Model Combining Tumor Molecular and Clinicopathologic Risk Factors Predicts Sentinel Lymph Node Metastasis in Primary Cutaneous Melanoma
| Autor: | Tina J. Hieken, Sindhuja Sominidi-Damodaran, Barbara A. Pockaj, Jason C. Sluzevich, David J. DiCaudo, Charles Perniciaro, Alina G. Bridges, Joel B. Heim, Enrica Quattrocchi, Martin H. van Vliet, Mark R. Pittelkow, James W. Jakub, Sanjay P. Bagaria, Félicia J. Tjien-Fooh, Julia S. Lehman, Mark A. Cappel, Alexander Meves, Suzette M. Arias-Mejias, Domenico Bellomo, Jvalini Dwarkasing, Chandru Ramana |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Oncology Cancer Research medicine.medical_specialty medicine.diagnostic_test business.industry Nodal metastasis Sentinel lymph node medicine.disease Article Metastasis 03 medical and health sciences 030104 developmental biology 0302 clinical medicine Text mining 030220 oncology & carcinogenesis Internal medicine Cutaneous melanoma Biopsy medicine business |
| Zdroj: | JCO Precis Oncol |
| Popis: | PURPOSE More than 80% of patients who undergo sentinel lymph node (SLN) biopsy have no nodal metastasis. Here, we describe a model that combines clinicopathologic and molecular variables to identify patients with thin- and intermediate-thickness melanomas who may forgo the SLN biopsy procedure because of their low risk of nodal metastasis. PATIENTS AND METHODS Genes with functional roles in melanoma metastasis were discovered by analysis of next-generation sequencing data and case-control studies. We then used polymerase chain reaction to quantify gene expression in diagnostic biopsy tissue across a prospectively designed archival cohort of 754 consecutive thin- and intermediate-thickness primary cutaneous melanomas. Outcome of interest was SLN biopsy metastasis within 90 days of melanoma diagnosis. A penalized maximum likelihood estimation algorithm was used to train logistic regression models in a repeated cross-validation scheme to predict the presence of SLN metastasis from molecular, clinical, and histologic variables. RESULTS Expression of genes with roles in epithelial-to-mesenchymal transition (glia-derived nexin, growth differentiation factor 15, integrin-β3, interleukin 8, lysyl oxidase homolog 4, transforming growth factor-β receptor type 1, and tissue-type plasminogen activator) and melanosome function (melanoma antigen recognized by T cells 1) were associated with SLN metastasis. The predictive ability of a model that only considered clinicopathologic or gene expression variables was outperformed by a model that included molecular variables in combination with the clinicopathologic predictors Breslow thickness and patient age (area under the receiver operating characteristic curve, 0.82; 95% CI, 0.78 to 0.86; SLN biopsy reduction rate, 42%; negative predictive value, 96%). CONCLUSION A combined model that included clinicopathologic and gene expression variables improved the identification of patients with melanoma who may forgo the SLN biopsy procedure because of their low risk of nodal metastasis. |
| Databáze: | OpenAIRE |
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