Personalized medicine: challenges and opportunities for translational bioinformatics

Translational bioinformatics approach to combat cardiovascular disease and cancers

Bioinformatics is an interconnected subject of science dealing with diverse fields including biology, chemistry, physics, statistics, mathematics, and computer science as the key fields to answer complicated physiological problems. Key intention of bioinformatics is to store, analyze, organize, and retrieve essential information about genome, proteome, transcriptome, metabolome, as well as organisms to investigate the biological system along with its dynamics, if any. The outcome of bioinformatics depends on the type, quantity, and quality of the raw data provided and the algorithm employed to analyze the same. Despite several approved medicines available, cardiovascular disorders (CVDs) and cancers comprises of the two leading causes of human deaths. Understanding the unknown facts of both these non-communicable disorders is inevitable to discover new pathways, find new drug targets, and eventually newer drugs to combat them successfully. Since, all these goals involve complex investigation and handling of various types of macro- and small- molecules of the human body, bioinformatics plays a key role in such processes. Results from such investigation has direct human application and thus we call this filed as translational bioinformatics. Current book chapter thus deals with diverse scope and applications of this translational bioinformatics to find cure, diagnosis, and understanding the mechanisms of CVDs and cancers. Developing complex yet small or long algorithms to address such problems is very common in translational bioinformatics. Structure-based drug discovery or AI-guided invention of novel antibodies that too with super-high accuracy, speed, and involvement of considerably low amount of investment are some of the astonishing features of the translational bioinformatics and its applications in the fields of CVDs and cancers.

From genome to clinic: The power of translational bioinformatics in improving human health

Translational bioinformatics (TBI) has transformed healthcare by providing personalized medicine and tailored treatment options by integrating genomic data and clinical information. In recent years, TBI has bridged the gap between genome and clinical data because of significant advances in informatics like quantum computing and utilizing state-of-the-art technologies. This chapter discusses the power of translational bioinformatics in improving human health, from uncovering disease-causing genes and variations to establishing new therapeutic techniques. We discuss key application areas of bioinformatics in clinical genomics, such as data sources and methods used in translational bioinformatics, the impact of translational bioinformatics on human health, and how machine learning and artificial intelligence are being used to mine vast amounts of data for drug development and precision medicine. We also look at the problems, constraints, and ethical concerns connected with exploiting genomic data and the future of translational bioinformatics and its potential impact on medicine and human health. Ultimately, this chapter emphasizes the great potential of translational bioinformatics to alter healthcare and enhance patient outcomes.

Patients' Experiences of a Precision Medicine Clinic

The purpose of this study is to provide an overview of patients' experiences using a precision medicine (PM) clinic that conducts pharmacogenomics-based (PGx) testing for adverse drug reactions. The study aimed to identify the features of the clinic valued most by patients and areas for improvement. A paper survey was used to collect data. Survey questions focused on patients' perceptions of the PM testing and the overall clinic experience. Sixty-seven patients completed the survey. Quantitative data were analyzed using SPSS and frequencies were reported. Open-ended responses were coded and organized thematically. Patients reported that the clinic services increased confidence in their medication usage. Feeling respected by staff, receiving education, and quick appointments were highly valued by patients. Suggested areas for improvement included better communication from the clinic to patients, expansion of clinic services, and education for other healthcare providers. The findings demonstrate that patient experience goes beyond the clinical care provided. Current and potential future providers of PM should invest the time and energy to configure their care delivery system to enhance the patient experience.

3D Bioprinting for Next-Generation Personalized Medicine

In the past decade, immense progress has been made in advancing personalized medicine to effectively address patient-specific disease complexities in order to develop individualized treatment strategies. In particular, the emergence of 3D bioprinting for in vitro models of tissue and organ engineering presents novel opportunities to improve personalized medicine. However, the existing bioprinted constructs are not yet able to fulfill the ultimate goal: an anatomically realistic organ with mature biological functions. Current bioprinting approaches have technical challenges in terms of precise cell deposition, effective differentiation, proper vascularization, and innervation. This review introduces the principles and realizations of bioprinting with a strong focus on the predominant techniques, including extrusion printing and digital light processing (DLP). We further discussed the applications of bioprinted constructs, including the engraftment of stem cells as personalized implants for regenerative medicine and in vitro high-throughput drug development models for drug discovery. While no one-size-fits-all approach to bioprinting has emerged, the rapid progress and promising results of preliminary studies have demonstrated that bioprinting could serve as an empowering technology to resolve critical challenges in personalized medicine.

Recommendations of the International Medical Informatics Association (IMIA) on Education in Biomedical and Health Informatics: Second Revision

BACKGROUND

The purpose of educational recommendations is to assist in establishing courses and programs in a discipline, to further develop existing educational activities in the various nations, and to support international initiatives for collaboration and sharing of courseware. The International Medical Informatics Association (IMIA) has published two versions of its international recommendations in biomedical and health informatics (BMHI) education, initially in 2000 and revised in 2010. Given the recent changes to the science, technology, the needs of the healthcare systems, and the workforce of BMHI, a revision of the recommendations is necessary.

OBJECTIVE

The aim of these updated recommendations is to support educators in developing BMHI curricula at different education levels, to identify essential skills and competencies for certification of healthcare professionals and those working in the field of BMHI, to provide a tool for evaluators of academic BMHI programs to compare and accredit the quality of delivered programs, and to motivate universities, organizations, and health authorities to recognize the need for establishing and further developing BMHI educational programs.

