biomarker

Overview

A biomarker is a measurable biological indicator used to assess normal biological processes, pathological conditions, or pharmaceutical responses in patients. Biomarkers can be detected in various biological matrices including blood, tissue, cerebrospinal fluid, and imaging data, serving as objective indicators of disease presence, progression, or treatment response. In contemporary medical practice, biomarkers have become central to precision medicine, enabling early diagnosis, risk stratification, and monitoring of therapeutic efficacy. The validation and integration of biomarkers into clinical workflows continues to accelerate due to advances in molecular profiling, neuroimaging, digital pathology, and artificial intelligence technologies that enable the discovery and interpretation of complex biomarker signatures.

Biomarkers span multiple categories based on their application: diagnostic biomarkers identify disease presence; prognostic biomarkers predict future disease course or patient outcomes; and predictive biomarkers indicate likelihood of response to specific treatments. The increasing sophistication of biomarker research reflects growing recognition that individual patient variability—at genetic, metabolic, and imaging levels—necessitates personalized diagnostic and therapeutic approaches. Modern biomarker discovery increasingly relies on integrated multi-omics platforms, machine learning algorithms, and real-world clinical data to identify robust, clinically actionable indicators that can improve patient stratification and treatment selection across diverse disease areas.

Recent Publications Summary

Recent publications examined biomarker use across several clinical and translational settings, with strong emphasis on predictive, prognostic, and treatment-response applications. In anterior uveitis, a systematic review and meta-analysis protocol was designed to synthesize immunological features of the human cornea and tear film as candidate biomarkers for disease classification, prognosis, and monitoring treatment responsiveness 42586731Aug. In chronic pain, another protocol proposed to evaluate whether baseline neuroimaging biomarkers can predict acupuncture treatment response, motivated by prior reports of classification accuracies exceeding 80% 42486530Jul.

Several studies focused on biomarker-guided decision-making and model development in oncology and critical care. A prospective pediatric intensive care unit study assessed an integrated antimicrobial stewardship strategy centered on procalcitonin-guided decisions, with serial clinical and microbiological reassessment, and also measured pancreatic stone protein and C-reactive protein as additional biomarkers in antibiotic de-escalation 42560479Aug. In breast cancer, multi-omics integration and machine learning were used to identify lipid droplet-associated prognostic biomarkers, yielding a model that stratified patients across molecular subtypes and outperformed ER/PR/HER2 status; SQLE and SOCS3 emerged as key genes associated with immune infiltration and T-cell dysfunction 42484744Jul.

Advances in artificial intelligence and spatial profiling were also highlighted as enabling biomarker discovery and prediction. A deep learning framework for pathology image analysis, EAGLE, was benchmarked across 43 tasks from nine cancer types spanning morphology, biomarker prediction, treatment response, and prognosis, and was reported to outperform patch aggregation methods while greatly reducing computation time 42386722Jul. A separate perspective emphasized that integrating spatial omics with artificial intelligence could strengthen biomarker research and diagnostics by linking mechanistic spatial target biology with scalable, reproducible quantification in routine pathology 41955187Apr. In 3D cancer models, optical coherence tomography combined with machine learning was investigated as a way to identify biomarkers of drug efficacy in tumor spheroids treated with cisplatin 42497140Jul.

Biomarker infrastructure and clinical trial use were also prominent themes. EPIC4ND was introduced as a large case-cohort study within the EPIC cohort designed to identify biomarkers predicting future onset of dementia, Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis using pre-disease blood samples and multi-omics data 42484778Jul. In Alzheimer's disease therapeutic development, biomarkers were reported to be widely used across active disease-targeted trials, appearing in 84% of studies overall and commonly serving as inclusion criteria and outcome measures, with especially high use in Phase 2 and Phase 3 trials 41716297Feb.

What Changes, What Holds

1. Biomarkers are being extended into disease-specific classification and response prediction
NEW DIRECTION Corneal and tear-film immunological features in anterior uveitis, together with baseline neuroimaging in chronic pain, push biomarker use into settings where the main question is not diagnosis alone but whether a marker can classify disease, forecast prognosis, or anticipate treatment responsiveness 42586731Aug42486530Jul. That extends the Overview’s diagnostic/prognostic/predictive framework into narrower translational use cases without displacing it.

2. Biomarker-guided treatment decisions are becoming more operational in critical care and oncology
REINFORCES Procalcitonin-driven antimicrobial stewardship, with adjunct inflammatory markers, sharpens the Overview’s point that biomarkers now support therapeutic monitoring and selection in real practice 42560479Aug. Multi-omics lipid droplet-associated prognostic modeling in breast cancer likewise reinforces biomarker stratification across molecular subtypes and treatment-relevant risk groups, while showing that machine-learning integration can outperform single conventional subtype markers 42484744Jul.

3. AI and spatial profiling are changing how biomarkers are discovered and quantified
METHOD Deep learning pathology systems, spatial-omics perspectives, and optical coherence tomography in 3D tumor models mainly alter the analytic pipeline for biomarker discovery, prediction, and drug-response readout rather than redefining what a biomarker is 42386722Jul41955187Apr42497140Jul. The baseline already anticipated AI-enabled interpretation; these studies make that infrastructure more concrete, scalable, and reproducible.

4. Pre-disease biospecimens and trial embeddedness are making biomarkers more central to prevention and development
NEW DIRECTION Blood-based multi-omics from people sampled before symptom onset moves biomarker work toward preclinical risk prediction for dementia and motor-neurodegenerative disease, a role not explicit in the Overview’s emphasis on current disease assessment 42484778Jul. Biomarkers’ heavy use in Alzheimer’s disease trials also shows they are now entrenched as inclusion and outcome tools in therapeutic development, strengthening the case for biomarker-driven trial design 41716297Feb.

Overview update candidates: biomarkers for pre-disease risk prediction in neurodegeneration; biomarkers as routine inclusion and outcome measures in Alzheimer’s trials.