Advances in the computational and molecular understanding of the prostate cancer cell nucleus

Abstract

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.

Keywords: machine learning in medicine; molecular-level nuclear changes; nuclear architecture; prostate cancer; quantitative nuclear morphometry.

Figure 1

The nucleus can be a critical component of the visual diagnosis of PCa. Subvisual structural and architectural alterations can precede visible histopathological changes. As such, we identify and implicate the ultrastructure and microenvironment for the subvisual nuclear changes that occur during cancer progression, which can ultimately be identified by quantitative nuclear morphometric analysis for the identification and prediction of PCa. These components include: (1) The nuclear lamina and matrix, which undergo significant expression and conformation changes during oncogenesis and can be responsible for driving nuclear size and shape irregularities; (2) Three-dimensional chromatin organization and epigenetic changes, which drive textural changes to the nucleus; and (3) Spatial topological and architectural arrangement patterns, which can be characteristic of pre-malignant changes.

Figure 2

Quantitative nuclear morphometric involves three critical steps: nuclear segmentation, nuclear morphometric feature extraction, and prognostication of PCa-related outcomes. (A) proper segmentation of nuclei from benign nuclei and background features relies on novel segmentation schemes that resolve nuclear overlapping and poor staining contrast. [Image adapted from Ali et al., 2011 with permission]. (B) Feature extraction can include nuclear size and shape features, textural features, or spatial topographical features. Left panel: depicts number of neighboring epithelial nuclei; center panel: depicts categorization of nuclear shape contexts; right panel: depicts distance fractal dimensions. [Image adapted from Kwak and Hewitt, 2017 with permission]. (C) Quantitative algorithms that utilize a number of different machine learning classifiers can use these features to create models to distinguish cancer from non-cancer, identify aggressive cancer phenotypes, or predict important PCa-related outcomes such as biochemical recurrence, progression, or mortality. Image depicts utilization of nuclear features to discriminate a more advanced PCa phenotype. [Image adapted from Carleton et al., 2018 with permission].