METHOD

An IMIA taskforce, established in 2017, updated the recommendations. The taskforce included representatives from all IMIA regions, with several having been involved in the development of the previous version. Workshops were held at different IMIA conferences, and an international Delphi study was performed to collect expert input on new and revised competencies.

RESULTS

Recommendations are provided for courses/course tracks in BMHI as part of educational programs in biomedical and health sciences, health information management, and informatics/computer science, as well as for dedicated programs in BMHI (leading to bachelor's, master's, or doctoral degree). The educational needs are described for the roles of BMHI user, BMHI generalist, and BMHI specialist across six domain areas - BMHI core principles; health sciences and services; computer, data and information sciences; social and behavioral sciences; management science; and BMHI specialization. Furthermore, recommendations are provided for dedicated educational programs in BMHI at the level of bachelor's, master's, and doctoral degrees. These are the mainstream academic programs in BMHI. In addition, recommendations for continuing education, certification, and accreditation procedures are provided.

CONCLUSION

The IMIA recommendations reflect societal changes related to globalization, digitalization, and digital transformation in general and in healthcare specifically, and center on educational needs for the healthcare workforce, computer scientists, and decision makers to acquire BMHI knowledge and skills at various levels. To support education in BMHI, IMIA offers accreditation of quality BMHI education programs. It supports information exchange on programs and courses in BMHI through its Working Group on Health and Medical Informatics Education.

Systematic Identification of Genomic Markers for Guiding Iron Oxide Nanoparticles in Cervical Cancer Based on Translational Bioinformatics

Purpose

Magnetic iron oxide nanoparticle (MNP) drug delivery system is a novel promising therapeutic option for cancer treatment. Material issues such as fabrication and functionalized modification have been investigated; however, pharmacologic mechanisms of bare MNPs inside cancer cells remain obscure. This study aimed to explore a systems pharmacology approach to understand the reaction of the whole cell to MNPs and suggest drug selection in MNP delivery systems to exert synergetic or additive anti-cancer effects.

Methods

HeLa and SiHa cell lines were used to estimate the properties of bare MNPs in cervical cancer through 3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide (MTT) and enzyme activity assays and cellular fluorescence imaging. A systems pharmacology approach was utilized by combining bioinformatics data mining with clinical data analysis and without a predefined hypothesis. Key genes of the MNP onco-pharmacologic mechanism in cervical cancer were identified and further validated through transcriptome analysis with quantitative reverse transcription PCR (qRT-PCR).

Results

Low cytotoxic activity and cell internalization of MNP in HeLa and SiHa cells were observed. Lysosomal function was found to be impaired after MNP treatment. Protein tyrosine kinase 2 beta (PTK2B), liprin-alpha-4 (PPFIA4), mothers against decapentaplegic homolog 7 (SMAD7), and interleukin (IL) 1B were identified as key genes relevant for MNP pharmacology, clinical features, somatic mutation, and immune infiltration. The four key genes also exhibited significant correlations with the lysosome gene set. The qRT-PCR results showed significant alterations in the expression of the four key genes after MNP treatment in HeLa and SiHa cells.

Conclusion

Our research suggests that treatment of bare MNPs in HeLa and SiHa cells induced significant expression changes in PTK2B, PPFIA4, SMAD7, and IL1B, which play crucial roles in cervical cancer development and progression. Interactions of the key genes with specific anti-cancer drugs must be considered in the rational design of MNP drug delivery systems.

Multi-omics strategies for personalized and predictive medicine: past, current, and future translational opportunities

Precision medicine is driven by the paradigm shift of empowering clinicians to predict the most appropriate course of action for patients with complex diseases and improve routine medical and public health practice. It promotes integrating collective and individualized clinical data with patient specific multi-omics data to develop therapeutic strategies, and knowledgebase for predictive and personalized medicine in diverse populations. This study is based on the hypothesis that understanding patient's metabolomics and genetic make-up in conjunction with clinical data will significantly lead to determining predisposition, diagnostic, prognostic and predictive biomarkers and optimal paths providing personalized care for diverse and targeted chronic, acute, and infectious diseases. This study briefs emerging significant, and recently reported multi-omics and translational approaches aimed to facilitate implementation of precision medicine. Furthermore, it discusses current grand challenges, and the future need of Findable, Accessible, Intelligent, and Reproducible (FAIR) approach to accelerate diagnostic and preventive care delivery strategies beyond traditional symptom-driven, disease-causal medical practice.

Understanding value in a healthcare setting: An application of the business model canvas

The business model canvas is a popular tool used to develop value-driven business models. Specific emphasis is placed on understanding what customers value and providing users with steps on how to design and deliver value for their customers. In health care, creating and delivering value for patients is an often-discussed topic, with the provision of patient-centered care becoming a standard for many health care organizations. While patients play a key role in determining value, providers are the key to delivering value. Therefore, effective health care management relies on integrating multiple perspectives from key stakeholders. This process requires consideration of the key needs that must be addressed, the resources and capabilities necessary to meet these needs, and the interests and values specific to each set of stakeholders. The business model canvas lends itself well to health care service planning as it incorporates the factors described above into the business model’s conceptualization and subsequent realization. This article outlines how the business model canvas was applied to assess the needs of physician stakeholders to help guide the expansion of a pharmacogenomic-based precision medicine clinic that conducts genetic testing for patients at risk of experiencing adverse drug reactions. The article provides a detailed description of how the business model canvas was used and adapted to understand physician’s responsibilities and challenges related to drug prescription and dosing, and how the clinic could address physician needs and create value by mapping clinic services onto physician needs and wants. Interviews were conducted with physicians and the data were analyzed following the recommendations of the developers of the business model canvas. The article examines the strengths and limitations of the business model canvas and discusses its applicability to a health care setting.

