Biology subjects

Deng, S.

Publications and source records attributed to Deng, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Multi-resolution humanness scoring of antibodies via a region-isolated language model

Antibodies recognized as foreign can elicit anti-drug antibodies that compromise efficacy and safety, making humanness scoring, estimating how human-like a sequence appears, an early, repeated step in antibody development. Widely used scores reduce a variable domain to a single framework-dominated number, leaving the complementarity-determining regions (CDRs) grafted during humanization, diversified by generative models weakly supervised and rarely audited. Here we introduce IgScope, a 23-million-parameter antibody language model trained from scratch that scores humanness at chain, IMGT-region, and residue resolution; a region-isolated attention module confines attention to within-region pairs, and chimeric-sequence training supplies region-level supervision. On 2.38 million held-out chains, masking all framework tokens leaves CDR accuracy unchanged, and back-mutated murine residues in human frameworks are detected at 93.4% accuracy. At chain level, IgScope matches the strongest published correlation with clinical anti-drug-antibody incidence (r = 0.567, n = 217) and outperforms a same-protocol recomputation. Region-level scoring exposes what chain-level scoring conceals: antibodies designed de novo by four generative models pass whole-chain humanness (0.966-0.988), while their CDRs score far below natural (P < 1e-30) in every chain class. Quality control turns the same resolution to the mirror-image question: in humanized-mouse platforms, whose domains are human by design, IgScope scores the domain as human and, in HUGO-Nano chimeric VHHs, isolates only the FR2 signal of four camelid hallmark residues deliberately retained to stabilize the VHH. IgScope gives antibody humanization a verified foundation and generative design an audit.

bioinformatics↗

bioRxiv2026

CryoFAST: A Fab-Aided Cryo-EM Workflow for High-Resolution Structure Determination of Sub-50-kDa Protein Targets

Single-particle cryo-electron microscopy (cryo-EM) plays an increasingly important role in structure-based drug discovery (SBDD) by revealing target protein-ligand interactions at the molecular level, but its application has largely focused on large- and medium-sized protein complexes. Many therapeutically important targets are smaller than 50 kDa and remain highly challenging for cryo-EM analysis because of low signal-to-noise ratios and difficulties in particle alignment. Here, we describe cryoFAST, an integrated workflow that couples ligand-compatible Fab discovery and structure-guided computational maturation with biochemical triage, fiducial-complex assembly, and single-particle cryo-EM. Through phage-display screening and/or AI-guided design, we developed high-affinity Fab binders of approximately 50 kDa that stabilize the target without disrupting ligand-target interactions and then assembled Fab-target complexes for cryo-EM analysis. Using the therapeutically relevant extracellular domain (~20 kDa) of interleukin-4 receptor (IL-4R) as a proof of concept, we determined its cryo-EM structure at 2.74 Angstrom resolution and established a framework for elucidating ligand-binding modes. This workflow provides a practical, efficient, and potentially broadly applicable route to the structural characterization of small protein targets that are otherwise challenging to study. By extending the applicability of cryo-EM to high-value but technically challenging small targets, cryoFAST may open new opportunities for structure-based drug discovery.

biophysics↗

bioRxiv2026

Bridging the gap between omics and structural data: A framework for interpreting protein-RNA interaction specificity

Protein-RNA interactions play a central role in many cellular processes, such as gene regulation, protein synthesis and viral infections. Although a few thousand protein-RNA complexes have been structurally characterized, they represent only a small fraction of all interactions. This lack of experimental data limits the ability of deep-learning based approaches such as AlphaFold3 to predict structures of protein-RNA interactions. In this study, we investigate protein-RNA binding specificity by enriching experimental structures with omics data. To this end, we present a scoring approach and associated web resource quantifying the agreement between experimental structures of protein-RNA interfaces and their omics-derived binding preferences, allowing us to identify interaction motif cores. Through key structural and evolutionary features, we further highlight that these motif cores correspond to important interface regions. We then leverage the dataset of protein-RNA complexes for which structural information can be combined with binding preferences in order to benchmark the ability of AlphaFold3 to predict protein-RNA interaction specificity. To this aim, we run AlphaFold3 predictions using different RNA inputs, from the exact sequence present in the experimental structure to a non-specific sequence, including sequences embedding the consensus binding motif from in vitro experiments. We show that despite good prediction quality, the sensitivity of AlphaFold3 to the exact RNA sequence used as input indicates signs of memorization. We examine some particular complexes and uncover challenges encountered by our scoring workflow and AlphaFold3, especially in the case of alternative binding modes. Altogether, this work evidences both the promise and current limitations of deep learning approaches for protein-RNA structure prediction, and provides a resource to guide their further development.

