 My Research
|
|
Grants
|
|
Honors and Awards
|
|
Committee Member
|
|
My Publications
|
| Date of Publication: |
Aug 2009 |
|
| Publication Title: |
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. |
|
| Description of Publication (Abstract): |
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 (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. |
|
| Research Focus: |
Bioinformatics |
|
|
|
|
|
|
|
| Date of Publication: |
Apr 2009 |
|
| Publication Title: |
Valproic acid causes dose- and time-dependent changes in nuclear structure in prostate cancer cells in vitro and in vivo |
|
| Description of Publication (Abstract): |
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. |
|
| Research Focus: |
Microscopy |
|
|
|
|
|
|
|
| Date of Publication: |
Jul 2008 |
|
| Publication Title: |
p300 (histone acetyltransferase) biomarker predicts prostate cancer biochemical recurrence and correlates with changes in epithelia nuclear size and shape. |
|
| Description of Publication (Abstract): |
Prostate |
|
| Research Focus: |
Biomarkers |
|
|
|
|
|
|
|
| Date of Publication: |
Feb 2008 |
|
| Publication Title: |
Using nuclear morphometry to predict the need for treatment among men with low grade, low stage prostate cancer enrolled in a program of expectant management with curative intent. |
|
| Description of Publication (Abstract): |
Prostate |
|
| Research Focus: |
Pathology |
|
|
|
|
|
|
|
| Date of Publication: |
Jan 2008 |
|
| Publication Title: |
Prediction of prostate-specific antigen recurrence in men with long-term follow-up postprostatectomy using quantitative nuclear morphometry. |
|
| Description of Publication (Abstract): |
Cancer Epidemiol Biomarkers Prev. |
|
| Research Focus: |
Biomarkers |
|
|
|
|
|
|
|
| Date of Publication: |
Aug 2007 |
|
| Publication Title: |
Significant variations in nuclear structure occur between and within Gleason grading patterns 3, 4, and 5 determined by digital image analysis. |
|
| Description of Publication (Abstract): |
Prostate |
|
| Research Focus: |
Bioinformatics |
|
|
|
|
|
|
|
| Date of Publication: |
Oct 2006 |
|
| Publication Title: |
Alterations in nuclear structure and expression of proPSA predict differences between native Japanese and Japanese-American prostate cancer. |
|
| Description of Publication (Abstract): |
Urology |
|
| Research Focus: |
Immunoassays/Immunochemistry |
|
|
|
|
|
|
|
| Date of Publication: |
Oct 2006 |
|
| Publication Title: |
Immunohistochemical staining of precursor forms of prostate-specific antigen (proPSA) in metastatic prostate cancer. |
|
| Description of Publication (Abstract): |
Am J Surg Pathol |
|
| Research Focus: |
Immunoassays/Immunochemistry |
|
|
|
|
|
|
|
| Date of Publication: |
Sep 2006 |
|
| Publication Title: |
Ultrasound verification of bladder damage is associated with known biomarkers of bladder cancer in adults chronically infected with Schistosoma haematobium in Ghana. |
|
| Description of Publication (Abstract): |
Trans R Soc Trop Med Hyg |
|
| Research Focus: |
Biomarkers |
|
|
|
|
|
|
| |
|
My Contributions
|
|
Articles in Refereed Journals
|
|
Posters
|
|
|
| Title of Poster: |
SURVEILLANCE OF MEN WITH LOW GRADE AND STAGE PROSTATE CANCER ENROLLED IN AN EXPECTANT MANAGEMENT PROGRAM: CHANGES IN CLINICAL, PATHOLOGICAL AND NUCLEAR MORPHOMETRY PATTERNS OBSERVED OVER TIME |
|
| Description of Poster: |
AUA Meeting 2007 Introduction and Objective: To assess the alterations of quantitative nuclear structure, histomorphometry, and clinical information at T0 (time of first biopsy) versus TL (time of last surveillance biopsy) in an expectant management (EM) cohort whom would eventually require treatment for prostate cancer (PCa). Methods: We identified 75 men; 30 with an unfavorable biopsy (Gleason grade>6, >2 cores involved with cancer, >50% of a core involved with cancer, or a palpable nodule) requiring treatment and 45 maintained favorable biopsies throughout a median followup of 2.7 years. We analyzed clinical, pathological features and 40 nuclear morphometric descriptors (NMDs) of EM entry (T0) and the last surveillance (TL) follow-up. The