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HIBRID: histology-based risk-stratification with deep learning and ctDNA in colorectal cancer

Although surgical resection is the standard therapy for stage II/III colorectal cancer, recurrence rates exceed 30%. Circulating tumor DNA (ctNDA) detects molecular residual disease (MRD), but lacks spatial and tumor microenvironment information. Here, we develop a deep learning (DL) model to predic...

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Main Authors: Löffler, Chiara (Author) , Bando, Hideaki (Author) , Sainath, Srividhya (Author) , Muti, Hannah Sophie (Author) , Jiang, Xiaofeng (Author) , van Treeck, Marko (Author) , Reitsam, Nic Gabriel (Author) , Carrero, Zunamys I. (Author) , Meneghetti, Asier Rabasco (Author) , Nishikawa, Tomomi (Author) , Misumi, Toshihiro (Author) , Mishima, Saori (Author) , Kotani, Daisuke (Author) , Taniguchi, Hiroya (Author) , Takemasa, Ichiro (Author) , Kato, Takeshi (Author) , Oki, Eiji (Author) , Yuan, Tanwei (Author) , Durgesh, Wankhede (Author) , Foersch, Sebastian (Author) , Brenner, Hermann (Author) , Hoffmeister, Michael (Author) , Nakamura, Yoshiaki (Author) , Yoshino, Takayuki (Author) , Kather, Jakob Nikolas (Author)
Format: Article (Journal)
Language:English
Published: 2025
In: Nature Communications
Year: 2025, Volume: 16, Pages: 1-11
ISSN:2041-1723
DOI:10.1038/s41467-025-62910-8
Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.1038/s41467-025-62910-8
Verlag, kostenfrei, Volltext: https://www.nature.com/articles/s41467-025-62910-8
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Author Notes:Chiara M. L. Loeffler, Hideaki Bando, Srividhya Sainath, Hannah Sophie Muti, Xiaofeng Jiang, Marko van Treeck, Nic Gabriel Reitsam, Zunamys I. Carrero, Asier Rabasco Meneghetti, Tomomi Nishikawa, Toshihiro Misumi, Saori Mishima, Daisuke Kotani, Hiroya Taniguchi, Ichiro Takemasa, Takeshi Kato, Eiji Oki, Yuan Tanwei, Wankhede Durgesh, Sebastian Foersch, Hermann Brenner, Michael Hoffmeister, Yoshiaki Nakamura, Takayuki Yoshino & Jakob Nikolas Kather
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Summary:Although surgical resection is the standard therapy for stage II/III colorectal cancer, recurrence rates exceed 30%. Circulating tumor DNA (ctNDA) detects molecular residual disease (MRD), but lacks spatial and tumor microenvironment information. Here, we develop a deep learning (DL) model to predict disease-free survival from hematoxylin & eosin stained whole slide images in stage II-IV colorectal cancer. The model is trained on the DACHS cohort (n = 1766) and validated on the GALAXY cohort (n = 1404). In GALAXY, the DL model categorizes 304 patients as DL high-risk and 1100 as low-risk (HR 2.31; p < 0.005). Combining DL scores with MRD status improves prognostic stratification in both MRD-positive (HR 1.58; p < 0.005) and MRD-negative groups (HR 2.1; p < 0.005). Notably, MRD-negative patients predicted as DL high-risk benefit from adjuvant chemotherapy (HR 0.49; p = 0.01) vs. DL low-risk (HR = 0.92; p = 0.64). Combining ctDNA with DL-based histology analysis significantly improves risk stratification, with the potential to improve follow-up and personalized adjuvant therapy decisions.
Item Description:Online veröffentlicht: 14. August 2025
Gesehen am 25.02.2026
Physical Description:Online Resource
ISSN:2041-1723
DOI:10.1038/s41467-025-62910-8

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