Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania.
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio.
J Cell Biochem. 2018 Sep;119(9):7127-7142. doi: 10.1002/jcb.27156. Epub 2018 Jun 20.
Nuclear alterations are a hallmark of many types of cancers, including prostate cancer (PCa). Recent evidence shows that subvisual changes, ones that may not be visually perceptible to a pathologist, to the nucleus and its ultrastructural components can precede visual histopathological recognition of cancer. Alterations to nuclear features, such as nuclear size and shape, texture, and spatial architecture, reflect the complex molecular-level changes that occur during oncogenesis. Quantitative nuclear morphometry, a field that uses computational approaches to identify and quantify malignancy-induced nuclear changes, can enable a detailed and objective analysis of the PCa cell nucleus. Recent advances in machine learning-based approaches can now automatically mine data related to these changes to aid in the diagnosis, decision making, and prediction of PCa prognoses. In this review, we use PCa as a case study to connect the molecular-level mechanisms that underlie these nuclear changes to the machine learning computational approaches, bridging the gap between the clinical and computational understanding of PCa. First, we will discuss recent developments to our understanding of the molecular events that drive nuclear alterations in the context of PCa: the role of the nuclear matrix and lamina in size and shape changes, the role of 3-dimensional chromatin organization and epigenetic modifications in textural changes, and the role of the tumor microenvironment in altering nuclear spatial topology. We will then discuss the advances in the applications of machine learning algorithms to automatically segment nuclei in prostate histopathological images, extract nuclear features to aid in diagnostic decision making, and predict potential outcomes, such as biochemical recurrence and survival. Finally, we will discuss the challenges and opportunities associated with translation of the quantitative nuclear morphometry methodology into the clinical space. Ultimately, accurate identification and quantification of nuclear alterations can contribute to the field of nucleomics and has applications for computationally driven precision oncologic patient care.
核改变是许多类型癌症的标志，包括前列腺癌 (PCa)。最近的证据表明，细胞核及其超微结构成分的亚视觉改变，即那些可能不会被病理学家肉眼察觉的改变，可能先于癌症的视觉组织病理学识别。核特征的改变，如核大小和形状、纹理和空间结构，反映了癌发生过程中复杂的分子水平变化。定量核形态计量学是一个使用计算方法来识别和量化恶性诱导的核变化的领域，可以对 PCa 细胞核进行详细和客观的分析。基于机器学习的方法的最新进展现在可以自动挖掘与这些变化相关的数据，以帮助诊断、决策和预测 PCa 预后。在这篇综述中，我们以 PCa 为例，将这些核改变背后的分子水平机制与机器学习计算方法联系起来，弥合了 PCa 临床和计算理解之间的差距。首先，我们将讨论我们对驱动 PCa 中核改变的分子事件的最新理解：核基质和核纤层在大小和形状改变中的作用、三维染色质组织和表观遗传修饰在纹理改变中的作用以及肿瘤微环境在改变核空间拓扑中的作用。然后，我们将讨论机器学习算法在自动分割前列腺组织病理学图像中的细胞核、提取核特征以辅助诊断决策以及预测生化复发和生存等潜在结果方面的应用进展。最后，我们将讨论将定量核形态计量学方法转化为临床领域所面临的挑战和机遇。最终，核改变的准确识别和定量可以为核组学领域做出贡献，并在计算驱动的精准肿瘤患者护理方面具有应用价值。
