Murchan Pierre, Baird Anne-Marie, Ó Broin Pilib, Sheils Orla, Finn Stephen P
Department of Histopathology and Morbid Anatomy, Trinity Translational Medicine Institute, Trinity College Dublin, D08 W9RT Dublin, Ireland.
The SFI Centre for Research Training in Genomics Data Science, University of Galway, H91 CF50 Galway, Ireland.
Diagnostics (Basel). 2024 Feb 20;14(5):462. doi: 10.3390/diagnostics14050462.
Recent advances in computational pathology have shown potential in predicting biomarkers from haematoxylin and eosin (H&E) whole-slide images (WSI). However, predicting the outcome directly from WSIs remains a substantial challenge. In this study, we aimed to investigate how gene expression, predicted from WSIs, could be used to evaluate overall survival (OS) in patients with lung adenocarcinoma (LUAD).
Differentially expressed genes (DEGs) were identified from The Cancer Genome Atlas (TCGA)-LUAD cohort. Cox regression analysis was performed on DEGs to identify the gene prognostics of OS. Attention-based multiple instance learning (AMIL) models were trained to predict the expression of identified prognostic genes from WSIs using the TCGA-LUAD dataset. Models were externally validated in the Clinical Proteomic Tumour Analysis Consortium (CPTAC)-LUAD dataset. The prognostic value of predicted gene expression values was then compared to the true gene expression measurements.
The expression of 239 prognostic genes could be predicted in TCGA-LUAD with cross-validated Pearson's R > 0.4. Predicted gene expression demonstrated prognostic performance, attaining a cross-validated concordance index of up to 0.615 in TCGA-LUAD through Cox regression. In total, 36 genes had predicted expression in the external validation cohort that was prognostic of OS.
Gene expression predicted from WSIs is an effective method of evaluating OS in patients with LUAD. These results may open up new avenues of cost- and time-efficient prognosis assessment in LUAD treatment.
计算病理学的最新进展已显示出从苏木精和伊红（H&E）全切片图像（WSI）预测生物标志物的潜力。然而，直接从WSI预测结果仍然是一项重大挑战。在本研究中，我们旨在探讨如何利用从WSI预测的基因表达来评估肺腺癌（LUAD）患者的总生存期（OS）。
从癌症基因组图谱（TCGA）-LUAD队列中鉴定出差异表达基因（DEG）。对DEG进行Cox回归分析，以确定OS的基因预后指标。使用TCGA-LUAD数据集训练基于注意力的多实例学习（AMIL）模型，以从WSI预测已鉴定的预后基因的表达。在临床蛋白质组肿瘤分析联盟（CPTAC）-LUAD数据集中对模型进行外部验证。然后将预测的基因表达值的预后价值与真实的基因表达测量值进行比较。
在TCGA-LUAD中可以预测239个预后基因的表达，交叉验证的Pearson's R>0.4。预测的基因表达显示出预后性能，通过Cox回归在TCGA-LUAD中获得的交叉验证一致性指数高达0.615。总共有36个基因在外部验证队列中有预测表达，且这些表达对OS具有预后意义。
从WSI预测的基因表达是评估LUAD患者OS的有效方法。这些结果可能为LUAD治疗中经济高效的预后评估开辟新途径。
