Improved prediction of prostate cancer recurrence through systems pathology

The yin and yang of chromosomal instability in prostate cancer

Metastatic prostate cancer remains an incurable lethal disease. Studies indicate that prostate cancer accumulates genomic changes during disease progression and displays the highest levels of chromosomal instability (CIN) across all types of metastatic tumours. CIN, which refers to ongoing chromosomal DNA gain or loss during mitosis, and derived aneuploidy, are known to be associated with increased tumour heterogeneity, metastasis and therapy resistance in many tumour types. Paradoxically, high CIN levels are also proposed to be detrimental to tumour cell survival, suggesting that cancer cells must develop adaptive mechanisms to ensure their survival. In the context of prostate cancer, studies indicate that CIN has a key role in disease progression and might also offer a therapeutic vulnerability that can be pharmacologically targeted. Thus, a comprehensive evaluation of the causes and consequences of CIN in prostate cancer, its contribution to aggressive advanced disease and a better understanding of the acquired CIN tolerance mechanisms can translate into new tumour classifications, biomarker development and therapeutic strategies.

Applications of artificial intelligence in prostate cancer histopathology

The diagnosis of prostate cancer (PCa) depends on the evaluation of core needle biopsies by trained pathologists. Artificial intelligence (AI) derived models have been created to address the challenges posed by pathologists' increasing workload, workforce shortages, and variability in histopathology assessment. These models with histopathological parameters integrated into sophisticated neural networks demonstrate remarkable ability to identify, grade, and predict outcomes for PCa. Though the fully autonomous diagnosis of PCa remains elusive, recently published data suggests that AI has begun to serve as an initial screening tool, an assistant in the form of a real-time interactive interface during histological analysis, and as a second read system to detect false negative diagnoses. Our article aims to describe recent advances and future opportunities for AI in PCa histopathology.

Seminal vesicle invasion combined with extraprostatic extension is associated with higher frequency of biochemical recurrence and lymph node metastasis than seminal vesicle invasion alone: Proposal for further pT3 prostate cancer subclassification

The 8th edition of the American Joint Committee on Cancer (AJCC) TNM staging system subdivides prostatic pT3 tumors into pT3a, which includes cases with extraprostatic extension (EPE) and pT3b, which is defined by the presence of seminal vesicle invasion (SVI) with or without EPE. Yet, it is not established whether combined SVI and EPE impart a worse prognosis compared to SVI alone. We studied a cohort of 69 prostatectomy patients with SVI with or without EPE. Patient age at the time of radical prostatectomy was documented and Gleason score and presence or absence of EPE and/or SVI were determined. Biochemical recurrence (BCR) was defined as a PSA rise >0.2 ng/mL. The frequency of BCR was 33.9% in cases with combined EPE and SVI versus 12.5% in cases with SVI alone (relative risk = 2.71). An additional cohort of 88 patients also showed a higher frequency of lymph node metastasis of 29% in patients with combined SVI and EPE at the time of radical prostatectomy versus a 10% frequency of lymph node metastasis in patients with SVI alone (relative risk = 2.9). Based on our data, we propose further subdividing pT3 prostate cancers into three groups: EPE alone (pT3a), SVI alone (pT3b), and combined EPE and SVI (pT3c). This classification system would more accurately identify patients with pT3 prostate cancer who are more likely to experience worse outcomes and provide clinicians with additional information to aid in follow-up and postoperative treatment decisions.

Applications of Artificial Intelligence to Prostate Multiparametric MRI (mpMRI): Current and Emerging Trends

Prostate carcinoma is one of the most prevalent cancers worldwide. Multiparametric magnetic resonance imaging (mpMRI) is a non-invasive tool that can improve prostate lesion detection, classification, and volume quantification. Machine learning (ML), a branch of artificial intelligence, can rapidly and accurately analyze mpMRI images. ML could provide better standardization and consistency in identifying prostate lesions and enhance prostate carcinoma management. This review summarizes ML applications to prostate mpMRI and focuses on prostate organ segmentation, lesion detection and segmentation, and lesion characterization. A literature search was conducted to find studies that have applied ML methods to prostate mpMRI. To date, prostate organ segmentation and volume approximation have been well executed using various ML techniques. Prostate lesion detection and segmentation are much more challenging tasks for ML and were attempted in several studies. They largely remain unsolved problems due to data scarcity and the limitations of current ML algorithms. By contrast, prostate lesion characterization has been successfully completed in several studies because of better data availability. Overall, ML is well situated to become a tool that enhances radiologists' accuracy and speed.

Impact of nuclear YAP1 expression in residual cancer after neoadjuvant chemohormonal therapy with docetaxel for high-risk localized prostate cancer

Background

Although docetaxel-based chemohormonal therapy (CHT) is one of the standard treatments for castration-resistant prostate cancer (CRPC), pertinent biomarkers and precise mechanisms involved in the resistance for CHT for CRPC remain unknown. We investigated the relationship between chemohormonal resistance and the expression of steroid receptors and Hippo pathway proteins using a docetaxel-resistant prostate cancer (PCa) cell line and human PCa tissues in patients who underwent surgery with and without neoadjuvant therapy.

Methods

A docetaxel-resistant subline (22Rv1-DR) was generated to assess Hippo pathway protein expression and the effect of YAP1 inhibition on cellular characteristics. A tissue microarray with 203 cores from 70 high-risk localized PCa tissues was performed to assess steroid receptor and Hippo pathway protein expressions.

Results

Nuclear YAP (nYAP) expression was higher in 22RV-1-DR than in parental 22Rv-1 and YAP1 knockdown suppressed cell proliferation of 22Rv1-DR. Steroid receptor and Hippo pathway protein expressions varied among three different neoadjuvant groups, and nYAP1 expression was the highest in the CHT group. The patients with high nYAP in residual cancer after neoadjuvant CHT had a significantly higher biochemical recurrence (BCR) rate than those with low nYAP1. On multivariate analysis, the high nYAP1 was an independent prognostic factor for BCR.

Conclusions

nYAP expression is a potential biomarker in high-risk patients treated with docetaxel-based CHT. Steroid receptors and Hippo pathway proteins may play a role in the chemohormonal resistance in advanced PCa.

Cost-effectiveness analyses and cost analyses in castration-resistant prostate cancer: A systematic review

Background

Treatment of metastatic prostate cancer is associated with high personal and economic burden. Recently, new treatment options for castration-resistant prostate cancer became available with promising survival advantages. However, cost-effectiveness of those new treatment options is sometimes ambiguous or given only under certain circumstances. The aim of this study was to systematically review studies on the cost-effectiveness of treatments and costs of castration-resistant prostate cancer (CRPC) and metastasizing castration-resistant prostate cancer (mCRPC) on their methodological quality and the risk of bias.

Methods

A systematic literature search was performed in the databases PubMed, CINAHL Complete, the Cochrane Library and Web of Science Core Collection for costs-effectiveness analyses, model-based economic evaluations, cost-of-illness analyses and budget impact analyses. Reported costs were inflated to 2015 US$ purchasing power parities. Quality assessment and risk of bias assessment was performed using the Consolidated Health Economic Evaluation Reporting Standards checklist and the Bias in Economic Evaluations checklist, respectively.

Results

In total, 38 articles were identified by the systematic literature search. The methodological quality of the included studies varied widely, and there was considerable risk of bias. The cost-effectiveness treatments for CRPC and mCRPC was assessed with incremental cost-effectiveness ratios ranging from dominance for mitoxantrone to $562,328 per quality-adjusted life year gained for sipuleucel-T compared with prednisone alone. Annual costs for the treatment of castration-resistant prostate cancer ranged from $3,067 to $77,725.

Conclusion

The cost-effectiveness of treatments of CRPC strongly depended on the willingness to pay per quality-adjusted life year gained/life-year saved throughout all included costs-effectiveness analyses and model-based economic evaluations. High-quality cost-effectiveness analyses based on randomized controlled trials are needed in order to make informed decisions on the management of castration-resistant prostate cancer and the resulting financial impact on the healthcare system.

