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Histone deacetylase inhibitors such as valproic acid (VPA) are promising anticancer agents that change the acetylation status of histones and loosen the chromatin structure. We assessed nuclear structure changes induced by VPA in prostate cancer LNCaP, CWR22R, DU145, and PC3 cell lines and xenografts and their potential use as a biomarker of treatment. In vitro tissue microarrays consisted of prostate cancer cell lines treated for 3, 7, or 14 days with 0, 0.6, or 1.2 mmol/L VPA. In vivo tissue microarrays consisted of cores from prostate cancer xenografts from nude mice treated for 30 days with 0.2% or 0.4% VPA in drinking water. Digital images of at least 200 Feulgen DNA-stained nuclei were captured using the Nikon CoolScope and nuclear alterations were measured. With a set of seven most frequently significant nuclear alterations (determined by univariate logistic regression analysis), control and VPA treatment nuclei were compared in vitro and in vivo. Depending on the cell line, area under the curve-receiver operating characteristics ranged between 0.6 and 0.9 and were dose- and time-dependent both in vitro and in vivo. Also, VPA treatment caused significant nuclear alterations in normal drug-filtering organs (liver and kidney tissue). In vitro and in vivo VPA treatment of prostate cancer cell lines results in significant dose- and time-dependent changes in nuclear structure. Further, VPA induces nuclear structural changes in normal liver and kidney tissue, which likely reflects a natural physiologic response. Therefore, nuclear structural alterations may serve as a biomarker for histone deacetylase inhibitor treatment. [Mol Cancer Ther 2009;8(4):802–8]

Background: Early prediction of disease progression in men with very low-risk (VLR) prostate cancer who selected active surveillance (AS) rather than immediate treatment could reduce morbidity associated with overtreatment.Methods: We evaluated the association of six biomarkers [Periostin, (−5, −7) proPSA, CACNA1D, HER2/neu, EZH2, and Ki-67] with different Gleason scores and biochemical recurrence (BCR) on prostate cancer TMAs of 80 radical prostatectomy (RP) cases. Multiplex tissue immunoblotting (MTI) was used to assess these biomarkers in cancer and adjacent benign areas of 5 μm sections. Multivariate logistic regression (MLR) was applied to model our results.Results: In the RP cases, CACNA1D, HER2/neu, and Periostin expression were significantly correlated with aggressive phenotype in cancer areas. An MLR model in the cancer area yielded a ROC-AUC = 0.98, whereas in cancer-adjacent benign areas, yielded a ROC-AUC = 0.94. CACNA1D and HER2/neu expression combined with Gleason score in a MLR model yielded a ROC-AUC = 0.79 for BCR prediction. In the small biopsies from an AS cohort of 61 VLR cases, an MLR model for prediction of progressors at diagnosis retained (−5, −7) proPSA and CACNA1D, yielding a ROC-AUC of 0.78, which was improved to 0.82 after adding tPSA into the model.Conclusions: The molecular profile of biomarkers is capable of accurately predicting aggressive prostate cancer on retrospective RP cases and identifying potential aggressive prostate cancer requiring immediate treatment on the AS diagnostic biopsy but limited in BCR prediction.Impact: Comprehensive profiling of biomarkers using MTI predicts prostate cancer aggressive phenotype in RP and AS biopsies. Cancer Epidemiol Biomarkers Prev; 24(12); 1864–72. ©2015 AACR.

Table S1. Information of antibodies used in MTI Table S2. Multiple logistic regression models for the prediction of aggressiveness and BCR in prostatectomy TMAs: parameters included. Table S3. Multiple logistic regression models for the prediction of aggressiveness and BCR in prostatectomy TMAs: Model accuracy Table S4. Standard/Multiple logistic regression models for the prediction of progressors in AS biopsies: parameters included Table S5. Standard/Multiple logistic regression models for the prediction of progressors in AS biopsies: Model accuracy

Supplementary Table S2 from Valproic acid causes dose- and time-dependent changes in nuclear structure in prostate cancer cells in vitro and in vivo

Purpose: We assessed the association of quantitative clinical and pathologic information, including serum and tissue pro–prostate-specific antigen (proPSA) measurements, with outcomes among men with prostate cancer in an expectant management (active surveillance) program.Experimental Design: We identified 71 men enrolled in expectant management with frozen serum and tissue available from diagnosis: 39 subsequently developed unfavorable biopsies (Gleason score ≥7, ≥3 cores positive for cancer, >50% of any core involved with cancer), whereas 32 maintained favorable biopsies (median follow-up, 3.93 years). Serum total PSA, free PSA (fPSA), and [−2]proPSA were measured by the Beckman Coulter immunoassay. [−5/−7]proPSA was evaluated in cancer and benign-adjacent areas (BAA) by quantitative immunohistochemistry. Cox proportional hazards and Kaplan-Meier analyses were used to identify significant associations with unfavorable biopsy conversion.Results: The ratio [−2]proPSA/% fPSA in serum was significantly higher at diagnosis (0.87 ± 0.44 versus 0.65 ± 0.36 pg/mL; P = 0.02) in men developing unfavorable biopsies. [−5/−7]proPSA tissue staining was more intense (4104.09 ± 3033.50 versus 2418.06 ± 1606.04; P = 0.03) and comprised a greater fractional area (11.58 ± 7.08% versus 6.88 ± 5.20%; P = 0.01) in BAA of these men. Serum [−2]proPSA/% fPSA [hazard ratio, 2.53 (1.18-5.41); P = 0.02], BAA [−5/−7]proPSA % area [hazard ratio, 1.06 (1.01-1.12); P = 0.02] and BAA [−5/−7]proPSA stain intensity [hazard ratio, 1.000213 (1.000071-1.000354); P = 0.003] were significantly associated with unfavorable biopsy in Kaplan-Meier and Cox analyses. Serum [−2]proPSA/% fPSA significantly correlated with BAA [−5/−7]proPSA % area (ρ = 0.40; P = 0.002) and BAA [−5/−7]proPSA stain intensity (ρ = 0.33; P = 0.016).Conclusions: In a prospective cohort of men enrolled into expectant management for prostate cancer, serum and tissue levels of proPSA at diagnosis are associated with need for subsequent treatment. The increase in serum proPSA/% fPSA might be driven by increased proPSA production from “premalignant” cells in the prostate BAA. (Clin Cancer Res 2009;15(23):7316–21)

Supplementary Tables 1-3, Figures 1-7 from Ligand-Independent Androgen Receptor Variants Derived from Splicing of Cryptic Exons Signify Hormone-Refractory Prostate Cancer

