Use of nuclear morphometry, gleason histologic scoring, clinical stage, and age to predict disease-free survival among patients with prostate cancer

AI powered quantification of nuclear morphology in cancers enables prediction of genome instability and prognosis

While alterations in nucleus size, shape, and color are ubiquitous in cancer, comprehensive quantification of nuclear morphology across a whole-slide histologic image remains a challenge. Here, we describe the development of a pan-tissue, deep learning-based digital pathology pipeline for exhaustive nucleus detection, segmentation, and classification and the utility of this pipeline for nuclear morphologic biomarker discovery. Manually-collected nucleus annotations were used to train an object detection and segmentation model for identifying nuclei, which was deployed to segment nuclei in H&E-stained slides from the BRCA, LUAD, and PRAD TCGA cohorts. Interpretable features describing the shape, size, color, and texture of each nucleus were extracted from segmented nuclei and compared to measurements of genomic instability, gene expression, and prognosis. The nuclear segmentation and classification model trained herein performed comparably to previously reported models. Features extracted from the model revealed differences sufficient to distinguish between BRCA, LUAD, and PRAD. Furthermore, cancer cell nuclear area was associated with increased aneuploidy score and homologous recombination deficiency. In BRCA, increased fibroblast nuclear area was indicative of poor progression-free and overall survival and was associated with gene expression signatures related to extracellular matrix remodeling and anti-tumor immunity. Thus, we developed a powerful pan-tissue approach for nucleus segmentation and featurization, enabling the construction of predictive models and the identification of features linking nuclear morphology with clinically-relevant prognostic biomarkers across multiple cancer types.

Development and Validation of an Artificial Intelligence-Powered Platform for Prostate Cancer Grading and Quantification

IMPORTANCE

The Gleason grading system has been the most reliable tool for the prognosis of prostate cancer since its development. However, its clinical application remains limited by interobserver variability in grading and quantification, which has negative consequences for risk assessment and clinical management of prostate cancer.

OBJECTIVE

To examine the impact of an artificial intelligence (AI)-assisted approach to prostate cancer grading and quantification.

DESIGN, SETTING, AND PARTICIPANTS

This diagnostic study was conducted at the University of Wisconsin-Madison from August 2, 2017, to December 30, 2019. The study chronologically selected 589 men with biopsy-confirmed prostate cancer who received care in the University of Wisconsin Health System between January 1, 2005, and February 28, 2017. A total of 1000 biopsy slides (1 or 2 slides per patient) were selected and scanned to create digital whole-slide images, which were used to develop and validate a deep convolutional neural network-based AI-powered platform. The whole-slide images were divided into a training set (n = 838) and validation set (n = 162). Three experienced academic urological pathologists (W.H., K.A.I., and R.H., hereinafter referred to as pathologists 1, 2, and 3, respectively) were involved in the validation. Data were collected between December 29, 2018, and December 20, 2019, and analyzed from January 4, 2020, to March 1, 2021.

MAIN OUTCOMES AND MEASURES

Accuracy of prostate cancer detection by the AI-powered platform and comparison of prostate cancer grading and quantification performed by the 3 pathologists using manual vs AI-assisted methods.

RESULTS

Among 589 men with biopsy slides, the mean (SD) age was 63.8 (8.2) years, the mean (SD) prebiopsy prostate-specific antigen level was 10.2 (16.2) ng/mL, and the mean (SD) total cancer volume was 15.4% (20.1%). The AI system was able to distinguish prostate cancer from benign prostatic epithelium and stroma with high accuracy at the patch-pixel level, with an area under the receiver operating characteristic curve of 0.92 (95% CI, 0.88-0.95). The AI system achieved almost perfect agreement with the training pathologist (pathologist 1) in detecting prostate cancer at the patch-pixel level (weighted κ = 0.97; asymptotic 95% CI, 0.96-0.98) and in grading prostate cancer at the slide level (weighted κ = 0.98; asymptotic 95% CI, 0.96-1.00). Use of the AI-assisted method was associated with significant improvements in the concordance of prostate cancer grading and quantification between the 3 pathologists (eg, pathologists 1 and 2: 90.1% agreement using AI-assisted method vs 84.0% agreement using manual method; P < .001) and significantly higher weighted κ values for all pathologists (eg, pathologists 2 and 3: weighted κ = 0.92 [asymptotic 95% CI, 0.90-0.94] for AI-assisted method vs 0.76 [asymptotic 95% CI, 0.71-0.80] for manual method; P < .001) compared with the manual method.

CONCLUSIONS AND RELEVANCE

In this diagnostic study, an AI-powered platform was able to detect, grade, and quantify prostate cancer with high accuracy and efficiency and was associated with significant reductions in interobserver variability. These results suggest that an AI-powered platform could potentially transform histopathological evaluation and improve risk stratification and clinical management of prostate cancer.

Circulating Tumor Cells: High-Throughput Imaging of CTCs and Bioinformatic Analysis

Circulating tumor cells (CTCs) represent novel biomarkers, since they are obtainable through a simple and noninvasive blood draw or liquid biopsy. Here, we review the high-definition single-cell analysis (HD-SCA) workflow, which brings together modern methods of immunofluorescence with more sophisticated image processing to rapidly and accurately detect rare tumor cells among the milieu of platelets, erythrocytes, and leukocytes in the peripheral blood. In particular, we discuss progress in methods to measure CTC morphology and subcellular protein expression, and we highlight some initial applications that lead to fundamental new insights about the hematogenous phase of cancer, as well as its performance in early-stage diagnosis and treatment monitoring. We end with an outlook on how to further probe CTCs and the unique advantages of the HD-SCA workflow for improving the precision of cancer care.

Models predicting survival to guide treatment decision-making in newly diagnosed primary non-metastatic prostate cancer: a systematic review

OBJECTIVES

Men diagnosed with non-metastatic prostate cancer require standardised and robust long-term prognostic information to help them decide on management. Most currently-used tools use short-term and surrogate outcomes. We explored the evidence base in the literature on available pre-treatment, prognostic models built around long-term survival and assess the accuracy, generalisability and clinical availability of these models.

DESIGN

Systematic literature review, pre-specified and registered on PROSPERO (CRD42018086394).

DATA SOURCES

MEDLINE, Embase and The Cochrane Library were searched from January 2000 through February 2018, using previously-tested search terms.

ELIGIBILITY CRITERIA

Inclusion required a multivariable model prognostic model for non-metastatic prostate cancer, using long-term survival data (defined as ≥5 years), which was not treatment-specific and usable at the point of diagnosis.

DATA EXTRACTION AND SYNTHESIS

Title, abstract and full-text screening were sequentially performed by three reviewers. Data extraction was performed for items in the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies checklist. Individual studies were assessed using the new Prediction model Risk Of Bias ASsessment Tool.