Predicting Advanced Prostate Cancer from Modeling Early Indications in Biopsy and Prostatectomy Samples via Transductive Semi-Supervised Survival Analysis

Prostate cancer is the most prevalent form of cancer and the second most common cause of cancer deaths among men in the United States. Accurate prognosis is important as it is the principal factor in determining the treatment plan. Prostate cancer is a complex disease which advances in stages. While clinical failure (including metastasis) is a significant endpoint following a radical prostatectomy, it can often take years to manifest, usually too late to be optimistically treated. In practice, the earlier endpoint of PSA Recurrence is frequently used as a surrogate in prognostic modeling. The central issue in these models is managing censored observations which challenge traditional regression techniques. The true target times of a majority of instances are unknown; what is known is a censored target representing some earlier indeterminate time. In this work we apply a novel transduction approach for semi-supervised survival analysis which has previously been shown to be powerful in medical prognosis. The approach considers censored samples as semi-supervised regression targets leveraging the partial nature of unsupervised information. We explore the use of this approach in building prostate cancer progression models from multimodal characteristics extracted from both biopsy and prostatectomy tissues samples. In this work, the approach leads to a significant increase in performance for predicting advanced prostate cancer from earlier endpoints and may also be useful in other diseases for predicting advanced endpoints from earlier stages of the disease.

Development and validation of a novel automated Gleason grade and molecular profile that define a highly predictive prostate cancer progression algorithm-based test

BACKGROUND

Postoperative risk assessment remains an important variable in the effective treatment of prostate cancer. There is an unmet clinical need for a test with the potential to enhance the Gleason grading system with novel features that more accurately reflect a personalized prediction of clinical failure.

METHODS

A prospectively designed retrospective study utilizing 892 patients, post radical prostatectomy, followed for a median of 8 years. In training, using digital image analysis to combine microscopic pattern analysis/machine learning with biomarkers, we evaluated Precise Post-op model results to predict clinical failure in 446 patients. The derived prognostic score was validated in 446 patients. Eligible subjects required complete clinical-pathologic variables and were excluded if they had received neoadjuvant treatment including androgen deprivation, radiation or chemotherapy prior to surgery. No patients were enrolled with metastatic disease prior to surgery. Evaluate the assay using time to event concordance index (C-index), Kaplan-Meier, and hazards ratio.

RESULTS

In the training cohort (n = 306), the Precise Post-op test predicted significant clinical failure with a C-index of 0.82, [95% CI: 0.76-0.86], HR:6.7, [95% CI: 3.59-12.45], p < 0.00001. Results were confirmed in validation (n = 284) with a C-index 0.77 [95% CI: 0.72-0.81], HR = 5.4, [95% CI: 2.74-10.52], p < 0.00001. By comparison, a clinical feature base model had a C-index of 0.70 with a HR = 3.7. The Post-Op test also re-classified 58% of CAPRA-S intermediate risk patients as low risk for clinical failure.

CONCLUSIONS

Precise Post-op tissue-based test discriminates low from intermediate high risk prostate cancer disease progression in the postoperative setting. Guided by machine learning, the test enhances traditional Gleason grading with novel features that accurately reflect the biology of personalized risk assignment.

Interval Kernels for Combining Biometric Measurements from Multiple Prostate Samples per Patient in Prognostic Models with Transductive Semi-Supervised Support Vector Regression

Prostate cancer is the most prevalent form of cancer and second most common form of cancer deaths among men in the United States. Physicians work with patients to make difficult treatment decisions. They are often aided by a multitude of prognostic models to assess risk and predict outcomes. These survival analysis models may analyze features characterizing biomolecular and histomorphic properties of the solid tumor sample. Commonly, multiple samples per patient are analyzed and features are combined in some way to generate a single result, such as by taking the median measurement. Recently, support vector regression approaches paired with a semi-supervised transduction framework have proven powerful in building such prognostic models. Separately, there has been work presenting the use of interval kernels to capture the range of measurements across multiple samples as an "interval" via the Hausdorff distance within a kernel based support vector approach. This paper presents the first results in exploring a combination of these two concepts. Namely, using an interval kernel based support vector approach within the aforementioned transduction framework to build prognostic models leveraging information from multiple tumor samples per patient. The results show that interval kernels yield more accurate prognostic models, and the semi-supervised transduction framework further improves performance. This suggests that this novel combination of unique recent advances can help build better prognostic models and improve the treatment of prostate cancer.

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.

Implementation of a Precision Pathology Program Focused on Oncology-Based Prognostic and Predictive Outcomes

Personalized or precision medicine as a diagnostic and therapeutic paradigm was introduced some 10-15 years ago, with the advent of biomarker discovery as a mechanism for identifying prognostic and predictive attributes associated with treatment indication and outcome. While the concept is not new, the successful development and implementation of novel 'companion diagnostics', especially in oncology, continues to represent a significant challenge and is currently at the forefront of smart trial design and therapeutic choice. The ability to determine patient selection for a specific therapy has broad implications including better chances for a positive outcome, limited exposure to potentially toxic drugs and improved health economics. Importantly, a significant step in this paradigm is the role of predictive pathology or the accurate assessment of morphology at the microscopic level. In breast cancer, this has been most useful where histologic attributes such as the classification of tubular and cribriform carcinoma dictates surgery while neoadjuvant studies suggest that patients with lobular carcinoma are not likely to benefit from chemotherapy. The next level of 'personalized pathology' at the tissue-cellular level is the use of 'protein biomarker panels' to classify the disease process and ultimately drive tumor characterization and treatment. The following review article will focus on the evolution of predictive pathology from a subjective, 'opinion-based' approach to a quantitative science. In addition, we will discuss the individual components of the precise pathology platform including advanced image analysis, biomarker quantitation with mathematical modeling and the integration with fluid-based (i.e. blood, urine) analytics as drivers of next generation precise patient phenotyping.

Vision 20/20: Molecular-guided surgical oncology based upon tumor metabolism or immunologic phenotype: Technological pathways for point of care imaging and intervention

Surgical guidance with fluorescence has been demonstrated in individual clinical trials for decades, but the scientific and commercial conditions exist today for a dramatic increase in clinical value. In the past decade, increased use of indocyanine green based visualization of vascular flow, biliary function, and tissue perfusion has spawned a robust growth in commercial systems that have near-infrared emission imaging and video display capabilities. This recent history combined with major preclinical innovations in fluorescent-labeled molecular probes, has the potential for a shift in surgical practice toward resection guidance based upon molecular information in addition to conventional visual and palpable cues. Most surgical subspecialties already have treatment management decisions partially based upon the immunohistochemical phenotype of the cancer, as assessed from molecular pathology of the biopsy tissue. This phenotyping can inform the surgical resection process by spatial mapping of these features. Further integration of the diagnostic and therapeutic value of tumor metabolism sensing molecules or immune binding agents directly into the surgical process can help this field mature. Maximal value to the patient would come from identifying the spatial patterns of molecular expression in vivo that are well known to exist. However, as each molecular agent is advanced into trials, the performance of the imaging system can have a critical impact on the success. For example, use of pre-existing commercial imaging systems are not well suited to image receptor targeted fluorophores because of the lower concentrations expected, requiring orders of magnitude more sensitivity. Additionally the imaging system needs the appropriate dynamic range and image processing features to view molecular probes or therapeutics that may have nonspecific uptake or pharmacokinetic issues which lead to limitations in contrast. Imaging systems need to be chosen based upon objective performance criteria, and issues around calibration, validation, and interpretation need to be established before a clinical trial starts. Finally, as early phase trials become more established, the costs associated with failures can be crippling to the field, and so judicious use of phase 0 trials with microdose levels of agents is one viable paradigm to help the field advance, but this places high sensitivity requirements on the imaging systems used. Molecular-guided surgery has truly transformative potential, and several key challenges are outlined here with the goal of seeing efficient advancement with ideal choices. The focus of this vision 20/20 paper is on the technological aspects that are needed to be paired with these agents.

