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In this paper, I set out two ethical complications for Rid and Wendler's proposal that a "Patient Preference Predictor" (PPP) should be used to aid decision making about incapacitated patients' care. Both of these worries concern how a PPP might categorize patients. In the first section of the paper, I set out some general considerations about the "ethics of apt categorization" within stratified medicine and show how these challenge certain PPPs. In the second section, I argue for a more specific--but more contentious--claim that proper respect for the autonomy of incapacitated patients might require us to act on reasons which they could endorse and show how this claim places important limits on the categories employed by an ethically acceptable PPP. The conclusion shows how these concerns about apt categorization relate to more familiar worries about Rid and Wendler's proposals.

Funding has been approved for a stratified medicine project as part of the National Unified Renal Translational Research Enterprise (NURTURE) programme. Maria Thompson, Moin Saleem and Florence Johnson describe the objectives of this project and explore the implications for patients with a rare kidney condition.

Background: Prior to direct-acting antiviral (DAA) therapy, personalised medicine played an important role in the treatment of hepatitis C virus (HCV). Whilst simplified treatment strategies are central to treatment scale-up, some patients will benefit from treatment optimisation. This systematic review and meta-analysis explores treatment optimisation strategies in the DAA era. Methods: We systematically searched Medline, Embase, and Web of Science for studies that adopted a stratified or personalised strategy using a licensed combination DAA regimen, alone or with additional agents. We performed a thematic analysis to classify optimisation strategies and a meta-analysis of sustained virologic response rates (SVR), exploring heterogeneity with subgroup analyses and meta-regression. Results: We included 64 studies (9450 participants). Thematic analysis found evidence of three approaches: duration, combination, and/or dose optimisation. We separated strategies into those aiming to maintain SVR in the absence of predictors of failure, and those aiming to improve SVR in the presence of predictors of failure. Shortened duration regimens achieve pooled SVR rates of 94.2% (92.3-95.9%) for 8 weeks, 81.1% (75.1-86.6%) for 6 weeks, and 63.1% (39.9-83.7%) for ≤4 weeks. Personalised strategies (100% vs 87.6%; p Conclusions: Treatment optimisation for individuals with multiple predictors of treatment failure can offer high SVR rates. More evidence is needed to identify with confidence those individuals in whom SVR can be achieved with shortened duration treatment.

The risk to develop anaphylaxis depends on the sensitization pattern, the proportion of the involved immunoglobulin classes, the avidity and affinity of immunoglobulins to bind an allergen, characteristics of the allergen, the route of allergen application, and, last but not least, the presence of cofactors of anaphylaxis. To be able to calculate the risk to develop anaphylaxis and to anticipate the severity of the reactions under certain conditions, it is necessary to understand how all these factors interact with each other. Important progress for risk assessment in anaphylaxis is based on component-resolved stratified diagnostics, which allow to (i) determine a patient's sensitization pattern on a molecular basis, (ii) correlate clinical responses to defined sensitization patterns, and (iii) better identify cross-reactive allergens. Together with the increasing knowledge regarding the role and mode of action of cofactors of anaphylaxis, these data pave the way to unscramble the complex interactions determining the clinical relevance of sensitizations, the risk of anaphylaxis, and the severity of reactions. As a consequence, this understanding allows to better determine the individual risk in response to an identified allergen and results in more specific advices and education for our patients to prevent further life-threatening anaphylactic reactions.

The preceding articles in this volume have identified and discussed a wide range of methodological and practical issues in the development of personalized medicine. This concluding article uses the resulting insights to identify implications for the economic incentives for evidence generation. It argues that promoting an efficient path to personalized medicine is going to require appropriate incentives for evidence generation including: 1) a greater willingness on the part of payers to accept prices that reflect value; 2) consideration of some form of intellectual property protection (e.g., data exclusivity) for diagnostics to incentivize generation of evidence of clinical utility; 3) realistic expectations around the standards for evidence; and 4) public investment in evidence collection to complement the efforts of payers and manufacturers. It concludes that such incentives could build and maintain a balance among: 1) realistic thresholds for evidence and the need for payers to have confidence in the clinical utility of the drugs and tests they use; 2) payment for value, with prices that ensure cost-effectiveness for health systems; and 3) levels of intellectual property protection for evidence generation that provide a return for those financing research and development, while encouraging competition to produce both better and more efficient tests.

