Abstract 5171: Gene expression in multi-parametric MRI visible and invisible prostate cancers predicts progression
Authors: Lehto Timo-Pekka K. , Stürenberg Carolin , Pylväläinen Juho , Sandeman Kevin , Kenttämies Anu , Nordling Stig , Tang Jing , Mirtti Tuomas , Rannikko Antti 
Published: June 2022

Prostate cancer (PCa) diagnostics are shifting towards multi-parametric magnetic resonance imaging (MRI) combined with targeted biopsies. However, some 10% of clinically significant PCa (csPCa) is missed with current MRI sequences. Evidence on biological differences between MRI visible and invisible csPCa is scarce. Our aim was to find out, whether MRI invisible lesions harbor more indolent characteristics compared to visible csPCa lesions, and to identify transcript markers of MRI visibility.

Methods: A retrospective cohort of 45 radical prostatectomy-treated men with csPCa, including 30 with MRI visible lesions, 15 with MRI invisible lesions and additional 19 benign controls. Apparent diffusion coefficient (ADC) values of the invisible lesions were measured based on their topographical locations in histological slides. The histological slides were checked for histological subtypes of PCa and the prostate epithelium and stroma surface area were measured using machine learning. mRNA was analyzed using Nanostring nCounter platform. First, hierarchical clustering of transcript copy counts was performed. Next, differentially expressed genes (DEGs) between study groups were analyzed and Reactome pathway analysis was performed to identify genes and pathways responsible for MRI visibility. Random forest models (RFMs) were trained to predict MRI visibility using DEGs, and genes in MSK-IMPACT, Decipher, Oncotype DX and Prolaris. RFMs were also trained to predict metastatic and lethal PCa using DEGs.

Results: MRI negative lesions remained invisible upon radiologist re-evaluation. Clinicopathological characteristics did not explain MRI invisibility, while ADC values were higher in invisible lesions than visible ones. Hierarchical clustering did not separate visible and invisible lesions, while benign samples formed a cluster. We identified a set of 10 and 52 DEGs and 65 enriched pathways between visible and invisible specimens. RFMs performed well in predicting MRI visibility for our 10- and 52-gene DEG sets (AUC = 0.92), for transcripts in Decipher, Oncotype DX, Prolaris (AUC = 0.69-0.86) and MSK-IMPACT (AUC = 0.93). RFMs based on 52- and 10-DEG sets showed independent value in predicting metastatic (AUC = 0.67 and 0.65) and lethal disease (AUC = 0.70 and 0.72) and the samples with predicted poor outcomes based on RFMs had significantly worse survival (p &lt; 0.0001).

Conclusions: MRI visibility is net result of many genes and might be linked to various biological processes. Invisible lesions harbor a less aggressive transcript signature than visible csPCa and might be considered for more conservative management. Further research is required to validate these findings on protein level and to find histological correlates. Promising markers altered in currently MRI invisible csPCa could be used to improve MRI sequences.

Citation Format: Timo-Pekka K. Lehto, Carolin Stürenberg, Juho Pylväläinen, Kevin Sandeman, Anu Kenttämies, Stig Nordling, Jing Tang, Tuomas Mirtti, Antti Rannikko. Gene expression in multi-parametric MRI visible and invisible prostate cancers predicts progression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5171.