Despite the blaze of advancing knowledge on its complex genetic architecture, hypertension remains an elusive condition. Genetic studies of blood pressure have yielded bitter-sweet results thus far with the identification of more than 2,000 genetic loci, though the candidate causal genes and biological pathways remain largely unknown. The era of big data and sophisticated statistical tools has propelled insights into pathophysiology and causal inferences. However, new genetic risk tools for hypertension are the tip of the iceberg, and applications of genomic technology are likely to proliferate. We review the genomics of hypertension, exploring the significant milestones in our current understanding of this condition and the progress towards personalised treatment and management for hypertension.

The discovery and development of immune checkpoint inhibitors (ICIs) has revolutionised the management of human cancers. However, only a subset of patients responds to ICI therapy, even though immune evasion is a hallmark of cancer. Initially, treatment was administered to patients on the basis of expression levels of one of the targets of ICI therapy, programmed cell death ligand 1. In clinical trials, the high response rate of melanoma and non-small cell lung cancer patients to ICI therapy supported the basic premise of cancer immunotherapy, that tumour-specific mutated proteins trigger an immune response. Tumour mutational burden subsequently emerged as a potential biomarker for response to ICI therapy. This review summarises the evidence supporting the scientific rationale for TMB as a biomarker for ICI therapy and focuses on some of the major challenges associated with incorporation of TMB into routine clinical practice.

Consortia like the Clinical Pharmacogenetic Implementation Consortium (CPIC) and the Dutch Pharmacogenetic Working Group (DPWG) provide clinical guidelines but pharmacogenomics implementation depends on population prevalence of actionable genetic variants and response phenotypes. We analyzed the distribution of actionable genetic variants and clinical recommendations in 14,354 adult Qataris, focusing only genes with guidelines (CYP2C19, CYP2D6, CYP2B6 and CYP3A4). Haplotypes and diplotypes were generated from 490 alleles using whole genome data and metabolizer phenotypes were predicted based on current knowledge. Qatari population predicted to have actionable metabolizer phenotypes of CYP2C19, CYP2B6 and CYP2D6 impacting response to antidepressants were in the range of 1%–58% and for antipsychotics 0.1%–33% based on CYP3A4 and CYP2D6. Fine-grained analysis based on clinical guidelines also revealed that while the Qataris may need prescription of an alternate antidepressant not metabolized by CYP2C19, patients from other populations may just need altering the dosage of tricyclic antidepressants like amitriptyline. Further studies incorporating other factors such as diet, environment and cultural habits alongwith population-specific variants will help in the pharmacogenomics implementation in the Qatari population.

eSource – particularly EHR-to-EDC – is an emerging paradigm in clinical research that enables automated transfer of electronic health record (EHR) data into electronic data capture (EDC) systems, with the potential to reduce site burden, improve data quality and accelerate oncology clinical trial workflows. However, widespread implementation remains limited due to technical, regulatory and operational barriers. To address these challenges, the European Institute for Innovation through Health Data (i~HD) launched the eSource Scale-Up Task Force in 2024. This multi-stakeholder initiative brings together leading oncology centres and pharmaceutical sponsors to establish a consensus-driven roadmap for eSource adoption. Central to this effort are three foundational resources: readiness criteria for early adopters, a performance indicator framework for monitoring success and an operational playbook to guide implementation. This article provides a structured overview of the Task Force’s objectives, collaborative model and outputs, with specific attention to its focus on interoperability, regulatory alignment and real-world validation. While initially developed for oncology, the Task Force’s framework is applicable across therapeutic areas characterized by data-intensive workflows.

Cancer heterogeneity presents a major obstacle to effective drug treatment, emphasizing the need for personalized approaches that can accurately predict drug responses. Advances in high-throughput technologies have driven precision medicine initiatives toward integrating multi-omics data, enabling a more comprehensive understanding of tumor biology. However, integration of diverse omics layers poses challenges for computational modeling, as many traditional machine learning (ML) and statistical methods are not designed to capture complex, high-dimensional and multimodal data. This review examines the studies that integrate multi-omics datasets, aiming to enhance drug response prediction (DRP). Specifically, it outlines the most used omics types and computational approaches – classical ML models, as well as advanced deep learning and multimodal integration frameworks for improving DRP, detailing key methodologies and evaluation metrics, such as area under the dose–response curve, F1 score and mean square error, which assess model performance. By summarizing the integrated omics data, computational methods and challenges encountered, this review provides an in-depth overview of the existing landscape of precision medicine and future directions for advancing drug-response prediction.

The concept of personalized medicine and its significant benefits for patients and society was introduced over three decades ago. The Human Genome Project (initiated in 1990 and completed in 2003) greatly accelerated the development of precision medicine. In many cancers, defined biomarkers are used to select patients for therapy. For example, KRAS mutations are used to guide treatment with Sotorasib, while tumor expression of (wild type) human epidermal growth factor receptor 2 and 3 (HER2 and HER3) are used to select patients for trastuzumab and cetuximab, respectively. Nonetheless, the clinical adoption of companion diagnostics to facilitate a patient-centric approach in inflammatory diseases remains disappointing. One key reason why the development of companion diagnostics may be delayed autoimmune and fibrotic diseases can be the timing when clinical development teams inform R&D teams about relevant biomarkers or companion diagnostic to select patients, disease monitoring or treatment termination decisions. For clinical practicality, it is highly preferred to measure a biomarker in the systemic circulation, as blood samples can be obtained relatively easily in most diseases. However, discovering systemic biomarkers during clinical development has proven extremely challenging. Here, we describe an alternative approach, which we have used to select the most appropriate target for IgA driven autoimmune and fibrotic diseases. In this specific context, autoantigen-specific assays to determine autoantibody serum levels are widely available for a variety of indications. A detailed analysis of the biological pathways that affect the biomarker can uncover multiple potential therapeutic targets, allowing selection of the most optimal target from a clinical development perspective. Identification of a relevant biomarker before clinical development is initiated, enabling patient stratification in early clinical studies. Selection of the appropriate patient population based on biomarker presence reduces the number of patients needed and consequently, clinical development costs. Moreover, such a patient stratification approach minimizes the risk of including patients who are unlikely to respond, thereby avoiding unnecessary adverse events. This approach was applied during the selection of an anti-CD89 antagonist monoclonal antibody for IgA-mediated autoimmune and fibrotic diseases, serving as an illustrative example of this novel strategy.