<img alt="" src="https://secure.soil5hear.com/223550.png" style="display:none;">
Contact Us Select Models
Select Models
kras-banner-home-box

KRAS Preclinical Platform

The KRAS benchmark has changed. Your preclinical strategy must change with it.

Champions helps KRAS teams understand where pan-RAS inhibition works, where it fails, and what the resistance biology points to next.

Daraxonrasib (RMC-6236) has moved pan-RAS(ON) inhibition from promise to clinical inflection point. If you are developing KRAS-directed therapies, the question is whether your program is ready to compete, combine, stratify, or differentiate against the emerging benchmark.

We have built a bespoke KRAS platform with clinically relevant tumor models, in vivo daraxonrasib response data, direct measurement of the molecular systems driving response, and a way to translate resistance biology into development decisions.

The landscape has shifted

A new preclinical decision point.

RASolute 302 marked one of the most important recent advances in KRAS-driven pancreatic ductal adenocarcinoma (PDAC). This impact extends beyond PDAC and remains highly relevant across NSCLC and colorectal cancer. As pan-RAS(ON) inhibition becomes a new reference point, every KRAS program faces a sharper set of preclinical questions:

01

Can your compound outperform or complement the new benchmark?

Benchmark activity against clinically relevant KRAS-mutant models, not generic tumor systems disconnected from the current clinical landscape.

02

Can you identify the tumors most likely to respond?

Move beyond allele status alone by connecting KRAS mutation, co-mutation context, tumor type, pathway activity, and measured protein biology.

03

Can you explain resistance before it becomes a clinical problem?

Use intrinsic and acquired resistance models to identify bypass programs, survival pathways, and molecular states that standard response screens can miss.

04

Can you turn that biology into a rational combination strategy?

Translate resistance mechanisms into testable hypotheses before making expensive clinical commitments.

SFP_5508

Understanding Resistance

Resistance to pan-RAS inhibition is not random.

Some KRAS-mutant tumors respond deeply to pan-RAS(ON) inhibition. Others show limited response, adaptive escape, or acquired resistance under drug pressure. The difference is in their biology.

For KRAS programs, the strategic value is not just knowing what happened in a model. It is understanding why it happened, whether that biology is shared across tumors, and how it can inform the next experiment.

This is where standard screening falls short. A response curve can tell you whether a model moved but it cannot tell you about PI3K bypass, MYC/E2F proliferation, epigenetic resistance, immune-cold biology, or if another molecular program is shaping that response.

The development questions

Built to answer

  • Which molecular features predict sensitivity to pan-RAS(ON) inhibition?
  • Lead optimizationHow do intrinsic resistance and acquired resistance differ?
  • Which resistance programs point toward combination partners?
  • How does response vary by allele, tumor type, co-mutation context, and prior treatment history?
  • How should a KRAS program prioritize models, biomarkers, and follow-on studies?

THE EVIDENCE BASE

We have built the KRAS evidence base.

Champions has screened more than 50 KRAS-mutant PDX models with daraxonrasib across NSCLC, PDAC, and colorectal cancer. Each model connects in vivo pharmacology with clinical annotation, and a multi-omic characterization stack built to investigate response and resistance.

This is not your usual generic model list. It is a response and resistance evidence base around the pan-RAS(ON) inhibitor now reshaping KRAS development strategy.

50+

KRAS-mutant PDX models screened with daraxonrasib

3

Core tumor types: NSCLC, PDAC, and CRC

4

KRAS alleles in the dataset: G12C, G12D, G12V, and G12R

100

Additional KRAS-mutant PDX models with resistance classifier applied

The dataset integrates in vivo pharmacology with WES, RNA-seq, whole-cell proteomics, phosphoproteomics, cell surface proteomics, KRAS allele status, co-mutations, tumor type, prior treatment history, and acquired resistance modeling under continuous drug pressure.