RESULTS

Database searches yielded 6581 studies after deduplication. Twelve studies were included in the final review. Nine were model development studies using data from over 231 888 men. However, only six of the nine studies included any conservatively managed cases and only three of the nine included treatment as a predictor variable. Every included study had at least one parameter for which there was high risk of bias, with failure to report accuracy, and inadequate reporting of missing data common failings. Three external validation studies were included, reporting two available models: The University of California San Francisco (UCSF) Cancer of the Prostate Risk Assessment score and the Cambridge Prognostic Groups. Neither included treatment effect, and both had potential flaws in design, but represent the most robust and usable prognostic models currently available.

CONCLUSION

Few long-term prognostic models exist to inform decision-making at diagnosis of non-metastatic prostate cancer. Improved models are required to inform management and avoid undertreatment and overtreatment of non-metastatic prostate cancer.

Individual prognosis at diagnosis in nonmetastatic prostate cancer: Development and external validation of the PREDICT Prostate multivariable model

BACKGROUND

Prognostic stratification is the cornerstone of management in nonmetastatic prostate cancer (PCa). However, existing prognostic models are inadequate-often using treatment outcomes rather than survival, stratifying by broad heterogeneous groups and using heavily treated cohorts. To address this unmet need, we developed an individualised prognostic model that contextualises PCa-specific mortality (PCSM) against other cause mortality, and estimates the impact of treatment on survival.

METHODS AND FINDINGS

Using records from the United Kingdom National Cancer Registration and Analysis Service (NCRAS), data were collated for 10,089 men diagnosed with nonmetastatic PCa between 2000 and 2010 in Eastern England. Median follow-up was 9.8 years with 3,829 deaths (1,202 PCa specific). Totals of 19.8%, 14.1%, 34.6%, and 31.5% of men underwent conservative management, prostatectomy, radiotherapy (RT), and androgen deprivation monotherapy, respectively. A total of 2,546 men diagnosed in Singapore over a similar time period represented an external validation cohort. Data were randomly split 70:30 into model development and validation cohorts. Fifteen-year PCSM and non-PCa mortality (NPCM) were explored using separate multivariable Cox models within a competing risks framework. Fractional polynomials (FPs) were utilised to fit continuous variables and baseline hazards. Model accuracy was assessed by discrimination and calibration using the Harrell C-index and chi-squared goodness of fit, respectively, within both validation cohorts. A multivariable model estimating individualised 10- and 15-year survival outcomes was constructed combining age, prostate-specific antigen (PSA), histological grade, biopsy core involvement, stage, and primary treatment, which were each independent prognostic factors for PCSM, and age and comorbidity, which were prognostic for NPCM. The model demonstrated good discrimination, with a C-index of 0.84 (95% CI: 0.82-0.86) and 0.84 (95% CI: 0.80-0.87) for 15-year PCSM in the UK and Singapore validation cohorts, respectively, comparing favourably to international risk-stratification criteria. Discrimination was maintained for overall mortality, with C-index 0.77 (95% CI: 0.75-0.78) and 0.76 (95% CI: 0.73-0.78). The model was well calibrated with no significant difference between predicted and observed PCa-specific (p = 0.19) or overall deaths (p = 0.43) in the UK cohort. Key study limitations were a relatively small external validation cohort, an inability to account for delayed changes to treatment beyond 12 months, and an absence of tumour-stage subclassifications.

CONCLUSIONS

'PREDICT Prostate' is an individualised multivariable PCa prognostic model built from baseline diagnostic information and the first to our knowledge that models potential treatment benefits on overall survival. Prognostic power is high despite using only routinely collected clinicopathological information.

Nuclear morphometry in histological specimens of canine prostate cancer: Correlation with histological subtypes, Gleason score, methods of collection and survival time

Ten normal prostates, 22 benign prostatic hyperplasia (BPH) and 29 prostate cancer (PC) were morphometrically analyzed with regard to mean nuclear area (MNA), mean nuclear perimeter (MNP), mean nuclear diameter (MND), coefficient of variation of the nuclear area (NACV), mean nuclear diameter maximum (MDx), mean nuclear diameter minimum (MDm), mean nuclear form ellipse (MNFe) and form factor (FF). The relationship between nuclear morphometric parameters and histological type, Gleason score, methods of sample collection, presence of metastases and survival time of canine PC were also investigated. Overall, nuclei from neoplastic cells were larger, with greater variation in nuclear size and shape compared to normal and hyperplastic cells. Significant differences were found between more (small acinar/ductal) and less (cribriform, solid) differentiated PCs with regard to FF (p<0.05). MNA, MNP, MND, MDx, and MDm were significantly correlated with the Gleason score of PC (p<0.05). MNA, MNP, MDx and MNFe may also have important prognostic implications in canine prostatic cancer since negatively correlated with the survival time. Biopsy specimens contained nuclei that were smaller and more irregular in comparison to those in prostatectomy and necropsy specimens and therefore factors associated with tissue sampling and processing may influence the overall morphometric evaluation. The results indicate that nuclear morphometric analysis in combination with Gleason score can help in canine prostate cancer grading, thus contributing to the establishment of a more precise prognosis and patient's management.

Cribriform morphology predicts upstaging after radical prostatectomy in patients with Gleason score 3 + 4 = 7 prostate cancer at transrectal ultrasound (TRUS)-guided needle biopsy

