Whole Slide Imaging-Based Prediction of TP53 Mutations Identifies an Aggressive Disease Phenotype in Prostate Cancer.
Whole Slide Imaging-Based Prediction of TP53 Mutations Identifies an Aggressive Disease Phenotype in Prostate Cancer.
复制标题
基于全切片成像的前列腺癌侵袭性疾病表型TP53突变预测
DOI：
10.1158/0008-5472.can-22-3113
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发表时间：
2023-09-01
期刊：
影响因子：
11.2
通讯作者：
Marchal, Kathleen
中科院分区：
文献类型：
--
作者：
Pizurica, Marija;Larmuseau, Maarten;Van der Eecken, Kim;de Schaetzen van Brienen, Louise;Carrillo-Perez, Francisco;Isphording, Simon;Lumen, Nicolaas;Van Dorpe, Jo;Ost, Piet;Verbeke, Sofie;Gevaert, Olivier;Marchal, Kathleen
从前列腺癌的整个幻灯片图像中预测 TP53 突变的深度学习模型捕获了与基质成分、淋巴结转移和生化复发相关的组织学表型，表明它们作为计算机预后生物标志物的潜力。在前列腺癌中，迫切需要客观的预后生物标志物来识别早期肿瘤的转移潜力。虽然最近的分析表明 TP53 突变是候选生物标志物，但临床环境中的分子分析因肿瘤异质性而变得复杂。预测整个幻灯片图像 (WSI) 中 TP53 突变空间存在的深度学习模型有可能缓解这一问题。为了评估 WSI 作为空间解析分析代理和侵袭性疾病生物标志物的潜力，我们开发了 TiDo，这是一种深度学习模型，在预测原发性前列腺肿瘤 WSI 的 TP53 突变方面实现了最先进的性能。在一个独立的多焦点队列中，该模型在患者和病变水平上都显示出成功的泛化能力。对模型预测的分析表明，假阳性 (FP) 预测至少可以部分地用 TP53 缺失来解释，这表明某些 FP 携带导致与 TP53 突变相同的组织学表型的改变。比较表达和组织学细胞类型分析确定了由影响基质组成的途径表达触发的TP53样细胞表型。总之，这些发现表明，基于 WSI 的模型可能无法完美预测单个 TP53 突变的空间存在，但它们有可能通过描述与侵袭性疾病生物标志物相关的下游表型来阐明肿瘤的预后。从前列腺癌的整个幻灯片图像中预测 TP53 突变的深度学习模型捕获了与基质成分、淋巴结转移和生化复发相关的组织学表型，表明它们作为计算机预后生物标志物的潜力。 参见 Bordeleau 的相关评论，第 17 页。 2809
Deep learning models predicting TP53 mutations from whole slide images of prostate cancer capture histologic phenotypes associated with stromal composition, lymph node metastasis, and biochemical recurrence, indicating their potential as in silico prognostic biomarkers. In prostate cancer, there is an urgent need for objective prognostic biomarkers that identify the metastatic potential of a tumor at an early stage. While recent analyses indicated TP53 mutations as candidate biomarkers, molecular profiling in a clinical setting is complicated by tumor heterogeneity. Deep learning models that predict the spatial presence of TP53 mutations in whole slide images (WSI) offer the potential to mitigate this issue. To assess the potential of WSIs as proxies for spatially resolved profiling and as biomarkers for aggressive disease, we developed TiDo, a deep learning model that achieves state-of-the-art performance in predicting TP53 mutations from WSIs of primary prostate tumors. In an independent multifocal cohort, the model showed successful generalization at both the patient and lesion level. Analysis of model predictions revealed that false positive (FP) predictions could at least partially be explained by TP53 deletions, suggesting that some FP carry an alteration that leads to the same histological phenotype as TP53 mutations. Comparative expression and histologic cell type analyses identified a TP53-like cellular phenotype triggered by expression of pathways affecting stromal composition. Together, these findings indicate that WSI-based models might not be able to perfectly predict the spatial presence of individual TP53 mutations but they have the potential to elucidate the prognosis of a tumor by depicting a downstream phenotype associated with aggressive disease biomarkers. Deep learning models predicting TP53 mutations from whole slide images of prostate cancer capture histologic phenotypes associated with stromal composition, lymph node metastasis, and biochemical recurrence, indicating their potential as in silico prognostic biomarkers. See related commentary by Bordeleau, p. 2809
影响因子：
5.2
作者：
de Schaetzen van Brienen L;Miclotte G;Larmuseau M;Van den Eynden J;Marchal K
通讯作者：
Marchal K