Uncovering influence links in molecular knowledge networks to streamline personalized medicine

OBJECTIVES

We developed Resource Description Framework (RDF)-induced InfluGrams (RIIG) - an informatics formalism to uncover complex relationships among biomarker proteins and biological pathways using the biomedical knowledge bases. We demonstrate an application of RIIG in morphoproteomics, a theranostic technique aimed at comprehensive analysis of protein circuitries to design effective therapeutic strategies in personalized medicine setting.

METHODS

RIIG uses an RDF "mashup" knowledge base that integrates publicly available pathway and protein data with ontologies. To mine for RDF-induced Influence Links, RIIG introduces notions of RDF relevancy and RDF collider, which mimic conditional independence and "explaining away" mechanism in probabilistic systems. Using these notions and constraint-based structure learning algorithms, the formalism generates the morphoproteomic diagrams, which we call InfluGrams, for further analysis by experts.

RESULTS

RIIG was able to recover up to 90% of predefined influence links in a simulated environment using synthetic data and outperformed a naïve Monte Carlo sampling of random links. In clinical cases of Acute Lymphoblastic Leukemia (ALL) and Mesenchymal Chondrosarcoma, a significant level of concordance between the RIIG-generated and expert-built morphoproteomic diagrams was observed. In a clinical case of Squamous Cell Carcinoma, RIIG allowed selection of alternative therapeutic targets, the validity of which was supported by a systematic literature review. We have also illustrated an ability of RIIG to discover novel influence links in the general case of the ALL.

CONCLUSIONS

Applications of the RIIG formalism demonstrated its potential to uncover patient-specific complex relationships among biological entities to find effective drug targets in a personalized medicine setting. We conclude that RIIG provides an effective means not only to streamline morphoproteomic studies, but also to bridge curated biomedical knowledge and causal reasoning with the clinical data in general.

Personalized preventive medicine: genetics and the response to regular exercise in preventive interventions

Regular exercise and a physically active lifestyle have favorable effects on health. Several issues related to this theme are addressed in this report. A comment on the requirements of personalized exercise medicine and in-depth biological profiling along with the opportunities that they offer is presented. This is followed by a brief overview of the evidence for the contributions of genetic differences to the ability to benefit from regular exercise. Subsequently, studies showing that mutations in TP53 influence exercise capacity in mice and humans are succinctly described. The evidence for effects of exercise on endothelial function in health and disease also is covered. Finally, changes in cardiac and skeletal muscle in response to exercise and their implications for patients with cardiac disease are summarized. Innovative research strategies are needed to define the molecular mechanisms involved in adaptation to exercise and to translate them into useful clinical and public health applications.

Treatment selections using risk-benefit profiles based on data from comparative randomized clinical trials with multiple endpoints

In a typical randomized clinical study to compare a new treatment with a control, oftentimes each study subject may experience any of several distinct outcomes during the study period, which collectively define the "risk-benefit" profile. To assess the effect of treatment, it is desirable to utilize the entirety of such outcome information. The times to these events, however, may not be observed completely due to, for example, competing risks or administrative censoring. The standard analyses based on the time to the first event, or individual component analyses with respect to each event time, are not ideal. In this paper, we classify each patient's risk-benefit profile, by considering all event times during follow-up, into several clinically meaningful ordinal categories. We first show how to make inferences for the treatment difference in a two-sample setting where categorical data are incomplete due to censoring. We then present a systematic procedure to identify patients who would benefit from a specific treatment using baseline covariate information. To obtain a valid and efficient system for personalized medicine, we utilize a cross-validation method for model building and evaluation and then make inferences using the final selected prediction procedure with an independent data set. The proposal is illustrated with the data from a clinical trial to evaluate a beta-blocker for treating chronic heart failure patients.

Update on research and clinical translation on specific clinical areas: From bench to bedside: How insight in immune pathogenesis can lead to precision medicine of severe juvenile idiopathic arthritis

Despite the enormous progress in the treatment of juvenile idiopathic arthritis (JIA), innovations based on true bench-to-bedside research, performed in JIA patients, are still scarce. This chapter describes novel developments in which clinical innovations go hand in hand with basic discoveries. For the purpose of this review, we will mainly focus on developments in severe forms of JIA, most notably systemic JIA and polyarticular JIA. However, also in less severe forms of JIA, such as oligoarticular JIA, better insight will help to improve diagnosis and treatment. Facilitating the transition from bench to bedside will prove crucial for addressing the major challenges in JIA management. If successful, it will set new standards for a safe, targeted and personalized therapeutic approach for children with JIA.

Vernakalant activates human cardiac K(2P)17.1 background K(+) channels

Atrial fibrillation (AF) contributes significantly to cardiovascular morbidity and mortality. The growing epidemic is associated with cardiac repolarization abnormalities and requires the development of more effective antiarrhythmic strategies. Two-pore-domain K(+) channels stabilize the resting membrane potential and repolarize action potentials. Recently discovered K2P17.1 channels are expressed in human atrium and represent potential targets for AF therapy. However, cardiac electropharmacology of K2P17.1 channels remains to be investigated. This study was designed to elucidate human K2P17.1 regulation by antiarrhythmic drugs. Two-electrode voltage clamp and whole-cell patch clamp electrophysiology was used to record K2P currents from Xenopus oocytes and Chinese hamster ovary (CHO) cells. The class III antiarrhythmic compound vernakalant activated K2P17.1 currents in oocytes an in mammalian cells (EC50,CHO=40 μM) in frequency-dependent manner. K2P17.1 channel activation by vernakalant was specific among K2P channel family members. By contrast, vernakalant reduced K2P4.1 and K2P10.1 currents, in line with K2P2.1 blockade reported earlier. K2P17.1 open rectification characteristics and current-voltage relationships were not affected by vernakalant. The class I drug flecainide did not significantly modulate K2P currents. In conclusion, vernakalant activates K2P17.1 background potassium channels. Pharmacologic K2P channel activation by cardiovascular drugs has not been reported previously and may be employed for personalized rhythm control in patients with AF-associated reduction of K(+) channel function.

Use of the comet assay technique for quick and reliable prediction of in vitro response to chemotherapeutics in breast and colon cancer

Background

Determination of response to chemotherapy is a major requirement of personalized medicine. Resistance, whether developed or native, critically affects a treatment’s success. Single Cell Gel lectrophoresis - also known as a comet assay - is used to detect DNA damage at the level of individual eukaryotic cells. We assessed the use of comet assays in determining response to chemotherapeutic drugs that are widely used in breast and colon cancer.

Results

We treated human breast and colon cancer cell lines with melphalan, cisplatin, mechlorethamine or doxorubicin, as monotherapies. Drug activities varied even in the same cancer types, further demonstrating the heterogeneity of different cancer types.