Selected patients with transrectal ultrasound (TRUS)-guided biopsies containing Gleason score 3 + 4 = 7 prostate cancer (PCa) may be considered candidates for active surveillance (AS). The purpose of this study was to determine if there are features that predict PCa upstaging and/or upgrading after radical prostatectomy (RP) in patients with Gleason score 3 + 4 = 7 PCa diagnosed on TRUS-guided biopsies. We searched our institution's database for patients with Gleason score 3 + 4 = 7 PCa diagnosed on TRUS-guided biopsy who underwent subsequent RP between January 2010 and January 2015. Two blinded genitourinary pathologists independently reviewed and assessed the following on biopsies: (a) nuclear size, nucleolar size and distribution of macronucleoli of PCa, which were subjectively graded using a semi-quantitative scale from 1 to 3, and (b) PCa with cribriform morphology and the size of cribriform disease. Patient age, serum prostate-specific antigen (PSA) and PSA density (PSAD) were also recorded. The Gleason score and stage (presence or absence of organ-confined disease (OCD)) were retrieved from RP reports. Comparisons were performed between groups using the chi-square test and Spearman correlation. One hundred and four patients were identified to have met inclusion criteria. The mean age was 63 (±6.1) years. Mean PSA and PSAD at diagnosis were 7.5 (±4.2) and 0.25 (±0.15) ng/mL, respectively. Gleason scores were upgraded to greater than 3 + 4 = 7 in 26.9 % (28/104) of patients, and 44.2 % (46/104) of patients had no OCD after RP. There was no correlation between age, PSA, PSAD or percent of biopsies with Gleason pattern 4 for either Gleason score upgrading or absence of OCD at the time of RP (p > 0.05). Thirty patients had cribriform morphology on TRUS-guided biopsy of which 60 % (18/30) had no OCD at RP (p = 0.04) while 36.7 % (11/30) were upgraded to Gleason score ≥3 + 4 = 7 after RP (p = 0.15). There was no association between nuclear size, nucleolar size and/or distribution of macronucleoli with upgrading and/or absence of OCD (p > 0.05). The size of cribriform pattern was not associated with the absence of OCD (p = 0.43) or Gleason score upgrade (p = 0.28). A proportion of patients with Gleason score 3 + 4 = 7 PCa at needle biopsy do not have OCD or are upgraded to higher Gleason scores after RP. In our study, patients with Gleason score 3 + 4 = 7 PCa with the presence of cribriform pattern 4 had a significantly increased chance of being found to have no OCD at the time of RP. There were no clinical or pathologic parameters at the time of TRUS-guided biopsy that identified risk factors for Gleason score upgrading at RP in this study. Cribriform morphology detected on biopsy in patients with Gleason score 3 + 4 = 7 PCa is associated with tumour upstaging after RP and may be considered a contraindication to active surveillance.

Subclassification of prostate cancer circulating tumor cells by nuclear size reveals very small nuclear circulating tumor cells in patients with visceral metastases

BACKGROUND

Although enumeration of circulating tumor cells (CTCs) has shown some clinical value, the pool of CTCs contains a mixture of cells that contains additional information that can be extracted. The authors subclassified CTCs by shape features focusing on nuclear size and related this with clinical information.

METHODS

A total of 148 blood samples were obtained from 57 patients with prostate cancer across the spectrum of metastatic states: no metastasis, nonvisceral metastasis, and visceral metastasis. CTCs captured and enumerated on NanoVelcro Chips (CytoLumina, Los Angeles, Calif) were subjected to pathologic review including nuclear size. The distribution of nuclear size was analyzed using a Gaussian mixture model. Correlations were made between CTC subpopulations and metastatic status.

RESULTS

Statistical modeling of nuclear size distribution revealed 3 distinct subpopulations: large nuclear CTCs, small nuclear CTCs, and very small nuclear CTCs (vsnCTCs). Small nuclear CTCs and vsnCTC identified those patients with metastatic disease. However, vsnCTC counts alone were found to be elevated in patients with visceral metastases when compared with those without (0.36 ± 0.69 vs 1.95 ± 3.77 cells/mL blood; P<.001). Serial enumeration studies suggested the emergence of vsnCTCs occurred before the detection of visceral metastases.

CONCLUSIONS

There are morphologic subsets of CTCs that can be identified by fundamental pathologic approaches, such as nuclear size measurement. The results of this observational study strongly suggest that CTCs contain relevant information regarding disease status. In particular, the detection of vsnCTCs was found to be correlated with the presence of visceral metastases and should be formally explored as a putative blood-borne biomarker to identify patients at risk of developing this clinical evolution of prostate cancer.

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

BACKGROUND

Prostate cancer progression is concomitant with quantifiable nuclear structure and texture changes as compared to non-cancer tissue. Malignant progression is associated with an epithelial-mesenchymal transition (EMT) program whereby epithelial cancer cells take on a mesenchymal phenotype and dissociate from a tumor mass, invade, and disseminate to distant metastatic sites. The objective of this study was to determine if epithelial and mesenchymal prostate cancer cells have different nuclear morphology.

METHODS

Murine tibia injections of epithelial PC3 (PC3-Epi) and mesenchymal PC3 (PC3-EMT) prostate cancer cells were processed and stained with H&E. Cancer cell nuclear image data was obtained using commercially available image-processing software. Univariate and multivariate statistical analysis were used to compare the two phenotypes. Several non-parametric classifiers were constructed and permutation-tested at various training set fractions to ensure robustness of classification between PC3-Epi and PC3-EMT cells in vivo.

RESULTS

PC3-Epi and PC3-EMT prostate cancer cells were separable at the single cell level in murine tibia injections on the basis of nuclear structure and texture remodeling associated with an EMT. Support vector machine and multinomial logistic regression models based on nuclear architecture features yielded AUC-ROC curves of 0.95 and 0.96, respectively, in separating PC3-Epi and PC3-EMT prostate cancer cells in vivo.

CONCLUSIONS

Prostate cancer cells that have undergone an EMT demonstrated an altered nuclear structure. The association of nuclear changes and a mesenchymal phenotype demonstrates quantitative morphometric image analysis may be used to detect cancer cells that have undergone EMT. This morphometric measurement could provide valuable prognostic information in patients regarding the likelihood of [future] metastatic disease.

Nuclear envelope invaginations and cancer

The nuclear envelope (NE) surrounds the nucleus and separates it from the cytoplasm. The NE is not a passive structural component, but rather contributes to various cellular processes such as genome organization, transcription, signaling, and stress responses. Although the NE is mostly a smooth surface, it also forms invaginations that can reach deep into the nucleoplasm and may even traverse the nucleus completely. Cancer cells are generally characterized by irregularities and invaginations of the NE that are of diagnostic and prognostic significance. In the current chapter, we describe the link between nuclear invaginations and irregularities with cancer and explore possible mechanistic roles they might have in tumorigenesis.

Development of a nuclear morphometric signature for prostate cancer risk in negative biopsies

Background

Our objective was to develop and validate a multi-feature nuclear score based on image analysis of direct DNA staining, and to test its association with field effects and subsequent detection of prostate cancer (PCa) in benign biopsies.

Methods

Tissue sections from 39 prostatectomies were Feulgen-stained and digitally scanned (400×), providing maps of DNA content per pixel. PCa and benign epithelial nuclei were randomly selected for measurement of 52 basic morphometric features. Logistic regression models discriminating benign from PCa nuclei, and benign from malignant nuclear populations, were built and cross-validated by AUC analysis. Nuclear populations were randomly collected <1 mm or >5 mm from cancer foci, and from cancer-free prostates, HGPIN, and PCa Gleason grade 3–5. Nuclei also were collected from negative biopsy subjects who had a subsequent diagnosis of PCa and age-matched cancer-free controls (20 pairs).

