Guangjing Zhu

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. J. Cell. Biochem. 116: 1341–1349, 2015.

A Novel Quantitative Multiplex Tissue Immunoblotting for Biomarkers Predicts a Prostate Cancer Aggressive Phenotype

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.

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

BACKGROUND Gleason scoring represents the standard for diagnosis of prostate cancer (PCa) and assessment of prognosis following radical prostatectomy (RP), but it does not account for patterns in neighboring normal-appearing benign fields that may be predictive of disease recurrence. OBJECTIVE To investigate (1) whether computer-extracted image features within tumor-adjacent benign regions on digital pathology images could predict recurrence in PCa patients after surgery and (2) whether a tumor plus adjacent benign signature (TABS) could better predict recurrence compared with Gleason score or features from benign or cancerous regions alone. DESIGN, SETTING, AND PARTICIPANTS We studied 140 tissue microarray cores (0.6mm each) from 70 PCa patients following surgery between 2000 and 2004 with up to 14 yr of follow-up. Overall, 22 patients experienced recurrence (biochemical [prostate-specific antigen], local, or distant recurrence and cancer death) and 48 did not. INTERVENTION RP was performed in all patients. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS The top 10 features identified as most predictive of recurrence within both the benign and cancerous regions were combined into a 10-feature signature (TABS). Computer-extracted nuclear shape and architectural features from cancerous regions, adjacent benign fields, and TABS were evaluated via random forest classification accuracy and Kaplan-Meier survival analysis. RESULTS AND LIMITATIONS Tumor-adjacent benign field features were predictive of recurrence (area under the receiver operating characteristic curve [AUC]: 0.72). Tumor-field nuclear shape descriptors and benign-field local nuclear arrangement were the predominant features found for TABS (AUC: 0.77). Combining TABS with Gleason sum further improved identification of recurrence (AUC: 0.81). All experiments were performed using threefold cross-validation without independent test set validation. CONCLUSIONS Computer-extracted nuclear features within cancerous and benign regions predict recurrence following RP. Furthermore, TABS was shown to provide added value to common predictors including Gleason sum and Kattan and Stephenson nomograms. PATIENT SUMMARY Future studies may benefit from evaluation of benign regions proximal to the tumor on surgically excised prostate cancer tissue for assessing risk of disease recurrence.

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

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.

RNA-Binding Motif 3 Protein Expression and Nuclear Architecture Changes as a Combined Biomarker to Predict Aggressive and Recurrent Prostate Cancer.

Purpose: The RNA-binding motif protein 3 (RBM3) has been shown to be up-regulated in several types of cancer, including prostate cancer (PCa). Increased RBM3 nuclear expression has been linked to improved clinical outcomes. Given this, we examined RBM3 expression and its relation to nuclear morphometric changes in PCa cells. Methods: 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 expression and nuclear structural changes could predict PCa aggressiveness and biochemical recurrence (BCR). A leave-one-out cross validation (LOOCV) was used to estimate predictive performance of the models. Results: 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 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. Significant Conclusions: The RBM3 biomarker alone is a strong prognostic marker for the prediction of aggressive PCa and biochemical recurrence.

Abstract 3922: Prediction of breast cancer progression using nuclear morphometry

Introduction: The progression of breast cancer (BrCa) involves nuclear morphometric changes, which are used in the pathological diagnosis of breast cancer. Although changes in nuclear morphometry (NM) contribute to histologic alterations observed in breast cancer, an accurate and autonomous quantification of NM changes have remained elusive. Here we created an image analysis macro to quantify changes in nuclear parameters, including size, shape, and DNA content and used these parameters to predict BrCa progression. Materials & Methods: Three tissue microarrays (TMAs) with 140 BrCa cases, stratified by TNM stage were used for this study. The TMAs were generated using 6 mm cores from primary tumors of a Korean cohort from Yengnam University Hospital, Daegu, South Korea. Specimens were obtained from surgical resection between January 1995 and January 2004. Each case was represented by 2 cores on the TMAs. Clinical information were provided by the pathology reports and patients’ medical records. HE 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3922.

