Zhou Catherine, Mooyaart Antien L, Kerkour Thamila, Louwman Marieke W J, Wakkee Marlies, Li Yunlei, Voorham Quirinus J M, Bruggink Annette, Nijsten Tamar E C, Hollestein Loes M
Department of Dermatology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.
Department of Pathology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.
Eur J Epidemiol. 2025 Jan;40(1):27-42. doi: 10.1007/s10654-024-01188-4. Epub 2025 Jan 9.
Early-stage cutaneous melanoma patients generally have a favorable prognosis, yet a significant proportion of metastatic melanoma cases arise from this group, highlighting the need for improved risk stratification using novel prognostic biomarkers. The Dutch Early-Stage Melanoma (D-ESMEL) study introduces a robust, population-based methodology to develop an absolute risk prediction model for stage I/II melanoma, incorporating clinical, imaging, and multi-omics data to identify patients at increased risk for distant metastases. Utilizing the Netherlands Cancer Registry and Dutch Nationwide Pathology Databank, we collected primary tumor samples from early-stage melanoma patients, with and without distant metastases during follow-up. Our study design includes a discovery set of metastatic cases and matched controls to identify novel prognostic factors, followed by a validation cohort using a nested case-control design to validate these factors and to build a risk prediction model. Tissue sections underwent Hematoxylin & Eosin (H&E) staining, RNA sequencing (RNAseq), DNA sequencing (DNAseq), immunohistochemistry (IHC), and multiplex immunofluorescence (MxIF).The discovery set included 442 primary melanoma samples (221 case-control sets), with 46% stage I and 54% stage II melanomas. The median time to distant metastasis was 3.4 years, while controls had a median follow-up time of 9.8 years. The validation cohort included 154 cases and 154 controls from a random population-based selection of 5,815 patients. Our approach enabled the collection of a large number of early-stage melanoma samples from population-based databases with extensive follow-up and a sufficient number of metastatic events. This methodology in prognostic cancer research holds the potential to impact clinical decision-making through absolute risk prediction.
早期皮肤黑色素瘤患者通常预后良好，但相当一部分转移性黑色素瘤病例就源于这一群体，这凸显了利用新型预后生物标志物改进风险分层的必要性。荷兰早期黑色素瘤（D - ESMEL）研究引入了一种强大的、基于人群的方法，以开发针对I/II期黑色素瘤的绝对风险预测模型，该模型纳入了临床、影像和多组学数据，以识别远处转移风险增加的患者。利用荷兰癌症登记处和荷兰全国病理数据库，我们收集了早期黑色素瘤患者的原发性肿瘤样本，这些患者在随访期间有或没有远处转移。我们的研究设计包括一个转移性病例发现集和匹配的对照组，以识别新型预后因素，随后是一个使用巢式病例对照设计的验证队列，以验证这些因素并建立风险预测模型。组织切片进行苏木精和伊红（H&E）染色、RNA测序（RNAseq）、DNA测序（DNAseq）、免疫组织化学（IHC）和多重免疫荧光（MxIF）。发现集包括442个原发性黑色素瘤样本（221个病例对照集），其中46%为I期黑色素瘤，54%为II期黑色素瘤。远处转移的中位时间为3.4年，而对照组的中位随访时间为9.8年。验证队列包括从5815名患者的随机人群选择中选取的154例病例和154例对照。我们的方法能够从基于人群的数据库中收集大量早期黑色素瘤样本，并进行广泛的随访以及有足够数量的转移事件。这种癌症预后研究方法有可能通过绝对风险预测影响临床决策。
