Biology subjects

Wang, J.

Publications and source records attributed to Wang, J..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

RDEmbed: A Sentence-Embedding Framework for Rare Disease Phenotype Representation and Phenotypic Similarity Analysis

Accurate phenotypic characterization of patients with rare diseases is essential for improving diagnosis, patient matchmaking, and gene discovery. Conventional ontology driven approaches based on the Human Phenotype Ontology (HPO) enable structured phenotype comparison but often limited by the incomplete annotations and reduced ability to capture the contextual information contained in free-text clinical narratives. We present RDEmbed, a sentence embedding framework that learns patient phenotype representations directly from rare disease case reports for similarity analysis. RDEmbed encodes narrative case reports into high-dimensional vectors to quantify phenotypic similarity. Unlike ontology-based approaches that rely on manually curated HPO annotations, RDEmbed encodes narrative case reports into dense semantic embeddings that capture complex phenotypic relationships from unstructured text. We further introduced retrieval-augmented embedding refinement (RAER), which enhances embedding representations by incorporating external domain knowledge at inference time without task-specific fine-tuning. RDEmbed was evaluated against HPO-based similarity methods (OWLSim, simGIC) using simulated datasets, published rare disease case reports and a curated case study dataset comprising 156 patient abstracts presenting six rare disease groups. Across the real world datasets and curated cohort. RDEmbed consistently outperformed ontology based approaches, improving AUROC and AUPRC by more than 5%. Embedding-based representations also produced more distinct disease-specific clustering in t-SNE visualizations and identified twice as many true positive rare disease pairs as ontology based methods (8 versus 4). Incorporating RAER further improved precision while maintaining high sensitivity compared with zero-shot embeddings. These results demonstrate that sentence embeddings provide an effective alternative to ontology-based phenotype representations by learning directly from free-text clinical narratives. RDEmbed enables more accurate phenotypic similarity analysis while reducing dependence on manually curated HPO annotations and offers a scalable framework for rare disease diagnosis, patient matchmaking, and gene discovery. RDEmbed is publicly available at https://github.com/xhwang2017/RDEmbed.

bioinformatics↗

bioRxiv2026

NeuroPilot: Longitudinal intravital neural imaging across scales and modalities through an agentic platform

Longitudinal intravital imaging is essential for understanding how neural populations evolve across behavioral states, yet converting large-scale recordings into biological insight remains a fundamental challenge because neural signals are degraded by noise, non-rigid motion, and heterogeneous recording conditions. Here we introduce NeuroPilot, an agentic framework that transforms neural imaging analysis into a closed-loop, context-aware process, integrating a multimodal large language model with callable computational imaging modules to adaptively coordinate workflows from raw recordings to population-level readouts. Across heterogeneous imaging and experimental regimes, NeuroPilot restores motion-degraded recordings and supports consistent neural signal recovery, detection, and tracking in challenging deep-brain, freely moving, and longitudinal settings. These enable neuronal population tracking across spinal stimulation paradigms, deep-brain imaging, and continuous 24-hour mPFC recordings aligned with spontaneous sleep-wake behavior. By converting heterogeneous intravital recordings into stable population activity maps, NeuroPilot supports behavioral decoding and reveals structured neural dynamics across behavioral states and transitions. NeuroPilot establishes a scalable agentic paradigm for neural imaging analysis, expanding experimental access to the measurement, visualization, and interpretation of neural population activity.

neuroscience↗

bioRxiv2026

Human brain white matter charts in international populations

Brain charts provide normative references for identifying neurodevelopmental milestones and clinically meaningful deviations. Existing white matter (WM) charts are derived predominantly from European and North American populations, limiting generalizability. Here, we established the first set of population-specific WM charts of 198 morphological and microstructural phenotypes for both Chinese (N=19,215) and Western (N=15,584) populations across the human lifespan. Developmental trajectories and spatial organization of WM maturation and degeneration were broadly conserved across the populations, whereas maturational timing varied across pathways and metrics. The Chinese population showed earlier maturation in several projection and sensorimotor pathways, but more protracted maturation in higher-order association and limbic pathways. Chinese population-matched charts more accurately predicted WM phenotypes and detected more extreme disease-related deviations in brain health in the Chinese population than Western-derived models. Individual deviation patterns further discriminated patients from healthy controls and predicted cognitive performance and plasma biomarkers. Our findings reveal population-specific WM maturation and show that reference-population mismatch can reduce the accuracy and clinical sensitivity of normative brain charts.

