Quantitative nuclear grade (QNG): a new image analysis-based biomarker of clinically relevant nuclear structure alterations

Nuclear IMPDH Filaments in Human Gliomas

The analysis of nuclear morphology plays an important role in glioma diagnosis and grading. We previously described intranuclear rods (rods) labeled with the SDL.3D10 monoclonal antibody against class III beta-tubulin (TUBB3) in human ependymomas. In a cohort of adult diffuse gliomas, we identified nuclear rods in 71.1% of IDH mutant lower-grade gliomas and 13.7% of IDH wild-type glioblastomas (GBMs). The presence of nuclear rods was associated with significantly longer postoperative survival in younger (≤65) GBM patients. Consistent with this, nuclear rods were mutually exclusive with Ki67 staining and their prevalence in cell nuclei inversely correlated with the Ki67 proliferation index. In addition, rod-containing nuclei showed a relative depletion of lamin B1, suggesting a possible association with senescence. To gain insight into their functional significance, we addressed their antigenic properties. Using a TUBB3-null mouse model, we demonstrate that the SDL.3D10 antibody does not bind TUBB3 in rods but recognizes an unknown antigen. In the present study, we show that rods show immunoreactivity for the nucleotide synthesizing enzymes inosine monophosphate dehydrogenase (IMPDH) and cytidine triphosphate synthetase. By analogy with the IMPDH filaments that have been described previously, we postulate that rods regulate the activity of nucleotide-synthesizing enzymes in the nucleus by sequestration, with important implications for glioma behavior.

Machine Learning in Drug Discovery: A Review

This review provides the feasible literature on drug discovery through ML tools and techniques that are enforced in every phase of drug development to accelerate the research process and deduce the risk and expenditure in clinical trials. Machine learning techniques improve the decision-making in pharmaceutical data across various applications like QSAR analysis, hit discoveries, de novo drug architectures to retrieve accurate outcomes. Target validation, prognostic biomarkers, digital pathology are considered under problem statements in this review. ML challenges must be applicable for the main cause of inadequacy in interpretability outcomes that may restrict the applications in drug discovery. In clinical trials, absolute and methodological data must be generated to tackle many puzzles in validating ML techniques, improving decision-making, promoting awareness in ML approaches, and deducing risk failures in drug discovery.

International Society of Urological Pathology Expert Opinion on Grading of Urothelial Carcinoma

CONTEXT

Grading is the mainstay for treatment decisions for patients with non-muscle-invasive bladder cancer (NMIBC).

OBJECTIVE

To determine the requirements for an optimal grading system for NMIBC via expert opinion.

EVIDENCE ACQUISITION

A multidisciplinary working group established by the International Society of Urological Pathology reviewed available clinical, histopathological, and molecular evidence for an optimal grading system for bladder cancer.

EVIDENCE SYNTHESIS

Bladder cancer grading is a continuum and five different grading systems based on historical grounds could be envisaged. Splitting of the World Health Organization (WHO) 2004 low-grade class for NMIBC lacks diagnostic reproducibility and molecular-genetic support, while showing little difference in progression rate. Subdividing the clinically heterogeneous WHO 2004 high-grade class for NMIBC into intermediate and high risk categories using the WHO 1973 grading is supported by both clinical and molecular-genetic findings. Grading criteria for the WHO 1973 scheme were detailed on the basis of literature findings and expert opinion.

CONCLUSIONS

Splitting of the WHO 2004 high-grade category into WHO 1973 grade 2 and 3 subsets is recommended. Provision of more detailed histological criteria for the WHO 1973 grading might facilitate the general acceptance of a hybrid four-tiered grading system or-as a preferred option-a more reproducible three-tiered system distinguishing low-, intermediate (high)-, and high-grade NMIBC.

PATIENT SUMMARY

Improvement of the current systems for grading bladder cancer may result in better informed treatment decisions for patients with bladder cancer.

Applications of machine learning in drug discovery and development

Drug discovery and development pipelines are long, complex and depend on numerous factors. Machine learning (ML) approaches provide a set of tools that can improve discovery and decision making for well-specified questions with abundant, high-quality data. Opportunities to apply ML occur in all stages of drug discovery. Examples include target validation, identification of prognostic biomarkers and analysis of digital pathology data in clinical trials. Applications have ranged in context and methodology, with some approaches yielding accurate predictions and insights. The challenges of applying ML lie primarily with the lack of interpretability and repeatability of ML-generated results, which may limit their application. In all areas, systematic and comprehensive high-dimensional data still need to be generated. With ongoing efforts to tackle these issues, as well as increasing awareness of the factors needed to validate ML approaches, the application of ML can promote data-driven decision making and has the potential to speed up the process and reduce failure rates in drug discovery and development.

Predictive Nuclear Chromatin Characteristics of Melanoma and Dysplastic Nevi

Background:

The diagnosis of malignant melanoma (MM) is among the diagnostic challenges pathologists encounter on a routine basis. Melanoma may arise in patients with preexisting dysplastic nevi (DN) and it is still the cause of 1.7% of all cancer-related deaths. Melanomas often have overlapping histological features with DN, especially those with severe dysplasia. Nucleotyping for identifying nuclear textural features can analyze nuclear DNA structure and organization. The aim of this study is to differentiate MM and DN using these methodologies.

Methods:

Dermatopathology slides diagnosed as MM and DN were retrieved. The glass slides were scanned using an Aperio ScanScopeXT at ×40 (0.25 μ/pixel). Whole slide images (WSI) were annotated for nuclei selection. Nuclear features to distinguish between MM and DN based on chromatin distributions were extracted from the WSI. The morphological characteristics for each nucleus were quantified with the optimal transport-based linear embedding in the continuous domain. Label predictions for individual cell nucleus are achieved through a modified version of linear discriminant analysis, coupled with the k-nearest neighbor classifier. Label for an unknown patient was set by the voting strategy with its pertaining cell nuclei.

Results:

Nucleotyping of 139 patient cases of melanoma (n = 67) and DN (n = 72) showed that our method had superior classification accuracy of 81.29%. This is a 6.4% gain in differentiating MM and DN, compared with numerical feature-based method. The distribution differences in nuclei morphology between MM and DN can be visualized with biological interpretation.

Conclusions:

Nucleotyping using quantitative and qualitative analyses may provide enough information for differentiating MM from DN using pixel image data. Our method to segment cell nuclei may offer a practical and inexpensive solution in aiding in the accurate diagnosis of melanoma.

Automated prostate tissue referencing for cancer detection and diagnosis

Background

The current practice of histopathology review is limited in speed and accuracy. The current diagnostic paradigm does not fully describe the complex and complicated patterns of cancer. To address these needs, we develop an automated and objective system that facilitates a comprehensive and easy information management and decision-making. We also develop a tissue similarity measure scheme to broaden our understanding of tissue characteristics.

Results

The system includes a database of previously evaluated prostate tissue images, clinical information and a tissue retrieval process. In the system, a tissue is characterized by its morphology. The retrieval process seeks to find the closest matching cases with the tissue of interest. Moreover, we define 9 morphologic criteria by which a pathologist arrives at a histomorphologic diagnosis. Based on the 9 criteria, true tissue similarity is determined and serves as the gold standard of tissue retrieval. Here, we found a minimum of 4 and 3 matching cases, out of 5, for ~80 % and ~60 % of the queries when a match was defined as the tissue similarity score ≥5 and ≥6, respectively. We were also able to examine the relationship between tissues beyond the Gleason grading system due to the tissue similarity scoring system.