Results

A multi-feature nuclear score discriminated cancer from benign cell populations with AUCs of 0.91 and 0.79, respectively, in training and validation sets of patients. In prostatectomy samples, both nuclear- and population-level models revealed cancer-like features in benign nuclei adjacent to PCa, compared to nuclei that were more distant or from PCa-free glands. In negative biopsies, a validated model with 5 variance features yielded significantly higher scores in cases than controls (P = 0.026).

Conclusions

A multifeature nuclear morphometric score, obtained by automated digital analysis, was validated for discrimination of benign from cancer nuclei. This score demonstrated field effects in benign epithelial nuclei at varying distance from PCa lesions, and was associated with subsequent PCa detection in negative biopsies.

Impact

This nuclear score shows promise as a risk predictor among men with negative biopsies and as an intermediate biomarker in Phase II chemoprevention trials. The results also suggest that subvisual disturbances in nuclear structure precede the development of pre-neoplastic lesions.

Gleason score 7 adenocarcinoma of the prostate with lymph node metastases: analysis of 184 radical prostatectomy specimens

CONTEXT

Prostate cancer (PC) with lymph node metastases (LN(+)) is relatively rare, whereas it is relatively common in disease with a Gleason score (GS) 8 to 10 and virtually never seen in PC with GS 6 or less. It is most variable in GS 7 PC.

OBJECTIVE

To determine clinicopathologic features associated with GS 7 PC with LN(+) compared with a control group without lymph node metastases (LN(-)).

DESIGN

We analyzed 184 GS 7 radical prostatectomies with LN(+) and the same number of LN(-) Gleason-matched controls. The LN(+) cases were GS 3 + 4 = 7 (n = 64; 34.8%), GS 4 + 3 = 7 (n = 66; 35.9%), GS 3 + 4 = 7 with tertiary 5 (n = 10; 5.4%), and GS 4 + 3 = 7 with tertiary 5 (n = 44; 23.9%).

RESULTS

The LN(+) cases demonstrated higher average values in preoperative prostate-specific antigen (12.2 versus 8.1 ng/mL), percentage of positive biopsy cores (59.1% versus 42.9%), prostate weight (54.4 versus 49.4 g), number of LNs submitted (12.7 versus 9.4), incidence of nonfocal extraprostatic extension (82.6% versus 63.6%), tumor volume (28.9% versus 14.8%), frequency of lymphovascular invasion (78.3% versus 38.6%), intraductal spread of carcinoma (42.4% versus 20.7%), incidence of satellite tumor foci (16.4% versus 4.3%), incidence of pT3b disease (49.5% versus 14.7%), and lymphovascular invasion in the seminal vesicles (52% versus 30%). There were differences in GS 4 patterns and cytology between LN(+) and LN(-) cases, with the former having higher volumes of cribriform and poorly formed patterns, larger nuclei and nucleoli, and more-frequent macronucleoli. All P ≤ .05.

CONCLUSION

Gleason score 7 PC with LN(+) has features highlighting a more-aggressive phenotype. These features can be assessed as prognostic markers in GS 7 disease on biopsy (eg, GS 4 pattern, intraductal spread, cytology) or at radical prostatectomies (all variables), even in men without LN dissection or LN(-) disease.

Nuclear features in oral squamous cell carcinoma: A computer-assisted microscopic study

Background and Objectives:

Oral squamous cell carcinoma (OSCC) is one of the most common tobacco-related cancers affecting the Indian population. Various malignancy-grading systems based on different histopathological features have been proposed for OSCC. Due to inherent subjectivity, inter-observer variation and reproducibility of a grading system remains a problem. Grading systems based on nuclear morphometry have been proposed for laryngeal, renal and pharyngeal carcinomas. In this study, an attempt was made to grade oral OSCC based on computer-assisted microscopic evaluation of nuclear features. Our intention was also to evaluate the use of Feulgen stain for studying nuclear features.

Materials and Methods:

Sections made from buccal mucosa biopsies of normal mucosa as well as different grades OSCC were stained by Feulgen reaction. The nuclear features were evaluated by computer-assisted microscopic image analysis for nuclear area (NA), nuclear perimeter (NP) and nuclear form factor (NF) and correlated with histologic grading of OSCC. Nuclear shape, membrane outline, chromatin clumps, nucleoli, and abnormal mitoses were also evaluated.

Results:

NA and NP were observed to be significantly increased in OSCC (P < 0.001) when compared with the control group. These values increased in correlation with increasing grades of OSCC. However, NF was found to more in the control group (P < 0.001).

Conclusion:

It may be concluded from the results that computer-assisted nuclear morphometry is a reliable tool for grading OSCC. A new grading system based on nuclear features for OSCC has been proposed.

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.

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.

Prostate cancer

Prostate cancer is biologically and clinically a heterogeneous disease and its imaging evaluation will need to be tailored to the specific phases of the disease in a patient-specific, risk-adapted manner. We first present a brief overview of the natural history of prostate cancer before discussing the role of various imaging tools, including opportunities and challenges, for different clinical phases of this common disease in men. We then review the preclinical and clinical evidence on the potential and emerging role of positron emission tomography with various radiotracers in the imaging evaluation of men with prostate cancer.

Novel diagnostic biomarkers for prostate cancer

Prostate cancer is the most frequently diagnosed malignancy in American men, and a more aggressive form of the disease is particularly prevalent among African Americans. The therapeutic success rate for prostate cancer can be tremendously improved if the disease is diagnosed early. Thus, a successful therapy for this disease depends heavily on the clinical indicators (biomarkers) for early detection of the presence and progression of the disease, as well as the prediction after the clinical intervention. However, the current clinical biomarkers for prostate cancer are not ideal as there remains a lack of reliable biomarkers that can specifically distinguish between those patients who should be treated adequately to stop the aggressive form of the disease and those who should avoid overtreatment of the indolent form.

A biomarker is a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention. A biomarker reveals further information to presently existing clinical and pathological analysis. It facilitates screening and detecting the cancer, monitoring the progression of the disease, and predicting the prognosis and survival after clinical intervention. A biomarker can also be used to evaluate the process of drug development, and, optimally, to improve the efficacy and safety of cancer treatment by enabling physicians to tailor treatment for individual patients. The form of the prostate cancer biomarkers can vary from metabolites and chemical products present in body fluid to genes and proteins in the prostate tissues.

Current advances in molecular techniques have provided new tools facilitating the discovery of new biomarkers for prostate cancer. These emerging biomarkers will be beneficial and critical in developing new and clinically reliable indicators that will have a high specificity for the diagnosis and prognosis of prostate cancer. The purpose of this review is to examine the current status of prostate cancer biomarkers, with special emphasis on emerging markers, by evaluating their diagnostic and prognostic potentials. Both genes and proteins that reveal loss, mutation, or variation in expression between normal prostate and cancerous prostate tissues will be covered in this article. Along with the discovery of prostate cancer biomarkers, we will describe the criteria used when selecting potential biomarkers for further development towards clinical use. In addition, we will address how to appraise and validate candidate markers for prostate cancer and some relevant issues involved in these processes. We will also discuss the new concept of the biomarkers, existing challenges, and perspectives of biomarker development.

