EGFR-mediated Beclin 1 phosphorylation in autophagy suppression, tumor progression, and tumor chemoresistance.

Cell 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.

High mobility group A1 (HMGA1) protein and gene expression correlate with ER-negativity and poor outcomes in breast cancer.

PURPOSE: 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. METHODS: 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 (< 5% = 0, 5-30% = 1, 30-60% = 2, > 60% = 3), and stratified into three groups: low (< 3), intermediate (3-6), high (> 6). We assessed HMGA1 and estrogen receptor (ESR1) gene expression from two large databases (TCGA, METABRIC). Overall survival was ascertained from the METABRIC cohort. RESULTS: Among 540 primary tumors from Korean women (181 ER-negative, 359 ER-positive), HMGA1 IRS was < 3 in 89 (16.5%), 3-6 in 215 (39.8%), and > 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 < 0.0001), including Asian, African American, and Caucasian subgroups. HMGA1 was highest in ER-negative tumors and there was a strong inverse correlation between HMGA1 and ESR1 gene expression (Pearson r = - 0.60, P < 0.0001). Most importantly, high HMGA1 predicted decreased overall survival (P < 0.0001) for all women with breast cancer and further stratified ER-positive tumors into those with inferior outcomes. CONCLUSIONS: Together, our results suggest that HMGA1 contributes to estrogen-independence, tumor progression, and poor outcomes. Moreover, further studies are warranted to determine whether HMGA1 could serve as a prognostic marker and therapeutic target for women with breast cancer.

Phosphorylation-induced conformational dynamics in an intrinsically disordered protein and potential role in phenotypic heterogeneity.

Intrinsically disordered proteins (IDPs) that lack a unique 3D structure and comprise a large fraction of the human proteome play important roles in numerous cellular functions. Prostate-Associated Gene 4 (PAGE4) is an IDP that acts as a potentiator of the Activator Protein-1 (AP-1) transcription factor. Homeodomain-Interacting Protein Kinase 1 (HIPK1) phosphorylates PAGE4 at S9 and T51, but only T51 is critical for its activity. Here, we identify a second kinase, CDC-Like Kinase 2 (CLK2), which acts on PAGE4 and hyperphosphorylates it at multiple S/T residues, including S9 and T51. We demonstrate that HIPK1 is expressed in both androgen-dependent and androgen-independent prostate cancer (PCa) cells, whereas CLK2 and PAGE4 are expressed only in androgen-dependent cells. Cell-based studies indicate that PAGE4 interaction with the two kinases leads to opposing functions. HIPK1-phosphorylated PAGE4 (HIPK1-PAGE4) potentiates c-Jun, whereas CLK2-phosphorylated PAGE4 (CLK2-PAGE4) attenuates c-Jun activity. Consistent with the cellular data, biophysical measurements (small-angle X-ray scattering, single-molecule fluorescence resonance energy transfer, and NMR) indicate that HIPK1-PAGE4 exhibits a relatively compact conformational ensemble that binds AP-1, whereas CLK2-PAGE4 is more expanded and resembles a random coil with diminished affinity for AP-1. Taken together, the results suggest that the phosphorylation-induced conformational dynamics of PAGE4 may play a role in modulating changes between PCa cell phenotypes. A mathematical model based on our experimental data demonstrates how differential phosphorylation of PAGE4 can lead to transitions between androgen-dependent and androgen-independent phenotypes by altering the AP-1/androgen receptor regulatory circuit in PCa cells.

Advances in the computational and molecular understanding of the prostate cancer cell nucleus.

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 PCa cell nucleus. Recent advances in machine learning-based approaches can now automatically mine data related to these changes to aid in the diagnosis, decision making, and prediction of PCa prognoses. In this review, we use PCa 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 will discuss recent developments to our understanding of the molecular events that drive nuclear alterations in the context of PCa: the role of the nuclear matrix and lamina in size and shape changes, the role of 3-dimensional chromatin organization and epigenetic modifications in textural changes, and the role of the tumor microenvironment in altering nuclear spatial topology. We will 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. Finally, we will 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.