Conclusion

The comet assay technique can provide reliable and quick results with minimum requirements and is applicable to a wide variety of drugs.

Characterization of distinct classes of differential gene expression in osteoblast cultures from non-syndromic craniosynostosis bone

Craniosynostosis, the premature fusion of one or more skull sutures, occurs in approximately 1 in 2500 infants, with the majority of cases non-syndromic and of unknown etiology. Two common reasons proposed for premature suture fusion are abnormal compression forces on the skull and rare genetic abnormalities. Our goal was to evaluate whether different sub-classes of disease can be identified based on total gene expression profiles. RNA-Seq data were obtained from 31 human osteoblast cultures derived from bone biopsy samples collected between 2009 and 2011, representing 23 craniosynostosis fusions and 8 normal cranial bones or long bones. No differentiation between regions of the skull was detected, but variance component analysis of gene expression patterns nevertheless supports transcriptome-based classification of craniosynostosis. Cluster analysis showed 4 distinct groups of samples; 1 predominantly normal and 3 craniosynostosis subtypes. Similar constellations of sub-types were also observed upon re-analysis of a similar dataset of 199 calvarial osteoblast cultures. Annotation of gene function of differentially expressed transcripts strongly implicates physiological differences with respect to cell cycle and cell death, stromal cell differentiation, extracellular matrix (ECM) components, and ribosomal activity. Based on these results, we propose non-syndromic craniosynostosis cases can be classified by differences in their gene expression patterns and that these may provide targets for future clinical intervention.

Molecular profiling of infiltrating urothelial carcinoma of bladder and nonbladder origin

BACKGROUND

Infiltrating UC represents the second most common genitourinary malignancy. Advanced UC has a poor prognosis and new treatments are needed. Molecular profiling of UC might identify biomarkers associated with targeted therapies or chemotherapeutics, providing physicians with new treatment options.

MATERIALS AND METHODS

Five hundred thirty-seven cases of locally advanced or metastatic UC of the bladder, 74 nonbladder, and 55 nonurothelial bladder cancers were profiled using mutation analysis, in situ hybridization, and immunohistochemistry assays for biomarkers predictive of therapy response.

RESULTS

Molecular profiling of UC showed high overexpression of topoisomerase 2α, common phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit alpha and/or phosophatase and tensin homolog (PTEN) alterations in nonbladder (27%) and bladder UC (21%), and rare gene mutations across subtypes. Compared with nonbladder, bladder UC consistently exhibited more frequent abnormal protein expression, including HER2 (10% vs. 3%; P = .04), tyrosine protein c-Kit receptor kinases (11% vs. 5%), c-Met proto-oncogene, receptor tyrosine kinases (25% vs. 8%), androgen receptor (16% vs. 6%), O(6)-methylguanine-methyltransferase (63% vs. 43%), ribonucleotide reductase M1 (32% vs. 11%), Serum protein acidic and rich in cysteine (SPARC) (69% vs. 33%), and topoisomerase 1 (63% vs. 39%). Bladder UC also exhibited increased amplification of HER2 (12% vs. 2%; P = .06).

CONCLUSION

Comprehensive molecular profiling of UC identified a large number of biomarkers aberrations that might direct treatment in conventional chemotherapies and targeted therapies, not currently recommended in this population. As a group, bladder UC exhibited higher levels of actionable biomarkers, suggesting that UC from different primary sites and non-UC are driven by different molecular pathways. These differences could have clinical implications resulting in different treatment regimens depending on the site of origin of UC.

An Indian eye to personalized medicine

Acknowledging the successful sequencing of the human genome and the valuable insights it has rendered, genetic drafting of non-human organisms can further enhance the understanding of modern biology. The price of sequencing technology has plummeted with time, and there is a noticeable enhancement in its implementation and recurrent usage. Sequenced genome information can be contained in a microarray chip, and then processed by a computer system for inferring analytics and predictions. Specifically, smart cards have been significantly applicable to assimilate and retrieve complex data, with ease and implicit mobility. Herein, we propose "The G-Card", a development with respect to the prevalent smart card, and an extension to the Electronic Health Record (EHR), that will hold the genome sequence of an individual, so that the medical practitioner can better investigate irregularities in a patient's health and hence recommend a precise prognosis.

Digital pathology and image analysis in tissue biomarker research

Digital pathology and the adoption of image analysis have grown rapidly in the last few years. This is largely due to the implementation of whole slide scanning, advances in software and computer processing capacity and the increasing importance of tissue-based research for biomarker discovery and stratified medicine. This review sets out the key application areas for digital pathology and image analysis, with a particular focus on research and biomarker discovery. A variety of image analysis applications are reviewed including nuclear morphometry and tissue architecture analysis, but with emphasis on immunohistochemistry and fluorescence analysis of tissue biomarkers. Digital pathology and image analysis have important roles across the drug/companion diagnostic development pipeline including biobanking, molecular pathology, tissue microarray analysis, molecular profiling of tissue and these important developments are reviewed. Underpinning all of these important developments is the need for high quality tissue samples and the impact of pre-analytical variables on tissue research is discussed. This requirement is combined with practical advice on setting up and running a digital pathology laboratory. Finally, we discuss the need to integrate digital image analysis data with epidemiological, clinical and genomic data in order to fully understand the relationship between genotype and phenotype and to drive discovery and the delivery of personalized medicine.

[Circulating tumor cells: cornerstone of personalized medicine]

Cancer treatment has evolved toward personalized medicine. It is mandatory for clinicians to ascertain tumor biological features in order to optimize patients' treatment. Identification and characterization of circulating tumor cells demonstrated a prognostic value in many solid tumors. Here, we describe the main technologies for identification and characterization of circulating tumor cells and their clinical application in gynecologic and breast cancers.

Personalized medicine in hematology - A landmark from bench to bed

Personalized medicine is the cornerstone of medical practice. It tailors treatments for specific conditions of an affected individual. The borders of personalized medicine are defined by limitations in technology and our understanding of biology, physiology and pathology of various conditions. Current advances in technology have provided physicians with the tools to investigate the molecular makeup of the disease. Translating these molecular make-ups to actionable targets has led to the development of small molecular inhibitors. Also, detailed understanding of genetic makeup has allowed us to develop prognostic markers, better known as companion diagnostics. Current attempts in the development of drug delivery systems offer the opportunity of delivering specific inhibitors to affected cells in an attempt to reduce the unwanted side effects of drugs.

Choosing inclusion criteria that minimize the time and cost of clinical trials

AIM: To present statistical tools to model and optimize the cost of a randomized clinical trial as a function of the stringency of patient inclusion criteria.