Gene-expression-based classification studies have changed the way cancer is traditionally perceived. It is becoming increasingly clear that many cancer types are in fact not single diseases but rather consist of multiple molecular distinct subtypes. In this review, we discuss unsupervised classification studies of common malignancies during the recent years. We found that the bioinformatic workflow of many of these studies follows a common main stream, although different statistical tools may be preferred from case to case. Here we summarize the employed methods, with a special focus on consensus clustering and classification. For each critical step of the bioinformatic analysis, we explain the biological relevance and implications of the technical principles. We think that a better understanding of these ever more frequently used methods to study cancer heterogeneity by the biomedical community is relevant as these type of studies will have an important impact on patient stratification and cancer subtype-specific drug development in the future.

Recent technical advances have brought insights into the biology of cancer in human, establishing it as a disease driven by genetic mutations. Beside inherited family tumour syndromes caused by germline mutations, somatic genetic alterations occur early in tumourigenesis, which accumulate during the progression of the disease and its treatment. Based on these observations, medical oncology has started to enter an era of stratified medicine, where treatment selection is becoming tailored to drugable molecular pathways. As a pre-requisite of an individualised treatment concept, molecular and genetic characterisation of the individual tumour has to be performed to align the most appropriate therapies according to the patient9s disease. Reading the individual molecular tumour profile and responding by a tailored treatment concept is the ‘communication’ required to fight this deadly disease. This way to communicate is currently changing the field of oncology dramatically, and fundamentally involves the discipline of molecular pathology. This review highlights the role of genetic characterisation of human malignancies by giving an overview on the basic methods of molecular pathology, the challenge of the instable tumour genome and its clinical consequences. Trial registration number EK1541/2012.

With the recent revolution in genomics and in stratified medicine, the development of molecular signatures is becoming more and more important for predicting the prognosis (prognostic biomarkers) and the treatment effect (predictive biomarkers) of each patient. However, the large quantity of information has rendered false positives more and more frequent in biomedical research. The high-dimensional space (i.e. number of biomarkers ≫ sample size) leads to several statistical challenges such as the identifiability of the models, the instability of the selected coefficients or the multiple testing issue.The aim of this thesis was to propose and evaluate statistical methods for the identification of these biomarkers and the individual predicted survival probability for new patients, in the context of the Cox regression model. For variable selection in a high-dimensional setting, the lasso penalty is commonly used. In the prognostic setting, an empirical extension of the lasso penalty has been proposed to be more stringent on the estimation of the tuning parameter λ in order to select less false positives. In the predictive setting, focus has been given to the biomarker-by-treatment interactions in the setting of a randomized clinical trial. Twelve approaches have been proposed for selecting these interactions such as lasso (standard, adaptive, grouped or ridge+lasso), boosting, dimension reduction of the main effects and a model incorporating arm-specific biomarker effects. Finally, several strategies were studied to obtain an individual survival prediction with a corresponding confidence interval for a future patient from a penalized regression model, while limiting the potential overfit.The performance of the approaches was evaluated through simulation studies combining null and alternative scenarios. The methods were also illustrated in several data sets containing gene expression data in breast cancer.; Avec la révolution récente de la génomique et la médecine stratifiée, le développement de signatures moléculaires devient de plus en plus important pour prédire le pronostic (biomarqueurs pronostiques) ou l’effet d’un traitement (biomarqueurs prédictifs) de chaque patient. Cependant, la grande quantité d’information disponible rend la découverte de faux positifs de plus en plus fréquente dans la recherche biomédicale. La présence de données de grande dimension (nombre de biomarqueurs ≫ taille d’échantillon) soulève de nombreux défis statistiques tels que la non-identifiabilité des modèles, l’instabilité des biomarqueurs sélectionnés ou encore la multiplicité des tests.L’objectif de cette thèse a été de proposer et d’évaluer des méthodes statistiques pour l’identification de ces biomarqueurs et l’élaboration d’une prédiction individuelle des probabilités de survie pour des nouveaux patients à partir d’un modèle de régression de Cox. Pour l’identification de biomarqueurs en présence de données de grande dimension, la régression pénalisée lasso est très largement utilisée. Dans le cas de biomarqueurs pronostiques, une extension empirique de cette pénalisation a été proposée permettant d’être plus restrictif sur le choix du paramètre λ dans le but de sélectionner moins de faux positifs. Pour les biomarqueurs prédictifs, l’intérêt s’est porté sur les interactions entre le traitement et les biomarqueurs dans le contexte d’un essai clinique randomisé. Douze approches permettant de les identifier ont été évaluées telles que le lasso (standard, adaptatif, groupé ou encore ridge+lasso), le boosting, la réduction de dimension des effets propres et un modèle implémentant les effets pronostiques par bras. Enfin, à partir d’un modèle de prédiction pénalisé, différentes stratégies ont été évaluées pour obtenir une prédiction individuelle pour un nouveau patient accompagnée d’un intervalle de confiance, tout en évitant un éventuel surapprentissage du modèle. La performance des approches ont été évaluées au travers d’études de simulation proposant des scénarios nuls et alternatifs. Ces méthodes ont également été illustrées sur différents jeux de données, contenant des données d’expression de gènes dans le cancer du sein.