neuroscience↗

bioRxiv2026

Fast Generalized Longitudinal Functional Principal Components Analysis with Applications to Population-Level Neuronal Dynamics

Functional data are widely collected across many fields. One approach for modeling functional data collected across repeated measures is longitudinal functional PCA (LFPCA), which allows for the decomposition of variability at multiple levels. However, existing estimation approaches for LFPCA can be computationally demanding for large or high-dimensional datasets. Here, we propose fast generalized LFPCA, a novel estimation technique that bins data along the functional domain and fits a marginal mixed model at each bin. Simulation results show that the proposed method is orders of magnitude faster than existing techniques while achieving similar accuracy, making LFPCA scalable to datasets with more densely sampled observations and larger sample sizes. We apply the proposed technique to model neuronal firing rate data both on and off deep brain stimulation (DBS) to investigate how the intervention modulates population-level neuronal activity. Our results suggest that the vast majority of between-neuron variation can be explained by four principal components, which relate to individual differences in neuronal firing as well as the arm manipulation paradigm under study. We also find that DBS significantly increases the variability of firing activity, likely driven by increased firing from a subset of neurons in response to DBS, a novel insight not obtained in previous analyses. Code to implement the proposed method and run simulation studies is included in the supplement.

neuroscience↗

bioRxiv2026

SubcellPert: Benchmarking Subcellular Perturbation Prediction

Perturbations change both RNA abundance and intracellular organization, but gene-count predictors omit the positions needed to model spatial responses. To address this limitation, we introduce \textbf{\textit{SubcellPert}}, a benchmark for perturbation-response prediction at single-RNA-molecule resolution. It comprises 14 imaging datasets with 13.88 million cells and 3.65 billion RNA molecules. Given source observations and a directed condition change, models generate target RNA populations in normalized two-dimensional coordinates. Two independently trained tasks separate abundance from localization: \textit{Oracle} predicts coordinates given exact target counts; \textit{Joint} predicts both. Eight baselines span source resampling, classical transport, and frozen foundation-model adapters. Fixed perturbation and cellular-context holdouts evaluate abundance error, target-gene coverage, and conditional spatial error. Through within-dataset analyses, we find context-dependent gains from spatial features in condition decoding. Interpreting spatial responses requires distinguishing changes within spatial clusters from population composition, and RNA fractions from enrichment relative to compartment geometry. Through comprehensive experiments, we find that Gaussian transport achieves the lowest Oracle spatial error on eight of eleven cellular-context OOD datasets. Audits show that better centroid accuracy can accompany worse radial distributions, and that 19 of 190 Joint adapter fits retain source counts after validation. SubcellPert opens a spatial frontier for virtual cell research, laying the groundwork for predictive models that capture both the molecular composition and intracellular organization of cellular responses. The code is available at \url{https://github.com/king159/SubcellPert}.

bioinformatics↗

bioRxiv2026

BioMINTREL expands biological problem solving through cross-disciplinary representations

Biological research agents often rely on familiar analytical methods, limiting the questions explored in existing data. We introduce BioMINTREL, an AI agent that finds methods from other disciplines and connects them to biological measurements. It can draw on relationships among datasets and complementary ideas from several methods to develop new questions and analyses. We evaluate it on BioMDC-Bench, which assesses 228 possible applications of computational methods to biological tasks. Compared with GPT-5.4 mini and Qwen3.6-35B, BioMINTREL improves F1 agreement with human experts by up to 45.3 percentage points and increases coverage of applications supported by studies published in subsequent years by up to 58.8 percentage points. Four case studies on real data go beyond previous analyses to reveal dose-specific variant effects, constraints on spatial cell communication and routes through perturbation-response states, and identify the sampling needed to test whether rare high-state cells predict later lineage size. These results highlight BioMINTREL's potential to expand questions in existing data and suggest directions for subsequent research.

bioinformatics↗

bioRxiv2026

Asymmetric flagellar filaments shape Borreliella burgdorferi morphology and motility

