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基于单细胞人工智能的结直肠癌 DNA 错配修复缺陷的检测及其预后和预测价值。

Single-cell AI-based detection and prognostic and predictive value of DNA mismatch repair deficiency in colorectal cancer.

机构信息

Department of Pathology and Molecular Pathology, Zurich, Zurich, Switzerland.

Cancer Genomics and Immunology Group, The Wellcome Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK.

出版信息

Cell Rep Med. 2024 Sep 17;5(9):101727. doi: 10.1016/j.xcrm.2024.101727.


DOI:10.1016/j.xcrm.2024.101727
PMID:39293403
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11525017/
Abstract

Testing for DNA mismatch repair deficiency (MMRd) is recommended for all colorectal cancers (CRCs). Automating this would enable precision medicine, particularly if providing information on etiology not captured by deep learning (DL) methods. We present AIMMeR, an AI-based method for determination of mismatch repair (MMR) protein expression at a single-cell level in routine pathology samples. AIMMeR shows an area under the receiver-operator curve (AUROC) of 0.98, and specificity of ≥75% at 98% sensitivity against pathologist ground truth in stage II/III in two trial cohorts, with positive predictive value of ≥98% for the commonest pattern of somatic MMRd. Lower agreement with microsatellite instability (MSI) testing (AUROC 0.86) reflects discordance between MMR and MSI PCR rather than AIMMeR misclassification. Analysis of the SCOT trial confirms MMRd prognostic value in oxaliplatin-treated patients; while MMRd does not predict differential benefit of chemotherapy duration, it correlates with difference in relapse by regimen (P = 0.04). AIMMeR may help reduce pathologist workload and streamline diagnostics in CRC.

摘要

检测 DNA 错配修复缺陷(MMRd)被推荐用于所有结直肠癌(CRC)。自动化检测可以实现精准医疗,特别是如果能提供深度学习(DL)方法无法捕捉的病因信息。我们提出了一种基于人工智能的方法 AIMMeR,用于在常规病理样本中单细胞水平上检测错配修复(MMR)蛋白的表达。在两个试验队列的 II/III 期,AIMMeR 与病理学家的真实数据相比,曲线下面积(AUROC)为 0.98,特异性≥75%,敏感性为 98%,对于最常见的体细胞 MMRd 模式,阳性预测值≥98%。与微卫星不稳定性(MSI)检测的一致性较低(AUROC 为 0.86),这反映了 MMR 和 MSI PCR 之间的不一致,而不是 AIMMeR 的分类错误。SCOT 试验的分析证实了 MMRd 在奥沙利铂治疗患者中的预后价值;虽然 MMRd 不能预测化疗持续时间的差异获益,但它与不同方案的复发差异相关(P=0.04)。AIMMeR 可能有助于减轻病理学家的工作负荷,并简化 CRC 的诊断。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0912/11525017/9be71b081d1f/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0912/11525017/b787981bffc0/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0912/11525017/b9d0e48d8b96/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0912/11525017/eb3303860b61/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0912/11525017/7ee1ee10e24c/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0912/11525017/b31a468d68de/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0912/11525017/2ea0732f1880/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0912/11525017/9be71b081d1f/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0912/11525017/b787981bffc0/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0912/11525017/b9d0e48d8b96/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0912/11525017/eb3303860b61/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0912/11525017/7ee1ee10e24c/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0912/11525017/b31a468d68de/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0912/11525017/2ea0732f1880/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0912/11525017/9be71b081d1f/gr6.jpg

相似文献

[1]
Single-cell AI-based detection and prognostic and predictive value of DNA mismatch repair deficiency in colorectal cancer.

Cell Rep Med. 2024-9-17

[2]
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[3]
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[4]
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[5]
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[6]
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[7]
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[8]
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[9]
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[10]
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引用本文的文献

[1]
Intelligent Care: A Scientometric Analysis of Artificial Intelligence in Precision Medicine.

Med Sci (Basel). 2025-4-19

本文引用的文献

[1]
A systematic pan-cancer study on deep learning-based prediction of multi-omic biomarkers from routine pathology images.

Commun Med (Lond). 2024-3-15

[2]
Validation of MSIntuit as an AI-based pre-screening tool for MSI detection from colorectal cancer histology slides.

Nat Commun. 2023-11-6

[3]
Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study.

Cancer Cell. 2023-9-11

[4]
Generalizable biomarker prediction from cancer pathology slides with self-supervised deep learning: A retrospective multi-centric study.

Cell Rep Med. 2023-4-18

[5]
Preoperative Chemotherapy for Operable Colon Cancer: Mature Results of an International Randomized Controlled Trial.

J Clin Oncol. 2023-3-10

[6]
Mismatch Repair and Microsatellite Instability Testing for Immune Checkpoint Inhibitor Therapy: Guideline From the College of American Pathologists in Collaboration With the Association for Molecular Pathology and Fight Colorectal Cancer.

Arch Pathol Lab Med. 2022-10-1

[7]
DeepSMILE: Contrastive self-supervised pre-training benefits MSI and HRD classification directly from H&E whole-slide images in colorectal and breast cancer.

Med Image Anal. 2022-7

[8]
Area under the curve may hide poor generalisation to external datasets.

ESMO Open. 2022-4

[9]
Artificial intelligence for detection of microsatellite instability in colorectal cancer-a multicentric analysis of a pre-screening tool for clinical application.

ESMO Open. 2022-4

[10]
Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study.

Lancet Digit Health. 2021-12

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