Introduction:\ud Cellular metabolism is altered during cancer initiation and progression, which allows cancer cells to increase anabolic synthesis, avoid apoptosis and adapt to low nutrient and oxygen availability. The metabolic nature of cancer enables patient cancer status to be monitored by metabolomics and lipidomics. Additionally, monitoring metabolic status of patients or biological models can be used to greater understand the action of anticancer therapeutics.\ud Objectives:\ud Discuss how metabolomics and lipidomics can be used to (i) identify metabolic biomarkers of cancer and (ii) understand the mechanism-of-action of anticancer therapies. Discuss considerations that can maximize the clinical value of metabolic cancer biomarkers including case–control, prognostic and longitudinal study designs.\ud Methods:\ud A literature search of the current relevant primary research was performed.\ud Results:\ud Metabolomics and lipidomics can identify metabolic signatures that associate with cancer diagnosis, prognosis and disease progression. Discriminatory metabolites were most commonly linked to lipid or energy metabolism. Case–control studies outnumbered prognostic and longitudinal approaches. Prognostic studies were able to correlate metabolic features with future cancer risk, whereas longitudinal studies were most effective for studying cancer progression. Metabolomics and lipidomics can help to understand the mechanism-of-action of anticancer therapeutics and mechanisms of drug resistance.\ud Conclusion:\ud Metabolomics and lipidomics can be used to identify biomarkers associated with cancer and to better understand anticancer therapies.

A case–control study from two randomised breast cancer prevention trials of tamoxifen and raloxifene (P-1 and P-2) identified single-nucleotide polymorphisms (SNPs) in or near genes ZNF423 and CTSO as factors which predict which women will derive most anti-cancer benefit from selective oestrogen receptor modulator (SERM) therapy. In this article, we further examine this question using blood samples from two randomised tamoxifen prevention trials: the International Breast Cancer Intervention Study I (IBIS-I) and the Royal Marsden trial (Marsden). A nested case–control study was designed with 2:1 matching in IBIS-I and 1:1 matching in Marsden. The OncoArray was used for genotyping and included two SNPs previously identified (rs8060157 in ZNF423 and rs10030044 near CTSO), and 102 further SNPs within the same regions. Overall, there were 369 cases and 662 controls, with 148 cases and 268 controls from the tamoxifen arms. Odds ratios were estimated by conditional logistic regression, with Wald 95 % confidence intervals. In the tamoxifen arms, the per-allele odds ratio for rs8060157 was 0.99 (95 %CI 0.73–1.34) and 1.00 (95 %CI 0.76–1.33) for rs10030044. In the placebo arm, the odds ratio was 1.10 (95 %CI 0.87–1.40) for rs8060157 and 1.01 (95 %CI 0.79–1.29) for rs10030044. There was no evidence to suggest that other SNPs in the surrounding regions of these SNPs might predict response to tamoxifen. Results from these two prevention trials do not support the earlier findings. rs8060157 in ZNF423 and rs10030044 near CTSO do not appear to predict response to tamoxifen.