The high mobility group A1 (HMGA1) chromatin remodeling protein is required for metastatic progression and cancer stem cell properties in preclinical breast cancer models, although its role in breast carcinogenesis has remained unclear. To investigate HMGA1 in primary breast cancer, we evaluated immunoreactivity score (IRS) in tumors from a large cohort of Asian women; HMGA1 gene expression was queried from two independent Western cohorts. HMGA1 IRS was generated from breast tumors in Korean women as the product of staining intensity (weak = 1, moderate = 2, strong = 3) and percent positive cells ( 60% = 3), and stratified into three groups: low ( 6). We assessed HMGA1 and estrogen receptor (ESR1) gene expression from two large databases (TCGA, METABRIC). Overall survival was ascertained from the METABRIC cohort. Among 540 primary tumors from Korean women (181 ER-negative, 359 ER-positive), HMGA1 IRS was 6 in 236 (43.7%). High HMGA1 IRS was associated with estrogen receptor (ER)-negativity (χ2 = 12.07; P = 0.002) and advanced nuclear grade (χ2 = 12.83; P = 0.012). In two large Western cohorts, the HMGA1 gene was overexpressed in breast cancers compared to non-malignant breast tissue (P

Simple Summary Active surveillance (AS) prostate cancer patients suffer from a lower quality of life, increased risk of anxiety and depression, and an increased risk of disease progression compared to patients who opt for curative treatment. The current inclusion criteria for AS patients is unable to accurately identify patients with increased risk of progression, and therefore there is a need for a risk stratification technique that can identify patients with a higher risk of disease progression. In this work, we leverage quantitative histomorphometric (QH) features describing nuclear position, shape, orientation, and clustering from initial H&E biopsy images to accurately identify AS-eligible patients who are at high risk for disease progression. Our findings indicate that QH features were correlated with the risk of clinical progression in AS-eligible patients and was able to out-perform judgements based on clinical variables such as Gleason score and pro-PSA. Abstract In this work, we assessed the ability of computerized features of nuclear morphology from diagnostic biopsy images to predict prostate cancer (CaP) progression in active surveillance (AS) patients. Improved risk characterization of AS patients could reduce over-testing of low-risk patients while directing high-risk patients to therapy. A total of 191 (125 progressors, 66 non-progressors) AS patients from a single site were identified using The Johns Hopkins University’s (JHU) AS-eligibility criteria. Progression was determined by pathologists at JHU. 30 progressors and 30 non-progressors were randomly selected to create the training cohort D1 (n = 60). The remaining patients comprised the validation cohort D2 (n = 131). Digitized Hematoxylin & Eosin (H&E) biopsies were annotated by a pathologist for CaP regions. Nuclei within the cancer regions were segmented using a watershed method and 216 nuclear features describing position, shape, orientation, and clustering were extracted. Six features associated with disease progression were identified using D1 and then used to train a machine learning classifier. The classifier was validated on D2. The classifier was further compared on a subset of D2 (n = 47) against pro-PSA, an isoform of prostate specific antigen (PSA) more linked with CaP, in predicting progression. Performance was evaluated with area under the curve (AUC). A combination of nuclear spatial arrangement, shape, and disorder features were associated with progression. The classifier using these features yielded an AUC of 0.75 in D2. On the 47 patient subset with pro-PSA measurements, the classifier yielded an AUC of 0.79 compared to an AUC of 0.42 for pro-PSA. Nuclear morphometric features from digitized H&E biopsies predicted progression in AS patients. This may be useful for identifying AS-eligible patients who could benefit from immediate curative therapy. However, additional multi-site validation is needed.

Background The RNA-binding motif protein 3 (RBM3) has been shown to be up-regulated in several types of cancer, including prostate cancer (PCa), compared to normal tissues. Increased RBM3 nuclear expression has been linked to improved clinical outcomes. Aims Given that RBM3 has been hypothesized to play a role in critical nuclear functions such as chromatin remodeling, DNA damage response, and other post-transcriptional processes, we sought to: (1) quantify RBM3 protein levels in archival PCa samples; (2) develop a nuclear morphometric model to determine if measures of RBM3 protein levels and nuclear features could be used to predict disease aggressiveness and biochemical recurrence. Methods & Results This study utilized two tissue microarrays (TMAs) stained for RBM3 that included 80 total cases of PCa stratified by Gleason score. A software-mediated image processing algorithm identified RBM3-positive cancerous nuclei in the TMA samples and calculated twenty-two features quantifying RBM3 expression and nuclear architecture. Multivariate logistic regression (MLR) modeling was performed to determine if RBM3 levels and nuclear structural changes could predict PCa aggressiveness and biochemical recurrence (BCR). Leave-one-out cross validation (LOOCV) was used to provide insight on how the predictive capabilities of the feature set might behave with respect to an independent patient cohort to address issues such as model overfitting. RBM3 expression was found to be significantly downregulated in highly aggressive GS ≥ 8 PCa samples compared to other Gleason scores (P < 0.0001) and significantly down-regulated in recurrent PCa samples compared to non-recurrent samples (P = 0.0377). An eleven-feature nuclear morphometric MLR model accurately identified aggressive PCa, yielding a receiver operating characteristic area under the curve (ROC-AUC) of 0.90 (P < 0.0001) in the raw data set and 0.77 (95% CI: 0.83-0.97) for LOOCV testing. The same eleven-feature model was then used to predict recurrence, yielding a ROC-AUC of 0.92 (P = 0.0004) in the raw data set and 0.76 (95% CI: 0.64-0.87) for LOOCV testing. Conclusions The RBM3 biomarker alone is a strong prognostic marker for the prediction of aggressive PCa and biochemical recurrence. Further, RBM3 appears to be down-regulated in aggressive and recurrent tumors.

Quantitative analysis of morphological changes in a cell nucleus is important for the understanding of nuclear architecture and its relationship with pathological conditions such as cancer. However, dimensionality of imaging data, together with a great variability of nuclear shapes, presents challenges for 3D morphological analysis. Thus, there is a compelling need for robust 3D nuclear morphometric techniques to carry out population-wide analysis. We propose a new approach that combines modeling, analysis, and interpretation of morphometric characteristics of cell nuclei and nucleoli in 3D. We used robust surface reconstruction that allows accurate approximation of 3D object boundary. Then, we computed geometric morphological measures characterizing the form of cell nuclei and nucleoli. Using these features, we compared over 450 nuclei with about 1,000 nucleoli of epithelial and mesenchymal prostate cancer cells, as well as 1,000 nuclei with over 2,000 nucleoli from serum-starved and proliferating fibroblast cells. Classification of sets of 9 and 15 cells achieved accuracy of 95.4% and 98%, respectively, for prostate cancer cells, and 95% and 98% for fibroblast cells. To our knowledge, this is the first attempt to combine these methods for 3D nuclear shape modeling and morphometry into a highly parallel pipeline workflow for morphometric analysis of thousands of nuclei and nucleoli in 3D.

