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PubMed2026

Pharmacogenomic and drug interactions risk in cardio-oncology: A precision medicine perspective for India.

Cardio-oncology patients may face complex treatment regimens due to the concurrent existence of cancer and cardiovascular disease, leading to a considerable polypharmacy burden. This significantly increases the prospect of drug-drug interactions (DDIs) and gene-drug interactions. The majority of these interactions arise from comparable pharmacokinetic and pharmacological pathways associated with drug transporters and cytochrome P450 enzymes. The significance of pharmacogenomics in tailored treatment strategies are emphasised by the fact that genetic variability enhances individual differences in drug response, safety, and efficacy. This narrative review focus on the effects of key genetic polymorphisms (e.g., DPYD, CYP2C19, and CYP2C9) on the metabolism and efficacy of commonly prescribed anticancer and cardiovascular medications such as fluoropyrimidines, clopidogrel, and warfarin. In addition it explore the role of pharmacogenomic variants on drug-drug interactions within the field of cardio-oncology. The study ultimately emphasizes the necessity of precision medicine in India to address the genetic diversity and underrepresentation in global genomic databases. The absence of pharmacogenomic testing, infrastructural deficiencies, financial constraints, and insufficient clinical integration hinder the widespread use of this technology in India. The Genome India Project and other national initiatives establish the foundation for pharmacogenomic-guided therapy. Utilizing genetic data, together with artificial intelligence-based predictive tools, for clinical decision-making may enhance medication safety and yield optimal outcomes in Indian cardio-oncology patients.

Humans↗
PubMed2026

Molecular clusters and precision medicine in pheochromocytomas and paragangliomas.

Pheochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumors derived from chromaffin cells of the adrenal medulla and extra-adrenal paraganglia. Over the past two decades, the genomic characterization of PPGLs has profoundly transformed their diagnosis, classification, risk stratification, and therapeutic management. Up to 40% of PPGLs harbor germline pathogenic variants, the highest proportion among human neoplasms, and somatic driver events are identified in a substantial fraction of the remaining cases. Integrative multi-omic studies have established three main molecular clusters: a pseudohypoxic cluster driven by Krebs-cycle alterations (SDHx, FH, MDH2, DLST) and HIF-2α pathway alterations (VHL, EPAS1, EGLN1/2); a kinase-signaling cluster driven by activation of RAS/MAPK and PI3K/AKT pathways (RET, NF1, HRAS, TMEM127, MAX); and a Wnt-signaling cluster characterized primarily by MAML3 fusions. This review summarizes progress in PPGL genomics, highlighting geographic and sex-related particularities. Using EPAS1/HIF-2α and RET as paradigmatic examples, we illustrate how diverse germline, somatic, mosaic, and fusion events converge on common core signaling hubs that can be therapeutically exploited with FDA-approved selective inhibitors for relevant targets (e.g. belzutifan for HIF-2α; selpercatinib and pralsetinib for RET). We further review the genomic determinants of metastatic risk (SDHB, ATRX, TERT, and MAML3 fusions), the immune microenvironment of metastatic disease, and emerging radionuclide theranostics, liquid biopsy biomarkers, and integrative multi-omic approaches that are reshaping precision medicine for PPGLs.

Humans↗
PubMed2026

Efforts towards a precision medicine approach in juvenile idiopathic arthritis.

Juvenile idiopathic arthritis (JIA) is the commonest group of childhood arthritides. Despite the availability of advanced therapeutics, many children and young people (CYP) with JIA experience disease flares, and in some, chronic joint damage. Tailoring treatment based on unique biological profiles would benefit CYP with JIA given their variable clinical presentation and disease course. To date, biomarkers to predict treatment response are lacking. With advances in single cell technologies, we are now able to profile the genes and proteins of target tissues at unprecedented resolution to define the biological basis of disease and guide novel treatment approaches. The complex analyses and combination of biological and clinical outcome data from large datasets across disease phenotypes have become possible with the development of computational and machine learning methods. Here, we summarize the strategies to integrate data through multimodal based approaches to maximize precision medicine and research priorities for CYP with JIA.

Humans↗
PubMed2026

Towards precision medicine for brain arteriovenous malformations.