Conclusions

Providing the closest matching cases and their clinical information with pathologists will help to conduct consistent and reliable diagnoses. Thus, we expect the system to facilitate quality maintenance and quality improvement of cancer pathology.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1086-6) contains supplementary material, which is available to authorized users.

Nuclear Morphometric Analysis of Leydig Cells of Male Pubertal Rats Exposed In Utero to Di(n-butyl) Phthalate

We recently reported that prenatal rat exposure to di(n-butyl) phthalate (DBP) induced Leydig cell (LC) hyperplasia after nine weeks (wks) of age, yet the number of LCs was similar to that of the vehicle group until seven weeks. Nuclear pleomorphism of hyperplastic LCs is common and is considered to be continuous progressive degeneration. Thus, computer-assisted image cell nuclear analysis of LCs was performed on 5- and 7-wk-old Sprague-Dawley (SD) rats whose dams had been administered DBP (i.g.) at 100 mg/kg/day or vehicle (corn oil) on gestation day 12 to 21. The results of the 5-wk-old DBP group were similar to those of the vehicle group; LC nuclei of the 7-wk-old DBP group showed normal ploidy and similar amounts of DNA. However, the size, elongation and peripheral chromatin aggregation parameters were significantly higher, and the reticular chromatin distribution and isolated chromatin aggregation parameters were significantly lower compared with the vehicle group. The present study quantitatively demonstrated nuclear morphological alterations in rat LCs at 7 wks old (puberty) due to the prenatal DBP administration before apparent LC hyperplasia developed.

Mechanisms of intracellular scaling

Cell size varies widely among different organisms as well as within the same organism in different tissue types and during development, which places variable metabolic and functional demands on organelles and internal structures. A fundamental question is how essential subcellular components scale to accommodate cell size differences. Nuclear transport has emerged as a conserved means of scaling nuclear size. A meiotic spindle scaling factor has been identified as the microtubule-severing protein katanin, which is differentially regulated by phosphorylation in two different-sized frog species. Anaphase mechanisms and levels of chromatin compaction both act to coordinate cell size with spindle and chromosome dimensions to ensure accurate genome distribution during cell division. Scaling relationships and mechanisms for many membrane-bound compartments remain largely unknown and are complicated by their heterogeneity and dynamic nature. This review summarizes cell and organelle size relationships and the experimental approaches that have elucidated mechanisms of intracellular scaling.

Quantitative characterization of preneoplastic progression using single-cell computed tomography and three-dimensional karyometry

The development of morphological biosignatures to precisely characterize preneoplastic progression necessitates high-resolution three-dimensional (3D) cell imagery and robust image processing algorithms. We report on the quantitative characterization of nuclear structure alterations associated with preneoplastic progression in human esophageal epithelial cells using single-cell optical tomography and fully automated 3D karyometry. We stained cultured cells with hematoxylin and generated 3D images of individual cells by mathematically reconstructing 500 projection images acquired using optical absorption tomographic imaging. For 3D karyometry, we developed novel, fully automated algorithms to robustly segment the cellular, nuclear, and subnuclear components in the acquired cell images, and computed 41 quantitative morphological descriptors from these segmented volumes. In addition, we developed algorithms to quantify the spatial distribution and texture of the nuclear DNA. We applied our methods to normal, metaplastic, and dysplastic human esophageal epithelial cell lines, analyzing 100 cells per line. The 3D karyometric descriptors elucidated quantitative differences in morphology and enabled robust discrimination between cell lines on the basis of extracted morphological features. The morphometric hallmarks of cancer progression such as increased nuclear size, elevated nuclear content, and anomalous chromatin texture and distribution correlated with this preneoplastic progression model, pointing to the clinical use of our method for early cancer detection.

Morphometric signature differences in nuclei of Gleason pattern 4 areas in Gleason 7 prostate cancer with differing primary grades on needle biopsy

PURPOSE

Since clinically significant upgrading of the biopsy Gleason score has an adverse clinical impact, ancillary tools besides the visual determination of primary Gleason pattern are essential to aid in better risk stratification.

MATERIALS AND METHODS

A total of 61 prostate biopsies were selected in patients with a diagnosis of Gleason score 7 prostatic adenocarcinoma, including 41 with primary Gleason pattern 3 and 20 with primary Gleason pattern 4. Slides from these tissues were stained using Feulgen stain, a nuclear DNA stain. Areas of Gleason pattern in all cases were analyzed for 40 nuclear morphometric descriptors of size, shape and chromatin using a CAS-200 system (BD). The primary outcome analyzed was the ability of morphometric features to identify visually determined primary Gleason pattern 4 on the biopsy. Data were analyzed using logistic regression as well as a C4.5 decision tree with and without preselection.

RESULTS

Decision tree analysis yielded the best model. Automatic feature selection identified minimum nuclear diameter as the most discriminative feature in a 3-parameter model with 85% classification accuracy. Using a preselected 3-parameter model including minimum diameter, angularity and sum optical density the decision tree yielded a slightly lesser accuracy of around 79%. Bootstrap validation of logistic regression results revealed that there was no unique model that could significantly explain the variance in primary Gleason pattern status, although minimum nuclear diameter was the most frequently selected parameter.

CONCLUSIONS

In this small cohort of patients with Gleason score 7 disease we report that Gleason pattern 4 nuclei from those with primary Gleason pattern 4 are generally larger with coarser chromatin compared with the Gleason pattern 4 in patients with primary Gleason pattern 3. These findings may aid in better risk stratification of the Gleason score 7 group by supplementing visual estimation of the percent of primary Gleason pattern 3 and 4 in the biopsy.

Quantification of diverse subcellular immunohistochemical markers with clinicobiological relevancies: validation of a new computer-assisted image analysis procedure

Tissue microarray technology and immunohistochemical techniques have become a routine and indispensable tool for current anatomical pathology diagnosis. However, manual quantification by eye is relatively slow and subjective, and the use of digital image analysis software to extract information of immunostained specimens is an area of ongoing research, especially when the immunohistochemical signals have different localization in the cells (nuclear, membrane, cytoplasm). To minimize critical aspects of manual quantitative data acquisition, we generated semi-automated image-processing steps for the quantification of individual stained cells with immunohistochemical staining of different subcellular location. The precision of these macros was evaluated in 196 digital colour images of different Hodgkin lymphoma biopsies stained for different nuclear (Ki67, p53), cytoplasmic (TIA-1, CD68) and membrane markers (CD4, CD8, CD56, HLA-Dr). Semi-automated counts were compared to those obtained manually by three separate observers. Paired t-tests demonstrated significant differences between intra- and inter-observer measurements, with more substantial variability when the cellular density of the digital images was > 100 positive cells/image. Overall, variability was more pronounced for intra-observer than for inter-observer comparisons, especially for cytoplasmic and membrane staining patterns (P < 0.0001 and P = 0.050). The comparison between the semi-automated and manual microscopic measurement methods indicates significantly lower variability in the results yielded by the former method. Our semi-automated computerized method eliminates the major causes of observer variability and may be considered a valid alternative to manual microscopic quantification for diagnostic, prognostic and therapeutic purposes.

