Personalized medicine: new genomics, old lessons

Proteogenomic Approaches for Diseasome Studies

During the last three decades, technological advancements in high-throughput next-generation sequencing have resulted in an increased understanding of proteomic and genomic data, aptly termed proteogenomics. Efforts in developing such approaches have not only been limited but also focused on protein identification and subcellular localization. These approaches, however, have also been explored for their broad understanding of how genomics/transcriptomics data have yielded measures, for example, gene expression regulation/signal cascading and diseasome studies. In this review, we discuss methods and tools developed through sequence-centric integrative modeling of proteogenomic approaches.

Precision medicine in pharmacy: assessing pharmacogenomics competence among pharmacists and pharmacy students

Background

Pharmacogenomics, a key component of precision medicine, aims to improve healthcare outcomes. As pharmacists play a pivotal role in this evolving field, an assessment of their preparedness to apply pharmacogenomics is imperative.

Methods

In this cross-sectional study, a validated questionnaire (Content Validity Ratio > 0.741, p < 0.05) that demonstrated reliability (Cronbach's alpha for all scales > 0.7) gathered data on demographics, knowledge, attitudes, barriers, and confidence in pharmacogenomics among pharmacists and pharmacy students in Jordan. Statistical analysis assessed associations and their strength within the collected data and variables.

Results

This study included 514 pharmacists and pharmacy students. Knowledge scores were moderate and correlated with academic level and pharmacy school attended. Most participants were open to providing pharmacogenomics testing and interpretation through pharmacy services, but the majority demonstrated concerns about potential misinterpretation of test results and the resulting patients' anxiety. Students cited limited accessibility, while pharmacists identified the lack of standardised guidelines as the main roadblock.

Conclusion

This study highlights the need for education to prepare pharmacists for their role in pharmacogenomics. Despite positive attitudes from pharmacists, addressing knowledge gaps, the low confidence in recommending pharmacogenomics tests, and concerns about implementation are essential.

Constructing and Validating a Nomogram Model for Short-Term Prognosis of Patients with AChR-Ab+ GMG

OBJECTIVE

This study aimed to establish and validate a nomogram prognostic model for predicting short-term efficacy of acetylcholine receptor antibody-positive (AChR-Ab+) generalized myasthenia gravis (GMG).

METHODS

A retrospective observational study was conducted at the First Hospital of Shanxi Medical University, enrolling patients diagnosed with AChR-Ab+ GMG from May 2020 to September 2022. The primary outcome was the change in the Myasthenia Gravis Foundation of America (MGFA) post-intervention status after 6 months of standard treatment. Predictive factors were identified through univariate and multivariate logistic regression analyses, with significant factors incorporated into the nomogram. The bootstrap test was used for internal validation of the nomogram model. Model performance was assessed using calibration curves, receiver-operating characteristic curve analysis, and decision curve analysis (DCA).

RESULTS

A total of 90 patients were enrolled, of whom 30 achieved unchanged or worse status after 6 months of standard therapy. Univariate logistic regression analysis showed that quantitative myasthenia gravis score, gender, body mass index, course of disease, hemoglobin levels, and white blood cell counts were six potential predictors. These factors were used for multivariate logistic regression analysis, and a nomogram was constructed. The calibration curve showed that the predicted value was in good agreement with the actual value (p = 0.707), and the area under the curve value (0.792, 95% CI 0.686-0.899) indicated good discrimination ability. DCA suggests that this model has potential clinical application value.

CONCLUSION

The constructed nomogram, based on key patient indicators, shows promise as a clinically useful tool for predicting the short-term efficacy of treatment of AChR-Ab+ GMG. Validation in larger, multicenter cohorts is needed to further substantiate its applicability.

Science Education for the Youth (SEFTY): A Neuroscience Outreach Program for High School Students in Southern Nevada during the COVID-19 Pandemic

Laboratory outreach programs for K-12 students in the United States from 2020 to 2022 were suspended or delayed due to COVID-19 restrictions. While Southern Nevada also observed similar closures for onsite programs, we and others hypothesized that in-person laboratory activities could be prioritized after increasing vaccine doses were available to the public and masking was encouraged. Here, we describe how the Laboratory of Neurogenetics and Precision Medicine at the University of Nevada Las Vegas (UNLV) collaborated with administrators from a local school district to conduct training activities for high school students during the COVID-19 pandemic. The Science Education for the Youth (SEFTY) program's curriculum was constructed to incorporate experiential learning, fostering collaboration and peer-to-peer knowledge exchange. Leveraging neuroscience tools from our UNLV laboratory, we engaged with 117 high school applicants from 2021 to 2022. Our recruitment efforts yielded a diverse cohort, with >41% Pacific Islander and Asian students, >9% African American students, and >12% multiracial students. We assessed the impact of the SEFTY program through pre- and postassessment student evaluations, revealing a significant improvement of 20.3% in science proficiency (p < 0.001) after participating in the program. Collectively, our laboratory curriculum offers valuable insights into the capacity of an outreach program to actively foster diversity and cultivate opportunities for academic excellence, even in the challenging context of a global pandemic.

Science Education for the Youth (SEFTY): A Neuroscience Outreach Program for High School Students in Southern Nevada During the COVID-19 Pandemic

Laboratory outreach programs for K-12 students in the United States from 2020-2022 were suspended or delayed due to COVID-19 restrictions. While Southern Nevada also observed similar closures for onsite programs, we and others hypothesized that in-person laboratory activities could be prioritized after increasing vaccine doses were available to the public and masking was encouraged. Here, we describe how the Laboratory of Neurogenetics and Precision Medicine at the University of Nevada Las Vegas (UNLV) collaborated with administrators from a local school district to conduct training activities for high school students during the COVID-19 pandemic. The Science Education for the Youth (SEFTY) program's curriculum was constructed to incorporate experiential learning, fostering collaboration and peer-to-peer knowledge exchange. Leveraging neuroscience tools from our UNLV laboratory, we engaged with 117 high school applicants from 2021-2022. Our recruitment efforts yielded a diverse cohort, with >41% Pacific Islander and Asian students, >9% African American students, and >12% multiracial students. We assessed the impact of the SEFTY program through pre- and post-assessment student evaluations, revealing a significant improvement of 20.3% in science proficiency ( p <0.001) after participating in the program. Collectively, our laboratory curriculum offers valuable insights into the capacity of an outreach program to actively foster diversity and cultivate opportunities for academic excellence, even in the challenging context of a global pandemic.

Significance Statement

The Science Education for the Youth (SEFTY) program at UNLV successfully engaged 117 diverse high school students in neuroscience-based experiential learning, demonstrating the viability of in-person education during a pandemic. Significant improvements in science proficiency (20.3% increase) underscore the program's effectiveness in fostering academic excellence and diversity. This initiative potentially serves as a model for maintaining high-quality, inclusive science education in challenging times.

