New functional signatures for understanding melanoma biology from tumor cell lineage-specific analysis
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PROVIDER: PRJNA280579 | ENA |
REPOSITORIES: ENA
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Project description:The identification of molecular signatures specific to distinct tumor types is urgently required for the design of therapies overcoming treatment resistance. It remains unclear whether such signatures are shared among tumors and corresponding cell lines, a key issue given the use of cell lines for drug development. We developed SCA (similarity core analysis) an unsupervised computational framework for extracting the core molecular features common to tumors and corresponding cell lines. We applied this algorithm to mRNA and miRNA expression data from various sources, comparing melanoma cell lines with melanoma metastases as an example. The signature obtained was associated with phenotypic characteristics in vitro and the core genes CAPN3 and TRIM63 were identified as new actors in melanoma cell migration and invasion. About 90% of the genes in the melanoma signature are potentially regulated by an intrinsic network of transcription factors known to regulate neural development (TFAP2A, DLX2, ALX1, MITF, PAX3, SOX10, LEF1 and GAS7) and three miRNAs (211-5p, 221-3p, 10a-5p). The SCA signature effectively discriminated between two subpopulations of melanoma patients with significant difference in overall survival. Furthermore, it classified MEKi and BRAFi resistant and sensitive melanoma cell lines. The SCA algorithm is potentially applicable to any tumor cell type.
2015-09-30 | GSE67636 | GEO
Project description:The identification of molecular signatures specific to distinct tumor types is urgently required for the design of therapies overcoming treatment resistance. It remains unclear whether such signatures are shared among tumors and corresponding cell lines, a key issue given the use of cell lines for drug development. We developed SCA (similarity core analysis) an unsupervised computational framework for extracting the core molecular features common to tumors and corresponding cell lines. We applied this algorithm to mRNA and miRNA expression data from various sources, comparing melanoma cell lines with melanoma metastases as an example. The signature obtained was associated with phenotypic characteristics in vitro and the core genes CAPN3 and TRIM63 were identified as new actors in melanoma cell migration and invasion. About 90% of the genes in the melanoma signature are potentially regulated by an intrinsic network of transcription factors known to regulate neural development (TFAP2A, DLX2, ALX1, MITF, PAX3, SOX10, LEF1 and GAS7) and three miRNAs (211-5p, 221-3p, 10a-5p). The SCA signature effectively discriminated between two subpopulations of melanoma patients with significant difference in overall survival. Furthermore, it classified MEKi and BRAFi resistant and sensitive melanoma cell lines. The SCA algorithm is potentially applicable to any tumor cell type.
2015-09-30 | GSE67635 | GEO
Project description:Molecular signatures specific to particular tumor types are required to design treatments for resistant tumors. However, it remains unclear whether tumors and corresponding cell lines used for drug development share such signatures. We developed similarity core analysis (SCA), a universal and unsupervised computational framework for extracting core molecular features common to tumors and cell lines. We applied SCA to mRNA/miRNA expression data from various sources, comparing melanoma cell lines and metastases. The signature obtained was associated with phenotypic characteristics in vitro, and the core genes CAPN3 and TRIM63 were implicated in melanoma cell migration/invasion. About 90% of the melanoma signature genes belong to an intrinsic network of transcription factors governing neural development (TFAP2A, DLX2, ALX1, MITF, PAX3, SOX10, LEF1, and GAS7) and miRNAs (211-5p, 221-3p, and 10a-5p). The SCA signature effectively discriminated between two subpopulations of melanoma patients differing in overall survival, and classified MEKi/BRAFi-resistant and -sensitive melanoma cell lines.
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Project description:Cell plasticity sustains intra-tumor heterogeneity and treatment resistance in melanoma. Deciphering the transcriptional mechanisms governing reversible phenotypic transitions between proliferative/differentiated and invasive/stem-like states is required. Expression of the ZEB1 transcription factor is frequently activated in melanoma, where it fosters adaptive resistance to targeted therapies. Here, we performed a genome-wide characterization of ZEB1 transcriptional targets, by combining ChIP-sequencing and RNA-sequencing, upon phenotype switching in melanoma models. We identified and validated ZEB1 binding peaks in the promoter of key lineage-specific genes crucial for melanoma cell identity. Mechanistically, ZEB1 negatively regulates SOX10-MITF dependent proliferative/melanocytic programs and positively regulates AP-1 driven invasive and stem-like programs. Comparative analyses with breast carcinoma cells revealed lineage-specific ZEB1 binding, leading to the design of a more reliable melanoma-specific ZEB1 regulon. We then developed single-cell spatial multiplexed analyses to characterize melanoma cell states intra-tumoral heterogeneity in human melanoma samples. Combined with scRNA-Seq analyses, our findings confirmed increased ZEB1 expression in Neural-Crest-like cells and mesenchymal cells, underscoring its significance in vivo in both populations. Overall, our results define ZEB1 as a major transcriptional regulator of cell states transitions and provide a better understanding of lineage-specific transcriptional programs sustaining intra-tumor heterogeneity in melanoma.
2024-03-26 | GSE246603 | GEO
Project description:Cell plasticity sustains intra-tumor heterogeneity and treatment resistance in melanoma. Deciphering the transcriptional mechanisms governing reversible phenotypic transitions between proliferative/differentiated and invasive/stem-like states is required. Expression of the ZEB1 transcription factor is frequently activated in melanoma, where it fosters adaptive resistance to targeted therapies. Here, we performed a genome-wide characterization of ZEB1 transcriptional targets, by combining ChIP-sequencing and RNA-sequencing, upon phenotype switching in melanoma models. We identified and validated ZEB1 binding peaks in the promoter of key lineage-specific genes crucial for melanoma cell identity. Mechanistically, ZEB1 negatively regulates SOX10-MITF dependent proliferative/melanocytic programs and positively regulates AP-1 driven invasive and stem-like programs. Comparative analyses with breast carcinoma cells revealed lineage-specific ZEB1 binding, leading to the design of a more reliable melanoma-specific ZEB1 regulon. We then developed single-cell spatial multiplexed analyses to characterize melanoma cell states intra-tumoral heterogeneity in human melanoma samples. Combined with scRNA-Seq analyses, our findings confirmed increased ZEB1 expression in Neural-Crest-like cells and mesenchymal cells, underscoring its significance in vivo in both populations. Overall, our results define ZEB1 as a major transcriptional regulator of cell states transitions and provide a better understanding of lineage-specific transcriptional programs sustaining intra-tumor heterogeneity in melanoma.
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