Fluorescent stains for quantification of DNA by confocal laser scanning microscopy in 3-D

Confocal microscopy requires the use of fluorophores to visualize structures of interest within a specimen. To perform reliable measurements of the intensity of fluorescence, the stain should be specific, penetrate well into tissue sections, and bind stoichiometrically. Furthermore, emission must be linear with respect to DNA content and brightness, and fluorescence should be stable. Confocal microscopy is used to determine DNA ploidy and to analyze texture of nuclei, which is accomplished in three dimensions, because nuclei can be measured within the original tissue context. For this purpose the sample must be stained with a DNA binding fluorophore with the properties described above. Stains with different properties have been developed for different applications. We review here the advantages and disadvantages of these different stains for analyzing DNA ploidy and nuclear texture using three-dimensional microscopy. We conclude that SYBR green I and TO-PRO-3 are the most suitable stains for this purpose at present.

p300 (histone acetyltransferase) biomarker predicts prostate cancer biochemical recurrence and correlates with changes in epithelia nuclear size and shape

BACKGROUND

p300 impacts the transcription of several genes involved in key pathways critical to PCa progression. Therefore, we evaluated the prognostic value of p300 expression and its correlation with nuclear alterations seen in tumor cells in men with long-term follow-up after radical prostatectomy (RP).

METHODS

NCI Cooperative Prostate Cancer Tissue Resource tissue microarray cores of 92 RP cases (56 non-recurrences and 36 PSA recurrences) were utilized for the study. p300 expression was assessed by quantitative immunohistochemistry and nuclear alterations in Feulgen-stained nuclei were evaluated by digital image analysis using the AutoCyte Pathology Workstation. Cox proportional hazards regression, Spearman's rank correlation, and Kaplan-Meier plots were employed to analyze the data.

RESULTS

p300 expression significantly correlated with nuclear alterations seen in tumor cells; specifically with circular form factor (P = 0.012) and minimum feret (P = 0.048). p300 expression in high grade tumors (Gleason score >or=7) was significantly higher compared to low grade tumors (Gleason score <7) [17.7% versus 13.7%, respectively, P = 0.03]. TNM stage, Gleason score, and p300 expression were univariately significant in the prediction of PCa biochemical recurrence-free survival (P <or= 0.05). p300 expression remained significant in the multivariate model (P = 0.03) while Gleason score showed a trend toward significance (P = 0.06). Patients with a Gleason score >or=7 and p300 expression >24% showed the highest risk for PCa biochemical recurrence (P = 0.002).

CONCLUSIONS

p300 expression correlates with nuclear alterations seen in tumor cells and has prognostic value in predicting long-term PCa biochemical recurrence-free survival.

Potential use of quantitative tissue phenotype to predict malignant risk for oral premalignant lesions

The importance of early diagnosis in improving mortality and morbidity rates of oral squamous cell carcinoma (SCC) has long been recognized. However, a major challenge for early diagnosis is our limited ability to differentiate oral premalignant lesions (OPL) at high risk of progressing into invasive SCC from those at low risk. We investigated the potential of quantitative tissue phenotype (QTP), measured by high-resolution image analysis, to identify severe dysplasia/carcinoma in situ (CIS; known to have an increased risk of progression) and to predict progression to cancer within hyperplasia or mild/moderate dysplasia. We generated a nuclear phenotype score (NPS), a combination of five nuclear morphometric features that best discriminate 4,027 "normal" nuclei (selected from 29 normal oral biopsies) from 4,298 "abnormal" nuclei (selected from 30 SCC biopsies). This NPS was then determined for a set of 69 OPLs. Severe dysplasia/CIS showed a significant increase in NPS compared with hyperplasia or mild/moderate dysplasia. However, within the latter group, elevated NPS was strongly associated with the presence of high-risk loss of heterozygosity (LOH) patterns. There was a statistical difference between NPS of hyperplasia or mild/moderate dysplasia that progressed to cancer and those that did not. Individuals with a high NPS had a 10-fold increase in relative risk of progression. In the multivariate Cox model, LOH and NPS together were the strongest predictors for cancer development. These data suggest that QTP could be used to identify lesions that require molecular evaluation and should be integrated with such approaches to facilitate the identification of hyperplasia or mild/moderate dysplasia OPLs at high risk of progression.

Dietary genistein inhibits metastasis of human prostate cancer in mice

Dietary genistein has been linked to lower prostate cancer (PCa) mortality. Metastasis is the ultimate cause of death from PCa. Cell detachment and invasion represent early steps in the metastatic cascade. We had shown that genistein inhibits PCa cell detachment and cell invasion in vitro. Genistein-mediated inhibition of activation of focal adhesion kinase (FAK) and of the p38 mitogen-activated protein kinase (MAPK)-heat shock protein 27 (HSP27) pathway has been shown by us to regulate PCa cell detachment and invasion effects, respectively. To evaluate the antimetastatic potential of genistein, we developed an animal model suited to evaluating antimetastatic drug efficacy. Orthotopically implanted human PC3-M PCa cells formed lung micrometastasis by 4 weeks in >80% of inbred athymic mice. Feeding mice dietary genistein before implantation led to blood concentrations similar to those measured in genistein-consuming men. Genistein decreased metastases by 96%, induced nuclear morphometric changes in PC3-M cells indicative of increased adhesion (i.e., decreased detachment) but did not alter tumor growth. Genistein increased tumor levels of FAK, p38 MAPK, and HSP27 "promotility" proteins. However, the ratio of phosphorylated to total protein trended downward, indicating a failure to increase relative amounts of activated protein. This study describes a murine model of human PCa metastasis well suited for testing antimetastatic drugs. It shows for the first time that dietary concentrations of genistein can inhibit PCa cell metastasis. Increases in promotility proteins support the notion of cellular compensatory responses to antimotility effects induced by therapy. Studies of antimetastatic efficacy in man are warranted and are under way.

Using nuclear morphometry to predict the need for treatment among men with low grade, low stage prostate cancer enrolled in a program of expectant management with curative intent

PURPOSE

We assessed the use of quantitative clinical and pathologic information to predict which patients would eventually require treatment for prostate cancer (CaP) in an expectant management (EM) cohort.

EXPERIMENTAL DESIGN

We identified 75 men having prostate cancer with favorable initial biopsy characteristics; 30 developed an unfavorable biopsy (Gleason grade >6, >2 cores with cancer, >50% of a core with cancer, or a palpable nodule) requiring treatment and 45 maintained favorable biopsies throughout a median follow-up of 2.7 years. Demographic, clinical data and quantitative tissue histomorphometry determined by digital image analysis were analyzed.

RESULTS

Logistic regression (LR) modeling generated a quantitative nuclear grade (QNG) signature based on the enrollment biopsy for differentiation of Favorable and Unfavorable groups using a variable LR selection criteria of P(z)<0.05. The QNG signature utilized 12 nuclear morphometric descriptors (NMDs) and had an area under the receiver operator characteristic curve (ROC-AUC) of 87% with a sensitivity of 82%, specificity of 70% and accuracy of 75%. A multivariable LR model combining QNG signature with clinical and pathological variables yielded an AUC-ROC of 88% and a sensitivity of 81%, specificity of 78% and accuracy of 79%. A LR model using prostate volume, PSA density, and number of pre-diagnosis biopsies resulted in an AUC-ROC of 68% and a sensitivity of 85%, specificity of 37% and accuracy of 56%.