Personalized redox biology: Designs and concepts

Personalized interventions are regarded as a next-generation approach in almost all fields of biomedicine, such as clinical medicine, exercise, nutrition and pharmacology. At the same time, an increasing body of evidence indicates that redox processes regulate, at least in part, multiple aspects of human physiology and pathology. As a result, the idea of applying personalized redox treatments to improve their efficacy has gained popularity among researchers in recent years. The aim of the present primer-style review was to highlight some crucial yet underappreciated methodological, statistical, and interpretative concepts within the redox biology literature, while also providing a physiology-oriented perspective on personalized redox biology. The topics addressed are: (i) the critical issue of investigating the potential existence of inter-individual variability; (ii) the importance of distinguishing a genuine and consistent response of a subject from a chance finding; (iii) the challenge of accurately quantifying the effect of a redox treatment when dealing with 'extreme' groups due to mathematical coupling and regression to the mean; and (iv) research designs and analyses that have been implemented in other fields, and can be reframed and exploited in a redox biology context.

Artificial Intelligence for Personalized Genetics and New Drug Development: Benefits and Cautions

As the global health care system grapples with steadily rising costs, increasing numbers of admissions, and the chronic defection of doctors and nurses from the profession, appropriate measures need to be put in place to reverse this course before it is too late [...].

Three-Dimensional Spheroids for Cancer Research

In vitro cell culture is one of the most widely used tools used today for increasing our understanding of various things such as protein production, mechanisms of drug action, tissue engineering, and overall cellular biology. For the past decades, however, cancer researchers have relied heavily on conventional two-dimensional (2D) monolayer culture techniques to test a variety of aspects of cancer research ranging from the cytotoxic effects of antitumor drugs to the toxicity of diagnostic dyes and contact tracers. However, many promising cancer therapies have either weak or no efficacy in real-life conditions, therefore delaying or stopping altogether their translating to the clinic. This is, in part, due to the reductionist 2D cultures used to test these materials, which lack appropriate cell-cell contacts, have altered signaling, do not represent the natural tumor microenvironment, and have different drug responses, due to their reduced malignant phenotype when compared to real in vivo tumors. With the most recent advances, cancer research has moved into 3D biological investigation. Three-dimensional (3D) cultures of cancer cells not only recapitulate the in vivo environment better than their 2D counterparts, but they have, in recent years, emerged as a relatively low-cost and scientifically accurate methodology for studying cancer. In this chapter, we highlight the importance of 3D culture, specifically 3D spheroid culture, reviewing some key methodologies for forming 3D spheroids, discussing the experimental tools that can be used in conjunction with 3D spheroids and finally their applications in cancer research.

Introducing AI to the molecular tumor board: one direction toward the establishment of precision medicine using large-scale cancer clinical and biological information

Since U.S. President Barack Obama announced the Precision Medicine Initiative in his New Year's State of the Union address in 2015, the establishment of a precision medicine system has been emphasized worldwide, particularly in the field of oncology. With the advent of next-generation sequencers specifically, genome analysis technology has made remarkable progress, and there are active efforts to apply genome information to diagnosis and treatment. Generally, in the process of feeding back the results of next-generation sequencing analysis to patients, a molecular tumor board (MTB), consisting of experts in clinical oncology, genetic medicine, etc., is established to discuss the results. On the other hand, an MTB currently involves a large amount of work, with humans searching through vast databases and literature, selecting the best drug candidates, and manually confirming the status of available clinical trials. In addition, as personalized medicine advances, the burden on MTB members is expected to increase in the future. Under these circumstances, introducing cutting-edge artificial intelligence (AI) technology and information and communication technology to MTBs while reducing the burden on MTB members and building a platform that enables more accurate and personalized medical care would be of great benefit to patients. In this review, we introduced the latest status of elemental technologies that have potential for AI utilization in MTB, and discussed issues that may arise in the future as we progress with AI implementation.

Knowledge, attitudes and practices of Egyptian healthcare professionals toward therapeutic drug monitoring service as a principal component of personalized medicine

Aim: To assess pharmacists' and physicians' knowledge, attitudes and practices toward therapeutic drug monitoring (TDM) service at the Children's Cancer Hospital Egypt 57357. Materials & methods: This was a single-site cross-sectional study where all practicing pharmacists and physicians were eligible to participate. Results: A statistically significant difference in the knowledge scores between pharmacists and physicians (p = 0.022) was found. In general, attitudes toward TDM among pharmacists and physicians were positive. Regarding practices, pharmacists were more likely than physicians to agree or strongly agree that they have studied some scientific references on TDM (p = 0.034), but more physicians recommend the TDM service (p = 0.046). Conclusion: A multidisciplinary educational program in Egypt for TDM for both medicine and pharmacy staff will improve interprofessional collaboration in the clinical setting, leading to better personalized medication management.

The importance of personalized medicine in urological cancers

A better understanding of key regulatory pathways involved in cancers has led to the development of molecularly targeted therapies. Molecular profiling based on genomics, proteomics, and metabolomics in tumors provides clinicians with the necessary information to maintain a personalized therapeutic regimen according to the patient's needs. for example, androgen deprivation therapy (ADT) for advanced prostate cancer is one of the earliest forms of targeted therapy and has remained a choice of treatment by physicians. Unfortunately, most patients will eventually become non-responsive to ADT and succumb to the disease. Since the emergence of ADT, the understanding of androgen receptor (AR) signaling and mechanisms driving the resistance to ADT has been significantly improved. Inactivation of the PTEN gene is a common occurrence in prostate cancers and is associated with metastatic potential, androgen independence, and poor prognosis. Several studies over personalized medicine for muscle-invasive and metastatic bladder cancer discussed potential molecular biomarkers which are currently under investigation and based on the excision repair cross-complementing group 1 (ERCC1) gene and its role in tumor development and therapeutic resistance to cytotoxic DNA-damaging chemotherapy and ionizing radiation. In this review, we consider personalized medicine for four urological cancers.

Adenosine deaminase gene variant in diabetes and obesity

Purpose

Personal medicine is a new notion for individualizing treatment in the future. Studying pathogenic markers including genetic variants would be beneficial in better diagnosis and management of complex diseases such as diabetes and obesity. Adenosine deaminase (ADA) is a purine metabolic enzyme and modulates insulin activity in various tissues through several different mechanisms. Increased ADA activity is associated with decreased glucose uptake. A significant increase in serum deaminase activity has been reported in patients with T2DM and obesity. ADA gene polymorphisms seem to affect ADA enzymatic activity and a polymorphism at the position 4223 in the first intron of ADA gene (ADA 4223 A/C) has been previously associated with obesity. The aim of this study was to explore ADA gene 4223 A/C polymorphism and its association with obesity in patients with Type 2 diabetes.

Methods

Obese patients (N = 133: 64 diabetic +69 non-diabetic) with BMI ≥ 30 and subjects with BMI < 30 (N = 152: 83 diabetics +69 non-diabetic) were recruited into a case-control association study. Blood samples were collected and after DNA extraction, the allele and genotype frequency for ADA gene polymorphism was determined using PCR-RFLP technique.

Results

We observed a significant increase for the frequency of AA+CA genotype in non-obese patients with diabetes compared to obese patients with diabetes (P = 0.04, OR = 2.1, 95%CI; 0.93-4.9).

Conclusion

The higher frequency of AA+CA genotype in none obese diabetes individuals and lower frequency of this genotype in obese diabetes subjects indicates an important role for ADA gene polymorphism in diabetes subjects without obesity.