METHODS: We consider a two treatment, dichotomous outcome trial that includes a proportion of patients who are strong responders to the tested intervention. Patients are screened for inclusion using an arbitrary number of test results that are combined into an aggregate suitability score. The screening score is regarded as a diagnostic test for the responsive phenotype, having a specific cutoff value for inclusion and a particular sensitivity and specificity. The cutoff is a measure of stringency of inclusion criteria. Total cost is modeled as a function of the cutoff value, number of patients screened, the number of patients included, the case occurrence rate, response probabilities for control and experimental treatments, and the trial duration required to produce a statistically significant result with a specified power. Regression methods are developed to estimate relevant model parameters from pilot data in an adaptive trial design.

RESULTS: The patient numbers and total cost are strongly related to the choice of the cutoff for inclusion. Clear cost minimums exist between 5.6 and 6.1 on a representative 10-point scale of exclusiveness. Potential cost savings for typical trial scenarios range in millions of dollars. As the response rate for controls approaches 50%, the proper choice of inclusion criteria can mean the difference between a successful trial and a failed trial.

CONCLUSION: Early formal estimation of optimal inclusion criteria allows planning of clinical trials to avoid high costs, excessive delays, and moral hazards of Type II errors.

Development of [18F]afatinib as new TKI-PET tracer for EGFR positive tumors

INTRODUCTION

Afatinib is an irreversible ErbB family blocker that was approved for the treatment of EGFR mutated non-small cell lung cancer in 2013. Positron emission tomography (PET) with fluorine-18 labeled afatinib provides a means to obtain improved understanding of afatinib tumor disposition in vivo. PET imaging with [(18)F]afatinib may also provide a method to select treatment responsive patients. The aim of this study was to label afatinib with fluorine-18 and evaluate its potential as TKI-PET tracer in tumor bearing mice.

METHODS

A radiochemically novel coupling, using peptide coupling reagent BOP, was explored and optimized to synthesize [(18)F]afatinib, followed by a metabolite analysis and biodistribution studies in two clinically relevant lung cancer cell lines, xenografted in nude mice.

RESULTS

A reliable [(18)F]afatinib radiosynthesis was developed and the tracer could be produced in yields of 17.0 ± 2.5% calculated from [(18)F]F(-) and >98% purity. The identity of the product was confirmed by co-injection on HPLC with non-labeled afatinib. Metabolite analysis revealed a moderate rate of metabolism, with >80% intact tracer in plasma at 45 min p.i. Biodistribution studies revealed rapid tumor accumulation and good retention for a period of at least 2 hours, while background tissues showed rapid clearance of the tracer.

CONCLUSION

We have developed a method to synthesize [(18)F]afatinib and related fluorine-18 labeled 4-anilinoquinazolines. [(18)F]Afatinib showed good stability in vivo, justifying further evaluation as a TKI-PET tracer.

What you are missing could matter: a rare, complex BRAF mutation affecting codons 599, 600, and 601 uncovered by next generation sequencing

Testing for somatic mutations in tumor samples is becoming standard practice in a number of tumor types where targeted therapies are available. Since clinical care is dependent on the identification of the presence or absence of specific mutations, it is important that these mutations be identified consistently and accurately. Here we identify in a patient with metastatic melanoma a novel, complex mutation involving BRAF c.1798A>T (p.T599T), c.1799T>A (p.V600E), and c.1803A>T (p.K601N) that was identified by next generation sequencing but not by standard testing methods. The patient was started on a combination therapy inhibiting both BRAF and MEK, and demonstrated a dramatic clinical response. This case highlights the need for improved methods of mutation testing in tumor samples and exposes a pitfall in allele-specific testing methods that can be overcome using next generation sequencing.

Technical desiderata for the integration of genomic data with clinical decision support

The ease with which whole genome sequence (WGS) information can be obtained is rapidly approaching the point where it can become useful for routine clinical care. However, significant barriers will inhibit widespread adoption unless clinicians are able to effectively integrate this information into patient care and decision-making. Electronic health records (EHR) and clinical decision support (CDS) systems may play a critical role in this integration. A previously published technical desiderata focused primarily on the integration of genomic data into the EHR. This manuscript extends the previous desiderata by specifically addressing needs related to the integration of genomic information with CDS. The objective of this study is to develop and validate a guiding set of technical desiderata for supporting the clinical use of WGS through CDS. A panel of domain experts in genomics and CDS developed a proposed set of seven additional requirements. These desiderata were reviewed by 63 experts in genomics and CDS through an online survey and refined based on the experts' comments. These additional desiderata provide important guiding principles for the technical development of CDS capabilities for the clinical use of WGS information.

Determination of personalized diabetes treatment plans using a two-delay model

Diabetes cases worldwide have risen steadily over the past few decades, lending urgency to the search for more efficient, effective, and personalized ways to treat the disease. Current treatment strategies, however, may fail to maintain oscillations in blood glucose concentration that naturally occur multiple times per day, an important element of normal human physiology. Building upon recent successes in mathematical modeling of the human glucose-insulin system, we show that both food intake and insulin therapy likely demand increasingly precise control over insulin sensitivity if oscillations at a healthy average glucose concentration are to be maintained. We then model and describe personalized treatment options for patients with diabetes that maintain these oscillations. We predict that for a person with type II diabetes, both blood glucose levels can be controlled and healthy oscillations maintained when the patient gets an hour of daily exercise and is placed on a combination of Metformin and sulfonylurea drugs. We note that insulin therapy and an additional hour of exercise will reduce the patient׳s need for sulfonylureas. Results of a modeling analysis suggest that, with constant nutrition and controlled exercise, the blood glucose levels of a person with type I diabetes can be properly controlled with insulin infusion between 0.45 and 0.7μU/mlmin. Lastly, we note that all suggested strategies rely on existing clinical techniques and established treatment measures, and so could potentially be of immediate use in the design of an artificial pancreas.

Personalized approach to primary and secondary prevention of ischemic stroke

Primary and secondary prevention of ischemic stroke represents a significant part of stroke management and health care. Although there are official guidelines concerning stroke management, new knowledge are introduced to them with a slight delay. This article provides an overview of current information on primary and secondary prevention of ischemic stroke. It summarizes information especially in the field of cardioembolic stroke, the use of new anticoagulants and the management of carotid stenosis based on the results of recent clinical studies. The optimal approach in stroke management is to follow these recommendations, to know new strategies and to apply an individual personalized approach in our clinical decisions.

