The KRAS landscape has changed. As next-generation inhibitors continue to demonstrate clinical impact, the expectations placed on preclinical programmes are increasing.
At the centre of this shift is a simple but critical concept. Benchmarking is no longer a differentiator, but a requirement.
As therapies such as daraxonrasib advance through clinical development, they establish a new baseline for efficacy. This baseline becomes the reference against which all new KRAS-targeting programmes are evaluated.
For preclinical scientists, this fundamentally changes the design of studies. It is no longer sufficient to show that a compound reduces tumor growth in a subset of models. The real question is whether that activity represents meaningful improvement over what is already achievable.
Without benchmarking, this question cannot be answered.
Benchmarking provides context and transforms isolated, siloed data into translatable data; the basis on which your decision-making can be trusted.
When preclinical results are generated against a known standard, researchers can:
In KRAS-mutant tumors, where heterogeneity is high and resistance is common, this context is essential for making informed decisions.
Not all benchmarking datasets are equal. To be meaningful, benchmarking must be built on three core components:
1. A sufficiently large and diverse model set
KRAS mutations span multiple tumor types and molecular contexts, and a robust dataset must reflect this diversity.
2. Deep molecular characterisation
Genomics and transcriptomics alone are not enough to fully capture tumor biology. Protein-level and pathway-level data provide additional resolution that is critical for understanding response.
3. Clinical relevance
Models must reflect real patient biology, including treatment history and resistance mechanisms.
At Champions Oncology, we have generated daraxonrasib response data across more than 50 KRAS-mutant PDX models spanning lung, colorectal, and pancreatic cancers. Each model is fully annotated with clinical and molecular data, enabling direct linkage between response and biology.
This is not a conventional screening dataset. It is a translational framework enabling decision-making.
Traditional preclinical studies often present results in binary terms. Tumors respond or they do not. While this can be useful at an early stage, it lacks the resolution needed for modern drug development.
Benchmarking enables a more nuanced view and instead of asking whether a tumor responds, scientists can ask:
This level of analysis is essential for designing therapies that succeed in the clinic.
The value of benchmarking extends beyond the preclinical stage and directly informs clinical development.
By linking response data to molecular features, benchmarking datasets can support:
In a competitive KRAS landscape, these insights provide a critical advantage.
As the field continues to evolve, benchmarking will become a standard component of preclinical workflows. Teams that fail to incorporate benchmarking risk generating data that lacks context and relevance.
The future of KRAS research lies in integrated, data-rich approaches that connect biology, pharmacology, and clinical strategy.
Benchmarking is the foundation of that future.
In the next blog in this series, we examine why many KRAS preclinical models fail to translate into clinical success and how integrated, multi-omic approaches are addressing this gap.