The Potential of Polygenic Risk Scores to Predict Antidepressant Treatment Response in Major Depression: A Systematic Review

Precision medicine in cardiovascular therapeutics: Evaluating the role of pharmacogenetic analysis prior to drug treatment

Pharmacogenomics is the examination of how genetic variation influences drug metabolism and response, in terms of both efficacy and safety. In cardiovascular disease, patient-specific diplotypes determine phenotypes, thereby influencing the efficacy and safety of drug treatments, including statins, antiarrhythmics, anticoagulants and antiplatelets. Notably, polymorphisms in key genes, such as CYP2C9, CYP2C19, VKORC1 and SLCO1B1, significantly impact the outcomes of treatment with clopidogrel, warfarin and simvastatin. Furthermore, the CYP2C19 polymorphism influences the pharmacokinetics and safety of the novel hypertrophic cardiomyopathy inhibitor, mavacamten. In this review, we critically assess the clinical application of pharmacogenomics in cardiovascular disease and delineate present and future utilization of pharmacogenomics. This includes insights into identifying missing heritability, the integration of whole genome sequencing and the application of polygenic risk scores to enhance the precision of personalized drug therapy. Our discussion encompasses health economic analyses that underscore the cost benefits associated with pre-emptive genotyping for warfarin and clopidogrel treatments, albeit acknowledging the need for further research in this area. In summary, we contend that cardiovascular pharmacogenomic analyses are underpinned by a wealth of evidence, and implementation is already occurring for some of these gene-drug pairs, but as with any area of medicine, we need to continually gather more information to optimize the use of pharmacogenomics in clinical practice.

Leveraging DNA methylation to predict treatment response in major depressive disorder: A critical review

Major depressive disorder (MDD) is a debilitating and prevalent mental disorder with a high disease burden. Despite a wide array of different treatment options, many patients do not respond to initial treatment attempts. Selection of the most appropriate treatment remains a significant clinical challenge in psychiatry, highlighting the need for the development of biomarkers with predictive utility. Recently, the epigenetic modification DNA methylation (DNAm) has emerged to be of great interest as a potential predictor of MDD treatment outcomes. Here, we review efforts to date that seek to identify DNAm signatures associated with treatment response in individuals with MDD. Searches were conducted in the databases PubMed, Scopus, and Web of Science with the concepts and keywords MDD, DNAm, antidepressants, psychotherapy, cognitive behavior therapy, electroconvulsive therapy, transcranial magnetic stimulation, and brain stimulation therapies. We identified 32 studies implicating DNAm patterns associated with MDD treatment outcomes. The majority of studies (N = 25) are focused on selected target genes exploring treatment outcomes in pharmacological treatments (N = 22) with a few studies assessing treatment response to electroconvulsive therapy (N = 3). Additionally, there are few genome-scale efforts (N = 7) to characterize DNAm patterns associated with treatment outcomes. There is a relative dearth of studies investigating DNAm patterns in relation to psychotherapy, electroconvulsive therapy, or transcranial magnetic stimulation; importantly, most existing studies have limited sample sizes. Given the heterogeneity in both methods and results of studies to date, there is a need for additional studies before existing findings can inform clinical decisions.

Susceptibility to Treatment-Resistant Depression Within Families

Importance

Antidepressant responses and the phenotype of treatment-resistant depression (TRD) are believed to have a genetic basis. Genetic susceptibility between the TRD phenotype and other psychiatric disorders has also been established in previous genetic studies, but population-based cohort studies have not yet provided evidence to support these outcomes.

Objective

To estimate the TRD susceptibility and the susceptibility between TRD and other psychiatric disorders within families in a nationwide insurance cohort with extremely high coverage and comprehensive health care data.

Design, Setting, and Participants

This cohort study assessed data from the Taiwan national health insurance database across entire population (N = 26 554 001) between January 2003 and December 2017. Data analysis was performed from August 2021 to April 2023. TRD was defined as having experienced at least 3 distinct antidepressant treatments in the current episode, each with adequate dose and duration, based on the prescribing records. Then, we identified the first-degree relatives of individuals with TRD (n = 34 467). A 1:4 comparison group (n = 137 868) of first-degree relatives of individuals without TRD was arranged for the comparison group, matched by birth year, sex, and kinship.