Abstract 4349: Cancer histologic and cell nucleus architecture differentiate prostate cancer Gleason patterns 3 from 4

There will be an anticipated 29,480 prostate cancer (PCa) deaths in 2014 in the US. Nearly 40% of PCa patients will undergo radical prostatectomy (RP), which reduces the risk of death from PCa. Roughly ∼15% of these PCa patients tend to recur and of these a portion will metastasize (∼40%). Hence, a more accurate and objective computer-assisted image analysis (CAIA) automated PCa grading system should be considered. The current standard for Gleason grading involves an expert pathologist visually scoring Gleason grade patterns based upon HE 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4349. doi:10.1158/1538-7445.AM2015-4349

Abstract 4352: Prediction of prostate cancer progression with biomarkers and tissue morphometry changes

Proceedings: AACR 106th Annual Meeting 2015; April 18-22, 2015; Philadelphia, PA Introduction: The progression of prostate cancer (PCa) involves both tissue morphometric changes and critical molecular alterations (such as cancer-associated markers). We have studied 80 men that underwent radical prostatectomy (RP) to evaluate this dual approach to discriminate Gleason score and progression. Next, we applied our approach at the diagnostic biopsy of men that chose active surveillance (AS) as an option for the management of their PCa, to predict the likelihood for re-classification of their disease to immediate treatment. Materials & Methods: Two tissue microarrays (TMAs) with 80 RP PCa cases, stratified by Gleason scores were used first. Next, a total of 27 favorable and 24 unfavorable AS biopsy PCa cases were evaluated. In both series we applied multiplex tissue immunoblotting (MTI) & quantitative nuclear morphometry studies. Data were first analyzed alone and then in combination using multivariate logistic regression (MLR) to predict the aggressive RP cases or AS biopsy reclassification status of the cancer. MTI was used to simultaneously detect 5∼6 biomarkers ((-5,-7)ProPSA, PCNA, RBM3, Her2/neu, & CACNA1D, etc) on a single 5 micron section. Proteins on the slide were transferred onto a series of 5∼6 P-film membranes and each membrane was probed with different primary antibody. The quantified FITC fluorescence signal of the biomarker were normalized to CY5 labelled total protein. For the nuclear morphometry analysis, quantification of the nuclear features were achieved either using ImagePro Premier 9.1 Software for the TMAs or the adaptive active contour scheme (AdACM) that uses nuclear shape, architectural and textural features extracted from AS biopsies. RESULTS: Using MLR, to differentiate aggressive PCa (Gleason score 4+3 & > = 8) from less aggressive PCa (Gleason score 3+3 & 3+4) on the TMAs of RP cases, our biomarkers model generated an ROC-AUC of 0.8 with accuracy of 71.43%, while morphometry model generate an ROC-AUC of 0.92 with accuracy of 82.50 %. When combined, it improved to an ROC-AUC of 0.96 and accuracy of 87.01%. Additionally, MLR was used for differentiation unfavorable biopsy cases that requires reclassification due to upgraded Gleason score, increased tumor volume, and/or PSA/PSAD during monitoring and need definitive treatment. On the contrary, favorable cases are very low risk (VLR) PCa biopsies that are not reclassified and can continue monitoring. The biomarkers model produced an ROC-AUC of 0.71 with an accuracy of 73.91% while morphometry model produces an ROC-AUC of 0.84 and accuracy of 74.51%. When combined, the new model produces an ROC-AUC of 0.88 and accuracy of 80.43%. CONCLUSIONS: Our method combining tissue morphometry with biomarkers demonstrated its translational clinical relevance since it can predict PCa aggressiveness in men that have undergone RP. Also, this approach predicts AS PCa cases requiring reclassification and immediate treatment at biopsy. Citation Format: Guangjing Zhu, George Lee, Christine Davis, Luciane Tsukamoto Kagohara, Jonathan I. Epstein, Patricia Landis, H. Ballantine Carter, Anant Madabhushi, Robert W. Veltri. Prediction of prostate cancer progression with biomarkers and tissue morphometry changes. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4352. doi:10.1158/1538-7445.AM2015-4352