TAD

TAD integrates clinical and molecular data from prostate cancer tissue microarrays to enable biomarker correlation, multivariable predictive modeling, and analysis of molecular heterogeneity.

Key Features:

• Integration of Clinical and Molecular Data: Links immunohistochemical data from tissue microarrays (TMAs) with patient-specific clinical information to contextualize prostate cancer biomarkers.
• Automated Data Management: Uses a relational SQL database to automate the recording and association of scoring data with clinical datasets.
• Automated Scoring: Implements automated recording of scores to ensure consistent data capture across large numbers of TMA cores.
• Biomarker Correlation Analysis: Analyzes expression levels of proteins implicated in prostate cancer pathogenesis, including bax, bcl-2, bin1, CD95 (FAS), mdm2, p21, p53, and NFkappaB, to identify associations with disease virulence.
• Support for Multivariable Analysis: Supports multivariable logistic regression methods for predictive modeling of biological potential, with specific application to Gleason score 7 prostate cancer.
• TMA Composition: Incorporates a 255-core prostate cancer TMA constructed from 47 prostatectomy specimens, including cores from transition and peripheral zone origins and replicate cores for Gleason grades 5–9.
• Analysis of Molecular Heterogeneity: Enables comparison of molecular differences among Gleason scores, including heterogeneity within Gleason grade 3 prostatectomy specimens.

Scientific Applications:

• Prostate Cancer Biomarker Research: Defines biomarker correlates of prostate cancer virulence to inform therapeutic decision-making for early-stage disease.
• Risk Stratification: Supports stratification of patients with tumors of uncertain prognosis by linking molecular profiles to Gleason grades and clinical outcomes.
• Investigation of Gleason 7 Biology: Facilitates predictive modeling and biological interpretation specifically for Gleason score 7 prostate cancers.

Methodology:

Relational SQL database storage and automated recording of TMA scores, automated scoring workflows, and multivariable logistic regression for predictive analysis.