COVID-19 Pharmacotherapy: Drug Development, Repurposing of Drugs, and the Role of Pharmacogenomics

The SARS-CoV-2 virus has been the subject of intense pharmacological research. Various pharmacotherapeutic approaches including antiviral and immunotherapy are being explored. A pandemic, however, cannot depend on the development of new drugs; the time required for conventional drug discovery and development is far too lengthy. As such, repurposing drugs is being used as a viable approach for identifying pharmacological agents for COVID-19 infections. Evaluation of repurposed drug candidates with pharmacogenomic analysis is being used to identify near-term pharmacological remedies for COVID-19.

Companion Diagnostics: State of the Art and New Regulations

Companion diagnostics (CDx) hail promise of improving the drug development process and precision medicine. However, there are various challenges involved in the clinical development and regulation of CDx, which are considered high-risk in vitro diagnostic medical devices given the role they play in therapeutic decision-making and the complications they may introduce with respect to their sensitivity and specificity. The European Union (E.U.) is currently in the process of bringing into effect in vitro Diagnostic Medical Devices Regulation (IVDR). The new Regulation is introducing a wide range of stringent requirements for scientific validity, analytical and clinical performance, as well as on post-market surveillance activities throughout the lifetime of in vitro diagnostics (IVD). Compliance with General Safety and Performance Requirements (GSPRs) adopts a risk-based approach, which is also the case for the new classification system. This changing regulatory framework has an impact on all stakeholders involved in the IVD Industry, including Authorized Representatives, Distributors, Importers, Notified Bodies, and Reference Laboratories and is expected to have a significant effect on the development of new CDx.

The Ultrastructural Analysis of Human Colorectal Cancer Stem Cell-Derived Spheroids and Their Mouse Xenograft Shows That the Same Cells Types Have Different Ratios

Spheroids from primary colorectal cancer cells and their mice xenografts have emerged as useful preclinical models for cancer research as they replicate tumor features more faithfully as compared to cell lines. While 3D models provide a reliable system for drug discovery and testing, their structural complexity represents a challenge and their structure-function relationships are only partly understood. Here, we present a comparative ultrastructural and flow citometric analysis of patient colorectal cancer-derived spheroids and their mice xenografts. Ultrastructural observations highlighted that multicellular spheroids and their xenografts contain the same cancer cell types but with different ratios, specifically multicellular spheroids were enriched in cells with a stem-like phenotype, while xenografts had an increased amount of lipid droplets-containing cells. The flow cytometric analysis for stem cell marker and activity showed enrichment of stem-like cells presence and activity in spheroids while xenografts had the inverse response. Our results evidence the effects on cancer cells of different in vitro and in vivo microenvironments. Those differences have to be paid into account in designing innovative experimental models for personalized drug testing.

Single Nucleotide Polymorphism in Prolactin Gene Is Associated With Clinical Aggressiveness and Outcome of Canine Mammary Malignant Tumors

Prolactin (PRL) is a key hormone involved in canine mammary development and tumorigenesis. In this study, the influence of a single nucleotide polymorphism (SNP) in the PRL gene (rs23932236) on the clinicopathological parameters and survival of dogs with canine mammary tumors (CMTs) was investigated. A total of 206 female dogs with spontaneous mammary tumors were enrolled in this study and circulating blood cells were genotyped. This specific SNP was associated with larger size (>3 cm diameter) for malignant tumors (P = .036), tumors with infiltrative/invasive growth pattern (P = .010), vascular invasion (P = .006), and lymph node metastasis (P = .004). Carriers of the variant allele had a shorter overall survival compared to the wild-type population with an overall survival of 18.7 months and 22.7 months, respectively (P = .004). These findings suggest that SNP rs23932236 of canine PRL gene may be used as an indicator for the development of clinically aggressive forms of CMTs.

Knowledge, attitude, future expectations and perceived barriers of medical students and physicians regarding pharmacogenomics in Jordan

BACKGROUND

Pharmacogenomics (PG) is a modern tool of personalising treatment protocols to improve the efficacy and safety of drug prescriptions. These benefits are offset by a slow uptake in clinical application due to a host of physician factors, patient factors and/or health system factors. Our study, thus, aimed to determine the knowledge, attitude, future expectations and perceived barriers of medical students and physicians in Jordan regarding PG testing.

METHODS

A descriptive, cross-sectional study was conducted between February and August 2019. Physicians and senior medical students from academic and non-academic institutions in North Jordan (n = 424) were surveyed. A structured, self-administered questionnaire was designed and piloted for the purpose of the study. A scoring system for each dimension assessed was calculated and presented using means. Mean scores were compared by sociodemographic and professional variables.

RESULTS

The response rate was 70.7%. The mean total PG knowledge score (±SD) was 5.42 (±1.51) out of 10, with a significantly higher mean among respondents aged <30 years (5.54 ± 1.43) compared with those ≥30 years old (5.21 ± 1.62; P = .03). The mean total PG attitude score was 21.18 (±2.58) out of 24, with significant differences by seniority levels evident (P = .03). The future expectations of PG among our sample were high, with a mean score of 10.44 (±1.64) out of 12. The top three perceived barriers in applying PG were the high cost, lack of clinical guidelines, and limited knowledge and awareness.

CONCLUSION

Physicians and medical students in Jordan have low overall knowledge, albeit strongly positive attitude and future expectations towards PG, despite the perceived high cost and lack of clinical guidelines. Thus, we strongly recommend adopting a comprehensive educational strategy that aims to integrate PG concepts into medical curricula, and promote the culture of continuous medical education about PG among practitioners.

Genomics, Proteomics and Metabolomics Approaches for Predicting Diabetic Nephropathy in Type 2 Diabetes Mellitus Patients

BACKGROUND

There is a continuous rise in the prevalence of type 2 diabetes mellitus (T2DM) worldwide and most patients are unaware of the presence of this chronic disease at the early stages. T2DM is associated with complications related to long-term damage and failure of multiple organ systems caused by vascular changes associated with glycated end products, oxidative stress, mild inflammation, and neovascularization. Among the most frequent complications of T2DM observed in about 20-40% of T2DM patients is diabetes nephropathy (DN).

METHODS

A literature search was made in view of highlighting the novel applications of genomics, proteomics and metabolomics, as the new prospective strategy for predicting DN in T2DM patients.

RESULTS

The complexity of DN requires a comprehensive and unbiased approach to investigate the main causes of disease and identify the most important mechanisms underlying its development. With the help of evolving throughput technology, rapidly evolving information can now be applied to clinical practice.

DISCUSSION

DN is also the leading cause of end-stage renal disease and comorbidity independent of T2DM. In terms of the comorbidity level, DN has many phenotypes; therefore, timely diagnosis is required to prevent these complications. Currently, urine albumin-to-creatinine ratio and estimated glomerular filtration rate (eGFR) are gold standards for assessing glomerular damage and changes in renal function. However, GFR estimation based on creatinine is limited to hyperfiltration status; therefore, this makes albuminuria and eGFR indicators less reliable for early-stage diagnosis of DN.

CONCLUSION

The combination of genomics, proteomics, and metabolomics assays as suitable biological systems can provide new and deeper insights into the pathogenesis of diabetes, as well as discover prospects for developing suitable and targeted interventions.

The impact of twenty-first century personalized medicine versus twenty-first century medicine's impact on personalization

BACKGROUND

Over the past decade, the exponential growth of the literature devoted to personalized medicine has been paralleled by an ever louder chorus of epistemic and ethical criticisms. Their differences notwithstanding, both advocates and critics share an outdated philosophical understanding of the concept of personhood and hence tend to assume too simplistic an understanding of personalization in health care.