Main Outcomes and Measures

Modified Poisson regression analyses were performed and adjusted relative risks (aRRs) and 95% CIs were calculated for the risk of TRD, the risk of other major psychiatric disorders, and different causes of mortality.

Results

This study included 172 335 participants (88 330 male and 84 005 female; mean [SD] age at beginning of follow-up, 22.9 [18.1] years). First-degree relatives of individuals with TRD had lower incomes, more physical comorbidities, higher suicide mortality, and increased risk of developing TRD (aRR, 9.16; 95% CI, 7.21-11.63) and higher risk of other psychiatric disorders than matched control individuals, including schizophrenia (aRR, 2.36; 95% CI, 2.10-2.65), bipolar disorder (aRR, 3.74; 95% CI, 3.39-4.13), major depressive disorder (aRR, 3.65; 95% CI, 3.44-3.87), attention-deficit/hyperactivity disorders (aRR, 2.38; 95% CI, 2.20-2.58), autism spectrum disorder (aRR, 2.26; 95% CI, 1.86-2.74), anxiety disorder (aRR, 2.71; 95% CI, 2.59-2.84), and obsessive-compulsive disorder (aRR, 3.14; 95% CI, 2.70-3.66). Sensitivity and subgroup analyses validated the robustness of the findings.

Conclusions and Relevance

To our knowledge, this study is the largest and perhaps first nationwide cohort study to demonstrate TRD phenotype transmission across families and coaggregation with other major psychiatric disorders. Patients with a family history of TRD had an increased risk of suicide mortality and tendency toward antidepressant resistance; therefore, more intensive treatments for depressive symptoms might be considered earlier, rather than antidepressant monotherapy.

The relationship between 11 different polygenic longevity scores, parental lifespan, and disease diagnosis in the UK Biobank

Large-scale genome-wide association studies (GWAS) strongly suggest that most traits and diseases have a polygenic component. This observation has motivated the development of disease-specific "polygenic scores (PGS)" that are weighted sums of the effects of disease-associated variants identified from GWAS that correlate with an individual's likelihood of expressing a specific phenotype. Although most GWAS have been pursued on disease traits, leading to the creation of refined "Polygenic Risk Scores" (PRS) that quantify risk to diseases, many GWAS have also been pursued on extreme human longevity, general fitness, health span, and other health-positive traits. These GWAS have discovered many genetic variants seemingly protective from disease and are often different from disease-associated variants (i.e., they are not just alternative alleles at disease-associated loci) and suggest that many health-positive traits also have a polygenic basis. This observation has led to an interest in "polygenic longevity scores (PLS)" that quantify the "risk" or genetic predisposition of an individual towards health. We derived 11 different PLS from 4 different available GWAS on lifespan and then investigated the properties of these PLS using data from the UK Biobank (UKB). Tests of association between the PLS and population structure, parental lifespan, and several cancerous and non-cancerous diseases, including death from COVID-19, were performed. Based on the results of our analyses, we argue that PLS are made up of variants not only robustly associated with parental lifespan, but that also contribute to the genetic architecture of disease susceptibility, morbidity, and mortality.

A genetic risk score to predict treatment nonresponse in psychotic depression

Psychotic depression is a severe and difficult-to-treat subtype of major depressive disorder for which higher rates of treatment-resistant depression were found. Studies have been performed aiming to predict treatment-resistant depression or treatment nonresponse. However, most of these studies excluded patients with psychotic depression. We created a genetic risk score (GRS) based on a large treatment-resistant depression genome-wide association study. We tested whether this GRS was associated with nonresponse, nonremission and the number of prior adequate antidepressant trials in patients with a psychotic depression. Using data from a randomized clinical trial with patients with a psychotic depression (n = 122), we created GRS deciles and calculated positive prediction values (PPV), negative predictive values (NPV) and odds ratios (OR). Nonresponse and nonremission were assessed after 7 weeks of treatment with venlafaxine, imipramine or venlafaxine plus quetiapine. The GRS was negatively correlated with treatment response (r = -0.32, p = 0.0023, n = 88) and remission (r = -0.31, p = 0.0037, n = 88), but was not correlated with the number of prior adequate antidepressant trials. For patients with a GRS in the top 10%, we observed a PPV of 100%, a NPV of 73.7% and an OR of 52.4 (p = 0.00072, n = 88) for nonresponse. For nonremission, a PPV of 100%, a NPV of 51.9% and an OR of 21.3 (p = 0.036, n = 88) was observed for patients with a GRS in the top 10%. Overall, an increased risk for nonresponse and nonremission was seen in patients with GRSs in the top 40%. Our results suggest that a treatment-resistant depression GRS is predictive of treatment nonresponse and nonremission in psychotic depression.

