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HOX code-based stratification reveals RUNX1T1-HDAC reprogramming as a targetable driver of lineage plasticity across cancers

Cancer Lett. 2026 Mar 28;648:218465. doi: 10.1016/j.canlet.2026.218465. Online ahead of print.

ABSTRACT

Cancer remains a leading cause of death worldwide, with lineage plasticity emerging as a hallmark that drives therapy resistance and tumor progression by enabling cancer cells to alter identity and evade targeted therapies. Although genomic and transcriptomic aberrations correlate with lineage plasticity, the absence of scalable cross-cancer markers to rapidly identify plastic subtypes has limited predictive utility. Homeobox (HOX) genes encode transcription factors that define tissue identity through distinct expression patterns, or HOX codes, within specific lineages. By analyzing multi-omics data encompassing 39 HOX genes across more than 80,000 RNA-seq samples across 23 cancer types spanning 114 cancer subtypes, we found that HOX code expression robustly stratifies lineage-constrained and lineage-plastic states at a cross-cancer level. This framework revealed previously unrecognized lineage-plastic subtypes in prostate cancer, lung cancer, and acute myeloid leukemia (AML), each displaying distinct HOX code divergence compared to non-plastic counterparts. Differential expression analysis across these representative malignancies identified RUNX1T1 as a consistent regulator associated with HOX-defined plastic states. We validated RUNX1T1 upregulation in bulk and single-cell RNA-seq from extensive preclinical and clinical cohorts and demonstrated that RUNX1T1 is functionally required for lineage-plastic programs in prostate cancer models. AI-based structural modeling and co-immunoprecipitation established the NCOR/HDAC3 complex as a critical binding partner of RUNX1T1. CUT&RUN profiling revealed that RUNX1T1 remodels chromatin by globally reducing active enhancer marks, thereby repressing lineage-defining differentiation programs and reshaping HOX positional identity. Selective pharmacologic inhibition of HDAC3 or targeted gene silencing via lipid nanoparticles suppressed the growth of lineage-plastic cancer cells, uncovering a therapeutically actionable vulnerability. Together, these findings establish RUNX1T1 as a cross-lineage regulator of HOX code-defined plasticity and identify the RUNX1T1-HDAC axis as a targetable mechanism underlying cancer lineage plasticity.

PMID:41912135 | DOI:10.1016/j.canlet.2026.218465

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HOX Code-Based Stratification Reveals RUNX1T1-HDAC Reprogramming as a Targetable Driver of Lineage Plasticity Across Cancers

Cancer Lett. 2026 Mar 28:218465. doi: 10.1016/j.canlet.2026.218465. Online ahead of print.

ABSTRACT

Cancer remains a leading cause of death worldwide, with lineage plasticity emerging as a hallmark that drives therapy resistance and tumor progression by enabling cancer cells to alter identity and evade targeted therapies. Although genomic and transcriptomic aberrations correlate with lineage plasticity, the absence of scalable cross-cancer markers to rapidly identify plastic subtypes has limited predictive utility. Homeobox (HOX) genes encode transcription factors that define tissue identity through distinct expression patterns, or HOX codes, within specific lineages. By analyzing multi-omics data encompassing 39 HOX genes across more than 80,000 RNA-seq samples across 23 cancer types spanning 114 cancer subtypes, we found that HOX code expression robustly stratifies lineage-constrained and lineage-plastic states at a cross-cancer level. This framework revealed previously unrecognized lineage-plastic subtypes in prostate cancer, lung cancer, and acute myeloid leukemia (AML), each displaying distinct HOX code divergence compared to non-plastic counterparts. Differential expression analysis across these representative malignancies identified RUNX1T1 as a consistent regulator associated with HOX-defined plastic states. We validated RUNX1T1 upregulation in bulk and single-cell RNA-seq from extensive preclinical and clinical cohorts and demonstrated that RUNX1T1 is functionally required for lineage-plastic programs in prostate cancer models. AI-based structural modeling and co-immunoprecipitation established the NCOR/HDAC3 complex as a critical binding partner of RUNX1T1. CUT&RUN profiling revealed that RUNX1T1 remodels chromatin by globally reducing active enhancer marks, thereby repressing lineage-defining differentiation programs and reshaping HOX positional identity. Selective pharmacologic inhibition of HDAC3 or targeted gene silencing via lipid nanoparticles suppressed the growth of lineage-plastic cancer cells, uncovering a therapeutically actionable vulnerability. Together, these findings establish RUNX1T1 as a cross-lineage regulator of HOX code-defined plasticity and identify the RUNX1T1-HDAC axis as a targetable mechanism underlying cancer lineage plasticity.

