Ability to predict biochemical progression using Gleason score and a computer-generated quantitative nuclear grade derived from cancer cell nuclei

Morphology-Predicted Large-Scale Transition Number in Circulating Tumor Cells Identifies a Chromosomal Instability Biomarker Associated with Poor Outcome in Castration-Resistant Prostate Cancer

Chromosomal instability (CIN) increases a tumor cell's ability to acquire chromosomal alterations, a mechanism by which tumor cells evolve, adapt, and resist therapeutics. We sought to develop a biomarker of CIN in circulating tumor cells (CTC) that are more likely to reflect the genetic diversity of patient's disease than a single-site biopsy and be assessed rapidly so as to inform treatment management decisions in real time. Large-scale transitions (LST) are genomic alterations defined as chromosomal breakages that generate chromosomal gains or losses of greater than or equal to10 Mb. Here we studied the relationship between the number of LST in an individual CTC determined by direct sequencing and morphologic features of the cells. This relationship was then used to develop a computer vision algorithm that utilizes CTC image features to predict the presence of a high (9 or more) versus low (8 or fewer) LST number in a single cell. As LSTs are a primary functional component of homologous recombination deficient cellular phenotypes, the image-based algorithm was studied prospectively on 10,240 CTCs in 367 blood samples obtained from 294 patients with progressing metastatic castration-resistant prostate cancer taken prior to starting a standard-of-care approved therapy. The resultant computer vision-based biomarker of CIN in CTCs in a pretreatment sample strongly associated with poor overall survival times in patients treated with androgen receptor signaling inhibitors and taxanes. SIGNIFICANCE: A rapidly assessable biomarker of chromosomal instability in CTC is associated with poor outcomes when detected in men with progressing mCRPC.

Quantification of large scale DNA organization for predicting prostate cancer recurrence

This study investigates whether Genomic Organization at Large Scales (which we propose to call GOALS) as quantified via nuclear phenotype characteristics and cell sociology features (describing cell organization within tissue) collected from prostate tissue microarrays (TMAs) can separate biochemical failure from biochemical nonevidence of disease (BNED) after radical prostatectomy (RP). Of the 78 prostate cancer tissue cores collected from patients treated with RP, 16 who developed biochemical relapse (failure group) and 16 who were BNED patients (nonfailure group) were included in the analyses (36 cores from 32 patients). A section from this TMA was stained stoichiometrically for DNA using the Feulgen-Thionin methodology, and scanned with a Pannoramic MIDI scanner. Approximately 110 nuclear phenotypic features, predominately quantifying large scale DNA organization (GOALS), were extracted from each segmented nuclei. In addition, the centers of these segmented nuclei defined a Voronoi tessellation and subsequent architectural analysis. Prostate TMA core classification as biochemical failure or BNED after RP using GOALS features was conducted (a) based on cell type and cell position within the epithelium (all cells, all epithelial cells, epithelial >2 cell layers away from basement membrane) from all cores, and (b) based on epithelial cells more than two cell layers from the basement membrane using a Classifier trained on Gleason 6, 8, 9 (16 cores) only and applied to a Test set consisting of the Gleason 7 cores (20 cores). Successful core classification as biochemical failure or BNED after RP by a linear classifier was 75% using all cells, 83% using all epithelial cells, and 86% using epithelial >2 layers. Overall success of predicted classification by the linear Classifier of (b) was 87.5% using the Training Set and 80% using the Test Set. Overall success of predicted progression using Gleason score alone was 75% for Gleason >7 as failures and 69% for Gleason >6 as failures. © 2017 International Society for Advancement of Cytometry.

Mechanisms of nuclear size regulation in model systems and cancer

Changes in nuclear size have long been used by cytopathologists as an important parameter to diagnose, stage, and prognose many cancers. Mechanisms underlying these changes and functional links between nuclear size and malignancy are largely unknown. Understanding mechanisms of nuclear size regulation and the physiological significance of proper nuclear size control will inform the interplay between altered nuclear size and oncogenesis. In this chapter we review what is known about molecular mechanisms of nuclear size control based on research in model experimental systems including yeast, Xenopus, Tetrahymena, Drosophila, plants, mice, and mammalian cell culture. We discuss how nuclear size is influenced by DNA ploidy, nuclear structural components, cytoplasmic factors and nucleocytoplasmic transport, the cytoskeleton, and the extracellular matrix. Based on these mechanistic insights, we speculate about how nuclear size might impact cell physiology and whether altered nuclear size could contribute to cancer development and progression. We end with some outstanding questions about mechanisms and functions of nuclear size regulation.