BACKGROUND: There are few tissue-based biomarkers that can accurately predict prostate cancer (PCa) progression and aggressiveness. We sought to evaluate the clinical utility of prostate and breast overexpressed 1 (PBOV1) as a potential PCa biomarker. METHODS: Patient tumor samples were designated by Grade Groups using the 2014 Gleason grading system. Primary radical prostatectomy tumors were obtained from 48 patients and evaluated for PBOV1 levels using Western blot analysis in matched cancer and benign cancer-adjacent regions. Immunohistochemical evaluation of PBOV1 was subsequently performed in 80 cancer and 80 benign cancer-adjacent patient samples across two tissue microarrays (TMAs) to verify protein levels in epithelial tissue and to assess correlation between PBOV1 proteins and nuclear architectural changes in PCa cells. Digital histomorphometric analysis was used to track twenty-two parameters that characterized nuclear changes in PBOV1-stained cells. Using a training and test set for validation, multivariate logistic regression (MLR) models were used to identify significant nuclear parameters that distinguish Grade Group 3 and above PCa from Grade Group 1 and 2 PCa regions. RESULTS: PBOV1 protein levels were increased in tumors from Grade Group 3 and above (GS 4+3 and ≥ 8) regions versus Grade Groups 1 and 2 (GS 3+3 and 3+4) regions (P = 0.005) as assessed by densitometry of immunoblots. Additionally, by immunoblotting, PBOV1 protein levels differed significantly between Grade Group 2 (GS 3+4) and Grade Group 3 (GS 4+3) PCa samples (P = 0.028). In the immunohistochemical analysis, measures of PBOV1 staining intensity strongly correlated with nuclear alterations in cancer cells. An MLR model retaining eight parameters describing PBOV1 staining intensity and nuclear architecture discriminated Grade Group 3 and above PCa from Grade Group 1 and 2 PCa and benign cancer-adjacent regions with a ROC-AUC of 0.90 and 0.80, respectively, in training and test sets. CONCLUSIONS: Our study demonstrates that the PBOV1 protein could be used to discriminate Grade Group 3 and above PCa. Additionally, the PBOV1 protein could be involved in modulating changes to the nuclear architecture of PCa cells. Confirmatory studies are warranted in an independent population for further validation.

Current clinical tests for prostate cancer (PCa), such as the PSA test, are not fully capable of discerning patients that are highly likely to develop metastatic prostate cancer (MPCa). Hence, more accurate prediction tools are needed to provide treatment strategies that are focused on the different risk groups. Cancer/testis antigens (CTAs) are expressed during embryonic development and present aberrant expression in cancer making them ideal tumor specific biomarkers. Here, the potential use of a panel of CTAs as a biomarker for PCa detection as well as metastasis prediction is explored. We initially identified eight CTAs (CEP55, NUF2, PAGE4, PBK, RQCD1, SPAG4, SSX2andTTK) that are differentially expressed in MPCa when compared to local disease and used this panel to compare the gene and protein expression profiles in paired PCa and normal adjacent prostate tissue. We identified differential expression of all eight CTAs at the protein level when comparing 80 paired samples of PCa and the adjacent non-cancer tissue. Using multiple logistic regression we also show that a panel of these CTAs present high accuracy to discriminate normal from tumor samples. In summary, this study provides evidence that a panel of CTAs, differentially expressed in aggressive PCa, is a potential biomarker for diagnosis and prognosis to be used in combination with the current clinically available tools and is also a potential target for immunotherapy development.

Quantitative analysis of morphological changes in a cell nucleus is important for the understanding of nuclear architecture and its relationship with pathological conditions such as cancer. However, dimensionality of imaging data, together with a great variability of nuclear shapes, presents challenges for 3D morphological analysis. Thus, there is a compelling need for robust 3D nuclear morphometric techniques to carry out population-wide analysis. We propose a new approach that combines modeling, analysis, and interpretation of morphometric characteristics of cell nuclei and nucleoli in 3D. We used robust surface reconstruction that allows accurate approximation of 3D object boundary. Then, we computed geometric morphological measures characterizing the form of cell nuclei and nucleoli. Using these features, we compared over 450 nuclei with about 1,000 nucleoli of epithelial and mesenchymal prostate cancer cells, as well as 1,000 nuclei with over 2,000 nucleoli from serum-starved and proliferating fibroblast cells. Classification of sets of 9 and 15 cells achieved accuracy of 95.4% and 98%, respectively, for prostate cancer cells, and 95% and 98% for fibroblast cells. To our knowledge, this is the first attempt to combine these methods for 3D nuclear shape modeling and morphometry into a highly parallel pipeline workflow for morphometric analysis of thousands of nuclei and nucleoli in 3D.

Background A 3.4kb deletion (3.4kbΔ ) in mitochondrial DNA (mtDNA) found in histologically normal prostate biopsy specimens has been reported to be a biomarker for the increased probability of prostate cancer. Increased mtDNA copy number is also reported as associated with cancer. Objective Independent evaluation of these two potential prostate cancer biomarkers using formalin-fixed paraffin-embedded (FFPE) prostate tissue and matched urine and serum from a high risk cohort of men with and without prostate cancer. Methods Biomarker levels were detected via qPCR. Results Both 3.4kbΔ and mtDNA levels were significantly higher in cancer patient FFPE cores (p= 0.045 and p= 0.070 respectively at > 90% confidence). Urine from cancer patients contained significantly higher levels of mtDNA (p= 0.006, 64.3% sensitivity, 86.7% specificity). Combining the 3.4kbΔ and mtDNA gave better performance of detecting prostate cancer than either biomarker alone (FFPE 73.7% sensitivity, 65% specificity; urine 64.3% sensitivity, 100% specificity). In serum, there was no difference for any of the biomarkers. Conclusions This is the first report on detecting the 3.4kbΔ in urine and evaluating mtDNA levels as a prostate cancer biomarker. A confirmation study with increased sample size and possibly with additional biomarkers would need to be conducted to corroborate and extend these observations.

Nuclear alterations are a hallmark of many types of cancers, including prostate cancer (PCa). Recent evidence shows that subvisual changes, ones that may not be visually perceptible to a pathologist, to the nucleus and its ultrastructural components can precede visual histopathological recognition of cancer. Alterations to nuclear features such as nuclear size and shape, texture, and spatial architecture reflect the complex molecular level changes that occur during oncogenesis. Quantitative nuclear morphometry, a field that uses computational approaches to identify and quantify malignancy-induced nuclear changes, can enable a detailed and objective analysis of the prostate cancer cell nucleus. Recent advances in machine learning-based approaches can now automatically mine data related to these changes to aid in diagnostic decision-making and prediction of PCa prognoses. In this review, we use prostate cancer as a case study to connect the molecular level mechanisms that underlie these nuclear changes to the machine learning computational approaches, bridging the gap between the clinical and computational understanding of PCa. First, we discuss recent developments to our understanding of molecular events that drive nuclear alterations in the context of prostate cancer: the role of the nuclear matrix and lamina in size and shape changes, the role of three-dimensional chromatin organization and epigenetic modifications in textural changes, and role the tumor microenvironment in altering nuclear spatial topology. We then discuss the advances in the applications of machine learning algorithms to automatically segment nuclei in prostate histopathological images, extract nuclear features to aid in diagnostic decision-making, and predict potential outcomes such as biochemical recurrence and survival. Lastly, we discuss the challenges and opportunities associated with translation of the quantitative nuclear morphometry methodology into the clinical space. Ultimately, accurate identification and quantification of nuclear alterations can contribute to the field of nucleomics and has applications for computationally driven precision oncologic patient care.

