Abstract 5159: From subcutaneous to orthotopic: Bridging the gap to human disease in lung cancer research
Abstract Subcutaneous (SC) models are commonly used in preclinical lung cancer research. However, differences in outcomes of mouse-tested therapies suggest that SC models fail to accurately mimic the complexity of the tumor microenvironment, limiting their reliability for evaluating therapeutic responses, tumor-immune interactions, and metastasis. To overcome these limitations, we developed and analyzed novel orthotopic lung cancer (LuO) models using syngeneic and xenograft approaches with human and murine lung cancer cell lines. Tumors were implanted into the left lung via thoracotomy, forming a single nodule per lung. Depending on the cell line, the model generates metastasis and allows for organotropism studies, closely resembling key traits of human lung cancer. Our study compares molecular and functional properties of SC and LuO models. Tumor microarrays (TMAs) were generated from 20 human and mouse lung cancer cell lines implanted either subcutaneously or orthotopically in mice. SC tumor cores were subcategorized into central and peripheral regions, while LuO cores were classified by parenchymal or pleural location when pleural seeding occurred. Two fluorescence-based multiplex (VECTRA®) panels characterized the tumor microenvironment (TME), profiling lymphoid and myeloid immune populations. Cancer hallmark-related proteins were also analyzed. Compared to SC tumors, LuO models showed significantly higher vascularization (CD31 area, p<0.0001), reduced hypoxia (HIF1-α, p<0.0001), and lower senescence marker expression (p16 and p21 H-scores, p<0.0001). Immune profiling revealed increased CD4+ T-cell infiltration (p=0.0128) and elevated CD4+PD1+ populations (p=0.0372), indicating enhanced T-cell exhaustion in LuO tumors. LuO tumors also exhibited significantly higher PD-L1 expression (p=0.0002), more M2 macrophages (p<0.0001), and an increase in MDSCs (p<0.0001), suggesting a reliance on immune suppression mediated by M2 macrophages and MDSCs rather than the PD-1/PD-L1 pathway. Proliferative, apoptotic, and metabolic activity were also compared between LuO and SC tumors. Although SC models are widely used for lung cancer research and therapy, our results show biological differences depending on the transplant location. Molecular features and TME profiles of SC tumors differ substantially from the clinically relevant LuO context. Our findings suggest that LuO models are more suitable for translational lung cancer studies. Therefore, beyond generating TMAs, we developed additional tools from our LuO models to support research into tumor biology and the TME. These resources are available to the scientific community for deeper understanding of lung cancer pathophysiology. In summary, LuO models improve the recapitulation value of mouse-transplanted models for human disease, offering a more robust platform for preclinical research and therapeutic development. Citation Format: Daniel Orive, Beñat Picabea, Maeva Houry, Ane Álava, Covadonga Rodriguez-Suarez, Alfonso Calvo, Luis M. Montuenga, Karmele Valencia. From subcutaneous to orthotopic: Bridging the gap to human disease in lung cancer research [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 5159.
Authors
- Maeva Houry (ORCID: https://orcid.org/0009-0005-0036-3017)
- Luis M. Montuenga (ORCID: https://orcid.org/0000-0002-8739-1387)
- Alfonso Calvo (ORCID: https://orcid.org/0000-0003-4074-4242)
- Carmen Rodríguez-Suárez
- Karmele Valencia (ORCID: https://orcid.org/0000-0002-2882-7427)
- Daniel Orive
- Beñat Picabea (ORCID: https://orcid.org/0000-0003-3956-0163)
- Ane Álava
Publication Details
- Journal
- Cancer Research
- Published
- 2025-04-21
- DOI
- https://doi.org/10.1158/1538-7445.am2025-5159
- Citations
- 1
- Primary Topic
- Cancer Research and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 1.23