The Lyme disease spirochete Borreliella burgdorferi possesses bipolar periplasmic flagella that are essential for its distinctive flat-wave morphology, motility, and infectivity. However, how the molecular architecture of these flagella contributes to spirochetal morphology and motility remains poorly understood. Here, using in situ single-particle cryo-electron microscopy, we determine near-atomic structures of native periplasmic flagellar filaments and reveal two distinct architectures: half-sheathed and unsheathed. We show that the spirochete-specific protein FlaA forms an asymmetric sheath along the convex surface of the conserved FlaB filament core, with sheath assembly restricted to the proximal filament region near the flagellar motor. Atomic models reveal that FlaA anchors to the FlaB core through specific interactions with two surface-exposed loops, while genetic and motility analyses demonstrate distinct roles for these loops in maintaining cell morphology and motility. Structural comparisons reveal that the FlaB core possesses intrinsic curvature and supercoiling, which are substantially amplified by asymmetric FlaA assembly. These findings support a model in which localized FlaA sheath assembly reinforces the proximal filament and potentially modulates its mechanical properties. Together, our results uncover a previously unrecognized mechanism by which asymmetric sheath assembly enhances flagellar supercoiling, providing a molecular basis for understanding the distinctive morphology and motility of spirochetes.

microbiology↗

bioRxiv2026

PatientPheX: A Full-Text Biomedical Corpus for Patient-Specific Phenotype Information Extraction

Patient-specific phenotype information extraction identifiers and normalizes phenotype entities and links them with individual patients, supporting rare disease research and genotype-phenotype analysis. However, existing datasets rarely combine full-text biomedical content with detailed annotations of patients, phenotypes, related entities, and their associations. In this work, we present PatientPheX, a corpus for patient-specific phenotype information extraction comprising 200 full-text articles, primarily case reports and case series. The corpus provides mention annotations and normalization for seven biomedical entity types, together with explicit associations between patients and entities across sentences, paragraphs, and sections. Negated findings and composite phenotype mentions are also annotated. Corpus construction combines automated pre-annotation, large language model (LLM)-assisted verification, and manual review and adjudication. Benchmarks cover entity recognition and normalization and patient-phenotype association extraction using gold-standard and predicted entities. The overall document-level entity F1 was 86.95%, while phenotype F1 was 79.33%. The best relation extraction macro-F1 decreased from 84.14% with gold-standard entities to 54.98% with predicted entities, highlighting the combined challenges of phenotype extraction and patient attribution. PatientPheX provides a resource for developing and evaluating patient-centered information extraction methods and supports the construction of structured patient-level knowledge resources for phenotype-driven biomedical research.

bioinformatics↗

bioRxiv2026

Cortex-wide single-cell activity mapping in freely moving mice

How the cortex supports complex, naturalistic behavior can be revealed only in freely moving animals. Although head-mounted microscopes promise access to the neural dynamics underlying such behavior, fundamental trade-offs among field of view, spatial resolution, device weight, and background contamination have prevented cortex-wide single-neuron recording, leaving distributed cortical coordination largely inaccessible. To meet these challenges, we introduce COMET (Cortex-wide Observational Miniature Epifluorescence Technique), a 3.75-g head-mounted mesoscope that integrates an aberration-controlled wide-field optical design, uniform high-efficiency excitation and single-coaxial power-and-data transmission with an end-to-end denoising and signal-extraction pipeline. This enables cortex-wide calcium imaging at single-cell resolution in freely moving mice. COMET captures activity from >15,000 layer 2/3 neurons per 5-min recording over fields of view up to 10 mm in diameter while preserving natural behavior. Benchmarking against benchtop two-photon microscopy demonstrates high spatial concordance of detected neurons and matched event-level calcium dynamics. We apply COMET to social interaction to quantify region-specific engagement and to an auditory trace fear-conditioning task that requires natural movement and behavioral expression, revealing time-ordered changes in regional firing-rate structure and functional connectivity preceding a global conditioned response. COMET provides an open, lightweight hardware-software framework for linking single-neuron activity to cortex-scale network dynamics during freely expressed behaviors under changing internal or external conditions.

neuroscience↗

bioRxiv2026

Structural Basis of γδ T-Cell Receptor Crosslinking and Activation by a Spontaneously Dimerized Fab