Nuclear roundness variance predicts prostate cancer progression, metastasis, and death: A prospective evaluation with up to 25 years of follow-up after radical prostatectomy

BACKGROUND

Nuclear structure is often altered in cancer due to spatial rearrangements of chromatin organization via activation of oncogenes and other chromatin remodeling genes. Therefore, we evaluated the prognostic value of nuclear roundness variance (NRV) for prostate cancer (PCa) progression, metastasis and PCa-specific death free survivals in a cohort of 116 men after radical prostatectomy (RP).

METHOD

NRV was calculated for each case using the variance of the nuclear roundness from approximately 150 nuclei captured at a magnification of 2,440x for each case in 1992-1993. $${\rm Nuclear}\,{\rm roundness} = {{{\rm Radius}({\rm circumference})} \over {{\rm radius}({\rm area})}} = {R \over r} = {{P/2\pi } \over {\sqrt {A/\pi } }}$$ NRV data were merged with clinical, pathologic, and follow-up data for all patients in 2009. Cox proportional hazards regression and Kaplan-Meier plots were employed to analyze the data.

RESULTS

Median follow-up time after RP for all patients was 19 years (range: 1-25 years, mean: 17 years), with approximately 92% (107/116), 71% (82/116), and 47% (55/116) patients having >or=10, 15, and 20 years of follow-up, respectively. NRV was the most significant parameter for prediction of all three outcomes and its concordance-index (C-Index) increased from progression (0.7080) to metastasis (0.7332) to PCa-specific death (0.8090) free survival predictions. Of note, NRV C-Index was significantly higher compared to Gleason Score C-Index for metastasis (0.7332 vs. 0.6046; P = 0.027) and PCa-specific death (0.8090 vs. 0.6336; P = 0.004) free survival predictions. However, the difference between NRV and Gleason Score C-Indexes was not statistically significant for progression free survival prediction (0.7080 vs. 0.6463; P = 0.106).

CONCLUSION

NRV is valuable nuclear structural feature that exceeds Gleason score to predict an aggressive phenotype of PCa.

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.

[Baltasar Llopis Mínguez (1934-1990). A pioneer in research on bladder cancer and introduction of computing in Urology]

OBJECTIVE

To recall the figure of a great Valencian urologist, to emphasize his great personality and humanity, and to draw particular attention to his significant contribution to the study of prognostic factors in urology and estimation of individual oncological risk, as well as to introduction of computing in urology.

METHOD

His work, the testimony of colleagues who treated him, and data obtained from his close relatives, as well as our own personal knowledge, are reviewed. Result. Baltasar Llopis was born in Valencia, and obtained his degree and doctorate in Medicine at the Valencia University. He specialized in urology with Dr. Tramoyeres Cases, for whom he acted as assistant surgeon and with whom he shared work at La Fe Hospital, where he carried out his complete urological activity, since its inception. Dr. Llopis opted for oncological research, with a special focus on urothelial tumors. He pioneered diagnosis of these tumors using tumor markers and the study of prognostic factors to assess the individual risk of relapse and to implement a specific chemotherapeutic treatment, which he introduced in clinical practice at La Fe Hospital. He thus demonstrated the two essential components of his personality, his investigative and human sides.

CONCLUSION

A multi-faceted person with great skills and intelligence, Dr. Llopis eagerly devoted himself to research aimed at understanding the biological behavior of cancer, particularly urothelial tumors. In the early 80s he pioneered worldwide the development of specific markers, estimations of individual oncological risk, and prognostic factors useful for planning treatment. He was 20 years ahead of the era of predictive nomograms and their clinical

INTRODUCTION

In addition to being a forerunner of computing applications in Urology, he designed a database for registration of superficial bladder tumors, which allowed him to perform statistical and multivariate analyses using multiple regression models to predict the risk of relapse.

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.

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.

Relationship of age to outcome and clinicopathologic findings in men submitted to radical prostatectomy

OBJECTIVE

It is controversial whether age is associated with higher grade and worse outcome. Some studies have not found age to be related to outcome nor younger age to be associated with better response to therapy.

MATERIALS AND METHODS

The study population consisted of 27 patients aged 55 years or younger and 173 patients 56 years or older submitted to radical prostatectomy. The variables studied were preoperative PSA, time to PSA progression following radical prostatectomy and pathologic findings in surgical specimens: Gleason score, Gleason predominant grade, positive surgical margins, tumor extent, extraprostatic extension (pT3a), and seminal vesicle invasion (pT3b).

RESULTS

Comparing patients aged 55 years or younger and 56 years or older, there was no statistically significant difference for all variables studied: preoperative PSA (p = 0.4417), Gleason score (p = 0.3934), Gleason predominant grade (p = 0.2653), tumor extent (p = 0.1190), positive surgical margins (p = 0.8335), extraprostatic extension (p = 0.3447) and seminal vesicle invasion (p > 0.9999). During the study period, 44 patients (22%) developed PSA progression. No difference was found in the time to biochemical progression between men aged 55 years or younger and 56 years or older.

CONCLUSIONS

Our findings suggest that age alone do not influence the biological aggressiveness of prostate cancer.

Prognostic factors in prostate cancer

Prognostic factors in organ confined prostate cancer will reflect survival after surgical radical prostatectomy. Gleason score, tumour volume, surgical margins and Ki-67 index have the most significant prognosticators. Also the origins from the transitional zone, p53 status in cancer tissue, stage, and aneuploidy have shown prognostic significance. Progression-associated features include Gleason score, stage, and capsular invasion, but PSA is also highly significant. Progression can also be predicted with biological markers (E-cadherin, microvessel density, and aneuploidy) with high level of significance. Other prognostic features of clinical or PSA-associated progression include age, IGF-1, p27, and Ki-67. In patients who were treated with radiotherapy the survival was potentially predictable with age, race and p53, but available research on other markers is limited. The most significant published survival-associated prognosticators of prostate cancer with extension outside prostate are microvessel density and total blood PSA. However, survival can potentially be predicted by other markers like androgen receptor, and Ki-67-positive cell fraction. In advanced prostate cancer nuclear morphometry and Gleason score are the most highly significant progression-associated prognosticators. In conclusion, Gleason score, capsular invasion, blood PSA, stage, and aneuploidy are the best markers of progression in organ confined disease. Other biological markers are less important. In advanced disease Gleason score and nuclear morphometry can be used as predictors of progression. Compound prognostic factors based on combinations of single prognosticators, or on gene expression profiles (tested by DNA arrays) are promising, but clinically relevant data is still lacking.