Predicting and replacing the pathological Gleason grade with automated gland ring morphometric features from immunofluorescent prostate cancer images

The Gleason grade is the most common architectural and morphological assessment of prostate cancer severity and prognosis. There have been numerous algorithms developed to approximate and duplicate the Gleason scoring system, mostly developed in standard H&E brightfield microscopy. Immunofluorescence (IF) image analysis of tissue pathology has recently been proven to be robust in developing prognostic assessments of disease, particularly in prostate cancer. We leverage a method of segmenting gland rings in IF images for predicting the pathological Gleason, both the clinical and the image specific grades, which may not necessarily be the same. We combine these measures with nuclear specific characteristics. In 324 images from 324 patients, our individual features correlate well univariately with the Gleason grades and in a multivariate setting have an accuracy of 85% in predicting the Gleason grade. Additionally, these features correlate strongly with clinical progression outcomes [concordance index (CI) of 0.89], significantly outperforming the clinical Gleason grades (CI of 0.78). Finally, in multivariate models for multiple prostate cancer progression endpoints, replacing the Gleason with these features results in equivalent or improved performances. This work presents the first assessment of morphological gland unit features from IF images for predicting the Gleason grade, and even replacing it in prostate cancer prognostics.

The prognostic value of stromal FK506-binding protein 1 and androgen receptor in prostate cancer outcome

BACKGROUND

Improving our ability to predict cancer progression and response to conservative or radical intent therapy is critical if we are to prevent under or over treatment of individual patients. Whereas the majority of solid tumors now have a range of molecular and/or immunological markers to help define prognosis and treatment options, prostate cancer still relies mainly on histological grading and clinical parameters. We have recently reported that androgen receptor (AR) expression in stroma inversely associates with prostate cancer-specific survival, and that stromal AR reduces metastasis. For this paper, we tested the hypothesis that the AR-regulated gene FKBP51 could be used as a marker of AR activity to better predict outcome.

METHODS

Using immunohistochemistry on a cohort of 64 patient-matched benign and malignant prostate tissues, we assessed patient outcome by FKBP51 and AR levels. Immunoblot and RT-qPCR were used to demonstrate androgen regulation of FKBP51 in primary and primary human prostatic fibroblasts and fibroblast cell-lines.

RESULTS

As predicted by FKBP51 level, high AR activity in cancer stroma was associated with longer median survival (1,306 days) compared with high AR alone (699 days), whereas those with low AR and/or low FKBP51 did poorly (384 and 338 days, respectively). Survival could not be predicted on the basis cancer epithelial AR levels or activity, and was not associated with immunoreactivity in patient matched benign tissues.

CONCLUSION

FKBP51 improves the ability of stromal AR to predict prostate cancer-specific mortality. By adding additional immunological assessment, similar to what is already in place in a number of other cancers, we could better serve patients with prostate cancer in prognosis and informed treatment choices. Prostate 77:185-195, 2017. © 2016 Wiley Periodicals, Inc.

Stromal Androgen Receptor in Prostate Cancer Development and Progression

Prostate cancer development and progression is the result of complex interactions between epithelia cells and fibroblasts/myofibroblasts, in a series of dynamic process amenable to regulation by hormones. Whilst androgen action through the androgen receptor (AR) is a well-established component of prostate cancer biology, it has been becoming increasingly apparent that changes in AR signalling in the surrounding stroma can dramatically influence tumour cell behavior. This is reflected in the consistent finding of a strong association between stromal AR expression and patient outcomes. In this review, we explore the relationship between AR signalling in fibroblasts/myofibroblasts and prostate cancer cells in the primary site, and detail the known functions, actions, and mechanisms of fibroblast AR signaling. We conclude with an evidence-based summary of how androgen action in stroma dramatically influences disease progression.

WOMEN IN CANCER THEMATIC REVIEW: New roles for nuclear receptors in prostate cancer

Prostate cancer has, for decades, been treated by inhibiting androgen signalling. This is effective in the majority of patients, but inevitably resistance develops and patients progress to life-threatening metastatic disease - hence the quest for new effective therapies for 'castrate-resistant' prostate cancer (CRPC). Studies into what pathways can drive tumour recurrence under these conditions has identified several other nuclear receptor signalling pathways as potential drivers or modulators of CRPC.The nuclear receptors constitute a large (48 members) superfamily of transcription factors sharing a common modular functional structure. Many of them are activated by the binding of small lipophilic molecules, making them potentially druggable. Even those for which no ligand exists or has yet been identified may be tractable to activity modulation by small molecules. Moreover, genomic studies have shown that in models of CRPC, other nuclear receptors can potentially drive similar transcriptional responses to the androgen receptor, while analysis of expression and sequencing databases shows disproportionately high mutation and copy number variation rates among the superfamily. Hence, the nuclear receptor superfamily is of intense interest in the drive to understand how prostate cancer recurs and how we may best treat such recurrent disease. This review aims to provide a snapshot of the current knowledge of the roles of different nuclear receptors in prostate cancer - a rapidly evolving field of research.

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

BACKGROUND

Gleason scoring represents the standard for diagnosis of prostate cancer (PCa) and assessment of prognosis following radical prostatectomy (RP), but it does not account for patterns in neighboring normal-appearing benign fields that may be predictive of disease recurrence.

OBJECTIVE

To investigate (1) whether computer-extracted image features within tumor-adjacent benign regions on digital pathology images could predict recurrence in PCa patients after surgery and (2) whether a tumor plus adjacent benign signature (TABS) could better predict recurrence compared with Gleason score or features from benign or cancerous regions alone.

DESIGN, SETTING, AND PARTICIPANTS

We studied 140 tissue microarray cores (0.6mm each) from 70 PCa patients following surgery between 2000 and 2004 with up to 14 yr of follow-up. Overall, 22 patients experienced recurrence (biochemical [prostate-specific antigen], local, or distant recurrence and cancer death) and 48 did not.

INTERVENTION

RP was performed in all patients.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The top 10 features identified as most predictive of recurrence within both the benign and cancerous regions were combined into a 10-feature signature (TABS). Computer-extracted nuclear shape and architectural features from cancerous regions, adjacent benign fields, and TABS were evaluated via random forest classification accuracy and Kaplan-Meier survival analysis.

RESULTS AND LIMITATIONS

Tumor-adjacent benign field features were predictive of recurrence (area under the receiver operating characteristic curve [AUC]: 0.72). Tumor-field nuclear shape descriptors and benign-field local nuclear arrangement were the predominant features found for TABS (AUC: 0.77). Combining TABS with Gleason sum further improved identification of recurrence (AUC: 0.81). All experiments were performed using threefold cross-validation without independent test set validation.

CONCLUSIONS

Computer-extracted nuclear features within cancerous and benign regions predict recurrence following RP. Furthermore, TABS was shown to provide added value to common predictors including Gleason sum and Kattan and Stephenson nomograms.

PATIENT SUMMARY

Future studies may benefit from evaluation of benign regions proximal to the tumor on surgically excised prostate cancer tissue for assessing risk of disease recurrence.