bioinformatics↗

bioRxiv2026

Reconstructing whole-organism cell phylogenies with resolved ancestral transcriptional states

Combining cell lineage tracing with single-cell RNA sequencing can reconstruct a phylogenetic tree to integrate single-cell transcriptomic atlas. However, the internal nodes of this tree - representing ancestral cells - remain transcriptionally silent, preventing along-lineage longitudinal tracing of cell state dynamics. Here, we overcome this by reconstructing high-resolution zygote-to-larva developmental cell phylogenies for 15 zebrafish larvae, with directly measured transcriptomes of the terminal nodes (i.e., sampled cells). Leveraging a set of lineage-committed upregulated genes (LUGs), we developed LUG-encoded ancestral projection (LEAP), a novel phylogeny-based computational framework, and successfully imputed the transcriptional states of internal nodes of the phylogenies. This enabled, for the first time in a non-nematode organism, lineage-informed longitudinal analysis of cell state dynamics throughout development. Our analysis revealed a major, previously unappreciated wave of fate specializations associated with hatching, distinct from the well-characterized events of gastrulation. Furthermore, we uncovered abundant incipient cell states that are already fate-determined but exhibit minimal transcriptional differentiation, revealing a hidden layer of developmental fate specializations. In sum, by resolving ancestral transcriptional states of a reconstructed cell phylogeny, this work paves the way for constructing lineage-resolved cell atlases in complex organisms to characterize comprehensive cell state dynamics.

developmental biology↗

bioRxiv2026

Transplanting ANXA1- CD8+ Naive T cells Delay Aging Through Senolysis

Immunosenescence is a hallmark of aging, yet strategies using defined immune subsets to counteract it are largely unexplored. We performed paired scRNA/TCR-seq on CD45 cells from human bone marrow (15 donors, 3-91 years). T cells were the most altered lineage, with CD8 naive T cell contraction and functional impairment. We identified an expanding ANXA1 CD8 naive subset with senescence signatures and impaired function. ANXA1-deficient CD8 T cells exhibited increased resting stemness and enhanced activation/cytotoxicity upon stimulation. ANXA1- cells showed senolytic activity in vitro and reduced senescence burden in vivo. Monthly transfer of syngeneic ANXA1- CD8 naive T cells into aged mice extended median lifespan by >30 weeks, improving cardiac function, bone density, motor coordination, and marrow immune microenvironment. Our study identifies ANXA1 as critical in CD8 naive T cell aging and establishes ANXA1- cell transfer as a strategy to counter immunosenescence and promote healthy aging.

immunology↗

bioRxiv2026

A small molecule PTER-selective inhibitor reduces food intake and body weight

PTER (phosphotriesterase-related) is an amidohydrolase that mediates catabolism of the anorexigenic taurine metabolite N-acetyltaurine. However, the structural basis of PTER ligand binding and catalysis remain unknown, limiting our ability to harness this pathway therapeutically. Here we solve crystal structures of a eukaryotic PTER in apo and product-bound forms. These structures uncover an unexpected pocket homology between PTER and histone deacetylase (HDAC) enzymes. We exploit this similarity to engineer a first-in-class substrate-competitive PTER inhibitor called PTERi with nanomolar potency and >100-fold selectivity for PTER over HDACs in vitro. Administration of PTERi to diet-induced obese mice reduces feeding, enhances GLP1-RA (glucagon like peptide 1 receptor agonist)-induced weight loss, and prevents weight regain after GLP1-RA discontinuation. The structure of PTER connects histone and metabolite deacetylation into a parallel conceptual framework and enables proof-of-concept data for pharmacological inhibition of PTER in obesity.

biochemistry↗

bioRxiv2026

Pan-cancer silencer transcription atlas reveals leukemia silencer hijacking FOXP1 to enhance MYC oncogene