Prognostic factors and reporting of prostate carcinoma in radical prostatectomy and pelvic lymphadenectomy specimens

This paper, based on the activity of the Morphology-Based Prognostic Factors Committee of the 2004 World Health Organization-sponsored International Consultation, describes various methods of handling radical prostatectomy specimens for both routine clinical use and research purposes. The correlation between radical prostatectomy findings and postoperative failure is discussed in detail. This includes issues relating to pelvic lymph node involvement, detected both at the time of frozen section and in permanent sections. Issues of seminal vesicle invasion, including its definition, routes of invasion and relationship to prognosis, are covered in detail. The definition, terminology and incidence of extra-prostatic extension are elucidated, along with its prognostic significance relating to location and extent. Margins of resection are covered in terms of their definition, the etiology, incidence and sites of positive margins, the use of frozen sections to assess the margins and the relationship between margin positivity and prognosis. Issues relating to grade within the radical prostatectomy specimen are covered in depth, including novel ways of reporting Gleason grade and the concept of tertiary Gleason patterns. Tumor volume, tumor location, vascular invasion and perineural invasion are the final variables discussed relating to the prognosis of radical prostatectomy specimens. The use of multivariate analysis to predict progression is discussed, together with proposed modifications to the TNM system. Finally, biomarkers to predict progression following radical prostatectomy are described, including DNA ploidy, microvessel density, Ki-67, neuroendocrine differentiation, p53, p21, p27, Bcl-2, Her-2/neu, E-cadherin, CD44, retinoblastoma proteins, apoptotic index, androgen receptor status, expression of prostate-specific antigen and prostatic-specific acid phosphatase and nuclear morphometry.

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.

Impacto de la edad y de la comorbilidad en la supervivencia y toxicidad del paciente con cáncer de próstata irradiado

BACKGROUND AND OBJECTIVE

We intended to assess the impact of age on radiation outcome in patients treated for prostate cancer with 2D planning radiation therapy in clinical practice at the Hospital Clínic of Barcelona Radiation Oncology Department.

PATIENTS AND METHOD

One hundred eighty three patients, treated from November 1993 to April 1999, were included. Median follow-up was 41.8 months and median age was 70 years old. Median dose to prostate was 70 Gy. Univariate (Kaplan-Meier with log rank test comparison) and multivariate analysis (Cox's regression models) were done to assess the effect of age on toxicity and to study prognostic factors for disease control, survival and radiation treatment toxicity.

RESULTS

Five years disease free survival probability was 61.94%, with an overall survival probability of 82.83%. Although comorbidity increased significantly with age, reduced overall survival by a factor of 0.4, from 94.85% to 78.55% at 5 years. No differences were seen with regard to age in acute or late toxicity. Five years toxicity free probability was 66.46%.

CONCLUSIONS

Comorbidities decrease life expectancy in prostate cancer patients treated with radiation. Age does not necessarily suppose an increased risk of late toxicity for selected patients.

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.

[Prognostic value of DNA ploidy and nuclear morphometry in metastatic prostate cancer]

PURPOSE

To assess the prognostic value of DNA ploidy and nuclear morphometry in metastatic prostate cancer after androgenic deprivation treatment.

METHODS

Fifty four patients with prostate cancer and bone metastases who had undergone androgenic suppression treatment were retrospectively studied. The deoxyribonucleic acid (DNA) content was analysed by flow cytometry. Nuclear morphometry characterized 14 nuclear descriptors. The study also included age, Gleason score, T classification, haematocrite, serum albumin, serum alkaline phosphatase, serum prostatic acid phosphatase and the amount of metastatic foci detected during radioisotope bone scan. Univariate survival analyses were performed and Cox's proportional hazards model was used to identify significant prognostic factors. To assess how the experimental factors improve the capacity of the classical factors for predicting the patients who reach median survival, logistic regression multivariate analysis was performed for the classical prognostic factors only and after added experimental variables (DNA content and Nuclear Area).

RESULTS

The univariate survival analyses assigned a prognostic value to T category, level of albumin, alkaline phosphatase, Gleason score, bone scan, DNA ploidy and mean nuclear area. In the case of the Cox regression model only Gleason score, bone scan, mean nuclear area and DNA ploidy provided independent prognostic information. In logistic regression for classic prognostic factors only Gleason score is significant (sensibility 89.3%, specificity 64%). However, when the experimental factors are added, in addition to Gleason score, radioisotope bone scan and DNA ploidy are of prognostic value (sensibility 90% and specificity 72%).

CONCLUSIONS

The study of DNA content and nuclear morphometry in the primitive tumor provides independent prognostic information in survival analysis for patients with metastatic prostate cancer. However, there is limited improvement with respect to the classical factors in predicting survival. This questions its utility in the daily clinical usage.

Molecular aspects of diagnostic nucleolar and nuclear envelope changes in prostate cancer

Prostate cancer is still diagnosed by pathologists based on subjective assessment of altered cell and tissue structure. The cellular-level structural changes diagnostic of some forms of cancer are known to be induced by cancer genes, but the relation between specific cellular-level structural features and cancer genes has not been explored in the prostate. Two important cell structural changes in prostate cancer-nucleolar enlargement and nuclear envelope (NE) irregularity-are discussed from the perspective that they should also relate to the function of the genes active in prostate cancer. Enlargement of the nucleolus is the key diagnostic feature of high-grade prostatic intraepithelial neoplasia (PIN), an early stage that appears to be the precursor to the majority of invasive prostate cancers. Nucleolar enlargement classically is associated with increased ribosome production, and production of new ribosomes appears essential for cell-cycle progression. Several cancer genes implicated in PIN are known (in other cell types) to augment ribosome production, including c-Myc, p27, retinoblastoma, p53, and growth factors that impact on ERK signaling. However, critical review of the available information suggests that increased ribosome production per se may be insufficient to explain nucleolar enlargement in PIN, and other newer functions of nucleoli may therefore need to be invoked. NE irregularity develops later in the clonal evolution of some prostate cancers, and it has adverse prognostic significance. Nuclear irregularity has recently been shown to develop dynamically during interphase following oncogene expression, without a requirement for post-mitotic NE reassembly. NE irregularity characteristic of some aggressive prostate cancers could reflect cytoskeletal forces exerted on the NE during active cell locomotion. NE irregularity could also promote chromosomal instability because it leads to chromosomal asymmetry in metaphase. Finally, NE irregularity could impact replication competence, transcriptional programming and nuclear pore function.

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.