Recent advances in cerebrovascular genomics, single-cell biology, pharmacology, and gene editing technology are transforming our understanding of brain arteriovenous malformations (bAVMs) - a leading cause of pediatric hemorrhagic stroke. Once considered static anatomical defects, bAVMs are now recognized as dynamic, genetically driven lesions resulting from somatic mutations in KRAS, BRAF, and pathways involved in arteriovenous specification, angiogenesis, and vascular remodeling. By integrating human genetics, animal models, and endovascular innovations, researchers have uncovered convergent mechanisms that link endothelial Ras/MAPK hyperactivation to abnormal vessel growth and higher rupture risk. These insights provide a foundation for precision medicine approaches that combine molecular diagnostics - such as liquid or endoluminal biopsies - with mutation-specific pharmacotherapies and emerging CRISPR-based gene editing strategies. We suggest that genotype-guided interventions, tailored by spatial and developmental cerebrovascular context, could ultimately reclassify bAVMs from surgically incurable malformations to treatable molecular conditions.

Humans↗
PubMed2025

Soluble Immune Checkpoint Protein and Lipid Network Associations with All-Cause Mortality Risk: Trans-Omics for Precision Medicine (TOPMed) Program.

Adverse cardiovascular events are emerging with the use of immune checkpoint therapies in oncology. Using datasets in the Trans-Omics for Precision Medicine program (Multi-Ethnic Study of Atherosclerosis, Jackson Heart Study [JHS], and Framingham Heart Study), we examined the association of immune checkpoint plasma proteins with each other, their associated protein network with high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C), and the association of HDL-C- and LDL-C-associated protein networks with all-cause mortality risk. Plasma levels of LAG3 and HAVCR2 showed statistically significant associations with mortality risk. Colocalization analysis using genome wide-association studies of HDL-C or LDL-C and protein quantitative trait loci from JHS and the Atherosclerosis Risk in Communities identified TFF3 rs60467699 and CD36 rs3211938 variants as significantly colocalized with HDL-C; in contrast, none colocalized with LDL-C. The measurement of plasma LAG3, HAVCR2, and associated proteins plus targeted genotyping may identify patients at increased mortality risk.

Journal Article↗
PubMed2026

The mighty microproteins: from versatile cellular regulators to precision medicine therapeutics.

Microproteins, are tiny proteins encoded by small open reading frame (sORF), translation of these non-canonical open reading frames (ncORFs) has been implicated in diverse biological processes and diseases. This review summarizes recent developments in the discovery, biogenesis, and functional characterization of microproteins, and their involvement in various disease, with special focus on their roles in cancer, cardiovascular, metabolic, neurodegenerative and immune-related disorders. We emphasize the regulation of key cellular pathways by microproteins, including mitochondrial homeostasis, apoptosis, metabolic reprogramming, and immune signaling, all of which affect disease initiation and progression. Emerging evidence also supports their potential as disease biomarkers and therapeutic candidates for precision medicine. Finally, the review critically discusses the current challenges including discrepancies in microprotein annotation, the limitations of ribosome profiling and proteogenomic approaches, the gap between computationally predicted and experimentally validated microproteins, and the need for rigorous orthogonal validation by means of CRISPR-based genome editing, ribosome release assays, mutational analysis, high-resolution mass spectrometry, and functional studies. Finally, we review recent development of AI-assisted ORF prediction, single-cell translatomics, spatial proteomics, and integrated multi-omics as emerging technologies reshaping. Microprotein discovery and functional annotation. Finally, we discuss the translational potential of microproteins and highlight the remaining challenges to clinical application, including peptide stability, pharmacokinetics, tissue-specific delivery, immunogenicity, and the need for rigorous preclinical and clinical validation. Together, this review provides an updated and critical overview of the rapidly evolving microprotein field and highlights future research priorities for translating these molecules into clinically useful biomarkers and precision therapeutics.

Microproteins↗
PubMed2026

Genomic and integrative based progression biomarker discovery in adult sepsis: toward clinical stratification and precision medicine.

Sepsis is a life-threatening syndrome characterized by a heterogeneous host response to infection that remains a major cause of mortality worldwide. Current clinical scoring systems capture organ dysfunction but fail to reflect the underlying biological diversity, limiting their utility for patient stratification and targeted therapy. This review provides a comprehensive overview of molecular biomarker approaches used to predict sepsis course and prognosis in adult patients, covering genetic, transcriptomic, proteomic, and integrative strategies up to May 2026. Here, we summarize findings from genetic association studies, along with analyses based on polygenic risk scores to aggregate genetic effects, Mendelian randomization, and rare-variant sequencing approaches. We also review transcriptomic and proteomic strategies for endotyping, and diagnostic and prognostic discrimination. Lastly, we discuss how multi-omics integration is emerging as a promising framework to assist in distinguishing causal therapeutic targets from non-causal biomarkers. We also address the challenges that still constrain clinical translation towards precision medicine.

