Abstract 4586: Multi-scale multi-omic assessment of matched synchronous colorectal cancer liver metastases using multiple spatial transcriptomic tools
Abstract Background: Primary colorectal cancer (CRC) is manageable with surgical and non-surgical therapies. However, high affinity for distant spread to the liver (CRLM) and lack of subsequent therapeutic options has maintained CRC as one of the most lethal malignant diseases worldwide. Tumors consist of malignant cells as well as a complex micro-environment (TME) of stromal and immune components, all of which have been implicated in disease severity and treatment resistance. Spatial transcriptomics (ST) is novel technology which allows molecular profiling of tissue while preserving tissue architecture. By studying matched CRC and CRLM with modern ST tools which allow scrutiny of the malignant cells and TME we aim to learn more about mechanisms of metastasis and why CRLM are resistant to treatment. Methods: 41 patients (29 untreated) underwent synchronous resection of CRC and CRLM within our institution between April 2002 and June 2010 (64% 5 year mortality). Paired CRC and CRLM from 4 patients underwent single-cell ST assessment on the Nanostring CosMx Spatial Molecular Imager (23 fields of view (FOV)). Paired CRC and CRLM from 4 patients underwent regional ST assessment (PanCk+, PanCk-, aSMA+ comprtments) on the Nanostring GeoMx Digital Spatial Profiler (116 FOV). Paired CRC and CRLM from 2 patients underwent regional ST assessment on the Visium 10X Genomics platform (4 FOV). Results/Conclusions: GeoMx offers entire slide visualization with bespoke and convenient FOV selection with reliable segmentation of TME components with whole transcriptome available. Visium 10X offers high resolution ST assessment of large pieces of tissue but slide preparation is difficult without specific tools. CosMx offers single-cell in-situ allowing the highest resolution with neighborhood and cellular interaction analysis possible but at lower plex and increased complexity. All technologies demonstrated differences between CRLM and their matched CRC. CRLM were generally more collagenous and chemokine rich. Furthermore, transcriptomic and morphological differences exist between CRLM from different patients. Specifically, CosMx demonstrated that epithelial cells and fibroblasts in CRC express MZTA compared to COL9A2, NEAT1 and IGF2 in CRLM. Neutrophils in CRLM under-express CXCL8. Desmoplastic CRLM were chemokine high whereas replacement CRLM over-expressed IGF2, DUSP5 and COL9A2. GeoMx identified MET and IL17 as potential drug targets in CRLM. These data suggest that CRLM evolve once metastasis is established and the immune response of the host to the metastasis is pivotal. Future therapeutic goals should emphasize personalized medicine and molecular staging of CRLM with specific targeted therapies. Citation Forpmat: Colin Wood, Luke McNickle, Andrew Cameron, Ritupam Sarma, Vaidehi Pandya, Joao Da Silva Filho, Colin steele, John Cole, Joanne Edwards, Paul Horgan, Campbell Roxburgh. Multi-scale multi-omic assessment of matched synchronous colorectal cancer liver metastases using multiple spatial transcriptomic tools [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 4586.
Authors
- Colin S. Wood (ORCID: https://orcid.org/0000-0001-7005-6171)
- Andrew J. Cameron (ORCID: https://orcid.org/0000-0002-7065-9033)
- Paul G. Horgan (ORCID: https://orcid.org/0000-0001-5557-7905)
- Joanne Edwards (ORCID: https://orcid.org/0000-0002-7192-6906)
- Colin William Steele (ORCID: https://orcid.org/0000-0001-8518-1223)
- Campbell S.D. Roxburgh (ORCID: https://orcid.org/0000-0002-2649-6695)
- João Luiz Silva‐Filho (ORCID: https://orcid.org/0000-0003-4762-2205)
- John J Cole (ORCID: https://orcid.org/0000-0001-6257-012X)
- R. Sarma
- Vaidehi Pandya
- Luke McNickle
Institutions
- University of Glasgow (GB)
Publication Details
- Journal
- Cancer Research
- Published
- 2025-04-21
- DOI
- https://doi.org/10.1158/1538-7445.am2025-4586
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00