METHODS

In this article, we question this philosophical understanding of personhood and personalization, as these concepts shape the field of personalized medicine. We establish a dialogue with phenomenology and hermeneutics (especially with E. Husserl, M. Merleau-Ponty and P. Ricoeur) in order to achieve a more sophisticated understanding of the meaning of these concepts We particularly focus on the relationship between personal subjectivity and objective data.

RESULTS

We first explore the gap between the ideal of personalized healthcare and the reality of today's personalized medicine. We show that the nearly exclusive focus of personalized medicine on the objective part of personhood leads to a flawed ethical debate that needs to be reframed. Second, we seek to contribute to this reframing by drawing on the phenomenological-hermeneutical movement in philosophy. Third, we show that these admittedly theoretical analyses open up new conceptual possibilities to tackle the very practical ethical challenges that personalized medicine faces.

CONCLUSION

Finally, we propose a reversal: if personalization is a continuous process by which the person reappropriates all manner of objective data, giving them meaning and thereby shaping his or her own way of being human, then personalized medicine, rather than being personalized itself, can facilitate personalization of those it serves through the data it provides.

Exploring Host Genetic Polymorphisms Involved in SARS-CoV Infection Outcomes: Implications for Personalized Medicine in COVID-19

Objective

To systematically explore genetic polymorphisms associated with the clinical outcomes in SARS-CoV infection in humans.

Methods

This comprehensive literature search comprised available English papers published in PubMed/Medline and SCOPUS databases following the PRISMA-P guidelines and PICO/AXIS criteria.

Results

Twenty-nine polymorphisms located in 21 genes were identified as associated with SARS-CoV susceptibility/resistance, disease severity, and clinical outcomes predominantly in Asian populations. Thus, genes implicated in key pathophysiological processes such as the mechanisms related to the entry of the virus into the cell and the antiviral immune/inflammatory responses were identified.

Conclusions

Although caution must be taken, the results of this systematic review suggest that multiple genetic polymorphisms are associated with SARS-CoV infection features by affecting virus pathogenesis and host immune response, which could have important applications for the study and understanding of genetics in SARS-CoV-2/COVID-19 and for personalized translational clinical practice depending on the population studied and associated environments.

Promising Applications of Tumor Spheroids and Organoids for Personalized Medicine

One of the promising directions in personalized medicine is the use of three-dimensional (3D) tumor models such as spheroids and organoids. Spheroids and organoids are three-dimensional cultures of tumor cells that can be obtained from patient tissue and, using high-throughput personalized medicine methods, provide a suitable therapy for that patient. These 3D models can be obtained from most types of tumors, which provides opportunities for the creation of biobanks with appropriate patient materials that can be used to screen drugs and facilitate the development of therapeutic agents. It should be noted that the use of spheroids and organoids would expand the understanding of tumor biology and its microenvironment, help develop new in vitro platforms for drug testing and create new therapeutic strategies. In this review, we discuss 3D tumor spheroid and organoid models, their advantages and disadvantages, and evaluate their promising use in personalized medicine.

Current Challenges and Implications of Proteogenomic Approaches in Prostate Cancer

In the recent past, next-generation sequencing (NGS) approaches have heralded the omics era. With NGS data burgeoning, there arose a need to disseminate the omic data better. Proteogenomics has been vividly used for characterising the functions of candidate genes and is applied in ascertaining various diseased phenotypes, including cancers. However, not much is known about the role and application of proteogenomics, especially Prostate Cancer (PCa). In this review, we outline the need for proteogenomic approaches, their applications and their role in PCa.

Genetic predisposition in type 2 diabetes: A promising approach toward a personalized management of diabetes

Diabetes mellitus, also known simply as diabetes, has been described as a chronic and complex endocrine metabolic disorder that is a leading cause of death across the globe. It is considered a key public health problem worldwide and one of four important non-communicable diseases prioritized for intervention through world health campaigns by various international foundations. Among its four categories, Type 2 diabetes (T2D) is the commonest form of diabetes accounting for over 90% of worldwide cases. Unlike monogenic inherited disorders that are passed on in a simple pattern, T2D is a multifactorial disease with a complex etiology, where a mixture of genetic and environmental factors are strong candidates for the development of the clinical condition and pathology. The genetic factors are believed to be key predisposing determinants in individual susceptibility to T2D. Therefore, identifying the predisposing genetic variants could be a crucial step in T2D management as it may ameliorate the clinical condition and preclude complications. Through an understanding the unique genetic and environmental factors that influence the development of this chronic disease individuals can benefit from personalized approaches to treatment. We searched the literature published in three electronic databases: PubMed, Scopus and ISI Web of Science for the current status of T2D and its associated genetic risk variants and discus promising approaches toward a personalized management of this chronic, non-communicable disorder.

Multiplex bioimaging of single-cell spatial profiles for precision cancer diagnostics and therapeutics

Cancers exhibit functional and structural diversity in distinct patients. In this mass, normal and malignant cells create tumor microenvironment that is heterogeneous among patients. A residue from primary tumors leaks into the bloodstream as cell clusters and single cells, providing clues about disease progression and therapeutic response. The complexity of these hierarchical microenvironments needs to be elucidated. Although tumors comprise ample cell types, the standard clinical technique is still the histology that is limited to a single marker. Multiplexed imaging technologies open new directions in pathology. Spatially resolved proteomic, genomic, and metabolic profiles of human cancers are now possible at the single-cell level. This perspective discusses spatial bioimaging methods to decipher the cascade of microenvironments in solid and liquid biopsies. A unique synthesis of top-down and bottom-up analysis methods is presented. Spatial multi-omics profiles can be tailored to precision oncology through artificial intelligence. Data-driven patient profiling enables personalized medicine and beyond.

Long-term overall survival and prognostic score predicting survival: the IMPACT study in precision medicine

Background

In 2007, we initiated IMPACT, a precision medicine program for patients referred for participation in early-phase clinical trials. We assessed the correlation of factors, including genomically matched therapy, with overall survival (OS).

Patients and methods

We performed molecular profiling (Clinical Laboratory Improvement Amendments) (genes ≤ 182) for patients with lethal/refractory advanced cancers referred to the Phase 1 Clinical Trials Program. Matched therapy, if available, was selected on the basis of genomics. Clinical trials varied over time and included investigational drugs against various targets (single agents or combinations). Patients were followed up for up to 10 years.

Results

Of 3487 patients who underwent tumor molecular profiling, 1307 (37.5%) had ≥ 1 alteration and received therapy (matched, 711; unmatched, 596; median age, 57 years; 39% men). Most common tumors were gastrointestinal, gynecologic, breast, melanoma, and lung. Objective response rates were: matched 16.4%, unmatched 5.4% (p < .0001); objective response plus stable disease ≥ 6 months rates were: matched 35.3% and unmatched 20.3%, (p < .001). Respective median progression-free survival: 4.0 and 2.8 months (p < .0001); OS, 9.3 and 7.3 months; 3-year, 15% versus 7%; 10-year, 6% vs. 1% (p < .0001). Independent factors associated with shorter OS (multivariate analysis) were performance status > 1 (p < .001), liver metastases (p < .001), lactate dehydrogenase levels > upper limit of normal (p < .001), PI3K/AKT/mTOR pathway alterations (p < .001), and non-matched therapy (p < .001). The five independent factors predicting shorter OS were used to design a prognostic score.