Nuclear morphometry, epigenetic changes, and clinical relevance in prostate cancer

Nuclear structure alterations in cancer involve global genetic (mutations, amplifications, copy number variations, translocations, etc.) and epigenetic (DNA methylation and histone modifications) events that dramatically and dynamically spatially change chromatin, nuclear body, and chromosome organization. In prostate cancer (CaP) there appears to be early (<50 years) versus late (>60 years) onset clinically significant cancers, and we have yet to clearly understand the hereditary and somatic-based molecular pathways involved. We do know that once cancer is initiated, dedifferentiation of the prostate gland occurs with significant changes in nuclear structure driven by numerous genetic and epigenetic processes. This review focuses upon the nuclear architecture and epigenetic dynamics with potential translational clinically relevant applications to CaP. Further, the review correlates changes in the cancer-driven epigenetic process at the molecular level and correlates these alterations to nuclear morphological quantitative measurements. Finally, we address how we can best utilize this knowledge to improve the efficacy of personalized treatment of cancer.

Clinical applications of recent molecular advances in urologic malignancies: no longer chasing a "mirage"?

As our understanding of the molecular events leading to the development and progression of genitourologic malignancies, new markers of detection, prognostication, and therapy prediction can be exploited in the management of these prevalent tumors. The current review discusses the recent advances in prostate, bladder, renal, and testicular neoplasms that are pertinent to the anatomic pathologist.

Quantitative DNA methylation analysis of genes coding for kallikrein-related peptidases 6 and 10 as biomarkers for prostate cancer

DNA methylation plays an important role in carcinogenesis and is being recognized as a promising diagnostic and prognostic biomarker for a variety of malignancies including Prostate cancer (PCa). The human kallikrein-related peptidases (KLKs) have emerged as an important family of cancer biomarkers, with KLK3, encoding for Prostate Specific Antigen, being most recognized. However, few studies have examined the epigenetic regulation of KLKs and its implications to PCa. To assess the biological effect of DNA methylation on KLK6 and KLK10 expression, we treated PC3 and 22RV1 PCa cells with a demethylating drug, 5-aza-2'deoxycytidine, and observed increased expression of both KLKs, establishing that DNA methylation plays a role in regulating gene expression. Subsequently, we have quantified KLK6 and KLK10 DNA methylation levels in two independent cohorts of PCa patients operated by radical prostatectomy between 2007-2011 (Cohort I, n = 150) and 1998-2001 (Cohort II, n = 124). In Cohort I, DNA methylation levels of both KLKs were significantly higher in cancerous tissue vs. normal. Further, we evaluated the relationship between DNA methylation and clinicopathological parameters. KLK6 DNA methylation was significantly associated with pathological stage only in Cohort I while KLK10 DNA methylation was significantly associated with pathological stage in both cohorts. In Cohort II, low KLK10 DNA methylation was associated with biochemical recurrence in univariate and multivariate analyses. A similar trend for KLK6 DNA methylation was observed. The results suggest that KLK6 and KLK10 DNA methylation distinguishes organ confined from locally invasive PCa and may have prognostic value.

Nuclear morphometry, nucleomics and prostate cancer progression

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.

Theranostic and prognostic biomarkers: genomic applications in urological malignancies

Compared to other solid tumours such as breast, colon, and lung, the current clinical management of urological malignancies is lagging behind in terms of utilisation of clinically robust molecular tests that can identify patients that are more likely to respond to a given targeted agent, or even those in need of a more aggressive treatment approach based on well-validated molecular prognosticators. Several promising biomarkers for detection, prognosis, and targeted therapeutics are now under evaluation. The following review discusses some of the candidate biomarkers that may soon make their transition into clinically applicable assays in urological oncology patients.

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.

Computer-assisted image analysis of breast fine needle aspiration in a randomized chemoprevention trial of fenretinide vs. placebo in HRT users

BACKGROUND

Digital nuclear morphometric analysis can capture subtle differences along neoplastic progression. Studies showed different profiles from normal to cancer lesions. Our goal is to utilize this method as biomarker in chemoprevention trials.

METHODS

Postmenopausal women were randomized to oral (CEE) or transdermal (E2) estrogen replacement therapy (ERT) in association with fenretinide or placebo. Ultrasound-guided fine needle aspiration (FNA) was performed at baseline and after 12 months in a subset of subjects.

RESULTS

Ten samples were analyzed by karyometry. E2 compared with CEE increased nuclear area (p=0.01). A similar pattern was observed for other DNA content and chromatin texture features. Fenretinide vs. placebo, increased nuclear area and shape while decreased slope, peak and entropy.

CONCLUSION

Preliminary results indicate that nuclear morphometry is feasible on FNA samples. ERT and fenretinide induced significant karyometric changes. These results support further investigation of this procedure as surrogate biomarker in chemoprevention trial.

