The Dutch Early-Stage Melanoma (D-ESMEL) study: a discovery set and validation cohort to predict the absolute risk of distant metastases in stage I/II cutaneous melanoma

Abstract

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.

Keywords: Biomarkers; Melanoma; Multi-omics; Population-based; Prognostic; Risk prediction.

Fig. 1

Matched case–control design of the discovery set and nested case–control design of the validation cohort. (Abbreviations: AJCC = American Joint Committee of Cancer)

Fig. 2

Clinical, imaging, RNA sequencing and DNA sequencing data derived from the discovery set will be integrated and the most prognostic features will be validated in the validation cohort

Fig. 3

Selection process of the matched case–control sets in the discovery set. (Abbreviations: NCR = Netherlands Cancer Registry, EMC = Erasmus MC Cancer Institute, H&E = Hematoxylin & Eosin)

Fig. 4

Selection process of the nested case–control design of the validation cohort (Abbreviations: NCR = Netherlands Cancer Registry, Palga = Dutch Nationwide Pathology Databank, H&E = Hematoxylin & Eosin)