Biomarker↗
PubMed2026

Artificial Intelligence in Predicting Systemic Complications From Retinal Findings: A New Frontier in Precision Medicine.

Innovations in retinal imaging technologies and growing evidence from retinal imaging of systemic and neurodegenerative diseases have begun to explore the utility of retinal imaging in diagnosing these conditions. Since the retina shares embryological origins with the central nervous system and reflects systemic microvascular characteristics, it is well positioned for noninvasive observation of patients' systemic and neural health. Moreover, accessibility of retinal imaging has improved with the increasing number of ophthalmology clinics. Rapid improvements in various deep learning (DL) tools have also catalyzed the automation of retinal imaging analysis. Systems that utilize DL for retinal imaging are being developed to assist with disease recognition, clinical judgment, and prognostic assessment of systemic health. Various imaging modalities are being integrated with existing genomic and clinical data to estimate an individual's predisposition to certain conditions. Contrary to many existing reviews, the objective of this review is to synthesize the most recent clinical and technological evidence on DL-based diagnostic systems for retinal imaging, with a focus on how different network architectures and their combinations have been developed, validated, and applied across systemic disease detection and prediction. Specifically, this review examines the datasets, model validation approaches, and automated diagnostic systems reported in recent literature. It discusses the extent to which these advancements address existing barriers toward real-time diagnostic application across clinical disciplines. Integrating retinal imaging with DL is an innovative and promising approach to precision medicine and health risk reduction.

artificial intelligence↗
PubMed2026

From standardization to precision medicine: Evolution of European Leukemianet recommendations in Philadelphia-negative myeloproliferative neoplasms.

The European LeukemiaNet (ELN) recommendations have been instrumental in shaping the diagnosis, risk stratification, and management of Philadelphia-negative myeloproliferative neoplasms (MPNs), including polycythemia vera, essential thrombocythemia, and primary myelofibrosis. Over the past two decades, these recommendations have evolved from consensus-based frameworks focused on response standardization to increasingly sophisticated, evidence-based and biologically informed approaches. Early efforts primarily aimed to harmonize response criteria across clinical studies, establishing a foundation for consistent therapeutic evaluation. Subsequent updates introduced risk-adapted treatment strategies centered on thrombotic risk, reflecting the major determinants of morbidity and mortality in these disorders. However, the limitations of surrogate endpoints prompted a shift toward clinically meaningful outcomes, including symptom burden, vascular events, and disease progression. More recent advances have been driven by the integration of molecular genetics and the adoption of structured evidence-based methodologies, enabling refined diagnostic classification and more accurate prognostic assessment. Contemporary ELN frameworks increasingly incorporate dynamic clinical parameters, genomic profiling, and emerging biomarkers, supporting a transition toward individualized, risk-adapted therapeutic strategies. Beyond their role in standardizing MPN management, ELN recommendations have also influenced clinical trial design, endpoint selection, risk stratification, and therapeutic decision-making. Evidence from prospective studies and real-world cohorts supports the clinical applicability of these frameworks, although their implementation remains heterogeneous across healthcare settings. This review provides a comprehensive and critical overview of the evolution and implementation of ELN recommendations, highlighting their impact on clinical research and routine practice, current limitations, and future directions toward precision medicine in Philadelphia-negative MPNs.

Essential thrombocythemia↗
PubMed2026

Organoids in translation: a bench-to-bedside framework for pancreatic cancer precision medicine.

