Enteric neuron–gastric cancer organoids expose lipid-metabolic Achilles’ heels — Strengths and limits of organoids for drug discovery, and what comes next

In a nutshell: Genome-wide CRISPR screens in patient-derived gastric cancer organoids uncovered strong dependencies on fatty-acid biosynthesis (ACC/ACACA) and cholesterol biosynthesis (LSS). Co-culture with enteric neurons (ENS) rewires metabolism, shifting responses toward resistance to ACC inhibition and increased sensitivity to LSS inhibition. These in vivo-relevant vulnerabilities are largely invisible in conventional 2D models.

• Key findings
• Why organoids help in drug discovery
• Pragmatic limitations
• Practical checklist
• My perspective: where organoids are headed

Key findings

• Models: Patient-derived gastric cancer organoids profiled by genome-wide CRISPR dropout screens; hits validated across multiple organoid lines and pharmacology.
• Top dependencies: Besides OXPHOS, fatty-acid biosynthesis (ACC/ACACA, FASN) and cholesterol biosynthesis (LSS) emerged as strong fitness genes.
• Pharmacology: ACC inhibitors (e.g., ND646) and LSS inhibitors (e.g., RO 48-8071) showed nano- to sub-micromolar efficacy across organoids; these effects were diminished or absent in 2D lines.
• Neuro-tumor crosstalk: ENS co-culture reprogrammed lipid metabolism, tilting responses toward ACC-inhibitor resistance and LSS-inhibitor sensitivity. ACC (ACACA) expression is a candidate biomarker for LSS-inhibitor response.
• In vivo: Genetic and pharmacological perturbations curtailed tumor growth with acceptable tolerability in preclinical settings.

Bottom line: Organoids make microenvironment-conditioned metabolic liabilities visible and testable.

Why organoids help in drug discovery

1. Patient heterogeneity retained: Organoids better preserve genetic, differentiation, and metabolic states than 2D monolayers.
2. Partial TME recapitulation: ECM/3D context plus co-culture with immune, fibroblast, and neuronal components can unmask in vivo-relevant dependencies.
3. Biomarker discovery engine: Functional screens linked with expression and metabolomics readily surface stratification axes (e.g., ACC expression → LSS-inhibitor sensitivity).
4. De-risking and hit rescue: Some efficacies fail in 2D yet appear in organoids, reducing false negatives and improving translational plausibility.
5. Toward personalized therapy: PDTOs enable patient-level testing and regimen optimization.

Pragmatic limitations

1. Incomplete TME: Vessels, immune cells, nerves, stroma, and microbiota are hard to model together in a stable, standardized way.
2. Matrix variability: Lot-to-lot changes in ECM (e.g., Matrigel) impact growth, drug response, and transcriptional states.
3. Throughput and cost: Higher hands-on time and consumable costs vs. 2D constrain large-scale screening.
4. Long-term drift: Genomic/epigenomic/metabolic drift during passaging may cause divergence from the parental tumor.
5. PK/PD blind spots: ADME and immune-mediated toxicities are not captured; must be bridged with liver/vascular models and in vivo studies.
6. Endpoint heterogeneity: Diverse readouts hinder cross-study comparability unless pre-registered and standardized.

Practical checklist

• Model paneling: Use multiple PDTOs with clear clinical annotations (site, stage, prior therapy).
• Matrix control: Fix lots where possible; benchmark synthetic ECM alternatives; include in-lot controls.
• Rational co-culture: Choose direct/indirect or microfluidic setups aligned with the pathway of interest (metabolic, immune, neural).
• Endpoint design: Pre-define multiparametric readouts (growth, death, metabolism, omics, imaging) and power the statistics.
• Translational ladder: Stage-gated triage from 2D → organoid → co-culture → PDX/animal → early clinical.
• Biomarker integration: Always couple functional hits with expression/metabolic/pathology data to propose stratification.

My perspective: where organoids are headed

This study exemplifies how neuro–tumor interactions can define metabolic liabilities. Looking ahead, I believe progress hinges on:

1. Modular multicellular crosstalk: Systematic, plug-and-play co-cultures (immune, fibroblast, neuronal, microbiome) to dissect causality.
2. Perfusion, vascularization, hypoxia control: Microfluidics/bioprinting to approximate drug penetration and nutrient gradients.
3. Synthetic, defined ECM: Standardized matrices for reproducibility and scale.
4. Multi-omics + imaging AI: Integrative models for response prediction and digital twins.
5. Prospective clinical linking: PDTO-guided treatment trials with outcomes to quantify predictive value.

Bottom line: Organoids are powerful engines for uncovering patient-proximal vulnerabilities, but they’re not a panacea. Coupling co-culture, perfusion, synthetic ECM, AI, and clinical linkage—step by step—can lift hit quality and boost translational success.

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This article was edited by the Morningglorysciences team.