Diagnostics are becoming increasingly central to oncology drug development, with their role expanding far beyond the traditional ‘One biomarker, one test’ model. Today, diagnostics support a much wider range of use cases, including early detection, prognosis, surrogate endpoints such as minimal residual disease (MRD), companion diagnostics and monitoring resistance mutations.
According to Guillaume Cettou, Executive Director, Diagnostics, Oncology Translational Medicine at GSK, the relationship between diagnostics and biomarker research is also becoming more closely connected. As therapies such as antibody-drug conjugates, T-cell engagers, and immunotherapies evolve, diagnostics are not only used to confirm biomarkers but may also help discover them. This means diagnostic strategy is now a key part of clinical development planning.
Pharma teams need to begin thinking about companion and complementary diagnostics as early as possible. In some programmes, regulatory submissions may be based on Phase I or Phase II data, making it essential to anticipate diagnostic needs as early as phase 1. Preclinical research, including organoid models, as well as real-world data, can also help teams generate hypotheses before trials begin.
Successful partnerships between pharma and diagnostic companies depend on transparency. Pharma companies need to clearly communicate the overall goals of a programme while leaving room for diagnostic partners to contribute ideas. Diagnostic companies, in turn, must be honest about what they have done, what they can do and where there are gaps. Overpromising can damage trust, while openness helps both sides identify risks early and work together to solve them.
Implementation remains challenging, particularly when moving from controlled clinical trial settings into real world practice. In trials, analytical validity of innovative approaches such as computational pathology or multimodal signature remains a challenge. In real-world practice, access to technology, pre-analytical factors and clinical practices further complicate deployment of new diagnostic tools. Early collaboration with diagnostic partners and pathologists can help to mitigate some of those challenges.
Looking ahead, three areas stand out as especially important for oncology diagnostics: computational pathology, liquid biopsy and multimodal diagnostics. Computational pathology is moving beyond replicating manual immunohistochemistry and toward discovering entirely new biomarkers. Liquid biopsy offers opportunities for monitoring molecular response, supporting MRD assessment and improving access where tissue biopsies are difficult. Multimodal diagnostics, combining for instance modalities such as pathology and molecular data into composite signatures, could reshape how biomarkers are discovered and applied to stratify patient populations.
As oncology becomes more complex, diagnostics will play an increasingly strategic role in identifying the right patients, guiding treatment decisions, supporting clinical trials and generating insights that feed future research.
