Predicting prostate cancer recurrence using a prognostic model that combines immunohistochemical staining and gene expression profiling

Abstract

A method that provides a graphical indication of whether a patient will have cancer recurrence uses univariate and bivariate prognostic features that were generated as part of a minimal spanning tree (MST). The method determines the values of first and second features. A first value is measured by detecting objects in an image of tissue from the cancer patient stained with a protein-specific IHC biomarker. A second value is measured using objects marked with an mRNA-specific probe biomarker detected in the tissue. The first feature is the univariate prognostic feature for cancer recurrence in a cohort of cancer patients. A combination of the first and second features is the bivariate prognostic feature for cancer recurrence in the cohort. The first and second features are elements of the MST. Nodes of the MST represent the univariate features, edges represent the bivariate features, and edge weights represent prognostic significance of bivariate features.