Polygenic risk for major depression, attention deficit hyperactivity disorder, neuroticism, and schizophrenia are correlated with experience of intimate partner violence

Research has suggested that mental illness may be a risk factor for, as well as a sequela of, experiencing intimate partner violence (IPV). The association between IPV and mental illness may also be due in part to gene-environment correlations. Using polygenic risk scores for six psychiatric disorders - attention-deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BPD), major depressive disorder (MDD), neuroticism, and schizophrenia-and a combined measure of overall genetic risk for mental illness, we tested whether women's genetic risk for mental illness was associated with the experience of three types of intimate partner violence. In this cohort of women of European ancestry (N = 11,095), participants in the highest quintile of genetic risk for ADHD (OR range: 1.38-1.49), MDD (OR range: 1.28-1.43), neuroticism (OR range: (1.18-1.25), schizophrenia (OR range: 1.30-1.34), and overall genetic risk (OR range: 1.30-1.41) were at higher risk for experiencing more severe emotional and physical abuse, and, except schizophrenia, more severe sexual abuse, as well as more types of abuse and chronic abuse. In addition, participants in the highest quintile of genetic risk for neuroticism (OR = 1.43 95% CI: 1.18, 1.72), schizophrenia (OR = 1.33 95% CI: 1.10, 1.62), and the overall genetic risk (OR = 1.40 95% CI: 1.15, 1.71) were at higher risk for experiencing intimate partner intimidation and control. Participants in the highest quintile of genetic risk for ADHD, ASD, MDD, schizophrenia, and overall genetic risk, compared to the lowest quintile, were at increased risk for experiencing harassment from a partner (OR range: 1.22-1.92). No associations were found between genetic risk for BPD with IPV. A better understanding of the salience of the multiple possible pathways linking genetic risk for mental illness with risk for IPV may aid in preventing IPV victimization or re-victimization.

The relationship between familial-genetic risk and pharmacological treatment in a Swedish national sample of patients with major depression, bipolar disorder, and schizophrenia

Using Swedish registers, we examine whether the prescription of and the response to antidepressants (AD), mood stabilizers (MS), and antipsychotics (AP) in the treatment of, respectively, major depression (MD), bipolar disorder (BD), and schizophrenia (SZ), are influenced by familial-genetic risk. We examined individuals born in Sweden 1960-1995 with a first diagnosis of MD (n = 257,177), BD (n = 23,032), and SZ (n = 4248) from 2006 to 2018. Drug classes and Defined Daily Dose (DDD) were obtained from the Pharmacy register using the Anatomical Therapeutic Chemical system. We utilized the Familial Genetic Risk Scores (FGRS) calculated from morbidity risks in first- through fifth degree relatives. Treatment with antidepressants (AD) in MD, mood-stabilizers (MS) in BD, and antipsychotics (AP) in SZ were associated with significantly higher disorder-specific familial-genetic risks. Using dosage trajectory analysis of AD, MS, and AP treatment for MD, BD, and SZ, respectively, familial-genetic risk was positively associated with higher and/or increasing drug dosages over time. For MD and BD, examining cases started on the most common pharmacologic treatment class (SSRIs for MD and "other anti-epileptics" for BD), familial-genetic risks were significantly lower in those who did not versus did later receive treatment from other AD and MS classes, respectively. Higher familial-genetic risk for BD predicted switching AD medication in cases of MD. Among pharmacologically treated cases of BD, familial-genetic risk was significantly higher for those treated with lithium. In a large population-based patient cohort, we found evidence of a wide-spread association between higher familial-genetic risk and i) increased likelihood of receiving pharmacologic treatment but 2) responding more poorly to it-as indicated by a switching of medications -- and/or requiring higher doses. Further investigations into the clinical utility of genetic risk scores in the clinical managements of MD, BD, and SZ are warranted.