PMID:41912135 | DOI:10.1016/j.canlet.2026.218465

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Not All Candidates are Created Equal: A Heterogeneity-Aware Approach to Pre-ranking in Recommender Systems

arXiv:2603.03770v1 Announce Type: cross Abstract: Most large-scale recommender systems follow a multi-stage cascade of retrieval, pre-ranking, ranking, and re-ranking. A key challenge at the pre-ranking stage arises from the heterogeneity of training instances sampled from coarse-grained retrieval results, fine-grained ranking signals, and exposure feedback. Our analysis reveals that prevailing pre-ranking methods, which indiscriminately mix heterogeneous samples, suffer from gradient conflicts: hard samples dominate training while easy ones remain underutilized, leading to suboptimal performance. We further show that the common practice of uniformly scaling model complexity across all samples is inefficient, as it overspends computation on easy cases and slows training without proportional gains. To address these limitations, this paper presents Heterogeneity-Aware Adaptive Pre-ranking (HAP), a unified framework that mitigates gradient conflicts through conflict-sensitive sampling coupled with tailored loss design, while adaptively allocating computational budgets across candidates. Specifically, HAP disentangles easy and hard samples, directing each subset along dedicated optimization paths. Building on this separation, it first applies lightweight models to all candidates for efficient coverage, and further engages stronger models on the hard ones, maintaining accuracy while reducing cost. This approach not only improves pre-ranking effectiveness but also provides a practical perspective on scaling strategies in industrial recommender systems. HAP has been deployed in the Toutiao production system for 9 months, yielding up to 0.4% improvement in user app usage duration and 0.05% in active days, without additional computational cost. We also release a large-scale industrial hybrid-sample dataset to enable the systematic study of source-driven candidate heterogeneity in pre-ranking.
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From Medical Records to Diagnostic Dialogues: A Clinical-Grounded Approach and Dataset for Psychiatric Comorbidity

arXiv:2510.25232v2 Announce Type: replace Abstract: Psychiatric comorbidity is clinically significant yet challenging due to the complexity of multiple co-occurring disorders. To address this, we develop a novel approach integrating synthetic patient electronic medical record (EMR) construction and multi-agent diagnostic dialogue generation. We create 502 synthetic EMRs for common comorbid conditions using a pipeline that ensures clinical relevance and diversity. Our multi-agent framework transfers the clinical interview protocol into a hierarchical state machine and context tree, supporting over 130 diagnostic states while maintaining clinical standards. Through this rigorous process, we construct PsyCoTalk, the first large-scale dialogue dataset supporting comorbidity, containing 3,000 multi-turn diagnostic dialogues validated by psychiatrists. This dataset enhances diagnostic accuracy and treatment planning, offering a valuable resource for psychiatric comorbidity research. Compared to real-world clinical transcripts, PsyCoTalk exhibits high structural and linguistic fidelity in terms of dialogue length, token distribution, and diagnostic reasoning strategies. Licensed psychiatrists confirm the realism and diagnostic validity of the dialogues. This dataset enables the development and evaluation of models capable of multi-disorder psychiatric screening in a single conversational pass.
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