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Supplementary Table S1 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)

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 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.

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.

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.