Summarizing studies using constitutive genetic deficiency to investigate behavioural influences of uptake 2 monoamine transporters

Burgeoning literature demonstrates that monoamine transporters with high transport capacity but lower substrate affinity (i.e., uptake 2) contribute meaningfully to regulation of monoamine neurotransmitter signalling. However, studying behavioural influences of uptake 2 is hindered by an absence of selective inhibitors largely free of off-target, confounding effects. This contrasts with study of monoamine transporters with low transport capacity but high substrate affinity (i.e., uptake 1), for which there are many reasonably selective inhibitors. To circumvent this dearth of pharmacological tools for studying uptake 2, researchers have instead employed mice with constitutive genetic deficiency in three separate transporters. By studying baseline behavioural shifts, plus behavioural responses to environmental and pharmacological manipulations-the latter primarily targeting uptake 1-investigators have been creatively characterizing the behavioural, and often sex-specific, influences of uptake 2. This non-systematic mini review summarizes current uptake 2 behaviour literature, highlighting emphases on stress responsivity in organic cation transporter 2 (OCT2) work, psychostimulant responsivity in OCT3 and plasma membrane monoamine transporter (PMAT) investigations, and antidepressant responsivity in all three. Collectively, this small but growing body of work reiterates the necessity for development of selective uptake 2-inhibiting drugs, with reviewed studies suggesting that these might advance personalized treatment approaches.

Transforming global approaches to chronic disease prevention and management across the lifespan: integrating genomics, behavior change, and digital health solutions

Chronic illnesses are a major threat to global population health through the lifespan into older age. Despite world-wide public health goals, there has been a steady increase in chronic and non-communicable diseases (e.g., cancer, cardiovascular and metabolic disorders) and strong growth in mental health disorders. In 2010, 67% of deaths worldwide were due to chronic diseases and this increased to 74% in 2019, with accelerated growth in the COVID-19 era and its aftermath. Aging and wellbeing across the lifespan are positively impacted by the presence of effective prevention and management of chronic illness that can enhance population health. This paper provides a short overview of the journey to this current situation followed by discussion of how we may better address what the World Health Organization has termed the "tsunami of chronic diseases." In this paper we advocate for the development, validation, and subsequent deployment of integrated: 1. Polygenic and multifactorial risk prediction tools to screen for those at future risk of chronic disease and those with undiagnosed chronic disease. 2. Advanced preventive, behavior change and chronic disease management to maximize population health and wellbeing. 3. Digital health systems to support greater efficiencies in population-scale health prevention and intervention programs. It is argued that each of these actions individually has an emerging evidence base. However, there has been limited research to date concerning the combined population-level health effects of their integration. We outline the conceptual framework within which we are planning and currently conducting studies to investigate the effects of their integration.

Response to anti-IL17 therapy in inflammatory disease is not strongly impacted by genetic background

Response to the anti-IL17 monoclonal antibody secukinumab is heterogeneous, and not all participants respond to treatment. Understanding whether this heterogeneity is driven by genetic variation is a key aim of pharmacogenetics and could influence precision medicine approaches in inflammatory diseases. Using changes in disease activity scores across 5,218 genotyped individuals from 19 clinical trials across four indications (psoriatic arthritis, psoriasis, ankylosing spondylitis, and rheumatoid arthritis), we tested whether genetics predicted response to secukinumab. We did not find any evidence of association between treatment response and common variants, imputed HLA alleles, polygenic risk scores of disease susceptibility, or cross-disease components of shared genetic risk. This suggests that anti-IL17 therapy is equally effective regardless of an individual's genetic background, a finding that has important implications for future genetic studies of biological therapy response in inflammatory diseases.

Clinical utility of polygenic risk scores: a critical 2023 appraisal

Since their first appearance in the context of schizophrenia and bipolar disorder in 2009, polygenic risk scores (PRSs) have been described for a large number of common complex diseases. However, the clinical utility of PRSs in disease risk assessment or therapeutic decision making is likely limited because PRSs usually only account for the heritable component of a trait and ignore the etiological role of environment and lifestyle. We surveyed the current state of PRSs for various diseases, including breast cancer, diabetes, prostate cancer, coronary artery disease, and Parkinson disease, with an extra focus upon the potential improvement of clinical scores by their combination with PRSs. We observed that the diagnostic and prognostic performance of PRSs alone is consistently low, as expected. Moreover, combining a PRS with a clinical score at best led to moderate improvement of the power of either risk marker. Despite the large number of PRSs reported in the scientific literature, prospective studies of their clinical utility, particularly of the PRS-associated improvement of standard screening or therapeutic procedures, are still rare. In conclusion, the benefit to individual patients or the health care system in general of PRS-based extensions of existing diagnostic or treatment regimens is still difficult to judge.

