Histomic and transcriptomic features of MRI-visible and invisible clinically significant prostate cancers are associated with prognosis

Magnetic resonance imaging (MRI) is increasingly used to triage patients for prostate biopsy. However, 9% to 24% of clinically significant (cs) prostate cancers (PCas) are not visible in MRI. We aimed to identify histomic and transcriptomic determinants of MRI visibility and their association to metastasis, and PCa-specific death (PCSD). We studied 45 radical prostatectomy-treated patients with csPCa (grade group [GG]2-3), including 30 with MRI-visible and 15 with MRI-invisible lesions, and 18 men without PCa. First, histological composition was quantified. Next, transcriptomic profiling was performed using NanoString technology. MRI visibility-associated differentially expressed genes (DEGs) and Reactome pathways were identified. MRI visibility was classified using publicly available genes in MSK-IMPACT and Decipher, Oncotype DX, and Prolaris. Finally, DEGs and clinical parameters were used to classify metastasis and PCSD in an external cohort, which included 76 patients with metastatic GG2-4 PCa, and 84 baseline-matched controls without progression. Luminal area was lower in MRI-visible than invisible lesions and low luminal area was associated with short metastasis-free and PCa-specific survival. We identified 67 DEGs, eight of which were associated with survival. Cell division, inflammation and transcriptional regulation pathways were upregulated in MRI-visible csPCas. Genes in Decipher, Oncotype DX and MSK-IMPACT performed well in classifying MRI visibility (AUC = 0.86-0.94). DEGs improved classification of metastasis (AUC = 0.69) and PCSD (AUC = 0.68) over clinical parameters. Our data reveals that MRI-visible csPCas harbor more aggressive histomic and transcriptomic features than MRI-invisible csPCas. Thus, targeted biopsy of visible lesions may be sufficient for risk stratification in patients with a positive MRI.

Prognostic impact of kallikrein-related peptidase transcript levels in prostate cancer

We aimed to study mRNA levels and prognostic impact of all 15 human kallikrein-related peptidases (KLKs) and their targets, proteinase-activated receptors (PARs), in surgically treated prostate cancer (PCa). Seventy-nine patients with localized grade group 2-4 PCas represented aggressive cases, based on metastatic progression during median follow-up of 11 years. Eighty-six patients with similar baseline characteristics, but no metastasis during follow-up, were assigned as controls. Transcript counts were detected with nCounter technology. KLK12 protein expression was investigated with immunohistochemistry. The effects of KLK12 and KLK15 were studied in LNCaP cells using RNA interference. KLK3, -2, -4, -11, -15, -10 and -12 mRNA, in decreasing order, were expressed over limit of detection (LOD). The expression of KLK2, -3, -4 and -15 was decreased and KLK12 increased in aggressive cancers, compared to controls (P < .05). Low KLK2, -3 and -15 expression was associated with short metastasis-free survival (P < .05) in Kaplan-Meier analysis. PAR1 and -2 were expressed over LOD, and PAR1 expression was higher, and PAR2 lower, in aggressive cases than controls. Together, KLKs and PARs improved classification of metastatic and lethal disease over grade, pathological stage and prostate-specific antigen combined, in random forest analyses. Strong KLK12 immunohistochemical staining was associated with short metastasis-free and PCa-specific survival in Kaplan-Meier analysis (P < .05). Knock-down of KLK15 reduced colony formation of LNCaP cells grown on Matrigel basement membrane preparation. These results support the involvement of several KLKs in PCa progression, highlighting, that they may serve as prognostic PCa biomarkers.

Abstract 5171: Gene expression in multi-parametric MRI visible and invisible prostate cancers predicts progression

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.

Transcript analysis of commercial prostate cancer risk stratification panels in hard-to-predict grade group 2-4 prostate cancers

BACKGROUND

Improved prognostication is needed to minimize overtreatment in grade group (GG) 2-4 prostate cancer. Our aim was to determine, at messenger RNA (mRNA) level, the performance of the genes in the commercial panels Decipher, Oncotype DX, Prolaris, and mutational panel MSK-IMPACT to predict metastasis-free and prostate cancer-specific death (PCSD) in patients with GG 2-4 prostate cancer at radical prostatectomy.

METHODS

The retrospective cohort consisted of GG 2-4 patients treated with radical prostatectomy (median follow-up 10.4 years). Seventy-six cases with postoperative metastasis or PCSD and 84 controls with similar clinical baseline risk, but without progression, were analyzed. Index lesion mRNA transcripts were analyzed using NanoString technology. Random forest models were trained using panel gene sets to predict clinical endpoints and area under the curve (AUC), sensitivity, specificity, Youden index, and number needed to diagnose (NND) was measured. Survival probability was assessed with Kaplan-Meier estimator.

RESULTS

All gene sets outperformed clinical parameters and predicted metastasis-free and prostate cancer-specific survival. However, there were significant differences between the panels. In metastasis prediction, the genes in Oncotype DX had inferior performance (area under the curve [AUC] = 0.65) compared to other panels (AUC = 0.73-0.74). Decipher, MSK-IMPACT and Prolaris showed similar NND (2.83-3.12) with Oncotype DX having highest NND (4.79). In PCSD prediction, the Prolaris gene set performed worse (AUC = 0.66) than MSK-IMPACT or Decipher (AUC = 0.72). Oncotype DX performed similarly to other panels (AUC = 0.69, p > .05). Oncotype DX demonstrated lowest NND (2.79) compared to other panels (4.22-5.66).

CONCLUSION

Transcript analysis of genes included in commercial panels is feasible in survival prediction of GG 2-4 patients after radical prostatectomy and may aid in clinical decision making. There were significant differences between the panels, and overall stronger predictive gene sets are needed. Prospective investigation is warranted in biopsy materials.