Metagenomics and personalized medicine

The microbiome is a complex community of Bacteria, Archaea, Eukarya, and viruses that infect humans and live in our tissues. It contributes the majority of genetic information to our metagenome and, consequently, influences our resistance and susceptibility to diseases, especially common inflammatory diseases, such as type 1 diabetes, ulcerative colitis, and Crohn's disease. Here we discuss how host-gene-microbial interactions are major determinants for the development of these multifactorial chronic disorders and, thus, for the relationship between genotype and phenotype. We also explore how genome-wide association studies (GWAS) on autoimmune and inflammatory diseases are uncovering mechanism-based subtypes for these disorders. Applying these emerging concepts will permit a more complete understanding of the etiologies of complex diseases and underpin the development of both next-generation animal models and new therapeutic strategies for targeting personalized disease phenotypes.     

Nutrigenomics research for personalized nutrition and medicine

Current nutritional and genetic epidemiological methods yield 'risk factors' on the basis of population studies. Risk factors, however, are statistical estimates of the percentage reduction in disease in the population if the risk were to be avoided or the gene variant were not present. These measures are often assumed to apply to individuals who are likely to differ in genetic make-up, lifestyle, and dietary patterns than to the individuals in the study population. Developing individual risk factors in light of the genetic diversity of human populations, the complexity of foods, culture and lifestyle, and the variety of metabolic processes that lead to health or disease is a significant challenge for personalizing dietary advice for healthy or individuals with chronic disease.     

Whole brain radiotherapy: Consequences for personalized medicine

Several studies focusing on brain irradiation are in progress. Reflecting updates of relevant outcomes in palliative treatment of patients suffering from brain metastases, the primary objective of these studies is the evaluation of neurocognitive function and quality of life. Improvements of technology in radiation oncology allows us to spare the hippocampal region while appropriately irradiating other parts of brain tissue. Irradiation of the hippocampus region is likely to lead to manifestations of adverse events with a subsequent impact on patient''s quality of life, which is in fact an improper approach in palliative medicine. Ongoing studies evaluate results of hippocampus avoiding radiotherapy compared to standard whole brain radiotherapy. Incorporation of neurocognitive function assessment may result in the confirmation of superiority of sparing the region of hippocampus and thus change current style of providing brain irradiation.     

The path to personalized medicine

Advances in personalized medicine, or the use of an individual's molecular profile to direct the practice of medicine, have been greatly enabled through human genome research. This research is leading to the identification of a range of molecular markers for predisposition testing, disease screening and prognostic assessment, as well as markers used to predict and monitor drug response. Successful personalized medicine research programs will not only require strategies for developing and validating biomarkers, but also coordinating these efforts with drug discovery and clinical development.     

A new molecular target for antidepressants

The mode of action of drugs used to treat depression has long remained mysterious.A new study now shows that unexpect-edly,this diverse group of chemicals binds...     

The implications of electronic health record for personalized medicine

The emerging concept of an electronic health record (EHR) targeted at a patient centric, cross-institutional and longitudinal information entity (possibly spanning the individuals lifetime) has great promise for personalized medicine. In fact, it is probably the only vehicle through which we may truly realize the personalization of medicine beyond population-based genetic profiles that are expected to become part of medication and treatment indications in the near future. The new EHR standards include mechanisms that integrate clinical data with genomic testing results obtained through applying research-type procedures, such as full DNA sequencing, to an individual patient. Although the most optimal process for the utilization of integrated clinical-genomic data in the EHR framework is still unclear, the new Health Level Seven (HL7) Clinical Genomics Draft Standard for Trial Use suggests using the 'encapsulate & bubble-up' approach, which includes two main phases: the encapsulation of raw genomic data and bubbling-up the most clinically significant portions of that data, while associating it with clinical phenotypes residing in the individual's EHR.     

Grand challenge commentary: Informative diagnostics for personalized medicine

Some of the most celebrated triumphs of chemical biology are molecularly targeted therapeutics to combat human disease. However, a grand challenge looms as informative diagnostic strategies must be developed to realize the full impact of these promising pharmaceutical agents.     

Ensemble survival trees for identifying subpopulations in personalized medicine

Recently, personalized medicine has received great attention to improve safety and effectiveness in drug development. Personalized medicine aims to provide medical treatment that is tailored to the patient's characteristics such as genomic biomarkers, disease history, etc., so that the benefit of treatment can be optimized. Subpopulations identification is to divide patients into several different subgroups where each subgroup corresponds to an optimal treatment. For two subgroups, traditionally the multivariate Cox proportional hazards model is fitted and used to calculate the risk score when outcome is survival time endpoint. Median is commonly chosen as the cutoff value to separate patients. However, using median as the cutoff value is quite subjective and sometimes may be inappropriate in situations where data are imbalanced. Here, we propose a novel tree‐based method that adopts the algorithm of relative risk trees to identify subgroup patients. After growing a relative risk tree, we apply k‐means clustering to group the terminal nodes based on the averaged covariates. We adopt an ensemble Bagging method to improve the performance of a single tree since it is well known that the performance of a single tree is quite unstable. A simulation study is conducted to compare the performance between our proposed method and the multivariate Cox model. The applications of our proposed method to two public cancer data sets are also conducted for illustration.     

Pharmacogenomics and personalized medicine in neuropsychiatry

Despite the need for more effective treatments for psychiatric disorders, development of new medications has stalled. Here we discuss the promise of personalized medicine in developing more efficacious and individualized pharmacotherapies that take into account genetic variation and target groups of patients who share biology, not just symptoms.     

An economic perspective on personalized medicine

The concept of personalized medicine not only promises to enhance the life of patients and increase the quality of clinical practice and targeted care pathways, but also to lower overall healthcare costs through early-detection, prevention, accurate risk assessments and efficiencies in care delivery. Current inefficiencies are widely regarded as substantial enough to have a significant impact on the economies of major nations like the US and China, and, therefore the world economy. A recent OECD report estimates healthcare expenditure for some of the developed western and eastern nations to be anywhere from 10% to 18%, and growing (with the US at the highest). Personalized medicine aims to use state-of-the-art genomic technologies, rich medical record data, tissue and blood banks and clinical knowledge that will allow clinicians and payors to tailor treatments to individuals, thereby greatly reducing the costs of ineffective therapies incurred through the current trial and error clinical paradigm. Pivotal to the field are drugs that have been designed to target a specific molecular pathway that has gone wrong and results in a diseased condition and the diagnostic tests that allow clinicians to separate responders from non-responders. However, the truly personalized approach in medicine faces two major problems: complex biology and complex economics; the pathways involved in diseases are quite often not well understood, and most targeted drugs are very expensive. As a result of all current efforts to translate the concepts of personalized healthcare into the clinic, personalized medicine becomes participatory and this implies patient decisions about their own health. Such a new paradigm requires powerful tools to handle significant amounts of personal information with the approach to be known as “P4 medicine”, that is predictive, preventive, personalized and participatory. P4 medicine promises to increase the quality of clinical care and treatments and will ultimately save costs. The greatest challenges are economic, not scientific.     

Magnetic resonance imaging in personalized medicine

正Continued biomedical advances and increased demands on quality health care have led to a new era of personalized medicine—a concept of medicine that uses specific information to an individual to help diagnose disease,plan treatment,assess treatment efficacy,and/or predict prognosis.This concept has evolved from the idea of"patient-centered care",which intends to shift the focus of health care from diseases to patients(Abujudeh et al.,2016).Medical imaging is essential in the practice of modern medicine,and its role in personalized medicine has never been greater.In particular,magnetic resonance(MR)