Evaluating associations between genomic classifier and digital pathology–based multi-modal AI biomarkers in oligometastatic castration-sensitive prostate cancer.
Authors: Sutera Philip Anthony , Song Yang , Shetty Amol , Wang Jarey , Van der Eecken Kim , Hakansson Alexander K. , Liu Yang , Mendes Adrianna , Shi Xiaolei , Davicioni Elai , Chen Emmalyn , Yamashita Rikiya , Showalter Timothy N , Lotan Tamara L. , DeWeese Theodore L. , Kiess Ana Ponce , Song Daniel Y. , Deek Matthew Pierre , Ost Piet , Tran Phuoc T. 
Published: February 2025

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Background:
Prostate cancer is a heterogeneous disease ranging from indolent localized to metastatic castration-resistance. Efforts to generate prognostic and predictive biomarkers to understand disease trajectory beyond clinical variables alone include the Decipher Prostate Genomic Classifier (GC) and Artera Multimodal AI (MMAI). Both are validated prognostic biomarkers within localized prostate cancer and are currently being evaluated in the metastatic setting. It is unknown if these biomarkers are reporting on similar biology through different means (gene expression vs digital pathology) or if they are complementary and provide orthogonal insights. Herein, we aim to correlate GC and MMAI scores in patients with metastatic prostate cancer.
Methods:
We conducted a retrospective review of patients with oligometastatic castration-sensitive prostate cancer (omCSPC) with available transcriptome and digital H&E images from prostate biopsy tissue. GC scores were calculated from RNA sequencing data using the same coefficients but scores were re-scaled to a reference cohort from GRID registry while missing features were imputed as 0. Following digitization of H&E slides, an AI-detected, 128 image feature vector (IFV) was generated per patient which was subsequently combined with Gleason score, PSA, and T stage for final MMAI scoring (Artera, Inc). The primary endpoint was to assess correlations between these biomarkers as continuous variables with linear regression. Given the MMAI score is composed of both AI-detected digital pathology features and clinical features, we evaluated any associations between the GC and AI-detected image features. Uniform Manifold Approximation and Projection (UMAP) was performed on the 128 IFV to generate digital pathology clusters which were then associated with GC both as a continuous and categorical variable using ANOVA and chi-square test, respectively.
Results:
85 patients (Metachronous n=74; Synchronous n=11) were included in the analysis. The median GC and MMAI scores were 0.60 and 0.52, respectively. Linear regression identified a very weak positive association between scores (R
2
=0.08, 95%CI 0.00-0.20). UMAP identified 4 digital pathology clusters. No cluster was found to be enriched with higher GC scores with median scores of 0.64, 0.67, 0.58, and 0.5 for clusters 1-4 respectively (p=0.138). Additionally, no cluster was enriched with either low (GC <.45, p=0.87), intermediate (GC ≥ 0.45-<0.6, p=0.73), or high (GC≥0.6, p=0.12) GC risk groups.
Conclusions:
We demonstrate for the first time that Decipher GC and Artera MMAI scores do not strongly correlate in a population of patients with omCSPC. This suggests these biomarkers may be complementary, identifying independently prognostic disease biology. Further work validating these findings is warranted.