Diverse stromal phenotypes at spatial resolution in colorectal cancer peritoneal metastasis.

218 Background: Peritoneal metastases (PM) in colorectal cancer (CRC) portend a poor prognosis. We sought to elucidate spatially resolved molecular features facilitating transcoelomic dissemination and progression. Methods: 50 PT, 116 PM and 37 primary normal samples from 96 patients were retrieved and profiled with digital spatial profiling (GeoMx DSP, Nanostring Technologies). Through unsupervised clustering, we characterized the microenvironment of tumor-stroma compartments and studied the roles of stromal phenotypes in promulgating tumorigenesis. These findings were orthogonally validated through spatial proteomics (multiplex IHC [mIHC] - COMET, Lunaphore Technologies) of 10 paired PT-PM samples retrieved from 6 patients. Results: A median of 37 region of interests (ROIs) (IQR: 32-38) were selected from a total of 9 tissue microarray (TMA) slides. A total of 269 ROIs (181 tumor, 88 stroma) from PM samples, 45 ROIs (34 tumor, 11 stroma) from PT samples and 28 (all stroma) ROIs from primary normal samples were retrieved and profiled. Through consensus clustering of stroma ROIs, we identify a fibro-collagenous and immune infiltrated stromal phenotype (stromal cluster [SC] 2) characterized by increased cancer associated fibroblasts (CAFs), memory B cells, M2 macrophages and T-cell exhaustion. Patients with SC2 stroma had poorer survival (p=0.036). SC2 stroma was also observed to support adjacent tumor compartments with enriched oncogenic pathways such as TGF-beta, TNF-alpha, hypoxia and JAK-STAT. Through discriminatory gene expression profiles, we developed a 20-gene composite bidirectional signature of SC2. The prognostic significance of the SC2-signature was externally validated through several cohorts including TCGA and MSI-H only cohorts. Next, by inspecting mIHC retrieved immune cell type densities across ROIs, we confirmed a similar phenomenon in which two distinct stromal clusters were found – one being fibroblast infiltrated but T-cell depleted (SC2-like, n=67 ROI) and another which is T-cell infiltrated but fibroblast depleted (SC1-like, n=104 ROI). Diverging spatial distributions of T-cells and fibroblasts between SC1 and SC2-like stroma was appreciated as well. Between both PT and PM, we find close clustering of T cells in SC1-like stromal compartments and fibroblasts in SC2-like stromal compartments. Conclusions: We describe SC2, a pro-tumorigenic stromal phenotype characterized by increased CAFs, T cell exhaustion and is associated with poor prognosis in CRC PM.

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Journal
Journal of Clinical Oncology
Published
2025-01-27
DOI
https://doi.org/10.1200/jco.2025.43.4_suppl.218
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00

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article

Diverse stromal phenotypes at spatial resolution in colorectal cancer peritoneal metastasis.

Xuewen Ong, Patrick Tan, Wei Peng Yong, Taotao Sheng et al.
Journal of Clinical Oncology
Radiomics and Machine Learning in Medical Imaging
article

Diverse stromal phenotypes at spatial resolution in colorectal cancer peritoneal metastasis.

Xuewen Ong, Patrick Tan, Wei Peng Yong, Taotao Sheng, Joseph Jonathan Zhao, Lit‐Hsin Loo, Qiu Xuan Tan, Raghav Sundar, J. Lee, Joey Wee‐Shan Tan, Gillian Ng, Filippo Pietrantonio, Robert J. Walsh, Haoran Ma, Su Ting Tay, Supriya Srivastava, Melissa Ching Ching Teo, Johnny Chin-Ann Ong, Ying Liu, Jeffrey Huey Yew Lum
article en

Abstract

218 Background: Peritoneal metastases (PM) in colorectal cancer (CRC) portend a poor prognosis. We sought to elucidate spatially resolved molecular features facilitating transcoelomic dissemination and progression. Methods: 50 PT, 116 PM and 37 primary normal samples from 96 patients were retrieved and profiled with digital spatial profiling (GeoMx DSP, Nanostring Technologies). Through unsupervised clustering, we characterized the microenvironment of tumor-stroma compartments and studied the roles of stromal phenotypes in promulgating tumorigenesis. These findings were orthogonally validated through spatial proteomics (multiplex IHC [mIHC] - COMET, Lunaphore Technologies) of 10 paired PT-PM samples retrieved from 6 patients. Results: A median of 37 region of interests (ROIs) (IQR: 32-38) were selected from a total of 9 tissue microarray (TMA) slides. A total of 269 ROIs (181 tumor, 88 stroma) from PM samples, 45 ROIs (34 tumor, 11 stroma) from PT samples and 28 (all stroma) ROIs from primary normal samples were retrieved and profiled. Through consensus clustering of stroma ROIs, we identify a fibro-collagenous and immune infiltrated stromal phenotype (stromal cluster [SC] 2) characterized by increased cancer associated fibroblasts (CAFs), memory B cells, M2 macrophages and T-cell exhaustion. Patients with SC2 stroma had poorer survival (p=0.036). SC2 stroma was also observed to support adjacent tumor compartments with enriched oncogenic pathways such as TGF-beta, TNF-alpha, hypoxia and JAK-STAT. Through discriminatory gene expression profiles, we developed a 20-gene composite bidirectional signature of SC2. The prognostic significance of the SC2-signature was externally validated through several cohorts including TCGA and MSI-H only cohorts. Next, by inspecting mIHC retrieved immune cell type densities across ROIs, we confirmed a similar phenomenon in which two distinct stromal clusters were found – one being fibroblast infiltrated but T-cell depleted (SC2-like, n=67 ROI) and another which is T-cell infiltrated but fibroblast depleted (SC1-like, n=104 ROI). Diverging spatial distributions of T-cells and fibroblasts between SC1 and SC2-like stroma was appreciated as well. Between both PT and PM, we find close clustering of T cells in SC1-like stromal compartments and fibroblasts in SC2-like stromal compartments. Conclusions: We describe SC2, a pro-tumorigenic stromal phenotype characterized by increased CAFs, T cell exhaustion and is associated with poor prognosis in CRC PM.

Journal of Clinical OncologyVol. 43(4_suppl)
National University Cancer Institute, Singapore (SG), Duke-NUS Medical School (SG), National Cancer Centre Singapore (SG), National University Hospital (SG), National University Health System (SG), Fondazione IRCCS Istituto Nazionale dei Tumori (IT)
National University Health System, National Medical Research Council
Openalex Percentile: Top 98%
Radiomics and Machine Learning in Medical Imaging
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