{"database":"GEO","file_versions":[{"headers":{"Content-Type":["application/json"]},"body":{"files":{"Other":["ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE319nnn/GSE319327/"]},"type":"primary"},"statusCode":"OK","statusCodeValue":200}],"scores":null,"additional":{"omics_type":["Transcriptomics"],"species":["Homo sapiens"],"gds_type":["Expression profiling by high throughput sequencing"],"full_dataset_link":["https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE319327"],"repository":["GEO"],"entry_type":["GSE"],"additional_accession":[]},"is_claimable":false,"name":"A Pan-Cancer Single-Cell Atlas to Evaluate Tumor Identity, Cell Line Concordance, and Dependency Mapping","description":"Bulk RNA sequencing enables pan-cancer transcriptional analyses, but obscures cancer cell-specific programs due to admixture with nonmalignant cells, limiting direct comparison between experimental models and human tumors. Single-cell RNA sequencing (scRNA-seq) overcomes these limitations, yet biological interpretability of public datasets is often compromised by variable data quality, inconsistent annotation, and atlas-scale aggregation strategies that favor data volume over biological coherence. We therefore developed a stringent integration framework that prioritizes representative malignant transcriptional states. Using Mahalanobis distance-based selection in batch-corrected latent space, we constructed a pan-cancer atlas of 135,441 high-quality malignant cells from 494 samples spanning 36 adult and pediatric cancer types. Atlas-derived signatures were used to assess tumor–cell line concordance and project ElasticNet models trained on DepMap CRISPR screens to infer cancer-specific gene dependencies. Together, the scTumor Atlas provides a scalable framework for tumor identity inference, cancer cell line benchmarking, and systematic identification of genetic vulnerabilities.","dates":{"publication":"2026/07/24"},"accession":"GSE319327","cross_references":{"GSM":["GSM9515643"],"GPL":["24676"],"GSE":["319327"],"taxon":["Homo sapiens"],"PMID":["[42124572]"]}}