SMART COVID Navigator: A Clinical Decision Support for COVID-19

Background

COVID-19 caused by SARS-CoV-2 has infected 219 million individuals at the time of writing of this paper. A large volume of research findings from observational studies about disease interactions with COVID-19 is being produced almost daily, making it difficult for physicians to keep track of the latest information on COVID-19’s effect on patients with certain pre-existing conditions.

Objective

In this paper, we describe the creation of a clinical decision support tool, the SMART COVID Navigator, a web application to assist clinicians in treating patients with COVID-19. Our application allows clinicians to access a patient’s electronic health records and identify disease interactions from a large set of observational research studies that affect the severity and fatality due to COVID-19.

Methods

The SMART COVID Navigator takes a 2-pronged approach to clinical decision support. The first part is a connection to electronic health record servers, allowing the application to access a patient’s medical conditions. The second is accessing data sets with information from various observational studies to determine the latest research findings about COVID-19 outcomes for patients with certain medical conditions. By connecting these 2 data sources, users can see how a patient’s medical history will affect their COVID-19 outcomes.

Results

The SMART COVID Navigator aggregates patient health information from multiple Fast Healthcare Interoperability Resources–enabled electronic health record systems. This allows physicians to see a comprehensive view of patient health records. The application accesses 2 data sets of over 1100 research studies to provide information on the fatality and severity of COVID-19 for several pre-existing conditions. We also analyzed the results of the collected studies to determine which medical conditions result in an increased chance of severity and fatality of COVID-19 progression. We found that certain conditions result in a higher likelihood of severity and fatality probabilities. We also analyze various cancer tissues and find that the probabilities for fatality vary greatly depending on the tissue being examined.

Conclusions

The SMART COVID Navigator allows physicians to predict the fatality and severity of COVID-19 progression given a particular patient’s medical conditions. This can allow physicians to determine how aggressively to treat patients infected with COVID-19 and to prioritize different patients for treatment considering their prior medical conditions.

Results of the Seventh Scientific Workshop of ECCO: Precision Medicine in IBD-What, Why, and How

Many diseases that affect modern humans fall in the category of complex diseases, thus called because they result from a combination of multiple aetiological and pathogenic factors. Regardless of the organ or system affected, complex diseases present major challenges in diagnosis, classification, and management. Current forms of therapy are usually applied in an indiscriminate fashion based on clinical information, but even the most advanced drugs only benefit a limited number of patients and to a variable and unpredictable degree. This 'one measure does not fit all' situation has spurred the notion that therapy for complex disease should be tailored to individual patients or groups of patients, giving rise to the notion of 'precision medicine' [PM]. Inflammatory bowel disease [IBD] is a prototypical complex disease where the need for PM has become increasingly clear. This prompted the European Crohn's and Colitis Organisation to focus the Seventh Scientific Workshop on this emerging theme. The articles in this special issue of the Journal address the various complementary aspects of PM in IBD, including what PM is; why it is needed and how it can be used; how PM can contribute to prediction and prevention of IBD; how IBD PM can aid in prognosis and improve response to therapy; and the challenges and future directions of PM in IBD. This first article of this series is structured on three simple concepts [what, why, and how] and addresses the definition of PM, discusses the rationale for the need of PM in IBD, and outlines the methodology required to implement PM in IBD in a correct and clinically meaningful way.

Healthcare professionals' knowledge, attitudes and future expectations towards personalized medicine

Aim: Personalized medicine (PM) is a novel approach to diagnose and treat disease. The study assessed the knowledge, attitudes and future expectations of healthcare professionals (HPs) towards PM in Ethiopia. Materials & methods: A cross-sectional survey with primary data and a simple random sampling technique was applied to collect data. Results: Our study revealed from a total of 384 respondents, 98 (25.5%), 146 (38%) and 140 (36.5%) had good, medium and poor knowledge of PM, respectively. However, 172 (44.8%), 185 (48.2%) and 27 (7%) had positive, neutral and negative attitudes towards PM, respectively. Conclusion: Most respondent's future expectations of PM were positive. Education level had a significant association with attitudes and other sociodemographic variables were not significant for both knowledge and attitude.