Nuclear envelope irregularity is induced by RET/PTC during interphase

Nuclear envelope (NE) irregularity is an important diagnostic feature of cancer, and its molecular basis is not understood. One possible cause is abnormal postmitotic NE re-assembly, such that a rounded contour is never achieved before the next mitosis. Alternatively, dynamic forces could deform the NE during interphase following an otherwise normal postmitotic NE re-assembly. To distinguish these possibilities, normal human thyroid epithelial cells were microinjected with the papillary thyroid carcinoma oncogene (RET/PTC1 short isoform, known to induce NE irregularity), an attenuated version of RET/PTC1 lacking the leucine zipper dimerization domain (RET/PTC1 Deltazip), H (V-12) RAS, and labeled dextran. Cells were fixed at 6 or 18 to 24 hours, stained for lamins and the products of microinjected plasmids, and scored blindly using previously defined criteria for NE irregularity. 6.5% of non-injected thyrocytes showed NE irregularity. Neither dextran nor RAS microinjections increased NE irregularity. In contrast, RET/PTC1 microinjection induced NE irregularity in 27% of cells at 6 hours and 37% of cells at 18 to 24 hours. RET/PTC1 Deltazip induced significantly less irregularity. Since irregularity develops quickly, and since no mitoses and only rare possible postmitotic cells were scored, postmitotic NE re-assembly does not appear necessary for RET/PTC signaling to induce an irregular NE contour.

Molecular and genetic prognostic factors of prostate cancer

Prostate cancer is the most commonly diagnosed cancer in Western males, responsible for 3% of all deaths in men over 55 years of age and second only to lung cancer as a cause of cancer death. Biomarkers have become an important diagnostic tool in prostate cancer. The discovery of the serum marker prostate-specific antigen (PSA) significantly facilitated the detection and management of prostate cancer. As we enter into the post-genomics era, novel biomarkers of prostate cancer of therapeutic significance will invariably emerge. Here we review a series of existing and emerging molecular-based prognostic markers particularly with radiotherapy.

Comparison of Logistic Regression and Neural Net Modeling for Prediction of Prostate Cancer Pathologic Stage

Background: Prostate cancer (PCa) pathologic staging remains a challenge for the physician using individual pretreatment variables. We have previously reported that UroScoreTM, a logistic regression (LR)-derived algorithm, can correctly predict organ-confined (OC) disease state with &gt;90% accuracy. This study compares statistical and neural network (NN) approaches to predict PCa stage.

Methods: A subset (756 of 817) of radical prostatectomy patients was assessed: 434 with OC disease, 173 with capsular penetration (NOC-CP), and 149 with metastases (NOC-AD) in the training sample. Additionally, an OC + NOC-CP (n = 607) vs NOC-AD (n = 149) two-outcome model was prepared. Validation sets included 120 or 397 cases not used for modeling. Input variables included clinical and several quantitative biopsy pathology variables. The classification accuracies achieved with a NN with an error back-propagation architecture were compared with those of LR statistical modeling.

Results: We demonstrated &gt;95% detection of OC PCa in three-outcome models, using both computational approaches. For training patient samples that were equally distributed for the three-outcome models, NNs gave a significantly higher overall classification accuracy than the LR approach (40% vs 96%, respectively). In the two-outcome models using either unequal or equal case distribution, the NNs had only a marginal advantage in classification accuracy over LR.

Conclusions: The strength of a mathematics-based disease-outcome model depends on the quality of the input variables, quantity of cases, case sample input distribution, and computational methods of data processing of inputs and outputs. We identified specific advantages for NNs, especially in the prediction of multiple-outcome models, related to the ability to pre- and postprocess inputs and outputs.

Prognostic factor analysis in patients with advanced prostate cancer treated by castration plus anandron or placebo: a final update

PURPOSE

Different outcome results have been published in trials comparing maximal androgen blockade (MAB) with chemical or surgical castration alone. The conflicting results could be explained by the fact that patients were included with different prognostic factors. In this new analysis of the Anandron European Study, independent prognostic factors have been evaluated in order to identify those which could influence the study outcome and the impact of the treatment.

MATERIAL AND METHODS

399 out of 457 patients recruited in this study were divided in a good or poor prognostic group depending on the presence of two or more poor prognostic factors, these were pain requiring treatment, >5 bone metastases, hydronephrosis, and alkaline phosphatase >2 ULN.

RESULTS

When expressed as a percentage, the improvement in time to progression, overall and cancer specific survival in the Anandron treated patients was identical in both groups. In absolute terms this improvement, however, was greater in the good prognostic group.

CONCLUSION

In comparison with surgical castration MAB using Anandron, in patients with metastatic prostate cancer improves the time to objective progression, overall and cancer specific survival, irrespective of certain poor prognostic factors.

Estramustine phosphate combined with orchidectomy as first-line therapy in patients with prostate carcinoma. Effect of age on survival

BACKGROUND

The role of age as a prognostic factor for survival remains debatable in patients with prostate carcinoma.

METHODS

The authors conducted a retrospective study of the significance of age as a prognostic factor for survival and progression free survival in 386 patients who underwent orchidectomy for locally advanced or metastatic prostate carcinoma, 75% of whom had T0-T4, M1 disease. After undergoing orchidectomy, 192 patients received no further therapeutic intervention, whereas 194 patients received additional treatment with estramustine phosphate (EMP) as first-line therapy.

RESULTS

The findings confirmed that age was a significant prognostic factor for survival and progression free survival in patients with prostate carcinoma as well as a predictor of response to chemotherapy. The data also showed that, although combining orchidectomy with EMP appeared to be beneficial in younger patients, using this relatively more aggressive therapeutic approach as first-line therapy in older patients (age > or = 80 years) may shorten their survival.

CONCLUSIONS

The current findings call for caution with the additional use of EMP as first-line therapy in older patient with prostate carcinoma.

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.

Does age influence the behaviour of localized prostate cancer?

OBJECTIVE

To assess, in a meta-analysis of published studies, whether age influences the behaviour of localized prostate cancer.

METHODS

The Medline database was searched from 1966 to 2000 to identify studies analysing the outcome of localized prostate cancer by age, using disease-specific outcome measures, and having controlled for the established prognostic factors of grade, T stage and, where available, serum prostate-specific antigen (PSA) level.

RESULTS

In all, 34 studies were identified, which included a total of 27 551 patients. The incomplete and heterogeneous nature of the reports precluded any quantitative overview. The findings of these reports are described and methodological shortcomings discussed.

CONCLUSION

The evidence suggests that young age was an adverse prognostic factor in some series of radiation therapy before the advent of PSA assays, when men typically presented clinically with locally advanced disease, but that age has no significant prognostic effect in contemporary series of localized prostate cancer. Possible explanations for this difference are discussed, together with implications for further studies.