Cell deformation is regulated by complex underlying biological mechanisms associated with spatial and temporal morphological changes in the nucleus that are related to cell differentiation, development, proliferation, and disease. Thus, quantitative analysis of changes in size and shape of nuclear structures in 3D microscopic images is important not only for investigating nuclear organization, but also for detecting and treating pathological conditions such as cancer. While many efforts have been made to develop cell and nuclear shape characteristics in 2D or pseudo-3D, several studies have suggested that 3D morphometric measures provide better results for nuclear shape description and discrimination. A few methods have been proposed to classify cell and nuclear morphological phenotypes in 3D, however, there is a lack of publicly available 3D data for the evaluation and comparison of such algorithms. This limitation becomes of great importance when the ability to evaluate different approaches on benchmark data is needed for better dissemination of the current state of the art methods for bioimage analysis. To address this problem, we present a dataset containing two different cell collections, including original 3D microscopic images of cell nuclei and nucleoli. In addition, we perform a baseline evaluation of a number of popular classification algorithms using 2D and 3D voxel-based morphometric measures. To account for batch effects, while enabling calculations of AUROC and AUPR performance metrics, we propose a specific cross-validation scheme that we compare with commonly used k-fold cross-validation. Original and derived imaging data are made publicly available on the project web-page: http://www.socr.umich.edu/projects/3d-cell-morphometry/data.html.

Resistance is a significant limitation to the effectiveness of cancer therapies. The PI3K/Akt and MAP kinase pathways play important roles in a variety of normal cellular processes and tumorigenesis. This study is designed to explore the relationship of these signaling pathways with multidrug resistance in prostate cancer (PCa). The PI3K/Akt and MAP kinase pathways were investigated utilizing paclitaxel resistant DU145-TxR PCa cells and their parental non-resistant DU145 cells to determine their relationship with resistance to paclitaxel and other anticancer drugs. Our results demonstrate that the PI3K/Akt and MAP kinase pathways are upregulated in DU145-TxR cells compared to the DU145 cells. Inactivating these pathways using the PI3K/Akt pathway inhibitor LY294002 or the MAP kinase pathway inhibitor PD98059 renders the DU145-TxR cells more sensitive to paclitaxel. We investigated the effects of these inhibitors on other anticancer drugs including docetaxel, vinblastine, doxorubicin, 10-Hydroxycamptothecin (10-HCPT) and cisplatin and find that both inhibitors induces DU145-TxR cells to be more sensitive only to the microtubule-targeting drugs (paclitaxel, docetaxel and vinblastine). Furthermore, the treatment with these inhibitors induces cleaved-PARP production in DU145-TxR cells, suggesting that apoptosis induction might be one of the mechanisms for the reversal of drug resistance. In conclusion, the PI3K/Akt and MAP kinase pathways are associated with resistance to multiple chemotherapeutic drugs. Inactivating these pathways renders these PCa cells more sensitive to microtubule-targeting drugs such as paclitaxel, docetaxel and vinblastine. Combination therapies with novel inhibitors of these two signaling pathways potentially represents a more effective treatment for drug resistant PCa.

Shape based active contours have emerged as a natural solution to overlap resolution. However, most of these shape-based methods are computationally expensive. There are instances in an image where no overlapping objects are present and applying these schemes results in significant computational overhead without any accompanying, additional benefit. In this paper we present a novel adaptive active contour scheme (AdACM) that combines boundary and region based energy terms with a shape prior in a multi level set formulation. To reduce the computational overhead, the shape prior term in the variational formulation is only invoked for those instances in the image where overlaps between objects are identified; these overlaps being identified via a contour concavity detection scheme. By not having to invoke all three terms (shape, boundary, region) for segmenting every object in the scene, the computational expense of the integrated active contour model is dramatically reduced, a particularly relevant consideration when multiple objects have to be segmented on very large histopathological images. The AdACM was employed for the task of segmenting nuclei on 80 prostate cancer tissue microarray images from 40 patient studies. Nuclear shape based, architectural and textural features extracted from these segmentations were extracted and found to able to discriminate different Gleason grade patterns with a classification accuracy of 86% via a quadratic discriminant analysis (QDA) classifier. On average the AdACM model provided 60% savings in computational times compared to a non-optimized hybrid active contour model involving a shape prior.

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. Prostate 75:218–224, 2015. © 2014 Wiley Periodicals, Inc.

SummaryCell surface growth factor receptors couple environmental cues to the regulation of cytoplasmic homeostatic processes, including autophagy, and aberrant activation of such receptors is a common feature of human malignancies. Here, we defined the molecular basis by which the epidermal growth factor receptor (EGFR) tyrosine kinase regulates autophagy. Active EGFR binds the autophagy protein Beclin 1, leading to its multisite tyrosine phosphorylation, enhanced binding to inhibitors, and decreased Beclin 1-associated VPS34 kinase activity. EGFR tyrosine kinase inhibitor (TKI) therapy disrupts Beclin 1 tyrosine phosphorylation and binding to its inhibitors and restores autophagy in non-small-cell lung carcinoma (NSCLC) cells with a TKI-sensitive EGFR mutation. In NSCLC tumor xenografts, the expression of a tyrosine phosphomimetic Beclin 1 mutant leads to reduced autophagy, enhanced tumor growth, tumor dedifferentiation, and resistance to TKI therapy. Thus, oncogenic receptor tyrosine kinases directly regulate the core autophagy machinery, which may contribute to tumor progression and chemoresistance.

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.