Rapid innovation in (epi)genetics and biomarker sciences is driving a new drug development and product development pathway, with the personalized medicine era dominated by biologic therapeutics and companion diagnostics. Companion diagnostics (CDx) are tests and assays that detect biomarkers and specific mutations to elucidate disease pathways, stratify patient populations, and target drug therapies. CDx can substantially influence the development and regulatory approval for certain high-risk biologics. However, despite the increasingly important role of companion diagnostics in the realization of personalized medicine, in the USA, there are only 23 Food and Drug Administration (FDA) approved companion diagnostics on the market for 11 unique indications. Personalized medicines have great potential, yet their use is currently constrained. A major factor for this may lie in the increased complexity of the companion diagnostic and corresponding therapeutic development and adoption pathways. Understanding the market dynamics of companion diagnostic/therapeutic (CDx/Rx) pairs is important to further development and adoption of personalized medicine. Therefore, data collected on a variety of factors may highlight incentives or disincentives driving the development of companion diagnostics. Statistical analysis for 36 hypotheses resulted in two significant relationships and 34 non-significant relationships. The sensitivity of the companion diagnostic was the only factor that significantly correlated with the price of the companion diagnostic. This result indicates that while there is regulatory pressure for the diagnostic and pharmaceutical industry to collaborate and co-develop companion diagnostics for the approval of personalized therapeutics, there seems to be a lack of parallel economic collaboration to incentivize development of companion diagnostics.

Precision medicine is defined by the National Institute of Health's Precision Medicine Initiative Working Group as an approach to disease treatment that takes into account individual variability in genes, environment, and lifestyle. There has been increased interest in applying the concept of precision medicine to idiopathic pulmonary fibrosis, in particular to search for genetic and molecular biomarker-based profiles (so called endotypes) that identify mechanistically distinct disease subgroups. The relevance of precision medicine to idiopathic pulmonary fibrosis is yet to be established, but we believe that it holds great promise to provide targeted and highly effective therapies to patients. In this manuscript, we describe the field's nascent efforts in genetic/molecular endotype identification and how environmental and behavioral subgroups may also be relevant to disease management.

There is general agreement that the manner medicine is currently practiced is not adequate for addressing all health relevant issues in a way that would be most beneficial for all patients and that we need a new modern medicine. Even candidates for such a modern medicine have been recently nominated in the form of (I) personalized medicine, (II) precision medicine, (III) P4 medicine, (IV) P5 medicine, and (V) stratified medicine, which we will review briefly. The purpose of our opinion paper is to emphasize the urgent need for a formal definition in terms of the experimental design of modern medicine in order to deal efficiently with the opportunities provided by high-throughput genomic data and that all the current candidate programs mentioned above are lacking such a formalization.

Objective: Reduced ex vivo lipopolysaccharide (LPS) stimulated whole blood pro-inflammatory cytokine release is a hallmark of immunosuppression in the critically ill and predicts adverse clinical outcomes. No standard technique for performing the assay currently exists. The impact of methodological heterogeneity was determined. Design, Setting, Subjects, and Interventions: Clinical experimental study set in a research laboratory. Venous blood from 5 to 10 healthy volunteers/experiment (total participant group: 18 subjects, 72% men, mean age 32) was stimulated ex vivo to evaluate the effect of variables identified via literature review on tumor necrosis factor-α (TNFα) release. These included sample handling, stimulation technique, and incubation conditions. Reporting convention was additionally assessed. Main Results: Measured TNFα release was significantly altered by source of LPS, concentration of LPS employed, duration and temperature of incubation prior to supernatant aspiration, and predilution of blood (repeated measures ANOVA, all P

Despite the demonstrated potential as an accurate cancer diagnostic tool, Raman spectroscopy (RS) is yet to be adopted by the clinic for histopathology reviews. The Stratified Medicine through Advanced Raman Technologies (SMART) consortium has begun to address some of the hurdles in its adoption for cancer diagnosis. These hurdles include awareness and acceptance of the technology, practicality of integration into the histopathology workflow, data reproducibility and availability of transferrable models. We have formed a consortium, in joint efforts, to develop optimised protocols for tissue sample preparation, data collection and analysis. These protocols will be supported by provision of suitable hardware and software tools to allow statistically sound classification models to be built and transferred for use on different systems. In addition, we are building a validated gastrointestinal (GI) cancers model, which can be trialled as part of the histopathology workflow at hospitals, and a classification tool. At the end of the project, we aim to deliver a robust Raman based diagnostic platform to enable clinical researchers to stage cancer, define tumour margin, build cancer diagnostic models and discover novel disease bio markers.