CONCLUSIONS

QNG using EM prostate biopsies improves the predictive accuracy of LR models based on traditional clinicopathologic variables in determining which patients will ultimately develop an unfavorable biopsy. Our QNG-based model must be rigorously, prospectively validated prior to use in the clinical arena.

Identification of patients with low-risk for aneuploidy: comparative discriminatory models using linear and machine-learning classifiers in prostate cancer

BACKGROUND

Prostate needle biopsy (PNB) ploidy status has proven utility to predict adverse outcomes after prostatectomy. We sought to develop models to predict ploidy status using clinicopathologic variables.

METHODS

We identified a cohort of 169 patients with a diagnosis of prostatic adenocarcinoma on PNB, and estimated ploidy status (determined using Feulgen stained biopsy tissue) using four predictors, including age, prebiopsy PSA, highest Gleason score (GS), and the percentage of involvement by carcinoma at the biopsy site with the highest GS (PCARBX). Logistic regression (LR), Neural Network (NN), and CART classifiers were constructed.

RESULTS

Univariate analyses revealed all four predictors to be significantly associated with ploidy status. On multivariable analyses, LR identified a 2-parameter model, including GS and PCARBX that had a significant ability to predict ploidy status with a 74% and 75% correct classification rate (CCR), respectively. Using the same variables, CART and NN yielded similar CCRs of 70.4%. Within GS = 6 cohort, the CART model classified over 90% of biopsies as diploid when patients had a PCARBX < 55% and a log(PSA) < 1.7.

CONCLUSIONS

Our study demonstrates that models using GS and PCARBX are able to predict PNB ploidy status with acceptable accuracy. While machine learning classifier-derived models yield similar accuracy as LR-derived models, the latter methodology has the distinct advantage of being applicable in future datasets to estimate case-specific predictions. This information may be useful in identifying potentially aneuploid patients, who can then be targeted for more aggressive therapy.

Improved prediction of prostate cancer recurrence through systems pathology

We have developed an integrated, multidisciplinary methodology, termed systems pathology, to generate highly accurate predictive tools for complex diseases, using prostate cancer for the prototype. To predict the recurrence of prostate cancer following radical prostatectomy, defined by rising serum prostate-specific antigen (PSA), we used machine learning to develop a model based on clinicopathologic variables, histologic tumor characteristics, and cell type-specific quantification of biomarkers. The initial study was based on a cohort of 323 patients and identified that high levels of the androgen receptor, as detected by immunohistochemistry, were associated with a reduced time to PSA recurrence. The model predicted recurrence with high accuracy, as indicated by a concordance index in the validation set of 0.82, sensitivity of 96%, and specificity of 72%. We extended this approach, employing quantitative multiplex immunofluorescence, on an expanded cohort of 682 patients. The model again predicted PSA recurrence with high accuracy, concordance index being 0.77, sensitivity of 77% and specificity of 72%. The androgen receptor was selected, along with 5 clinicopathologic features (seminal vesicle invasion, biopsy Gleason score, extracapsular extension, preoperative PSA, and dominant prostatectomy Gleason grade) as well as 2 histologic features (texture of epithelial nuclei and cytoplasm in tumor only regions). This robust platform has broad applications in patient diagnosis, treatment management, and prognostication.

Significant variations in nuclear structure occur between and within Gleason grading patterns 3, 4, and 5 determined by digital image analysis

BACKGROUND

Alterations in nuclei structure and DNA content captured from Gleason grading patterns 3, 4 and 5 of radical prostatectomy (RP) cases were determined by a computer-assisted microscope. Quantitative Nuclear Morphometry (QNM) profiles were created to evaluate variability in nuclear structure within each of these grades.

METHODS

A tissue microarray (TMA) was constructed using RP cases and the prostate cancer (PCa) TMA cores prepared from 20 GG-3, 9 GG-4, 10 GG-5 patterns, and 20 benign cancer-adjacent cases from RP archival paraffin blocks. Feulgen-stained nuclei were captured from 0.6 mm spots using the AutoCyte system. Pools of 1100 nuclei captured from each test group were used to calculate Multivariate Logistic Regression (MLR) models that generated predictive indices and predictive probabilities (PP) to make comparisons between and within each set of pooled nuclei.

RESULTS

A single QNM profiles yielded areas of receiver operator characteristic curves (ROC) that distinguished differences among benign cancer-adjacent nuclei and GG-3 (ROC-AUC = 0.78); GG-4 (ROC-AUC = 0.86) and GG-5 (ROC-AUC = 0.88) with accuracies of 73%, 78% and 80% respectively. Applying PP plots generated from MLR models of GG 3, 4, and 5 nuclei clearly demonstrated marked heterogeneity within each of these three GG patterns.

CONCLUSIONS

QNM signatures illustrate alterations in nuclei structure, based upon nuclear morphometry within each of these three GG patterns, and might signify potential variations in PCa disease risk of progression outcomes. In the future a modified system of Gleason grading that considers not only glandular architecture but also quantitative nuclear grade may ensure accuracy in prognosis.

Correlating routine cytology, quantitative nuclear morphometry by digital image analysis, and genetic alterations by fluorescence in situ hybridization to assess the sensitivity of cytology for detecting pancreatobiliary tract malignancy

Routine cytologic (RC), fluorescence in situ hybridization (FISH), digital image analysis (DIA), and quantifiable morphometric results from 284 pancreatobiliary stricture brushings were compared. We chose specific DIA nuclear features assessed by pathologists in evaluating RC specimens, such as area and shape. A visual nuclear morphometric score (VNMS) was calculated. There was a difference (P < .001) in the mean VNMS when RC results were classified as negative (11.5), atypical (12.5), suspicious (13.8), and positive (16.5). The mean VNMS of specimens diagnosed as disomy (11.3), trisomy 7 (12.1), and polysomy (14.7) by FISH was also different (P < .001). There was no difference in the VNMS of false-negative and true-negative cytologic specimens (P = .225). Our findings substantiate the relationship between cell nuclear visual alterations and genetic FISH abnormalities. The low sensitivity of cytologic examination for pancreatobiliary carcinoma is due to an absence of tumor cells or the presence of well-differentiated tumor lacking recognizable nuclear atypia.

Discrimination between benign and malignant prostate tissue using chromatin texture analysis in 3-D by confocal laser scanning microscopy

BACKGROUND

Analysis of chromatin texture may improve both the diagnosis and the assessment of the prognosis of prostate cancer. Confocal laser scanning microscopy (CLSM) allows performing measurements in nuclei reconstructed in 3-D. The aim of this study was to evaluate the clinical usefulness of 3-D texture analysis of prostate tissue.

METHODS

Image stacks of eight prostate cancer sections were obtained by CLSM of both benign and malignant areas. Texture feature values were computed for individual nuclei. The discriminative power of the texture features was established by receiver operating characteristic (ROC) analysis and linear discriminant analysis (LDA).

RESULTS

Texture features were identified that could discriminate between benign and malignant nuclei. LDA correctly classified 89% of the nuclei of the pooled set of benign and malignant nuclei.

CONCLUSIONS

3-D nuclear texture features allow discrimination of most benign and malignant prostate nuclei. We estimate that the classification rates can be increased by improving the image quality.