Study Type – Therapy (case series) Level of Evidence 4 What's known on the subject? and What does the study add? This international collaboration started in 2008 based upon the possible application of the ‘predictiveness curves’ (multinomial logistic regression method) developed at the Fred Hutchinson Cancer Research Center (FHCRC) to the internationally recognized Partin Tables for staging prostate cancer. Dr. Ying Huang, a biostatistician at the FHCRC, applied the ‘predictiveness curve’ statistical modeling concept to the Partin Tables and then created a new Partin Nomogram using total PSA (tPSA) as a continuous variable. The new ‘2010 Partin Nomogram’ stage risk prediction capacity among the total cohort and the individual patients is based on the ‘predictiveness curves’ using the method developed in Huang et al.[16]. For each pathological stage, we calculated ‘the risk’ for each subject in the cohort based on the risk model and made a quantile plot based on the estimated risks. If one considers a point on the ‘predictiveness curve’ with an x-coordinate of value v, then the value of its y-coordinate, which we name R(v), is the 100 × vth percentile of risk in the study cohort. On the other hand, for a particular point on the curve with y-coordinate p, the value of its x-coordinate, which we name R−1(p), corresponds to the percentage of subjects in the study cohort with risk ≤p[i.e. the cumulative distribution function (CDF) of risk at p]. It is likely that this CDF of risk will be useful for clinicians and patients (see Fig. 2 in the article). Dr. Huang has also written an operational R-program to calculate patient's risk and next we intend to develop a user friendly computer program based upon this program to allow the easy use by patients and physicians of the 2010 Partin Nomogram and the corresponding predictiveness curves for patient-specific pathological stage outcome prediction. OBJECTIVES • To develop a ‘2010 Partin Nomogram’ with total prostate-specific antigen (tPSA) as a continuous biomarker, in light of the fact that the current 2007 Partin Tables restrict the application of tPSA as a non-continuous biomarker by creating ‘groups’ for risk stratification with tPSA levels (ng/mL) of 0–2.5, 2.6–4.0, 4.1–6.0, 6.1–10.0 and >10.0. • To use a ‘predictiveness curve’ to calculate the percentile risk of a patient among the cohort. PATIENTS AND METHODS • In all, 5730 and 1646 patients were treated with radical prostatectomy (without neoadjuvant therapy) between 2000 and 2005 at the Johns Hopkins Hospital (JHH) and University Clinic Hamburg-Eppendorf (UCHE), respectively. • Multinomial logistic regression analysis was performed to create a model for predicting the risk of the four non-ordered pathological stages, i.e. organ-confined disease (OC), extraprostatic extension (EPE), and seminal vesicle (SV+) and lymph node (LN+) involvement. • Patient-specific risk was modelled as a function of the B-spline basis of tPSA (with knots at the first, second and third quartiles), clinical stage (T1c, T2a, and T2b/T2c) and biopsy Gleason score (5–6, 3 + 4 = 7, 4 + 3 = 7, 8–10). RESULTS • The ‘2010 Partin Nomogram’ calculates patient-specific absolute risk for all four pathological outcomes (OC, EPE, SV+, LN+) given a patient’s preoperative clinical stage, tPSA and biopsy Gleason score. • While having similar performance in terms of calibration and discriminatory power, this new model provides a more accurate prediction of patients’ pathological stage than the 2007 Partin Tables model. • The use of ‘predictiveness curves’ has also made it possible to obtain the percentile risk of a patient among the cohort and to gauge the impact of risk thresholds for making decisions regarding radical prostatectomy. CONCLUSION • The ‘2010 Partin Nomogram’ using tPSA as a continuous biomarker together with the corresponding ‘predictiveness curve’ will help clinicians and patients to make improved treatment decisions.

The aim of the current study was to identify a panel of cancer/testis antigens (CTAs) with the potential to be used as a complementary test to the prostate-specific antigen (PSA) test for prostate cancer (PCa) screening. PSA test is capable of identifying men under risk of PCa before the presence of symptoms. However, this screening has been considered a controversy assessment since many men presenting benign lesions (benign prostate hyperplasia or other inflammatory conditions) can also present increased PSA levels. Hence, the great current dilemma in PCa screening is to develop a test that in combination with PSA assay could present higher accuracy for PCa early diagnosis. CTAs constitute an important class of potential cancer biomarkers that are poorly studied in PCa. CTAs are normally expressed in testis and germ cells and are aberrantly over-expressed in cancers. This unique pattern of expression makes these genes potential candidates as specific tumor biomarkers. CTA aberrant expression in malignant tumors is associated with phenotypic changes that confer the cancer cells essential advantages for proliferation and survival. Our hypothesis is that a panel of CTAs differentially expressed in PCa compared to normal prostate tissue would be useful to develop a diagnostic biomarker panel to be used in combination with the PSA test. We initially evaluated the expression of 22 CTAs, using the Nanostring approach, in localized and metastatic PCa to identify those genes associated with more aggressive tumors. After validation by qRT-PCR, we verified that 8 genes were differentially expressed between indolent and aggressive tumors. Using immunohistochemistry and a quantitative image analysis to measure protein expression, we evaluated CTA protein levels in PCa and adjacent normal tissue paired samples. The CTAs CEP55, NUF2, PAGE4, PBK, RQCD1, SPAG4, SSX2 and TTK presented increased protein levels in PCa when compared to normal prostate tissue from the same patient. Increased levels of PAGE4, PBK, RQCD1, SPAG4 and SSX2 were more frequent among patients with Gleason Score 4+3 or higher. In an attempt to identify a panel of CTAs with increased expression in PCa vs. prostate normal samples, we used multiple logistic regression analysis (MLR). MLR analysis showed that a panel that includes all 8 CTAs analyzed correctly classified 88.9% of the cases (AUC=0.96; sensitivity=88.5%; specificity=89.2%). For the MLR analysis, we considered the intensity and the frequency of the cancer cells with positive CTA expression. The intensity of all 8 CTAs was significant in the analysis, while the frequency of staining was only relevant for NUF2, PBK, SSX2 and TTK. Our findings suggest that our 8 CTAs panel represent a potential biomarker with high accuracy to discriminate normal prostate from PCa. Further studies to evaluate their expression in bodily fluids will determine their potential as a non-invasive PCa screening test to be used in combination with PSA. Citation Format: Luciane T. Kagohara, Prakash Kulkarni, Takumi Shiraishi, Robert Vessella, Robert W. Veltri, Elana J. Fertig. Cancer/testis antigens: A biomarker panel for prostate cancer screening [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4525.

Study Type – Prognosis (case series)Level of Evidence 4 OBJECTIVE To assess the DNA content in benign-adjacent and cancer-tissue areas of a diagnostic biopsy, to predict which patients would subsequently develop an unfavourable biopsy necessitating treatment for prostate cancer in the expectant management (EM) programme. PATIENTS AND METHODS Of 71 patients who had benign-adjacent and cancer-tissue areas of diagnostic biopsies available, 39 developed unfavourable biopsies (Gleason score ≥7, Gleason pattern 4/5, three or more cores positive for cancer, >50% of any core involved with cancer), while 32 maintained favourable biopsies on annual surveillance examination (median follow-up 3.7 years). DNA content was measured on Feulgen-stained biopsy sections using an automatic imaging system (AutoCyteTM, TriPath Imaging Inc, Burlington, NC, USA). Cox proportional-hazard regression and Kaplan-Meier plots were used to identify significant predictors for unfavourable biopsy conversion. RESULTS Univariately, DNA content measurements i.e. an excess of optical density (OD) in the benign-adjacent tissuer area, and the sd of the OD in the cancer tissue were significant, with a hazard ratio and 95% confidence interval of 2.58 (1.17–5.68; P = 0.019) and 5.36 (1.89–15.24; P = 0.002), respectively, for predicting unfavourable biopsy conversion that required intervention. Also, several other DNA content measurements in benign-adjacent and cancer-tissue areas showed a trend to statistical significance. Further, benign-adjacent excess of OD (3.12, 1.4–6.95; P = 0.005) and cancer sd of OD (5.88, 2.06–16.82; P = 0.001) remained significant in the multivariate model to predict unfavourable biopsy conversion. Patients with benign-adjacent excess of OD > 25.0 and cancer sd of OD of >4.0 had the highest risk for unfavourable biopsy conversion (P

