Mechanism-Driven Diagnostic Development: A Specimen-Aware Framework Illustrated by Colorectal Cancer and Solid Tumours

Abstract

Simple Summary Cancer diagnostics have traditionally relied on radiological imaging and histopathological assessment of biopsied tissue, but modern translational oncology increasingly uses blood, urine, stool, saliva, cerebrospinal fluid, and minimally invasive tissue sampling to detect and characterise disease. Currently available tests still depend on a limited number of biomarkers in one biological layer, such as DNA mutations or protein abundance. Although broadly adopted across clinical practice, this reductive strategy does not consider the inter-play of molecular, cellular, immune, metabolic, and microbial regulation that shapes oncogenesis and tumour behaviour. In this review, we evaluate recent advancements in cancer biology, technology and how these combinations can and are being utilized for prospective diagnostic applications. We rationalise that cancer profiling should be “mechanistically-driven and specimen-aware.” and propose a conceptual framework that critically evaluates the benefits of using the clinical application as the driver for biological specimen selection, combined with multimodal understanding of oncogenesis to determine biomarker selection strategies. We discuss how multi-omics, single-cell and spatial methods, liquid biopsy, and artificial intelligence (AI) are enabling more informative disease models, and we examine how these advances can improve screening, diagnosis, primary treatment selection, residual disease detection, and monitoring of ongoing outcomes. We also address the practical barriers to translation, including pre-analytical variability, assay standardisation, contamination, model validation, and health-system implementation.