A Translational Lens on Synergistic Cancer Therapy: Insights from Our Recent Case Study

In cancer drug development, monotherapies often lose ground when tumors adapt. This is especially true for KRASmutant non–small cell lung cancers with concurrent LKB1 loss, where resistance and metabolic plasticity are common. We believe the path forward lies in strategically designed combination therapies and deep mechanistic insight.

Recently we partnered with Panome Bio to execute a preclinical study combining an HDAC inhibitor (GB3103) and a MEK inhibitor (Trametinib) in KRAS/LKB1mutant NSCLC. We led study selection, dosing strategy, and in vivo execution using our A549 xenograft model, while Panome brought their NextGeneration^® untargeted metabolomics platform to uncover the biochemical underpinnings of the observed synergy.

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Why This Model and Strategy Matter

Choosing the right in vivo model and treatment schedule is often underestimated, but it’s critical to decipher mechanism, not just efficacy. At TD2 Oncology, we optimized treatment arms and timing to ensure metabolic shifts would be detectable and interpretable. That allowed Panome’s metabolomics to work at full potential.

By integrating in vivo rigor with omics depth, we can move beyond “this works” and toward “this works because of these vulnerabilities.”

Key Findings: Mechanisms Behind Synergy

• The combination therapy produced significantly stronger tumor suppression compared to either agent alone.
• Out of ~3,942 metabolites assessed via four LC/MS assays, 73 met stringent significance criteria. The combination arm showed broader differential regulation.
• Pathway analysis revealed 19 altered metabolic routes, with notable changes in lipid metabolism, redox balance, and amino acid pathways.
• Metabolites such as PC O36:5 and TG 13:0/13:0/13:0 were depleted under combination treatment, pointing to disruption in membrane synthesis and energy reserves.
• Elevated levels of acetyl lysine and cystathionine indicated increased oxidative stress.
• The integrated profile revealed a tumor under metabolic duress, redox stress, reduced antioxidant defense, and energy imbalance.

These insights support a model in which HDAC + MEK inhibition forces tumors into a stressed metabolic state they cannot compensate for, helping explain the superior efficacy observed.

What This Means for Translational Strategy

For drug developers designing combination regimens, the lessons are clear:

• Model and design matter: Without the right study design, you may miss mechanistic signals entirely.
• Mechanism is more than tumor shrinkage: Metabolomics (and multiomics) reveal vulnerabilities that efficacy curves alone cannot.
• Predictive biomarkers come from integrated insight: The metabolic fingerprints identified here can guide patient stratification or next-generation combinations.
• Strategic partnerships unlock value: Pairing our in vivo expertise with metabolomics magnified what each standalone technology could reveal.

Next Steps

Moving forward, integrating proteomics and phosphoproteomics will help map upstream signaling and connect enzymatic control to metabolic outcomes. This multiomics view will further sharpen biomarker hypotheses and combination strategies for resistant cancers.

If you’re working on combination oncology, mechanism validation, or translational strategy and want a partner that bridges rigorous models with mechanistic depth, let’s talk. We’re ready to bring scientific clarity to your next breakthrough.

Download the full case study to view all figures, metabolic pathway analyses, and mechanistic insights uncovered through our multi-omics collaboration.