Purpose: We assessed the association of quantitative clinical and pathologic information, including serum and tissue pro–prostate-specific antigen (proPSA) measurements, with outcomes among men with prostate cancer in an expectant management (active surveillance) program. Experimental Design: We identified 71 men enrolled in expectant management with frozen serum and tissue available from diagnosis: 39 subsequently developed unfavorable biopsies (Gleason score ≥7, ≥3 cores positive for cancer, >50% of any core involved with cancer), whereas 32 maintained favorable biopsies (median follow-up, 3.93 years). Serum total PSA, free PSA (fPSA), and [−2]proPSA were measured by the Beckman Coulter immunoassay. [−5/−7]proPSA was evaluated in cancer and benign-adjacent areas (BAA) by quantitative immunohistochemistry. Cox proportional hazards and Kaplan-Meier analyses were used to identify significant associations with unfavorable biopsy conversion. Results: The ratio [−2]proPSA/% fPSA in serum was significantly higher at diagnosis (0.87 ± 0.44 versus 0.65 ± 0.36 pg/mL; P = 0.02) in men developing unfavorable biopsies. [−5/−7]proPSA tissue staining was more intense (4104.09 ± 3033.50 versus 2418.06 ± 1606.04; P = 0.03) and comprised a greater fractional area (11.58 ± 7.08% versus 6.88 ± 5.20%; P = 0.01) in BAA of these men. Serum [−2]proPSA/% fPSA [hazard ratio, 2.53 (1.18-5.41); P = 0.02], BAA [−5/−7]proPSA % area [hazard ratio, 1.06 (1.01-1.12); P = 0.02] and BAA [−5/−7]proPSA stain intensity [hazard ratio, 1.000213 (1.000071-1.000354); P = 0.003] were significantly associated with unfavorable biopsy in Kaplan-Meier and Cox analyses. Serum [−2]proPSA/% fPSA significantly correlated with BAA [−5/−7]proPSA % area (ρ = 0.40; P = 0.002) and BAA [−5/−7]proPSA stain intensity (ρ = 0.33; P = 0.016). Conclusions: In a prospective cohort of men enrolled into expectant management for prostate cancer, serum and tissue levels of proPSA at diagnosis are associated with need for subsequent treatment. The increase in serum proPSA/% fPSA might be driven by increased proPSA production from “premalignant” cells in the prostate BAA. (Clin Cancer Res 2009;15(23):7316–21)

Objectives Transformation of normal epithelium into cancer cells involves epigenetic and genetic changes and modifications in nuclear structure and tissue architecture. To evaluate nuclear morphometric alterations and clinicopathologic features for organ- vs nonorgan-confined prostate carcinoma (PCa) prediction. Methods Of 557 prospectively enrolled patients, 370 had complete information and sufficient tumor area for all evaluated parameters (281 organ-confined and 89 nonorgan-confined PCa cases). Digital images of Feulgen DNA-stained nuclei were captured from biopsies using the AutoCyte imaging system, and the nuclear morphometric alterations were calculated. Logistic regression analysis with bootstrap resampling was used to determine the factors important for differentiation of the 2 groups and to generate models for organ- vs nonorgan-confined PCa prediction. Results Several nuclear morphometric features were significantly altered and could differentiate organ- and nonorgan-confined disease. DNA ploidy was the most important factor among the significant nuclear morphometric features and was the second most important factor for organ- vs nonorgan-confined PCa prediction when considered with total prostate-specific antigen (PSA), complexed PSA, free/total PSA, biopsy Gleason score, and clinical stage. The combination of DNA ploidy with clinical stage, total PSA, and biopsy Gleason score showed an improvement of 1.5% in the area under the receiver operator characteristic curves compared with the combination of clinical stage, total PSA, and biopsy Gleason (73.97% vs 72.43%). The use of DNA ploidy in lieu of the biopsy Gleason score in each preoperative model evaluated resulted in equivalent or improved organ- vs nonorgan-confined PCa prediction. Conclusions The results of our study have shown that DNA ploidy can serve as a surrogate biomarker that has the potential to replace biopsy Gleason scores for organ- vs nonorgan-confined PCa prediction.

Histone deacetylase inhibitors such as valproic acid (VPA) are promising anticancer agents that change the acetylation status of histones and loosen the chromatin structure. We assessed nuclear structure changes induced by VPA in prostate cancer LNCaP, CWR22R, DU145, and PC3 cell lines and xenografts and their potential use as a biomarker of treatment. In vitro tissue microarrays consisted of prostate cancer cell lines treated for 3, 7, or 14 days with 0, 0.6, or 1.2 mmol/L VPA. In vivo tissue microarrays consisted of cores from prostate cancer xenografts from nude mice treated for 30 days with 0.2% or 0.4% VPA in drinking water. Digital images of at least 200 Feulgen DNA-stained nuclei were captured using the Nikon CoolScope and nuclear alterations were measured. With a set of seven most frequently significant nuclear alterations (determined by univariate logistic regression analysis), control and VPA treatment nuclei were compared in vitro and in vivo. Depending on the cell line, area under the curve-receiver operating characteristics ranged between 0.6 and 0.9 and were dose- and time-dependent both in vitro and in vivo. Also, VPA treatment caused significant nuclear alterations in normal drug-filtering organs (liver and kidney tissue). In vitro and in vivo VPA treatment of prostate cancer cell lines results in significant dose- and time-dependent changes in nuclear structure. Further, VPA induces nuclear structural changes in normal liver and kidney tissue, which likely reflects a natural physiologic response. Therefore, nuclear structural alterations may serve as a biomarker for histone deacetylase inhibitor treatment. [Mol Cancer Ther 2009;8(4):802–8]

Suppression of androgen production and function provides palliation but not cure in men with prostate cancer (PCa). Therapeutic failure and progression to hormone-refractory PCa (HRPC) are often accompanied by molecular alterations involving the androgen receptor (AR). In this study, we report novel forms of AR alteration that are prevalent in HRPC. Through in silico sequence analysis and subsequent experimental validation studies, we uncovered seven AR variant transcripts lacking the reading frames for the ligand-binding domain due to splicing of “intronic” cryptic exons to the upstream exons encoding the AR DNA-binding domain. We focused on the two most abundantly expressed variants, AR-V1 and AR-V7, for more detailed analysis. AR-V1 and AR-V7 mRNA showed an average 20-fold higher expression in HRPC (n = 25) when compared with hormone-naive PCa (n = 82; P < 0.0001). Among the hormone-naive PCa, higher expression of AR-V7 predicted biochemical recurrence following surgical treatment (P = 0.012). Polyclonal antibodies specific to AR-V7 detected the AR-V7 protein frequently in HRPC specimens but rarely in hormone-naive PCa specimens. AR-V7 was localized in the nuclei of cultured PCa cells under androgen-depleted conditions, and constitutively active in driving the expression of canonical androgen-responsive genes, as revealed by both AR reporter assays and expression microarray analysis. These results suggest a novel mechanism for the development of HRPC that warrants further investigation. In addition, as expression markers for lethal PCa, these novel AR variants may be explored as potential biomarkers and therapeutic targets for advanced PCa. [Cancer Res 2009;69(1):16–22]