Computerized morphometry as an aid in determining the grade of dysplasia and progression to adenocarcinoma in Barrett's esophagus

The aims of this study were to use computerized morphometry in order to differentiate between the degree of dysplasia and to predict progression to invasive adenocarcinoma in Barrett's esophagus (BE). Biopsies from 97 patients with BE graded by a consensus forum of expert gastrointestinal pathologists were available for morphometrical analysis. The study group included 36 biopsies negative for dysplasia (ND), none of which progressed to carcinoma; 16 indefinite for dysplasia (IND) and 21 low-grade dysplasia (LGD), of which three progressed in each group and 24 high-grade dysplasia (HGD), of which 15 progressed to invasive carcinoma. Computerized morphometry was used for measuring indices of size, shape, texture, symmetry and architectural distribution of the epithelial nuclei. Low-grade dysplasia was best differentiated from the ND group by nuclear pseudostratification (P=0.036), pleomorphism (P<0.01), and chromatin texture (margination, P<0.01) and from the HGD group by nuclear area (P<0.01), pleomorphism (P<0.01), chromatin texture (margination, P<0.01), symmetry (P<0.01), and orientation (P=0.027). These results were validated on a new set of cases (n=55) using a neural network model, resulting in an accuracy of 89% for differentiating between the ND and LGD groups and 86% for differentiating between the LGD and HGD groups. Within the HGD group, univariate significant predictors of the progression interval to carcinoma were: indices of nuclear texture (heterogeneity: P=0.0019, s.d.-OD: P=0.005) and orientation: P=0.022. Nuclear texture (heterogeneity) was the only independent predictor of progression (P=0.004, hazard=11.54) by Cox's multivariate test. This study proposes that computerized morphometry is a valid tool for determining the grade of dysplasia in BE. Moreover, histomorphometric quantification of nuclear texture is a powerful tool for predicting progression to invasive adenocarcinoma in patients with HGD.

Alterations in nuclear structure and expression of proPSA predict differences between native Japanese and Japanese-American prostate cancer

OBJECTIVES

To differentiate the benign and/or malignant epithelial cells in prostate cancer (PCa) glands of native Japanese (NJ) and Japanese-American (JA) men using biomarkers.

METHODS

Tissue microarrays from radical prostatectomy specimens of cancerous and adjacent benign areas from 25 NJ and 25 JA prostate glands were studied. Image analysis was used to quantify total prostate-specific antigen (PSA) and proPSA immunohistochemical staining, as well as the variance of several morphometric features from Feulgen-stained epithelial cell nuclei. Logistic regression analysis was applied to determine whether quantitative nuclear grade (QNG) calculations and PSA immunohistochemical staining could differentiate the two test groups.

RESULTS

The QNG model differentiated changes in the benign epithelium of the two Japanese groups with an area under the receiver operating characteristic curve of 84% and accuracy of 82% (P = 0.0001). A second QNG model differentiated changes in the malignant epithelium of the two groups with an area under the receiver operating characteristic curve of 84% and accuracy of 76% (P = 0.0023). Logistic regression models combining proPSA immunohistochemical data and QNG from either benign or malignant tissue components yielded areas under the receiver operating characteristic curve of 96% and 91% (P <0.0001) for differentiation of the JA and NJ groups, respectively.

CONCLUSIONS

Unique nuclear morphometric alterations demonstrated by QNG combined with proPSA immunohistologic localization independently predicted for significant differences between NJ and JA men with PCa. These preliminary observations indicate a basis for biologic and molecular alterations in the benign adjacent and malignant epithelium between these two groups.

Molecular markers in bladder cancer: A critical appraisal

The diagnosis of both primary and recurrent bladder tumors currently relies upon the urine cytology and cystoscopy. Neither of these diagnostic tools is completely accurate. Prognostication of bladder cancer is largely based on pathologic tumor grade and stage. Over the past 2 decades, there is accumulating evidence that like many other cancers, bladder cancer, too, has a distinct molecular signature that separates it from other cancers and normal bladder tissue. Bladder tumors of different grades and stages even possess unique, and specific genotypic and phenotypic characteristics. Although recognition of several of these molecular alterations is possible by analyzing tumor tissue, urine, and serum samples, few if any of these "molecular markers" for bladder cancer are widely used in clinical practice. These markers include some that can be applied during the diagnostic work-up of symptoms (e.g., hematuria), those under surveillance for recurrence of superficial disease and forecasting long-term prognosis, or response to chemotherapy. In this review of molecular markers for bladder cancer, effectiveness of markers in each of these categories that are identifiable in the urine of patients with bladder cancer was examined. Many of the diagnostic markers appear to hold an advantage over urine cytology in terms of sensitivity, especially for the detection of low-grade superficial tumors. However, most markers tend to be less specific than cytology, yielding more false-positives. This result is more commonly observed in patients with concurrent bladder inflammation or other benign bladder conditions. Although there are several candidate markers for assessing prognosis or response to chemotherapy, studies of large patient populations are lacking. Further studies involving larger numbers of patients are required to determine their accuracy and widespread applicability in guiding treatment of bladder cancer.

Ultrasound verification of bladder damage is associated with known biomarkers of bladder cancer in adults chronically infected with Schistosoma haematobium in Ghana

Long-term infection with urinary schistosomiasis has been associated with development of bladder cancer. However, bladder cancer is difficult to diagnose without invasive measures such as cystoscopy, thus there is little information on the epidemiological extent of the problem. Studies have been either case-control studies or case examinations in different geographical areas, estimating a schistosome-associated bladder cancer incidence of 3-4 cases per 100,000. We have used three indicators to examine the potential bladder cancer problem in an adult rural population in Ghana endemic for urinary schistosomiasis: (i) parasitological positivity; (ii) age prevalence of bladder damage from ultrasound scans; and (iii) detection of biomarkers associated with the presence of bladder cancer. Biomarkers were BLCA-4 test (urine) and nuclear morphometry or quantitative nuclear grading (QNG) of epithelial cells (urine sediment), which quantifies DNA ploidy status and nuclear morphometric descriptors, both of which can detect the presence of bladder cancer. Our data show an increasing association between age, severe bladder abnormalities and the occurrence of these biomarkers. Sixty-two of 73 cytopathology Papanicolaou-stained smears were seen to have squamous metaplasia. Although further investigations are needed, we suggest that schistosome-associated bladder cancer is an important public health concern in areas where Schistosoma haematobium is prevalent.

Prediction of radiosensitivity in human bladder cell lines using nuclear chromatin phenotype

BACKGROUND

Nuclear texture analysis measures phenotypic changes in chromatin distribution within a cell nucleus, while the alkaline Comet assay is a sensitive method for measuring the extent of DNA breakage in individual cells. The authors aim to use both methods to provide information about the sensitivity of cells to ionizing radiation.

METHODS

The alkaline Comet assay was performed on six human bladder carcinoma cell lines and one human urothelial cell line exposed to gamma-radiation doses from 0 to 10 Gy. Nuclear chromatin texture analysis of 40 features was then performed in the same cell lines exposed to 0, 2, and 6 Gy to explore if nuclear phenotype was related to radiation sensitivity.

RESULTS

Comet assay results demonstrated that the cell lines exhibited different levels of radiosensitivity and could be divided into a radiosensitive and a radioresistant group at >6 Gy. Using stepwise discriminant analysis, a subset of important nuclear texture features that best discriminated between sensitive and resistant cell lines were identified A classification function, defined using these features, correctly classified 81.75% of all cells into their radiosensitive or radioresistant groups based on their pretreatment chromatin phenotype. Posttreatment chromatin changes also varied between cell lines, with sensitive cell lines showing a relaxed chromatin conformation following radiation, whereas resistant cell lines exhibited chromatin condensation.

