Improved prediction of prostate cancer recurrence through systems pathology

Abstract

We have developed an integrated, multidisciplinary methodology, termed systems pathology, to generate highly accurate predictive tools for complex diseases, using prostate cancer for the prototype. To predict the recurrence of prostate cancer following radical prostatectomy, defined by rising serum prostate-specific antigen (PSA), we used machine learning to develop a model based on clinicopathologic variables, histologic tumor characteristics, and cell type-specific quantification of biomarkers. The initial study was based on a cohort of 323 patients and identified that high levels of the androgen receptor, as detected by immunohistochemistry, were associated with a reduced time to PSA recurrence. The model predicted recurrence with high accuracy, as indicated by a concordance index in the validation set of 0.82, sensitivity of 96%, and specificity of 72%. We extended this approach, employing quantitative multiplex immunofluorescence, on an expanded cohort of 682 patients. The model again predicted PSA recurrence with high accuracy, concordance index being 0.77, sensitivity of 77% and specificity of 72%. The androgen receptor was selected, along with 5 clinicopathologic features (seminal vesicle invasion, biopsy Gleason score, extracapsular extension, preoperative PSA, and dominant prostatectomy Gleason grade) as well as 2 histologic features (texture of epithelial nuclei and cytoplasm in tumor only regions). This robust platform has broad applications in patient diagnosis, treatment management, and prognostication.

Limitations of Prostate Specific Antigen Doubling Time Following Biochemical Recurrence After Radical Prostatectomy: Results From the SEARCH Database

“…Therefore, it is essential new markers be developed, ideally based upon pathological analysis of the tissue and thus available immediately following surgery. To this end, recent studies have demonstrated this approach accurately predicts PSA recurrence, 20 but whether it accurately predicts more delayed end-points remains to be determined. However, given the limitations that not all men have PSADT data, it is clear that this approach is desperately needed.…”

Zinc α2-Glycoprotein: A Multidisciplinary Protein

“…They have also shown that this observation is similar to that of breast cancer, where the loss of ZAG production was associated with the lack of tumor differentiation. It is interesting to note that the expression of ZAG in prostate cancer is similar to that of other relevant prostatic proteins (57), such as prostate-specific antigen and prostatic acid phosphatase, which are significantly decreased in prostatic tumors (63)(64)(65). Moreover, immunohistochemical studies assured the partial loss of ZAG expression in prostatic tissue after malignant transformation (47).…”

Molecular genetics of prostate cancer: new prospects for old challenges

“…In principle, suitable combinations of markers may be successful in cumulatively predicting outcome, as enabled by new technologies such as molecular systems pathology (Cordon-Cardo et al 2007). Alternatively, system biology approaches that identify master regulatory genes of disease progression may enable the effective stratification of patients, as has been applied for other cancer types (Carro et al 2009).…”