Abstract 3615
Background
As primary cutaneous melanoma patients with a positive sentinel lymph node (SLN, stage III) are now considered candidates for adjuvant systemic therapy, a SLN biopsy (SLNB) is indicated in more patients. However, SLNB is an invasive procedure and is negative in approximately 80% of patients. Therefore, there is a need for a non-invasive test to accurately identify patients with primary cutaneous melanoma without nodal metastases. Here we describe the first independent validation of a recently developed CP-GEP model to predict nodal metastasis. This risk model combines Breslow thickness, age, and gene expression variables from the primary melanoma.
Methods
This study focused on all patients >18 years who underwent a SLNB at the Erasmus Medical Center (between January 2007 and December 2017), within 90 days after diagnosis of primary cutaneous melanoma. Total RNA was extracted from formalin-fixed paraffin-embedded (FFPE) primary cutaneous melanomas, reversed transcribed into cDNA and subsequently analyzed for the expression of 8 target genes involved in melanoma metastasis (ITGB3, PLAT, SERPINE2, GDF15, TGFBR1, LOXL4, IL8, MLANA) using an optimized qPCR protocol.
Results
FFPE tissue samples from 211 patients were analyzed using the CP-GEP model. At diagnosis, the median age was 55 years (interquartile range [IQR] 45-65), and the median Breslow thickness was 2.1mm (IQR 1.4-3.4). Most patients presented with a T2 or T3 melanoma, accounting for 94 and 70 patients, respectively. Overall, 27.5% of patients had a positive SLN. The CP-GEP model had a negative predictive value (NPV) of 89.4%. In patients with stage T1-T2 melanoma, the model was able to achieve an SLNB reduction rate of 40.1% with an NPV of 90.7%.
Conclusions
The CP-GEP model is a non-invasive and validated tool that is able to predict nodal metastasis in an independent Dutch population. Consequently, this risk model is able to accurately identify patients with primary cutaneous melanoma that can safely forego SLNB. Therefore, the CP-GEP model is a promising tool for patient care, preventing unnecessary surgery in the majority of patients.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
Erasmus University Medical Center, Department of Dermatology.
Funding
SkylineDx.
Disclosure
J.T. Dwarkasing: Shareholder / Stockholder / Stock options, Full / Part-time employment: SkylineDx BV. D. Tempel: Shareholder / Stockholder / Stock options, Full / Part-time employment: SkylineDx BV. L. Bosman: Shareholder / Stockholder / Stock options, Full / Part-time employment: SkylineDx. A.A.M. Van der Veldt: Advisory / Consultancy: BMS; Advisory / Consultancy: MSD; Advisory / Consultancy: Roche; Advisory / Consultancy: Novartis; Advisory / Consultancy: Pierre Fabre; Advisory / Consultancy: Pfizer; Advisory / Consultancy: Sanofi; Advisory / Consultancy: Ipsen. All other authors have declared no conflicts of interest.
Resources from the same session
5517 - Molecular fingerprinting in breast cancer (BC) screening using Quantum Optics (QO) technology combined with an artificial intelligence (AI) approach applying the concept of “molecular profiles at n variables (MPnV)”: a prospective pilot study.
Presenter: Jean-Marc Nabholtz
Session: Poster Display session 3
Resources:
Abstract
2152 - Inferring the correlation between incidence rates of melanoma and the average tumor-specific epitope binding ability of HLA class I molecules in different populations
Presenter: Istvan Miklos
Session: Poster Display session 3
Resources:
Abstract
4382 - Thermal Liquid Biopsy as a Valuable Tool in Lung Cancer Screening Programs
Presenter: Alberto Rodrigo
Session: Poster Display session 3
Resources:
Abstract
2465 - Towards a screening test for cancer by circulating DNA analysis
Presenter: Rita Tanos
Session: Poster Display session 3
Resources:
Abstract
3788 - Evaluation of a successful launch of the MammaPrint and BluePrint NGS kit
Presenter: Leonie Delahaye
Session: Poster Display session 3
Resources:
Abstract
3863 - Analysis of prognostic factors on overall survival in elderly women treated for early breast cancer using data mining and machine learning
Presenter: Pierre Heudel
Session: Poster Display session 3
Resources:
Abstract
1993 - Circulating tumor cell detection in epithelial ovarian cancer using dual-component antibodies targeting EpCAM and FRα
Presenter: Na Li
Session: Poster Display session 3
Resources:
Abstract
4281 - CEUS of the breast: Is it feasible in improved performance of BI-RADS evaluation of critical breast lesions?——A multi-center prospective study in China
Presenter: Jun Luo
Session: Poster Display session 3
Resources:
Abstract
2268 - Classification of abnormal findings on ring-type dedicated breast PET for detecting breast cancer
Presenter: Shinsuke Sasada
Session: Poster Display session 3
Resources:
Abstract
4035 - Prediction of benign and malignant breast masses using digital mammograms texture features
Presenter: Cui Yanhua
Session: Poster Display session 3
Resources:
Abstract