Precision Medicine in Hematology 2021: Definitions, Tools, Perspectives, and Open Questions

During the past few years, our understanding of molecular mechanisms and cellular interactions relevant to malignant blood cell disorders has improved substantially. New insights include a detailed knowledge about disease-initiating exogenous factors, endogenous (genetic, somatic, epigenetic) elicitors or facilitators of disease evolution, and drug actions and interactions that underlie efficacy and adverse event profiles in defined cohorts of patients. As a result, precision medicine and personalized medicine are rapidly growing new disciplines that support the clinician in making the correct diagnosis, in predicting outcomes, and in optimally selecting patients for interventional therapies. In addition, precision medicine tools are greatly facilitating the development of new drugs, therapeutic approaches, and new multiparametric prognostic scoring models. However, although the emerging roles of precision medicine and personalized medicine in hematology and oncology are clearly visible, several questions remain. For example, it remains unknown how precision medicine tools can be implemented in healthcare systems and whether all possible approaches are also affordable. In addition, there is a need to define terminologies and to relate these to specific and context-related tools and strategies in basic and applied science. To discuss these issues, a working conference was organized in September 2019. The outcomes of this conference are summarized herein and include a proposal for definitions, terminologies, and applications of precision and personalized medicine concepts and tools in hematologic neoplasms. We also provide proposals aimed at reducing costs, thereby making these applications affordable in daily practice.

Pharmacogenomics Implementation and Hurdles to Overcome; In the Context of a Developing Country

Having multiple dimensions, uncertainties and several stakeholders, the costly pharmacogenomics (PGx) is associated with dynamic implementation complexities. Identification of these challenges is critical to harness its full potential, especially in developing countries with fragile healthcare systems and scarce resources. This is the first study aimed to identify most salient challenges related to PGx implementation, with respect to the experiences of early-adopters and local experts' prospects, in the context of a developing country in the Middle East. To perform a comprehensive reconnaissance on PGx adoption challenges a scoping literature review was conducted based on national drug policy components: efficacy/safety, access, affordability and rational use of medicine (RUM). Strategic option development and analysis workshop method with cognitive mapping as the technique was used to evaluate challenges in the context of Iran. The cognitive maps were face-validated and analyzed via Decision Explorer XML. The findings indicated a complex network of issues relative to PGx adoption, categorized in national drug policy indicators. In the rational use of medicine category, ethics, education, bench -to- bedside strategies, guidelines, compliance, and health system issues were found. Clinical trial issues, test's utility, and biomarker validation were identified in the efficacy group. Affordability included pricing, reimbursement, and value assessment issues. Finally, access category included regulation, availability, and stakeholder management challenges. The current study identified the most significant challenges ahead of clinical implementation of PGx in a developing country. This could be the basis of a policy-note development in future work, which may consolidate vital communication among stakeholders and accelerate the efficient implementation in developing new-comer countries.

Milestones in Personalized Medicine: From the Ancient Time to Nowadays-the Provocation of COVID-19

The first evidence of individual targeting medicine appeared in ancient times thousands of years ago. Various therapeutic approaches have been established since then. However, even nowadays, conventional therapies do not take into consideration individuals' idiosyncrasy and genetic make-up, failing thus to be effective in some cases. Over time, the necessity of a more precise and effective treatment resulted in the development of a scientific field currently known as “personalized medicine.” The numerous technological breakthroughs in this field have acknowledged personalized medicine as the next generation of diagnosis and treatment. Although personalized medicine has attracted a lot of attention the last years, there are still several obstacles hindering its application in clinical practice. These limitations have come to light recently, due to the COVID-19 pandemic. This review describes the “journey” of personalized medicine over time, emphasizing on important milestones achieved through time. Starting from the treatment of malaria, as a first more personalized therapeutic approach, it highlights the need of new diagnostic tools and therapeutic regimens based on individuals' genetic background. Furthermore, it aims at raising global awareness regarding the current limitations and the necessity of a personalized strategy to overpass healthcare problems and hence, the current crisis.

Analysis of the Efficacy of Cinnamon for Patients with Diabetes Mellitus Type II Based on Traditional Persian Medicine Syndrome Differentiation: A Randomized Controlled Trial

Background: Conflicting results have been obtained from the studies on the hypoglycemic effect of cinnamon in patients with diabetes mellitus type II (T2DM). Objectives: This research aimed at assessing the effect of applying the syndrome differentiation model based on traditional Persian medicine (TPM) to identify the patients who benefit more from cinnamon supplementation. Methods: This study was a randomized, triple-blind, placebo-controlled trial based on a parallel design. One hundred and forty eligible patients referring to the Diabetes Clinic of Yazd University of Medical Sciences, who were diagnosed with T2DM, were randomly divided into the cinnamon and placebo groups. Then, 500-mg capsules of cinnamon bark powder or placebo were administered to them twice a day for three months. All the participants completed a standard questionnaire for syndrome diagnosis in traditional Persian medicine (TPM). Fasting plasma glucose (FPG), glycated hemoglobin (HbA1C), fasting insulin (FI), and insulin resistance (IR), calorie intake, and physical activity were measured before and after the interventions. The glycemic outcomes were analyzed according to the TPM syndrome. Results: There was no statistically significant difference between the patients’ demographic and clinical features in the two groups. Glycemic indices were improved in patients receiving cinnamon supplementation compared to placebo group (-13.1 ± 1.7, -1.7 ± 1.9, P < 0.001 for change in FPG and -0.27 ± 0.039 vs. 0.001 ± 0.019, P < 0.001 for change in HbA1C respectively). All the outcomes were more improved in the patients with wet syndrome compared to dry ones, which was compatible with Persian syndrome differentiation (P < 0.05). Conclusions: Diabetic patients with wet syndrome based on TPM benefit more form cinnamon supplementation.

