We aim to develop prediction models based on patient and tumor characteristics to identify patients with melanoma who are at high risk for disease progression.
Aim 1: Identify patients with a low risk of nodal metastasis
Patients with AJCC stage IB melanoma or higher may undergo a sentinel lymph node biopsy (SLNB) in order to detect nodal metastasis. The SLNB is used for accurate disease staging, prognosis determination, and since the introduction of adjuvant therapy, also for treatment planning. The SLNB is an invasive procedure and associated with possible morbidity. Moreover, ~80% of all SLNB’s is negative. Identification of patients with low risk of nodal metastasis may lead to a reduction of SLNB’s.
Aim 2: Identify patients with a high risk of disease progression:
Most patients with melanoma (90%) are diagnosed without any nodal or distant metastasis. Although it seems controversial to the good prognosis of most early stage melanomas, death due to melanoma after diagnosis of an early stage melanoma concerns 41% of all melanoma deaths (i.e. >300 of 800 melanoma deaths in the Netherlands). We aim to develop prognostic models based on both patient and tumor characteristics in order to identify patients with early stage melanoma who are at high risk for disease progression. Those patients can then be treated with adjuvant targeted or immunotherapies to increase the probability of cure.
Our research focus
Prediction of nodal metastasis
SkylineDx developed a clinicopathological gene expression profile (CP-GEP) model in collaboration with the Mayo Clinics in the United States in order to predict which patients have a low risk of nodal metastasis.
We recently validated the CP-GEP model among >200 patients who underwent a SLNB in the Erasmus MC. We observed that the CP-GEP model can accurately identify those patients with a low risk of nodal metastasis.
Currently, we are validating the prognostic value of the CP-GEP model. Moreover, Erasmus MC, the Netherlands Comprehensive Cancer Organization (IKNL) and SkylineDx collaborate on the development of a new prediction model for distant metastasis of cutaneous melanoma, including both patient characteristics (e.g. age, sex, immunosuppressive drug use) and molecular tumor characteristics (e.g. gene expression, immune cell infiltration).