Search bioRxiv⌕ Search

Biology subjects

Sherpa, N.

Publications and source records attributed to Sherpa, N..

2 recordsLinked to original sources

Integrated spatial and single-cell transcriptomic analysis of aggressive glioblastoma growth dynamics.

Glioblastoma (GBM) develops within a complex tumor ecosystem whose temporal dynamics remain poorly understood. Here, we performed longitudinal single-cell RNA sequencing and spatial transcriptomics across multiple timepoints in two widely used murine GBM models - CT2A and GL261 - which differ markedly in aggressiveness and response to immune checkpoint blockade. Tumor cell transcriptomes revealed model-specific programs: CT2A cells progressively upregulated epithelial-mesenchymal transition (EMT), non-classical MHC Class I, and progressively, hypoxia response pathways, resembling the human mesenchymal GBM cell state, while GL261 cells exhibited MHC Class II expression and developmental signatures resembling oligodendrocyte progenitor and astrocytic states. Ligand-receptor interaction analyses identified thrombospondins (Thbs1, Thbs2) and osteopontin (Spp1) as CT2A-specific tumor ligands mediating tumorigenic interactions with immune cells, with downstream targets enriched for EMT and TGF-{beta} pathways. Conversely, the GL261 model presented a differential potential to engage neuronal and perivascular guidance networks, with Glutamate and L1 cell adhesion molecule (L1cam) as lead signaling partners. The CT2A immune compartment exhibited progressive microglia-to-macrophage phenotypic conversion, enhanced macrophage infiltration driven by Spp1, and elevated T cell exhaustion, while GL261 maintained a distinct adaptive immune communication hub via MHC class II-CD4 signaling. Elevated THBS1, THBS2, and SPP1 expression correlated with poor survival in human GBM datasets. Together, these findings reveal divergent tumor-immune ecosystems in CT2A and GL261 that recapitulate distinct aspects of human GBM, with implications for therapeutic targeting.

cancer biology↗

Enforced ZFP281 expression delays breast cancer initiation and can provide lifelong protection against breast cancer metastasis

Breast cancer metastatic reactivation and its links to mammary development are largely unknown. Here, using conditional knockout and overexpression in normal and HER2+ mammary glands, we show that the dormancy regulator ZFP281 promotes branching and dissemination while suppressing growth, and its overexpression can even override HER2-driven cancer initiation. Notably, while ZFP281 does not limit HER2-driven early dissemination, it constrains DCC plasticity, confining cells to a dormant mesenchymal/hybrid-like state and effectively suppressing metastasis throughout the mouse lifespan. ZFP281 is induced by estrogen, progesterone, and glucocorticoid signaling, and RNA sequencing of early lesions revealed that it regulates glutathione metabolism and ferroptosis, potentially supporting fitness during dormancy, while repressing angiogenesis, Th17-like inflammation, innate immune genes, and pro-inflammatory programs that might otherwise trigger awakening. Integrating these findings with human data, we show that DCIS and IBC (invasive breast cancer) lesions that later relapse are selectively enriched for ZFP281-regulated M-like and dormancy signatures and, after pregnancy, depleted for a ZFP281-induced stress-autophagy module, indicating that erosion or imbalance of these programs marks lesions that seed DCCs with higher reactivation potential. We propose that ZFP281 acts as a hormone-regulated dormancy gatekeeper that uncouples dissemination from growth, enforcing a metabolically fit, angiogenesis-low, immune-evasive dormant state in breast DCCs, thereby shaping the timing of metastatic relapse and potentially exploitable for durable prevention of metastasis. STATEMENT OF SIGNIFICANCEZFP281, a hormone-regulated dormancy gatekeeper, uncouples dissemination from growth and constrains DCCs into long-term arrest, defining human gene signatures that distinguish dormancy-prone from awakening-prone lesions and predict breast cancer relapse dynamics.

cancer biology↗