CONCLUSIONS

The authors conclude that the alkaline Comet assay and nuclear texture methodologies may prove to be valuable aids in predicting the response of tumor cells to radiotherapy.

Risk stratification after radical prostatectomy in men with pathologically organ-confined prostate cancer using volume-weighted mean nuclear volume

OBJECTIVE

We examined the impact of volume-weighted mean nuclear volume (MNV) on biochemical failure after radical prostatectomy (RP) in pathologically organ-confined prostate cancer (PC) and developed a prognostic factor-based stratification model for these patients.

PATIENTS AND METHODS

We analyzed 141 patients with pathologically organ-confined PC treated solely with RP. Unbiased estimates of MNV were calculated from biopsy specimens based on a stereological method, and compared with other clinical and pathologic findings including patient age, pre-treatment PSA, biopsy and RP specimen Gleason score, pathologic stage, total cancer volume, index cancer volume, tumor differentiation, number of tumor foci, main tumor location, and surgical margin status, with regard to prediction of disease outcome after RP using Cox proportional hazard models.

RESULTS

The median follow-up was 38.6 months (range 4--119 months). Twenty patients (14.2%) experienced biochemical failure. On multivariate analysis, MNV was demonstrated to be an independent prognostic factor, along with pre-treatment PSA and total cancer volume (P=0.0004, 0.0184, and 0.0285, respectively). All patients were stratified into three groups according to their prognostic scores developed on the basis of multivariate analysis, with statistically significant prognostic differences revealed for each of the between-group comparisons.

CONCLUSION

The results demonstrated that estimates of MNV contribute most significantly to the prediction of biochemical control of pathologically organ-confined PC. The combination of MNV with other independent predictors such as pre-treatment PSA and total cancer volume provided a statistically verifiable basis for risk stratification, facilitating more accurate prediction of disease outcome.

Java Web Start based software for automated quantitative nuclear analysis of prostate cancer and benign prostate hyperplasia

Background

Androgen acts via androgen receptor (AR) and accurate measurement of the levels of AR protein expression is critical for prostate research. The expression of AR in paired specimens of benign prostate and prostate cancer from 20 African and 20 Caucasian Americans was compared to demonstrate an application of this system.

Methods

A set of 200 immunopositive and 200 immunonegative nuclei were collected from the images using a macro developed in Image Pro Plus. Linear Discriminant and Logistic Regression analyses were performed on the data to generate classification coefficients. Classification coefficients render the automated image analysis software independent of the type of immunostaining or image acquisition system used. The image analysis software performs local segmentation and uses nuclear shape and size to detect prostatic epithelial nuclei. AR expression is described by (a) percentage of immunopositive nuclei; (b) percentage of immunopositive nuclear area; and (c) intensity of AR expression among immunopositive nuclei or areas.

Results

The percent positive nuclei and percent nuclear area were similar by race in both benign prostate hyperplasia and prostate cancer. In prostate cancer epithelial nuclei, African Americans exhibited 38% higher levels of AR immunostaining than Caucasian Americans (two sided Student's t-tests; P < 0.05). Intensity of AR immunostaining was similar between races in benign prostate.

Conclusion

The differences measured in the intensity of AR expression in prostate cancer were consistent with previous studies. Classification coefficients are required due to non-standardized immunostaining and image collection methods across medical institutions and research laboratories and helps customize the software for the specimen under study. The availability of a free, automated system creates new opportunities for testing, evaluation and use of this image analysis system by many research groups who study nuclear protein expression.

p300 Modulates Nuclear Morphology in Prostate Cancer

Alterations in nuclear structure distinguish cancer cells from noncancer cells. These nuclear alterations can be translated into quantifiable features by digital image analysis in a process known as quantitative nuclear morphometry. Recently, quantitative nuclear morphometry has been shown to predict metastasis and biochemical recurrence of prostate cancer. However, little is known about the cellular mechanisms underlying these nuclear morphometric changes. Alterations of nuclear matrix proteins are frequently involved in changes of nuclear structure. A number of co-activators interact with these nuclear structure–related proteins, suggesting that they might be involved in quantitative nuclear morphometry changes. We have shown previously that the transcriptional co-activator p300 is involved in prostate cancer progression. However, the ability of a transcriptional regulator like p300 to modulate nuclear morphology has not been described previously. In the present study, we show that p300 expression in prostate cancer biopsy tissue from 95 patients correlates with quantifiable nuclear alterations. Moreover, we show that transfection of p300 into prostate cancer cells in culture induces quantifiable nuclear alterations, such as diameter, perimeter, and absorbance among others, as assessed by digital image analysis. These alterations correlate individually with aggressive features in prostate cancer, such as expression of the proliferation marker Ki-67 and extraprostatic extension of the tumor. Finally, we found that transfection of p300 into prostate cancer cells specifically increases mRNA and protein levels of nuclear matrix peptides lamins A and C, suggesting that these proteins mediate the p300-induced effects. These findings reveal a new insight into the transcriptional and structural regulation of prostate cancer.

Ability to predict metastasis based on pathology findings and alterations in nuclear structure of normal-appearing and cancer peripheral zone epithelium in the prostate

PURPOSE

Malignant transformation in the prostate produces significant alterations in glandular architecture (Gleason grade) and nuclear structure that provide valuable prognostic information. Normal-appearing nuclei (NN) adjacent to cancer may also have altered functions in response to malignancy. We studied NN adjacent to peripheral zone (PZ) prostate cancer (PCa), as well as the PZ cancer nuclei (CaN) using quantitative image cytometry. The nuclear structure information was combined with routine pathological findings to predict metastatic PCa progression and/or death.

EXPERIMENTAL DESIGN

Tissue microarrays of normal-appearing and cancer areas were prepared from 182 pathologist-selected paraffin blocks. Feulgen-stained CaN and NN were captured from the tissue microarrays using the AutoCyte Pathology Workstation. Multivariate logistic regression was used to calculate quantitative nuclear grade (QNG) solutions based on nuclear morphometric descriptors determined from NN and CaN. Multivariate logistic regression and Kaplan-Meier plots were also used to predict risk for distant metastasis and/or PCa-specific death using QNG solutions and routine pathology.

RESULTS

The pathology model yielded an area under the receiver operator characteristic curve of 72.5%. The QNG-NN and QNG-CaN solutions yielded an area under the receiver operator characteristic curve of 81.6 and 79.9%, respectively, but used different sets of nuclear morphometric descriptors. Kaplan-Meier plots for the pathology variables, the QNG-NN and QNG-CaN solutions, were combined with pathology to defined three statistically significantly distinct risk groups for distant metastasis and/or death (P < 0.0001).

CONCLUSIONS

Alterations in cancer or normal-appearing nuclei adjacent to peripheral zone cancer areas can predict PCa progression and/or death. The QNG-NN and QNG-CA solutions could be combined with pathology variables to improve the prediction of distant metastasis.

Prostate cancer in native Japanese and Japanese-American men: Effects of dietary differences on prostatic tissue

OBJECTIVES

To investigate the relationship between diet and prostate cancer (CaP) among native Japanese (NJ) and second-generation or third-generation Japanese-American (J-A) men--focusing on the effects of animal fat and soy on prostatic tissues.

