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Related Concept Videos

Skin Cancer01:30

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Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...
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A 3D Organotypic Melanoma Spheroid Skin Model
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The Dutch Early-Stage Melanoma (D-ESMEL) study: a discovery set and validation cohort to predict the absolute risk of

Catherine Zhou1, Antien L Mooyaart2, Thamila Kerkour1

  • 1Department of Dermatology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.

European Journal of Epidemiology
|January 9, 2025
PubMed
Summary

This study developed a new method to predict distant melanoma metastasis risk in early-stage patients using clinical, imaging, and multi-omics data. The goal is to improve risk stratification and guide clinical decisions for better patient outcomes.

Keywords:
BiomarkersMelanomaMulti-omicsPopulation-basedPrognosticRisk prediction

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Area of Science:

  • Oncology
  • Genomics
  • Biomarker Discovery

Background:

  • Early-stage cutaneous melanoma has a generally favorable prognosis, but a subset develops distant metastases, necessitating improved risk stratification.
  • Novel prognostic biomarkers are crucial for identifying high-risk early-stage melanoma patients who may benefit from closer monitoring or adjuvant therapies.

Purpose of the Study:

  • To develop a robust, population-based methodology for creating an absolute risk prediction model for distant metastases in stage I/II cutaneous melanoma.
  • To identify and validate novel prognostic biomarkers by integrating clinical, imaging, and multi-omics data.
  • To enhance clinical decision-making through precise risk stratification.

Main Methods:

  • Utilized the Dutch Early-Stage Melanoma (D-ESMEL) study cohort, leveraging the Netherlands Cancer Registry and Dutch Nationwide Pathology Databank.
  • Employed a discovery cohort (442 primary melanoma samples) and a validation cohort (154 cases and 154 controls) using a nested case-control design.
  • Performed Hematoxylin & Eosin (H&E) staining, RNA sequencing (RNAseq), DNA sequencing (DNAseq), immunohistochemistry (IHC), and multiplex immunofluorescence (MxIF) on tissue samples.

Main Results:

  • The study successfully collected a large, population-based cohort of early-stage melanoma samples with extensive follow-up and sufficient metastatic events.
  • The methodology integrates diverse data types (clinical, imaging, multi-omics) for comprehensive risk assessment.
  • The developed risk prediction model aims to provide absolute risk estimates for distant metastasis.

Conclusions:

  • The D-ESMEL study established a robust methodology for prognostic biomarker discovery and absolute risk prediction in early-stage melanoma.
  • This approach has the potential to significantly impact clinical decision-making by enabling more accurate risk stratification.
  • Identifying patients at high risk for distant metastasis can lead to personalized treatment strategies and improved patient outcomes.