Spohn S K B, Aebersold D M, Albrecht C, Boehmer D, Ganswindt U, Schmidt-Hegemann N-S, Hoecht S, Hölscher T, Koerber S A, Mueller A-C, Niehoff P, Peeken J C, Pinkawa M, Polat B, Shelan M, Wolf F, Zamboglou C, Zips D, Wiegel T
Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Robert-Koch-Straße 3, 79106, Freiburg, Germany.
Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, Freiburgstraße 4, 3010, Bern, Switzerland.
Strahlenther Onkol. 2025 Mar 25. doi: 10.1007/s00066-025-02388-x.
Prostate cancer (PCa) is the most frequently diagnosed malignancy among men in Germany. Advances in diagnostics and treatment have transformed PCa into a chronic disease. Given the heterogeneity of PCa, there is a need for additional stratification tools. This review focuses on updating the evidence for genomic classifiers (GC; Decipher [Veracyte Inc. San Diego, CA, USA], Prolaris [Myriad Genetics, Inc., Salt Lake City, UT], and Oncotype DX [Exact Sciences, Madison, WI, USA] tests) and artificial intelligence (AI)-based digital histopathology biomarkers (ArteraAI Prostate Test) in the context of radiotherapy (RT) for PCa.
The members of the Prostate Cancer Working Group of the German Society of Radiation Oncology (DEGRO) conducted an updated literature search on GCs and histopathological biomarkers in PCa, covering original articles published between January 2022 and February 2024 in the PubMed database.
In addition to previous reviews, 11 relevant studies were identified, of which nine studies analyzed biomarkers within prospective phase II or III trials. Eight trials focused on genomic biomarkers, of which three addressed GCs in primary localized PCa, three in recurrent PCa in the setting of salvage RT, and two in metastatic castration-sensitive PCa. In localized PCa, GCs could be validated in a retrospective analysis of randomized controlled trials. Additionally, three studies reported on AI-based histopathology biomarkers.
Genomic classifiers and AI-based digital histopathology models might have superior prognostic and predictive value compared to established clinical and pathological parameters in localized, recurrent, and metastatic PCa. Despite promising results, prospective validation of these biomarkers in randomized trials remains limited. This review underscores the need for further prospective trials to confirm the usefulness of these biomarkers in PCa.
前列腺癌（PCa）是德国男性中最常被诊断出的恶性肿瘤。诊断和治疗方面的进展已将PCa转变为一种慢性病。鉴于PCa的异质性，需要更多的分层工具。本综述重点更新基因组分类器（GC；Decipher[美国加利福尼亚州圣地亚哥市Veracyte公司]、Prolaris[美国犹他州盐湖城Myriad Genetics公司]和Oncotype DX[美国威斯康星州麦迪逊市Exact Sciences公司]检测）以及基于人工智能（AI）的数字组织病理学生物标志物（ArteraAI前列腺检测）在PCa放射治疗（RT）背景下的证据。
德国放射肿瘤学会（DEGRO）前列腺癌工作组的成员对PCa中的GC和组织病理学生物标志物进行了更新的文献检索，涵盖2022年1月至2024年2月在PubMed数据库中发表的原始文章。
除了之前的综述外，还确定了11项相关研究，其中9项研究在II期或III期前瞻性试验中分析了生物标志物。8项试验聚焦于基因组生物标志物，其中3项涉及原发性局限性PCa中的GC，3项涉及挽救性RT背景下复发性PCa中的GC，2项涉及转移性去势敏感性PCa中的GC。在局限性PCa中，GC可在随机对照试验的回顾性分析中得到验证。此外，有3项研究报告了基于AI的组织病理学生物标志物。
与局限性、复发性和转移性PCa中既定的临床和病理参数相比，基因组分类器和基于AI的数字组织病理学模型可能具有更高的预后和预测价值。尽管结果令人鼓舞，但这些生物标志物在随机试验中的前瞻性验证仍然有限。本综述强调需要进一步进行前瞻性试验，以证实这些生物标志物在PCa中的有用性。