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.

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.

Prostate pathology: histologic and molecular perspectives

The field of prostate cancer research is poised for dramatic improvements in our ability to better diagnose men at risk of prostate cancer and to better predict prognosis and response to treatment. Histopathologic and molecular analyses lie at the heart of these issues. Improvements in our understanding of the mechanisms of prostate carcinogenesis and in determining why the prostate seems to be so highly targeted for cancer development will lead to rational strategies of disease prevention.

Nuclear morphometry for the prediction of regional lymph nodes metastases in patients with cancer of the larynx

The changes in cell nuclei reflect their activity. Quantitative morphometric analyses of tumor nuclei could be instrumental in providing prognostic information. We studied whether, and if so, which specific nuclear parameters and histoclinical factors in patients with cancer of the larynx could be related to the lymph node metastases. Specimens were taken from 61 patients surgically treated in the Department of Otolaryngology, Jagiellonian University, Cracow, Poland, between 1987 and 1988. The period between the onset of the first symptoms and the actual commencement of the treatment spanned no more than 9 months. The follow-up period was no shorter than 5 years. Histologically confirmed metastases in the regional lymph nodes were found in 16 patients. The histologic grading and tumor front grading was pursued in all cases. Fourteen parameters of the nuclei were studied with the aid of a computer-assisted system of image analysis. The morphometric parameters and the histoclinical factors were analyzed by the chi(2) test and the stepwise logistic regression. It was established that nuclear area > or =66 microm (P = 0.042), perimeter > or =32 microm (P = 0.087), optical density > or =22,500 (P = 0.027), long axis > or =10.15 microm (P = 0.025), short axis > or =7.3 microm (P = 0.003), TFG assessed (> or =15 points) and tumor advancement (T3, T4) were related to more frequent metastases to the lymph nodes. The morphometric parameters of the greatest significance were short axis and optical density. The quantitative morphometric analysis could prove a useful tool in predicting metastases to the lymph nodes.

Analysis of the mechanism of discrepant nuclear morphometric results comparing preoperative biopsy and prostatectomy specimens

OBJECTIVES

To explore the mechanism for the differing nuclear morphometric results between needle biopsy and surgical specimens of the prostate.

METHODS

In experiment 1, a comparison of mean nuclear area (MNA), volume-weighted mean nuclear volume (MNV), and form factor (FF) for prostatic epithelial cells was performed between preoperative needle biopsy and prostatectomy specimens from 5 patients with benign prostatic hyperplasia (BPH). In experiment 2, a scheduled, sequential ex vivo needle sampling from the enucleated prostates (at 0, 2, 6, and 24 hours after surgical resection) was also performed for 7 patients with BPH. The prostatectomy specimens were left unfixed for 2 hours until the second needle sampling was done. Nuclear morphometric parameters were measured on the needle-sampled as well as on the prostatectomy specimens.

RESULTS

MNA, MNV, and FF of BPH cells measured on preoperative biopsy specimens were smaller than those of surgical specimens in all 5 of the cases. The results of nuclear morphometry on the materials obtained by ex vivo needle sampling of prostates before and during fixation revealed that the MNA, MNV, and FF for BPH cells of 0-hour specimens were significantly smaller than those for needle samples at 2, 6, and 24 hours after surgical resection as well as those for prostatectomy specimens.

CONCLUSIONS

The present study provided further evidence that the ischemic damage caused by delayed fixation could result in a substantial change of the nuclear morphology of prostate cells. An immediate start, as well as a rapid completion, of the fixation procedure seems critical for an accurate nuclear morphometry of prostatectomy specimens.

Prognostic factors in prostate cancer. College of American Pathologists Consensus Statement 1999

BACKGROUND

Under the auspices of the College of American Pathologists, a multidisciplinary group of clinicians, pathologists, and statisticians considered prognostic and predictive factors in prostate cancer and stratified them into categories reflecting the strength of published evidence and taking into account the expert opinions of the Prostate Working Group members.

MATERIALS AND METHODS

Factors were ranked according to the previous College of American Pathologists categorical rankings: category I, factors proven to be of prognostic importance and useful in clinical patient management; category II, factors that have been extensively studied biologically and clinically but whose importance remains to be validated in statistically robust studies; and category III, all other factors not sufficiently studied to demonstrate their prognostic value. Factors in categories I and II were considered with respect to variations in methods of analysis, interpretation of findings, reporting of data, and statistical evaluation. For each factor, detailed recommendations for improvement were made. Recommendations were based on the following aims: (1) increasing uniformity and completeness of pathologic evaluation of tumor specimens, (2) enhancing the quality of data collected pertaining to existing prognostic factors, and (3) improving patient care.

RESULTS AND CONCLUSIONS

Factors ranked in category I included preoperative serum prostate-specific antigen level, TNM stage grouping, histologic grade as Gleason score, and surgical margin status. Category II factors included tumor volume, histologic type, and DNA ploidy. Factors in category III included perineural invasion, neuroendocrine differentiation, microvessel density, nuclear roundness, chromatin texture, other karyometric factors, proliferation markers, prostate-specific antigen derivatives, and other factors (oncogenes, tumor suppressor genes, apoptosis genes, etc).

Prognostic value of nuclear morphometry on needle biopsy from patients with prostate cancer: is volume-weighted mean nuclear volume superior to other morphometric parameters?

OBJECTIVES

To compare the prognostic value of stereologically estimated volume-weighted mean nuclear volume (MNV) with other nuclear morphometric parameters using pretreatment needle-biopsy specimens of prostate cancer.

METHODS

The MNV, mean nuclear area, form factor, and coefficients of variation for nuclear area (VNA) and form factor were measured on pretreatment needle biopsy specimens from 66 patients with prostate cancer (clinical Stage B, n = 9; Stage C, n = 14; and Stage D, n = 43), all of whom underwent androgen deprivation therapy. The prognostic value of those morphometric parameters, as well as Gleason score and clinical stage, was examined in terms of cause-specific patient survival using univariate and multivariate analysis (Cox proportional hazard model).

RESULTS

Univariate analysis of the nuclear morphometric parameters revealed that MNV, mean nuclear area, VNA, coefficient of variation for form factor, and clinical stage were significant prognostic factors for cause-specific patient survival. However, when the patients with Stage D disease were selectively analyzed for survival, only the VNA was a significant prognostic parameter. Furthermore, the multivariate analysis, including the morphometric parameters, clinical stage, and Gleason score revealed that only VNA and clinical stage were independent variables.

CONCLUSIONS

The present comparative study could not demonstrate any prognostic superiority of MNV over other nuclear morphometric parameters in patients with prostate cancer.