PBOV1 as a potential biomarker for more advanced prostate cancer based on protein and digital histomorphometric analysis.

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 22 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.

The Upregulation of PI3K/Akt and MAP Kinase Pathways is Associated with Resistance of Microtubule-Targeting Drugs in Prostate Cancer.

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.

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

BACKGROUND: Prostate cancer progression is concomitant with quantifiable nuclear structure and texture changes as compared to non-cancer tissue. Malignant progression is associated with an epithelial-mesenchymal transition (EMT) program whereby epithelial cancer cells take on a mesenchymal phenotype and dissociate from a tumor mass, invade, and disseminate to distant metastatic sites. The objective of this study was to determine if epithelial and mesenchymal prostate cancer cells have different nuclear morphology. METHODS: Murine tibia injections of epithelial PC3 (PC3-Epi) and mesenchymal PC3 (PC3-EMT) prostate cancer cells were processed and stained with H&E. Cancer cell nuclear image data was obtained using commercially available image-processing software. Univariate and multivariate statistical analysis were used to compare the two phenotypes. Several non-parametric classifiers were constructed and permutation-tested at various training set fractions to ensure robustness of classification between PC3-Epi and PC3-EMT cells in vivo. RESULTS: PC3-Epi and PC3-EMT prostate cancer cells were separable at the single cell level in murine tibia injections on the basis of nuclear structure and texture remodeling associated with an EMT. Support vector machine and multinomial logistic regression models based on nuclear architecture features yielded AUC-ROC curves of 0.95 and 0.96, respectively, in separating PC3-Epi and PC3-EMT prostate cancer cells in vivo. CONCLUSIONS: Prostate cancer cells that have undergone an EMT demonstrated an altered nuclear structure. The association of nuclear changes and a mesenchymal phenotype demonstrates quantitative morphometric image analysis may be used to detect cancer cells that have undergone EMT. This morphometric measurement could provide valuable prognostic information in patients regarding the likelihood of [future] metastatic disease.

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

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

Acquisition of paclitaxel resistance is associated with a more aggressive and invasive phenotype in prostate cancer.

Drug resistance is a major limitation to the successful treatment of advanced prostate cancer (PCa). Patients who have metastatic, castration-resistant PCa (mCRPC) are treated with chemotherapeutics. However, these standard therapy modalities culminate in the development of resistance. We established paclitaxel resistance in a classic, androgen-insensitive mCRPC cell line (DU145) and, using a suite of molecular and biophysical methods, characterized the structural and functional changes in vitro and in vivo that are associated with the development of drug resistance. After acquiring paclitaxel-resistance, cells exhibited an abnormal nuclear morphology with extensive chromosomal content, an increase in stiffness, and faster cytoskeletal remodeling dynamics. Compared with the parental DU145, paclitaxel-resistant (DU145-TxR) cells became highly invasive and motile in vitro, exercised greater cell traction forces, and formed larger and rapidly growing tumors in mouse xenografts. Furthermore, DU145-TxR cells showed a discrete loss of keratins but a distinct gain of ZEB1, Vimentin and Snail, suggesting an epithelial-to-mesenchymal transition. These findings demonstrate, for the first time, that paclitaxel resistance in PCa is associated with a trans-differentiation of epithelial cell machinery that enables more aggressive and invasive phenotype and portend new strategies for developing novel biomarkers and effective treatment modalities for PCa patients.

Association of [-2]proPSA with biopsy reclassification during active surveillance for prostate cancer.

