Dataset Information

Malignant Melanoma - Improved survival prognostication of node-positive malignant melanoma patients utilizing shotgun proteomics guided by histopathological characterization and genomic data

ABSTRACT: In depth histopathology analysis of melanoma lymph node metastases combined with deep-mining protein expression analysis, clinical data and genomic profile information, in the search for novel protein markers to predict progression and survival of melanoma

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Melanocyte, Lymph Node

DISEASE(S): Skin Melanoma

SUBMITTER: Lazaro Hiram Betancourt Nunez

LAB HEAD: Gyorgy Marko-Varga

PROVIDER: PXD009630 | Pride | 2019-03-29

REPOSITORIES: Pride

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Improved survival prognostication of node-positive malignant melanoma patients utilizing shotgun proteomics guided by histopathological characterization and genomic data.

Betancourt Lazaro Hiram LH Pawłowski Krzysztof K Eriksson Jonatan J Szasz A Marcell AM Mitra Shamik S Pla Indira I Welinder Charlotte C Ekedahl Henrik H Broberg Per P Appelqvist Roger R Yakovleva Maria M Sugihara Yutaka Y Miharada Kenichi K Ingvar Christian C Lundgren Lotta L Baldetorp Bo B Olsson Håkan H Rezeli Melinda M Wieslander Elisabet E Horvatovich Peter P Malm Johan J Jönsson Göran G Marko-Varga György G

Scientific reports 20190326 1

Metastatic melanoma is one of the most common deadly cancers, and robust biomarkers are still needed, e.g. to predict survival and treatment efficiency. Here, protein expression analysis of one hundred eleven melanoma lymph node metastases using high resolution mass spectrometry is coupled with in-depth histopathology analysis, clinical data and genomics profiles. This broad view of protein expression allowed to identify novel candidate protein markers that improved prediction of survival in mel ...[more]

PMID: 30914758

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Project description:Melanoma recurrence frequently occurs after a latency period of several years. In vivo studies demonstrated that tumor cells overcoming latency show a T cell-edited phenotype, suggesting a relevant role for CD8+ T cells in maintaining metastatic latency. Here, in a patient model of multiple recurrent lesions, we illustrate the genetic evolution of poorly immunogenic melanoma phenotypes, evolving in the presence of autologous tumor antigen-specific CD8+ T cells. Melanoma cells from two of three late recurrent metastases, developing within a 6-year latency period, lacked HLA class I expression. HLA class I-negative tumor cells became clinically apparent 1.5 and 6 years into stage IV disease. Genome profiling by SNP arrays revealed total T-cell resistance in both metastases originating from a shared chromosome 15q alteration and independently acquired focal B2M gene deletions. A third HLA class I-positive lesion developed in year 3 of stage IV disease. By HLA haplotype loss lesion-derived melanoma cells acquired resistance towards dominant T-cell clonotypes targeting early stage III tumor cells. Early disease melanoma cells showed a dedifferentiated MITFnegative phenotype, recently described to be associated with immunosuppression, in contrast to the MITFhigh phenotype of T cell-edited tumor cells from late metastases. In summary, our study demonstrates that tumor recurrences after long-term latency develop towards T-cell resistance by independent genetic events, suggesting a mechanism of T cell-driven genetic evolution of melanoma as a means to evade immune recognition and tumor immunotherapy. Genetic alterations lead to loss of tumor antigen presentation. Cell lines were generated from tumor material, differences in T cell recognition were observed and Affymetrix SNP arrays were performed according to the manufacturer's directions on DNA extracted from the cell lines. SNP analysis of different melanoma cell lines obtained from one melanoma patient (4 cell lines from different metastasis of one patient with matching germline DNA).

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