Discuss the Dataset
kras-video-thumb

Webinar

Targeting KRAS Mutations Webinar

The KRAS landscape is rapidly evolving, with new clinical findings highlighting the potential of G12C, G12D, G12V, pan-KRAS, and pan-RAS(ON) approaches. Discover the latest developments, key clinical insights, and innovative combination strategies driving progress across NSCLC, colorectal, and pancreatic cancers.

 

Watch On Demand

OUR OFFERINGS

Our KRAS Platform.

Different KRAS programs need different kinds of evidence, which is why we provide you with three ways to use our KRAS platform; A way to start with existing data, commission prospective studies, or move from multi-omic profiling into predictive intelligence.

EXISTING DATA ASSET

RAS(ON) Intelligence Dataset

License an existing multi-omic response and resistance dataset across 50+ KRAS-mutant PDX models screened with daraxonrasib. Use it to evaluate sensitivity, resistance biology, allele context, tumor type effects, and biomarker hypotheses, for risk mitigation prior to launching a new study.

Learn more

PROSPECTIVE IN VIVO STUDY

KRAS Panel

Run a prospective benchmarking study across 50+ KRAS-mutant PDX models spanning G12C, G12D, G12V, G12R, G13D, and 11 tumor types. Design model selection, study arms, omic layers, and endpoints around your program from the start to generate new evidence for your compound.

Learn more

PREDICTIVE INTELLIGENCE

KRAS Intelligence Graph

Connect in vivo response, genomics, transcriptomics, proteomics, phosphoproteomics, cell surface proteomics, and pathway activity into predictive, translatable models that surface resistance mechanisms and combination hypotheses.

Learn more

OUR FOUNDATION

Why Champions for KRAS programs?

Champions did not build a KRAS capability in response to the latest clinical readout. We have been generating KRAS evidence in patient-derived models for years.

Before daraxonrasib, Champions tested adagrasib across patient-derived xenograft models prior to its FDA approval in 2022. The same foundation, metastatic and heavily pretreated patient-derived models, full clinical annotation, and deep molecular characterization, now supports a broader KRAS platform for response, resistance, benchmarking, and translational strategy.
Clinically relevant, low passage PDX models (approximately half from metastatic, pretreated patients)
1500+
Models with multi-omic profiling across whole-cell proteomics, cell surface proteomics, genomics, and transcriptomics for direct protein measurement
1050+
Positive predictive value for clinical disease control (Izumchenko et al., 2017)
85%
Negative predictive value for clinical progression
91%
SFP_5180

FAQS

Frequently Asked Questions

Champions' KRAS platform covers G12C, G12D, G12V, G12R, and G13D across a broad set of clinically relevant tumor types. The existing RAS(ON) Intelligence Dataset includes G12C, G12D, G12V, and G12R models screened with daraxonrasib.

The RAS(ON) Intelligence Dataset is an existing multi-omic response and resistance dataset available for licensing. The KRAS Panel is a prospective in vivo benchmarking program designed around your compound and experimental questions. The KRAS Intelligence Graph integrates response and multi-omic data into predictive models that support resistance analysis, combination hypotheses, and patient stratification strategy.

Yes. The RAS(ON) Intelligence Dataset is available for licensing and includes in vivo pharmacology, multi-omic profiling, resistance analysis, and clinical annotations across 50+ KRAS-mutant PDX models screened with daraxonrasib.

RNA expression does not reliably predict protein expression or pathway activation. For KRAS biology, signaling states, phosphorylation, and surface target expression can materially affect drug response and resistance. Champions directly measures whole-cell proteomics, phosphoproteomics, and cell surface proteomics from tumor material rather than relying only on DNA and RNA inference.

Yes. Champions uses response data, acquired resistance modeling, multi-omic profiling, and predictive modeling to identify molecular programs associated with non-response or escape. Those programs can inform rational combination hypotheses and follow-on study design.

Champions combines clinically annotated PDX models, prior treatment history, multi-omic characterization, in vivo pharmacology, and predictive modeling. The goal is to help teams connect preclinical response and resistance biology to translational decisions, including model selection, biomarker strategy, combination testing, and patient stratification.

resources

Related resources

kras

Get started

Get in contact

Fill out the form to get in contact with us.