Cohort-scale Spatial Host-Microbiome Predicts Post-Resection Recurrence in Colorectal Cancer
- 1City University of Hong Kong; Hong Kong Centre for Cerebro-Cardiovascular Health Engineering; COSDAF, City University of Hong Kong; Shenzhen Research Institute,
Abstract
The tumor microenvironment in colorectal cancer (CRC) is a heterogeneous ecosystem in which host cells and microbial communities interact dynamically, influencing disease progression. However, the clinical utility is limited by the lack of a scalable spatial host-microbiome technique and by insufficient integration of artificial intelligence for the interpretation of high-dimensional multi-omics data. To overcome the barriers, we present AlphaFISH, a platform technology integrating both technical and computational innovations for multi-omics spatial analysis of clinical biopsies at subcellular resolution. The system uses a sequencing-free, high-throughput, spatial profiling technique to construct, to date, the largest clinical spatial transcriptomics and spatial microbiome datasets acquired from 149 colorectal biopsies from 68 human subjects, supported by a comprehensive scRNA-seq atlas covering 4.27 million cells across 650 patients for robust cell annotation. Deep learning of the cohort-scale dual-omics data, consisting of more than 10 million subcellular sampling vectors, enables the development of a transformer model with joint embeddings of gene expression, spatial architecture, and the microbial microenvironment in colon tissues, achieving nearly 90% accuracy in predicting CRC-associated pathological features using unseen spatial omics inputs. The AI interrogation further predicts tumour recurrence at 81% accuracy in 28 patients followed within 1 year post tumor resection period. AlphaFISH reveals that spatial interactions between Fusobacterium and cellular niche consisting of tumor and T cells serve as key markers of CRC malignancy, progression, and recurrence, indicating the critical role of spatial bacterial-immune crosstalks.
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BibTeX
@misc{guo2026cohortscale,
title = {{Cohort-scale Spatial Host-Microbiome Predicts Post-Resection Recurrence in Colorectal Cancer}},
author = {F. Guo and H. Sun and C. Hu and M. Hao and Y. Wan and C. Xiong and F. Gao and L.-Y. Mak and X. Deng and A. Goel and J. Ke and J. Ma and P. Shi},
howpublished = {medRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.29.26363734},
url = {https://www.medrxiv.org/content/10.64898/2026.09.29.26363734v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Cohort-scale Spatial Host-Microbiome Predicts Post-Resection Recurrence in Colorectal Cancer
AU - F. Guo
AU - H. Sun
AU - C. Hu
AU - M. Hao
AU - Y. Wan
AU - C. Xiong
AU - F. Gao
AU - L.-Y. Mak
AU - X. Deng
AU - A. Goel
AU - J. Ke
AU - J. Ma
AU - P. Shi
PY - 2026
DA - 2026/09/29
PB - medRxiv
DO - 10.64898/2026.09.29.26363734
UR - https://www.medrxiv.org/content/10.64898/2026.09.29.26363734v1
AB - The tumor microenvironment in colorectal cancer (CRC) is a heterogeneous ecosystem in which host cells and microbial communities interact dynamically, influencing disease progression. However, the clinical utility is limited by the lack of a scalable spatial host-microbiome technique and by insufficient integration of artificial intelligence for the interpretation of high-dimensional multi-omics data. To overcome the barriers, we present AlphaFISH, a platform technology integrating both technical and computational innovations for multi-omics spatial analysis of clinical biopsies at subcellular resolution. The system uses a sequencing-free, high-throughput, spatial profiling technique to construct, to date, the largest clinical spatial transcriptomics and spatial microbiome datasets acquired from 149 colorectal biopsies from 68 human subjects, supported by a comprehensive scRNA-seq atlas covering 4.27 million cells across 650 patients for robust cell annotation. Deep learning of the cohort-scale dual-omics data, consisting of more than 10 million subcellular sampling vectors, enables the development of a transformer model with joint embeddings of gene expression, spatial architecture, and the microbial microenvironment in colon tissues, achieving nearly 90% accuracy in predicting CRC-associated pathological features using unseen spatial omics inputs. The AI interrogation further predicts tumour recurrence at 81% accuracy in 28 patients followed within 1 year post tumor resection period. AlphaFISH reveals that spatial interactions between Fusobacterium and cellular niche consisting of tumor and T cells serve as key markers of CRC malignancy, progression, and recurrence, indicating the critical role of spatial bacterial-immune crosstalks.
ER -