With the recent revolution in genomics and in stratified medicine, the development of molecular signatures is becoming more and more important for predicting the prognosis (prognostic biomarkers) and the treatment effect (predictive biomarkers) of each patient. However, the large quantity of information has rendered false positives more and more frequent in biomedical research. The high-dimensional space (i.e. number of biomarkers ≫ sample size) leads to several statistical challenges such as the identifiability of the models, the instability of the selected coefficients or the multiple testing issue.The aim of this thesis was to propose and evaluate statistical methods for the identification of these biomarkers and the individual predicted survival probability for new patients, in the context of the Cox regression model. For variable selection in a high-dimensional setting, the lasso penalty is commonly used. In the prognostic setting, an empirical extension of the lasso penalty has been proposed to be more stringent on the estimation of the tuning parameter λ in order to select less false positives. In the predictive setting, focus has been given to the biomarker-by-treatment interactions in the setting of a randomized clinical trial. Twelve approaches have been proposed for selecting these interactions such as lasso (standard, adaptive, grouped or ridge+lasso), boosting, dimension reduction of the main effects and a model incorporating arm-specific biomarker effects. Finally, several strategies were studied to obtain an individual survival prediction with a corresponding confidence interval for a future patient from a penalized regression model, while limiting the potential overfit.The performance of the approaches was evaluated through simulation studies combining null and alternative scenarios. The methods were also illustrated in several data sets containing gene expression data in breast cancer., Avec la révolution récente de la génomique et la médecine stratifiée, le développement de signatures moléculaires devient de plus en plus important pour prédire le pronostic (biomarqueurs pronostiques) ou l’effet d’un traitement (biomarqueurs prédictifs) de chaque patient. Cependant, la grande quantité d’information disponible rend la découverte de faux positifs de plus en plus fréquente dans la recherche biomédicale. La présence de données de grande dimension (nombre de biomarqueurs ≫ taille d’échantillon) soulève de nombreux défis statistiques tels que la non-identifiabilité des modèles, l’instabilité des biomarqueurs sélectionnés ou encore la multiplicité des tests.L’objectif de cette thèse a été de proposer et d’évaluer des méthodes statistiques pour l’identification de ces biomarqueurs et l’élaboration d’une prédiction individuelle des probabilités de survie pour des nouveaux patients à partir d’un modèle de régression de Cox. Pour l’identification de biomarqueurs en présence de données de grande dimension, la régression pénalisée lasso est très largement utilisée. Dans le cas de biomarqueurs pronostiques, une extension empirique de cette pénalisation a été proposée permettant d’être plus restrictif sur le choix du paramètre λ dans le but de sélectionner moins de faux positifs. Pour les biomarqueurs prédictifs, l’intérêt s’est porté sur les interactions entre le traitement et les biomarqueurs dans le contexte d’un essai clinique randomisé. Douze approches permettant de les identifier ont été évaluées telles que le lasso (standard, adaptatif, groupé ou encore ridge+lasso), le boosting, la réduction de dimension des effets propres et un modèle implémentant les effets pronostiques par bras. Enfin, à partir d’un modèle de prédiction pénalisé, différentes stratégies ont été évaluées pour obtenir une prédiction individuelle pour un nouveau patient accompagnée d’un intervalle de confiance, tout en évitant un éventuel surapprentissage du modèle. La performance des approches ont été évaluées au travers d’études de simulation proposant des scénarios nuls et alternatifs. Ces méthodes ont également été illustrées sur différents jeux de données, contenant des données d’expression de gènes dans le cancer du sein.

Background: Stratified Medicine (SM) is becoming a practical reality with the targeting of medicines by using a biomarker or genetic-based diagnostic to identify the eligible patient sub-population. Like any healthcare intervention, SM interventions have costs and consequences that must be considered by reimbursement authorities with limited resources. Methodological standards and guidelines exist for economic evaluations in clinical pharmacology and are an important component for health technology assessments (HTAs) in many countries. However, these guidelines have initially been developed for traditional pharmaceuticals and not for complex interventions with multiple components. This raises the issue as to whether these guidelines are adequate to SM interventions or whether new specific guidance and methodology is needed to avoid inconsistencies and contradictory findings when assessing economic value in SM.Objective: This article describes specific methodological challenges when conducting health economic (HE) evaluations for SM interventions and outlines potential modifications necessary to existing evaluation guidelines/principles that would promote consistent economic evaluations for SM.Results/Conclusions: Specific methodological aspects for SM comprise considerations on the choice of comparator, measuring effectiveness and outcomes, appropriate modeling structure and the scope of sensitivity analyses. Although current HE methodology can be applied for SM, greater complexity requires further methodology development and modifications in the guidelines.