FHIR Genomics: enabling standardization for precision medicine use cases

The development of Fast Healthcare Interoperability Resources (FHIR) Genomics, a feasible and efficient method for exchanging complex clinical genomic data and interpretations, is described. FHIR Genomics is a subset of the emerging Health Level 7 FHIR standard and targets data from increasingly available technologies such as next-generation sequencing. Much care and integration of feedback have been taken to ease implementation, facilitate wide-scale interoperability, and enable modern app development toward a complete precision medicine standard. A new use case, the integration of the Variant Interpretation for Cancer Consortium (VICC) “meta-knowledgebase” into a third-party application, is described.

The National MDS Natural History Study: design of an integrated data and sample biorepository to promote research studies in myelodysplastic syndromes

Myelodysplastic syndromes (MDS), a spectrum of heterogeneous hematopoietic stem cell diseases, vary in clinical severity, response to therapy, and propensity toward progression to acute myeloid leukemia. These are acquired clonal disorders resulting from somatic mutations within the hematopoietic stem or progenitor cell population. Understanding the natural history and the risk of developing leukemia and other adverse outcomes is dependent on access to well-annotated biospecimens linked to robust clinical and molecular data. To facilitate the acquisition and distribution of MDS biospecimens to the wider scientific community and support scientific discovery in this disease, the National MDS Natural History study was initiated by the National Heart, Lung, and Blood Institute (NHLBI) and is being conducted in collaboration with community hospitals and academic medical centers supported by the National Cancer Institute (NCI). The study will recruit up to 2000 MDS patients or overlapping myeloproliferative neoplasms (MDS/MPN) and up to 500 cases of idiopathic cytopenia of undetermined significance (ICUS). The National MDS Natural History Study (NCT02775383) will offer the world's largest disease-focused tissue biobank linked to longitudinal clinical and molecular data in MDS. Here, we report on the study design features and describe the vanguard phase of 200 cases. The study assembles a comprehensive clinical database, quality of life results, laboratory data, histopathology slides and images, genetic information, hematopoietic and germline tissues representing high-quality biospecimens and data from diverse centers across the United States. These resources will be available to the scientific community for investigator-initiated research.

100 Years of evolving gene-disease complexities and scientific debutants

It's been over 100 years since the word `gene' is around and progressively evolving in several scientific directions. Time-to-time technological advancements have heavily revolutionized the field of genomics, especially when it's about, e.g. triple code development, gene number proposition, genetic mapping, data banks, gene-disease maps, catalogs of human genes and genetic disorders, CRISPR/Cas9, big data and next generation sequencing, etc. In this manuscript, we present the progress of genomics from pea plant genetics to the human genome project and highlight the molecular, technical and computational developments. Studying genome and epigenome led to the fundamentals of development and progression of human diseases, which includes chromosomal, monogenic, multifactorial and mitochondrial diseases. World Health Organization has classified, standardized and maintained all human diseases, when many academic and commercial online systems are sharing information about genes and linking to associated diseases. To efficiently fathom the wealth of this biological data, there is a crucial need to generate appropriate gene annotation repositories and resources. Our focus has been how many gene-disease databases are available worldwide and which sources are authentic, timely updated and recommended for research and clinical purposes. In this manuscript, we have discussed and compared 43 such databases and bioinformatics applications, which enable users to connect, explore and, if possible, download gene-disease data.

Big Data and the Study of Social Inequalities in Health: Expectations and Issues

Understanding the construction of the social gradient in health is a major challenge in the field of social epidemiology, a branch of epidemiology that seeks to understand how society and its different forms of organization influence health at a population level. Attempting to answer these questions involves large datasets of varied heterogeneous data suggesting that Big Data approaches could be then particularly relevant to the study of social inequalities in health. Nevertheless, real challenges have to be addressed in order to make the best use of the development of Big Data in health for the benefit of all. The main purpose of this perspective is to discuss some of these challenges, in particular: (i) the perimeter and the particularity of Big Data in health, which must be broader than a vision centerd solely on care, the individual and his or her biological characteristics; (ii) the need for clarification regarding the notion of data, the validity of data and the question of causal inference for various actors involved in health, such data as researchers, health professionals and the civilian population; (iii) the need for regulation and control of data and their uses by public authorities for the common good and the fight against social inequalities in health. To face these issues, it seems essential to integrate different approaches into a close dialog, integrating methodological, societal, and ethical issues. This question cannot escape an interdisciplinary approach, including users or patients.

In vitro label-free screening of chemotherapeutic drugs using Raman microspectroscopy: Towards a new paradigm of spectralomics

This overview groups some of the recent studies highlighting the potential application of Raman microspectroscopy as an analytical technique in preclinical development to predict drug mechanism of action and in clinical application as a companion diagnostic and in personalised therapy due to its capacity to predict cellular resistance and therefore to optimise chemotherapeutic treatment efficacy. Notably, the anthracyclines, doxorubicin and actinomycin D, elicit similar spectroscopic signatures of subcellular interaction characteristic of the mode of action of intercalation. Although cisplatin and vincristine show markedly different signatures, at low exposure doses, their signatures at higher doses show marked similarities to those elicited by the intercalating anthracyclines, confirming that anticancer agents can have different modes of action with different spectroscopic signatures, depending on the dose. The study demonstrates that Raman microspectroscopy can elucidate subcellular transport and accumulation pathways of chemotherapeutic agents, characterise and fingerprint their mode of action, and potentially identify cell-resistant strains. The consistency of the spectroscopic signatures for drugs of similar modes of action, in different cell lines, suggests that this fingerprint can be considered a "spectralome" of the drug-cell interaction suggesting a new paradigm of representing spectroscopic responses.

