Definiens AG, Munich, Germany.
Institute for Pathology, Ludwig-Maximilians-University, Munich, Germany.
Sci Rep. 2018 Mar 13;8(1):4470. doi: 10.1038/s41598-018-22564-7.
Tissue Phenomics is the discipline of mining tissue images to identify patterns that are related to clinical outcome providing potential prognostic and predictive value. This involves the discovery process from assay development, image analysis, and data mining to the final interpretation and validation of the findings. Importantly, this process is not linear but allows backward steps and optimization loops over multiple sub-processes. We provide a detailed description of the Tissue Phenomics methodology while exemplifying each step on the application of prostate cancer recurrence prediction. In particular, we automatically identified tissue-based biomarkers having significant prognostic value for low- and intermediate-risk prostate cancer patients (Gleason scores 6-7b) after radical prostatectomy. We found that promising phenes were related to CD8(+) and CD68(+) cells in the microenvironment of cancerous glands in combination with the local micro-vascularization. Recurrence prediction based on the selected phenes yielded accuracies up to 83% thereby clearly outperforming prediction based on the Gleason score. Moreover, we compared different machine learning algorithms to combine the most relevant phenes resulting in increased accuracies of 88% for tumor progression prediction. These findings will be of potential use for future prognostic tests for prostate cancer patients and provide a proof-of-principle of the Tissue Phenomics approach.
组织表型组学是一门从组织图像中挖掘与临床结果相关的模式的学科，提供潜在的预后和预测价值。这涉及从测定开发、图像分析和数据挖掘到最终发现的解释和验证的发现过程。重要的是，这个过程不是线性的，而是允许在多个子过程中进行回溯步骤和优化循环。我们提供了组织表型组学方法的详细描述，同时在前列腺癌复发预测的应用中举例说明了每个步骤。特别是，我们自动确定了组织标志物，这些标志物对根治性前列腺切除术后低危和中危前列腺癌患者（Gleason 评分 6-7b）具有显著的预后价值。我们发现有前途的表型与癌症腺体微环境中的 CD8(+)和 CD68(+)细胞以及局部微血管化有关。基于所选表型的复发预测准确率高达 83%，明显优于基于 Gleason 评分的预测。此外，我们比较了不同的机器学习算法来组合最相关的表型，从而提高肿瘤进展预测的准确率至 88%。这些发现对于未来前列腺癌患者的预后测试具有潜在的应用价值，并为组织表型组学方法提供了原理验证。