In this chapter, I extend the analysis beyond the innovation communities and funding agencies that have driven new biology and trans-lational approaches to health innovation, and explore the reciprocal impact of regulation and policy on the therapeutic innovation strategies that are driving the health bioeconomy. The question is: How has new biology both challenged and transformed conventional regulatory systems and the resilience and adaptive capabilities of health-care systems to innovative therapies? Using the case examples of regenerative medicine (RM) and personalized/stratified medicine, I reveal the regulatory and policy challenges facing disruptive therapies that do not have established routes to market, nor conventional business models and value chains to facilitate entry to the clinic. Many of these therapies, and their underlying or companion technologies, if they are to be successful, must find a way to fit into or transform existing health-care pathways. They must also navigate complex and onerous regulatory and reimbursement systems, which have built up incrementally over many decades.

Rapid innovation in (epi)genetics and biomarker sciences is driving a new drug development and product development pathway, with the personalized medicine era dominated by biologic therapeutics and companion diagnostics. Companion diagnostics (CDx) are tests and assays that detect biomarkers and specific mutations to elucidate disease pathways, stratify patient populations, and target drug therapies. CDx can substantially influence the development and regulatory approval for certain high-risk biologics. However, despite the increasingly important role of companion diagnostics in the realization of personalized medicine, in the United States, there are only twenty-three Food and Drug Administration (FDA) approved companion diagnostics on the market for eleven unique indications. Personalized medicines have great potential, yet their use is currently constrained. A major factor for this may lie in the increased complexity of the companion diagnostic and corresponding therapeutic development and adoption pathways. Understanding the market dynamics of companion diagnostic/therapeutic (CDx/Rx) pairs is important to further development and adoption of personalized medicine. Therefore, data collected on a variety of factors may highlight incentives or disincentives driving the development of companion diagnostics. Statistical analysis for thirty-six hypotheses resulted in two significant relationships and thirty-four non-significant relationships. The sensitivity of the companion diagnostic was the only factor that significantly correlated with the price of the companion diagnostic. This result indicates that while there is regulatory pressure for the diagnostic and pharmaceutical industry to collaborate and co-develop companion diagnostics for the approval of personalized therapeutics, there seems to be a lack of parallel economic collaboration to incentivize development of companion diagnostics.

International audience; Defining thresholds of prognostic markers is essential for stratified medicine. Such thresholds are mostly estimated from purely statistical measures regardless of patient preferences potentially leading to unacceptable medical decisions. Quality-Adjusted Life-Years are a widely used preferences-based measure of health outcomes. We develop a time-dependent Quality-Adjusted Life-Years-based expected utility function for censored data that should be maximized to estimate an optimal threshold. We performed a simulation study to compare estimated thresholds when using the proposed expected utility approach and purely statistical estimators. Two applications illustrate the usefulness of the proposed methodology which was implemented in the R package ROCt ( www.divat.fr ). First, by reanalysing data of a randomized clinical trial comparing the efficacy of prednisone vs. placebo in patients with chronic liver cirrhosis, we demonstrate the utility of treating patients with a prothrombin level higher than 89%. Second, we reanalyze the data of an observational cohort of kidney transplant recipients: we conclude to the uselessness of the Kidney Transplant Failure Score to adapt the frequency of clinical visits. Applying such a patient-centered methodology may improve future transfer of novel prognostic scoring systems or markers in clinical practice.

