The power of dual-layer genomics in precision oncology
Integrating DNA and RNA profiling is a promising approach to advance cancer characterisation and guide clinical decision-making
Shaping the use of AI in oncology
ESMO plays a central role in developing long-lasting recommendations for a safe and effective use of artificial intelligence supporting oncology stakeholders
Artificial intelligence is expediting the development of therapeutic immunotherapies
AI-driven approaches can help to design minibinders, which may bring new T-cell receptor-based therapies closer to the clinic
Neoadjuvant immunotherapy continues to rewrite our understanding of anticancer immunity
Integrating spatial profiling, single-cell technologies and blood-based correlates may help reveal why immunotherapy is effective in some tumours but not others
Exploring synergy to keep the immunotherapy flame alight
2025 ESMO Immuno-Oncology Awardee, Ignacio Melero, is confident that combinations and novel multispecific agents will result in synergistic strategies to ensure the continued success of cancer immunotherapy
Novel ADCs from Asia open new clinical opportunities
Phase I–II studies showcase the clinical potential of novel ADCs with enhanced targeting and/or payload strategies
First-in-class p53 reactivator shows activity in advanced solid tumours
Initial results of a phase II trial with rezatapopt suggest real promise in an area that has eluded druggable targeting
Going beyond protocol is key for young people with cancer
For adolescents and young adults with cancer, individualised care that addresses their unique medical and psychosocial needs is essential to improving outcomes and quality of life
Bridging the interpretation gap in cancer genomics – the next frontier for ai in oncology
Can artificial intelligence finally unlock the full potential of cancer genomics? Discover how new digital tools are closing the gap between data and clinical action in oncology.
AI-powered three-dimensional tumour pathology
Computational models can overcome current limitations of 3D analysis of tumour samples