单细胞肿瘤图谱定义了稳健的通路和基因特征，可用于癌症细胞系保真度评估

The aim of this study was to determine whether rigorous quality control applied across multiple single-cell RNA (scRNA-seq) sequencing datasets could generate reproducible transcriptional signatures that accurately reflect tumor biology and support evaluation of cancer model fidelity. We aggregated publicly available scRNA-seq datasets and processed all samples through a high-stringency quality-control pipeline that included thresholds of >5,000 counts, <10% mitochondrial content, removal of samples with < 200 cells, and doublet identification using Scrublet. The resulting atlas included 135,441 high-quality tumor cells across 494 samples representing 36 adult and pediatric tumor types. We identified tumor specific gene signatures through differential expression analysis and computed hallmark pathways analysis. Strict QC markedly improved the clarity and biological coherence of tumor-specific signatures enabling us to group otherwise unrelated primary tumors into reproducible transcriptional archetypes (proliferative, immune-signaling, and metabolic) states. These atlas-derived gene signatures showed strong concordance with independent bulk RNA-seq datasets and spatial transcriptomic signatures validating the approach/model. To examine the utility of these signatures, we projected gene expression profiles from established cancer cell lines onto the atlas-derived signatures. This analysis scored cell lines based on how representative they remained to their tumor of origin. Culture adaptation, metabolic drift, or the loss or gain of hallmark pathways, are known causes of transcriptional divergence in in-vitro models. These findings demonstrate that rigorous QC enables construction of a reproducible, pan-cancer single-cell atlas that yields stable transcriptomic signatures suitable for more reliable tumor characterization than offered by the publicly resources (HTAN, EcoTyper, DepMap, Cancer SCEM etc) which vary significantly in their QC measures. This atlas provides a high-quality reference for tumor biology and a framework for evaluating the fidelity of cancer cell lines, with implications for model selection, assessment of therapeutic vulnerabilities, and translational research.