Goethe University Frankfurt, University Hospital, Clinic for Radiology and Nuclear Medicine, Frankfurt am Main, Germany.
Goethe University Frankfurt, University Hospital, Dr. Senckenberg Institute for Pathology, Frankfurt am Main, Germany.
J Cancer Res Clin Oncol. 2024 Aug 24;150(8):396. doi: 10.1007/s00432-024-05921-0.
While epigenetic profiling discovered biomarkers in several tumor entities, its application in prostate cancer is still limited. We explored DNA methylation-based deconvolution of benign and malignant prostate tissue for biomarker discovery and the potential of radiomics as a non-invasive surrogate.
We retrospectively included 30 patients (63 [58-79] years) with prostate cancer (PCa) who had a multiparametric MRI of the prostate before radical prostatectomy between 2014 and 2019. The control group comprised four patients with benign prostate tissue adjacent to the PCa lesions and four patients with benign prostatic hyperplasia. Tissue punches of all lesions were obtained. DNA methylation analysis and reference-free in silico deconvolution were conducted to retrieve Latent Methylation Components (LCMs). LCM-based clustering was analyzed for cellular composition and correlated with clinical disease parameters. Additionally, PCa and adjacent benign lesions were analyzed using radiomics to predict the epigenetic signatures non-invasively.
LCMs identified two clusters with potential prognostic impact. Cluster one was associated with malignant prostate tissue (p < 0.001) and reduced immune-cell-related signatures (p = 0.004) of CD19 and CD4 cells. Cluster one comprised exclusively malignant prostate tissue enriched for significant prostate cancer and advanced tumor stages (p < 0.03 for both). No radiomics model could non-invasively predict the epigenetic clusters.
Epigenetic clusters were associated with prognostically and clinically relevant metrics in prostate cancer. Further, immune cell-related signatures differed significantly between prognostically favorable and unfavorable clusters. Further research is necessary to explore potential diagnostic and therapeutic implications.
尽管表观遗传分析已经在多个肿瘤实体中发现了生物标志物,但在前列腺癌中的应用仍然有限。我们探索了基于 DNA 甲基化的良性和恶性前列腺组织去卷积,以发现生物标志物,并探讨了放射组学作为一种非侵入性替代方法的潜力。
我们回顾性纳入了 30 名(63 [58-79] 岁)前列腺癌(PCa)患者,这些患者在 2014 年至 2019 年间接受了根治性前列腺切除术之前的多参数前列腺 MRI。对照组包括 4 名紧邻 PCa 病变的良性前列腺组织患者和 4 名良性前列腺增生患者。获取所有病变的组织活检。进行 DNA 甲基化分析和无参考的计算机模拟去卷积,以检索潜在的甲基化成分(LCMs)。基于 LCM 的聚类分析细胞组成,并与临床疾病参数相关联。此外,使用放射组学分析 PCa 和相邻良性病变,以非侵入性方式预测表观遗传特征。
LCMs 确定了两个具有潜在预后影响的聚类。聚类 1 与恶性前列腺组织相关(p<0.001),并且免疫细胞相关标志物(CD19 和 CD4 细胞)的签名减少(p=0.004)。聚类 1 仅包含富含显著前列腺癌和高级肿瘤分期的恶性前列腺组织(均 p<0.03)。没有放射组学模型可以非侵入性地预测表观遗传聚类。
表观遗传聚类与前列腺癌的预后和临床相关指标相关。此外,预后良好和预后不良的聚类之间的免疫细胞相关标志物有显著差异。需要进一步研究以探讨潜在的诊断和治疗意义。