Over the past two decades, approaches to describing and understanding human biology, disease, and response to pharmacological treatment have undergone a major paradigm shift—from an average-based description and assessment of organismal functions and dysfunctions to the introduction of the individual into science, both conceptually and concretely. For most of the 20th century, any biological or pharmacological factor was assessed by measuring multiple individuals to determine the average value as an approximation to the hypothetical “true” value, eliminating the variation of measured values as “standard error.” Thus, “the organism,” “the brain,” and essentially all biochemical, physiological, and pharmacological functions as described in the textbooks were an abstraction from the individual, although face value and clinical experience said otherwise. The search for causative factors underlying disease, or the characterization of pharmacological effects, was approached by comparing average values between cases and controls, which often were not representative of any individual in the samples, with huge standard deviations masking different molecular subgroups or response types. Needless to say, translating such knowledge to the clinic resulted in “trial-and-error” health care for large proportions of patients, with the diagnostics and treatment measures not taking into account their individual molecular make-up.
Then came the dawn. The advent of next-generation sequencing (NGS) technologies enabled the deciphering of whole human genomes at an increasingly rapid pace, unveiling an immense amount of interindividual DNA sequence variation. Further technological advances now not only allow sequencing multiple genomes per machine run for about $1000 per genome, but also transcriptomes, epigenomes, and metagenomes, and can be applied to characterize the proteome. In addition to faster disease gene discovery, high-throughput “omics” techniques allow comprehensive characterization of individuals’ biological networks and molecular processes acting on, or predisposing to, disease and differential drug response. The detailed characterization of individual patients can include many other techniques such as imaging and sensor techniques. Huge collective science endeavors, for example The BRAIN Initiative, the establishment of comprehensive knowledge databases integrating the genome sequences, molecular and clinical data from many thousands of individuals, and approaches to handling “big data” all support these developments, laying the foundation for precision medicine and individualized patient care. This issue of Dialogues in Clinical Neuroscience provides a highly timely survey of work from leading experts that covers various aspects of these developments.
In the first, State of the art, article (p 237), Anna Need and David Goldstein provide a most comprehensive synopsis of the application of NGS to neuropsychiatric disease. They convincingly demonstrate that NGS is becoming an essential part of clinical care, transforming the diagnosis of rare neurological conditions, especially of early onset and severe presentations. Moreover, they show that NGS has led to a remarkable number of “phenotype expansions” in which already known genes are found to be implicated in unexpected presentations. Beyond its diagnostic role, NGS has led to an explosion in disease gene discovery, which evidently is not limited to Mendelian disease; the more complex neuropsychiatric disorders also now appear to be systematically tractable through NGS. The authors’ comprehensive evaluation of results provides an emerging picture of the genetic architectures of neuropsychiatric diseases. In conclusion, they discuss the relevance of current results as pointers for underlying disease biology and their implications for disease stratification, individual treatment optimization, and clinical trial design.
In the first Basic research article (p 253), Hans Lehrach makes the case that true personalization of drug therapies will rely on “virtual patient models” based on a detailed characterization of the individual patient by molecular, imaging, and sensor techniques. In contrast to common stratification approaches, an “N-of-1” design is used, considering the individual patient as the sole unit of observation to assess his or her unique characteristics. Lehrach outlines the different types of biological information that are needed and that can be collected at all omics levels by use of DNA sequencing and other advanced technologies. Data can be integrated computationally to model the biological networks and molecular processes acting in individual (disease) tissues. This “virtual patient/in-silico self” model is constructed to allow predictions about therapy response, and ultimately to become a central element of our health care system throughout our lives, to help us deal intelligently with our health and well-being. In the second Basic research article (p. 267), David Gurwitz evaluates in vitro cell systems as models for human diversity as they provide a credible resource for personalized medicine research of neuropsychiatric disorders. The author elaborates in detail the advances, but also limitations of human induced pluripotent stem cell (iPSC) derived neurons, a rather new tool providing access to brain-specific biology and, importantly, specific human brain areas. He comparatively evaluates the long known human lymphoblastoid cell lines (LCLs) derived from peripheral blood B-lymphocytes and outlines their advantages for personalized medicine research in neuropsychiatric disease, in particular for examining individual drug response phenotypes and genomes along with their transcriptomes, epigenomes, proteomes, metabolomes and many other in vitro phenotypes. In the third Basic research article (p. 277), Emily Finn and R. Todd Constable evaluate functional brain connectivity measured with fMRI and considered an indicator for neural organization, as a potential brain-based biomarker of mental illness. Analyzing fMRI data, the authors provide evidence for intrinsic functional connectivity profiles that are both reliable within individuals and unique across individuals. In other words, a person’s brain activity appears to be as unique as his or her fingerprints. The authors demonstrate, moreover, how these functional connectivity profiles allow prediction of behavioral phenotypes in individual subjects. Notably, these individually different profiles became apparent through repeated measurement of individual cognitive performance and would have been obscured by comparing groups of patients and controls. Taken together, these results promise to help clinicians predict or even treat neuropsychiatric diseases based on individual brain connectivity profiles, contributing to the development of personalized approaches to mental illness.