Background Molecular pathways of proliferation, angiogenesis, neuroendocrine differentiation, apoptosis and alterations in nuclear structure of cancer epithelial cells are important in the pathogenesis of prostate cancer (PCa). Therefore, we evaluated the prognostic value of these parameters in 105 clinically localized PCa tumors with long-term follow-up after radical prostatectomy for progression-free survival (PFS). Method Nuclear roundness variance (NRV) was calculated for tumor nuclei using the graphic tracing DynaCELL system. Immunohistochemistry assessed expression of Ki67, PCNA (proliferation), Chromogranin A (neuroendocrine differentiation), CD31 (angiogenesis), BCL2 (apoptosis), and Her-2/neu (oncogene) in the tumors. Cox proportional hazards regression, Spearman's rank correlation, and Kaplan–Meier plots were employed to analyze the data. Results Gleason score, focal vs. non-focal extra-prostatic extension, organ confined status, NRV, Her-2/neu, CD-31 and Ki67 were univariately significant predictors of PFS. NRV was the most significant prognostic indicator with the highest concordance index (0.7) for PFS. Gleason score, NRV and Her-2/neu were multivariately significant and yielded a concordance index of 0.77. Conclusion Her-2/neu oncogene and NRV were shown to be significant in the prediction of PFS. The assessment of alterations in nuclear structure using NRV proved to be the most significant factor in the prediction of PFS. Integration of image analysis-based NRV and molecular biomarkers with pathologic parameters should be considered for validation in the prediction of PFS. Prostate © 2008 Wiley-Liss, Inc.

p300 impacts the transcription of several genes involved in key pathways critical to PCa progression. Therefore, we evaluated the prognostic value of p300 expression and its correlation with nuclear alterations seen in tumor cells in men with long-term follow-up after radical prostatectomy (RP).NCI Cooperative Prostate Cancer Tissue Resource tissue microarray cores of 92 RP cases (56 non-recurrences and 36 PSA recurrences) were utilized for the study. p300 expression was assessed by quantitative immunohistochemistry and nuclear alterations in Feulgen-stained nuclei were evaluated by digital image analysis using the AutoCyte Pathology Workstation. Cox proportional hazards regression, Spearman's rank correlation, and Kaplan-Meier plots were employed to analyze the data.p300 expression significantly correlated with nuclear alterations seen in tumor cells; specifically with circular form factor (P = 0.012) and minimum feret (P = 0.048). p300 expression in high grade tumors (Gleason scoreor=7) was significantly higher compared to low grade tumors (Gleason score7) [17.7% versus 13.7%, respectively, P = 0.03]. TNM stage, Gleason score, and p300 expression were univariately significant in the prediction of PCa biochemical recurrence-free survival (Por= 0.05). p300 expression remained significant in the multivariate model (P = 0.03) while Gleason score showed a trend toward significance (P = 0.06). Patients with a Gleason scoreor=7 and p300 expression24% showed the highest risk for PCa biochemical recurrence (P = 0.002).p300 expression correlates with nuclear alterations seen in tumor cells and has prognostic value in predicting long-term PCa biochemical recurrence-free survival.

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 2.7years. 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 groupsusingavariableLRselectioncriteriaofPz

The histologic distinction between high-grade prostate cancer and infiltrating high-grade urothelial cancer may be difficult, and has significant implications because each disease may be treated very differently (ie, hormone therapy for prostate cancer and chemotherapy for urothelial cancer). Immunohistochemistry of novel and established prostatic and urothelial markers using tissue microarrays (TMAs) were studied. Prostatic markers studied included: prostate-specific antigen (PSA), prostein (P501s), prostate-specific membrane antigen (PSMA), NKX3.1 (an androgen-related tumor suppressor gene), and proPSA (pPSA) (precursor form of PSA). "Urothelial markers" included high molecular weight cytokeratin (HMWCK), p63, thrombomodulin, and S100P (placental S100). TMAs contained 38 poorly differentiated prostate cancers [Gleason score 8 (n=2), Gleason score 9 (n=18), Gleason score 10 (n=18)] and 35 high-grade invasive urothelial carcinomas from radical prostatectomy and cystectomy specimens, respectively. Each case had 2 to 8 tissue spots (0.6-mm diameter). If all spots for a case showed negative staining, the case was called negative. The sensitivities for labeling prostate cancers were PSA (97.4%), P501S (100%), PSMA (92.1%), NKX3.1 (94.7%), and pPSA (94.7%). Because of PSA's high sensitivity on the TMA, we chose 41 additional poorly differentiated primary (N=36) and metastatic (N=5) prostate carcinomas which showed variable PSA staining at the time of diagnosis and performed immunohistochemistry on routine tissue sections. Compared to PSA, which on average showed 18.8% of cells with moderate to strong positivity, cases stained for P501S, PSMA, and NKX3.1 had on average 42.5%, 53.7%, 52.9% immunoreactivity, respectively. All prostatic markers showed excellent specificity. HMWCK, p63, thrombomodulin, and S100P showed lower sensitivities in labeling high-grade invasive urothelial cancer in the TMAs with 91.4%, 82.9%, 68.6%, and 71.4% staining, respectively. These urothelial markers were relatively specific with only a few prostate cancers showing scattered (or=2%) weak-moderate positive cells. In summary, PSA can be used as the first screening marker for differentiating high-grade prostate adenocarcinoma from high-grade urothelial carcinoma. Immunohistochemistry for P501S, PSMA, NKX3.1, and pPSA are useful when high-grade prostate cancer is suspected based on the morphology or clinical findings, yet shows negative or equivocal PSA staining. HMWCK and p63 are superior to the novel markers thrombomodulin and S100P.

BACKGROUND Alterations in nuclei structure and DNA content captured from Gleason grading patterns 3, 4 and 5 of radical prostatectomy (RP) cases were determined by a computer-assisted microscope. Quantitative Nuclear Morphometry (QNM) profiles were created to evaluate variability in nuclear structure within each of these grades. METHODS A tissue microarray (TMA) was constructed using RP cases and the prostate cancer (PCa) TMA cores prepared from 20 GG-3, 9 GG-4, 10 GG-5 patterns, and 20 benign cancer-adjacent cases from RP archival paraffin blocks. Feulgen-stained nuclei were captured from 0.6 mm spots using the AutoCyte™ system. Pools of 1100 nuclei captured from each test group were used to calculate Multivariate Logistic Regression (MLR) models that generated predictive indices and predictive probabilities (PP) to make comparisons between and within each set of pooled nuclei. RESULTS A single QNM profiles yielded areas of receiver operator characteristic curves (ROC) that distinguished differences among benign cancer-adjacent nuclei and GG-3 (ROC-AUC = 0.78); GG-4 (ROC-AUC = 0.86) and GG-5 (ROC-AUC = 0.88) with accuracies of 73%, 78% and 80% respectively. Applying PP plots generated from MLR models of GG 3, 4, and 5 nuclei clearly demonstrated marked heterogeneity within each of these three GG patterns. CONCLUSIONS QNM signatures illustrate alterations in nuclei structure, based upon nuclear morphometry within each of these three GG patterns, and might signify potential variations in PCa disease risk of progression outcomes. In the future a modified system of Gleason grading that considers not only glandular architecture but also quantitative nuclear grade may ensure accuracy in prognosis. Prostate 67: 1202–1210, 2007. © 2007 Wiley-Liss, Inc.

