Dataset Information

Melanoblast transcriptome analysis reveals pathways promoting melanoma metastasis.

ABSTRACT: Cutaneous malignant melanoma is an aggressive cancer of melanocytes with a strong propensity to metastasize. We posit that melanoma cells acquire metastatic capability by adopting an embryonic-like phenotype, and that a lineage approach would uncover metastatic melanoma biology. Using a genetically engineered mouse model to generate a rich melanoblast transcriptome dataset, we identify melanoblast-specific genes whose expression contribute to metastatic competence and derive a 43-gene signature that predicts patient survival. We identify a melanoblast gene, KDELR3, whose loss impairs experimental metastasis. In contrast, KDELR1 deficiency enhances metastasis, providing the first example of different disease etiologies within the KDELR-family of retrograde transporters. We show that KDELR3 regulates the metastasis suppressor, KAI1, and report an interaction with the E3 ubiquitin-protein ligase gp78, a regulator of KAI1 degradation. Our work demonstrates that the melanoblast transcriptome can be mined to uncover targetable pathways for melanoma therapy.

SUBMITTER: Marie KL

PROVIDER: S-EPMC6965108 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Melanoblast transcriptome analysis reveals pathways promoting melanoma metastasis.

Marie Kerrie L KL Sassano Antonella A Yang Howard H HH Michalowski Aleksandra M AM Michael Helen T HT Guo Theresa T Tsai Yien Che YC Weissman Allan M AM Lee Maxwell P MP Jenkins Lisa M LM Zaidi M Raza MR Pérez-Guijarro Eva E Day Chi-Ping CP Ylaya Kris K Hewitt Stephen M SM Patel Nimit L NL Arnheiter Heinz H Davis Sean S Meltzer Paul S PS Merlino Glenn G Mishra Pravin J PJ

Nature communications 20200116 1

Cutaneous malignant melanoma is an aggressive cancer of melanocytes with a strong propensity to metastasize. We posit that melanoma cells acquire metastatic capability by adopting an embryonic-like phenotype, and that a lineage approach would uncover metastatic melanoma biology. Using a genetically engineered mouse model to generate a rich melanoblast transcriptome dataset, we identify melanoblast-specific genes whose expression contribute to metastatic competence and derive a 43-gene signature ...[more]

PMID: 31949145

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Project description:Purpose: We hypothesized that embryonic melanocytic pathways would be reinitiated in melanoma metatastasis. To identify nove melanoblast signaling pathways with which to explore our hypothesis we used the inducible Dct-GFP mouse model, in which GFP is expressed in both immature melanoblasts and mature melanocytes. Mouse melanoblasts/melanoytes were extracted using Fluorescence Activated Cell Sorting and transcriptomes were analyzed using RNA-sequencing. Abstract: Cutaneous malignant melanoma is an aggressive cancer of melanocytes with a strong propensity to metastasize. We posit that melanoma cells acquire metastatic capability by adopting an embryonic-like phenotype, and that a lineage approach would uncover novel metastatic melanoma biology. Using a genetically engineered mouse model to generate a rich melanoblast transcriptome dataset, we identify melanoblast-specific genes whose expression contribute to metastatic competence and derive a 43-gene signature that predicts patient survival. We identify a melanoblast gene, KDELR3, whose loss impairs experimental metastasis. In contrast, KDELR1 deficiency enhances metastasis, providing the first example of different disease etiologies within the KDELR-family of retrograde transporters. We show that KDELR3 regulates the metastasis suppressor, KAI1, and report an interaction with the E3 ubiquitin-protein ligase gp78, a regulator of KAI1 degradation. Our work demonstrates that the melanoblast transcriptome can be mined to uncover targetable pathways for melanoma therapy. Method: FVB/N iDct-GFP mice were used and doxycycline was administered to activate GFP expression. Multiple litters were used for each developmental stage, and embryos/pups from each stage were pooled to ensure adequate numbers of GFP+ cells. At E17.5, P1 and P7 stages, most melanocytes have reached the dermis, thus only the skin was collected from these developmental stages. Back skin was immersed in a shallow layer of 1X PBS and subcutaneous fat was scraped off until skin appeared translucent. E15.5 was the youngest age assessed due to the necessity to capture sufficient cells for RNA-sequencing. Embryos of the same developmental age that were heterozygous for the TRE-H2B-GFP gene but lacked the Dct-rtTA gene were used as negative controls. Cell doublets were excluded from the analysis. Cells were sorted based on GFP expression and SSC-A. Based on these reference sorts, gates were set so that background cells represented less than 10% of sorted cells. Cells were lysed in 10-fold TRIzol reagent (w/v), phases were separated by addition of 0.2X volume of chloroform, the aqueous phase was combined with an equal volume of 70% ethanol and applied to a RNeasy Micro column (Qiagen) and processed as per the manufacturer’s instructions. Paired-end sequencing libraries were prepared using 1 μg of purified RNA following the mRNA-Seq Sample Prep Kit according to the manufacturer’s instructions (Illumina). RNA-Seq libraries were sequenced on two lanes each of an Illumina GAIIx Genome Analyser to a minimum depth of 49 million reads. Sequence reads were aligned to the mm9 genome using the TopHat software (https://ccb.jhu.edu/software/tophat/index.shtml). Quantified Fragments Per Kilobase of transcript per Million mapped reads (FPKM) values were generated using the Cufflinks software (http://cole-trapnell-lab.github.io/cufflinks/). The UCSC KnownGenes gene models were used for guided alignment and quantification. Results: DESeq2 analysis identified melanoblast-specific gene expression, which was shown by further analyses using patient data and wet lab techniques to likewise be upregulated in metastatic melanoma and facilitate metastasis.

Dataset Information

Melanoblast transcriptome analysis reveals pathways promoting melanoma metastasis.

Publications

Melanoblast transcriptome analysis reveals pathways promoting melanoma metastasis.

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