Abstract 655: Spatiotemporally deciphering the response dynamics of micro-satellite stable rectal cancer to neoadjuvant short-course radiotherapy and PD-1 blockade
Abstract Neoadjuvant chemoradiotherapy is the stand of care for patients with locally advanced rectal cancer (LARC). For microsatellite-stable (MSS) LARC patients, combining immune checkpoint inhibitors (ICIs) with radiotherapy has shown improved tumor regression, though the mechanisms behind tumor response remain unclear. Here, we analyzed 60 pre-, mid- and post-treatment tumor samples from 39 LARC patients in the TORCH clinical trial, utilizing single-cell sequencing and spatial transcriptomics to explore cell dynamics and their association with therapeutic efficacy. In well responders, combination therapy expanded the CD8+ and CD4+ memory T cell pool, which interacted with type 2 dendritic cells and clustered around non-malignant epithelial cells, forming part of tertiary lymphoid structures, as validated by spatial transcriptomics. In poor responders, the tumor microenvironment was enriched with immunosuppressive cells clustering around cancer-associated fibroblasts. This study uncovers mechanisms underlying the efficacy of radiotherapy combined with ICIs and provides valuable resources for identifying targets in MSS LARC patients. Citation Format: Jingwen Wang, Zhen Zhang. Spatiotemporally deciphering the response dynamics of micro-satellite stable rectal cancer to neoadjuvant short-course radiotherapy and PD-1 blockade. [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 655.
Authors
- Jingwen Wang (ORCID: https://orcid.org/0000-0001-6716-4614)
- Zhen Zhang (ORCID: https://orcid.org/0000-0001-6869-7981)
Institutions
- Fudan University Shanghai Cancer Center (CN)
Publication Details
- Journal
- Cancer Research
- Published
- 2025-04-21
- DOI
- https://doi.org/10.1158/1538-7445.am2025-655
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00