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.

Morphometric sum optical density as a surrogate marker for ploidy status in prostate cancer: an analysis in 180 biopsies using logistic regression and binary recursive partitioning

We sought to identify morphometric descriptors predictive of nondiploidy in prostatic adenocarcinoma on prostate needle biopsies using logistic regression (LR) and binary recursive partitioning (BRP) and compare the equivalence of both methods. A total of 180 prostate needle biopsies diagnosed as prostatic adenocarcinoma were selected. Deoxyribonucleic acid ploidy and morphometry were performed separately on Feulgen-stained sections from these biopsies using the CAS-200 system and Nuclear Morphometry Suite, respectively. Seven morphometric predictors were tested as predictor variables, including nuclear area, circularity, elongation, sum optical density (OD), configuration run length, coefficient of variation (CV), and angularity. Logistic regression (LR) identified a two-parameter model including sum OD and circularity that had a 93.9% overall correct prediction rate (area under curve=0.950; 95% CI: 0.913, 0.987). A reduced model including only sum OD was equally good without any significant loss of predictive accuracy (93.3% correct overall classification rate). BRP also selected sum OD as the most predictive parameter; a sum OD cut-off of 7.73 in this model identified 93.3% of the nondiploid cases correctly. Morphometric OD can be used as a surrogate marker of nondiploidy. LR and BRP models are both equivalent in identifying and correctly classifying nondiploid cases of prostate cancer using sum OD as the predictor variable.

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.

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.

Effectiveness and cost-effectiveness of prognostic markers in prostate cancer

This paper demonstrates how economic modelling can be used to derive estimates of the cost-effectiveness of prognostic markers in the management of clinically localised and moderately graded prostate cancer. The model uses a Markov process and is populated using published evidence and local data. The robustness of the results has been tested using sensitivity analysis. Three treatment policies of 'monitoring' (observation), radical prostatectomy, or a selection-based management policy using DNA-ploidy as an experimental marker, have been evaluated. Modelling indicates that a policy of managing these tumours utilising experimental markers has an estimated cost per quality-adjusted life year (QALY) of pound 12 068. Sensitivity analysis shows the results to be relatively sensitive to quality-of-life variables. If novel and experimental markers can achieve specificity in excess of 80%, then a policy of radical surgery for those identified as being at high risk and conservative treatment for the remainder would be both better for patients and cost-effective. The analysis suggests that a radical prostatectomy treatment policy for the moderately graded tumours (Gleason grades -7) modelled in this paper may be inferior to a conservative approach in the absence of reliable prognostic markers, being both more costly and yielding fewer QALYs.

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.

Prediction of pelvic lymph node metastasis by the ratio of cathepsin B to stefin A in patients with prostate carcinoma

BACKGROUND

Pathologic grade and/or histologic score, extraprostatic extension indicated by invasion of the prostatic capsule, margin, and/or seminal vesicles by prostate cancer cells, serum total prostate-specific antigen (PSA), free PSA, complexed PSA levels and/or their ratios, regional pelvic lymph node metastases, and clinical staging have been used to diagnose and monitor the treatment of prostate carcinoma (PC) patients. The Gleason grading system is also used to grade/score a patient's stage of disease, with lower to higher scores indicating progression of PC. However, Gleason's system cannot be used to distinguish biologically aggressive PCs within a single Gleason score. Our objective was to identify subpopulations (or clones) of aggressive prostate cancers within an individual Gleason score by utilizing biological molecule(s) that also facilitate cancer cell invasion to prostatic stroma and metastasis to the lymph nodes.

MATERIALS AND METHODS

Specimens were collected from 97 patients with PC and from 8 patients with benign prostatic hyperplasia. These patients had not been treated with hormonal and/or chemotherapeutic agents before undergoing a prostatectomy at the Minneapolis Veterans Affairs Medical Center. Formalin-fixed, paraffin or paraplast-embedded prostate tissue sections were stained with hematoxylin and eosin for pathologic diagnosis and adjacent sections were stained for for immunohistochemical study. We also collected data on age, race, extraprostatic extension, margin status, seminal vesicle, and lymph node invasion by cancer cells, clinical stage at prostatectomy, and mortality/survival data, including the available presurgery and postsurgery serum total PSA and prostatic acid phosphatase concentrations in patients. Immunohistochemical localization of mouse or rabbit anti-cathepsin B (CB) antibody IgG and mouse antihuman stefin (cystatin) A IgG was quantified using a computer-based image analysis system equipped with Metamorph software.