Numerous non-coding regulatory elements have been identified in the human genome. However, our understanding of silencers remains largely incomplete. Here, we proposed the concept of silencer transcriptional activity (STA) to investigated silencers from a new perspective using >20,000 transcriptomes. We demonstrated that lower STAs predicted better patient outcomes in most cancers, and a subset of STAs distinguished tumors from noncancerous tissues for most cancer types. Importantly, we discovered a silencer-linked RNA (slRNA) of a leukemia cell-specific silencer (LeSSi). Knocking down (KD) of the slRNA significantly decreased leukemia cell viability and migration and increased cellular apoptosis. Furthermore, the direct link and functional similarity between LeSSi and slRNA were established by CRISPR/Cas9 knocking out (KO) of LeSSi. Mechanistically, the slRNA hijacked FOXP1, which functions here both as a known transcription factor (TF), and more importantly, as a novel RNA binding protein (RBP), thereby preventing FOXP1 from binding to the promoter of proto-oncogene MYC and subsequently promoting MYC transcription. Collectively, our work provides insights into the functions of silencers in cancer and identified a slRNA hijacking FOXP1 to enhance MYC expression in leukemia cells.

genomics↗

bioRxiv2025

ATHENA: A deep learning-based AI for functional prediction of genomic mutations and synergistic vulnerabilities in prostate cancer

Identifying functional mutations that drive therapy resistance remains a major challenge in prostate cancer. Large-scale sequencing often produces extensive lists of mutations but provides limited insight into which alterations are functionally relevant. To overcome this gap, we developed ATHENA (Attention-based Therapeutic Network Analyzer), a deep learning-based AI framework that predicts the functional impact of genomic mutations and reveals their synergistic vulnerabilities. Integrated with our RNA/DNA-informed variant discovery pipeline OncoVar, ATHENA models nonlinear dependencies among mutations to distinguish driver events from passenger variants. Trained on large multi-cohort datasets and interpreted using SHAP analysis, ATHENA not only stratifies patients by clinical outcomes but also predicts which specific mutations alter tumor behavior and therapy response, enabling direct validation through base editing experiments. Applied to prostate cancer progression models, the OncoVar-ATHENA framework identified stage-specific driver signatures across castration-resistant, AR-variant-driven, and metastatic disease, and uncovered cooperative interactions such as SYVN1-STC2 that promote tumor proliferation. By moving beyond simple mutation identification, ATHENA enables functional prediction of genomic interactions. This approach accelerates the discovery of actionable targets and provides a foundation for rational design of next-generation combination therapies in advanced prostate cancer.

cancer biology↗

bioRxiv2025

A deep learning framework for building INDEL mutation rate maps

Germline short insertions and deletions (INDELs) are pervasive genetic variants that shape genome evolution and contribute to human disease. However, accurately quantifying fine-scale INDEL mutation rates remains challenging due to data limitations and the diversity of INDEL subtypes. Here, we present MuRaL-indel, a deep learning framework that predicts germline INDEL mutation rates by leveraging long-range sequence context through a U-Net architecture. Using extensive rare variant data from large population cohorts, MuRaL-indel generates base-resolution, length-specific mutation rate maps for the human genome and achieves superior accuracy compared with existing models across multiple genomic scales. We successfully apply MuRaL-indel to three non-human species (Macaca mulatta, Drosophila melanogaster, and Arabidopsis thaliana), demonstrating its broad applicability across taxa. Using the predicted mutation rate maps, we reveal the mutational landscape around human coding genes and show that MuRaL-indel-derived constraint scores better prioritize pathogenic INDELs than previous models. Through deep learning interpretability analyses, we uncovered sequence motifs--including both repeat and non-repeat elements--associated with elevated INDEL mutability, providing insights into underlying mutational mechanisms. Together, MuRaL-indel establishes a generalizable and scalable framework for building high-resolution INDEL mutation rate maps, offering a valuable resource for studies of genome evolution, mutational mechanism, variant interpretation, and genetic disease.

genomics↗

bioRxiv2025

LEM-3/ANKLE1 nuclease prevents the formation of syncytium between postmitotic sister cells and safeguards neuronal differentiation