Conclusions

Matched targeted therapy was an independent factor predicting longer OS. A score to predict an individual patient’s risk of death is proposed.

Trial registration

ClinicalTrials.gov, NCT00851032, date of registration February 25, 2009.

Understanding diseases as increased heterogeneity: a complex network computational framework

Owing to the complexity of the human body, most diseases present a high interpersonal variability in the way they manifest, i.e. in their phenotype, which has important clinical repercussions-for instance, the difficulty in defining objective diagnostic rules. Here we explore the hypothesis that signs and symptoms used to define a disease should be understood in terms of the dispersion (as opposed to the average) of physical observables. To that end, we propose a computational framework, based on complex networks theory, to map groups of subjects to a network structure, based on their pairwise phenotypical similarity. We demonstrate that the resulting structure can be used to improve the performance of classification algorithms, especially in the case of a limited number of instances, with both synthetic and real datasets. Beyond providing an alternative conceptual understanding of diseases, the proposed framework could be of special relevance in the growing field of personalized, or N-to-1, medicine.

A risk assessment model of acute liver allograft rejection by genetic polymorphism of CD276

Background

Liver transplantation is an effective therapy for end‐stage liver diseases and acute liver failure. After the operation, however, recipients may suffer grafts loss induced by alloimmune reaction, which is termed as acute allograft rejection. The interaction between costimulatory molecules, CD276, and its ligand, TREML2, promotes T cell‐mediated immune response, as well as acute or chronic allograft rejection. Our research aimed at correlating genetic polymorphisms of CD276/TREML2 with acute rejection, and evaluating its prognostic value of acute rejection after liver transplantation.

Methods

The study enrolled a total of 388 recipients. Among them, acute allograft rejection was observed in 54 cases. We performed single nucleotide polymorphism genotyping of CD276, including rs11072431, rs11574495, rs12593558, rs12594627, rs2127015, rs3816661 and rs7176654, and TREML2, including rs4714431, rs6915083, rs7754593, and rs9394767 from preoperative peripheral blood genome DNA.

Results

We found rs2127015 of CD276, rs6915083 and rs7754593 of TREML2, and HBV infection as well were associated with acute rejection. And, rs2127015 influences CD276 expression. Moreover, we established a risk assessment model, composited by statistically proved risk factors.

Conclusion

By integrating both clinical and genetic variables, liver transplant recipients can be categorized into different risk groups, and might benefit from individualized therapies.

Microevolution of medically important mosquitoes - A review

This review intends to discuss central issues regarding the microevolution of mosquito (Culicidae) vectors of several pathogens and how this process impacts vector biology, disease transmission, and vector control attempts. On the microevolutionary context, it comparatively discusses the current knowledge on the population genetics of representatives of the genera Aedes, Anopheles and Culex, and comments on insecticide resistance of culicids. It also discusses other biological aspects of culicids that are not usually addressed in microevolutionary studies, such as vectorial competence, endosymbiosis, and wing morphology. One conclusion is that mosquitoes are highly genetically variable, adaptable, fast evolving, and have versatile vectorial competence. Unveiling microevolutionary patterns is fundamental for the design and maintenance of all control programs. Sampling methods for assessing microevolution must be standardized and must follow meaningful guidelines, such as those of "landscape genetics". A good understanding of microevolution requires more than a collection of case studies on population genetics and resistance. Future research could deal not only with the microevolution sensu stricto, but also with evolutionarily meaningful issues, such as inheritable characters, epigenetics, physiological cost-free plasticity, vector immunity, symbiosis, pathogen-mosquito co-evolution and environmental variables. A genotyping panel for seeking adaptive phenotypes as part of the standardization of population genetics methods is proposed. The investigative paradigm should not only be retrospective but also prospective, despite the unpredictability of evolution. If we integrate all suggestions to tackle mosquito evolution, a global revolution to counter vector-borne diseases can be provoked.

Progress of Individualized Chemotherapy for Gastric Carcinoma Under the Guidance of Genetic Testing

BACKGROUND

Gastric cancer is a major malignancy that has high incidence rates worldwide. Approximately 30% of patients with gastric cancer have progressed into advanced stages at the time of diagnosis. Chemotherapy is the standard-of-care for most advanced gastric cancer and elicits variable responses among patients. Personalized chemotherapy based on genetic information of individual patients with gastric cancer has gained increasing attention among oncologists for guiding chemotherapeutic regimens.

METHODS

This review summarizes recent progress of individualized chemotherapy in gastric cancer guided by pharmacogenomics. Variable medical research search engines, such as PubMed, Google Scholar, SpringerLink and ScienceDirect, were used to retrieve related literature. Only peerreviewed journal articles were selected for further analyses.

RESULTS AND CONCLUSION

The efficiency of chemotherapy in patients with gastric cancer is not only determined by chemotherapeutic drugs but is also directly and indirectly influenced by functionally correlative genes. Individual gene alteration or polymorphism remarkably affects patients' responses to particular chemotherapy. Most studies have focused on the influence of single-gene alteration on a selected drug, and only a few works explored the interaction between therapeutics and a panel of genes. Individualized chemotherapy regimens guided by a genetic survey of a multiple-gene panel are expected to remarkably improve the treatment efficacy in patients with advanced gastric cancer and may become the new standard for personalizing chemotherapy for gastric cancer in the near future.

Genetic Predictors of Response to Acupuncture for Aromatase Inhibitor-Associated Arthralgia Among Breast Cancer Survivors

Objective

To evaluate the associations between polymorphisms in two genes, catechol-O-methyltransferase and T-cell leukemia/lymphoma 1 A, and acupuncture-mediated pain reduction among breast cancer survivors with aromatase inhibitor-associated arthralgia.

Design, Setting, and Subjects

Biospecimens were obtained from 38 patients enrolled in a clinical trial of acupuncture for aromatase inhibitor-associated arthralgia in postmenopausal hormone receptor-positive breast cancer survivors.

Methods

We used polymerase chain reaction to genotype the rs4680 (Val158Met) and rs4633 (His62His) variants in the catechol-O-methyltransferase gene and rs2369049 (A > G) and rs7158782 (A > G) variants in the T-cell leukemia/lymphoma 1 A gene. Response to acupuncture was defined by 30% reduction in end-of-treatment average pain, measured by the Brief Pain Inventory. We used Fisher exact tests to evaluate associations between genotype and treatment response.

Results

Among participants, all six (15.8%) subjects who expressed AA in locus rs4680 responded to acupuncture. In a combined analysis, the 18 (47.4%) subjects with the responder genotype at either rs4680 (AA) or rs2369049 (GG or AG) were significantly more likely to respond to acupuncture than those without (77.8% vs 45.0%, P = 0.039).

Conclusions

Specific genetic variations at loci rs4680 and rs2369049 are associated with response to acupuncture-type intervention for management of arthralgia. These results serve as a proof of concept for applying a precision medicine framework to the study of cancer pain management.