RESULTS

CB and stefin A identified aggressive and less aggressive clones of PCs within an individual Gleason score. Tumors with a Gleason Score of 6 that are similar histologically and morphologically were heterogeneous with respect to the ratios of CB to stefin A (CB > stefin A, CB = stefin A, and CB < stefin A). We also found a significant positive association (P = 0.0066) between ratios of CB and stefin A (CB > stefin A) and the incidence of pelvic lymph node metastases, but not with ratios of CB less than stefin A and/or ratios of CB equal to stefin A. Patients with Gleason 7 PCs had a higher incidence of positive lymph nodes than those with Gleason Score 6 tumors. Our data indicated that mortality rates increased in patients when the ratios of CB were greater than stefin A.

CONCLUSIONS

PC within an individual Gleason score is a heterogeneous tumor that contains clones or subpopulations of aggressive and less aggressive tumors that can be defined by the ratios of CB to stefin A. PC with an aggressive clone can be identified when the ratio of CB is greater than that of stefin A. Less aggressive clones are identified when the ratio of CB is less than that of stefin A or when the ratio of CB is equal to that of stefin A. The ratios of CB to stefin A can be used in the differential diagnosis and treatment of patients with PC. This is the first report to identify phenotypes of aggressive and less aggressive PCs within a Gleason score.

Prognostic value of DNA ploidy and nuclear morphometry in prostate cancer treated with androgen deprivation

OBJECTIVES

To assess the prognostic value of flow cytometry and nuclear morphometry in prostate cancer after androgen deprivation treatment.

METHODS

A total of 127 patients with a prostate cancer diagnosis who had undergone androgen suppression were retrospectively studied. The DNA content by flow cytometry and nuclear morphometry was studied from biopsy specimens. In the patients with Stage M0, two multivariate analyses by the Cox proportional regression model were performed to determine whether the experimental variables (DNA content and nuclear area) added independent information to the classic prognostic factors (Gleason score and stage). Using the statistical analysis results, risk groups were created.

RESULTS

T and M categories, Gleason score, DNA ploidy, and mean nuclear area proved to have prognostic value in the univariate analysis. For the group of patients free of metastasis (M0), it was possible to create low, intermediate, and high-risk groups using stage and Gleason score with statistically significant differences in survival. Multivariate analysis, combining the classic and experimental variables, selected Gleason score and DNA content as prognostic independent factors. Also, risk groups with statistically significant differences in survival were created. However, the net result of combining both kinds of factors was at least as valuable as the combination of stage and Gleason score in predicting survival.

CONCLUSIONS

The determination of DNA ploidy and mean nuclear area do not add enough independent information to improve the predictive value to justify their use in this group of patients treated with hormonal therapy.

Contemporary identification of patients at high risk of early prostate cancer recurrence after radical retropubic prostatectomy

OBJECTIVES

To develop a model that will identify a contemporary cohort of patients at high risk of early prostate cancer recurrence (greater than 50% at 36 months) after radical retropubic prostatectomy for clinically localized disease. Data from this model will provide important information for patient selection and the design of prospective randomized trials of adjuvant therapies.

METHODS

Proportional hazards regression analysis was applied to two patient cohorts to develop and cross-validate a multifactorial predictive model to identify men with the highest risk of early prostate cancer recurrence. The model and validation cohorts contained 904 and 901 men, respectively, who underwent radical retropubic prostatectomy at Johns Hopkins Hospital. This model was then externally validated using a cohort of patients from the Mayo Clinic.

RESULTS

A model for weighted risk of recurrence was developed: R(W)'=lymph node involvement (0/1)x1.43+surgical margin status (0/1)x1.15+modified Gleason score (0 to 4)x0.71+seminal vesicle involvement (0/1)x0.51. Men with an R(W)' greater than 2.84 (9%) demonstrated a 50% biochemical recurrence rate (prostrate-specific antigen level greater than 0.2 ng/mL) at 3 years and thus were placed in the high-risk group. Kaplan-Meier analyses of biochemical recurrence-free survival demonstrated rapid deviation of the curves based on the R(W)'. This model was cross-validated in the second group of patients and performed with similar results. Furthermore, similar trends were apparent when the model was externally validated on patients treated at the Mayo Clinic.

CONCLUSIONS

We have developed a multivariate Cox proportional hazards model that successfully stratifies patients on the basis of their risk of early prostate cancer recurrence.

Ratio of cathepsin B to stefin A identifies heterogeneity within Gleason histologic scores for human prostate cancer

BACKGROUND

Cathepsin B (CB), a lysosomal cysteine protease, is involved in degradation of extracellular matrix proteins and progression of tumor cells from one biological compartment to another in many solid organ cancers, including prostate cancer. Our objective was to identify patterns of distribution of CB and its endogenous cellular inhibitor stefin A in cryostat sections of frozen BPH and prostate cancer tissue samples and to define these patterns in relation to Gleason histologic scores, clinical stages, and serum total PSA levels.

