Nuclear morphometry, nucleomics and prostate cancer progression

Abstract

Prostate cancer (PCa) results from a multistep process. This process includes initiation, which occurs through various aging events and multiple insults (such as chronic infection, inflammation and genetic instability through reactive oxygen species causing DNA double-strand breaks), followed by a multistep process of progression. These steps include several genetic and epigenetic alterations, as well as alterations to the chromatin structure, which occur in response to the carcinogenic stress-related events that sustain proliferative signaling. Events such as evading growth suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis, and activating invasion and metastasis are readily observed. In addition, in conjunction with these critical drivers of carcinogenesis, other factors related to the etiopathogenesis of PCa, involving energy metabolism and evasion of the immune surveillance system, appear to be involved. In addition, when cancer spread and metastasis occur, the 'tumor microenvironment' in the bone of PCa patients may provide a way to sustain dormancy or senescence and eventually establish a 'seed and soil' site where PCa proliferation and growth may occur over time. When PCa is initiated and progression ensues, significant alterations in nuclear size, shape and heterochromatin (DNA transcription) organization are found, and key nuclear transcriptional and structural proteins, as well as multiple nuclear bodies can lead to precancerous and malignant changes. These series of cellular and tissue-related malignancy-associated events can be quantified to assess disease progression and management.

Figure 1

Illustration of the hematoxylin and eosin (H&E) and the Feulgen DNA-stained cancer and benign-appearing histology preparation (cancer-adjacent area) of prostate tissue microarray (TMA) spots.

Figure 2

Digital image analysis of Feulgen-stained prostate nuclei. These artificially colored images are based on the pixel maps obtained from the AutoCyte Pathology Workstation of the two representative cells that are illustrated above.

Figure 3

Dimensionality reduction and manifold learning. Reproduced from Madabhushi et al. Non-linear dimensionality reduction used to classify prostate adenocarcinoma into Gleason grade 3 and grade 4 patterns. (a) Low dimensional embedding of the high dimensional attribute space via local linear embedding of 20 images representing prostate cancer grades 3 (circles) and 4 (squares). Each image is displayed as a point in 3D eigenspace. The clustering clearly shows very good discrimination between these two classes, which clinically is the most challenging problem in terms of Gleason grading. (b) Bar plots reflecting the classification accuracy obtained via a supervised classifier in distinguishing between pairs of tissue classes—grade 3/4, grade 3 vs. benign epithelium, and grade 4 vs. benign epithelium via a Support Vector Machine classifier. Note that in every case, the classification accuracy is over 90%.

Figure 4

Automated imaging technology for the diagnosis and prognostic evaluation of prostate cancer. ANN, Artificial Neural Networks; QIHC, quantitative immunohistochemistry; SVM, Support Vector Machines.

Figure 5

Personalized concept to decision making for prostate cancer. IR, irradiation; QIHC, quantitative immunohistochemistry; WW, watchful waiting or active surveillance for very low or low risk prostate cancers observed on a diagnostic biopsy.