Despite tremendous advances in making an accurate diagnosis, most therapeutic program are designed for the “average model patient.” However, “one-size-fits-all-approach,” treatments can be very successful for only some patients but not for many others. On the other hand, this has changed with rapid advances made in evidence-based personalized medicine. The whole process involves a stepwise approach in building the holistic picture referred to as stratified medicine with the ultimate aim of individualized or personalized therapeutic interventions. Emerging prospects of affordable and highly sensitive next generation genome sequencing methods offer to unravel specific clinically relevant personalized genomic signatures enabling the busy clinicians to offer bespoke diagnostic, prognostic, and therapeutic information. The availability of targeted molecular therapy in tumor stratification and in other systemic disorders has opened doors to a new era of clinical medicine. This chapter briefly summarizes scope and limitations of genetic and genomic applications in an new exciting opportunity for stratified, personalized medicine and precision medicine. Exciting new developments and promising future lies ahead.

In this chapter, a broad history of the field of metabolic phenotyping (also known as metabonomics and metabolomics) is presented. Largely overlooked pioneering studies from the 1960s and 1970s are described, and this is then followed by a description of the main development of the field using modern analytical chemistry techniques, mainly nuclear magnetic resonance spectroscopy and mass spectrometry. The use of multivariate statistics and related methods that are used to interpret the complex data led to the dramatic progress made in the field. The expansion into animal models of disease and drug toxicity is described, and the current explosive increase in large-scale epidemiologic studies at one end of the range and high precision clinical studies for stratified medicine at the other is discussed. This has led to the concept of dedicated phenome centers for metabolic phenotyping and the current state of the art is explained.

In this final chapter, the current state of the art in metabolic phenotyping is summarized. The challenges for future integration of the topic into mainstream clinical and health studies are delineated and discussed. These include the need for validation, standard protocols, and the problems associated with “big data.” The prospects for metabolic phenotyping in clinical and epidemiologic studies are described. Potential new phenotyping outputs and possible impacts of metabolic phenotyping on medicine are listed.

The twin forces of payors seeking fair pricing and the rising costs of developing new medicines has driven a closer relationship between pharmaceutical companies and diagnostics companies, because stratified medicines, guided by companion diagnostics, offer better commercial, as well as clinical, outcomes. Stratified medicines have created clinical success and provided rapid product approvals, particularly in oncology, and indeed have changed the dynamic between drug and diagnostic developers. The commercial payback for such partnerships offered by stratified medicines has been less well articulated, but this has shifted as the benefits in risk management, pricing and value creation for all stakeholders become clearer. In this larger healthcare setting, stratified medicine provides both physicians and patients with greater insight on the disease and provides rationale for providers to understand cost-effectiveness of treatment. This article considers how the economic value of stratified medicine relationships can be recognized and translated into better outcomes for all healthcare stakeholders.

The field of personalised or stratified medicine is evolving alongside the formation of a plethora of public/private partnerships and collaborations. These new institutional forms, or 'social technologies', are varied and emerge in response to several drivers, including the need to draw on a broader base of data inputs relating to genomics, patient behaviour and healthcare system differentiation. This paper discusses some of these drivers of partnerships and collaborations. Although the number of such partnerships is growing, their rationale and basis for collaboration remains unclear. Public-private collaborations are at the core of the set of new life sciences policies in the UK but there is little indication in the policy documents of clear boundaries for these partnerships. In part, this is due to the lack of empirical evidence at the system level for conceptualising what is still a relatively new approach. The collection of evidence in the form of broad evaluations, rather than tightly focused theoretical studies, is more likely to be related back to systems and be of more use for formulating policy rationales.

Stratified medicine is a new term that figures highly in current MRC and NHS strategy. It has developed from the earlier terms individualised or personalised medicine and refers to the use of genetic and/or endophenotypic measures to allow better targeting of treatments. The best exemplar is HER2 positivity in breast cancer to determine the efficacy of Herceptin. Clinical trials of this anti-cancer drug were initially unpromising, but once the HER2 positive subgroup was identified it was found, in this subgroup only, to be highly effective. It is presumed that similar subgroups will be found for many common disorders not just cancers, and that these will lead to much better targeted treatments. Such an advance may be necessary to develop new treatments in certain fields where the development of broad-spectrum/blockbuster treatments appears to have reached the end of the road; a particular example of this is in psychiatry. In this paper we discuss this issue in relation to psychiatry using a new and interesting example of how genotyping might help rescue an apparently failed novel treatment in anxiety disorders.