The gamma delta T-cell receptor ({gamma}{delta} TCR) mediates {gamma}{delta} T-cell responses that are crucial for immune defense, tissue surveillance, and homeostasis. While recent studies suggest that {gamma}{delta} TCRs engage ligands multivalently, how multivalent engagement activates the {gamma}{delta} TCR remains elusive. Here, we present the structures of the V{gamma}9V{delta}2 (clone G115) TCR in both the inactive and the agonist-crosslinked active states. In the {gamma}{delta} TCR lipoprotein complex, cholesterol and phospholipids form a "barrier" that isolates CD3{zeta}{zeta} from the other subunits and likely prevents autoactivation. As an agonist, the Fab fragment of the UCHT1 antibody dimerizes upon binding the V{gamma}9V{delta}2 TCR, thereby crosslinking and activating the receptor. Cryo-EM analysis of Fab-crosslinked V{gamma}9V{delta}2 TCR identifies three Fab dimer interfaces, one between the two light chains and the other two between light and heavy chains. These Fab-Fab interfaces, together with the Fab-CD3{epsilon} interface, mediate the assembly of the V{gamma}9V{delta}2 TCR and UCHT1 Fab into 2:2 tetramers. Upon activation, the V{gamma}9V{delta}2 TCR becomes conformationally plastic and loses enriched lipids, including cholesterol and phosphatidylethanolamine. Our findings reveal a dimerization-driven activation mechanism, inform the design of rigid TCR-crosslinking agonists, and suggest multivalency-induced clustering as a general paradigm for {gamma}{delta} TCR activation.

immunology↗

bioRxiv2026

A molecular clutch governs stator engagement and flagellar torque

Motile bacteria must continuously adjust their motility to navigate and colonize diverse environments, including mammalian hosts, yet the underlying mechanisms remain poorly understood. Here, using in situ single-particle cryo-electron microscopy, we determine near-atomic structures of the flagellar motor in the Lyme disease spirochete Borrelia burgdorferi and uncover a novel mechanism for controlling torque generation. We identify a distinct neutral state in which individual torque-generating MotA5B2 stator complexes remain incorporated in the motor but adopt an extended, tilted conformation disengaged from the rotor. Two conserved flagellar proteins, FliL and FlcA, form a transmembrane scaffold that retains 16 stator complexes, while FapA, a newly identified multidomain flagellar protein, binds the cytoplasmic base of the stator complex and stabilizes its rotor-disengaged conformation. Strikingly, rotor-engaged and rotor-disengaged stators coexist within individual motors, demonstrating that stator retention and torque engagement are separable states. Our findings establish a motor-retained neutral stator state and reveal a molecular-clutch mechanism that independently controls the engagement of individual stator units with the rotor, enabling rapid and precise regulation of flagellar torque and motility.

microbiology↗

bioRxiv2026

Dehydration-Responsive Left-Handed G-Quadruplexes Expandthe Sequence Repertoire

G-quadruplexes (G4s) are widely studied noncanonical nucleic-acid structures that carry important biological functions. Typically, G4s form from G-rich sequences containing at least four tracts of more than two consecutive guanines, yet a plethora of unconventional G4-forming sequences have been identified, including sequences that fold into G4s only under specific conditions. These G4 states remain poorly resolved. Here we identify dehydration-responsive left-handed G4s (drLHG4s), a class of left-handed G4 (LHG4) structures accessible under dehydrating conditions. Using d(GT)n repeats, we show that left-handed folding depends on repeat length, salt condition and solvent conditions. Systematic mutational analysis reveals a broader but register-constrained sequence space, while high-resolution crystal structures of d-GTGGTGTGTGGTG, a sequence that folded robustly under dehydrating conditions, establish a bulged LHG4 architecture and reveal a distinctive hydration sphere. We further show that selected peptides and a cationic zinc phthalocyanine derivative, ZnTEGQPyrPc, containing four triethylene glycol methyl ether tails can induce LHG4 states. Together, our findings reveal a dehydration-responsive G4 landscape and expand the experimentally accessible sequence space of LHG4 folding.

biophysics↗

bioRxiv2026

SVC: A Vision Transformer-based Spatially and Virtually Embedded Cell Model for Deciphering Subcellular Spatial Transcriptomic Heterogeneity

