Keys from KRAS: Drug Discovery Takeaways from Recent Research

A recent article in Science Signaling shines a spotlight on the complex and interconnected world of oncogenic signaling, providing new insight into the intricacies of KRAS signaling in cancer cells while also raising larger questions about how we understand critical molecular mechanisms. In this article – “Proteomic analyses identify targets, pathways, and cellular consequences of oncogenic KRAS signaling” – scientists from Bernhard Kuster’s group at the Technical University of Munich sought to evaluate how KRAS mutations (G12C and G12D) shape the cellular response to pharmacological inhibition of KRAS and other key signaling proteins. By leveraging a broad range of chemoproteomic techniques – including global profiling of post-translational modifications and reactive cysteine residues – these scientists were able to comprehensively characterize cellular responses to signaling pathway perturbation. In addition to reporting valuable findings about the nature of KRAS signaling in cancer cells, this work illuminates universal themes relevant to a broad range of research efforts. Read on to learn more about how these takeaways can inform your drug discovery and development project.

Finding #1: Response to KRAS inhibition is largely determined by cell line, not by the particular inhibitor used.

Takeaway: Drug responses are highly context-dependent and arise from cell-type-specific pathway wiring.

As part of this work, researchers treated cells with a number of different KRAS inhibitors (sotorasib, adagrasib, ARS-1620, MRTX1257, and MRTX1133), then used DecryptM profiling to characterize treatment-induced changes in protein phosphorylation. Interestingly, analysis of these responses showed that they clustered by cell line – not by drug. This result indicates that, within a single cell type, different KRAS inhibitors tend to produce the same effect, while the treatment of different cell lines with the same KRAS inhibitor can lead to divergent outcomes. In one example, KRAS inhibition in ASPC1 cells was shown to have a particularly strong effect on Rho signaling, suggesting that actin cytoskeletal dynamics are closely related to KRAS signaling in this cellular context. The authors conclude that “the molecular composition and wiring of the KRAS pathway are substantially different between cell lines.”

Beyond its importance to the study of KRAS and KRAS-mutant cancers, this finding has broader implications for strategic drug development. It highlights the importance of choosing an appropriate cell model for preclinical research efforts and cautions researchers against over-generalizing findings obtained in any one cell line. Furthermore, it supports the potential value of repurposing existing drugs for novel applications, leveraging cell-type-specific signaling networks to effect distinct downstream consequences. Investigating existing therapeutics in different disease contexts – or targeting their delivery to different cell populations – could have unexpected effects beyond what previous studies have demonstrated.

Finding #2: Of the subset of proteins affected by inhibitor treatment in all mutation contexts, roughly half were not previously known to be connected to KRAS signaling.

Takeaway: The functional breadth of KRAS signaling (and other key signaling networks) is not yet fully appreciated. Limiting yourself to known targets can mean missing out on fundamental aspects of a biological system.

While much is known about the landscape of cellular signaling, much still remains to be discovered. In this work, researchers identified 196 proteins (241 phospho-peptides) that were affected by KRAS inhibitor treatment in all three tested cell lines (MiaPaCa-2 and ASPC1 pancreatic cancer cells and NCI-H23 lung cancer cells) and designated these proteins as the “KRAS core signaling signature.” Despite this functional corroboration, STRING protein interaction analysis identified only about half of these proteins as connected to KRAS or to each other. The authors interpret these findings as “suggesting that KRAS signaling extends far beyond well-researched biology.”

Indeed, protein interaction networks are extensive, diverse, and dynamic. Proteins that interact in one cell type may not interact in another, and even recurring interactions may be subject to different regulatory forces in different contexts. While targeted interrogation of known interactors may provide an easy starting point, this kind of restricted analysis can prevent the discovery of novel biological relationships. By incorporating unbiased, proteome-wide profiling of protein levels and phosphorylation events, researchers can appreciate the full extent of cellular interactions in a specific, relevant context.

Finding #3: KRAS inhibitor treatment induces immediate cellular changes, followed by a later adaptive response.

Takeaway: Cells readily compensate for pharmacological perturbations, potentially obscuring the direct effects of drug treatment.

To understand the kinetics of cellular response to KRAS inhibition, researchers performed concentration-dependent phosphoproteome profiling of sotorasib-treated MiaPaCa-2 cells across a dynamic time course (1, 2, 8, and 16 hours). Analysis of these results identified a subset of phosphopeptides that were altered after 1, 2, or 8 hours of treatment but not after 16 hours. Another subset of phosphopeptides were unaffected at early time points but exhibited significant changes after 8 or 16 hours of inhibition. This distinction between immediate and delayed responders prompted the authors to hypothesize that the initial cellular response to KRAS inhibition is followed by a subsequent adaptive response that maintains cell viability in the absence of this key signaling pathway.

This type of compensatory response to perturbation has been reported following inhibition of other proteins, such as the chromatin remodeler BRG1/SMARCA4. These findings underscore the importance of choosing experimental time points consistent with a project’s research goals. For example, a study that seeks to understand the basic biological function of a protein might be best served by focusing on early time points, before compensation obscures the direct effects of inhibition. In contrast, a clinically oriented study may prioritize later time points, which reflect the functional long-term outcomes of inhibitor treatment in a cellular context.

Finding #4: The transition of KRAS-inhibited cells to a resting state occurs without substantial changes in protein abundance and is instead mediated by post-translational modifications.

Takeaway: Protein levels alone don’t tell the whole story.

Researchers observed that, as part of their adaptive response, KRAS-inhibited cells transition from a proliferative state to a quiescent one. Perhaps surprisingly, analysis of protein levels during this shift revealed very few changes in protein abundance. Much more dramatic changes, however, were observed in phosphopeptide and ubi-peptide levels, indicating that the change in cell state is mediated largely on the level of post-translational modifications. The authors suggest that this mechanism may “allow cells to avoid the energy costs associated with […] transcriptional remodeling,” while also facilitating a rapid return to the proliferative state under favorable conditions.

This finding not only emphasizes the functional importance of post-translational modifications, it also highlights the limits of proteome profiling alone. In isolation, proteomic analysis was not sufficient to reveal the mechanisms underlying the shift to quiescence, and targeted ‘omic’ analysis of relevant modifications was required to fully appreciate the dynamics of this adaptation. Researchers should be wary of relying solely on protein levels to capture complex cellular phenomena, instead complementing these approaches with additional analyses of the phosphoproteome, ubiquitome, etc. as appropriate.

At Momentum, we can help you leverage these takeaways and design a customized experimental workflow to advance your drug discovery and development research. In fact, our team members contributed critical cysteine profiling data to Kabella et al., quantifying 12.5k – 18.6k reactive cysteines per cell line following sotorasib and adagrasib treatment. This information confirmed the selectivity of these inhibitors for KRAS G12C, validating their use as chemical probes. You can learn more about our cysteine profiling technology (CysScout™) here. When you’re ready to kick off your CysScout™ experiment – or to learn more about any of our comprehensive suite of bioanalytical services – send us a message.

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