Doxorubicin kinetics and effects on lung cancer cell lines using in vitro Raman micro-spectroscopy: binding signatures, drug resistance and DNA repair

Raman micro-spectroscopy is a non-invasive analytical tool, whose potential in cellular analysis and monitoring drug mechanisms of action has already been demonstrated, and which can potentially be used in pre-clinical and clinical applications for the prediction of chemotherapeutic efficacy. To further investigate such potential clinical application, it is important to demonstrate its capability to differentiate drug mechanisms of action and cellular resistances. Using the example of Doxorubicin (DOX), in this study, it was used to probe the cellular uptake, signatures of chemical binding and subsequent cellular responses, of the chemotherapeutic drug in two lung cancer cell lines, A549 and Calu-1. Multivariate statistical analysis was used to elucidate the spectroscopic signatures associated with DOX uptake and subcellular interaction. Biomarkers related to DNA damage and repair, and mechanisms leading to apoptosis were also measured and correlated to Raman spectral profiles. Results confirm the potential of Raman spectroscopic profiling to elucidate both drug kinetics and pharmacodynamics and differentiate cellular drug resistance associated with different subcellular accumulation rates and subsequent cellular response to DNA damage, pointing towards a better understanding of drug resistance for personalised targeted treatment.

Comprehensive benchmarking of SNV callers for highly admixed tumor data

Precision medicine attempts to individualize cancer therapy by matching tumor-specific genetic changes with effective targeted therapies. A crucial first step in this process is the reliable identification of cancer-relevant variants, which is considerably complicated by the impurity and heterogeneity of clinical tumor samples. We compared the impact of admixture of non-cancerous cells and low somatic allele frequencies on the sensitivity and precision of 19 state-of-the-art SNV callers. We studied both whole exome and targeted gene panel data and up to 13 distinct parameter configurations for each tool. We found vast differences among callers. Based on our comprehensive analyses we recommend joint tumor-normal calling with MuTect, EBCall or Strelka for whole exome somatic variant calling, and HaplotypeCaller or FreeBayes for whole exome germline calling. For targeted gene panel data on a single tumor sample, LoFreqStar performed best. We further found that tumor impurity and admixture had a negative impact on precision, and in particular, sensitivity in whole exome experiments. At admixture levels of 60% to 90% sometimes seen in pathological biopsies, sensitivity dropped significantly, even when variants were originally present in the tumor at 100% allele frequency. Sensitivity to low-frequency SNVs improved with targeted panel data, but whole exome data allowed more efficient identification of germline variants. Effective somatic variant calling requires high-quality pathological samples with minimal admixture, a consciously selected sequencing strategy, and the appropriate variant calling tool with settings optimized for the chosen type of data.

Physicians' pharmacogenomics information needs and seeking behavior: a study with case vignettes

Background

Genetic testing, especially in pharmacogenomics, can have a major impact on patient care. However, most physicians do not feel that they have sufficient knowledge to apply pharmacogenomics to patient care. Online information resources can help address this gap. We investigated physicians’ pharmacogenomics information needs and information-seeking behavior, in order to guide the design of pharmacogenomics information resources that effectively meet clinical information needs.

Methods

We performed a formative, mixed-method assessment of physicians’ information-seeking process in three pharmacogenomics case vignettes. Interactions of 6 physicians’ with online pharmacogenomics resources were recorded, transcribed, and analyzed for prominent themes. Quantitative data included information-seeking duration, page navigations, and number of searches entered.

Results

We found that participants searched an average of 8 min per case vignette, spent less than 30 s reviewing specific content, and rarely refined search terms. Participants’ information needs included a need for clinically meaningful descriptions of test interpretations, a molecular basis for the clinical effect of drug variation, information on the logistics of carrying out a genetic test (including questions related to cost, availability, test turn-around time, insurance coverage, and accessibility of expert support).Also, participants sought alternative therapies that would not require genetic testing.

Conclusion

This study of pharmacogenomics information-seeking behavior indicates that content to support their information needs is dispersed and hard to find. Our results reveal a set of themes that information resources can use to help physicians find and apply pharmacogenomics information to the care of their patients.

Electronic supplementary material

The online version of this article (doi:10.1186/s12911-017-0510-9) contains supplementary material, which is available to authorized users.

Systems Biology in Kidney Transplantation: The Application of Multi-Omics to a Complex Model

In spite of reduction of rejection rates and improvement in short-term survival post-kidney transplantation, modest progress has occurred in long-term graft attrition over the years. Timely identification of molecular events that precede clinical and histopathological changes might help in early intervention and thereby increase the graft half-life. Evolution of "omics" tools has enabled systemic investigation of the influence of the whole genome, epigenome, transcriptome, proteome and microbiome on transplant function and survival. In this omics era, systemic approaches, in-depth clinical phenotyping and use of strict validation methods are the key for further understanding the complex mechanisms associated with graft function. Systems biology is an interdisciplinary holistic approach that focuses on complex and dynamic interactions within biological systems. The complexity of the human kidney transplant is unlikely to be captured by a reductionist approach. It appears essential to integrate multi-omics data that can elucidate the multidimensional and multilayered regulation of the underlying heterogeneous and complex kidney transplant model. Herein, we discuss studies that focus on genetic biomarkers, emerging technologies and systems biology approaches, which should increase the ability to discover biomarkers, understand mechanisms and stratify patients and responses post-kidney transplantation.

