Development of a Nuclear Morphometric Signature for Prostate Cancer Risk in Negative Biopsies

Abstract

BackgroundOur objective was to develop and validate a multi-feature nuclear score based on image analysis of direct DNA staining, and to test its association with field effects and subsequent detection of prostate cancer (PCa) in benign biopsies.MethodsTissue sections from 39 prostatectomies were Feulgen-stained and digitally scanned (400×), providing maps of DNA content per pixel. PCa and benign epithelial nuclei were randomly selected for measurement of 52 basic morphometric features. Logistic regression models discriminating benign from PCa nuclei, and benign from malignant nuclear populations, were built and cross-validated by AUC analysis. Nuclear populations were randomly collected <1 mm or >5 mm from cancer foci, and from cancer-free prostates, HGPIN, and PCa Gleason grade 3–5. Nuclei also were collected from negative biopsy subjects who had a subsequent diagnosis of PCa and age-matched cancer-free controls (20 pairs).ResultsA multi-feature nuclear score discriminated cancer from benign cell populations with AUCs of 0.91 and 0.79, respectively, in training and validation sets of patients. In prostatectomy samples, both nuclear- and population-level models revealed cancer-like features in benign nuclei adjacent to PCa, compared to nuclei that were more distant or from PCa-free glands. In negative biopsies, a validated model with 5 variance features yielded significantly higher scores in cases than controls (P = 0.026).ConclusionsA multifeature nuclear morphometric score, obtained by automated digital analysis, was validated for discrimination of benign from cancer nuclei. This score demonstrated field effects in benign epithelial nuclei at varying distance from PCa lesions, and was associated with subsequent PCa detection in negative biopsies.ImpactThis nuclear score shows promise as a risk predictor among men with negative biopsies and as an intermediate biomarker in Phase II chemoprevention trials. The results also suggest that subvisual disturbances in nuclear structure precede the development of pre-neoplastic lesions.

Epithelial-mesenchymal transition in prostate cancer is associated with quantifiable changes in nuclear structure

“…A current difficulty in the area of diagnostic pathology of prostate and breast biopsy specimens is the differential diagnosis of the very small volume of tumor samples. Recent studies with nuclear morphometry techniques successfully detect the cancer field effect in adjacent tissue in both prostate and breast cancers allowing for detection of cancer without direct observation of a lesion [35][36][37][38]. Applying these types of technical advancements to the prognostic space, future studies using nuclear morphometry to detect the degree of mesenchymal transformation or EMT fraction of CTCs within a prostate biopsy may have considerable value in predicting patient outcome and appropriate course of treatment.…”

Nuclear Mechanopathology and Cancer Diagnosis

“…To circumvent this subjectivity, recent advances in imaging combined with machine learning have provided new avenues [103,104]. These include parametric machine-learning techniques that use quantitative nuclear morphometric information, such as nuclear size, shape, nucleus:cytoplasm ratio, and chromatin texture, for classifying histopathology images [105,106]. More recently, various nonparametric methods, including deep learning, have been applied for diagnosing various cancers, including breast, skin, and thyroid cancer, with high accuracies [107,108].…”

Nuclear Shape and Architecture in Benign Fields Predict Biochemical Recurrence in Prostate Cancer Patients Following Radical Prostatectomy: Preliminary Findings

“…There has been evidence to suggest that the microenvironment surrounding the prostate tumor may play a role in cancer progression [5,6], a phenomena known as the field effect . Epigenetic changes within the benign regions surrounding the tumor have been shown to be capable of initiating PCa [5].…”

“…Veltri et al [7] and Gann et al [6] showed that there are quantifiable morphometric attributes within tumor-adjacent benign regions that can provide additional information related to disease outcome. These studies, however, have been limited to the correlation of outcome and nuclear shape parameters alone.…”