PURPOSE: Previous studies have suggested an association between [-2]proPSA expression and prostate cancer detection. Less is known about the usefulness of this marker in following patients with prostate cancer on active surveillance. Thus, we examined the relationship between [-2]proPSA and biopsy results in men enrolled in an active surveillance program. MATERIALS AND METHODS: In 167 men from our institutional active surveillance program we used Cox proportional hazards models to examine the relationship between [-2]proPSA and annual surveillance biopsy results. The outcome of interest was biopsy reclassification (Gleason score 7 or greater, more than 2 positive biopsy cores or more than 50% involvement of any core with cancer). We also examined the association of biopsy results with total prostate specific antigen, %fPSA, [-2]proPSA/%fPSA and the Beckman Coulter Prostate Health Index phi ([-2]proPSA/free prostate specific antigen) × (total prostate specific antigen)(½)). RESULTS: While on active surveillance (median time from diagnosis 4.3 years), 63 (37.7%) men demonstrated biopsy reclassification based on the previously mentioned criteria, including 28 (16.7%) of whom had reclassification based on Gleason score upgrading (Gleason score 7 or greater). Baseline and longitudinal %fPSA, %[-2]proPSA, [-2]proPSA/%fPSA and phi measurements were significantly associated with biopsy reclassification, and %[-2]proPSA and phi provided the greatest predictive accuracy for high grade cancer. CONCLUSIONS: In men on active surveillance, measures based on [-2]proPSA such as phi appear to provide improved prediction of biopsy reclassification during followup. Additional validation is warranted to determine whether clinically useful thresholds can be defined, and to better characterize the role of %[-2]proPSA and phi in conjunction with other markers in monitoring patients enrolled in active surveillance.

Prediction of patient-specific risk and percentile cohort risk of pathological stage outcome using continuous prostate-specific antigen measurement, clinical stage and biopsy Gleason score.

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.

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

BACKGROUND: Nuclear structure is often altered in cancer due to spatial rearrangements of chromatin organization via activation of oncogenes and other chromatin remodeling genes. Therefore, we evaluated the prognostic value of nuclear roundness variance (NRV) for prostate cancer (PCa) progression, metastasis and PCa-specific death free survivals in a cohort of 116 men after radical prostatectomy (RP). METHOD: NRV was calculated for each case using the variance of the nuclear roundness from approximately 150 nuclei captured at a magnification of 2,440x for each case in 1992-1993. $${\rm Nuclear}\,{\rm roundness} = {{{\rm Radius}({\rm circumference})} \over {{\rm radius}({\rm area})}} = {R \over r} = {{P/2\pi } \over {\sqrt {A/\pi } }}$$ NRV data were merged with clinical, pathologic, and follow-up data for all patients in 2009. Cox proportional hazards regression and Kaplan-Meier plots were employed to analyze the data. RESULTS: Median follow-up time after RP for all patients was 19 years (range: 1-25 years, mean: 17 years), with approximately 92% (107/116), 71% (82/116), and 47% (55/116) patients having >or=10, 15, and 20 years of follow-up, respectively. NRV was the most significant parameter for prediction of all three outcomes and its concordance-index (C-Index) increased from progression (0.7080) to metastasis (0.7332) to PCa-specific death (0.8090) free survival predictions. Of note, NRV C-Index was significantly higher compared to Gleason Score C-Index for metastasis (0.7332 vs. 0.6046; P = 0.027) and PCa-specific death (0.8090 vs. 0.6336; P = 0.004) free survival predictions. However, the difference between NRV and Gleason Score C-Indexes was not statistically significant for progression free survival prediction (0.7080 vs. 0.6463; P = 0.106). CONCLUSION: NRV is valuable nuclear structural feature that exceeds Gleason score to predict an aggressive phenotype of PCa.

DNA content in the diagnostic biopsy for benign-adjacent and cancer-tissue areas predicts the need for treatment in men with T1c prostate cancer undergoing surveillance in an expectant management programme.

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 > or =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 (AutoCyte(TM), 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 < 0.001). CONCLUSIONS: DNA content measurements in the benign-adjacent and cancer-tissue areas appear to be useful for predicting unfavourable biopsy conversion (a recommendation for intervention) on annual surveillance examinations in the EM programme.