Research over the past two decades has untangled the molecular heterogeneity of cancer at the cellular level. The molecular techniques that have been developed in the past 5 to 10 years are dramatically improving our understanding of the genomic aberrations that underlie the malignant transformation of normal cells. At the same time, however, advances in our understanding of the genetic basis of solid tumours and hematologic malignancies have revealed how complex cancer is, and consequently how much more challenging it is in many of the more common cancers to identify the right drug for the right patient in the adequate dose at the right time. Given the slow pace of translation from genome science to individualised medicine in oncology, this expert review, mainly taking into account recently published perspective articles and reviews, describes the molecular heterogeneity of cancer, the difficulties in developing novel molecularly targeted agents, and the need for developing biomarkers to optimise drug development and clinical use. Distinct types of biomarkers, breakthroughs as well as disappointments in the clinical implementation of biomarkers and targeted therapeutics into clinical practice are discussed by focusing on four targeted anti-cancer drugs. Finally, as clinical biomarker tests that predict response to particular therapies will play an important role in achieving stratified medicine in the near future, this article will conclude by giving some recommendations for effective biomarker evaluation and clinical trial designs for predictive biomarker validation.

Modern humans evolved in Africa and spread across the world, successfully colonizing diverse geographical locations including extremes of latitudes, altitudes, arid and wet conditions as well as regions endemic for di" erent pathogens. Besides geo-climatic conditions, human genomes have also been shapedbyanaggregateofsocioculturalfactorssuchasadmixture, mating patterns, food sources, and dietary habits. These footprints ofhuman history inthe form ofgenetic variationsreside in our genomes, and many of them have become relatively ! xed in the past millions of years in certain populations. Nearly 11 millionsingle-nucleotidepolymorphisms(SNPs)havenowbeen catalogued in humans across diverse populations by the International HapMap Consortium. Coupled to this, the individual genome projects have also revealed a large fraction of variations that are speci! c to an individual. With this enormous amount of variability, it now seems that there are as many human genomes as there are humans. Thougha majorityof these variations might be neutral, a large number of these di" erences could contribute to adaptation, phenotypic variability, di" erences in disease susceptibility, and response to environment even within healthy individuals of a population. With global socioeconomic and cultural changes leading to altered lifestyles, diet patterns, and

The UK healthcare system holds a favorable position in the development of stratified medicines through strong scientific innovation, robust biotechnology and pharmaceutical industries and comparatively simple regulatory and reimbursement processes. Organizations such as its robust health technology assessment agency, the NICE and its mature socialized healthcare system, the National Health Service, enable innovative medicines, including stratified treatments for cancer and infectious disease, to be rapidly assessed for their effectiveness and value to patients in the UK. However, our recent observations with a variety of UK healthcare stakeholders suggest that certain features need to be improved if the favorable position in stratified medicine development, and consequential beneficial outcome to patients, is to be sustained and indeed further enhanced to a position of pre-eminence. Key changes suggested are removing healthcare silos and enabling multidisciplinary teams to translate scientific and medical innovation into the best practice; expanding the UK skill base in certain disciplines including medical pathology, health economics and clinical informatics; and using successful pilot cases of stratified medicines to better educate stakeholders in a drive to change healthcare culture. Through this cultural change, the UK would offer healthcare based on prediction and prevention rather than symptom-based diagnosis and reactive treatment.

The implementation of stratified medicine in modern cancer care presents substantial opportunity to refine diagnosis and treatment but also numerous challenges. Through experience in a UK tumor profiling initiative, we have gained valuable insights into the complexities and possible solutions for routine delivery of stratified cancer medicine.

Prescribers have been practicing stratified medicine for many years. Patient characteristics, usually non-genetic, including age, comorbidities and concomitant medications are taken into account when deciding which drug to prescribe. In addition, the majority of drugs require dose adjustments across patient subgroups, usually determined by non-genetic differences between the subgroups. Whilst pharmacogenetics hold promise for enhancing treatment stratification and even treatment individualisation, non-genetic factors will continue to be very important. Both non-genetic and genetic factors must be considered to improve understanding and quantification of the variability in treatment outcomes and to guide stratification and targeting of patient subgroups to the right drug and also to the right range of doses within that subgroup. Development of stratified medicines must consider non-genetic as well as genetic factors and, where appropriate, include stratification through optimising the dose for each patient or subgroup as well as by choosing the drug most likely to deliver efficacy to that patient or group.