METHODS

We localized CB and stefin A in the same sections using polyclonal and monoclonal antibody immunoglobulin G (IgGs) against CB and stefin A using immunofluorescence and confocal microscopic techniques. Only cryostat sections of frozen prostates were used in localizations of CB and stefin A.

RESULTS

Benign prostatic hyperplasia (BPH) showed similar localization patterns for CB and stefin A and a ratio of 1 was indicated by CB = stefin A. Confocal studies indicated that most CB and stefin A sites in BPH glandular cells overlapped as shown by the yellow fluorescence of their co-localization. We found considerable variability in individual localization of CB and stefin A within and between Gleason histologic scores for prostate cancers. This variability was also found in Gleason score 6 tumors that are otherwise considered similar histologically and morphologically. Negative control sections did not show localization of CB by FITC, stefin A by Cy3 or yellow fluorescence for co-localization. Our analysis of the ratio of CB to stefin A showed three patterns, namely CB = stefin A, CB > stefin A, and CB < stefin A, within each Gleason score evaluated by us. Confocal microscopy showed more sites of yellow fluorescence when the ratio was CB = stefin A than those found in CB > stefin A or CB < stefin A. Statistical analyses showed prostate cancer cases with ratios of CB > stefin A (P < 0.05) and CB < stefin A (P < 0.05) significantly different from normal prostate and BPH which had ratios of CB = stefin A. Regression analysis did not show any specific relationship between the ratio of CB to stefin A and Gleason scores, clinical stages, and serum total prostate specific antigen (PSA) levels in prostate cancers. Analysis of our data indicates that the homeostatic balance between the enzyme and inhibitor was altered even in Gleason histologic score 6 tumors that are usually considered histologically similar by glandular differentiation.

CONCLUSIONS

We have shown that prostate cancer is a heterogeneous tumor within each Gleason histological score regardless of the progression indicated by lower to higher Gleason score tumors. The ratio of CB > stefin A would indicate a preponderance of enzyme that would favor degradation of extracellular matrix proteins and progression of tumor cells in biological compartments. These tumors are expected to be aggressive prostate cancers. In contrast, prostate tumors showing ratios of CB < stefin A and CB = stefin A are expected to be less aggressive prostate cancers. This is the first report to define heterogeneity within any Gleason score for prostate cancers by the ratios of CB to stefin A.

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

This review addresses the potential clinical value of using quantitative nuclear morphometry information derived from computer-assisted image analysis for cancer detection and predicting outcomes such as tumor stage, recurrence, and progression. Today's imaging technology uses sophisticated hardware platforms coupled with powerful and user-friendly software packages that are commercially available as complete image analysis systems. There are many different mathematically derived nuclear morphometric descriptors (NMD's) (i.e. texture features) that can be calculated by these image analysis systems, but for the most part, these NMD's quantify nuclear size, shape, DNA content (ploidy), and chromatin organization (i.e. texture, both Markovian and non-Markovian) parameters. We have utilized commercially available image analysis systems and the NMD's calculated by these systems to create a mathematical solution, termed quantitative nuclear grade (QNG), for making clinical, diagnostic, and prognostic outcome predictions in both prostate and bladder cancer. A separate computational model is calculated for each outcome of interest using well-characterized and robust training, testing, and validation patient sample sets that adequately represent the selected population and clinical dilemma. A specific QNG solution may be calculated either by non-parametric statistical methods or non-linear mathematics employed by artificial neural networks (ANNs). The QNG solution, a measure of genomic instability, provides a unique independent variable to be used alone or to be included in an algorithm to assess a specific clinical outcome. This approach of customization of the nuclear morphometric descriptor (NMD) information through the calculation of a QNG solution mathematically adjusts for redundancy of features and reduces the complexity of the inputs used to create decision support tools for patient disease management. J. Cell. Biochem. Suppl. 35:151-157, 2000.

Indicators of pathologic stage of prostate cancer and their use in clinical practice

Pathologic stage is the most reliable means of predicting the likelihood of curable prostate cancer at the time of definitive treatment. Its prediction is of the greatest importance to individuals with clinically localized disease, principally because of the therapeutic and prognostic implications. Multivariate models integrating variables that can be derived from clinical and pathologic assessment have been shown to be reliable and useful in urologic practice. Among these variables, the combination of clinical stage, serum PSA, and biopsy Gleason score provides reliable assessment of the risk for extraprostatic disease that can be used readily for counseling individual patients. Other biopsy-derived parameters may contribute additional information, but their value in multivariate analysis has not been validated in a multi-institutional setting. The development of new prognostic markers is a priority objective in current research to distinguish patients in whom cancer cannot be controlled by surgical treatment. For patients undergoing radical prostatectomy, definitive pathologic stage certainly will remain an important prognostic factor; therefore, clinical practice will continue to be determined by its accurate prediction.