In the Translational research article (p. 289), J. David Sweatt and Carol A. Tamminga review epigenetic mechanisms and their roles in conferring inter-individual differences especially as related to experientially acquired and genetically driven changes in CNS function. They address the possible ways in which epigenomic changes may contribute to neuropsychiatric conditions and disorders. They provide a primer on epigenetics to begin with, and describe special techniques for characterizing the individual epigenotype. The authors advocate epigenotyping and the integrated analysis of epigenomes, genomes, and transcriptomes, together with deep phenotyping, as a means towards advanced precision medicine. Finally, they address two disorders, Rett syndrome and schizophrenia, which provide an interesting compare-and-contrast regarding the possible epigenotypic regulation of behavior.
In a comprehensive Clinical research article, (p. 299), Joel Krier, Sarah Kalia, and Robert Green address a very important and timely issue, the implementation of genomic sequencing (GS) in clinical practice. They outline the current clinical applications of diagnostic sequencing as well as emerging applications such as preconception carrier screening and predispositional genetic screening in healthy individuals. Moreover, they address the challenges in clinical GS including the standardization of variant interpretation, clinical contextualization of results, handling of secondary findings, genomics education for nongeneticist clinicians, and issues such as clinical utility and cost effectiveness. Finally, the authors address ethical, legal, and social implications (ELSI) of GS such as concerns about risks to privacy and confidentiality of genetic information, and the emotional impact of receiving sequencing results. Notably, the authors probe key issues paradigmatically in cutting-edge studies such as the MedSeq Project.
Two companion Pharmacological aspects articles provide state-of-the-art pharmacogenomics information concerning the application of psychotropic drugs: a survey of available pharmacogenetic tests and an evaluation of their effectiveness for the treatment of psychiatric disorders. In this context, Chin Bin Eap (p. 313) provides a thorough and comprehensive evaluation of the clinical usefulness of pharmacogenetic tests (evaluating polymorphisms in drug metabolizing enzymes and drug targets) to improve the efficacy of classical and recent antidepressants, and decrease the risk of adverse reactions. In conclusion, he suggests increasing the use of pharmacogenetic tests in combination with careful clinical evaluations and other tools such as therapeutic drug monitoring and phenotyping to provide the best possible care for psychiatric patients. In the second article, Seenae Eum, Adam Lee and Jeffrey Bishop (p. 323) provide a comprehensive survey of presently available commercial and noncommercial pharmacogenetic testing panels for antipsychotic treatment. For each of these tests, they outline the specific pharmacokinetic and pharmacodynamic gene variants that are evaluated. Their associations with clinical efficacy and adverse effects, as well as clinical implications in antipsychotic pharmacotherapy, are discussed. Taken together, these two reviews provide a clear, complementary and useful evaluation of the—still limited—improvement that can be expected from the application of these pharmacogenetic tests to optimize personal treatment.
Technical innovations in medicine and informatics, some of them addressed in this issue, lead to the generation of unprecedentedly huge volumes of different types of data. In their Brief report (p. 339), S.M. Reza Soroushmehr and Kayvan Najarian describe the nature and complexities of these “big data” related to health care systems. They elaborate on computational approaches to personalized medicine and their requirements to improve personal diagnosis, prognosis, prevention, pharmacotherapy, and other therapeutic measures. Moreover, they review the challenges of collecting, handling, analysing, and comparing the data and develop comprehensive, integrated computer models.
In sum, this issue provides both insights into cutting-edge research by leading experts in this field and a sense of the huge challenges remaining to fully understand human individuality as a biological and pathophysiological phenomenon, and find ways to improve it.
Margret R. Hoehe, MD, PhD; Pierre Schulz, MD