Automated prostate tissue referencing for cancer detection and diagnosis

Background

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

Results

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

Conclusions

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

Electronic supplementary material

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

Quantitative Time-Resolved Fluorescence Imaging of Androgen Receptor and Prostate-Specific Antigen in Prostate Tissue Sections

Androgen receptor (AR) and prostate-specific antigen (PSA) are expressed in the prostate and are involved in prostate cancer (PCa). The aim of this study was to develop reliable protocols for reproducible quantification of AR and PSA in benign and malignant prostate tissue using time-resolved fluorescence (TRF) imaging techniques. AR and PSA were detected with TRF in tissue microarrays from 91 PCa patients. p63/ alpha-methylacyl-CoA racemase (AMACR) staining on consecutive sections was used to categorize tissue areas as benign or cancerous. Automated image analysis was used to quantify staining intensity. AR intensity was significantly higher in AMACR+ and lower in AMACR- cancer areas as compared with benign epithelium. The PSA intensity was significantly lower in cancer areas, particularly in AMACR- glands. The AR/PSA ratio varied significantly in the AMACR+ tumor cells as compared with benign glands. There was a trend of more rapid disease progression in patients with higher AR/PSA ratios in the AMACR- areas. This study demonstrates the feasibility of developing reproducible protocols for TRF imaging and automated image analysis to study the expression of AR and PSA in benign and malignant prostate. It also highlighted the differences in AR and PSA protein expression within AMACR- and AMACR+ cancer regions.

Computational pathology of pre-treatment biopsies identifies lymphocyte density as a predictor of response to neoadjuvant chemotherapy in breast cancer

BACKGROUND

There is a need to improve prediction of response to chemotherapy in breast cancer in order to improve clinical management and this may be achieved by harnessing computational metrics of tissue pathology. We investigated the association between quantitative image metrics derived from computational analysis of digital pathology slides and response to chemotherapy in women with breast cancer who received neoadjuvant chemotherapy.

METHODS

We digitised tissue sections of both diagnostic and surgical samples of breast tumours from 768 patients enrolled in the Neo-tAnGo randomized controlled trial. We subjected digital images to systematic analysis optimised for detection of single cells. Machine-learning methods were used to classify cells as cancer, stromal or lymphocyte and we computed estimates of absolute numbers, relative fractions and cell densities using these data. Pathological complete response (pCR), a histological indicator of chemotherapy response, was the primary endpoint. Fifteen image metrics were tested for their association with pCR using univariate and multivariate logistic regression.

RESULTS

Median lymphocyte density proved most strongly associated with pCR on univariate analysis (OR 4.46, 95 % CI 2.34-8.50, p < 0.0001; observations = 614) and on multivariate analysis (OR 2.42, 95 % CI 1.08-5.40, p = 0.03; observations = 406) after adjustment for clinical factors. Further exploratory analyses revealed that in approximately one quarter of cases there was an increase in lymphocyte density in the tumour removed at surgery compared to diagnostic biopsies. A reduction in lymphocyte density at surgery was strongly associated with pCR (OR 0.28, 95 % CI 0.17-0.47, p < 0.0001; observations = 553).

CONCLUSIONS

A data-driven analysis of computational pathology reveals lymphocyte density as an independent predictor of pCR. Paradoxically an increase in lymphocyte density, following exposure to chemotherapy, is associated with a lack of pCR. Computational pathology can provide objective, quantitative and reproducible tissue metrics and represents a viable means of outcome prediction in breast cancer.

TRIAL REGISTRATION

ClinicalTrials.gov NCT00070278 ; 03/10/2003.

Immunopathologic Assessment of PTEN Expression

Immunohistochemistry (IHC) is an excellent technique used routinely to define the phenotype in pathology laboratories through the analysis of molecular expression in cells and tissues. The PTEN protein is ubiquitously expressed in the majority of human tissues, and allelic or complete loss of PTEN is frequently observed in different types of malignancies leading to an activation of the AKT/mTOR pathways. IHC-based analyses are best to determine the level of PTEN expression in histological samples, but not to assess partial or heterozygous deletions, for which FISH analyses are more appropriate. Interpretation of the IHC results is the most critical point in the assessment of PTEN expression, since it is used both as a prognostic factor and as a tool to guide therapeutic intervention and response to therapy. Importantly, analyses of well-known downstream markers, such as AKT or mTOR, may be used to further analyze PTEN functional status.

SIGIRR/TIR8, an important regulator of TLR4 and IL-1R-mediated NF-κB activation, predicts biochemical recurrence after prostatectomy in low-grade prostate carcinomas

Single Ig IL-1-related receptor (SIGIRR) is a negative regulator of toll-like receptor 4 and IL-1-mediated activation of nuclear factor κ-light-chain enhancer of activated B cells. The purpose of this study was to qualitatively and quantitatively determine SIGIRR protein expression in human prostate tissues and associate SIGIRR expression with clinical parameters. SIGIRR expression was quantified in glandular prostate tissue using immunohistochemistry and multispectral imaging, and expression was evaluated in relation to clinicopathological features of benign prostatic hyperplasia and prostate cancer (PCa). Subgroupings of low Gleason score (≤ 6 and 3 + 4) and high Gleason score (4 + 3 and ≥ 8) were used for patient outcomes. SIGIRR was predominantly expressed in the cytoplasm and nucleus of the prostatic epithelium with little expression within the stroma. Compared with normal prostate, cytoplasmic SIGIRR expression was similar in benign prostatic hyperplasia, high-grade prostatic intraepithelial neoplasia, PCa, and metastases. A decrease in nuclear expression was found in metastasis samples (P = .04). Changes in SIGIRR expression were not associated with Gleason score, pathological stage, tumor volume, surgical margin status, or serum prostate-specific antigen (P > .05). Nuclear (P = .96) and cytoplasmic (P = .89) SIGIRR expressions were not related to patient outcomes in univariable analysis, but in the analysis of patients with low Gleason scores, high cytoplasmic SIGIRR expression was associated with biochemical recurrence in both univariable (P = .01) and multivariable (hazard ratio, 2.31 [95% confidence interval 1.05-5.06]; P = .04) analyses. Similarly, in multivariable analysis of only low-stage (pT2) tumors, SIGIRR independently predicted biochemical recurrence (P = .009). We conclude that SIGIRR predicts biochemical recurrence in patients with low Gleason score and low pathological stage PCa.

Expression of ERG protein in prostate cancer: variability and biological correlates

Prostate cancer is the second leading cause of cancer-related death of men in the USA. The TMPRSS2/ERG (T/E) fusion gene is present in approximately 50% of prostate cancers and promotes tumor progression in vivo. The presence of the T/E fusion gene is strongly associated with the expression of ERG protein, but emerging evidence indicates a significant interfocal and intrafocal variability in the levels of ERG protein expression. We therefore analyzed ERG protein expression by image analysis to objectively quantitate the extent of such heterogeneity, and confirmed significant interfocal and intrafocal variability of ERG protein expression levels in cancer expressing ERG. To define the pathways associated with ERG and its variable expression in prostate cancer, we have analyzed the correlations of ERG expression, as evaluated by immunohistochemistry, with 46 key proteins associated with signal transduction, transcriptional control, and other processes using a large tissue microarray with more than 500 prostate cancers. We found a significant correlation of ERG expression with the markers of activation of the PI3K, MYC, and NFκB pathways, which had previously been linked directly or indirectly to ERG expression. We have also identified significant correlations with novel proteins that have not been previously linked to ERG expression, including serum response factor, the p160 coactivator SRC1, and Sprouty1. Notably, SKP2 only correlated with a high level of ERG protein expression. Thus ERG expression is variable in prostate cancer and is associated with activation of multiple pathways and proteins including several potentially targetable pathways.

Improving prediction of prostate cancer recurrence using chemical imaging

Precise Outcome prediction is crucial to providing optimal cancer care across the spectrum of solid cancers. Clinically-useful tools to predict risk of adverse events (metastases, recurrence), however, remain deficient. Here, we report an approach to predict the risk of prostate cancer recurrence, at the time of initial diagnosis, using a combination of emerging chemical imaging, a diagnostic protocol that focuses simultaneously on the tumor and its microenvironment, and data analysis of frequent patterns in molecular expression. Fourier transform infrared (FT-IR) spectroscopic imaging was employed to record the structure and molecular content from tumors prostatectomy. We analyzed data from a patient cohort that is mid-grade dominant – which is the largest cohort of patients in the modern era and in whom prognostic methods are largely ineffective. Our approach outperforms the two widely used tools, Kattan nomogram and CAPRA-S score in a head-to-head comparison for predicting risk of recurrence. Importantly, the approach provides a histologic basis to the prediction that identifies chemical and morphologic features in the tumor microenvironment that is independent of conventional clinical information, opening the door to similar advances in other solid tumors.