METHODS

The subjects were 50 Japanese men undergoing radical prostatectomy, 25 NJ living in Nagoya, Japan and 25 U.S.-born J-A men, living in Los Angeles, California. A priori, the NJ men were believed to be a low-fat, high-soy group and the J-A men, a high-fat, low-soy group. The studies included postoperative measurements of diet (Block questionnaire), body fat (bioimpedance), blood, urine, and prostatic biomarkers in malignant and adjacent normal tissue, using a tissue microarray made from the original paraffin blocks.

RESULTS

The NJ and J-A men were similar in age (65 to 70 years old; P <0.05), prostate-specific antigen level (7.1 to 8.6 ng/mL), prostate volume (35 to 38 cm3), and Gleason score (5.6 to 6.6), but their body composition differed. J-A men had more body fat (24% versus 19%), higher serum triglyceride levels (245 versus 106 mg/dL), lower estradiol levels (27 versus 31 ng/mL), and much lower urinary soy-metabolite levels (1:3) than NJ men (P <0.02). In both NJ and J-A groups, expression of numerous tissue biomarkers separated normal from CaP tissue, including markers for apoptosis (Bcl-2, caspase-3), growth factor receptors (epidermal growth factor receptor), racemase, 5-lipoxygenase, kinase inhibition (p27), and cell proliferation (Ki-67; all P <0.02). Furthermore, within both normal and CaP tissues, caspase-3 and 5-lipoxygenase were expressed more in NJ than in J-A men (P <0.01). Nuclear morphometry showed that the chromatin in each of the four groups (normal versus CaP, NJ versus J-A) was different (area under the curve 85% to 94%, P <0.01), despite fundamental genetic homogeneity.

CONCLUSIONS

NJ and J-A men, products of similar genetics but differing environments, were shown to have differences in body composition that could influence CaP evolution. The CaP specimens from the NJ and J-A men were histologically similar, but tissue biomarker expression, especially of lipoxygenase and the caspase family, suggested differing mechanisms of carcinogenesis. Differences in nuclear morphometry suggested the additional possibility of gene-nutrient interactions.

Using Nuclear Morphometry to Discriminate the Tumorigenic Potential of Cells: A Comparison of Statistical Methods

Despite interest in the use of nuclear morphometry for cancer diagnosis and prognosis as well as to monitor changes in cancer risk, no generally accepted statistical method has emerged for the analysis of these data. To evaluate different statistical approaches, Feulgen-stained nuclei from a human lung epithelial cell line, BEAS-2B, and a human lung adenocarcinoma (non-small cell) cancer cell line, NCI-H522, were subjected to morphometric analysis using a CAS-200 imaging system. The morphometric characteristics of these two cell lines differed significantly. Therefore, we proceeded to address the question of which statistical approach was most effective in classifying individual cells into the cell lines from which they were derived. The statistical techniques evaluated ranged from simple, traditional, parametric approaches to newer machine learning techniques. The multivariate techniques were compared based on a systematic cross-validation approach using 10 fixed partitions of the data to compute the misclassification rate for each method. For comparisons across cell lines at the level of each morphometric feature, we found little to distinguish nonparametric from parametric approaches. Among the linear models applied, logistic regression had the highest percentage of correct classifications; among the nonlinear and nonparametric methods applied, the Classification and Regression Trees model provided the highest percentage of correct classifications. Classification and Regression Trees has appealing characteristics: there are no assumptions about the distribution of the variables to be used, there is no need to specify which interactions to test, and there is no difficulty in handling complex, high-dimensional data sets containing mixed data types.

Impact of Volume Weighted Mean Nuclear Volume on Outcomes Following Salvage Radiation Therapy After Radical Prostatectomy

PURPOSE

Although salvage radiation therapy (RT) is a potentially curative treatment option for men with biochemical failure after radical prostatectomy (RP), to our knowledge there are no definitive pretreatment factors predicting patients likely to benefit from this treatment. We examined the impact of volume weighted mean nuclear volume (MNV) of biopsy specimens on disease outcomes and describe its usefulness as a new independent predictor.

MATERIALS AND METHODS

We analyzed 33 patients who received salvage RT for biochemical failure after RP, including 11 who had received neoadjuvant hormone therapy before RP. Salvage RT was delivered to the prostatic bed at a total dose of 60 Gy with a 4-field contoured technique. Unbiased estimates of MNV were calculated from more than 100 cancer nuclei per patient captured from biopsy specimens based on a stereological method and compared with other clinical and pathological findings, including patient age, pretreatment prostate specific antigen (PSA), PSA density, biopsy Gleason score, neoadjuvant therapy, surgical Gleason score, pathological stage, tumor volume, surgical margin status, biochemical disease-free duration before RT, nadir PSA and PSA doubling time before RT, and pre-RT PSA with regard to predicting the disease outcome after salvage RT.

RESULTS

The median followup after salvage RT was 43.4 months. A total of 17 patients (52%) experienced biochemical failure a median of 6.7 months (range 0 to 48.1) after RT. On univariate analysis MNV and log(pre-RT PSA) were significant predictors of disease outcome in all patients and in the 22 nonneoadjuvant patient subset (p = 0.0124 and 0.0159, respectively). Log(nadir PSA) and PSA doubling time were also significant in the latter subset (p = 0.0287 and 0.0475, respectively). However, dual multivariate analysis revealed that MNV was the only independent predictor in the 2 groups (logistic regression analysis p = 0.00931 and 0.03511, and Cox proportional hazards analysis p = 0.00483 and 0.02277, respectively). There was a statistically significant biochemical disease-free survival advantage for small vs large MNV in each data set (p = 0.0072 and 0.0036, respectively).

CONCLUSIONS

Our results suggest that an estimate of MNV contributes significantly to the prediction of biochemical control after salvage RT. However, further investigation in a larger nonneoadjuvant population is needed to confirm its significance in combination with other clinical and pathological findings.

Quantitative alterations in nuclear structure predict prostate carcinoma distant metastasis and death in men with biochemical recurrence after radical prostatectomy

BACKGROUND

Microscopic histologic grade has been the best predictor of prostate carcinoma (PCa) progression in men after surgical therapy. The ability to predict accurately, at the time of surgery, which patients are likely to develop metastatic PCa would enable optimization of disease management with adjuvant therapy. The authors assessed the ability of pathologic, nuclear morphometric, and chromatin parameters to predict metastatic PCa progression and/or death in 227 men with biochemical recurrence and long-term follow-up after undergoing radical prostatectomy.

METHODS

Multivariate logistic regression (LR) was used to calculate quantitative nuclear grade (QNG) solutions using the variances of 60 nuclear morphometric descriptors (NMDs) of nuclear size, shape, DNA content, and chromatin organization that predicted distant metastasis and/or PCa-specific death. An LR model also was generated to predict this outcome using a combination of pathologic variables and the best QNG solution. Cox proportional hazards models were generated, and Kaplan-Meier plots were used to display three risk groups based on pathology, QNG, and a combination of these variables.