INTRODUCTION: Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies with a 5-year survival rate of < 13%. Standard treatments such as FOLFIRINOX or gemcitabine/nab-paclitaxel yield modest response rates, underscoring the urgent need for precision oncology approaches. Patient-derived organoids (PDOs) preserve the genomic, phenotypic, and histopathological features of the source tumor and offer a promising platform for drug screening, biomarker development, and personalized therapy. However, a systematic evaluation of their translational capacities is lacking. METHODS: A systematic review was conducted according to the PRISMA 2020 guidelines (PROSPERO registration pending) using PubMed, EMBASE, and Cochrane CENTRAL (December 10, 2024) to identify English-language PDAC PDO studies that incorporated therapeutic testing. Ninety-five studies met the inclusion criteria. Data extraction captured >75 variables per study, including spanning culture methodology, therapeutic profiling, biomarker integration, and clinical correlation. A 13-domain weighted Translatability Scoring Framework adapted from Wehling et al. assessed predictive validity, biomarker strength, pharmacogenetics, and clinical trial alignment. Scores ranged from 0 to 5 and were categorized as good (>4.0), moderate (3.0-4.0), or low (<3.0) translational potential. RESULTS: Of the 95 studies, 70.5% have been published since 2021, reflecting the rapid growth in this field. The mean PDO generation success rate was 89.7%, with the primary tumor tissue being the predominant source (48.4%). Only 24.8% were directly linked to clinical trials and 5.3% incorporated multi-omic profiling. The median translatability score was 3.13 (range, 1.72-4.59): 45.3% of the studies had low translatability, 50.5% moderate, and only 4.2% had good translational potential. High-scoring studies consistently combine multi-omic biomarker platforms, in vivo validation, clinical outcome correlation, and prospective trial integration. Conversely, the weakest domains were pharmacogenetics, endpoint strategies, and biomarker validation, limiting their overall clinical relevance. CONCLUSIONS: PDOs have demonstrated strong feasibility and in vitro clinical correlation in PDAC; however, their clinical translation remains constrained by limited multi-omic integration, absence of pharmacogenomic modeling, and sparse clinical trial embedding. Standardization of protocols, adoption of harmonized and clinically relevant endpoints, and systematic incorporation of biomarker-driven co-clinical trial frameworks are urgently needed to transition PDOs from promising experimental surrogates to validating precision oncology tools capable of informing therapeutic decision-making in PDAC.

Humans↗
PubMed2026

Precision Medicine in Pancreatic Cancer: Targeting KRAS and Beyond.

For the past decades, chemotherapy constituted the therapeutic foundation in advanced or metastatic pancreatic cancer. Despite significant advances in the molecular understanding, translation into tangible patient benefit has remained modest. Until recently, mutant KRAS, the dominant oncogenic driver, was considered undruggable, and only a small subgroup of patients potentially benefited from targeted therapies. With the emergence of KRAS inhibitors, most patients with pancreatic cancer in theory qualify for targeted therapeutics. Final results from the RASolute 302 trial showed clinically meaningful activity of RAS inhibition in patients with metastatic pancreatic cancer and paved the way for approval. Ongoing preclinical and coclinical studies have documented both intrinsic and acquired mechanisms of resistance to KRAS inhibition. Given the cellular plasticity seen in pancreatic cancer, the identification and anticipation of resistance mechanisms will be critical to exploit emerging therapeutic vulnerabilities through novel combination strategies. In view of the increasing number of trials and the growing body of evidence for targeted therapies, pancreatic cancer is entering a transitional phase in which precision oncology strategies must be redefined beyond rare molecular subgroups. In this review, we will briefly revisit targeted therapeutic approaches in pancreatic cancer to then discuss the clinical implications of genomic and transcriptomic heterogeneity in KRAS-mutant and KRAS wild-type disease. We will outline how our expanding biological insights into pancreatic cancer could inform combination and sequential therapeutic approaches.

Humans↗
PubMed2026

Acute leukemia therapy at a crossroads: from conventional chemotherapy to the era of precision medicine.

Since the discovery of cytotoxic agents in the mid-20th century, acute leukemia has consistently served as a model for oncology research. As the Human Genome Project and subsequent genomic profiling elucidated the landscape of somatic mutations and cytogenetic aberrations driving leukemogenesis, the development of molecularly targeted therapies has dramatically accelerated, yielding significant improvements in patient outcomes. In acute myeloid leukemia (AML), the emergence of selective inhibitors targeting high-frequency alterations such as FLT3, NPM1, and IDH1/2 has redefined the standard of care, demonstrating superior efficacy when combined with conventional intensive chemotherapy or hypomethylating agents. Simultaneously, for acute lymphoblastic leukemia (ALL), in addition to the significant improvements achieved by tyrosine kinase inhibitors (TKIs) for BCR-ABL-positive ALL, the advent of CD19- or CD22-targeted monoclonal antibodies and CAR-T cell therapies has marked an epoch-making milestone, representing a major paradigm shift in the management of relapsed or refractory cases. Bridging these two distinct lineages, menin inhibitors have emerged as a novel class of agents targeting a common pathogenic mechanism in KMT2A-rearranged AML/ALL and NPM1-mutated AML, exhibiting promising antileukemic activity across these subtypes. In this review, we describe the evolution of leukemia therapy-highlighting historical trajectory across AML, APL, and ALL from uniform cytotoxic chemotherapy to molecularly targeted agents, antibody-based therapies, and chemo-free paradigms, while outlining future perspectives for precision hematology.