Epoxyeicosatrienoic acids and cardioprotection: the road to translation

Cardiovascular disease, including acute myocardial infarction (AMI), is the leading cause of morbidity and mortality globally, despite well-established treatments. The discovery and development of novel therapeutics that prevent the progression of devastating consequences following AMI are thus important in reducing the global burden of this devastating disease. Scientific evidence for the protective effects of epoxyeicosatrienoic acids (EETs) in the cardiovascular system is rapidly emerging and suggests that promoting the effects of these cytochrome P450-derived epoxyeicosanoids is a potentially viable clinical therapeutic strategy. Through a translational lens, this review will provide insight into the potential clinical utility of this therapeutic strategy for AMI by 1) outlining the known cardioprotective effects of EETs and underlying mechanisms demonstrated in preclinical models of AMI with a particular focus on myocardial ischemia-reperfusion injury, 2) describing studies in human cohorts that demonstrate a relationship between EETs and associated pathways with coronary artery disease risk, and 3) discussing preclinical and clinical areas that require further investigation in order to increase the probability of successfully translating this rapidly emerging body of evidence into a clinically applicable therapeutic strategy for AMI.

Discussion of combining biomarkers to optimize patient treatment recommendations

Kang, Janes and Huang propose an interesting boosting method to combine biomarkers for treatment selection. The method requires modeling the treatment effects using markers. We discuss an alternative method, outcome weighted learning. This method sidesteps the need for modeling the outcomes, and thus can be more robust to model misspecification.

Seeing through the trick of cancer cells via 2D gels

The advancement of modern therapy concepts has dramatically extended the postsurvival rates of patients with malignant gastric cancer. However, a remaining setback is the drug resistance of recurrent cancer, which casts a dark shadow over disease prognosis. The original work of Klein et al. [Proteomics Clin. Appl. 2013, 7, 813-824] has outlined a rational experimental approach to decipher the mechanistic pathway of cancer drug resistance by proteomic approach. They used gel-based comparative proteomics to analyze the nuclear proteome of a human gastric cancer cell line (AGS) with and without inactivation of hypoxia-inducible factor 1 (HIF-1), a transcription factor and master regulator of hypoxia adaptation. Using the classical 2DE-MS approach, these researchers observed 163 HIF-1 responsive proteins, among which over half of them could be confidently identified by MS. From this large dataset, the authors proposed an enhanced nuclear translocation of some proteasomal proteins upon inactivation of HIF-1. Overall, this work appropriately used proteomics as a hypothesis-free, top-down approach to dissect imperative clinical problems.

Evaluating marker-guided treatment selection strategies

A potential venue to improve healthcare efficiency is to effectively tailor individualized treatment strategies by incorporating patient level predictor information such as environmental exposure, biological, and genetic marker measurements. Many useful statistical methods for deriving individualized treatment rules (ITR) have become available in recent years. Prior to adopting any ITR in clinical practice, it is crucial to evaluate its value in improving patient outcomes. Existing methods for quantifying such values mainly consider either a single marker or semi-parametric methods that are subject to bias under model misspecification. In this article, we consider a general setting with multiple markers and propose a two-step robust method to derive ITRs and evaluate their values. We also propose procedures for comparing different ITRs, which can be used to quantify the incremental value of new markers in improving treatment selection. While working models are used in step I to approximate optimal ITRs, we add a layer of calibration to guard against model misspecification and further assess the value of the ITR non-parametrically, which ensures the validity of the inference. To account for the sampling variability of the estimated rules and their corresponding values, we propose a resampling procedure to provide valid confidence intervals for the value functions as well as for the incremental value of new markers for treatment selection. Our proposals are examined through extensive simulation studies and illustrated with the data from a clinical trial that studies the effects of two drug combinations on HIV-1 infected patients.

Managing customization in health care: a framework derived from the services sector literature

Organizations that provide health services are increasingly in need of systems and approaches that will enable them to be more responsive to the needs and wishes of their clients. Two recent trends, namely, patient-centered care (PCC) and personalized medicine, are first steps in the customization of care. PCC shifts the focus away from the disease to the patient. Personalized medicine, which relies heavily on genetics, promises significant improvements in the quality of healthcare through the development of tailored and targeted drugs. We need to understand how these two trends can be related to customization in healthcare delivery and, because customization often entails extra costs, to define new business models. This article analyze how customization of the care process can be developed and managed in healthcare. Drawing on relevant literature from various services sectors, we have developed a framework for the implementation of customization by the hospital managers and caregivers involved in care pathways.

Personalized prediction of chronic wound healing: an exponential mixed effects model using stereophotogrammetric measurement

STUDY AIM

Stereophotogrammetric digital imaging enables rapid and accurate detailed 3D wound monitoring. This rich data source was used to develop a statistically validated model to provide personalized predictive healing information for chronic wounds.

MATERIALS

147 valid wound images were obtained from a sample of 13 category III/IV pressure ulcers from 10 individuals with spinal cord injury.

METHODS

Statistical comparison of several models indicated the best fit for the clinical data was a personalized mixed-effects exponential model (pMEE), with initial wound size and time as predictors and observed wound size as the response variable. Random effects capture personalized differences.

RESULTS

Other models are only valid when wound size constantly decreases. This is often not achieved for clinical wounds. Our model accommodates this reality. Two criteria to determine effective healing time outcomes are proposed: r-fold wound size reduction time, t(r-fold), is defined as the time when wound size reduces to 1/r of initial size. t(δ) is defined as the time when the rate of the wound healing/size change reduces to a predetermined threshold δ < 0. Healing rate differs from patient to patient. Model development and validation indicates that accurate monitoring of wound geometry can adaptively predict healing progression and that larger wounds heal more rapidly. Accuracy of the prediction curve in the current model improves with each additional evaluation.

CONCLUSION

Routine assessment of wounds using detailed stereophotogrammetric imaging can provide personalized predictions of wound healing time. Application of a valid model will help the clinical team to determine wound management care pathways.

Molecular pathology of malignant melanoma: changing the clinical practice paradigm toward a personalized approach

Melanocytic proliferations are notoriously difficult lesions to evaluate histologically, even among experts, as there is a lack of objective, highly reproducible criteria, which can be broadly applied to the wide range of melanocytic lesions encountered in daily practice. These difficult diagnoses are undeniably further compounded by the substantial medicolegal risks of an "erroneous" diagnosis. Molecular information and classification of melanocytic lesions is already vast and constantly expanding. The application of molecular techniques for the diagnosis of benignity or malignancy is, at times, confusing and limits its utility if not used properly. In addition, current and future therapies will necessitate molecular classification of melanoma into one of several distinct subtypes for appropriate patient-specific therapy. An understanding of what different molecular markers can and cannot predict is of the utmost importance. We discuss both mutational analysis and chromosomal gains/losses to help clarify this continually developing and confusing facet of pathology.

