Advanced tissue-based image analysis techniques integrated with biomarker-morphometric multiplex immunofluorescence to provide prostate cancer risk stratification models post-surgery.

Abstract

47 Background: We have previously validated the importance of utilizing the intact tissue section and quantitative biomarker strategies for developing prognostic prostate cancer outcome models. We sought to further expand upon these constructs by applying next-gen advanced image analysis tools using multiplex immunofluorescence (IF) integrated with clinical variables on an independent cohort. Methods: Utilizing a 686 patient radical prostatectomy tissue microarray population with median 8 years follow-up from Roswell Park Cancer Institute, NY, we selected an initial subset of 98 patients to construct a training model; applying multiplex immunofluorescence with CK-18 (epithelial cells), CK 5/6 (basal cells), androgen receptor (AR), Ki67, AMACR and DAPI (nuclear marker). Images were captured with an Nikon i90 microscope equipped with a CRI multiplex camera, a Bayesian probabilistic approach was used to derive features that reflect tumor grade and co-locate - quantify IF biomarker expression within various glandular sub-types. Multivariate models were constructed to predict disease progression (i.e., clinical failure (CF) = PSA rise post-adjuvant therapy, or metastasis, or death) with c-index (CI) to estimate performance. Results: A 98 patient with the following clinical features: mean PSA 6.6 ng/mL, prostatectomy Gleason score, PGS 6, 34%; >/=PGS 7, 55%; LN+ 2%, +SM 19%; + ECE 49% ; + SVI 3%. CFevent rate 13%. Two models (i.e., +/- LN status) employing 47 features with 7 clinical variables (e.g., PSA and Gleason) had comparable outcomes, CI 0.84. The number one feature selected in both models was an imaging feature reflecting prostate cancer gland architecture and differentiation. Combined AR levels with gland phenotype and CK-18 composition characterized a relevant phenotype dependent upon feature inclusion in the model generated. Conclusions: We have established the importance of utilizing the intact tissue section when developing accurate prognostic prostate cancer models. Additional studies with expanded training and test patient cohorts are underway to confirm and validate these initial findings.