Genomic analysis in active surveillance: predicting high-risk disease using tissue biomarkers

PURPOSE OF REVIEW

For patients newly diagnosed with prostate cancer, the most significant question is whether the 'truly malignant' disease has been identified. This review will provide an overview of current prostate cancer genomic and biomarker discovery - validation strategies geared towards identifying aggressive, clinically significant disease at the time of diagnosis.

RECENT FINDINGS

Based on recent findings the prostate cancer aggressive disease phenotype develops as a result of mutations (TP53, PTEN), structural events (TMPRSS2-ETS), epigenetic changes (EZH2, DAB2IP, histone alteration), and transcriptional modifications (SChLAP, PCAT-1). Copy number variability and dysregulation of specific pathways including androgen receptor signaling, PTEN/PAKT and TGF-β continue to play an important role in invasion and metastasis.

SUMMARY

Given the current challenges for applying prostate cancer genomics to clinical management, this review will incorporate some of the current novel genomic approaches and techniques including systems-based precise pathology platforms, and the role of fluid-based assays, notably, exosomes and circulating tumor cells (liquid biopsy), as tools for future diagnostic-treatment algorithms.

Overcoming tumor heterogeneity in the molecular diagnosis of urological cancers

Our understanding of tumor heterogeneity and impact on treatment response is still in its infancy, presenting significant challenges to the molecular pathologist, treating physician and ultimately for the patient. Given that tumor recurrence due to treatment resistance is the most common cause of cancer death, there remains a critical unmet need to change the current paradigm. The mechanisms which underlie tumor heterogeneity can be broadly divided into genomic instability and non-mutational processes, including stochastic variations in cellular responses, modulation by tumor microenvironment and or phenotypic/ functional plasticity relating to cancer stem cells. We believe that these biological mechanisms are not mutually exclusive and emphasize the need for more suitable methodologies to exploit the spatiotemporal patterns of intratumoral heterogeneity using novel approaches such as quantitative tissue-based biomarker assessment and systemic fluid analytics. Generating a comprehensive patient-centric phenotypic disease profile should generate a 'codex' which can be employed to change the current treatment decision process.

Digitization of medicine: how radiology can take advantage of the digital revolution

In the era of medical cost containment, radiologists must continually maintain their actual and perceived value to patients, payers, and referring providers. Exploitation of current and future digital technologies may be the key to defining and promoting radiology's "brand" and assure our continued relevance in providing predictive, preventive, personalized, and participatory medicine. The Association of University of Radiologists Radiology Research Alliance Digitization of Medicine Task Force was formed to explore the opportunities and challenges of the digitization of medicine that are relevant to radiologists, which include the reporting paradigm, computational biology, and imaging informatics. In addition to discussing these opportunities and challenges, we consider how change occurs in medicine, and how change may be effected in medical imaging community. This review article is a summary of the research of the task force and hopefully can be used as a stimulus for further discussions and development of action plans by radiology leaders.

Multispectral Imaging

The field of anatomic pathology has changed significantly over the last decades and, as a result of the technological developments in molecular pathology and genetics, has had increasing pressures put on it to become quantitative and to provide more information about protein expression on a cellular level in tissue sections. Multispectral imaging (MSI) has a long history as an advanced imaging modality and has been used for over a decade now in pathology to improve quantitative accuracy, enable the analysis of multicolor immunohistochemistry, and drastically reduce the impact of contrast-robbing tissue autofluorescence common in formalin-fixed, paraffin-embedded tissues. When combined with advanced software for the automated segmentation of different tissue morphologies (eg, tumor vs stroma) and cellular and subcellular segmentation, MSI can enable the per-cell quantitation of many markers simultaneously. This article covers the role that MSI has played in anatomic pathology in the analysis of formalin-fixed, paraffin-embedded tissue sections, discusses the technological aspects of why MSI has been adopted, and provides a review of the literature of the application of MSI in anatomic pathology.

Predicting high-risk disease using tissue biomarkers

PURPOSE OF REVIEW

For men newly diagnosed with prostate cancer, there are limited tools to understand the risk of disease progression and guide the treatment decision process. We will provide an overview of current prostate cancer biomarker discovery and validation strategies that are geared toward identifying aggressive, clinically significant disease at the time of diagnosis.

RECENT FINDINGS

The prostate gland exhibits multiple genetic events leading to both latent and clinically significant prostate cancer. Recent evidence from clinical translational studies has implicated the role of aneuploidy and copy-number variation as significant predictors of aggressive disease. Furthermore, the regulation of NKX3.1 by Pim-1 has provided a novel mechanism for the balance between indolence and disease course. Although promising, there are no routine clinically used tissue-based biomarkers for identifying risk of prostate cancer progression at diagnosis. The TMPRSS2-ERG gene fusion has provided insight into the early development of prostate cancer but has not been unequivocally associated with aggressive disease. Importantly, the only platform relying on intact tissue profiles is the systems pathology analysis program that includes histomorphometry and quantitative multiplex biomarker assessment (including the evaluation of the prostate cancer stem cell) to construct prognostic algorithms for pretreatment and post-treatment assessment.

SUMMARY

Our objective for this review was to explore the effective use of prostate tissue samples, including fluids, to identify relevant markers of clinically significant disease. We believe that the inherent molecular heterogeneity in prostate cancer requires a multimodal approach, in the context of a systems pathology platform, to create the personalized tools for future diagnostic treatment algorithms.

Astronomical algorithms for automated analysis of tissue protein expression in breast cancer

BACKGROUND

High-throughput evaluation of tissue biomarkers in oncology has been greatly accelerated by the widespread use of tissue microarrays (TMAs) and immunohistochemistry. Although TMAs have the potential to facilitate protein expression profiling on a scale to rival experiments of tumour transcriptomes, the bottleneck and imprecision of manually scoring TMAs has impeded progress.

METHODS

We report image analysis algorithms adapted from astronomy for the precise automated analysis of IHC in all subcellular compartments. The power of this technique is demonstrated using over 2000 breast tumours and comparing quantitative automated scores against manual assessment by pathologists.

RESULTS

All continuous automated scores showed good correlation with their corresponding ordinal manual scores. For oestrogen receptor (ER), the correlation was 0.82, P<0.0001, for BCL2 0.72, P<0.0001 and for HER2 0.62, P<0.0001. Automated scores showed excellent concordance with manual scores for the unsupervised assignment of cases to 'positive' or 'negative' categories with agreement rates of up to 96%.

CONCLUSION

The adaptation of astronomical algorithms coupled with their application to large annotated study cohorts, constitutes a powerful tool for the realisation of the enormous potential of digital pathology.

The role of treatment modality on the utility of predictive tissue biomarkers in clinical prostate cancer: a systematic review

BACKGROUND

Tissue biomarkers could pivotally improve clinical outcome prediction following prostate cancer therapy. Clinically, prostate cancer is managed by diverse treatment modalities whose individual influence on a biomarker's predictive ability is not well understood and poorly investigated in the literature.

OBJECTIVE

We conducted a systematic review to assess the predictive value of biomarkers in different treatment contexts in prostate cancer.

STUDY METHODOLOGY

A literature search was performed using the MeSH headings "prostate neoplasms" and "biological markers". Rigorous selection criteria identified studies correlating expression with clinical outcomes from primary androgen deprivation therapy (ADT), radical prostatectomy and radiotherapy (± neoadjuvant ADT).