A rare missense variant in RCL1 segregates with depression in extended families

Depression is the most prevalent psychiatric disorder with a complex and elusive etiology that is moderately heritable. Identification of genes would greatly facilitate the elucidation of the biological mechanisms underlying depression, however, its complex etiology has proved to be a major bottleneck in the identification of its genetic risk factors, especially in genome-wide association-like studies. In this study, we exploit the properties of a genetic isolate and its family-based structure to explore whether relatively rare exonic variants influence the burden of depressive symptoms in families. Using a multistep approach involving linkage and haplotype analyses followed by exome sequencing in the Erasmus Rucphen Family (ERF) study, we identified a rare (minor allele frequency (MAF)=1%) missense c.1114C>T mutation (rs115482041) in the RCL1 gene segregating with depression across multiple generations. Rs115482041 showed significant association with depressive symptoms (N=2393, β_T-allele=2.33, P-value=1 × 10^-4) and explained 2.9% of the estimated genetic variance of depressive symptoms (22%) in ERF. Despite being twice as rare (MAF<0.5%), c.1114C>T showed similar effect and significant association with depressive symptoms in samples from the independent population-based Rotterdam study (N=1604, β_T-allele=3.60, P-value=3 × 10^-2). A comparison of RCL1 expression in human and mouse brain revealed a striking co-localization of RCL1 with the layer 1 interlaminar subclass of astrocytes found exclusively in higher-order primates. Our findings identify RCL1 as a novel candidate gene for depression and offer insights into mechanisms through which RCL1 may be relevant for depression.

Stages of behavioural change after direct-to-consumer disease risk profiling: study protocol of two integrated controlled pragmatic trials

Background

The incidence and prevalence of chronic diseases have reached epidemic proportions during the last decades and are not expected to diminish. Chronic diseases increasingly affect younger individuals too, with over 40% of all deaths due to non-communicable diseases occurring before the age of 70. This has led to the development of information services aimed at preventive health care, such as Health Potential®. This counselling service estimates a personal disease risk of a carefully selected list of preventable common chronic diseases that have both a genetic and a lifestyle component of development. The results are delivered face-to-face by a lifestyle counsellor, simultaneously stimulating initial steps towards behaviour change. This information can assist in lifestyle decision-making.

Methods/design

The primary aim is to study the effect of the Health Potential® service on change in lifestyle behaviour in distinguishable customer populations. The secondary aims are (1) to study the effect of the Health Potential® service on determinants of behaviour change, (2) to study the effect of additional lifestyle counselling on behaviour change and determinants thereof, and (3) to describe the characteristics of the Health Potential® customer. The study consists of two integrated designs: (A) a two-armed non-randomised controlled pre-test/post-test trial (1.5:1 ratio), followed by (B) a two-armed randomised controlled pre-test/post-test trial (1:1 ratio), resulting in three study arms. Participants are clients of local prevention clinics, purchasing a personalised health check (PHC; intervention condition), consisting of Health Potential® and a general health check, or the general health check alone (GHC; control condition) (part A). PHC participants will be randomised to receive four additional lifestyle counselling sessions over a period of 3 months (part B).

Discussion

This research can provide valuable insights into the effectiveness of and possible ways forward in the field of personalised prevention making use of lifestyle interventions enriched with modern genetic advancements.

Trial registration

Nederlands Trial Register, NTR6289 and NTR6288. Registered on 24 February 2017.

Electronic supplementary material

The online version of this article (10.1186/s13063-018-2630-7) contains supplementary material, which is available to authorized users.

Studying microbial functionality within the gut ecosystem by systems biology

Humans are not autonomous entities. We are all living in a complex environment, interacting not only with our peers, but as true holobionts; we are also very much in interaction with our coexisting microbial ecosystems living on and especially within us, in the intestine. Intestinal microorganisms, often collectively referred to as intestinal microbiota, contribute significantly to our daily energy uptake by breaking down complex carbohydrates into simple sugars, which are fermented to short-chain fatty acids and subsequently absorbed by human cells. They also have an impact on our immune system, by suppressing or enhancing the growth of malevolent and beneficial microbes. Our lifestyle can have a large influence on this ecosystem. What and how much we consume can tip the ecological balance in the intestine. A "western diet" containing mainly processed food will have a different effect on our health than a balanced diet fortified with pre- and probiotics. In recent years, new technologies have emerged, which made a more detailed understanding of microbial communities and ecosystems feasible. This includes progress in the sequencing of PCR-amplified phylogenetic marker genes as well as the collective microbial metagenome and metatranscriptome, allowing us to determine with an increasing level of detail, which microbial species are in the microbiota, understand what these microorganisms do and how they respond to changes in lifestyle and diet. These new technologies also include the use of synthetic and in vitro systems, which allow us to study the impact of substrates and addition of specific microbes to microbial communities at a high level of detail, and enable us to gather quantitative data for modelling purposes. Here, we will review the current state of microbiome research, summarizing the computational methodologies in this area and highlighting possible outcomes for personalized nutrition and medicine.

Pharmacogenomic Impact of CYP2C19 Variation on Clopidogrel Therapy in Precision Cardiovascular Medicine

Variability in response to antiplatelet therapy can be explained in part by pharmacogenomics, particularly of the CYP450 enzyme encoded by CYP2C19. Loss-of-function and gain-of-function variants help explain these interindividual differences. Individuals may carry multiple variants, with linkage disequilibrium noted among some alleles. In the current pharmacogenomics era, genomic variation in CYP2C19 has led to the definition of pharmacokinetic phenotypes for response to antiplatelet therapy, in particular, clopidogrel. Individuals may be classified as poor, intermediate, extensive, or ultrarapid metabolizers, based on whether they carry wild type or polymorphic CYP2C19 alleles. Variant alleles differentially impact platelet reactivity, concentration of plasma clopidogrel metabolites, and clinical outcomes. Interestingly, response to clopidogrel appears to be modulated by additional factors, such as sociodemographic characteristics, risk factors for ischemic heart disease, and drug-drug interactions. Furthermore, systems medicine studies suggest that a broader approach may be required to adequately assess, predict, preempt, and manage variation in antiplatelet response. Transcriptomics, epigenomics, exposomics, miRNAomics, proteomics, metabolomics, microbiomics, and mathematical, computational, and molecular modeling should be integrated with pharmacogenomics for enhanced prediction and individualized care. In this review of pharmacogenomic variation of CYP450, a systems medicine approach is described for tailoring antiplatelet therapy in clinical practice of precision cardiovascular medicine.

Initiative for Molecular Profiling and Advanced Cancer Therapy (IMPACT): An MD Anderson Precision Medicine Study

PURPOSE

Genomic profiling is increasingly used in the management of cancer. We have previously reported preliminary results of our precision medicine program. Here, we present response and survival outcomes for 637 additional patients who were referred for phase I trials and were treated with matched targeted therapy (MTT) when available.

PATIENTS AND METHODS

Patients with advanced cancer who underwent tumor genomic analyses were treated with MTT when available.

