{"database":"GEO","file_versions":[{"headers":{"Content-Type":["application/json"]},"body":{"files":{"Other":["ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE311nnn/GSE311521/"]},"type":"primary"},"statusCode":"OK","statusCodeValue":200}],"scores":null,"additional":{"omics_type":["Transcriptomics"],"species":["Homo sapiens"],"gds_type":[" Genome binding/occupancy profiling by high throughput sequencing","Expression profiling by high throughput sequencing"],"full_dataset_link":["https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE311521"],"repository":["GEO"],"entry_type":["GSE"],"additional_accession":[]},"is_claimable":false,"name":"Single-cell multimodal profiling of pan-cancer cell lines uncovers gene regulatory principles underlying intrinsic cell states and environmental features","description":"Cancer arises from somatic mutations whose effects are executed through dysregulated gene-regulatory programs that reshape chromatin, transcription, and malignant phenotypes. To uncover gene regulatory principles underlying heterogeneous cancer cell states and their linked environmental features, here we present a pan-cancer single-cell, multi-omic atlas of human cancer cell lines, including a compendium of 240,957 transcriptomes and 223,347 chromatin-accessibility profiles from primary cancers spanning 20 tumor types. We revealed extensive pan-cancer cell-state heterogeneity, core gene-regulatory networks, and consensus epithelial–mesenchymal transition (EMT) trajectories that transcend tissue of origin and are governed by conserved epigenomic and transcriptomic features. In addition, our copy-number variation analysis implicated transcription factor amplification, followed by hyperactive downstream regulation, as a major driver of malignant states. Further focused analysis of acral versus cutaneous melanoma cell lines uncovers a universal inflammation-suppressive program in acral melanoma versus an inflamed regulatory landscape in cutaneous melanoma, highlighting the JAK–STAT axis as a key discriminator. Finally, by integrating single-cell and bulk datasets across models and patient cohorts, we revealed tumor–microenvironment co-adaptation in vivo, and this was associated with immunotherapy responsiveness.","dates":{"publication":"2026/03/02"},"accession":"GSE311521","cross_references":{"GSM":["GSM9326419","GSM9326418","GSM9326409","GSM9326415","GSM9326414","GSM9326417","GSM9326416","GSM9326411","GSM9326422","GSM9326421","GSM9326410","GSM9326413","GSM9326412","GSM9326423","GSM9326420"],"GPL":["30882"],"GSE":["311521"],"taxon":["Homo sapiens"]}}