Abstract 4624: Analysis of the characteristics of immune microenvironment in non-small cell lung cancer patients with EGFR amplification and exploration of immunotherapy
Abstract Background: EGFR is the most common driver gene in Asian NSCLC patients, and its association with tumors and the efficacy of targeted therapy for sensitive mutations are well-established. According to current guidelines, first-line immunotherapy is not recommended for patients with EGFR-sensitive mutations. However, the treatment strategy for EGFR variants with limited sensitivity to TKIs remains unclear. EGFR amplification has been shown to exhibit resistance to TKIs in several studies, and its relationship with immunity has yet to be definitively established. Therefore, exploring the immune microenvironment to assess the efficacy of immunotherapy in this context holds significant clinical value. Methods: Tumor tissue samples from 210 Chinese lung adenocarcinoma (LUAD) patients were analyzed using next-generation sequencing (NGS) and multiplex immunohistochemistry (mIHC). The mIHC analysis, performed with the Akoya Detection Kit targeting 13 markers, quantified immune cell percentages among total cells, offering insights into the distribution of cell subpopulations within the tumor and stromal compartments. Result: Among the enrolled patients, 114 had EGFR point mutations and insertion-deletion mutations (54.3%), with 83 cases of L858R and 19del mutations, and 31 cases of EGFR amplification (14.8%), which is consistent with previous studies. An analysis of the immune microenvironment characteristics among patients with EGFR wild-type (EGFRwt), EGFR-sensitive mutations (EGFRm), and EGFR amplification (EGFRa) revealed that the levels of CD8+PD-1+ cells in the tumor stroma were significantly higher in the EGFRwt (P=0.008) and EGFRa (P=0.025) groups compared to the EGFRm group. Additionally, the NK cell levels in the tumor stroma were significantly higher in the EGFRwt group (P=0.037) compared to the EGFRm group, while no significant difference was found between the EGFRa and EGFRm groups (P=0.071). Although there were no significant differences in the infiltration levels of macrophages, T cells, and B cells between the three groups in the tumor parenchyma and stroma, the numbers were generally slightly higher in the EGFRwt and EGFRa groups compared to the EGFRm group. Conclusions: The results of this study suggest that the level of immune cell infiltration in the EGFR amplification group is higher than in the EGFR-sensitive mutation group. This may indicate that EGFR amplification patients could have better outcomes with immunotherapy compared to those with EGFR mutations. Therefore, immunotherapy as a first-line treatment may offer greater benefits for EGFR amplification patients, providing a potential mechanistic basis for the treatment strategy of this patient population. Citation Format: Zhi Xian Jin, Min Chen, Xu He, Hong Bi, Chao Yang, Jing Shao, Zhaoming Huang, Yuyan Zhou, Cuilin Yang. Analysis of the characteristics of immune microenvironment in non-small cell lung cancer patients with EGFR amplification and exploration of immunotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4624.
Authors
- Zhi Xian Jin
- He Xu (ORCID: https://orcid.org/0000-0002-1223-116X)
- Zhaoming Huang (ORCID: https://orcid.org/0009-0003-5126-8293)
- Min Chen (ORCID: https://orcid.org/0000-0002-6413-6973)
- Yuyan Zhou
- Chao Yang (ORCID: https://orcid.org/0009-0001-2472-1393)
- Chen-Chang Yang (ORCID: https://orcid.org/0000-0003-1391-8323)
- Jing Shao (ORCID: https://orcid.org/0000-0002-2500-2366)
- Hong Bi
Institutions
- Kunming Medical University (CN)
- The First Hospital of Kunming (CN)
- First Affiliated Hospital of Kunming Medical University (CN)
Publication Details
- Journal
- Cancer Research
- Published
- 2025-04-21
- DOI
- https://doi.org/10.1158/1538-7445.am2025-4624
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00