In animal development, cells exit the cell cycle after a final division to differentiate into specialized types. However, the impact of failed cytokinesis on the fate specification of postmitotic cells, especially neurons, remain poorly understood. We analysed the Caenorhabditis elegans mutants lacking the midbody-tethered endonuclease LEM-3/ANKLE1, which resolves chromatin bridges during cytokinesis. Using genetic analysis, fluorescence microscopy, and RNA visualization, we found that unresolved DNA bridges lead to the formation of stable intercellular canals, creating binucleate syncytia between touch receptor neurons and sisters, which allows thorough cytoplasmic mixing despite independent nuclear transcription. In some cases, the cytoplasmic linkage between sister cells also led to the suppression of neuronal fates and prevention of apoptotic fates. Thus, LEM-3 prevents syncytium formation to ensure proper cellular differentiation.

developmental biology↗

bioRxiv2025

MAESTRO uncovers tandem paralog dynamics as a core driver of fungal stress adaptation

Understanding how fungi adapt to diverse stresses is critical for mitigating emerging drug resistance and harnessing their robustness for biotechnology. Pichia kudriavzevii is a stress-tolerant and intrinsically drug-resistant yeast, with dual industrial and clinical importance. We analyzed 170 strains using MAESTRO, a machine-learning-assisted GWAS pipeline optimized for small cohorts. MAESTRO identified biologically meaningful features and revealed that copy number variation (CNV) of tandem paralogs (TPs) is a core mechanism of multi-stress adaptation. TPs were recurrently linked to tolerance of industrial inhibitors (HMF, phenolics, heat) and antifungal drugs (fluconazole, azoles), and deletion of the TP pair gene4260/gene4261 confirmed pleiotropic effects across stresses. These findings support a TP CNV model where recombination-driven TP CNVs and gene fusions enable rapid stress adaptation. Importantly, our results suggest that antifungal resistance can arise through co-option of mechanisms originally evolved for environmental stressors, raising a One Health concern about the environmental origins of drug-resistant pathogens.

genomics↗

bioRxiv2025

Mechanism-Aware Protein-Protein Interaction Prediction via Contact-Guided Dual Attention on Protein Language Models

Robust prediction of protein-protein interactions (PPIs) requires models that generalize beyond the training distribution. Here, we present PLMDA-PPI, a mechanism-aware framework that incorporates biophysical inductive biases into a dual-attention architecture with residue-level contact supervision applied to protein language model embedded geometric representations. This design enables the model to jointly learn global interaction likelihoods and the residue pairs mediating interactions, producing interpretable, mechanism-grounded predictions. PLMDA-PPI demonstrates strong out-of-distribution generalization, substantially outperforming lightweight deep learning models (D-SCRIPT, Topsy-Turvy, TT3D, and TUnA) and achieving comparable or even superior performance to computationally intensive methods (AF2Complex, RF2-Lite, and RF2-PPI) derived from AlphaFold2 and RoseTTAFold2, while requiring orders of magnitude fewer computational resources. These results show that incorporating mechanistic priors as architectural inductive biases enhances generalization, interpretability, and computational efficiency, providing a principled foundation for AI-driven prediction of complex biological interactions.

bioinformatics↗

bioRxiv2025

Phase-separating enzyme-responsive condensates for inhalable delivery of biomacromolecules

Inhaled delivery of biomacromolecules shows extensive promise for treating a large variety of diseases, especially pulmonary and respiratory diseases. Although nanoparticle-based vehicles enhance intracellular uptake efficiency, it still remains challenging for biomacromolecules inhaled delivery to overcome cytotoxicity, cargo leakage and off-target effect. Here, we present an enzyme-responsive condensates based highly biocompatible and controllable platform for inhaled delivery of biomacromolecules. The condensates that can be modularly designed for rapid recruitment and enrichment of biomolecules, including Cas9 ribonucleoprotein (RNP) and mRNA, were internalized by cells via lysosomal-independent endocytosis, and subsequently released payloads triggered with endogenous physicochemical stimulus for controlled regulation of genes and proteins expression. Additionally, the micrometer sized condensates with highly structural and biological stability during lyophilization and nebulization enable inhalable delivery of biomacromolecules to lung. This condensate-based vehicle provides a promising platform for biomacromolecules inhaled delivery and controlled release, targeted cell regulation, and precise therapy.

bioengineering↗

bioRxiv2025

Pp6-Pfkfb1 axis modulates intracellular bacterial proliferation by orchestrating host-pathogen metabolic crosstalk