In silico Mapping of Protein Unfolding Mutations for Inherited Disease

The effect of disease-causing missense mutations on protein folding is difficult to evaluate. To understand this relationship, we developed the unfolding mutation screen (UMS) for in silico evaluation of the severity of genetic perturbations at the atomic level of protein structure. The program takes into account the protein-unfolding curve and generates propensities using calculated free energy changes for every possible missense mutation at once. These results are presented in a series of unfolding heat maps and a colored protein 3D structure to show the residues critical to the protein folding and are available for quick reference. UMS was tested with 16 crystal structures to evaluate the unfolding for 1391 mutations from the ProTherm database. Our results showed that the computational accuracy of the unfolding calculations was similar to the accuracy of previously published free energy changes but provided a better scale. Our residue identity control helps to improve protein homology models. The unfolding predictions for proteins involved in age-related macular degeneration, retinitis pigmentosa, and Leber's congenital amaurosis matched well with data from previous studies. These results suggest that UMS could be a useful tool in the analysis of genotype-to-phenotype associations and next-generation sequencing data for inherited diseases.

Text mining for precision medicine: automating disease-mutation relationship extraction from biomedical literature

OBJECTIVE

Identifying disease-mutation relationships is a significant challenge in the advancement of precision medicine. The aim of this work is to design a tool that automates the extraction of disease-related mutations from biomedical text to advance database curation for the support of precision medicine.

MATERIALS AND METHODS

We developed a machine-learning (ML) based method to automatically identify the mutations mentioned in the biomedical literature related to a particular disease. In order to predict a relationship between the mutation and the target disease, several features, such as statistical features, distance features, and sentiment features, were constructed. Our ML model was trained with a pre-labeled dataset consisting of manually curated information about mutation-disease associations. The model was subsequently used to extract disease-related mutations from larger biomedical literature corpora.

RESULTS

The performance of the proposed approach was assessed using a benchmarking dataset. Results show that our proposed approach gains significant improvement over the previous state of the art and obtains F-measures of 0.880 and 0.845 for prostate and breast cancer mutations, respectively.

DISCUSSION

To demonstrate its utility, we applied our approach to all abstracts in PubMed for 3 diseases (including a non-cancer disease). The mutations extracted were then manually validated against human-curated databases. The validation results show that the proposed approach is useful in a real-world setting to extract uncurated disease mutations from the biomedical literature.

CONCLUSIONS

The proposed approach improves the state of the art for mutation-disease extraction from text. It is scalable and generalizable to identify mutations for any disease at a PubMed scale.

Dynamic software design for clinical exome and genome analyses: insights from bioinformaticians, clinical geneticists, and genetic counselors

BACKGROUND

The transition of whole-exome and whole-genome sequencing (WES/WGS) from the research setting to routine clinical practice remains challenging.

OBJECTIVES

With almost no previous research specifically assessing interface designs and functionalities of WES and WGS software tools, the authors set out to ascertain perspectives from healthcare professionals in distinct domains on optimal clinical genomics user interfaces.

METHODS

A series of semi-scripted focus groups, structured around professional challenges encountered in clinical WES and WGS, were conducted with bioinformaticians (n = 8), clinical geneticists (n = 9), genetic counselors (n = 5), and general physicians (n = 4).

RESULTS

Contrary to popular existing system designs, bioinformaticians preferred command line over graphical user interfaces for better software compatibility and customization flexibility. Clinical geneticists and genetic counselors desired an overarching interactive graphical layout to prioritize candidate variants--a "tiered" system where only functionalities relevant to the user domain are made accessible. They favored a system capable of retrieving consistent representations of external genetic information from third-party sources. To streamline collaboration and patient exchanges, the authors identified user requirements toward an automated reporting system capable of summarizing key evidence-based clinical findings among the vast array of technical details.

CONCLUSIONS

Successful adoption of a clinical WES/WGS system is heavily dependent on its ability to address the diverse necessities and predilections among specialists in distinct healthcare domains. Tailored software interfaces suitable for each group is likely more appropriate than the current popular "one size fits all" generic framework. This study provides interfaces for future intervention studies and software engineering opportunities.

Participant views on consent in cancer genetics research: preparing for the precision medicine era

The Precision Medicine Initiative (PMI) has created considerable discussions about research participant issues including re-consent and how and when to incorporate the patient experience into clinical trials. Within the changing landscape of genetic and genomic research, the preferences of participants are lacking yet are needed to inform policy. With the growing use of biobanks intended to support studies, including the national research cohort proposed under the PMI, understanding participant preferences, including re-consent, is a pressing concern. The Participant Issues Project (PIP) addresses this gap, and here we present data on participant attitudes regarding re-consent and broad consent in research studies. PIP study participants came from the Northwest Cancer Genetics Registry and included cancer patients, relatives, and controls. Thirty telephone interviews were conducted and analyzed using content and thematic analysis. Results indicate that in some scenarios, re-consent is needed. Most participants agreed that re-consent was necessary when the study direction changed significantly or a child participant became an adult, but not if the genetic variant changed. Most participants' willingness to participate in research would not be affected if the researcher or institution profited or if a broad consent form were used. Participants emphasized re-consent to provide information and control of the use of their data, now relevant for tailored treatment, while also prioritizing research as important. In the era of precision medicine, it is essential that policy makers consider participant preferences with regard to use of their materials and that participants understand genetic and genomic research and its harms and benefits as well as what broad consent entails, including privacy and re-identification risks.