Ethical, legal, and social implications of genetic risk prediction for multifactorial disease: a narrative review identifying concerns about interpretation and use of polygenic scores

Advances in genomics have enabled the development of polygenic scores (PGS), sometimes called polygenic risk scores, in the context of multifactorial diseases and disorders such as cancer, cardiovascular disease, and schizophrenia. PGS estimate an individual's genetic predisposition, as compared to other members of a population, for conditions which are influenced by both genetic and environmental factors. There is significant interest in using genetic risk prediction afforded through PGS in public health, clinical care, and research settings, yet many acknowledge the need to thoughtfully consider and address ethical, legal, and social implications (ELSI). To contribute to this effort, this paper reports on a narrative review of the literature, with the aim of identifying and categorizing ELSI relating to genetic risk prediction in the context of multifactorial disease, which have been raised by scholars in the field. Ninety-two articles, spanning from 1977 to 2021, met the inclusion criteria for this study. Identified ELSI included potential benefits, challenges and risks that focused on concerns about interpretation and use, and ethical obligations to maximize benefits, minimize risks, promote justice, and support autonomy. This research will support geneticists, clinicians, genetic counselors, patients, patient advocates, and policymakers in recognizing and addressing ethical concerns associated with PGS; it will also guide future empirical and normative research.

Deep phenotyping towards precision psychiatry of first-episode depression - the Brain Drugs-Depression cohort

BACKGROUND

Major Depressive Disorder (MDD) is a heterogenous brain disorder, with potentially multiple psychosocial and biological disease mechanisms. This is also a plausible explanation for why patients do not respond equally well to treatment with first- or second-line antidepressants, i.e., one-third to one-half of patients do not remit in response to first- or second-line treatment. To map MDD heterogeneity and markers of treatment response to enable a precision medicine approach, we will acquire several possible predictive markers across several domains, e.g., psychosocial, biochemical, and neuroimaging.

METHODS

All patients are examined before receiving a standardised treatment package for adults aged 18-65 with first-episode depression in six public outpatient clinics in the Capital Region of Denmark. From this population, we will recruit a cohort of 800 patients for whom we will acquire clinical, cognitive, psychometric, and biological data. A subgroup (subcohort I, n = 600) will additionally provide neuroimaging data, i.e., Magnetic Resonance Imaging, and Electroencephalogram, and a subgroup of patients from subcohort I unmedicated at inclusion (subcohort II, n = 60) will also undergo a brain Positron Emission Tomography with the [¹¹C]-UCB-J tracer binding to the presynaptic glycoprotein-SV2A. Subcohort allocation is based on eligibility and willingness to participate. The treatment package typically lasts six months. Depression severity is assessed with the Quick Inventory of Depressive Symptomatology (QIDS) at baseline, and 6, 12 and 18 months after treatment initiation. The primary outcome is remission (QIDS ≤ 5) and clinical improvement (≥ 50% reduction in QIDS) after 6 months. Secondary endpoints include remission at 12 and 18 months and %-change in QIDS, 10-item Symptom Checklist, 5-item WHO Well-Being Index, and modified Disability Scale from baseline through follow-up. We also assess psychotherapy and medication side-effects. We will use machine learning to determine a combination of characteristics that best predict treatment outcomes and statistical models to investigate the association between individual measures and clinical outcomes. We will assess associations between patient characteristics, treatment choices, and clinical outcomes using path analysis, enabling us to estimate the effect of treatment choices and timing on the clinical outcome.

DISCUSSION

The BrainDrugs-Depression study is a real-world deep-phenotyping clinical cohort study of first-episode MDD patients.

TRIAL REGISTRATION

Registered at clinicaltrials.gov November 15th, 2022 (NCT05616559).

