Résumé
There is no available tumor marker that can detect primary melanoma. Proteomics analysis has been proposed as a novel tool that would lead to the discovery of potential new tumor markers.
We developed a serum proteomic fingerprinting approach coupled with a classification method to determine whether proteomic profiling could discriminate between melanoma and healthy volunteers. A total of 108 serum samples from 30 early-stage [American Joint Committee on Cancer (AJCC) stage I or II] and 30 advanced-stage (AJCC stage III or IV) melanoma patients and 48 healthy volunteers were analyzed by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) utilizing protein chip technology and artificial neural networks.
In a first step, a multiprotein classifier was built using a training set of 30 pathologically confirmed melanoma and 24 healthy volunteer serum samples, resulting in good classification accuracy for correct diagnosis and stage classification assignment. Subsequently, our multiprotein classifier was tested in an independent validation set of 30 melanoma and 24 non-cancer serum samples patients, maintained in a good diagnostic accuracy of 98.1% (sensitivity 96.7%, specificity 100%), and 100% stage I/II classification assignment.
Although results remain to be confirmed in larger collective patient cohorts, we could demonstrate the usefulness of proteomic profiling as a sensitive and specific assay to detect melanoma, including non-metastatic melanoma, from the serum.