An index of barriers for the implementation of personalised medicine and pharmacogenomics in Europe

BACKGROUND

Personalised medicine (PM) is an innovative way to produce better patient outcomes by using an individualised or stratified approach to disease and treatment rather than a collective treatment approach for patients. Despite its tangible advantages, the complex process to translate PM into the member states and European healthcare systems has delayed its uptake. The aim of this study is to identify relevant barriers represented by an index to summarise challenging areas for the implementation of PM in Europe.

METHODS

A systematic literature review was conducted, and a gaps-and-needs assessment together with a strengths-weaknesses-opportunities-and-threats analysis were applied to review strategic reports and conduct interviews with key stakeholders. Furthermore, surveys were sent out to representatives of stakeholder groups. The index was constructed based on the priorisation of relevant factors by stakeholders.

RESULTS

A need for stakeholder-agreed standards at all levels of implementation of PM exists, from validating biomarkers to definitions of 'informed consent'. The barriers to implement PM are identified in 7 areas, namely, stakeholder involvement, standardisation, interoperable infrastructure, European-level policy making, funding, data and research, and healthcare systems.

CONCLUSIONS

Challenges in the above-mentioned areas can and must be successfully tackled if we are to create a healthier Europe through PM. In order to create an environment in which PM can thrive for the patients' best outcomes, there is an urgent need for systematic actions to remove as many barriers as possible.

Empowering Personalized Medicine with Big Data and Semantic Web Technology: Promises, Challenges, and Use Cases

In healthcare, big data tools and technologies have the potential to create significant value by improving outcomes while lowering costs for each individual patient. Diagnostic images, genetic test results and biometric information are increasingly generated and stored in electronic health records presenting us with challenges in data that is by nature high volume, variety and velocity, thereby necessitating novel ways to store, manage and process big data. This presents an urgent need to develop new, scalable and expandable big data infrastructure and analytical methods that can enable healthcare providers access knowledge for the individual patient, yielding better decisions and outcomes. In this paper, we briefly discuss the nature of big data and the role of semantic web and data analysis for generating "smart data" which offer actionable information that supports better decision for personalized medicine. In our view, the biggest challenge is to create a system that makes big data robust and smart for healthcare providers and patients that can lead to more effective clinical decision-making, improved health outcomes, and ultimately, managing the healthcare costs. We highlight some of the challenges in using big data and propose the need for a semantic data-driven environment to address them. We illustrate our vision with practical use cases, and discuss a path for empowering personalized medicine using big data and semantic web technology.

Pragmatic and Ethical Challenges of Incorporating the Genome into the Electronic Medical Record

Recent successes in the use of gene sequencing for patient care highlight the potential of genomic medicine. For genomics to become a part of usual care, pertinent elements of a patient's genomic test must be communicated to the most appropriate care providers. Electronic medical records may serve as a useful tool for storing and disseminating genomic data. Yet, the structure of existing EMRs and the nature of genomic data pose a number of pragmatic and ethical challenges in their integration. Through a review of the recent genome-EMR integration literature, we explore concrete examples of these challenges, categorized under four key questions: What data will we store? How will we store it? How will we use it? How will we protect it? We conclude that genome-EMR integration requires a rigorous, multi-faceted and interdisciplinary approach of study. Problems facing the field are numerous, but few are intractable.

Bioinformatics for precision medicine in oncology: principles and application to the SHIVA clinical trial

Precision medicine (PM) requires the delivery of individually adapted medical care based on the genetic characteristics of each patient and his/her tumor. The last decade witnessed the development of high-throughput technologies such as microarrays and next-generation sequencing which paved the way to PM in the field of oncology. While the cost of these technologies decreases, we are facing an exponential increase in the amount of data produced. Our ability to use this information in daily practice relies strongly on the availability of an efficient bioinformatics system that assists in the translation of knowledge from the bench towards molecular targeting and diagnosis. Clinical trials and routine diagnoses constitute different approaches, both requiring a strong bioinformatics environment capable of (i) warranting the integration and the traceability of data, (ii) ensuring the correct processing and analyses of genomic data, and (iii) applying well-defined and reproducible procedures for workflow management and decision-making. To address the issues, a seamless information system was developed at Institut Curie which facilitates the data integration and tracks in real-time the processing of individual samples. Moreover, computational pipelines were developed to identify reliably genomic alterations and mutations from the molecular profiles of each patient. After a rigorous quality control, a meaningful report is delivered to the clinicians and biologists for the therapeutic decision. The complete bioinformatics environment and the key points of its implementation are presented in the context of the SHIVA clinical trial, a multicentric randomized phase II trial comparing targeted therapy based on tumor molecular profiling versus conventional therapy in patients with refractory cancer. The numerous challenges faced in practice during the setting up and the conduct of this trial are discussed as an illustration of PM application.