Subcellular transcript localization shapes gene regulation and cellular function, yet profiling it at scale remains difficult, limiting our ability to study subcellular heterogeneity across tissues and disease. Here, we present SVC, a Vision Transformer-based spatially and virtually embedded cell model that learns subcellular transcript organization within native tissue context directly from subcellular-resolution spatial transcriptomics. SVC represents each gene's subcellular localization as an image patch and uses other genes within the same cell as context, enabling subcellular contextual learning of spatial dependencies that govern intracellular organization. By integrating gene-function priors, cell morphology, spatial context, and, when available, cell identity, SVC captures unified, spatially grounded representations across genes and cells that connect subcellular organization to cellular state and tissue microenvironment. Across diverse subcellular-resolution spatial transcriptomics datasets, SVC accurately predicts cell-specific subcellular transcript localization and co-organization, generalizes across tissues and technical platforms, and supports subcellular imputation of unmeasured genes and in silico perturbation. Leveraging these capabilities, SVC highlights disease-associated patterns of intracellular transcript reorganization and spatially heterogeneous responses to gene perturbations. Beyond prediction, SVC reveals systematic variation in subcellular transcript organization associated with gene function, cellular states, and tissue microenvironments, uncovering de novo spatial biology that is not accessible by cell-level analysis. SVC's learned representations further transfer to cell- and tissue-level tasks, including cell-level expression imputation, cell clustering, and spatial domain detection. Together, SVC provides a general framework for modeling and interrogating subcellular transcript organization in situ, enabling scalable discovery of subcellular spatial biology.

bioinformatics↗

bioRxiv2026

Charting sex divergence in the human brain across the lifespan

Biological sex fundamentally shapes human brain organization, but sex-specific normative neuroanatomical trajectories across the lifespan remain largely uncharted. Here, we constructed independent, sex-specific lifespan brain charts using structural neuroimaging data from 59,915 healthy individuals (29,760 males and 30,155 females) ranging in age from 266 postconception days to 100 years. By examining 296 structural phenotypes across global, cortical, and subcortical measures, these models revealed widespread sex differences in maturational timing, with males reaching peak milestones later than females. These trajectories demonstrate that sex differences evolve dynamically, with phenotype-specific windows of emergence and maximal separation. Compared with conventional sex-pooled references, sex-specific models achieved superior predictive accuracy and reduced misestimation of individual deviations in healthy populations. Across five neuropsychiatric conditions, sex-specific models improved the detection of extreme deviations and revealed both shared and sex-dependent patterns of disorder-related neuroanatomical abnormalities. These sex-specific charts establish tailored normative references for assessing brain development, ageing, and disease.

neuroscience↗

bioRxiv2026

Co-targeting KRAS and BCL-2 induces pyroptosis and antitumor tertiary lymphoid structures

Mutant KRAS drives lung tumorigenesis through both cell-intrinsic and cell-extrinsic mechanisms, yet mutation subtype-specific differences in metabolic regulation and immune evasion remain incompletely understood. Here we show that the KRAS G12D mutation, particularly in the KRAS/STK11 co-mutant (KL) context, is associated with poor prognosis and a suppressed immune microenvironment characterized by impaired antigen presentation. At the cell-intrinsic level, G12D exhibits weakened interaction with hexokinase 1 (HK1), reduced spare respiratory capacity, and diminished mitochondrial stress resistance compared with G12C. Based on this mitochondrial vulnerability, we screened a panel of clinically available agents that induce mitochondrial stress and identified that co-targeting KRAS and BCL-2 synergistically triggers GSDME-mediated pyroptosis predominantly in G12D tumors. This combination further induces functional tertiary lymphoid structures (TLS) in vivo. Immune cell depletion experiments further demonstrate that the antitumor efficacy is dramatically dependent on B cells. Moreover, this treatment strategy induced membrane attack complex (MAC) deposition in situ and elicited memory antitumor responses. Collectively, these findings establish that combined KRAS and BCL-2 inhibition elicits pyroptosis and TLS formation, leading to B-cell-dependent in situ and memory antitumor immunity, providing a mechanistic rationale for targeting mitochondrial-immune crosstalk in KRAS G12D-mutant lung cancer.

cancer biology↗

bioRxiv2026

Warming responses of microbial respiration emerge from spatially structured species interactions