RESULTS

Overall, 1,179 (82.1%) of 1,436 patients had one or more alterations (median age, 59.7 years; men, 41.2%); 637 had one or more actionable aberrations and were treated with MTT (n = 390) or non-MTT (n = 247). Patients who were treated with MTT had higher rates of complete and partial response (11% v 5%; P = .0099), longer failure-free survival (FFS; 3.4 v 2.9 months; P = .0015), and longer overall survival (OS; 8.4 v 7.3 months; P = .041) than did unmatched patients. Two-month landmark analyses showed that, for MTT patients, FFS for responders versus nonresponders was 7.6 versus 4.3 months (P < .001) and OS was 23.4 versus 8.5 months (P < .001), whereas for non-MTT patients (responders v nonresponders), FFS was 6.6 versus 4.1 months (P = .001) and OS was 15.2 versus 7.5 months (P = .43). Patients with phosphatidylinositol 3-kinase (PI3K) and mitogen-activated protein kinase pathway alterations matched to PI3K/Akt/mammalian target of rapamycin axis inhibitors alone demonstrated outcomes comparable to unmatched patients.

CONCLUSION

Our results support the use of genomic matching. Subset analyses indicate that matching patients who harbor a PI3K and mitogen-activated protein kinase pathway alteration to only a PI3K pathway inhibitor does not improve outcome. We have initiated IMPACT2, a randomized trial to compare treatment with and without genomic selection.

The Personalization of Clopidogrel Antiplatelet Therapy: The Role of Integrative Pharmacogenetics and Pharmacometabolomics

Dual antiplatelet therapy of aspirin and clopidogrel is pivotal for patients undergoing percutaneous coronary intervention. However, the variable platelets reactivity response to clopidogrel may lead to outcome failure and recurrence of cardiovascular events. Although many genetic and nongenetic factors are known, great portion of clopidogrel variable platelets reactivity remain unexplained which challenges the personalization of clopidogrel therapy. Current methods for clopidogrel personalization include CYP2C19 genotyping, pharmacokinetics, and platelets function testing. However, these methods lack precise prediction of clopidogrel outcome, often leading to insufficient prediction. Pharmacometabolomics which is an approach to identify novel biomarkers of drug response or toxicity in biofluids has been investigated to predict drug response. The advantage of pharmacometabolomics is that it does not only predict the response but also provide extensive information on the metabolic pathways implicated with the response. Integrating pharmacogenetics with pharmacometabolomics can give insight on unknown genetic and nongenetic factors associated with the response. This review aimed to review the literature on factors associated with the variable platelets reactivity response to clopidogrel, as well as appraising current methods for the personalization of clopidogrel therapy. We also aimed to review the literature on using pharmacometabolomics approach to predict drug response, as well as discussing the plausibility of using it to predict clopidogrel outcome.

Two-way learning with one-way supervision for gene expression data

BACKGROUND

A family of parsimonious Gaussian mixture models for the biclustering of gene expression data is introduced. Biclustering is accommodated by adopting a mixture of factor analyzers model with a binary, row-stochastic factor loadings matrix. This particular form of factor loadings matrix results in a block-diagonal covariance matrix, which is a useful property in gene expression analyses, specifically in biomarker discovery scenarios where blood can potentially act as a surrogate tissue for other less accessible tissues. Prior knowledge of the factor loadings matrix is useful in this application and is reflected in the one-way supervised nature of the algorithm. Additionally, the factor loadings matrix can be assumed to be constant across all components because of the relationship desired between the various types of tissue samples. Parameter estimates are obtained through a variant of the expectation-maximization algorithm and the best-fitting model is selected using the Bayesian information criterion. The family of models is demonstrated using simulated data and two real microarray data sets. The first real data set is from a rat study that investigated the influence of diabetes on gene expression in different tissues. The second real data set is from a human transcriptomics study that focused on blood and immune tissues. The microarray data sets illustrate the biclustering family's performance in biomarker discovery involving peripheral blood as surrogate biopsy material.

RESULTS

The simulation studies indicate that the algorithm identifies the correct biclusters, most optimally when the number of observation clusters is known. Moreover, the biclustering algorithm identified biclusters comprised of biologically meaningful data related to insulin resistance and immune function in the rat and human real data sets, respectively.

CONCLUSIONS

Initial results using real data show that this biclustering technique provides a novel approach for biomarker discovery by enabling blood to be used as a surrogate for hard-to-obtain tissues.

Needs and Requirements of Modern Biobanks on the Example of Dystonia Syndromes

Dystonia belongs to a group of rare diseases (RDs) characterized by etiologic heterogeneity, affection often in childhood, severe and variable clinical manifestation. The burden of this disease is aggravated by the lack of effective and specific treatment. In the field of dystonia as in other RDs the number of available biospecimens is, in general, limited. Here, we report a new approach to collect clinical and genetic data in biospecimens maintained collaboratively by researchers and their associated institutions in a decentralized system. Allowing researchers to have access to significant numbers of samples and corresponding clinical data, biobanking in dystonia might not only provide a powerful tool in the identification of disease genes but also the classification of variants detected in known genes with respect to their clinical relevance. Growing data in genetics due to the technical progress demand for well-annotated and well-managed biobanks, which in near future hold even the potential for biomarker research and generating medical treatment based on clinical and genetic data currently summarized as “personalized medicine.”

Interleukin 1 genetic tests provide no support for reduction of preventive dental care

BACKGROUND

It has been proposed that the PST and PerioPredict genetic tests that are based on polymorphisms in interleukin 1 (IL-1) genes identify a subset of patients who experience fewer tooth extractions if provided with 2 annual preventive visits. Economic analyses indicate rationing preventive care to only "high-risk" genotypes, smokers, patients with diabetes, or combinations of these risk factors would reduce the cost of dental care by $4.8 billion annually in the United States.

METHODS

Data presented in the study that claimed clinical utility for the PST and PerioPredict tests were obtained for reanalysis using logistic regression to assess whether the PST genetic test, smoking, diabetes, or number of preventive visits were risk factors for tooth extraction during a span of 16 years. Consistency of risk classification by the PST (version 1) and PerioPredict (version 2) genetic tests was evaluated in different ethnic groups from the 1000 Genomes database.

RESULTS

Multivariate analyses revealed association of tooth extraction with diabetes (P < .0001), smoking (P < .0001), and number of preventive visits (P = .004), but no support for the PST genetic test (P = .96) nor indication that the benefit of 2 preventive visits was affected by this genetic test (P = .58). Classification of risk was highly inconsistent between the PST (version 1) and PerioPredict (version 2) genetic tests.

CONCLUSIONS

Two annual preventive visits were supported as beneficial for all patients, and there was no evidence that the IL-1 PST genetic test has any effect on tooth extraction risk or influences the benefits of 2 annual preventive visits.

PRACTICAL IMPLICATIONS

Neither IL-1 PST nor PerioPredict genetic tests are useful for rationing preventive dental care. Further research is needed to identify genetic biomarkers with robust clinical validity and clinical utility to effectively personalize the practice of dentistry.

The future of clinical cancer genomics

The current and future applications of genomics to the practice of preventive oncology are being impacted by a number of challenges. These include rapid advances in genomic science and technology that allow massively parallel sequencing of both tumors and the germline, a diminishing of intellectual property restrictions on diagnostic genetic applications, rapid expansion of access to the internet which includes mobile access to both genomic data and tools to communicate and interpret genetic data in a medical context, the expansion of for-profit diagnostic companies seeking to monetize genetic information, and a simultaneous effort to depict medical professionals as barriers to rather than facilitators of understanding one's genome. Addressing each of these issues will be required to bring "personalized" germline genomics to cancer prevention and care. A profound future challenge will be whether clinical cancer genomics will be "de-medicalized" by commercial interests and their advocates, or whether the future course of this field can be modulated in a responsible way that protects the public health while implementing powerful new medical tools for cancer prevention and early detection.