Cluster headache polygenetic risk and known functional variants of CYP3A4 are not associated with treatment response

BACKGROUND AND PURPOSE

The response to cluster headache treatments has a high interindividual variation. To date, treatment response has only been assessed by a candidate gene approach and no investigations into metabolic pathways have been performed. Our aim was to investigate the association between the polygenetic risk of cluster headache and treatment response to first-line cluster headache treatments as well as known functional variants of CYP3A4 and the response to verapamil. Further, it was aimed to replicate previous single nucleotide polymorphisms found to be associated with treatment response in cluster headache and/or migraine.

METHODS

In, 508 cluster headache patients diagnosed according to the International Classification of Headache Disorders were genotyped and participated in a semi-structured interview to evaluate treatment response. Polygenetic risk scores were calculated by the effect retrieved from a meta-analysis of the latest two genome-wide association studies on cluster headache.

RESULTS

Inferior treatment response to oxygen, triptans and verapamil is associated with chronicity of cluster headache were confirmed but no evidence was found that a response could be predicted by a high genetic risk of cluster headache. Likewise, verapamil response was not associated with functional variants of CYP3A4. No support of the genetic variants previously reported to be associated with treatment response to triptans or verapamil was found.

CONCLUSION

The clinically relevant variation in treatment response for cluster headache was not influenced by genetic factors in the present study.

The MAOA rs979605 Genetic Polymorphism Is Differentially Associated with Clinical Improvement Following Antidepressant Treatment between Male and Female Depressed Patients

Major depressive disorder (MDD) is the leading cause of disability worldwide. Treatment with antidepressant drugs (ATD), which target monoamine neurotransmitters including serotonin (5HT), are only modestly effective. Monoamine oxidase (MAO) metabolizes 5HT to 5-hydroxy indoleacetic acid (5HIAA). Genetic variants in the X-chromosome-linked MAO-encoding genes, MAOA and MAOB, have been associated with clinical improvement following ATD treatment in depressed patients. Our aim was to analyze the association of MAOA and MAOB genetic variants with (1) clinical improvement and (2) the plasma 5HIAA/5HT ratio in 6-month ATD-treated depressed individuals. Clinical (n = 378) and metabolite (n = 148) data were obtained at baseline and up to 6 months after beginning ATD treatment (M6) in patients of METADAP. Mixed-effects models were used to assess the association of variants with the Hamilton Depression Rating Scale (HDRS) score, response and remission rates, and the plasma 5HIAA/5HT ratio. Variant × sex interactions and dominance terms were included to control for X-chromosome-linked factors. The MAOA rs979605 and MAOB rs1799836 polymorphisms were analyzed. The sex × rs979605 interaction was significantly associated with the HDRS score (p = 0.012). At M6, A allele-carrying males had a lower HDRS score (n = 24, 10.9 ± 1.61) compared to AA homozygous females (n = 14, 18.1 ± 1.87; p = 0.0067). The rs1799836 polymorphism was significantly associated with the plasma 5HIAA/5HT ratio (p = 0.018). Overall, CC/C females/males had a lower ratio (n = 44, 2.18 ± 0.28) compared to TT/T females/males (n = 60, 2.79 ± 0.27; p = 0.047). The MAOA rs979605 polymorphism, associated with the HDRS score in a sex-dependent manner, could be a useful biomarker for the response to ATD treatment.

Polygenic risk scores for predicting outcomes and treatment response in psychiatry: hope or hype?

Over the last years, the decreased costs and enhanced accessibility to large genome-wide association studies datasets have laid the foundations for the development of polygenic risk scores (PRSs). A PRS is calculated on the weighted sum of single nucleotide polymorphisms and measures the individual genetic predisposition to develop a certain phenotype. An increasing number of studies have attempted to utilize the PRSs for risk stratification and prognostic evaluation. The present narrative review aims to discuss the potential clinical utility of PRSs in predicting outcomes and treatment response in psychiatry. After summarizing the evidence on major mental disorders, we have discussed the advantages and limitations of currently available PRSs. Although PRSs represent stable trait features with a normal distribution in the general population and can be relatively easily calculated in terms of time and costs, their real-world applicability is reduced by several limitations, such as low predictive power and lack of population diversity. Even with the rapid expansion of the psychiatric genetic knowledge base, pure genetic prediction in clinical psychiatry appears to be out of reach in the near future. Therefore, combining genomic and exposomic vulnerabilities for mental disorders with a detailed clinical characterization is needed to personalize care.