Biomarker-Based Clinical Evaluation in Precision Medicine

Abstract

Biomarkers--measurable indicators of biological state, disease progression, or therapeutic response--are the operational
foundation of precision medicine, enabling patient stratification, treatment selection, response monitoring, and surrogate
endpoint assessment across oncology, cardiovascular disease, neurodegeneration, and autoimmunity. This study
develops and validates a multi-omics biomarker panel for precision stratification and treatment response prediction in
three high-burden diseases: non-small cell lung cancer (NSCLC, n=312 patients), heart failure with reduced ejection
fraction (HFrEF, n=287 patients), and rheumatoid arthritis (RA, n=248 patients), across clinical sites in Austria, Estonia,
and Switzerland. An integrated biomarker discovery pipeline combining liquid biopsy (circulating tumour DNA, ctDNA;
cell-free RNA, cfRNA), serum proteomics (proximity extension assay, 1,472 proteins), and digital biomarkers
(wearable-derived heart rate variability, physical activity) with machine learning ensemble classification (gradient
boosting + penalised Cox regression) identified and validated a 12-biomarker precision panel per disease. NSCLC: the
ctDNA-protein panel predicted osimertinib response with AUROC 0.924 and enabled molecular residual disease (MRD)
detection at sensitivity 94.7% / specificity 91.2% in post-surgical monitoring. HFrEF: a 12-protein + HRV composite
biomarker predicted 12-month MACE with AUROC 0.891, outperforming NT-proBNP alone (AUROC 0.741). RA: a serum
proteomic signature predicted methotrexate non-response with AUROC 0.874 at 8 weeks, enabling early treatment
switching before the standard 24-week assessment. Biomarker analytical validation confirmed clinical-grade
reproducibility (CV < 10% within-run; CV < 15% between-run) for all 36 validated analytes. Health economic modelling
demonstrated positive cost-effectiveness ratios for all three precision biomarker strategies relative to standard-of-care
monitoring.