Single-cell multimodal profiling of pan-cancer cell lines uncovers gene regulatory principles underlying intrinsic cell states and environmental features
A pan-cancer single cell transcriptomic and epigenomic atlas is generated and subtype-specific gene-regulatory programs that shape cancer cell-state plasticity are identified that shape cancer cell-state plasticity.
Abstract
Cancer arises from genetic and epigenetic alterations that reshape chromatin, transcriptional regulation, and malignant cell states. To chart cancer-intrinsic regulatory programs, we build a pan-cancer single-cell atlas of 60 cancer cell lines spanning 16 tissue origins and 20 cancer types, comprising 240,957 snRNA-seq and 223,347 snATAC-seq profiles. Integrative analyses reveal cell-state heterogeneity, core gene-regulatory networks, and a conserved EMT axis transcending tissue of origin; copy-number analysis identifies transcription factor amplification and hyperactivation as drivers of state reprogramming. Comparing cutaneous melanoma with acral melanoma, a rare subtype underrepresented in previous studies, uncovers a universal inflammation-suppressive program in acral and an inflamed landscape in cutaneous melanoma, with JAK-STAT activity as the central discriminator. Integrating data across models and patient cohorts links tumor-intrinsic regulation to microenvironmental composition and therapeutic response. By profiling rare alongside common subtypes, this atlas offers a resource for mapping pan-cancer and subtype-specific regulatory programs shaping cell-state plasticity. ‘The characterization of cancer intrinsic regulatory landscape remains elusive. Here, the authors generate a pan-cancer single cell transcriptomic and epigenomic atlas and identify subtype-specific gene-regulatory programs that shape cancer cell-state plasticity.
Integrative single-cell RNA sequencing analysis of publicly available datasets from non-small cell lung cancer and breast cancer is performed to systematically map transcriptional heterogeneity and regulatory networks within the TME, providing a systems-level framework of TME organization.
M. O. Odubote, Chiemeka Elochi Emeribe· bioRxiv· 0 citations
Epigenetic aberrations are a hallmark of cancer; however, systematic chromatin state maps of cancer cells are unavailable. We generated and analyzed 803 histone mark profiles in 142 cancer cell lines and 114 human tumors belonging to 9 solid tumor types. Irrespective of their cell-of-origin, cancer cells segregate from normal tissues based on their enhancer patterns, suggesting enhancer deregulation is a fundamental epigenetic feature in cancer. Enhancer based clustering defined 5 distinct subgroups of cancer cells (EpiC1-5) with unique developmental trajectories, molecular features and dependencies. Importantly, we define a set of core TFs that are critical for EpiC-specific enhancer patterns and survival. Notably, EpiC4 represented a predominantly epigenetic, pan-cancer subtype that displays poor survival, activation and dependence on a FN1-CAV1-SRC-PI3K-AKT signaling network. Together, these data uncover enhancer heterogeneity in pan-cancer systems with identification of a novel enhancer-based subtype and identify potential new therapeutic targets associated with unique epigenetic features.
M. Mattohti, E. Arslan, Ayush T. Raman et al.· bioRxiv· 0 citations
Background/Objectives: Cancer cell migration is a hallmark of cancer and is associated with metastasis. While large-scale functional screens have identified regulators of migration, less is known about how intrinsic transcriptional heterogeneity drives highly migratory phenotypes within individual cancer models or whether these transcriptional changes are conserved across different models of varying tissues of origin. This study aims to define shared and cell line-specific transcriptional programs associated with cancer cell migration and analyze their relevance to patient datasets. Methods: Five cancer cell lines across three cancer types (breast, colorectal, and melanoma) were subjected to transwell-based migratory sorting to isolate highly and weakly migratory subpopulations. Bulk RNA sequencing, differential gene expression analysis, Gene Ontology (GO) enrichment, and upstream regulator prediction were performed. Public tumor datasets were analyzed to evaluate gene expression and its association with patient survival. Results: EVA1A was consistently upregulated in all highly migratory (HM) subpopulations. Multiple GO terms were enriched across all cell lines, often driven by distinct gene signatures, indicating convergence at the level of biological processes despite transcriptional divergence. TEAD4 was predicted as an upstream regulator, and increased TEAD4 nuclear localization was observed in four of the five HM subpopulations. EVA1A and TEAD4 expression were elevated in tumors relative to normal tissues, with cancer type-dependent survival outcomes. Conclusions: Migratory selection was accompanied by extensive transcriptional change within each model, yet across five models spanning three tissue types these changes converged on shared biological processes rather than shared genes. Migration-associated phenotypes may therefore be better defined by pathway-level than single-gene analyses, and the clinical relevance of regulatory nodes such as TEAD4 appears conditional on cancer type.