Valproic acid causes dose- and time-dependent changes in nuclear structure in prostate cancer cells in vitro and in vivo.

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.

Computer Extracted Features from Initial H&E Tissue Biopsies Predict Disease Progression for Prostate Cancer Patients on Active Surveillance.

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.

Characterization of RNA-Binding Motif 3 (RBM3) Protein Levels and Nuclear Architecture Changes in Aggressive and Recurrent Prostate Cancer.

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.

Prognostic value of Her-2/neu and DNA index for progression, metastasis and prostate cancer-specific death in men with long-term follow-up after radical prostatectomy.

Abnormal DNA content in tumor cells represents large scale chromosomal alterations and reflects later changes of genetic instability. Her-2/neu oncogene is amplified in 20-30% of breast and ovarian cancer patients and is associated with poor prognosis. Therefore, we evaluated prognostic value of Her-2/neu expression and DNA content measurements in 252 clinically localized PCa patients with long-term follow-up after radical prostatectomy for progression, metastasis and PCa-specific death. Her-2/neu expression was determined by immunohistochemistry and DNA content measurements employed Feulgen-stained cancer nuclei captured using static image cytometry system. Cox proportional hazard regression and Kaplan-Meir plots were used to identify significant prognostic factors for progression, metastasis and PCa-specific death. The proportions of Her-2/neu positive and high %DNA index tumors significantly increased from nonprogressor to progressors without metastasis to progressors with metastasis (p < 0.0001; <0.0001). Further, the proportions of Her-2/neu positive and high %DNA index tumors significantly increased from patients who died from another cause without progression to those who died from another cause with progression to those died with PCa-specific death (p = 0.027; <0.0001). Her-2/neu expression and %DNA index were significant prognosticators for progression (p

Long-term assessment of prostate cancer progression free survival: evaluation of pathological parameters, nuclear shape and molecular biomarkers of pathogenesis.

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.

Prediction of prostate-specific antigen recurrence in men with long-term follow-up postprostatectomy using quantitative nuclear morphometry.

BACKGROUND: Nuclear morphometric signatures can be calculated using nuclear size, shape, DNA content, and chromatin texture descriptors [nuclear morphometric descriptor (NMD)]. We evaluated the use of a patient-specific quantitative nuclear grade (QNG) alone and in combination with routine pathologic features to predict biochemical [prostate-specific antigen (PSA)] recurrence-free survival in patients with prostate cancer. METHODS: The National Cancer Institute Cooperative Prostate Cancer Tissue Resource (NCI-CPCTR) tissue microarray was prepared from radical prostatectomy cases treated in 1991 to 1992. We assessed 112 cases (72 nonrecurrences and 40 PSA recurrences) with long-term follow-up. Images of Feulgen DNA-stained nuclei were captured and the NMDs were calculated using the AutoCyte system. Multivariate logistic regression was used to calculate QNG and pathology-based solutions for prediction of PSA recurrence. Kaplan-Meier survival curves and predictive probability graphs were generated. RESULTS: A QNG signature using the variance of 14 NMDs yielded an area under the receiver operator characteristic curve (AUC-ROC) of 80% with a sensitivity, specificity, and accuracy of 75% at a predictive probability threshold of > or =0.39. A pathology model using the pathologic stage and Gleason score yielded an AUC-ROC of 67% with a sensitivity, specificity, and accuracy of 70%, 50%, and 57%, respectively, at a predictive probability threshold of > or =0.35. Combining QNG, pathologic stage, and Gleason score yielded a model with an AUC-ROC of 81% with a sensitivity, specificity, and accuracy of 75%, 78%, and 77%, respectively, at a predictive probability threshold of > or =0.34. CONCLUSIONS: PSA recurrence is more accurately predicted using the QNG signature compared with routine pathology information alone. Inclusion of a morphometry signature, routine pathology, and new biomarkers should improve the prognostic value of information collected at surgery.