Predictive genomics: a cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data

Tumor genome sequencing leads to documenting thousands of DNA mutations and other genomic alterations. At present, these data cannot be analyzed adequately to aid in the understanding of tumorigenesis and its evolution. Moreover, we have little insight into how to use these data to predict clinical phenotypes and tumor progression to better design patient treatment. To meet these challenges, we discuss a cancer hallmark network framework for modeling genome sequencing data to predict cancer clonal evolution and associated clinical phenotypes. The framework includes: (1) cancer hallmarks that can be represented by a few molecular/signaling networks. 'Network operational signatures' which represent gene regulatory logics/strengths enable to quantify state transitions and measures of hallmark traits. Thus, sets of genomic alterations which are associated with network operational signatures could be linked to the state/measure of hallmark traits. The network operational signature transforms genotypic data (i.e., genomic alterations) to regulatory phenotypic profiles (i.e., regulatory logics/strengths), to cellular phenotypic profiles (i.e., hallmark traits) which lead to clinical phenotypic profiles (i.e., a collection of hallmark traits). Furthermore, the framework considers regulatory logics of the hallmark networks under tumor evolutionary dynamics and therefore also includes: (2) a self-promoting positive feedback loop that is dominated by a genomic instability network and a cell survival/proliferation network is the main driver of tumor clonal evolution. Surrounding tumor stroma and its host immune systems shape the evolutionary paths; (3) cell motility initiating metastasis is a byproduct of the above self-promoting loop activity during tumorigenesis; (4) an emerging hallmark network which triggers genome duplication dominates a feed-forward loop which in turn could act as a rate-limiting step for tumor formation; (5) mutations and other genomic alterations have specific patterns and tissue-specificity, which are driven by aging and other cancer-inducing agents. This framework represents the logics of complex cancer biology as a myriad of phenotypic complexities governed by a limited set of underlying organizing principles. It therefore adds to our understanding of tumor evolution and tumorigenesis, and moreover, potential usefulness of predicting tumors' evolutionary paths and clinical phenotypes. Strategies of using this framework in conjunction with genome sequencing data in an attempt to predict personalized drug targets, drug resistance, and metastasis for cancer patients, as well as cancer risks for healthy individuals are discussed. Accurate prediction of cancer clonal evolution and clinical phenotypes will have substantial impact on timely diagnosis, personalized treatment and personalized prevention of cancer.

Cell therapies and regenerative medicine

Molecular and cell biology has resulted in major advances in our understanding of disease pathogenesis as well as in novel strategies for the diagnosis, therapy and prevention of human diseases. Based on modern molecular, genetic and biochemical methodologies, it is on the one hand possible to identify disease-related point mutations and single nucleotide polymorphisms, for example. On the other hand, using high throughput array and other technologies, it is for example possible to simultaneously analyze thousands of genes or gene products (RNA and proteins), resulting in an individual gene or gene expression profile ('signature'). Such data increasingly allow defining the individual disposition for a given disease and predicting disease prognosis as well as the efficacy of therapeutic strategies in the individual patient ('personalized medicine'). At the same time, the basic discoveries in cell biology, including embryonic and adult stem cells, induced pluripotent stem cells, genetically modified cells and others, have moved regenerative medicine into the center of biomedical research worldwide with a major translational impact on tissue engineering as well as transplantation medicine. All these aspects have greatly contributed to the recent advances in regenerative medicine and the development of novel concepts for the treatment of many human diseases, including liver diseases.

Model predictive control for optimally scheduling intermittent androgen suppression of prostate cancer

Mathematical modeling of prostate cancer under intermittent androgen suppression revealed that we may be able to delay relapse by optimally scheduling the hormone therapy for each patient. However, our previous study showed the difficulty of the scheduling by minimizing the maximal tumor growth rate because the transient dynamics is also important and can help to delay the relapse for a finite time. Here, we propose to use model predictive control for scheduling intermittent androgen suppression. We find that model predictive control tends to delay the relapse of prostate specific antigen more than the method with minimizing the maximal tumor growth rate. Therefore, model predictive control is a promising approach for practically applying the mathematical model to optimally schedule intermittent androgen suppression.

Theranostic applications of antibodies in oncology

Targeted therapies, including antibodies, are becoming increasingly important in cancer therapy. Important limitations, however, are that not every patient benefits from a specific antibody therapy and that responses could be short-lived due to acquired resistance. In addition, targeted therapies are quite expensive and are not completely devoid of side-effects. This urges the need for accurate patient selection and response monitoring. An important step towards personalizing antibody treatment could be the implementation of theranostics. Antibody theranostics combine the diagnostic and therapeutic potential of an antibody, thereby selecting those patients who are most likely to benefit from antibody treatment. This review focuses on the clinical application of theranostic antibodies in oncology. It provides detailed information concerning the suitability of antibodies for theranostics, the different types of theranostic tests available and summarizes the efficacy of theranostic antibodies used in current clinical practice. Advanced theranostic applications, including radiolabeled antibodies for non-invasive functional imagining, are also addressed. Finally, we discuss the importance of theranostics in the emerging field of personalized medicine and critically evaluate recent data to determine the best way to apply antibody theranostics in the future.

The Theranostic Path to Personalized Nanomedicine

Advances in nanotechnology and chemical engineering have led to the development of many different drug delivery systems. These 1-100(0) nm-sized carrier materials aim to increase drug concentrations at the pathological site, while avoiding their accumulation in healthy non-target tissues, thereby improving the balance between the efficacy and the toxicity of systemic (chemo-) therapeutic interventions. An important advantage of such nanocarrier materials is the ease of incorporating both diagnostic and therapeutic entities within a single formulation, enabling them to be used for theranostic purposes. We here describe the basic principles of using nanomaterials for targeting therapeutic and diagnostic agents to pathological sites, and we discuss how nanotheranostics and image-guided drug delivery can be used to personalize nanomedicine treatments.

Personalized medicine for breast cancer: it is a new day!

Breast cancer remains the most common cancer diagnosed in women in the United States and is second only to lung cancer as a cause of cancer mortality. Breast cancer has become the prototypical solid tumor where targets have been identified within the tumor allowing for a personalized approach of systemic therapy.

Mcl-1 dependence predicts response to vorinostat and gemtuzumab ozogamicin in acute myeloid leukemia

Older adults with acute myeloid leukemia (AML) are commonly considered for investigational therapies, which often only benefit subsets of patients. In this study, we assessed whether BH3 profiling of apoptotic functionality could predict outcomes following treatment with vorinostat (histone deacetylase inhibitor) and gemtuzumab ozogamicin (GO; CD33-targeted immunoconjugate). Flow cytometry of BH3 peptide priming with Noxa (anti-apoptotic protein Mcl-1 modulator) correlated with remission induction (p=.026; AUC=0.83 [CI: 0.65-1.00; p=.00042]: AUC=0.88 [CI:0.75-1.00] with age adjustment) and overall survival (p=.027 logistic regression; AUC=0.87 [0.64-1.00; p=.0017]). This Mcl-1-dependence suggests a pivotal role of Bcl-2 family protein-mediated apoptosis to vorinostat/GO in AML patients.