Intracellular bacterial pathogens exhibit heterogeneous replication rates within host macrophages, but the mechanisms by which they manipulate host factors for survival remain incompletely understood. Using a fluorescence-dilution reporter system in Salmonella typhimurium (Salmonella)-infected macrophages, we found that Protein Phosphatase 6 (Pp6) was downregulated in macrophages harboring growing bacteria. Conditional knockout of Pp6 elevated host susceptibility to Salmonella-mediated lethality due to compromised antimicrobial defenses. MicroRNA-31 (miR-31) was identified as a negative regulator of Pp6, and its conditional ablation enhanced bacterial clearance. Yeast two-hybrid screening identified 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 1 (Pfkfb1), a metabolic regulator, as a substrate of Pp6. Pp6 deficiency resulted in significantly elevated expression of Pfkfb1, which was highly expressed in macrophages containing replicating Salmonella. Pfkfb1 deletion restricted bacterial proliferation by promoting nitric oxide (NO) production while concurrently suppressing arginase-1 (Arg-1) expression and impairing arginine metabolism in macrophages. Collectively, these results establish the Pp6-Pfkfb1 axis as a key regulator of host metabolic adaptation and intracellular bacterial survival, offering potential therapeutic targets against multidrug-resistant pathogens. Author summaryIntracellular pathogens secrete effector proteins that intercept and modify host cells to usurp host defenses and establish habitable intracellular niches, yet the host factors they manipulate for survival and virulence are not fully understood. Using a fluorescence-dilution reporter system in Salmonella typhimurium (Salmonella)-infected macrophages, we found Protein Phosphatase 6 (Pp6) was significantly decreased in macrophages harboring replicating Salmonella and its deficiency indulged bacterial growth in macrophages. However, the deletion of microRNA-31 (miR-31), a negative regulator of Pp6, enhanced Salmonella clearance. Furthermore, through yeast two- hybrid screening, we identified 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 1 (Pfkfb1) is a substrate of Pp6. And we demonstrated Pfkfb1, abundantly expressed in macrophages containing growing bacteria, is crucial for Salmonella proliferation by regulating nitric oxide (NO) levels, arginase-1 (Arg-1) expression and arginine metabolism. Overall, the molecular changes in Pp6-Pfkfb1 axis drive host metabolic adaptations that enable intracellular bacterial survival.

microbiology↗

bioRxiv2025

Cholesterol-Responsive Interaction of NFE2L1-INSIG1 Controls VLDL Secretion and MASH Pathogenesis

Cholesterol overload drives metabolic dysfunction-associated steatohepatitis (MASH). While the liver maintains homeostasis by exporting cholesterol via very low-density lipoprotein (VLDL) secretion, this process is paradoxically suppressed under cholesterol excess by insulin-induced gene 1 (INSIG1), which inhibits sterol regulatory element-binding protein 1 (SREBP1) activity. How VLDL secretion persists during cholesterol overload to prevent lipotoxicity remains unresolved. We identify a cholesterol-responsive interaction between nuclear factor erythroid 2 related factor-1 (NFE2L1) and INSIG1 that sustains cholesterol balance. Hepatic NFE2L1 deficiency elevates INSIG1 levels, suppressing SREBP1 activation and impairing VLDL secretion, leading to cholesterol accumulation and liver injury. Mechanistically, NFE2L1 binds to INSIG1 via its N-terminal homology box 2 (NHB2) domain, with cholesterol enhancing this interaction to drive INSIG1 degradation and SREBP1 activation. In NFE2L1-deficient mice, wild-type NFE2L1 restores SREBP1 activity and VLDL secretion, while the NHB2-deleted mutant ({Delta}NHB2) fails. Lipidomics reveal that NFE2L1 deficiency reduces serum triglyceride composition, restored exclusively by wild-type NFE2L1. In a murine MASH model, NFE2L1 overexpression activates SREBP1/2, enhances cholesterol secretion, and alleviates liver injury, inflammation and fibrosis, without elevating atherogenic lipoproteins due to compensatory LDL receptor upregulation. Our findings resolve the paradox of cholesterol-driven VLDL secretion and establish the NFE2L1-INSIG1 axis as a therapeutic target for metabolic diseases.

physiology↗

bioRxiv2025

Altered susceptibility of rare yeast species causing vulvovaginal candidiasis at acidic vaginal environment

