Pathology in Personalised Oncology: Biomarkers, Predictive Models, and Therapeutic Targets

Pathology has become central to the advancement of personalised oncology, offering deep insights into disease mechanisms and supporting the development of precision cancer therapies.

With continuous innovations in molecular, genetic, and computational methodologies, the ability to identify robust biomarkers, develop predictive models, and uncover novel therapeutic targets is revolutionising the discipline. Despite substantial progress, major challenges remain, including the clinical validation and implementation of these discoveries and translating them for meaningful patient benefit.

This Research Topic aims to highlight cutting-edge research at the intersection of pathology and personalised oncology, focusing on the discovery, validation, and clinical application of diagnostic and prognostic biomarkers, mechanistic pathophysiology, as well as the integration of advanced analytics for therapeutic stratification. We are particularly interested in studies that:

• Elucidate new pathological biomarkers for cancer diagnosis, prognosis, and monitoring
• Develop and validate predictive models based on pathological and molecular data
• Investigate the mechanistic basis of pathological processes relevant to cancer treatment
• Explore novel therapeutic targets revealed by pathological analyses
• Advance digital pathology, including AI-driven image analysis and computational pathology
• Facilitate the translation of pathological findings into patient-specific clinical applications
• The use of in silico methodologies for the advancement of personalised oncological care

We welcome original research, systematic or narrative reviews, case reports, and perspectives that advance the understanding of pathological processes within oncology and underscore the progress and potential for personalised medicine.

Our aim is to foster a deeper integration between innovative pathology research and clinical oncology, ultimately driving improved outcomes for patients.