STUDY RESULTS

Of 10,668 studies identified, 481 papers matched initial inclusion criteria. Following rescreening, 384 studies identified 236 individual tissue biomarkers, of which 29 were predictive on multivariate analysis in at least 2 independent cohorts. The majority were only tested in surgical cohorts. Only 8 predictive biomarkers were tested across all 3 treatments with Ki67 identified as universal predictive marker. p16 showed potential for treatment stratification between surgery and radiotherapy but needs further validation in independent studies.

CONCLUSIONS

Despite years of research, very few tissue biomarkers retain predictive value in independent validation across therapy context. Currently, none have conclusive ability to help treatment selection. Future biomarker research should consider the therapy context and use uniform methodology and evaluation criteria.

Systematic analysis of breast cancer morphology uncovers stromal features associated with survival

The morphological interpretation of histologic sections forms the basis of diagnosis and prognostication for cancer. In the diagnosis of carcinomas, pathologists perform a semiquantitative analysis of a small set of morphological features to determine the cancer's histologic grade. Physicians use histologic grade to inform their assessment of a carcinoma's aggressiveness and a patient's prognosis. Nevertheless, the determination of grade in breast cancer examines only a small set of morphological features of breast cancer epithelial cells, which has been largely unchanged since the 1920s. A comprehensive analysis of automatically quantitated morphological features could identify characteristics of prognostic relevance and provide an accurate and reproducible means for assessing prognosis from microscopic image data. We developed the C-Path (Computational Pathologist) system to measure a rich quantitative feature set from the breast cancer epithelium and stroma (6642 features), including both standard morphometric descriptors of image objects and higher-level contextual, relational, and global image features. These measurements were used to construct a prognostic model. We applied the C-Path system to microscopic images from two independent cohorts of breast cancer patients [from the Netherlands Cancer Institute (NKI) cohort, n = 248, and the Vancouver General Hospital (VGH) cohort, n = 328]. The prognostic model score generated by our system was strongly associated with overall survival in both the NKI and the VGH cohorts (both log-rank P ≤ 0.001). This association was independent of clinical, pathological, and molecular factors. Three stromal features were significantly associated with survival, and this association was stronger than the association of survival with epithelial characteristics in the model. These findings implicate stromal morphologic structure as a previously unrecognized prognostic determinant for breast cancer.

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.

Predictive and prognostic molecular markers for cancer medicine

Over the last 10 years there has been an explosion of information about the molecular biology of cancer. A challenge in oncology is to translate this information into advances in patient care. While there are well-formed routes for translating new molecular information into drug therapy, the routes for translating new information into sensitive and specific diagnostic, prognostic and predictive tests are still being developed. Similarly, the science of using tumor molecular profiles to select clinical trial participants or to optimize therapy for individual patients is still in its infancy. This review will summarize the current technologies for predicting treatment response and prognosis in cancer medicine, and outline what the future may hold. It will also highlight the potential importance of methods that can integrate molecular, histopathological and clinical information into a synergistic understanding of tumor progression. While these possibilities are without doubt exciting, significant challenges remain if we are to implement them with a strong evidence base in a widely available and cost-effective manner.

Lycopene for the prevention of prostate cancer

BACKGROUND

Prostate cancer is a common cause of death in developed countries, yet the benefits of screening for prostate cancer still remain controversial. A prostate-specific antigen (PSA) test result greater than 4 ng/mL (nanograms/millilitre) has commonly been used as the cut-off level for seeking further tests to diagnose the presence (or absence) of prostate cancer. An increase in PSA levels may not necessarily be associated with an increased risk of prostate cancer, as PSA levels may also be increased in men with benign prostatic hyperplasia and prostatitis. Despite the uncertainty of the net benefit of early detection and treatment, safe and effective methods to prevent prostate cancer are of value. Consumers, seeking greater involvement in their healthcare, are increasingly turning to lifestyle modification and complementary and alternative medicines (CAMs) to maintain their health and prevent disease. Lycopene is a member of the carotenoid family, which is found abundantly in tomatoes, tomato-based products, strawberries, and watermelon. It has been hypothesised that lycopene is a strong antioxidant, which may lower the risk of cancer (including prostate cancer) in people who have diets rich in lycopene.

OBJECTIVES

To determine whether lycopene reduces the incidence of prostate cancer and prostate cancer-specific mortality. Secondary objectives include changes in PSA levels, prostate symptoms and the nature of adverse events associated with lycopene use.

SEARCH METHODS

Electronic searches were conducted across MEDLINE, EMBASE and the Cochrane Central Register of Controlled Trials (CENTRAL) databases. No language or other limitations were imposed.

SELECTION CRITERIA

Randomised controlled trials (RCTs) that investigated the use of lycopene for the prevention of prostate cancer were eligible for inclusion in this review.

DATA COLLECTION AND ANALYSIS

A search of electronic databases, performed in August 2011, identified 64 citations. All articles were selected for full-text review. From these citations, three studies were identified as meeting the inclusion criteria. Handsearching did not provide any additional studies.

MAIN RESULTS

Three RCTs, with a total of 154 participants were included in this review. None of the studies reported data on prostate cancer mortality. All of the included studies differed with respect to design, participants included and allocation of lycopene. This clinical heterogeneity limits the value on the pooled estimated of the meta-analyses. The methodological quality of two of the three included studies was assessed as posing a 'high' risk of bias. Meta-analysis indicated no statistical difference in PSA levels between men randomised to receive lycopene and the comparison group (MD (mean difference) -0.34, 95% CI (confidence interval) -2.01, 1.32). Only one study reported incidence of prostate cancer (10% in the lycopene group versus 30% in control group). The level of lycopene was also not statistically different in men randomised to receive lycopene and the comparison group (MD 0.39 µg/mL (micrograms/millilitre), 95% CI -0.19, 0.98). No other meta-analyses were possible since other outcomes assessed only had one study contributing data.

AUTHORS' CONCLUSIONS

Given that only three RCTs were included in this systematic review, and the high risk of bias in two of the three studies, there is insufficient evidence to either support, or refute, the use of lycopene for the prevention of prostate cancer. Similarly, there is no robust evidence from RCTs to identify the impact of lycopene consumption upon the incidence of prostate cancer, prostate symptoms, PSA levels or adverse events.

Independent diagnostic and post-treatment prognostic models for prostate cancer demonstrate significant correlation with disease progression end points

BACKGROUND AND PURPOSE

A major advance in the standard practice of tissue-based pathology is the new discipline of systems pathology (SP) that uses computational modeling to combine clinical, pathologic, and molecular measurements to predict biologic activity. Recently, a SP-based prostate cancer (PCa) predictive model for both preoperative (Px+) and postoperative (Px) prostatectomy has been developed. The purpose of this study is to calculate the percent agreement and the concordance between the Px+ and Px end points.

PATIENTS AND METHODS

Fifty-three patients underwent robot-assisted prostatectomy for PCa, and had Px+ and Px testing performed. Data were collected on Px+ end points and Px end points along with pathologic specimen results. The percent agreement and the degree of correlation between the Px+ and Px end points were then calculated.

RESULTS

The percent agreement (PA) between Px+ end points and Px end points ranged from 77% to 87%. The PA between a high Px+ favorable pathology (FP) classification and dominant Gleason score ≤ 3 and Gleason sum ≤ 6 was 71.7% and 37.4%, respectively. On univariate analysis, Px+ disease progression (DP) score significantly correlated with Px prostate-specific antigen recurrence (PSAR) score (P<0.001), while Px+ DP probability significantly correlated with PxPSAR probability (P<0.001). Px+ FP probability significantly correlated with postprostatectomy dominant Gleason grade ≤ 3 (P<0.001) and Gleason sum (P<0.001).

CONCLUSION

The PA between Px+ and Px testing end points for radical prostatectomy patients was very good. Furthermore, there was a direct correlation between most Px+ and Px end points. While the Px+FP classification and Gleason sum demonstrated a poor PA, Px+FP score still maintained a direct correlation to prostatectomy Gleason sum.

