Phenotypic plasticity is a hallmark of cancer1; however the molecular switches required for cell-fate reprogramming are poorly understood. During intestinal wound-healing and colorectal cancer (CRC) metastasis, differentiated cells can dynamically dedifferentiate into an intestinal stem cell (ISC) state to drive epithelial regeneration and metastatic outgrowth2-10. Here we show that the RNA-binding protein ZFP36L2, which is mutated in 5-10% of CRC11-15, is a pivotal stress-responsive orchestrator of dynamic dedifferentiation. In mouse colon regeneration models, ZFP36L2 ablation inhibits dedifferentiation, ISC gene expression and function and impairs intestinal regeneration. In human CRC, loss of ZFP36L2 function abrogates metastatic seeding and the outgrowth of LGR5+ canonical metastases while promoting lineage plasticity and non-canonical differentiation into heterogeneous cell states. Mechanistically, ZFP36L2 binds to stress-associated mRNAs that contain AU-rich 3' untranslated regions, which induces the formation of dynamic biomolecular condensates associated with mRNA degradation and termination of the stress response. Together, these data show that ZFP36L2 acts as an important molecular switch that couples stress sensing with phenotypic plasticity. This in turn drives cellular dedifferentiation essential for re-establishing the ISC state during wound healing and metastasis. In ZFP36L2-deficient CRC, the inability to re-enter the LGR5+ state during metastatic outgrowth promotes non-canonical lineage plasticity, which is associated with poor clinical outcomes.
Qi Jiang, Manisha S Raghavan, Aileen M Rodriguez et al.· Nature· 0 citations
An ensemble approach (enFoldX) that leverages structure prediction models such as AlphaFold3 to build sensitive binding predictors and outperforms the current co-folding methods which rely on predictions from the single top ranked structure.
O. Lyudovyk, Jonathan A. Levine, M. Pathil et al.· Clinical Cancer Research· 0 citations
An ensemble approach (enFoldX) that leverages structure prediction models such as AlphaFold3 to build sensitive binding predictors and outperforms the current co-folding methods which rely on predictions from the single top ranked structure.
O. Lyudovyk, Jonathan A. Levine, M. Pathil et al.· Clinical Cancer Research· 0 citations
Adaptive immunity relies on T-cell receptor (TCR) recognition of peptides presented by the major histocompatibility complex (pMHC). Accurate prediction of TCR:pMHC binding pairs from sequence data remains a longstanding challenge in computational immunology, limiting the development of precision immunotherapies like cancer vaccines and adoptive cell therapies. Here, we present enFoldX (ensemble of Folded compleXes), a structure-based approach leveraging biophysical characterization of AlphaFold3-generated ensembles to classify TCR:pMHC sequence pairs as cognate versus non-cognate. Unlike previous methods reliant on only sequence data or a single, static predicted structure, enFoldX extracts features from an entire generated ensemble with a custom focus on the biophysical binding interface. Our model distinguishes T cell reactivity between peptides differing by a single amino acid substitution, the resolution required for cancer neoantigens, and generalizes to unseen peptides, MHCs, and TCRs, a major objective for artificial intelligence (AI) in immunology. Our performance on these crucial tasks demonstrates that diverse, structural sampling of biophysical interactions over an ensemble is fundamental for accurate AI-driven binding predictions and offers lessons for efficient future data generation to improve models. Our findings therefore offer a scalable framework to accelerate therapeutic binder design, and we provide access to a publicly available code repository.
O. Lyudovyk, JA Levine, M. Pathil et al.· bioRxiv· 1 citation
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