The value of genomics in dissecting the RAS-network and in guiding therapeutics for RAS-driven cancers

The rise in genomic knowledge over the past decade has revealed the molecular etiology of many diseases, and has identified intricate signaling network activity in human cancers. Genomics provides the opportunity to determine genome structure and capture the activity of thousands of molecular events concurrently, which is important for deciphering highly complex genetic diseases such as cancer. In this review, we focus on genomic efforts directed towards one of cancer's most frequently mutated networks, the RAS pathway. Genomic tools such as gene expression signatures and assessment of mutations across the RAS network enable the capture of RAS signaling complexity. Due to this high level of interaction and cross-talk within the network, efforts to target RAS signaling in the clinic have generally failed, and we currently lack the ability to directly inhibit the RAS protein with high efficacy. We propose that the use of gene expression data can identify effective treatments that broadly inhibit the RAS network as this approach measures pathway activity independent of mutation status or any single mechanism of activation. Here, we review the genomic studies that map the complexity of the RAS network in cancer, and that show how genomic measurements of RAS pathway activation can identify effective RAS inhibition strategies. We also address the challenges and future directions for treating RAS-driven tumors. In summary, genomic assessment of RAS signaling provides a level of complexity necessary to accurately map the network that matches the intricacy of RAS pathway interactions in cancer.

Between hype and hope: What is really at stake with personalized medicine?

Over the last decade, personalized medicine has become a buzz word, which covers a broad spectrum of meanings and generates many different opinions. The purpose of this article is to achieve a better understanding of the reasons why personalized medicine gives rise to such conflicting opinions. We show that a major issue of personalized medicine is the gap existing between its claims and its reality. We then present and analyze different possible reasons for this gap. We propose an hypothesis inspired by the Windelband's distinction between nomothetic and idiographic methodology. We argue that the fuzzy situation of personalized medicine results from a mix between idiographic claims and nomothetic methodological procedures. Hence we suggest that the current quandary about personalized medicine cannot be solved without getting involved in a discussion about the complex epistemological and methodological status of medicine. To conclude, we show that the Gadamer's view of medicine as a dialogical process can be fruitfully used and reveals that personalization is not a theoretical task, but a practical one, which takes place within the clinical encounter.

From "Personalized" to "Precision" Medicine: The Ethical and Social Implications of Rhetorical Reform in Genomic Medicine

Since the late 1980s, the human genetics and genomics research community has been promising to usher in a "new paradigm for health care"-one that uses molecular profiling to identify human genetic variants implicated in multifactorial health risks. After the completion of the Human Genome Project in 2003, a wide range of stakeholders became committed to this "paradigm shift," creating a confluence of investment, advocacy, and enthusiasm that bears all the marks of a "scientific/intellectual social movement" within biomedicine. Proponents of this movement usually offer four ways in which their approach to medical diagnosis and health care improves upon current practices, arguing that it is more "personalized," "predictive," "preventive," and "participatory" than the medical status quo. Initially, it was personalization that seemed to best sum up the movement's appeal. By 2012, however, powerful opinion leaders were abandoning "personalized medicine" in favor of a new label: "precision medicine." The new label received a decisive seal of approval when, in January 2015, President Obama unveiled plans for a national "precision medicine initiative" to promote the development and use of genomic tools in health care.

Personalized medicine, genomics, and pharmacogenomics: a primer for nurses

Personalized medicine is the study of patients' unique environmental influences as well as the totality of their genetic code-their genome-to tailor personalized risk assessments, diagnoses, prognoses, and treatments. The study of how patients' genomes affect responses to medications, or pharmacogenomics, is a related field. Personalized medicine and genomics are particularly relevant in oncology because of the genetic basis of cancer. Nurses need to understand related issues such as the role of genetic and genomic counseling, the ethical and legal questions surrounding genomics, and the growing direct-to-consumer genomics industry. As genomics research is incorporated into health care, nurses need to understand the technology to provide advocacy and education for patients and their families.

APLICACIONES DE LA BIOINFORMÁTICA EN LA MEDICINA: EL GENOMA HUMANO. ¿CÓMO PODEMOS VER TANTO DETALLE?

<p lang="es-ES" align="JUSTIFY">La bioinformática es un campo novedoso que soporta parte de la investigación biológica dirigida a la identificación de variantes génicas que pueden ser descubiertas desde los estudios de genomas completos. Basados en esta motivación se presenta el panorama general de los aportes principales de la bioinformática en el desarrollo del secuenciamiento del primer genoma humano. Adicionalmente se resumen los principales avances en cómputo desarrollados para responder a las demandas requeridas por los métodos de secuenciamiento de última generación para lograr re-secuenciar un genoma humano. Finalmente se introducen algunos de los nuevos retos que deben asumirse para aplicar la genómica personalizada en el desarrollo de la medicina. </p><p lang="es-ES" align="JUSTIFY"> </p><p lang="es-ES" align="JUSTIFY">Abstract</p><p lang="es-ES" align="JUSTIFY">Bioinformatics is a new field that supports part of the biological research aimed at identifying gene variants that can be discovered from studies of whole genomes. Based on this motivation the overview of the main contributions of bioinformatics in the development of sequencing the first human genome is presented. Additionally it is summarized the main advances in computing developed to meet the demands to re-sequence a human genome by using the next generation sequencing technologies. Finally some new challenges that must be faced to apply the personalized genomics into the medicine development are introduced.</p>

Genomic Biomarkers for Breast Cancer Risk

Clinical risk assessment for cancer predisposition includes a three-generation pedigree and physical examination to identify inherited syndromes. Additionally genetic and genomic biomarkers may identify individuals with a constitutional basis for their disease that may not be evident clinically. Genomic biomarker testing may detect molecular variations in single genes, panels of genes, or entire genomes. The strength of evidence for the association of a genomic biomarker with disease risk may be weak or strong. The factors contributing to clinical validity and utility of genomic biomarkers include functional laboratory analyses and genetic epidemiologic evidence. Genomic biomarkers may be further classified as low, moderate or highly penetrant based on the likelihood of disease. Genomic biomarkers for breast cancer are comprised of rare highly penetrant mutations of genes such as BRCA1 or BRCA2, moderately penetrant mutations of genes such as CHEK2, as well as more common genomic variants, including single nucleotide polymorphisms, associated with modest effect sizes. When applied in the context of appropriate counseling and interpretation, identification of genomic biomarkers of inherited risk for breast cancer may decrease morbidity and mortality, allow for definitive prevention through assisted reproduction, and serve as a guide to targeted therapy .

Reads meet rotamers: structural biology in the age of deep sequencing

Structure has traditionally been interrelated with sequence, usually in the framework of comparing sequences across species sharing a common fold. However, the nature of information within the sequence and structure databases is evolving, changing the type of comparisons possible. In particular, we now have a vast amount of personal genome sequences from human populations and a greater fraction of new structures contain interacting proteins within large complexes. Consequently, we have to recast our conception of sequence conservation and its relation to structure-for example, focusing more on selection within the human population. Moreover, within structural biology there is less emphasis on the discovery of novel folds and more on relating structures to networks of protein interactions. We cover this changing mindset here.