Cystic fibrosis: toward personalized therapies

Cystic fibrosis (CF), the most common, life-threatening monogenetic disease in Caucasians, is caused by mutations in the CFTR gene, encoding a cAMP- and cGMP-regulated epithelial chloride channel. Symptomatic therapies treating end-organ manifestations have increased the life expectancy of CF patients toward a mean of 40 years. The recent development of CFTR-targeted drugs that emerged from high-throughput screening and are capable of correcting the basic defect promises to transform the therapeutic landscape from a trial-and-error prescription to personalized medicine. This stratified approach is tailored to a specific functional class of mutations in CFTR, but can be refined further to an individual level by exploiting recent advances in ex vivo drug testing methods. These tests range from CFTR functional measurements in rectal biopsies donated by a CF patient to the use of patient-derived intestinal or pulmonary organoids. Such organoids may serve as an inexhaustible source of epithelial cells that can be stored in biobanks and allow medium- to high-throughput screening of CFTR activators, correctors and potentiators on the basis of a simple microscopic assay monitoring organoid swelling. Thus the recent breakthrough in stem cell biology allowing the culturing of mini-organs from individual patients is not only relevant for future stem cell therapy, but may also allow the preclinical testing of new drugs or combinations that are optimally suited for an individual patient.

Genetic modifiers of obesity and bariatric surgery outcomes

Obesity is a highly heritable trait. While acute and chronic changes in body weight or obesity-related comorbidities are heavily influenced by environmental factors, there are still strong genomic modifiers that help account for inter-subject variability in baseline traits and in response to interventions. This review is intended to provide an up-to-date overview of our current understanding of genetic influences on obesity, with emphasis on genetic modifiers of baseline traits and responses to intervention. We begin by reviewing how genetic variants can influence obesity. We then examine genetic modifiers of weight loss via different intervention strategies, focusing on known and potential modifiers of surgical weight loss outcomes. We will pay particular attention to the effects of patient age on outcomes, addressing the risks and benefits of adopting early intervention strategies. Finally, we will discuss how the field of bariatric surgery can leverage knowledge of genetic modifiers to adopt a personalized medicine approach for optimal outcomes across this widespread and diverse patient population.

Essential elements of personalized medicine

OBJECTIVES

Genomic information has been promoted as the basis for "personalized" health care. We considered the benefits provided by genomic testing in context of the concept of personalized medicine.

MATERIALS AND METHODS

We evaluated current and potential uses of genomic testing in health care, using prostate cancer as an example, and considered their implications for individualizing or otherwise improving health care.

RESULTS AND CONCLUSIONS

Personalized medicine is most accurately seen as a comprehensive effort to tailor health care to the individual, spanning multiple dimensions. While genomic tests will offer many potential opportunities to improve the delivery of care, including the potential for genomic research to offer opportunities to improve prostate cancer screening and treatment, such advances do not in themselves constitute a paradigm shift in the delivery of health care. Rather, personalized medicine is based on a partnership between clinician and patient that utilizes shared decision making to determine the best health care options among the available choices, weighing the patient's personal values and preferences together with clinical findings. This approach is particularly important for difficult clinical decisions involving uncertainty and trade-offs, such as those involved in prostate cancer screening and management. The delivery of personalized medicine also requires adequate health care access and assurance that basic health needs have been met. Substantial research investment will be needed to identify how genomic tests can contribute to this effort.

Communication with parents concerning withholding or withdrawing of life-sustaining interventions in neonatology

The nature and content of the conversations between the healthcare team and the parents concerning withholding or withdrawing of life-sustaining interventions for neonates vary greatly. These depend upon the status of the infant; for some neonates, death may be imminent, while other infants may be relatively stable, yet with a potential risk for surviving with severe disability. Healthcare providers also need to communicate with prospective parents before the birth of premature infants or neonates with uncertain outcomes. Many authors recommend that parents of fragile neonates receive detailed information about the potential outcomes of their children and the choices they have provided in an unbiased and empathetic manner. However, the exact manner this is to be achieved in clinical practice remains unclear. Parents and healthcare providers may have different values regarding the provision of life-sustaining interventions. However, parents base their decisions on many factors, not just probabilities. The role of emotions, regret, hope, quality of life, resilience, and relationships is rarely discussed. End-of-life discussions with parents should be individualized and personalized. This article suggests ways to personalize these conversations. The mnemonic "SOBPIE" may help providers have fruitful discussions: (1) What is the Situation? Is the baby imminently dying? Should withholding or withdrawing life-sustaining interventions be considered? (2) Opinions and options: personal biases of healthcare professionals and alternatives for patients. (3) Basic human interactions. (4) Parents: their story, their concerns, their needs, and their goals. (5) Information: meeting parental informational needs and providing balanced information. (6) Emotions: relational aspects of decision making which include the following: emotions, social supports, coping with uncertainty, adaptation, and resilience. In this paper, we consider some aspects of this complex process.

Cancer nanotechnology: the impact of passive and active targeting in the era of modern cancer biology

Cancer nanotherapeutics are progressing at a steady rate; research and development in the field has experienced an exponential growth since early 2000's. The path to the commercialization of oncology drugs is long and carries significant risk; however, there is considerable excitement that nanoparticle technologies may contribute to the success of cancer drug development. The pace at which pharmaceutical companies have formed partnerships to use proprietary nanoparticle technologies has considerably accelerated. It is now recognized that by enhancing the efficacy and/or tolerability of new drug candidates, nanotechnology can meaningfully contribute to create differentiated products and improve clinical outcome. This review describes the lessons learned since the commercialization of the first-generation nanomedicines including DOXIL® and Abraxane®. It explores our current understanding of targeted and non-targeted nanoparticles that are under various stages of development, including BIND-014 and MM-398. It highlights the opportunities and challenges faced by nanomedicines in contemporary oncology, where personalized medicine is increasingly the mainstay of cancer therapy. We revisit the fundamental concepts of enhanced permeability and retention effect (EPR) and explore the mechanisms proposed to enhance preferential "retention" in the tumor, whether using active targeting of nanoparticles, binding of drugs to their tumoral targets or the presence of tumor associated macrophages. The overall objective of this review is to enhance our understanding in the design and development of therapeutic nanoparticles for treatment of cancers.

mTOR is a promising therapeutical target in a subpopulation of pancreatic adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) remains a highly lethal disease, unusually resistant against therapy. It is generally felt that stratification of patients for personalized medicine is the way forward. Here, we report that a subpopulation of PDACs shows strong activation of the mTOR signaling cassette. Moreover, we show that inhibition of mTOR in pancreatic cancer cell lines showing high levels of mTOR signaling is associated with cancer cell death. Finally, we show using fine needle biopsies the existence of a subpopulation of PDAC patients with high activation of the mTOR signaling cassette and provide evidence that inhibition of mTOR might be clinically useful for this group. Thus, our results define an unrecognized subpopulation of PDACs, characterized by high activation of mTOR and show that identification of this specific patient group in the early phase of diagnosis is feasible.

