Beschreibung:
Abstract Background With the recent improvements in genomic analysis tools, research groups all over the world are intensively deep-sequencing different tumor entities, including malignant melanomas. However, most of these studies are using only a single biopsy per patient, providing only limited insights into genomic tumor evolution. In this study, we performed genomic profiling of sorted clonal tumor populations from several specimens per patient in order to infer genomic heterogeneity in malignant melanomas and genomic evolution during tumor progression. Methods A multi-parameter sorting approach was used to flow-sort multiple biopsies from individual melanoma patients. Therefore nuclei were extracted from frozen and FFPE tissues and sorted according to DNA ploidy. SOX10 was used as an additional melanoma parameter to ensure sorting of pure tumor populations. Resulting clonal tumor populations were genomically characterized by usage of high resolution aCGH and deep-sequencing with NGS technologies. Results Array CGH and NGS performed on flow-sorted clonal tumor populations (DNA and SOX10) revealed the clonal relationship between multiple tumor biopsies from individual patients. The tumor marker SOX10 allowed us to uncover and sort pure tumor populations, excluding the risk of contribution of non-neoplastic cells in downstream analysis with aCGH and NGS. Taken together the information of ploidy, aCGH and NGS data, we were able to detect chromosome imbalances and allelic frequencies from these populations. Our approach allows us to track the evolution of clonal populations across time and organs and in the context of therapeutic interventions. Conclusions Human malignant melanomas are composed of different clonal populations with population-specific genomic aberrations and mutations. The use of SOX10 allowed unraveling of pure tumor populations within the diploid peaks, which would have been obscured by the use of DNA content alone. NGS and aCGH helped to characterize these sorted clonal tumor populations. Further bioinformatic analyses of these sorted clonal tumor populations are fundamental for the understanding of the clonal relationship and genomic heterogeneity and their potential impact on metastasis and therapy response in malignant melanoma. Citation Format: Thomas Lorber, Tanja Dietsche, Valeria Perrina, Michael Barret, Kathrin Glatz, Christian Ruiz, Lukas Bubendorf. Deciphering the genomic heterogeneity and evolution in malignant melanoma by genomic profiling of clonal tumor populations. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3424. doi:10.1158/1538-7445.AM2014-3424