We recently showed that lymph node disaggregation followed by immunocytology enables precise quantification of disseminated cancer cells (DCCs) and demonstrated that this approach has a 20-fold higher sensitivity to detect melanoma DCCs than routine histopathology (Ulmer et al., PLoS Medicine, 2014). Moreover, genetic profiling of single melanoma DCCs identified a colonization signature consisting of specific copy number alterations and point mutations that identify patients with high risk of progression (Werner-Klein et al., Nature Communications 2018). Here, we present the adaptation of this method to a semi-automated workflow for detection, isolation and molecular analysis of single melanoma DCCs. The developed workflow includes a mechanical disaggregation of lymph node tissue and collection of the mononuclear cells, immunofluorescence staining against melanoma-associated markers gp100 and MCSP and depletion of CD45-positive cells. Individual melanoma cells are then detected and isolated by DEPArrayTM technology enabling single cell whole genome amplification (Ampli1TM) for subsequent molecular analysis. In total, we processed 20 lymph nodes of melanoma patients and detected melanoma DCCs in 11/20 samples (55%). The quality of isolated cells was checked by Ampli1TM QC and 174 isolated single cells were further analyzed by Sanger sequencing for specific point mutations (BRAF, NRAS and cKIT) and Ampli1TM low pass kit for Illumina for copy number variation (CNV). Successful molecular analysis was correlated with genome integrity score (GII) as determined by Ampli1TM QC, with more than 95% of cells with GII 3-4 showing good performance in low pass sequencing. In conclusion, a new DepArrayTM based application for marker-dependent single cell isolation from malignant melanoma lymph nodes was successfully established and tested on a cohort of 20 melanoma patients. Molecular analyses of isolated single cells confirmed the tumor origin by CNV profiling and mutational analysis of melanoma-associated mutations. In the future, this approach could help to select individualized therapies for melanoma patients.

Citation Format: Barbara Alberter, Sebastian Scheitler, Giancarlo Feliciello, Alberto Ferrarini, Melanie Werner-Klein, Sebastian Haferkamp, Christoph A. Klein, Bernhard Polzer. Semi-automated detection, isolation and molecular analysis of single disseminated melanoma cells from lymph nodes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 431.