García-Silva et al. show for the first time that extracellular vesicles isolated from the exudative seroma obtained from the lymphatic drainage implanted in melanoma patients after lymphadenectomy can be interrogated for melanoma markers and BRAF mutations.,Profiling the BRAFV600E mutation in this biofluid is a novel approach to predict disease relapse.,Liquid biopsies from cancer patients have the potential to improve diagnosis and prognosis.,The assessment of surrogate markers of tumor progression in circulating extracellular vesicles could be a powerful non-invasive approach in this setting.,We have characterized extracellular vesicles purified from the lymphatic drainage also known as exudative seroma (ES) of stage III melanoma patients obtained after lymphadenectomy.,Proteomic analysis showed that seroma-derived exosomes are enriched in proteins resembling melanoma progression.,In addition, we found that the BRAFV600E mutation can be detected in ES-derived extracellular vesicles and its detection correlated with patients at risk of relapse.
Tremelimumab is an antibody that blocks CTLA-4 and demonstrates clinical efficacy in a subset of advanced melanoma patients.,An unmet clinical need exists for blood-based response-predictive gene signatures to facilitate clinically effective and cost-efficient use of such immunotherapeutic interventions.,Peripheral blood samples were collected in PAXgene® tubes from 210 treatment-naïve melanoma patients receiving tremelimumab in a worldwide, multicenter phase III study (discovery dataset).,A central panel of radiologists determined objective response using RECIST criteria.,Gene expression for 169 mRNA transcripts was measured using quantitative PCR.,A 15-gene pre-treatment response-predictive classifier model was identified.,An independent population (N = 150) of refractory melanoma patients receiving tremelimumab after chemotherapy enrolled in a worldwide phase II study (validation dataset).,The classifier model, using the same genes, coefficients and constants for objective response and one-year survival after treatment, was applied to the validation dataset.,A 15-gene pre-treatment classifier model (containing ADAM17, CDK2, CDKN2A, DPP4, ERBB2, HLA-DRA, ICOS, ITGA4, LARGE, MYC, NAB2, NRAS, RHOC, TGFB1, and TIMP1) achieved an area under the curve (AUC) of 0.86 (95% confidence interval 0.81 to 0.91, p < 0.0001) for objective response and 0.6 (95% confidence interval 0.54 to 0.67, p = 0.0066) for one-year survival in the discovery set.,This model was validated in the validation set with AUCs of 0.62 (95% confidence interval 0.54 to 0.70 p = 0.0455) for objective response and 0.68 for one-year survival (95% confidence interval 0.59 to 0.75 p = 0.0002).,To our knowledge, this is the largest blood-based biomarker study of a checkpoint inhibitor, tremelimumab, which demonstrates a validated pre-treatment mRNA classifier model that predicts clinical response.,The data suggest that the model captures a biological signature representative of genes needed for a robust anti-cancer immune response.,It also identifies non-responders to tremelimumab at baseline prior to treatment.,The online version of this article (doi:10.1186/s40425-017-0272-z) contains supplementary material, which is available to authorized users.