Precision medicine in complex diseases—Molecular subgrouping for improved prediction and treatment stratification

Abstract

Complex diseases are caused by a combination of genetic, lifestyle, and environmental factors and comprise common noncommunicable diseases, including allergies, cardiovascular disease, and psychiatric and metabolic disorders. More than 25% of Europeans suffer from a complex disease, and together these diseases account for 70% of all deaths. The use of genomic, molecular, or imaging data to develop accurate diagnostic tools for treatment recommendations and preventive strategies, and for disease prognosis and prediction, is an important step toward precision medicine. However, for complex diseases, precision medicine is associated with several challenges. There is a significant heterogeneity between patients of a specific disease—both with regards to symptoms and underlying causal mechanisms—and the number of underlying genetic and nongenetic risk factors is often high. Here, we summarize precision medicine approaches for complex diseases and highlight the current breakthroughs as well as the challenges. We conclude that genomic‐based precision medicine has been used mainly for patients with highly penetrant monogenic disease forms, such as cardiomyopathies. However, for most complex diseases—including psychiatric disorders and allergies—available polygenic risk scores are more probabilistic than deterministic and have not yet been validated for clinical utility. However, subclassifying patients of a specific disease into discrete homogenous subtypes based on molecular or phenotypic data is a promising strategy for improving diagnosis, prediction, treatment, prevention, and prognosis. The availability of high‐throughput molecular technologies, together with large collections of health data and novel data‐driven approaches, offers promise toward improved individual health through precision medicine.

Polymorphisms of the GCLC Gene Are Novel Genetic Markers for Susceptibility to Psoriasis Associated with Alcohol Abuse and Cigarette Smoking

“…Thus, this approach has potential in dermatological practice as a means of adjuvant therapy for psoriasis and the prevention of disease progression. Furthermore, pharmacogenetic and precision medicine approaches [ 88 , 89 ] would make it possible to subclassify patient groups based on environmental risk factors (e.g., cigarette smoking and alcohol abuse) and clinically significant genetic variants affecting glutathione metabolism, thus personalizing and improving the treatment and prevention of psoriasis.…”

Clinical data mining: challenges, opportunities, and recommendations for translational applications

“…As an alternative, one can use a aposteriori variable to divide into subgroups, to identify subgroups with beneficial characteristics [ 81 ], such as improved therapeutic responses [ 34 ] or fewer treatment-related complications [ 82 ]. Researchers are increasingly reporting these created variables, such as polygenic scores [ 83 , 84 ], on a continuous scale, enabling them to investigate how the effectiveness of treatment changes as the value of the novel variable increases [ 85 – 87 ]. Despite the potential of multivariable continuous models to reveal complex interactions, investigators should ultimately rely on binary subgroups that require a reasonable cut-off for a simpler explanation.…”

Human Pangenomics: Promises and Challenges of a Distributed Genomic Reference

“…The use of bioinformatics tools to integrate SNP data with other types of data, such as transcriptomic and proteomic data, has also enabled researchers to identify correlations between SNPs and phenotypic variation [ 92 , 93 , 94 ]. This has been particularly useful in the field of personalized medicine, where the identification of SNPs associated with disease can help to develop more precise treatments and therapies [ 95 , 96 ]. The pangenome has enabled a much better understanding of genetic variation and diversity among organisms, allowing for an unprecedented level of SNP discovery and mappability [ 97 , 98 ].…”