At the 2026 ASCO Annual Meeting, the RASolute 302 Phase 3 trial delivered what many are calling the most significant advance in pancreatic cancer treatment in decades. Daraxonrasib, a first-in-class oral RAS(ON) multi-selective inhibitor developed by Revolution Medicines, nearly doubled overall survival (OS) in previously treated metastatic pancreatic ductal adenocarcinoma (PDAC), achieving a median OS of 13.2 months compared to 6.7 months with standard chemotherapy (HR 0.40; p<0.0001). Progression-free survival followed the same trajectory: 7.3 months versus 3.5 months. The results, published simultaneously in The New England Journal of Medicine, drew a standing ovation during the plenary session.
ASCO's chief medical officer called it "a grand slam," and the ASCO-selected commentator described starting to cry in clinic after seeing the data. This is no longer a drug to watch. Daraxonrasib is poised to become the new standard of care for second-line metastatic PDAC, with first-line combination trials already underway.
For drug developers working in KRAS-driven cancers, the question is no longer whether daraxonrasib will reshape the treatment landscape, but how fast, and whether your preclinical program is ready.
More than 90% of pancreatic cancers harbor KRAS mutations, making it one of the most RAS-addicted tumor types in oncology. As daraxonrasib transitions from investigational therapy to standard of care, it will become the benchmark against which new agents, novel combinations, and next-generation therapies are measured.
This shift has immediate implications for preclinical strategy. Scientists developing therapies in KRAS-mutant NSCLC, colorectal cancer, and PDAC need access to clinically annotated models with existing daraxonrasib response data to design meaningful benchmarking and combination studies. Without this data, preclinical programs risk testing against an outdated treatment landscape.
Yet despite the urgency, a significant gap exists. Most contract research organizations and preclinical providers do not have daraxonrasib response data, the KRAS-mutant patient-derived xenograft (PDX) models needed to generate it, or the multi-omic depth required to interpret results in a translational context.
At Champions Oncology, we recognized early that daraxonrasib would become a defining compound in the KRAS space. That is why we have already screened over 50 KRAS-mutant PDX models across NSCLC, colorectal cancer, and PDAC, generating a unique and comprehensive daraxonrasib benchmarking dataset that is available today.
Figure 1: Tumor response and genomic landscape of KRAS-mutant
PDX models treated with Daraxonrasib (RMC-6236)
This is not a standard drug screen. Every model in the cohort is fully annotated with clinical history, treatment status, and deep molecular profiling, including whole exome sequencing, RNA sequencing, and integrated genomic analysis. The resulting waterfall plots and genomic annotation panels allow scientists to rapidly identify responder and non-responder models matched to their therapeutic hypothesis, and to understand the molecular context driving each outcome.
Understanding what drives resistance. Differential gene expression analysis of responding versus non-responding KRAS G12D-mutant PDAC models has identified transcriptional programs associated with primary resistance to daraxonrasib, providing actionable insights for biomarker development and patient selection strategies.
Modeling acquired resistance in real time. Because secondary resistance is an inevitable feature of targeted therapy, we have developed acquired resistance models under continuous daraxonrasib treatment pressure. Models CTG-2473 (pancreatic, KRAS G12D) and CTG-0068 (colorectal, KRAS G12D) both demonstrated initial tumor regression followed by regrowth, recapitulating the resistance patterns expected in patients. These models create a powerful translational framework for testing next-line agents, rational combination strategies, and treatment sequencing approaches.
Going deeper with multi-omic prediction. Champions' Pharmaco-Pheno-Multiomic (PPMO) integration platform takes this further by layering whole-cell proteomics, cell surface proteomics, genomics, and transcriptomics across 56 KRAS-mutant PDX models treated with daraxonrasib. The resulting computational model achieves 80% prediction accuracy for drug response, substantially outperforming RNA-only approaches, which plateau at approximately 60%. This level of biological resolution is critical for identifying the molecular determinants of sensitivity and resistance that standard biomarker panels miss.
These findings were first presented at AACR 2026 (Poster 1884): Molecular Determinants of Sensitivity and Resistance to the Pan-RAS(ON) Inhibitor Daraxonrasib (RMC-6236) Across KRAS-Mutant Patient-Derived Models. For the complete dataset, including response profiles, genomic annotations, and resistance modeling data, download the full poster here.
For additional context, read our companion blog: Understanding Sensitivity and Resistance to Pan-RAS(ON) Inhibition Across KRAS-Mutant Tumors.
Daraxonrasib is changing how KRAS-targeted therapies will be developed and evaluated. Whether you are benchmarking a novel compound against an emerging standard of care, designing combination strategies, or investigating mechanisms of primary and acquired resistance, we have the models, the data, and the translational depth to support your program.