Affymetrix SNP array data of melanoma cell lines II
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE96884 | GEO | 2017/05/24
SECONDARY ACCESSION(S): PRJNA379986
REPOSITORIES: GEO
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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.
2016-07-27 | GSE80736 | GEO
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).
2016-07-27 | E-GEOD-80736 | biostudies-arrayexpress
Project description:B2M losses are seen as an early event, shared by all cell lines presented here
2015-01-01 | GSE60218 | GEO
Project description: Not available
| S-DIXA-D-1096 | biostudies-other
Project description:SNP arrays were used to derive copy number estimates and identify amplifications and deletions in melanomas These copy number breakpoints were compared to gene fusions identified by second generation sequencing of cDNA
2010-02-01 | GSE17359 | GEO
Project description:Comparison between the copy number of differentially methylated sites between lymph node metastasis from melanoma patients with good prognosis and melanoma brain metastasis. All samples are taken from different patients, and were established as cell lines in the John Wayne Cancer Institute.
2013-09-19 | GSE44019 | GEO
Project description:DNA from resected colon cancer primary tumour tissue was analyzed for association between tumour dissemination and copy number alterations.
2014-12-01 | GSE62875 | GEO
Project description:Affymetrix SNP arrays were performed according to the manufacturer's directions on DNA extracted from fresh frozen tissues. To obtain a profile of copy number alterations in RMS, we studied 65 samples in 60 RMS cases. Other data of 38 samples are deposited in GSE41263: GSM1528059 GSM1528057 GSM1528061 GSM1528058 GSM1528054 GSM1528060 GSM1012723 GSM1012722 GSM1012746 GSM1528055 GSM1528056 GSM1530028 GSM1012751 GSM1012724 GSM1012726 GSM1012747 GSM1012725 GSM1012716 GSM1012735 GSM1012713 GSM1012736 GSM1012737 GSM1012738 GSM1012730 GSM1012739 GSM1012740 GSM1012750 GSM1012717 GSM1012718 GSM1012719 GSM1012741 GSM1012732 GSM1012715 GSM1012720 GSM1012742 GSM1012721 GSM1012743 GSM1012744
2015-06-30 | GSE63891 | GEO
Project description:Allele call files from on 250K StyI SNP array using DNA from 60 human cell lines from metastasized melanoma and from 44 corresponding peripheral blood mononuclear cells (CEL and CHP files provided).
2010-04-13 | GSE17534 | GEO
Project description:BackgroundCopy number data are routinely being extracted from genome-wide association study chips using a variety of software. We empirically evaluated and compared four freely-available software packages designed for Affymetrix SNP chips to estimate copy number: Affymetrix Power Tools (APT), Aroma.Affymetrix, PennCNV and CRLMM. Our evaluation used 1,418 GENOA samples that were genotyped on the Affymetrix Genome-Wide Human SNP Array 6.0. We compared bias and variance in the locus-level copy number data, the concordance amongst regions of copy number gains/deletions and the false-positive rate amongst deleted segments.ResultsAPT had median locus-level copy numbers closest to a value of two, whereas PennCNV and Aroma.Affymetrix had the smallest variability associated with the median copy number. Of those evaluated, only PennCNV provides copy number specific quality-control metrics and identified 136 poor CNV samples. Regions of copy number variation (CNV) were detected using the hidden Markov models provided within PennCNV and CRLMM/VanillaIce. PennCNV detected more CNVs than CRLMM/VanillaIce; the median number of CNVs detected per sample was 39 and 30, respectively. PennCNV detected most of the regions that CRLMM/VanillaIce did as well as additional CNV regions. The median concordance between PennCNV and CRLMM/VanillaIce was 47.9% for duplications and 51.5% for deletions. The estimated false-positive rate associated with deletions was similar for PennCNV and CRLMM/VanillaIce.ConclusionsIf the objective is to perform statistical tests on the locus-level copy number data, our empirical results suggest that PennCNV or Aroma.Affymetrix is optimal. If the objective is to perform statistical tests on the summarized segmented data then PennCNV would be preferred over CRLMM/VanillaIce. Specifically, PennCNV allows the analyst to estimate locus-level copy number, perform segmentation and evaluate CNV-specific quality-control metrics within a single software package. PennCNV has relatively small bias, small variability and detects more regions while maintaining a similar estimated false-positive rate as CRLMM/VanillaIce. More generally, we advocate that software developers need to provide guidance with respect to evaluating and choosing optimal settings in order to obtain optimal results for an individual dataset. Until such guidance exists, we recommend trying multiple algorithms, evaluating concordance/discordance and subsequently consider the union of regions for downstream association tests.
| S-EPMC3146450 | biostudies-literature