Acute lymphoblastic leukemia↗
PubMed2026

Predictive Biomarkers for Immune Checkpoint Inhibitor Efficacy: Challenges, Innovations, and a Pathway to Precision Medicine in the Era of Cancer Immunotherapy.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have transformed oncology practice. However, treatment response remains heterogeneous, rendering predictive biomarkers critical for optimal patient care. The 3 established biomarkers, programmed death-ligand 1, tumor mutational burden (TMB), and microsatellite instability-high/deficient mismatch repair, are approved and clinically validated but are modest predictors of benefit. As a result, multiple novel predictive biomarkers remain under investigation. CONTENT: This review highlights established and investigational predictive ICI efficacy biomarkers. For established biomarkers, we describe biology, assay modalities, approved companion diagnostics, landmark studies, and notable limitations. Due to the multisystem nature of antitumor immune effects, investigational biomarkers span multiple domains, including tumor genomic biomarkers (e.g., mutational signatures, TMB, neoantigen clonality), tumor microenvironment (e.g., tumor-infiltrating lymphocytes [TILs], tertiary lymphoid structures), systemic immune biomarkers (e.g., cytokines, autoantibodies, glycoproteins, peripheral blood mononuclear cells), and the microbiome (e.g., gastrointestinal microbial diversity, responder-enriched taxa). SUMMARY: The established biomarkers PD-L1, TMB, and microsatellite instability-high/deficient mismatch repair inform ICI use in clinical practice but have important limitations. Multiple investigational biomarkers show promise in refining patient selection and optimizing therapy. Moving forward, increased assay harmonization, prospective validation, and standardized parameters may improve performance. Composite models integrating complementary signals across domains may further individualize treatment and lead to an era of personalized cancer immunotherapy.

Humans↗
PubMed2026

Unraveling lung cancer complexity: Spatial omics in tumor microenvironment characterization and precision medicine.

Heterogeneous tumor microenvironment (TME) in lung cancer plays a crucial role in disease progression and resistance to therapy. Despite advances in single-cell and bulk omics profiling, these methods often overlook spatial context, which is vital for understanding cell-cell interactions and regional heterogeneity. In recent years, spatial omics technologies-including spatial genomics, transcriptomics, proteomics, and metabolomics-have revolutionized the ability to map molecular landscapes while maintaining tissue architecture. These advancements have become essential components of next-generation lung cancer management. By providing unprecedented resolution in characterizing the lung cancer TME, spatial omics could reveal prognostic and predictive biomarkers and identify new therapeutic vulnerabilities. This review will provide the first critical evaluation of spatial multi-omics approaches for lung cancer prognosis. It will also assess various integration strategies for multi-omics data to explore the clinical translational potential of these tools for therapy selection and patient stratification. Therefore, a deeper understanding of spatial omics technologies and their application in lung cancer can significantly improve precision diagnostics and therapeutic decision-making.

Lung cancer↗
PubMed2026

The Inflammation-Thrombosis Genetic Axis in Abdominal Aortic Aneurysm: A Shared Roadmap to Precision Medicine.

Abdominal Aortic Aneurysm (AAA) is characterized by persistent inflammation, extracellular matrix loss, and intraluminal thrombus formation, yet the genetic links among these processes remain incompletely defined. Here, we examine evidence that genetic variation can influence inflammatory and thrombotic responses at the same time. Findings from genome-wide association studies are considered alongside transcriptomic, proteomic, single-cell, epigenetic, and experimental data. Particular attention is given to candidate genes and signaling networks relevant to AAA susceptibility, enlargement, and rupture. The available evidence supports a model in which inherited susceptibility alters the balance between vascular inflammation, coagulation, fibrinolysis, and wall repair. However, many reported loci still lack functional confirmation, and most genetic data come from populations of European ancestry. Defining the causal variants and the cells in which they act will be necessary before these findings can be used for individualized screening or treatment.

abdominal aortic aneurysm↗