Postoperative systems models more accurately predict risk of significant disease progression than standard risk groups and a 10-year postoperative nomogram: potential impact on the receipt of adjuvant therapy after surgery

OBJECTIVE

To compare the performance of a systems-based risk assessment tool with standard defined risk groups and the 10-year postoperative nomogram for predicting disease progression, including biochemical relapse and clinical (systemic) failure.

PATIENTS AND METHODS

Clinical variables, biometric profiles and outcome results from a training cohort comprising 373 patients in a published postoperative systems-based prognostic model were obtained. Patients were stratified according to D'Amico standard risk groups, Kattan 10-year postoperative nomogram and prognostic scores from the postoperative tissue model. The association of pathological variables and calculated risk groups with biochemical recurrence and clinical (systemic) failure was assessed using the concordance index (C-index) and hazard ratio (HR).

RESULTS

Systems-based post-prostatectomy models to predict significant disease progression (post-treatment clinical failure) were more accurate than the D'Amico defined risk groups and the Kattan 10-year postoperative nomogram (systems model: C-index, 0.84; HR, 17.46; P < 0.001 vs D'Amico: C-index, 0.73; HR, 11; P = 0.001; 10-year nomogram: C-index, 0.79; HR, 5.06; P < 0.001). The systems models were also more accurate than standard risk groups for predicting prostate-specific antigen recurrence (systems model: C-index, 0.76; HR, 8.94; P < 0.001 vs D'Amico C- index, 0.70; HR, 4.67; P < 0.001) and showed incremental improvement over the 10-year postoperative nomogram (C-index, 0.75; HR, 5.83; P < 0.001). The postoperative tissue model provided additional risk discrimination over surgical margin status and extracapsular extension for predicting disease outcome, and was most significant for the clinical (systemic) failure endpoint (surgical margin: C-index, 0.58; HR, 1.57; P= 0.2; extracapsular extension: C-index, 0.62; HR, 2.06; P = 0.04).

CONCLUSIONS

Risk assessment models that incorporate characteristics from the patient's own tumour specimen are more accurate than clinical-only nomograms for predicting significant disease outcome. Systems-based tools should provide useful information concerning the appropriate receipt of adjuvant therapy in the post-surgical setting.

A systems-based modelling approach using transurethral resection of the prostate (TURP) specimens yielded incremental prognostic significance to Gleason when predicting long-term outcome in men with localized prostate cancer

OBJECTIVE

To develop a systems-based model for predicting prostate cancer-specific survival (PCSS) using a conservatively managed cohort with clinically localized prostate cancer and long-term follow-up.

PATIENTS AND METHODS

Transurethral prostate (TURP) specimens in tissue microarray format and medical records from a 758 patient cohort were obtained. Slides were stained with haematoxylin and eosin (H&E), imaged and digitally outlined for invasive tumour. Additional sections were analysed with two multiplex quantitative immunofluorescence (IF) assays for cytokeratin-18 (epithelial cells), 4'-6-diamidino-2-phenylindole(nuclei), p63/high-molecular-weight keratin (basal cells), androgen receptor (AR) and α-methyl CoA-racemase, Ki67, phosphorylated AKT (pAKT)and CD34. Images were acquired with spectral imaging software. H&E and IF images were evaluated with image analysis algorithms; feature data were integrated with clinical variables to construct prognostic models for outcome.

RESULTS

Using a training set of 256 patients with 24% events, one clinical variable (Gleason score) and two tissue-specific characteristics (H&E morphometry and tumour-specific pAKT levels) were identified (concordance index [CoI] 0.79, sensitivity 76%, specificity 86%, hazard ratio [HR] 6.6) for predicting PCSS. Validation on an independent cohort of 269 patients with 29% events yielded a CoI of 0.76, sensitivity 59%, specificity 80% and HR of 3.6. Both H&E and IF features were selected in a multivariate setting and added incremental prognostic value to the Gleason score alone (CoI 0.77 to CoI 0.79). Furthermore, global Ki67 expression and AR levels in Gleason grade 3 tumours were both univariately associated with outcome; however, neither was selected in the final model.

CONCLUSION

A previously validated prostate needle-biopsy systems modelling approach that integrates clinical data with reproducible methods to assess H&E morphometry and biomarker expression, provided incremental benefit to the TURP Gleason score for predicting PCSS. Ki67 and AR, known to be associated with outcome in the prostate needle biopsy, were not associated with PCSS in multivariate models using TURP specimens.

Apoptosis resistance of mucosal lymphocytes and IL-10 deficiency in patients with steroid-refractory Crohn's disease

BACKGROUND

Apoptosis resistance of T-cells is considered an abnormality of immune pathways in Crohn's disease (CD). It has been previously shown that corticosteroids induce apoptosis of cells involved in inflammation. Thus, our aim was to assess the apoptosis of mononuclear cells and pro/antiinflammatory cytokines in the intestinal mucosa of patients with active CD, related to steroid response, and identify cellular and molecular factors that may predict this response to therapy.

METHODS

Patients with CD (n = 26), ulcerative colitis (UC) (n = 32), and controls (n = 10) were prospectively studied with mucosal biopsies before and 7-10 days after corticosteroid treatment. Immunophenotype and apoptosis of T and B lymphocytes, plasma cells, and macrophages were assessed by flow cytometry, immunohistochemistry, and immunofluorescence. The cytokine expression pattern was evaluated by quantitative polymerase chain reaction (PCR).

RESULTS

Apoptosis resistance of T and B lymphocytes was observed only in steroid-refractory and -dependent CD patients as compared to responsive patients (P = 0.032; P = 0.004, respectively), being evident after steroid treatment. Interleukin (IL)-10 was markedly increased at baseline in steroid-responsive patients compared to the nonresponders (P = 0.006; sensitivity: 88.8%; specificity: 66.6% to predict steroid response).

CONCLUSIONS

Apoptosis resistance of mucosal T and B cells in steroid-refractory and -dependent CD patients appears during the evolution of the acute phase, limiting its clinical application as a predictor marker. In contrast, increased expression of IL-10 at an early stage of active steroid-sensitive CD patients supports its usefulness at predicting a good steroid response. Steroid-dependent and -refractory CD patients share similar molecular and cellular pathophysiological mechanisms.

Androgen receptors are differentially expressed in Gleason patterns of prostate cancer and down-regulated in matched lymph node metastases

BACKGROUND

Androgen receptor (AR) expression profile in the different Gleason patterns (GP) of primary prostate cancers and nodal metastases is unknown. More information about AR distribution is needed to optimize evaluation methods and to better understand the role of AR in development and progression of prostate cancer.

METHODS

A tissue microarray was constructed from 119 hormone-naïve nodal positive, surgically treated prostate cancers containing tissues from all GP present in every primary tumor and the matched metastases. ARs were evaluated immunohistochemically and an expression score (intensity × percentage of positive cells) was assigned for each tissue spot.

RESULTS

ARs were up-regulated in primary tumors compared to normal glands and significantly different expressed in the GP (mean AR scores: GP 3=128.7, GP 4=159.1, GP 5=123.5; P=0.016). A similar expression profile was observed in metastases, however, on significantly (P<0.001) lower level (mean AR scores: GP 3=70.5, GP 4=90.4, GP 5=71.7; P=0.114). High AR expression in metastases was associated with larger total size of metastases (P=0.008). All other correlations of AR expression in primary tumors and metastases with quantitative (age, prostate cancer volume, number of metastases) or categorical (tumor stage, Gleason score of the primary tumor and metastases) tumor characteristics or with survival were insignificant.

CONCLUSION

ARs are differentially expressed in GP what should be considered in prognostic models which include AR. In nodal metastases, ARs are significantly down-regulated suggesting decreased dependence on androgens already under hormone-naïve conditions. AR expression level is not prognostic in nodal positive disease.

