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Updated: Aug 14, 2025

Profiling Sensitivity to Targeted Therapies in EGFR-Mutant NSCLC Patient-Derived Organoids
Published on: November 22, 2021
Features of tumor-microenvironment images predict targeted therapy survival benefit in patients with EGFR-mutant lung
Shidan Wang1, Ruichen Rong1, Donghan M Yang1
1Quantitative Biomedical Research Center, The Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Abstract:
Tyrosine kinase inhibitors (TKIs) targeting epidermal growth factor receptor (EGFR) are effective for many patients with lung cancer with EGFR mutations. However, not all patients are responsive to EGFR TKIs, including even those harboring EGFR-sensitizing mutations. In this study, we quantified the cells and cellular interaction features of the tumor microenvironment (TME) using routine H&E-stained biopsy sections. These TME features were used to develop a prediction model for survival benefit from EGFR TKI therapy in patients with lung adenocarcinoma and EGFR-sensitizing mutations in the Lung Cancer Mutation Consortium 1 (LCMC1) and validated in an independent LCMC2 cohort. In the validation data set, EGFR TKI treatment prolonged survival in the predicted-to-benefit group but not in the predicted-not-to-benefit group. Among patients treated with EGFR TKIs, the predicted-to-benefit group had prolonged survival outcomes compared with the predicted not-to-benefit group. The EGFR TKI survival benefit positively correlated with tumor-tumor interaction image features and negatively correlated with tumor-stroma interaction. Moreover, the tumor-stroma interaction was associated with higher activation of the hepatocyte growth factor/MET-mediated PI3K/AKT signaling pathway and epithelial-mesenchymal transition process, supporting the hypothesis of fibroblast-involved resistance to EGFR TKI treatment.
Insights
Tumor microenvironment features can predict lung cancer patient response to epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs). This model identifies patients likely to benefit from EGFR TKI therapy, guiding personalized treatment strategies.
Area of Science:
- Oncology
- Translational Research
- Computational Pathology
Background:
- Epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) improve outcomes for lung cancer patients with specific mutations.
- However, not all patients respond to EGFR TKIs, necessitating predictive biomarkers.
Purpose of the Study:
- To develop and validate a predictive model for survival benefit from EGFR TKI therapy using tumor microenvironment (TME) features.
- To investigate the relationship between TME characteristics and resistance mechanisms to EGFR TKIs.
Main Methods:
- Quantification of cellular and interaction features in H&E-stained biopsy sections.
- Development of a predictive model using TME features in the LCMC1 cohort.
- Validation of the model in an independent LCMC2 cohort.
Main Results:
- The predictive model successfully identified patient groups with differential survival benefits from EGFR TKI treatment.
- Positive correlation between EGFR TKI survival benefit and tumor-tumor interactions.
- Negative correlation between EGFR TKI survival benefit and tumor-stroma interactions.
Conclusions:
- Tumor microenvironment features, particularly cell-cell interactions, can predict response to EGFR TKI therapy in lung adenocarcinoma.
- Tumor-stroma interactions are linked to resistance pathways, including MET signaling and epithelial-mesenchymal transition, suggesting a role for fibroblasts.
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