This study evaluated potency of antifungals against the rare emerging yeast causing VVC under the influence of acid pH (4.5). The antifungal susceptibility was evaluated by CLSI-M27A4 for 25 isolates represented 11 rare yeast species recovered from VVC patients. Among 11 yeast species (25 isolates), Clavispora lusitaniae (n=9) was the most common species, followed by Cyberlindnera fabianii (n=4), Kodamaea ohmeri, Candida metapsilosis and Candida bracarensis (n=2, respectively), Pichia norvegensis, Kluyveromyces marxianus, Diutina rugosa, Tricholoma matsutake, Candida nivariensis and Candida africana (n=1, respectively). The antifungal agents most affected by a lower pH were terconazole (4-fold higher, MIC >0.5-8mg/L) to all 11 yeast species; followed by amphotericin B (3-fold higher, MIC >2mg/L) to 9 species except K. ohmeri and D. rugose; nystatin (2-fold higher, MIC >2 - 4 mg/L) to 9 species except C. nivariensis and C.africana; micafungin (2-fold higher, MIC >0.125 - 1 mg/L) to 8 species except C. metapsilosis, D. rugosa and Tricholoma matsutake by acid pH, respectively. 5-flucytosine was not affected by pH to all tested yeast species, and maintained activity at low PH. The rare yeast species tested shared high MICs of terconazole, amphotericin B, nystatin at pH4.5 compared with those at pH7.0. The in vitro antifungal susceptibility at pH 7.0 did not consistently predict the activity of antifungals against those rare yeast species at vaginal acidic pH. Further investigations assessing the clinical utility of antifungal susceptibility testing should take these findings into consideration.

microbiology↗

bioRxiv2025

Notch as a Driver of Lineage Plasticity and Therapeutic Target in Enzalutamide-Resistant Prostate Cancer

Resistance to androgen receptor (AR)-targeted therapies, such as enzalutamide, in castration-resistant prostate cancer (CRPC) remains a significant clinical challenge, often driven by mechanisms including lineage plasticity. The precise molecular mechanisms driving this process, particularly downstream effectors, remain incompletely understood. Given its established roles in cell fate and stemness, alongside its complex functions in prostate cancer, the Notch signaling pathway presented a compelling focus for study. This study investigates the role of Notch signaling in mediating lineage plasticity and therapeutic resistance in CRPC. Employing transcriptomic analysis and functional assays, we identified Notch activity is elevated across prostate cancer progression resistance. Notably, both CRISPR-mediated knockout and targeted inhibition of Notch reversed enzalutamide resistance in vitro. Collectively, this study delineates dynamic alterations in Notch signaling activity during prostate cancer progression and establishes its function as a crucial and druggable driver of therapy resistance. These findings underscore Notch signaling as a promising therapeutic target to counteract resistance to AR-targeted therapies in advanced prostate cancer.

cancer biology↗

bioRxiv2025

Pan-cancer Analysis Identified Ectopic RUNX1T1 Associated with Lineage Plasticity

Cancer remains a leading cause of death worldwide, with lineage plasticity emerging as a hallmark that drives therapy resistance and tumor progression, allowing cancer cells to rapidly alter their identity and evade targeted therapies. Although various genomic and transcriptomic aberrations correlate with lineage plasticity, the lack of pan-cancer gene markers quantifying lineage plasticity has limited its utility as a predictive biomarker. Homeobox (HOX) genes encode a family of transcription factors that play critical roles in embryonic development and tissue identity by establishing distinct expression patterns, known as HOX codes, in specific cell lineages. Through comprehensive bioinformatic analysis of multi-omics data--including expression profiles of 39 HOX genes from over 80,000 RNA sequencing samples across 114 cancer types--we first demonstrated that HOX codes effectively represent the lineages of cancer cells. We then identified multiple lineage-plastic cancer subtypes by applying the calculated HOX codes. Specifically, we identified lineage-plastic tumor subtypes in prostate cancer, lung cancer, and acute myeloid leukemia (AML), which exhibit altered HOX codes compared to non-plastic subtypes. Downstream differential expression analysis revealed significantly elevated RUNX1T1 levels across all three lineage-plastic cancer types, which was further validated through bulk and single-cell RNA sequencing data derived from preclinical and clinical samples. Together, our findings provide a novel strategy for characterizing lineage plasticity in pan-cancer cells and suggest ectopic RUNX1T1 expression as a pan-cancer marker and critical mediator of lineage plasticity.

cancer biology↗