Pathologic assessment of the surgical specimen

Despite the wealth of information obtained by conventional histology, long-term studies are needed to provide novel information on the correlation of pathologic findings with prognosis. Findings need to be correlated not only with PSA progression but with the more clinically important parameters of distant metastases and survival. Although conventional histology still will have a role in the evaluation of prostate cancer at radical prostatectomy and its correlation with outcome, it undoubtedly will be augmented by newer techniques. These developments must be approached critically and rationally to determine whether they provide additional prognostic information beyond that currently available using more conventional parameters.

Standardization, analytical validation, and quality control of intermediate endpoint biomarkers

Standardized processes should be used in the identification and development of intermediate endpoint biomarkers (IEB) for the prediction of patient-specific disease outcomes. Using our own experiences, we outline some of our standardized processes. Using computer-assisted image analysis, we developed a new biomarker of genetic instability, termed quantitative nuclear grade (QNG). The QNG biomarker is derived using nuclear images analyzed from the tumor areas of Feulgen-stained 5-microm biopsy or radical prostatectomy tissue sections. From the variances of 41 to 60 different nuclear size, shape, and chromatin organization features, a QNG solution is computed using either logistic regression or artificial neural networks. QNG can then be used as an input for models that solve for a patient-specific probability to accurately predict disease outcomes. Preoperatively, QNG predicted both the pathologic stage and progression of prostate cancer using biopsies (P <0.0001). Postoperatively, QNG proved extremely valuable in the prediction of biochemical progression using radical prostatectomy specimens with more than 10 years of follow-up (P <0.0001). We also demonstrate the identification of novel, differentially expressed, prostate cancer genes using RNA fingerprinting methods and the clinical utility of testing for these genes in both blood and tissue samples. Also illustrated is the improvement of serum biomarker performance by combining molecular forms of PSA with new biomarkers. In conclusion, the development of new IEBs requires planning based upon an understanding of the molecular pathogenesis of disease. IEB selection and clinical evaluation should employ standardized methods of testing and validation, followed by publication. QNG is 1 example of a new, highly predictive, IEB for prostate cancer that has been developed using these processes.

Computer-assisted image analysis-derived intermediate endpoints

The development of prostatic lesions undergoes a slow progression. To establish efficacy of chemopreventive intervention it is therefore necessary to define surrogate endpoint biomarkers. Such biomarkers should be sensitive in their ability to indicate response. They should be objective, ie, the result of measurement, and numerically defined so that a statistical validation of response is possible. They should be able to indicate not only a halt of progression of a lesion, but also a reversal of progression. The spatial and statistical distribution of nuclear chromatin in the secretory and luminal cells in prostatic intraepithelial neoplastic lesions has been shown to be well defined. It can be represented by a set of features. These have been used to define a progression curve along which progression or regression of a lesion can be assessed. One could define a fixed endpoint, or one might choose to accept a statistically significant regression along the progression curve as criterion for chemopreventive efficacy. Expected difficulties could arise from lesion heterogeneity, as it would affect the sampling, and from multifocal lesions of differing progressions. Lesion heterogeneity thus limits the precision with which regression could be detected. These problems might be partially overcome by observations taken in histologically normal appearing regions of the prostate. The nuclear chromatin pattern of secretory cell nuclei measured in such tissue regions from prostates harboring intraepithelial or malignant lesions has been shown to exhibit distinctive changes from the chromatin pattern seen in secretory cell nuclei from prostates free from any such lesions. These changes appear to be expressed in the tissue up to a substantial distance from a lesion. The expression of changes in the nuclear chromatin suggests the existence of an intraepithelial preneoplastic lesion that can be detected by biomarkers, but which is not apparent from visual microscopic inspection. Since chemoprevention might be expected to be most effective at the earliest stages of lesion development, the assessment of such early alterations is seen as highly relevant to efforts to validate the efficacy of chemopreventive intervention.

Using outcome data and patient satisfaction surveys to develop policies regarding minimum length of hospitalization after radical prostatectomy

OBJECTIVES

Changes in health care economics have prompted new clinical pathways for radical prostatectomy to reduce length of hospitalization after surgery to 1 day. We evaluated satisfaction, outcomes, and short-term morbidity in 187 consecutive patients with overnight hospitalization after radical retropubic prostatectomy (RRP).

