CAG Precision Psychiatry (PSYCH)

Psychiatric disorders are a leading cause of disability world-wide, causing enormous suffering and lost capacity for millions of affected individuals, their family and loved ones, and society at large. Despite decades of intense  investments and milestone achievements in psychiatric epidemiology, human genetics, and cellular/molecular neurosciences, for most mental disorders we still lack a mechanistic understanding of etiology, have no clinically actionable biomarkers, and struggle to provide effective interventions efficiently. Progress requires an integrative and translational scientific agenda that aligns perspectives and expertise from data science, basic science, and clinical science.

With this CAG we propose to integrate the disjoint potential to accelerate the translation of scientific discovery towards clinical impact. To do so, we will organize this CAG around five main goals. First, we will build a connected community of cross-disciplinary researchers in the Greater Copenhagen Area around the internationally unique, integrated register, clinical, imaging, high resolution-omics, large-scale genetic and biobank data available in Denmark through key members of this group. Second, we will seek to identify clinically actionable (bio)markers by developing models for stratifying patient subgroups, predicting disorder onset, course, and treatment outcomes, and monitoring risk. Third, we will seek mechanistic understanding of these enigmatic disorders by characterizing the cellular and molecular impact
of genetic risk. Fourth, we will pursue both in silico target emulation trials and real-life clinical trials together with novel machine learning approaches to close the loop from discovery to clinical impact. Fifth, and finally, we will provide a unique opportunity for cross-disciplinary, translational research training for students, PhD fellows, postdoctoral fellows, and junior group leaders, with cross-group projects, annual meetings, and mentorship programs.

Challenge:
Mental disorders are highly prevalent across the lifespan and are major causes of disability and associated with reduced life expectancy (1-8). A substantial number of patients with mental disorders suffer from unmet needs stemming from imprecise diagnoses and treatment strategies (9-10).
Currently, diagnoses in mental health are mainly based on psychometric measures while paraclinical data supporting diagnosis and treatment are lacking. Thus, the one specific etiology – out of a broad range of many different mechanisms leading to, e.g. schizophrenia – is in most cases not identified. Therefore, guideline-driven clinical treatments apply the same standard regimen irrespective of etiology, which may explain the insufficient current treatments. Currently, many of the treatments provided in mental health services barely exceeds the effect of placebo or active control groups, and have a very broad and unprecise effects, leaving a tremendous unmet need for patients with mental disorders (11-12). Targeting these needs might not only reduce morbidity but also mortality among patients with severe mental disorders.

The likelihood of developing a severe mental disorder with adverse outcomes varies significantly among patients, ranging from rapid and full recovery to chronicity and increased mortality. Effectively allocating resources to those at high risk poses a considerable challenge, requiring the use of risk algorithms grounded in evidence-based data. While significant progress has been made in psychiatric genetics over recent decades, revealing the polygenic nature of mental disorders and significant overlap among current diagnostic categories, this understanding has not yet translated to the development of new diagnostic or therapeutic approaches in psychiatry (9, 13-14).

Opportunities:
To advance mechanistic understandings, biomarker discovery, and treatment outcomes in psychiatry, we will:
• Align an internationally unique collection of data resources from national registers, electronic health records, biobanks and large-scale genetic cohorts, epidemiological surveys, and targeted clinical cohorts
• Translate genetic and epidemiological findings into mechanistic understandings using advanced cellular and molecular bioassays and models
• Identify digital, imaging, or high resolution-omics based biomarkers with prognostic and diagnostic validity
• Identify indicators of heterogeneous treatment response to stratify patients into more homogeneous subgroups
• Test insights in next generation clinical trials that emphasize predictive algorithms, stratification, and individualized interventions to translate the findings into impact for the patients

We believe an organized CAG can systematize and accelerate the path from discovery to implementation, activating an untapped potential in the Greater Copenhagen Area and providing clinical psychiatry with a pipeline to the next generation of treatment and prevention.