International Society of Urological Pathology (ISUP) Consensus Conference on Handling and Staging of Radical Prostatectomy Specimens. Working group 4: seminal vesicles and lymph nodes

The 2009 International Society of Urological Pathology Consensus Conference in Boston made recommendations regarding the standardization of pathology reporting of radical prostatectomy specimens. Issues relating to the infiltration of tumor into the seminal vesicles and regional lymph nodes were coordinated by working group 4. There was a consensus that complete blocking of the seminal vesicles was not necessary, although sampling of the junction of the seminal vesicles and prostate was mandatory. There was consensus that sampling of the vas deferens margins was not obligatory. There was also consensus that muscular wall invasion of the extraprostatic seminal vesicle only should be regarded as seminal vesicle invasion. Categorization into types of seminal vesicle spread was agreed by consensus to be not necessary. For examination of lymph nodes, there was consensus that special techniques such as frozen sectioning were of use only in high-risk cases. There was no consensus on the optimal sampling method for pelvic lymph node dissection specimens, although there was consensus that all lymph nodes should be completely blocked as a minimum. There was also a consensus that a count of the number of lymph nodes harvested should be attempted. In view of recent evidence, there was consensus that the diameter of the largest lymph node metastasis should be measured. These consensus decisions will hopefully clarify the difficult areas of pathological assessment in radical prostatectomy evaluation and improve the concordance of research series to allow more accurate assessment of patient prognosis.

Molecular genetics of prostate cancer: new prospects for old challenges

Despite much recent progress, prostate cancer continues to represent a major cause of cancer-related mortality and morbidity in men. Since early studies on the role of the androgen receptor that led to the advent of androgen deprivation therapy in the 1940s, there has long been intensive interest in the basic mechanisms underlying prostate cancer initiation and progression, as well as the potential to target these processes for therapeutic intervention. Here, we present an overview of major themes in prostate cancer research, focusing on current knowledge of principal events in cancer initiation and progression. We discuss recent advances, including new insights into the mechanisms of castration resistance, identification of stem cells and tumor-initiating cells, and development of mouse models for preclinical evaluation of novel therapuetics. Overall, we highlight the tremendous research progress made in recent years, and underscore the challenges that lie ahead.

Molecular processes leading to aberrant androgen receptor signaling and castration resistance in prostate cancer

Hormone therapies targeting androgen receptor signaling are the mainstay of treatment for patients with advanced prostate cancer. The length of clinical remission induced by hormone therapies varies substantially among treated patients. Why some patients progress rapidly after treatment while others benefit with prolonged remission is a question that remains unsolved. The androgen receptor signaling pathway is the key molecular determinant of castration resistance, and a key target for prostate cancer drug design. Recent advances in characterizing molecular processes leading to the development of castration-resistant prostate cancer, including the discovery of multiple androgen receptor splicing variants, offer opportunities for rational development of new clinical tools or approaches to predict, monitor or control/prevent prostate cancer progression in the castrate setting.

PathGrid: a service-orientated architecture for microscopy image analysis

This paper describes 'PathGrid'--an analysis and data integration system, developed initially to meet the demands in the analysis of medical microscopy imaging data. An overview of the current system is given, describing the techniques used in developing the data handling infrastructure and the analysis algorithm development. The use of software created in the context of systems designed for the astronomy domain is noted, specifically infrastructure from the astronomy virtual observatory movement for data discovery, access and workflow management, and astronomical image analysis software adapted for the analysis of high-throughput astronomy imaging surveys. This paper notes the applicability of the techniques from the astronomy domain. The testbed infrastructure deployment is described, emphasizing its speed and ease of use and support. The validity of the analysis techniques is confirmed through the pilot study described here--with the application to a large sample of immunohistochemistry microscopy data obtained in part for assessing the oestrogen receptor status of breast cancers. The analysis showed that the specificity and sensitivity values for the automatic scoring using PathGrid were within the errors of those obtained via a 'gold standard' manual pathologist scoring.

Beta-2-microglobulin expression correlates with high-grade prostate cancer and specific defects in androgen signaling

BACKGROUND

Previously, we identified Beta-2-microglobulin (beta2M) as an androgen-regulated secreted protein elevated in the serum of prostate cancer patients. In this study, we explore an interaction between beta2M expression, prostate cancer tissue, and the androgen signaling axis.

METHODS

beta2M expression in relation to clinical and pathologic variables was examined in a tissue microarray representing specimens obtained at the time of radical prostatectomy. Viral vectors were designed to down-regulate beta2M expression, and the effects on androgen-dependent growth, transcriptional regulation, and androgen receptor recruitment was investigated in human prostate cancer cell lines.

RESULTS

Variation in beta2M expression in human prostate cancer is associated with characteristics of clinically aggressive disease such as high tumor grade. Knockdown of beta2M expression in human prostate cancer cells resulted in selective defects in androgen-dependent events including growth, gene regulation, and chromatin assembly.

CONCLUSIONS

beta2M expression may provide prognostic information in patients treated with surgery for prostate cancer. Targeting beta2M expression or activity may represent a new and important mechanism to manipulate the androgen signaling axis in patients with prostate cancer.

Serum biomarkers as signals for risk and severity of work-related musculoskeletal injury

Work-related musculoskeletal disorders (MSDs) have accounted for a significant proportion of work injuries and workers' compensation claims in industrialized nations since the late 1980s. Despite epidemiological evidence for the role of repetition and force in the onset and progression of work-related MSDs, complete understanding of these important occupational health problems requires further elucidation of the underlying pathogenesis. Results from several clinical and experimental studies indicate that pathological and/or adaptive tissue changes occur as a consequence of performing repetitive and/or forceful tasks. Here, we review evidence of these tissue changes as revealed by the testing of serum biomarkers. Biomarkers of inflammation (inflammatory cytokines and C-reactive protein), cell stress or injury (malondialdehyde and creatine kinase), and collagen synthesis and degradation (collagen I carboxy-terminal propeptide and type-I collagen cross-linked C-telopeptide, respectively) and their association with MSDs will be reviewed.

High-throughput detection of prostate cancer in histological sections using probabilistic pairwise Markov models

In this paper we present a high-throughput system for detecting regions of carcinoma of the prostate (CaP) in HSs from radical prostatectomies (RPs) using probabilistic pairwise Markov models (PPMMs), a novel type of Markov random field (MRF). At diagnostic resolution a digitized HS can contain 80Kx70K pixels - far too many for current automated Gleason grading algorithms to process. However, grading can be separated into two distinct steps: (1) detecting cancerous regions and (2) then grading these regions. The detection step does not require diagnostic resolution and can be performed much more quickly. Thus, we introduce a CaP detection system capable of analyzing an entire digitized whole-mount HS (2x1.75cm(2)) in under three minutes (on a desktop computer) while achieving a CaP detection sensitivity and specificity of 0.87 and 0.90, respectively. We obtain this high-throughput by tailoring the system to analyze the HSs at low resolution (8microm per pixel). This motivates the following algorithm: (Step 1) glands are segmented, (Step 2) the segmented glands are classified as malignant or benign, and (Step 3) the malignant glands are consolidated into continuous regions. The classification of individual glands leverages two features: gland size and the tendency for proximate glands to share the same class. The latter feature describes a spatial dependency which we model using a Markov prior. Typically, Markov priors are expressed as the product of potential functions. Unfortunately, potential functions are mathematical abstractions, and constructing priors through their selection becomes an ad hoc procedure, resulting in simplistic models such as the Potts. Addressing this problem, we introduce PPMMs which formulate priors in terms of probability density functions, allowing the creation of more sophisticated models. To demonstrate the efficacy of our CaP detection system and assess the advantages of using a PPMM prior instead of the Potts, we alternately incorporate both priors into our algorithm and rigorously evaluate system performance, extracting statistics from over 6000 simulations run across 40 RP specimens. Perhaps the most indicative result is as follows: at a CaP sensitivity of 0.87 the accompanying false positive rates of the system when alternately employing the PPMM and Potts priors are 0.10 and 0.20, respectively.