METHODS

In 1995, we initiated a critical pathway for RRP that included epidural anesthesia with or without spinal anesthesia and postoperative methadone, acetaminophen, and ibuprofen for pain control. Patients were discharged when they were afebrile, tolerating a regular diet, ambulating without assistance, and using oral medications for analgesia. An 18-item satisfaction survey was mailed to each patient 3 weeks after discharge. Responses to the postoperative survey, morbidity, blood loss, and use of transfusions were recorded.

RESULTS

Of 252 patients who underwent RRP, 187 (74. 2%) were discharged 1 day after surgery. The mean age of patients was 61.4 years (range 42 to 73). A pelvic lymphadenectomy was performed in addition to the RRP in 32 men (17%). Epidural anesthesia with or without spinal anesthesia was used for all but 3 patients. The mean estimated blood loss was 1166 mL, and 24 patients (12.8%) required transfusion, with a mean of 1.9 U (range 1 to 6) of packed red blood cells. The postoperative complication rate was 11. 8%, of which 2.1% (n = 4) were definitely or probably related to our protocol. These complications included clot retention (n = 2), gastrointestinal bleeding (n = 1), and spinal headache (n = 1). Three of 187 patients were readmitted to the hospital within 30 days but only one (0.5%) required admission because of our protocol. The survey response rate was 91.4%. No patient was dissatisfied with his overall care, and only 10.5% of patients would have preferred to stay in the hospital longer.

CONCLUSIONS

One-day hospitalization after RRP is associated with minimal postoperative morbidity and high patient satisfaction. Similar data are needed for RRP from other centers before policy decisions regarding the length of stay after this procedure are made.

Genetically engineered neural networks for predicting prostate cancer progression after radical prostatectomy

OBJECTIVES

To use pathologic, morphometric, DNA ploidy, and clinical data to develop and test a genetically engineered neural network (GENN) for the prediction of biochemical (prostate-specific antigen [PSA]) progression after radical prostatectomy in a select group of men with clinically localized prostate cancer.

METHODS

Two hundred fourteen men who underwent anatomic radical retropubic prostatectomy for clinically localized prostate cancer were selected on the basis of adequate follow-up, pathologic criteria indicating an intermediate risk of progression, and availability of archival tissue. The median age was 58.9 years (range 40 to 87). Men with Gleason score 5 to 7 and clinical Stage T1b-T2c tumors were included. Follow-up was a median of 9.5 years. Three GENNs were developed using pathologic findings (Gleason score, extraprostatic extension, surgical margin status), age, quantitative nuclear grade (QNG), and DNA ploidy. These networks were developed using three randomly selected training (n = 136) and testing (n = 35) sets. Different variable subsets were compared for the ability to maximize prediction of progression. Both standard logistic regression and Cox regression analyses were used concurrently to calculate progression risk.

RESULTS

Biochemical (PSA) progression occurred in 84 men (40%), with a median time to progression of 48 months (range 1 to 168). GENN models were trained using inputs consisting of (a) pathologic features and patient age; (b) QNG and DNA ploidy; and (c) all variables combined. These GENN models achieved an average accuracy of 74.4%, 63.1 %, and 73.5%, respectively, for the prediction of progression in the training sets. In the testing sets, the three GENN models had an accuracy of 74.3%, 80.0%, and 78.1%, respectively.

CONCLUSIONS

The GENN models developed show promise in predicting progression in select groups of men after radical prostatectomy. Neural networks using QNG and DNA ploidy as input variables performed as well as networks using Gleason score and staging information. All GENN models were superior to logistic regression modeling and to Cox regression analysis in prediction of PSA progression. The development of models using improved input variables and imaging systems in larger, well-characterized patient groups with long-term follow-up is ongoing.

Markov optical texture parameters as prognostic indicators in ovarian carcinoma

Texture is a descriptive property of a surface describing the morphometric heterogeneity of complex structures. Computer aided image analysis allows optical texture measurement and analysis of gray-scale images. The authors, utilizing image analysis, prospectively studied Markov nuclear texture features to determine their relevance as prognostic indicators of survival in patients with epithelial ovarian carcinoma. Ninety-nine consecutive patients with ovarian cancer, treated initially with surgery were evaluated for their length of survival, level of cytoreduction, FIGO stage, grade, histology, and DNA index, as well as 20 Markov texture features. Markov nuclear texture features were quantified using image analysis. Mean follow-up for the study population was 64 months (median 59) with a range from 51 to 89 months. Five optical texture features showed significant correlation with length of survival. Difference entropy (P = 0.033) and information measure A (P = 0.041) were both indirectly correlated with survival while information measure B (P = 0.030), correlation coefficient (P = 0.045), and the maximum correlation coefficient (P = 0.041) were directly correlated. Only sum entropy (P = 0.035), FIGO stage (P = 0.0031), and level of cytoreduction (P < 0.0001) were independent predictors of survival in this population. Optical texture can be quantified by image analysis. Utilizing multivariate analysis, the Markov texture feature, sum entropy, was demonstrated to be an independent prognostic indicator of survival in patients with epithelial ovarian cancer. FIGO stage and optimal cytoreduction also were independent prognostic indicators of survival.