Background: Early prediction of disease progression in men with very low-risk (VLR) prostate cancer who selected active surveillance (AS) rather than immediate treatment could reduce morbidity associated with overtreatment. Methods: We evaluated the association of six biomarkers [Periostin, (−5, −7) proPSA, CACNA1D, HER2/neu, EZH2, and Ki-67] with different Gleason scores and biochemical recurrence (BCR) on prostate cancer TMAs of 80 radical prostatectomy (RP) cases. Multiplex tissue immunoblotting (MTI) was used to assess these biomarkers in cancer and adjacent benign areas of 5 μm sections. Multivariate logistic regression (MLR) was applied to model our results. Results: In the RP cases, CACNA1D, HER2/neu, and Periostin expression were significantly correlated with aggressive phenotype in cancer areas. An MLR model in the cancer area yielded a ROC-AUC = 0.98, whereas in cancer-adjacent benign areas, yielded a ROC-AUC = 0.94. CACNA1D and HER2/neu expression combined with Gleason score in a MLR model yielded a ROC-AUC = 0.79 for BCR prediction. In the small biopsies from an AS cohort of 61 VLR cases, an MLR model for prediction of progressors at diagnosis retained (−5, −7) proPSA and CACNA1D, yielding a ROC-AUC of 0.78, which was improved to 0.82 after adding tPSA into the model. Conclusions: The molecular profile of biomarkers is capable of accurately predicting aggressive prostate cancer on retrospective RP cases and identifying potential aggressive prostate cancer requiring immediate treatment on the AS diagnostic biopsy but limited in BCR prediction. Impact: Comprehensive profiling of biomarkers using MTI predicts prostate cancer aggressive phenotype in RP and AS biopsies. Cancer Epidemiol Biomarkers Prev; 24(12); 1864–72. ©2015 AACR.

This paper is aimed at the development of an approach of applying the curvelet transform to images of prostatectomy pathological specimens of critical Gleason grades for computer-aided classification. A set of Tissue MicroArray (TMA) images from the Johns Hopkins University have been used as the data base. We utilize a moving window to sample multiple patches of a given image leading to a majority decision by the patches for image class assignment. The curvelet-based feature extraction may capture both textural and, implicitly, structural information in an image patch. A tree-structured classifier consisting of three Gaussian-kernel support vector machines each with an embedded voting mechanism has been successfully trained and tested yielding high accuracy to classify tissue images of four critical Gleason scores (GS) 3+3, 3+4, 4+3 and 4+4. The experimental result has demonstrated an enhanced performance as compared to other reported works.

Plasma sMet levels accurately distinguished patients with PCa (n1⁄483) from those without (n1⁄480, Pearson r1⁄40.440, p < 0.0001; area under the curve AUC1⁄40.9385, threshold value of 146 ng/ml) with good sensitivity (87%) and excellent specificity (94%), and patients with pathologic stage 2A and higher from normals (Pearson r1⁄40.468, p

Background Prostate cancer (PCa) is the most common malignancy among men in the United States. Though highly sensitive, the often-used prostate-specific antigen (PSA) test has low specificity which leads to overdiagnosis and overtreatment of PCa. This paper presents results of a retrospective study that indicates that testing for macrophage inhibitory cytokine 1 (MIC-1) concentration along with the PSA assay could provide much improved specificity to the assay. Methods The MIC-1 serum level was determined by a novel p-Chip-based immunoassay run on 70 retrospective samples. The assay was configured on p-Chips, small integrated circuits (IC) capable of storing in their electronic memories a serial number to identify the molecular probe immobilized on its surface. The distribution of MIC-1 and pre-determined PSA concentrations were displayed in a 2D plot and the predictive power of the dual MIC-1/PSA assay was analyzed. Results MIC-1 concentration in serum was elevated in PCa patients (1.44 ng/ml) compared to normal and biopsy-negative individuals (0.93 ng/ml and 0.88 ng/ml, respectively). In addition, the MIC-1 level was correlated with the progression of PCa. The area under the receiver operator curve (AUC-ROC) was 0.81 providing an assay sensitivity of 83.3% and specificity of 60.7% by using a cutoff of 0.494 for the logistic regression value of MIC-1 and PSA. Another approach, by defining high-frequency PCa zones in a two-dimensional plot, resulted in assay sensitivity of 78.6% and specificity of 89.3%. Conclusions The analysis based on correlation of MIC-1 and PSA concentrations in serum with the patient PCa status improved the specificity of PCa diagnosis without compromising the high sensitivity of the PSA test alone and has potential for PCa prognosis for patient therapy strategies.

Background Periostin is an important extracellular matrix protein involved in cell development and adhesion. Previously, we identified periostin to be up-regulated in aggressive prostate cancer (CaP) using quantitative glycoproteomics and mass spectrometry. The expression of periostin was further evaluated in primary radical prostatectomy (RP) prostate tumors and adjacent non-tumorous prostate tissues using immunohistochemistry (IHC). Our IHC results revealed a low background periostin levels in the adjacent non-tumorous prostate tissues, but overexpressed periostin levels in the peritumoral stroma of primary CaP tumors. Methods In this study, periostin expression in CaP was further examined on multiple tissue microarrays (TMAs), which were conducted in four laboratories. To achieve consistent staining, all TMAs were stained with same protocol and scored by same image computation tool to determine the total periostin staining intensities. The TMAs were further scored by pathologists to characterize the stromal staining and epithelial staining. Results The periostin staining was observed mainly in peritumoral stromal cells and in some cases in tumor epithelial cells though the stronger staining was found in peritumoral stromal cells. Both periostin stromal staining and epithelial staining can differentiate BPH from CaP including low grade CaP (Gleason score ≤6), with significant p-value of 2.2e-16 and 0.001, respectively. Periostin epithelial staining differentiated PIN from low grade CaP (Gleason score ≤6) (p=0.001), while periostin stromal staining differentiated low grade Cap (Gleason score ≤6) from high grade Cap (Gleason score ≤6) (p=1.7e-05). In addition, a positive correlation between total periostin staining and Gleason score was observed (r=0.87, p=0.002). Conclusions The results showed that periostin staining was positively correlated with increasing Gleason score and the aggressiveness of prostate disease.