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Development and interpretation of a pathomics-based model for the prediction of microsatellite instability in Colorectal Cancer
Microsatellite instability (MSI) has been approved as a pan-cancer biomarker for immune checkpoint blockade (ICB) therapy. However, current MSI identification methods are not available for all patients. We proposed an ensemble multiple instance deep learning model to predict microsatellite status ba...
Autores principales: | , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Ivyspring International Publisher
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7532670/ https://www.ncbi.nlm.nih.gov/pubmed/33042271 http://dx.doi.org/10.7150/thno.49864 |
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author | Cao, Rui Yang, Fan Ma, Si-Cong Liu, Li Zhao, Yu Li, Yan Wu, De-Hua Wang, Tongxin Lu, Wei-Jia Cai, Wei-Jing Zhu, Hong-Bo Guo, Xue-Jun Lu, Yu-Wen Kuang, Jun-Jie Huan, Wen-Jing Tang, Wei-Min Huang, Kun Huang, Junzhou Yao, Jianhua Dong, Zhong-Yi |
author_facet | Cao, Rui Yang, Fan Ma, Si-Cong Liu, Li Zhao, Yu Li, Yan Wu, De-Hua Wang, Tongxin Lu, Wei-Jia Cai, Wei-Jing Zhu, Hong-Bo Guo, Xue-Jun Lu, Yu-Wen Kuang, Jun-Jie Huan, Wen-Jing Tang, Wei-Min Huang, Kun Huang, Junzhou Yao, Jianhua Dong, Zhong-Yi |
author_sort | Cao, Rui |
collection | PubMed |
description | Microsatellite instability (MSI) has been approved as a pan-cancer biomarker for immune checkpoint blockade (ICB) therapy. However, current MSI identification methods are not available for all patients. We proposed an ensemble multiple instance deep learning model to predict microsatellite status based on histopathology images, and interpreted the pathomics-based model with multi-omics correlation. Methods: Two cohorts of patients were collected, including 429 from The Cancer Genome Atlas (TCGA-COAD) and 785 from an Asian colorectal cancer (CRC) cohort (Asian-CRC). We established the pathomics model, named Ensembled Patch Likelihood Aggregation (EPLA), based on two consecutive stages: patch-level prediction and WSI-level prediction. The initial model was developed and validated in TCGA-COAD, and then generalized in Asian-CRC through transfer learning. The pathological signatures extracted from the model were analyzed with genomic and transcriptomic profiles for model interpretation. Results: The EPLA model achieved an area-under-the-curve (AUC) of 0.8848 (95% CI: 0.8185-0.9512) in the TCGA-COAD test set and an AUC of 0.8504 (95% CI: 0.7591-0.9323) in the external validation set Asian-CRC after transfer learning. Notably, EPLA captured the relationship between pathological phenotype of poor differentiation and MSI (P < 0.001). Furthermore, the five pathological imaging signatures identified from the EPLA model were associated with mutation burden and DNA damage repair related genotype in the genomic profiles, and antitumor immunity activated pathway in the transcriptomic profiles. Conclusions: Our pathomics-based deep learning model can effectively predict MSI from histopathology images and is transferable to a new patient cohort. The interpretability of our model by association with pathological, genomic and transcriptomic phenotypes lays the foundation for prospective clinical trials of the application of this artificial intelligence (AI) platform in ICB therapy. |
format | Online Article Text |
id | pubmed-7532670 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Ivyspring International Publisher |
record_format | MEDLINE/PubMed |
spelling | pubmed-75326702020-10-08 Development and interpretation of a pathomics-based model for the prediction of microsatellite instability in Colorectal Cancer Cao, Rui Yang, Fan Ma, Si-Cong Liu, Li Zhao, Yu Li, Yan Wu, De-Hua Wang, Tongxin Lu, Wei-Jia Cai, Wei-Jing Zhu, Hong-Bo Guo, Xue-Jun Lu, Yu-Wen Kuang, Jun-Jie Huan, Wen-Jing Tang, Wei-Min Huang, Kun Huang, Junzhou Yao, Jianhua Dong, Zhong-Yi Theranostics Research Paper Microsatellite instability (MSI) has been approved as a pan-cancer biomarker for immune checkpoint blockade (ICB) therapy. However, current MSI identification methods are not available for all patients. We proposed an ensemble multiple instance deep learning model to predict microsatellite status based on histopathology images, and interpreted the pathomics-based model with multi-omics correlation. Methods: Two cohorts of patients were collected, including 429 from The Cancer Genome Atlas (TCGA-COAD) and 785 from an Asian colorectal cancer (CRC) cohort (Asian-CRC). We established the pathomics model, named Ensembled Patch Likelihood Aggregation (EPLA), based on two consecutive stages: patch-level prediction and WSI-level prediction. The initial model was developed and validated in TCGA-COAD, and then generalized in Asian-CRC through transfer learning. The pathological signatures extracted from the model were analyzed with genomic and transcriptomic profiles for model interpretation. Results: The EPLA model achieved an area-under-the-curve (AUC) of 0.8848 (95% CI: 0.8185-0.9512) in the TCGA-COAD test set and an AUC of 0.8504 (95% CI: 0.7591-0.9323) in the external validation set Asian-CRC after transfer learning. Notably, EPLA captured the relationship between pathological phenotype of poor differentiation and MSI (P < 0.001). Furthermore, the five pathological imaging signatures identified from the EPLA model were associated with mutation burden and DNA damage repair related genotype in the genomic profiles, and antitumor immunity activated pathway in the transcriptomic profiles. Conclusions: Our pathomics-based deep learning model can effectively predict MSI from histopathology images and is transferable to a new patient cohort. The interpretability of our model by association with pathological, genomic and transcriptomic phenotypes lays the foundation for prospective clinical trials of the application of this artificial intelligence (AI) platform in ICB therapy. Ivyspring International Publisher 2020-09-02 /pmc/articles/PMC7532670/ /pubmed/33042271 http://dx.doi.org/10.7150/thno.49864 Text en © The author(s) This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions. |
spellingShingle | Research Paper Cao, Rui Yang, Fan Ma, Si-Cong Liu, Li Zhao, Yu Li, Yan Wu, De-Hua Wang, Tongxin Lu, Wei-Jia Cai, Wei-Jing Zhu, Hong-Bo Guo, Xue-Jun Lu, Yu-Wen Kuang, Jun-Jie Huan, Wen-Jing Tang, Wei-Min Huang, Kun Huang, Junzhou Yao, Jianhua Dong, Zhong-Yi Development and interpretation of a pathomics-based model for the prediction of microsatellite instability in Colorectal Cancer |
title | Development and interpretation of a pathomics-based model for the prediction of microsatellite instability in Colorectal Cancer |
title_full | Development and interpretation of a pathomics-based model for the prediction of microsatellite instability in Colorectal Cancer |
title_fullStr | Development and interpretation of a pathomics-based model for the prediction of microsatellite instability in Colorectal Cancer |
title_full_unstemmed | Development and interpretation of a pathomics-based model for the prediction of microsatellite instability in Colorectal Cancer |
title_short | Development and interpretation of a pathomics-based model for the prediction of microsatellite instability in Colorectal Cancer |
title_sort | development and interpretation of a pathomics-based model for the prediction of microsatellite instability in colorectal cancer |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7532670/ https://www.ncbi.nlm.nih.gov/pubmed/33042271 http://dx.doi.org/10.7150/thno.49864 |
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