Biology subjects

Liew, A. Y.

Publications and source records attributed to Liew, A. Y..

3 recordsLinked to original sources

bioRxiv2026

Integrative AI-Enabled Virtual Cell Modeling Reveals a Clinically Relevant Latent Effector State of Human CD8 T Cells Undetectable by Conventional Analyses

Understanding how immune checkpoint inhibitors (ICIs) reshape human T-cell responses requires models that move beyond static transcriptomic snapshots and discrete cell-state classifications. Here, we present an integrative AI-enabled virtual cell framework that represents human CD8 T-cell responses as dynamic and computable systems during ICI therapy. By integrating single-cell RNA sequencing with paired T-cell receptor sequencing within the C2S-scale foundation model, we construct a virtual representation of individual T cells, in which each cell is encoded by a unique functional identity that captures its transcriptional, signaling, and clonal characteristics. Using this framework, we identify a previously unrecognized dynamic latent effector state of CD8 T cells characterized by intermediate expression of effector genes, distinct signaling activity, and ongoing clonal expansion. Across independent patient cohorts, the virtual cell model consistently indicates that ICI therapy mainly acts by unmasking pre-existing effector potential rather than inducing de novo effector differentiation. Notably, this latent effector population remains transcriptionally restrained despite active signaling and clonal expansion, revealing a hidden reservoir of antitumor immune capacity. More broadly, our study demonstrates how AI-enabled virtual cell modeling can reconstruct latent cellular states and their dynamic transitions from multidimensional single-cell data. By incorporating functional identity into virtual cell model, this framework uncovers biologically meaningful yet non-obvious T-cell effector program during cancer immunotherapy and provides a generalizable approach for studying immune dynamics in human disease.

immunology↗

bioRxiv2026

ME1 Programs Latent Effector Capacity and Grounds a Mathematical Model of Reversible T Cell Exhaustion

A central paradox in cancer immunotherapy is that patients harboring apparently exhausted or dysfunctional CD8+ T cells can exhibit rapid and durable responses to immune checkpoint inhibition (ICI). Although such rebound responses do not occur in all ICI-treated patients, resolving this paradox is essential for identifying and therapeutically maximizing reversible T cell exhaustion before clinical benefit is achieved. Here, we identify malic enzyme 1 (ME1) as a key molecular determinant of a latent, epigenetically poised effector CD8+ T cell state that is both necessary and sufficient for responsiveness to ICI. Genetic loss of ME1 abolishes therapeutic efficacy despite intact checkpoint blockade, whereas enforced ME1 expression enables robust antitumor responses even in otherwise ICI-resistant tumor models. Chromatin accessibility profiling, together with bulk and single-cell transcriptomic analyses, demonstrates that ME1 preserves effector readiness by maintaining latent effector capacity as a measurable biological state. These findings also experimentally ground a mechanistically interpretable dynamical model of T cell exhaustion, in which latent effector capacity, E(t), evolves according to a first-order differential equation shaped by activation history and decay. By defining latent effector capacity as a quantifiable, history-dependent state variable, our findings further enable the rational design of AI-driven models to predict patient-specific responsiveness and optimize therapeutic strategies in T cell-based immunotherapy. Together, our results redefine exhaustion as a state of masked potential governed by history-dependent latent effector capacity and provide a unified framework explaining rapid functional rebound, therapeutic heterogeneity, and the boundary between reversible and irreversible T cell exhaustion.

immunology↗

bioRxiv2026

Latent Effector Capacity Governs Reversible T Cell Exhaustion: A Mathematical Model for Mechanistically Predictive AI in PD-1 Blockade

T cell exhaustion is commonly viewed as a terminal differentiation state marked by irreversible loss of effector function during chronic infection and cancer. However, the rapid restoration of cytotoxic activity following PD-1 checkpoint blockade challenges this view, revealing a central paradox: T cells that appear functionally inert can regain effector function on timescales incompatible with de novo differentiation or extensive epigenetic reprogramming. To resolve this contradiction, we present a mathematical framework that explicitly decouples latent effector capacity from active effector output. We define latent effector capacity as a slow, history-dependent state variable representing preserved epigenetic accessibility and regulatory readiness at effector loci, distinct from instantaneous transcriptional activity. Within this framework, PD-1 signaling functions as a reversible, graded masking mechanism that suppresses effector realization without erasing latent capacity, thereby explaining the coexistence of preserved chromatin accessibility, rapid functional rebound, and heterogeneous responses to checkpoint blockade. Incorporating nonlinear self-maintenance of epigenetic programs together with checkpoint-dependent erosion of latent capacity reveals a bistable regime and a history-dependent point of no return, beyond which exhaustion becomes irreversible. Critically, the model demonstrates that PD-1 checkpoint blockade unmasks pre-existing effector potential but cannot recreate lost capacity, because therapeutic reversibility is governed by the prior dynamical stability of a latent epigenetic state rather than by instantaneous transcriptional output. This framework establishes a mathematical foundation for mechanistically predictive AI in PD-1 blockade therapy by identifying latent, history-dependent variables that can be inferred from epigenetic and transcriptional data to predict therapeutic responsiveness and irreversibility.

immunology↗