature effectively discriminated between two subpopulations of melanoma patients differing in overall survival, and classified MEKi/BRAFi-resistant and -sensitive melanoma cell lines.

Systems biology approaches to adverse drug effects: the example of cardio-oncology

Increased awareness of the cardiovascular toxic effects of chemotherapy has led to the emergence of cardio-oncology (or onco-cardiology), which focuses on screening, monitoring and treatment of patients with cardiovascular dysfunctions resulting from chemotherapy. Anthracyclines, such as doxorubicin, and HER2 inhibitors, such as trastuzumab, both have cardiotoxic effects. The biological rationale, mechanisms of action and cardiotoxicity profiles of these two classes of drugs, however, are completely different, suggesting that cardiotoxic effects can occur in a range of different ways. Advances in genomics and proteomics have implicated several genomic variants and biological pathways that can influence the susceptibility to cardiotoxicity from these, and other drugs. Established pathways include multidrug resistance proteins, energy utilization pathways, oxidative stress, cytoskeletal regulation and apoptosis. Gene-expression profiles that have revealed perturbed pathways have vastly increased our knowledge of the complex processes involved in crosstalk between tumours and cardiac function. Utilization of mathematical and computational modelling can complement pharmacogenomics and improve individual patient outcomes. Such endeavours should enable identification of variations in cardiotoxicity, particularly in those patients who are at risk of not recovering, even with the institution of cardioprotective therapy. The application of systems biology holds substantial potential to advance our understanding of chemotherapy-induced cardiotoxicity.