Genomic insights into ayurvedic and western approaches to personalized medicine

Ayurveda, an ancient Indian system of medicine documented and practised since 1500 B.C., follows a systems approach that has interesting parallels with contemporary personalized genomic medicine approaches to the understanding and management of health and disease. It is based on the trisutra, which are the three aspects of causes, features and therapeutics that are interconnected through a common organizing principle termed ‘tridosha’. Tridosha comprise three ascertainable physiological entities; vata (kinetic), pitta (metabolic) and kapha (potential) that are pervasive across systems, work in conjunction with each other, respond to the external environment and maintain homeostasis. Each individual is born with a specific proportion of tridosha that are not only genetically determined but also influenced by the environment during foetal development. Jointly they determine a person’s basic constitution, which is termed their ‘prakriti’. Development and progression of different diseases with their subtypes are thought to depend on the origin and mechanism of perturbation of the doshas, and the aim of therapeutic practice is to ensure that the doshas retain their homeostatic state. Similarly, western systems biology epitomized by translational P4 medicine envisages the integration of multiscalar genetic, cellular, physiological and environmental networks to predict phenotypic outcomes of perturbations. In this perspective article, we aim to outline the shape of a unifying scaffold that may allow the two intellectual traditions to enhance one another. Specifically, we illustrate how a unique integrative ‘Ayurgenomics’ approach can be used to integrate the trisutra concept of Ayurveda with genomics. We observe biochemical and molecular correlates of prakriti and show how these differ significantly in processes that are linked to intermediate patho-phenotypes, known to take different course in diseases. We also observe a significant enrichment of the highly connected hub genes which could explain differences in prakriti, focussing on EGLN1, a key oxygen sensor that differs between prakriti types and is linked to high altitude adaptation. Integrating our observation with the current literature, we demonstrate how EGLN1 could qualify as a molecular equivalent of tridosha that can modulate different phenotypic outcomes, where hypoxia is a cause or a consequence both during health and diseased states. Our studies affirm that integration of the trisutra framework through Ayurgenomics can guide the identification of predisposed groups of individuals and enable discovery of actionable therapeutic points in an individualized manner.     

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.     

Redefining clinical trials: the age of personalized medicine

The triumph of personalized cancer therapeutics in recent years is prompting some oncologists to rethink clinical trial design; other researchers have different priorities for trial reform.     

Engineering neoantigen vaccines to improve cancer personalized immunotherapy

Immunotherapy treatments harnessing the immune system herald a new era of personalized medicine, offering considerable benefits for cancer patients. Over the past years, tumor neoantigens emerged as a rising star in immunotherapy. Neoantigens are tumor-specific antigens arising from somatic mutations, which are proceeded and presented by the major histocompatibility complex on the cell surface. With the advancement of sequencing technology and bioinformatics engineering, the recognition of neoantigens has accelerated and is expected to be incorporated into the clinical routine. Currently, tumor vaccines against neoantigens mainly encompass peptides, DNA, RNA, and dendritic cells, which are extremely specific to individual patients. Due to the high immunogenicity of neoantigens, tumor vaccines could activate and expand antigen-specific CD4+ and CD8+ T cells to intensify anti-tumor immunity. Herein, we introduce the origin and prediction of neoantigens and compare the advantages and disadvantages of multiple types of neoantigen vaccines. Besides, we review the immunizations and the current clinical research status in neoantigen vaccines, and outline strategies for enhancing the efficacy of neoantigen vaccines. Finally, we present the challenges facing the application of neoantigens.     

Learning from clinical medicine to improve the use of surrogates in ecology

Surrogates are used widely in ecology to detect or monitor changes in the environment that are too difficult or costly to assess directly. Yet most work on surrogates to date has been correlative, with little work on their predictive capacity or the circumstances under which they work. Our suggestion is to revisit and learn from research in the clinical medical sciences, including the causal statistical frameworks available to validate relationships between treatments, surrogate variables, and the outcome of interest. We adapt this medical thinking to ecology by providing a new framework that involves specification of the surrogate model, statistical validation, and subsequent evaluation in a range of spatial and temporal contexts. An inter‐disciplinary surrogate concept will allow for a more rigorous approach to validating and evaluating proxy variables, thus advancing the selection and application of surrogates in ecology. Synthesis We draw together ideas from the medical sciences to define an explicit surrogate concept that has not previously been used in ecology. We present a new framework for specifying surrogate models involving validation using a causal framework, and subsequent re‐evaluation in different spatial and temporal contexts – an approach closely aligned with that used by researchers in the clinical medical sciences. This rigorous method can advance the science underpinning the application of surrogates in ecology by shifting the focus away from correlative understanding to one that focuses instead on causation and prediction. An improved use of surrogates is imperative if we are to meet the challenge of properly measuring and understanding the multifarious and complex problems in contemporary ecology.     

Deciphering signaling pathways in clinical tissues for personalized medicine using protein microarrays

The current transition in cancer therapy from general treatment approaches, based mainly on chemotherapy and radiotherapy, to more directed approaches that aim to inhibit specific molecular targets has brought about new challenges for pathology. In the past, classical assignment of pathology consisted of tumor diagnosis and staging for further therapy decisions; nowadays, pathologists are asked to predict possible therapeutic results by detecting and quantifying therapeutic targets in tumors such as the human epidermal growth factor receptor 2 (HER2). The best approach to analyze such molecular targets is to provide a tumor‐specific protein expression profile prior to therapy. To further elucidate signaling networks underlying cancer development and to identify new targets, it is necessary to implement tools that allow fast, precise, cheap, and simultaneous analysis of many network components while requiring only a small amount of clinical material. Reverse phase protein microarray (RPPA) is a promising technology that meets these requirements while enabling quantitative measurement of proteins. Recently, methods for the extraction of proteins from formalin‐fixed, paraffin‐embedded (FFPE) tissues have become available. In this article, we demonstrate how the use of RPPA to analyze signaling pathways from FFPE tissues may improve quantification of therapeutic targets and diagnostic markers in the near future. J. Cell. Physiol. 225: 364–370, 2010. © 2010 Wiley‐Liss, Inc.     

Cancer genomics: from discovery science to personalized medicine

Recent advances in genome technologies and the ensuing outpouring of genomic information related to cancer have accelerated the convergence of discovery science and clinical medicine. Successful examples of translating cancer genomics into therapeutics and diagnostics reinforce its potential to make possible personalized cancer medicine. However, the bottlenecks along the path of converting a genome discovery into a tangible clinical endpoint are numerous and formidable. In this Perspective, we emphasize the importance of establishing the biological relevance of a cancer genomic discovery in realizing its clinical potential and discuss some of the major obstacles to moving from the bench to the bedside.