I. Ortiz, Paul V. Taufalele, Victor L. Dunagan et al.· Genes· 0 citations
Breast cancer comprises heterogeneous transcriptional states that are incompletely captured by discrete clinical or molecular subtype labels. To visualize this heterogeneity in a unified framework, we integrated bulk RNA-seq data from 2,284 patient samples across 13 studies using 18,089 protein coding genes, a harmonized processing pipeline, batch correction, consensus clustering and PaCMAP dimensionality reduction to construct an interactive breast cancer transcriptional landscape. Consensus clustering identified five major regions, which were annotated using PAM50 scores calculated for each sample: Luminal A, Luminal B, HER2-enriched, and two basal-associated clusters. The basal clusters separated into an immune-rich region marked by T cell–inflamed, tumor-associated macrophages (TAM), and low-purity signatures, and a cell-cycle–driven region enriched for proliferation and DNA replication programs. Overlay of marker genes, pathways, kinases, neuronal-like signaling programs, cancer associated fibroblasts (CAF) states, and TAM programs revealed spatially organized subtype biology and microenvironmental heterogeneity. Finally, projection of therapy-associated resistance signatures identified landscape regions linked to predicted resistance to HER2-targeted therapy and hormone receptor–directed endocrine therapies. By enabling interactive exploration of transcriptional states, marker genes, pathways, and therapeutic response programs, this resource provides a community framework for biomarker discovery in breast cancer. Significance statement We present a unified, multi-cohort transcriptional landscape of breast cancer that organizes canonical subtypes along continuous biological axes and reveals spatially structured regions of therapeutic sensitivity and resistance. By enabling projection of patient samples and patient-derived models into this framework, we provide a practical tool for interpreting tumor biology and guiding translational discovery. Graphical abstract caption We integrate 13 breast cancer transcriptomic datasets using a harmonized processing pipeline with batch correction and dimensionality reduction to construct a unified landscape spanning basal, HER2-enriched, luminal A, luminal B, and normal-like subtypes. Continuous gradients of proliferation, endocrine signaling, metabolic activity, and tissue architecture organize the space and recapitulate subtype biology. Overlay of clinical outcomes reveals high-risk regions beyond discrete subtype boundaries, while integration of gene expression and copy number alterations links genomic events to pathway-level programs. Microenvironmental features, including cancer-associated fibroblast programs, map to distinct regions of the landscape. Projection of drug response signatures identifies zones of sensitivity and intrinsic resistance. The framework supports embedding of patient samples and patient-derived xenograft models and is available as an interactive resource for exploration and projection of new datasets. One Sentence Summary A landscape built using only transcriptomic analysis for breast cancer reveals novel insights about subtype-specific biology.
Sonali Arora, Ramya Suresh, N. Holland et al.· bioRxiv· 0 citations
This study identified UBE2C+ proliferative tumor cells as a functionally relevant malignant subpopulation in SCLC and links this state to immune-stromal communication networks within the tumor microenvironment and provides a systems-level framework for investigating the cancer-immunity regulome in SCLC.
Hong-Ling Jia, Yongxuan An, Bing Chen et al.· Frontiers in Immunology· 0 citations
Single-cell transcriptomics of publicly available BLCA data identifies two transcriptionally distinct fibroblast states in adjacent tissue and supports an exploratory ligand–receptor interaction framework, warranting prospective validation with primary CAF populations and adequately powered multi-specimen cohorts.
Yan-Dong He, Wen-Long Lu, Guan-Qun Ju et al.· Frontiers in Cell and Develo...· 0 citations
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