Redox biology: interface of the exposome with the proteome, epigenome and genome

The exposome is the cumulative measure of environmental influences and associated biological responses throughout lifespan, including exposures from the environment, diet, behavior and endogenous processes. Much of the direct interaction of an individual's exposome involves redox biology as the body responds to environmental, dietary and behavioral risk factors of disease. The present commentary addresses this critical interface and the need for redox biologists to lead development of concepts and strategies to sequence the exposome.

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The exposome is a sequence of lifelong environmental exposures to complement the genome.

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Redox biology provides a mechanistic bridge between the exposome and the genome.

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Redox biologists should lead efforts to sequence the exposome and integrate its use with the genome for personalized medicine.

Set-valued dynamic treatment regimes for competing outcomes

Dynamic treatment regimes (DTRs) operationalize the clinical decision process as a sequence of functions, one for each clinical decision, where each function maps up-to-date patient information to a single recommended treatment. Current methods for estimating optimal DTRs, for example Q-learning, require the specification of a single outcome by which the "goodness" of competing dynamic treatment regimes is measured. However, this is an over-simplification of the goal of clinical decision making, which aims to balance several potentially competing outcomes, for example, symptom relief and side-effect burden. When there are competing outcomes and patients do not know or cannot communicate their preferences, formation of a single composite outcome that correctly balances the competing outcomes is not possible. This problem also occurs when patient preferences evolve over time. We propose a method for constructing DTRs that accommodates competing outcomes by recommending sets of treatments at each decision point. Formally, we construct a sequence of set-valued functions that take as input up-to-date patient information and give as output a recommended subset of the possible treatments. For a given patient history, the recommended set of treatments contains all treatments that produce non-inferior outcome vectors. Constructing these set-valued functions requires solving a non-trivial enumeration problem. We offer an exact enumeration algorithm by recasting the problem as a linear mixed integer program. The proposed methods are illustrated using data from the CATIE schizophrenia study.

Dynamic treatment regimes: technical challenges and applications

Dynamic treatment regimes are of growing interest across the clinical sciences because these regimes provide one way to operationalize and thus inform sequential personalized clinical decision making. Formally, a dynamic treatment regime is a sequence of decision rules, one per stage of clinical intervention. Each decision rule maps up-to-date patient information to a recommended treatment. We briefly review a variety of approaches for using data to construct the decision rules. We then review a critical inferential challenge that results from nonregularity, which often arises in this area. In particular, nonregularity arises in inference for parameters in the optimal dynamic treatment regime; the asymptotic, limiting, distribution of estimators are sensitive to local perturbations. We propose and evaluate a locally consistent Adaptive Confidence Interval (ACI) for the parameters of the optimal dynamic treatment regime. We use data from the Adaptive Pharmacological and Behavioral Treatments for Children with ADHD Trial as an illustrative example. We conclude by highlighting and discussing emerging theoretical problems in this area.

Biomarkers in rare neuromuscular diseases

Neuromuscular diseases (NMDs) comprise a range of rare disorders that include both hereditary peripheral neuropathies and myopathies. The heterogeneity and rarity of neuromuscular disorders are challenges for researchers seeking to develop effective diagnosis and treatment strategies. In particular, clinical trials of new therapies are made more difficult due to lack of reliable and monitorable clinical outcome measures. Biomarkers could be a way to speed up research in this field, shedding light on the pathophysiological mechanisms behind such diseases and providing invaluable tools for monitoring their progression, prognosis and response to drug treatment. Furthermore, biomarkers could represent a surrogate endpoint for clinical trials, enabling better stratification of patient cohorts through more accurate diagnosis and prognosis prediction. This review summarizes the types, applications, characteristics and best strategies for biomarker discovery to date.

Developing an institutional cancer biorepository for personalized medicine

High quality human biospecimens, such as tissue, blood, cell derivatives, and associated patient clinical information, are key elements of a scientific infrastructure that supports discovery and identification of molecular biomarkers and diagnostic agents. The goal of most biorepositories is to collect, process, store, and distribute human biospecimen for use in basic, translational and clinical research. A biorepository serving as the central hub provides investigators with an invaluable resource with appropriately examined and characterized biospecimens with associated patient clinical information. Expertise in standardization, quality control, and information technology, and awareness of cutting edge research developments are generally required for biorepository development and management. The availability of low cost whole genome profiles of individual tumors has opened up new possibilities for personalized medicine to deliver the most appropriate treatments to individual patients with minimal toxicity. A biorepository in support of personalized medicine thus requires the highest standards of operation and adequate funding, training and certification. This review provides an overview of the development of an institutional cancer biorepository for clinical research and personalized medicine advancement.

Non-linear EEG analyses predict non-response to rTMS treatment in major depressive disorder

OBJECTIVE

Several linear electroencephalographic (EEG) measures at baseline have been demonstrated to be associated with treatment outcome after antidepressant treatment. In this study we investigated the added value of non-linear EEG metrics in the alpha band in predicting treatment outcome to repetitive transcranial magnetic stimulation (rTMS).

METHODS

Subjects were 90 patients with major depressive disorder (MDD) and a group of 17 healthy controls (HC). MDD patients were treated with rTMS and psychotherapy for on average 21 sessions. Three non-linear EEG metrics (Lempel-Ziv Complexity (LZC); False Nearest Neighbors and Largest Lyapunov Exponent) were applied to the alpha band (7-13 Hz) for two 1-min epochs EEG and the association with treatment outcome was investigated.

RESULTS

No differences were found between a subgroup of unmedicated MDD patients and the HC. Non-responders showed a significant decrease in LZC from minute 1 to minute 2, whereas the responders and HC showed an increase in LZC.

CONCLUSIONS

There is no difference in EEG complexity between MDD and HC and the change in LZC across time demonstrated value in predicting outcome to rTMS.

SIGNIFICANCE

This is the first study demonstrating utility of non-linear EEG metrics in predicting treatment outcome in MDD.