Microbial communities are the main drivers of global carbon cycling. Yet, predicting their respiratory responses to warming is challenging because the underlying mechanisms remain elusive. Here, we developed a spatially explicit model, integrating single-cell metabolism with metabolic exchange and endogenous heat production, to disentangle the mechanisms driving community-level temperature responses. We reveal that mutualism amplifies respiratory temperature sensitivity compared to isolated taxa, as exchanged metabolites sustain a secondary growth phase with higher respiratory demand, and this phase creates disproportionately large CO2; emissions under warming (apparent activation energy Ea; increases from 0.65 to 0.77 eV). Spatial structure amplifies this further through threshold effects, whereby diffusion limitation shifts from constraining to enabling mutualistic benefits as metabolic rates increase with temperature (Ea; increases to 1.18 {+/-} 0.09 eV). Endogenous heat production drives additional amplification through positive feedback between local temperature rises and resource turnover rates (Ea; further increasing to 1.78 {+/-} 0.14 eV). Collectively, our results demonstrate that community-level temperature responses are not determined by enzyme kinetics alone but emerge from the interplay of ecological, physical, and metabolic interactions. Our model provides a general framework for understanding the combined effects of the underlying mechanisms on microbial temperature responses. Future climate carbon models should incorporate these considerations to improve predictions.

ecology↗

bioRxiv2026

The ammonia-senescence ratchet drives an adaptation-senescence-ammonioptosis transition in liver cancer

Senescence suppresses tumor proliferation while exposing survival dependencies, motivating separate, sequential steps of senescence induction and senescent-cell clearance. Whether senescence-sustaining metabolism can instead be overdriven to trigger cell-autonomous death remains unknown. Senescent cells generate ammonia through glutaminolysis to buffer intracellular acidification, yet extracellular ammonia can impair anti-tumor immunity, leaving unexplored whether ammonia can instead drive tumor-cell-intrinsic death. Here, we reverse this logic and identify a self-reinforcing ammonia-senescence ratchet in which ammonia accumulation promotes senescence, whereas senescence reciprocally enhances ammoniagenesis, progressively driving tumor cells from metabolic adaptation through senescence to ammonioptosis. Reducing intracellular ammonia suppressed senescence and cell death, whereas ammonia repletion restored lethality. We engineered acid-responsive H3B-120@CoGd-LDH (HBCG) to preferentially amplify tumor-cell ammonia by reinforcing senescence and ammoniagenesis, restricting ammonia disposal and weakening lysosomal buffering. HBCG limited extracellular ammonia accumulation and CD8+ T-cell exposure in co-cultures, induced ammonioptosis in patient-derived liver-cancer organoids and suppressed tumor growth in immunocompetent mice. This work reveals that adaptation, senescence and ammonioptosis can be connected through escalating intracellular ammonia stress, providing a mechanistic framework for understanding stress-driven tumor-cell fate transitions.

bioengineering↗

bioRxiv2026

FLASH X-rays preserve neutrophil chromatin to boost antitumor immunity

Ultrahigh dose-rate FLASH radiotherapy spares normal tissue while preserving antitumor activity, positioning it as a promising technology in radiation oncology, but its immunomodulatory mechanisms remain elusive. Here, using a bilateral subcutaneous tumor model in mice, we show that 8-Gy FLASH X-rays, versus an equal dose of conventional dose-rate (CONV) X-rays, elicit a robust abscopal antitumor effect with anti-PD-1 therapy. Single-cell transcriptome and T cell receptor (TCR) profiling reveal that FLASH X-rays plus anti-PD-1 enhance antitumor immune infiltration and TCR clonal expansion in both irradiated and abscopal tumor lesions. Mechanistically, the ultrashort temporal continuity of FLASH dose delivery conserves chromatin compaction in neutrophils during exposure and inhibits neutrophil extracellular trap (NET) formation, thereby dismantling NET-mediated CD8+ T-cell exclusion within primary tumors. Conversely, CONV or split-dose FLASH X-rays induce marked chromatin modifications prior to dose completion, leading to chromatin hyper-relaxation which promotes NETosis and consequently abrogates systemic antitumor immunity. Notably, this detrimental effect can be reversed by pharmacologically suppressing PARylation-mediated chromatin modification and decondensation in neutrophils. These findings identify radiation-induced aberrant chromatin relaxation in neutrophils as a critical determinant of radioimmunotherapy outcomes, providing a rationale for redefining the FLASH effect and advancing its clinical translation.

cancer biology↗