RESULTS

A multivariate LR model using pathology retained lymph node (LN) status, seminal vesicle status, and prostatectomy Gleason score, yielding an area under the curve-receiver operator characteristic (AUC-ROC) of 75% with an accuracy of 59% at 90% sensitivity. The best QNG solution used the variance of 25 NMDs, yielding an AUC-ROC of 84% and an accuracy of 70% at 90% sensitivity. The combined pathology-QNG model retained LN status, prostatectomy Gleason score, and QNG, yielding an AUC-ROC of 86% with an accuracy of 76% at 90% sensitivity. The Cox proportional hazards models produced the following significant univariate and multivariate hazard ratios: QNG, 3.5 and 2.9, respectively; LN, 2.7 and 1.8, respectively; and prostatectomy Gleason score, 2.8 and 2.1, respectively.

CONCLUSIONS

Alterations in the structure of tumor nuclei measured by computer-assisted image analysis were strong predictors of PCa progression and death in men with long-term follow-up who had biochemical recurrence after undergoing radical prostatectomy. QNG solutions can serve as a new supplemental biomarker for accurate prediction of PCa progression at the time of surgery.

Light scattering from cervical cells throughout neoplastic progression: influence of nuclear morphology, DNA content, and chromatin texture

A number of noninvasive fiber optic optical technologies are under development for real-time diagnosis of neoplasia. We investigate how the light scattering properties of cervical cells are affected by changes in nuclear morphology, DNA content, and chromatin texture, which occur during neoplastic progression. We used a Cyto-Savant computer-assisted image analysis system to acquire quantitative nuclear features measurements from 122 Feulgen-thionin-stained histopathologic sections of cervical tissue. A subset of the measured nuclear features was incorporated into a finite-difference time-domain (FDTD) model of cellular light scattering. The magnitude and angular distribution of scattered light was calculated for cervical cells as a function of pathologic grade. The nuclear atypia strongly affected light scattering properties. The increased size and elevated DNA content of nuclei in high-grade lesions caused the most significant changes in scattering intensity. The spatial dimensions of chromatin texture features and the amplitude of refractive index fluctuations within the nucleus impacted both the angular distribution of scattering angles and the total amount of scattered light. Cellular scattering is sensitive to changes in nuclear morphology that accompany neoplastic progression. Understanding the quantitative relationships between nuclear features and scattering properties will aid in the development of noninvasive optical technologies for detection of precancerous conditions.

Saw palmetto alters nuclear measurements reflecting DNA content in men with symptomatic BPH: evidence for a possible molecular mechanism

OBJECTIVES

To examine the nuclear chromatin characteristics of epithelial cells, looking for an SPHB-mediated effect on nuclear DNA structure and organization. Saw palmetto herbal blend (SPHB) causes contraction of prostate epithelial cells and suppression of tissue dihydrotestosterone levels in men with symptomatic benign prostatic hyperplasia, but a fundamental mechanism remains unknown.

METHODS

A 6-month randomized trial, comparing prostatic tissue of men treated with SPHB (n = 20) or placebo (n = 20), was performed. At baseline, the two groups were similar in age (65 versus 64 years), symptoms (International Prostate Symptom Score 18 versus 17), uroflow (maximal urinary flow rate 10 versus 11 mL/s), prostate volume (59 versus 58 cm(3)), prostate-specific antigen (4.2 versus 2.7 ng/mL), and percentage of epithelium (17% versus 16%). Prostatic tissue was obtained by sextant biopsy before and after treatment. Five-micron sections were Feulgen stained and quantitatively analyzed using the AutoCyte QUIC-DNA imaging system. Images were captured from 200 randomly selected epithelial cell nuclei, and 60 nuclear morphometric descriptors (NMDs) (eg, size, shape, DNA content, and textural features) were determined for each nucleus. Logistic regression analysis was used to assess the differences in the variances of the NMDs between the treated and untreated prostate epithelial cells.

RESULTS

At baseline, the SPHB and placebo groups had similar NMD values. After 6 months of placebo, no significant change from baseline was found in the NMDs. However, after 6 months of SPHB, 25 of the 60 NMDs were significantly different compared with baseline, and a multivariate model for predicting treatment effect using 4 of the 25 was created (P <0.001). The multivariate model had an area under the receiver operating characteristic curve of 94% and an accuracy of 85%.

CONCLUSIONS

Six months of SPHB treatment appears to alter the DNA chromatin structure and organization in prostate epithelial cells. Thus, a possible molecular basis for tissue changes and therapeutic effect of the compound is suggested.

The prognostic molecular markers in hepatocellular carcinoma

The prognosis of hepatocellular carcinoma (HCC) still remains dismal, although many advances in its clinical study have been made. It is important for tumor control to identify the factors that predispose patients to death. With new discoveries in cancer biology, the pathological and biological prognostic factors of HCC have been studied quite extensively. Analyzing molecular markers (biomarkers) with prognostic significance is a complementary method. A large number of molecular factors have been shown to associate with the invasiveness of HCC, and have potential prognostic significance. One important aspect is the analysis of molecular markers for the cellular malignancy phenotype. These include alterations in DNA ploidy, cellular proliferation markers (PCNA, Ki-67, Mcm2, MIB1, MIA, and CSE1L/CAS protein), nuclear morphology, the p53 gene and its related molecule MD M2, other cell cycle regulators (cyclin A, cyclin D, cyclin E, cdc2, p27, p73), oncogenes and their receptors (such as ras, c-myc, c-fms, HGF, c-met, and erb-B receptor family members), apoptosis related factors (Fas and FasL), as well as telomerase activity. Another important aspect is the analysis of molecular markers involved in the process of cancer invasion and metastasis. Adhesion molecules (E-cadherin, catenins, serum intercellular adhesion molecule-1, CD44 variants), proteinases involved in the degradation of extracellular matrix (MMP-2, MMP-9, uPA, uPAR, PAI), as well as other molecules have been regarded as biomarkers for the malignant phenotype of HCC, and are related to prognosis and therapeutic outcomes. Tumor angiogenesis is critical to both the growth and metastasis of cancers including HCC, and has drawn much attention in recent years. Many angiogenesis-related markers, such as vascular endothelial growth factor (VEGF), basic fibroblast growth factor (bFGF), platelet-derived endothelial cell growth factor (PD-ECGF), thrombospondin (TSP), angiogenin, pleiotrophin, and endostatin (ES) levels, as well as intratumor microvessel density (MVD) have been evaluated and found to be of prognostic significance. Body fluid (particularly blood and urinary) testing for biomarkers is easily accessible and useful in clinical patients. The prognostic significance of circulating DNA in plasma or serum, and its genetic alterations in HCC are other important trends. More attention should be paid to these two areas in future. As the progress of the human genome project advances, so does a clearer understanding of tumor biology, and more and more new prognostic markers with high sensitivity and specificity will be found and used in clinical assays. However, the combination of some items, i.e., the pathological features and some biomarkers mentioned above, seems to be more practical for now.

Predictive non-linear modeling of complex data by artificial neural networks

An artificial neural network (ANN) is an artificial intelligence tool that identifies arbitrary nonlinear multiparametric discriminant functions directly from experimental data. The use of ANNs has gained increasing popularity for applications where a mechanistic description of the dependency between dependent and independent variables is either unknown or very complex. This machine learning technique can be roughly described as a universal algebraic function that will distinguish signal from noise directly from experimental data. The application of ANNs to complex relationships makes them highly attractive for the study of biological systems. Recent applications include the analysis of expression profiles and genomic and proteomic sequences.