Are nuclear texture features a suitable tool for predicting non-organ-confined prostate cancer?

PURPOSE

We investigated the possibility of determining organ confinement of prostate cancer using multiple nuclear texture features determined by fully automated high resolution image analysis combined with preoperative serum PSA levels.

MATERIALS AND METHODS

The study population consisted of 145 patients (61 organ confined and 84 non-organ-confined cases). Nuclear texture features were determined using single cell preparations of radical prostatectomy specimens. Nuclear texture features were extracted and analyzed by multivariate logistic regression analysis in order to build a classifier for distinguishing between organ confined and non-organ-confined tumors. The classifier was designed in a cell by cell model and tested on a case by case analysis.

RESULTS

The predictive probability of the trained classifier in the cell by cell analysis had a sensitivity of 63%, a specificity of 53%, a positive predictive value of 75% and a negative predictive value of 38% and an area under the ROC curve of 0.58. In the case by case analysis the sensitivity was 70%, the specificity was 54%, positive predictive value 78%, negative predictive value 74%, area under the ROC curve 0.62. When preoperative PSA was included in the algorithm, sensitivity raised to 80%, specificity to 60%, the positive predictive value raised to 79%, the negative predictive value to 52% and the area under the ROC curve to 0.70.

CONCLUSIONS

In contrast to former studies using tissue sections, our results suggest that nuclear texture features extracted from single cell preparations cannot be used as a reliable parameter for the determination of organ confinement in prostatic adenocarcinomas.

E-cadherin expression as a marker of tumor aggressiveness in routinely processed radical prostatectomy specimens

OBJECTIVES

Approximately 30% of clinically localized prostate adenocarcinomas treated by radical prostatectomy (RP) will recur within 10 years. To prevent recurrence, new adjuvant therapies are in development that seek to treat high-risk patients after surgery. To identify patients as candidates for these treatments, improved biomarkers for predicting prognosis are needed. Reduced expression of E-cadherin has been proposed as a new marker for predicting prognosis in prostate adenocarcinoma. Since few studies have examined the relation between risk factors for disease progression and E-cadherin expression using routinely processed RP specimens, we used RP specimens to correlate downregulation of E-cadherin and pathologic stage at RP.

METHODS

Primary adenocarcinomas (n = 76) from formalin-fixed and paraffin-embedded RP specimens were evaluated by immunohistochemistry against E-cadherin (HECD-1) using heat-induced epitope retrieval and automated staining (BioTek Solutions). Normal appearing prostate epithelium was used as an internal control for each specimen. Staining was scored as an estimate of the percentage of tumor cells in each specimen that showed strong plasma membrane staining.

RESULTS

Specimens were divided into three categories with respect to Gleason score: intermediate (score 5 to 6, n = 31), intermediate to high (score 7, n = 25), and high (score 8 to 9, n = 20). For pathologic stage, specimens were divided into three categories: low stage/organ confined (pT2, n = 30), intermediate stage/limited extraprostatic extension (pT3a, n = 25), and high stage/seminal vesicle-pelvic lymph node metastases (pT3b-any pTN1, n = 21). In univariate analysis, reduced levels of E-cadherin correlated with advanced Gleason score (P = 0.003) and advanced pathologic stage (P = 0.008). In multivariate analysis, E-cadherin, preoperative prostate-specific antigen, and Gleason score all contributed independently to the prediction of high-stage disease (P<0.0001). Ten pelvic lymph node metastases from this same patient cohort were stained for E-cadherin. All were positive and 9 of 10 were moderately to strongly positive.

CONCLUSIONS

Since essentially all patients in the high-stage category have a very high likelihood of disease recurrence, we conclude that the study of E-cadherin in a prospective manner as a potential biomarker of disease progression in patients with clinically organ-confined prostate cancer who undergo RP is warranted. Additionally, our finding that most metastatic tumor cells in pelvic lymph nodes express E-cadherin supports the notion that the establishment of the distant colonization and growth of metastatic tumor cells may be facilitated by expression or re-expression of previously downregulated E-cadherin. This would strongly suggest that irreversible genetic inactivation through mutation or allelic loss at 16q2.3 is probably not the mechanism of E-cadherin downregulation in most abnormally expressing primary prostate carcinomas.