<HashMap><database>GEO</database><file_versions><headers><Content-Type>application/xml</Content-Type></headers><body><files><Other>ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE311nnn/GSE311521/</Other></files><type>primary</type></body><statusCode>OK</statusCode><statusCodeValue>200</statusCodeValue></file_versions><scores/><additional><omics_type>Transcriptomics</omics_type><species>Homo sapiens</species><gds_type> Genome binding/occupancy profiling by high throughput sequencing</gds_type><gds_type>Expression profiling by high throughput sequencing</gds_type><full_dataset_link>https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE311521</full_dataset_link><repository>GEO</repository><entry_type>GSE</entry_type></additional><is_claimable>false</is_claimable><name>Single-cell multimodal profiling of pan-cancer cell lines uncovers gene regulatory principles underlying intrinsic cell states and environmental features</name><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.</description><dates><publication>2026/03/02</publication></dates><accession>GSE311521</accession><cross_references><GSM>GSM9326419</GSM><GSM>GSM9326418</GSM><GSM>GSM9326409</GSM><GSM>GSM9326415</GSM><GSM>GSM9326414</GSM><GSM>GSM9326417</GSM><GSM>GSM9326416</GSM><GSM>GSM9326411</GSM><GSM>GSM9326422</GSM><GSM>GSM9326421</GSM><GSM>GSM9326410</GSM><GSM>GSM9326413</GSM><GSM>GSM9326412</GSM><GSM>GSM9326423</GSM><GSM>GSM9326420</GSM><GPL>30882</GPL><GSE>311521</GSE><taxon>Homo sapiens</taxon></cross_references></HashMap>