Establishment of a human 3D lung cancer model based on a biological tissue matrix combined with a Boolean in silico model

For the development of new treatment strategies against cancer, understanding signaling networks and their changes upon drug response is a promising approach to identify new drug targets and biomarker profiles. Pre‐requisites are tumor models with multiple read‐out options that accurately reflect the clinical situation. Tissue engineering technologies offer the integration of components of the tumor microenvironment which are known to impair drug response of cancer cells. We established three‐dimensional (3D) lung carcinoma models on a decellularized tissue matrix, providing a complex microenvironment for cell growth. For model generation, we used two cell lines with (HCC827) or without (A549) an activating mutation of the epidermal growth factor receptor (EGFR), exhibiting different sensitivities to the EGFR inhibitor gefitinib. EGFR activation in HCC827 was inhibited by gefitinib, resulting in a significant reduction of proliferation (Ki‐67 proliferation index) and in the induction of apoptosis (TUNEL staining, M30‐ELISA). No significant effect was observed in conventional cell culture. Results from the 3D model correlated with the results of an in silico model that integrates the EGFR signaling network according to clinical data. The application of TGFβ1 induced tumor cell invasion, accompanied by epithelial–mesenchymal transition (EMT) both in vitro and in silico. This was confirmed in the 3D model by acquisition of mesenchymal cell morphology and modified expression of fibronectin, E‐cadherin, β‐catenin and mucin‐1. Quantitative read‐outs for proliferation, apoptosis and invasion were established in the complex 3D tumor model. The combined in vitro and in silico model represents a powerful tool for systems analysis.

Combination of a human 3D lung tumor tissue model with a Boolean in silico Model.

Establishment of in silico signaling network topology for personalized medicine.

Significant decrease of tumor proliferation and induction of apoptosis upon in vitro treatment with tyrosine kinase inhibitors.

Decreased proliferation of tumor cells in the 3D model compared to 2D conditions.

Induction of invasion with EMT by TGFβ stimulation.

Evaluation of symptom, clinical chemistry and metabolomics profiles during Rehmannia six formula (R6) treatment: an integrated and personalized data analysis approach

ETHNOPHARMACOLOGICAL RELEVANCE

Rehmannia Six Formula (R6, Chinese name is Liu Wei Di Huang Wan) is one of the most important classic Chinese medicine formula used to treat metabolic disorders related to aging. It was first reported in the Chinese medicine book titled 'Xiao Er Yao Zheng Zhi Jue by Qian Yi' (Chinese Song dynasty: 1035-1117). In modern times it is therefore often used to treat diabetes, pre-diabetes, fatigue and people with metabolic syndrome. The aim of this study is to measure changes in symptoms, clinical parameters and serum metabolite profiles during R6 treatment of human subjects with features of metabolic syndrome.

MATERIALS AND METHODS

Symptoms, clinical parameters and serum metabolites were measured before and after 4 and 8 weeks of R6 treatment. Nonlinear Principal Component Analysis was applied for the first time to conduct an integrated analysis of the three data sets. Correlation structures were compared before treatment and after 4 and 8 weeks of treatment. Additionally, a State Space Grid approach was used to study personalized changes in symptom profiles.

RESULTS

The symptoms 'hectic fever' and 'spontaneous sweating' were found to be most relieved during R6 treatment. Most of the symptoms were less correlated with other variables after 8 weeks of R6 treatment. LDL-C, total cholesterol, systolic blood pressure and waist size were found to decrease during R6 treatment. Additionally, 10 of the 15 measured phosphatidylcholines were found to decrease. Personalized symptom profiles as described by Chinese medical terms show that most Yin deficiencies are addressed first by R6 treatment. However, in subjects with reduced or less Yin deficiency but which do have a substantial Qi deficiency a reduction of Qi deficiency is subsequently observed.

CONCLUSIONS

R6 treatment was shown to improve the lipid profile indicating a reduction of cardiovascular risk. Additionally, the changes observed in correlation structure indicate a different angle of looking at treatment effects. Less strong correlations between symptoms and metabolites suggest a healthier situation after R6 treatment. A State Space Grid analysis showed that the effect of R6 was different for the Yin deficiency subjects and the Qi deficiency subjects. The observed decrease of Yin deficiency related symptoms is in agreement with the use of R6 in Chinese medicine to nourish Yin. Observing individual differences in treatment effects is therefore an essential step in the development of personalized medicine.

Perspectives in immunopharmacology: the future of immunosuppression

Modulation of immune responses for therapeutic purposes is a particularly relevant area, given the central role of anomalous immunity in a wide variety of diseases, from the most typically immune-related syndromes (autoimmune diseases, allergy and asthma, immunodeficiencies) to those in which altered immunity and inflammation define the pathological outcomes (chronic infections, tumors, chronic inflammatory and degenerative diseases, metabolic disorders, etc.). This brief review will summarize some of the most promising perspectives of immunopharmacology, in particular in the area of immunosuppression, by considering the following aspects:

Targeted cancer therapy--are the days of systemic chemotherapy numbered?

Targeted therapy or molecular targeted therapy has been defined as a type of treatment that blocks the growth of cancer cells by interfering with specific cell molecules required for carcinogenesis and tumor growth, rather than by simply interfering with all rapidly dividing cells as with traditional chemotherapy. There is a growing number of FDA approved monoclonal antibodies and small molecules targeting specific types of cancer suggestive of the growing relevance of this therapeutic approach. Targeted cancer therapies, also referred to as "Personalized Medicine", are being studied for use alone, in combination with other targeted therapies, and in combination with chemotherapy. The objective of personalized medicine is the identification of patients that would benefit from a specific treatment based on the expression of molecular markers. Examples of this approach include bevacizumab and olaparib, which have been designated as promising targeted therapies for ovarian cancer. Combinations of trastuzumab with pertuzumab, or T-DM1 and mTOR inhibitors added to an aromatase inhibitor are new therapeutic strategies for breast cancer. Although this approach has been seen as a major step in the expansion of personalized medicine, it has substantial limitations including its high cost and the presence of serious adverse effects. The Cancer Genome Atlas is a useful resource to identify novel and more effective targets, which may help to overcome the present limitations. In this review we will discuss the clinical outcome of some of these new therapies with a focus on ovarian and breast cancer. We will also discuss novel concepts in targeted therapy, the target of cancer stem cells.

Predicting the restricted mean event time with the subject's baseline covariates in survival analysis

For designing, monitoring, and analyzing a longitudinal study with an event time as the outcome variable, the restricted mean event time (RMET) is an easily interpretable, clinically meaningful summary of the survival function in the presence of censoring. The RMET is the average of all potential event times measured up to a time point τ and can be estimated consistently by the area under the Kaplan-Meier curve over $[0, \tau ]$. In this paper, we study a class of regression models, which directly relates the RMET to its "baseline" covariates for predicting the future subjects' RMETs. Since the standard Cox and the accelerated failure time models can also be used for estimating such RMETs, we utilize a cross-validation procedure to select the "best" among all the working models considered in the model building and evaluation process. Lastly, we draw inferences for the predicted RMETs to assess the performance of the final selected model using an independent data set or a "hold-out" sample from the original data set. All the proposals are illustrated with the data from the an HIV clinical trial conducted by the AIDS Clinical Trials Group and the primary biliary cirrhosis study conducted by the Mayo Clinic.