analysis included ProPSA (pPSA) tissue quantitative immunohistochemistry (QIHC) and nuclear morphometry using Feulgen-stained nuclei captured from biopsies. Logistic regression (LR) modeling was used to monitor changes from T0 to TL follow-ups. Also, a novel method to perform “correlation network analysis” was used to detect and visualize co-regulatory patterns among a large number including ~40 NMDs and clinical variables in a system observed across predefined EM populations (i.e. progressors or non-progressors) or over time courses (T0 vs. TL). The method uses differential analysis to detect high-order co-regulatory structures in complex data that are informative about phenotypic differences in our EM patients. Results: We identified different QNG morphometry signatures that utilized 12/40 NMDs for both T0 and TL EM groups, generating areas under the receiver operator characteristic curves respectively (ROC-AUC) of 87% and 91% respectively. Also, significant changes in the ProPSA QIHC (P < 0.01) were noted of the T0 and TL groups. The correlation network analysis clearly mapped marked visual differences (nodes) in progressors and non-progressors as well as across time (T0 vs. TL). Conclusions: Significant changes in QNG morphometry and ProPSA QIHC were observed during surveillance of men undergoing EM monitoring. Using “correlation network analysis” we mapped unique co-regulatory structural patterns among the variables measured in EM patients. Hence, monitoring alterations in high order structure of our data derived from surveillance of EM patients we will be able to derive the best parameters to predict unfavorable biopsy pathology and need for treatment. |
|
| Research Focus: |
Immunoassays/Immunochemistry |
|
|
|
|
|
|
|
|
|
| Title of Poster: |
PREDICTING THE NEED FOR TREATMENT AMONG MEN WITH LOW GRADE, LOW STAGE PROSTATE CANCER ENROLLED IN A PROGRAM OF EXPECTANT MANAGEMENT WITH CURATIVE INTENT |
|
| Description of Poster: |
AUA Meeting 2007 Introduction and Objective: To assess the use of quantitative clinical and pathologic information to predict a priori which patients would eventually require treatment for prostate cancer (CaP) in an expectant management (EM) cohort. Methods: We identified 75 men; 30 developed an unfavorable biopsy (Gleason grade>6, >2 cores involved with cancer, >50% of a core involved with cancer, or a palpable nodule) requiring treatment and 45 maintained favorable biopsies throughout a median followup of 2.7 years. Demographic and clinical data from the time of enrollment in EM were analyzed, including serum results (tPSA, fPSA) and tissue histomorphometry, based both on image analysis of DNA in Feulgen stained nuclei and on pPSA immunohistochemical staining. Results: Logistic regression (LR) modeling was used to generate a quantitative nuclear grade (QNG) signature based on the enrollment biopsy for differentiation of Favorable and Unfavorable groups using a variable selection criteria of pz<0.05. The QNG signature utilized 12 nuclear morphometric descriptors (NMDs) and had an area under the receiver operator characteristic curve (ROC-AUC) of 87%. A LR model using prostate volume, PSA density, and number of biopsies prior to diagnosis resulted in an AUC-ROC of 68%, while a LR model utilizing mean pPSA staining density in the cancer area yielded an AUC-ROC of 63%. A multivariable LR model combining QNG signature with clinical and pathological variables yielded an AUC-ROC of 88%. Conclusions: QNG analysis of initial EM prostate biopsies improves the predictive accuracy of LR models based on traditional clinicopathologic variables in determining which patients will ultimately develop an unfavorable biopsy. Our QNG-based model must be rigorously validated in a prospective manner before it can be utilized in the clinical arena. |
|
| Research Focus: |
Pathology |
|
|
|
|
|
|
Books
|
|
Presentations Webinars, Speeches, Lectures, Video
|
|
Dissertations and Theses
|
|
Events and Conferences
|
|
Courses Taught
|
|
Other Document Sharing
|
|
| |
Home
Videos
Podcasts
Groups
Reviews
Blogs
Q&A Forum
News
Publications
Events
Jobs
PDF
