Normal view
-
cs.AI, q-bio.NC updates on arXiv.org
-
Tracing and Coordinating Cross-Layer Influence for Multimodal Model Merging
arXiv:2609.12897v1 Announce Type: new Abstract: Multimodal model merging aims to consolidate task experts into a single model that retains their complementary capabilities. Most unimodal model merging methods combine expert updates within individual layers, and multimodal approaches largely follow this design. However, an expert update changes the representations passed to subsequent layers, allowing its influence to propagate across depth and affect how visual and textual information interact.
-
cs.AI, q-bio.NC updates on arXiv.org
-
Behavior Quotient Learning for Low-Rank Adaptation of LLM Agents
arXiv:2609.12896v1 Announce Type: cross Abstract: LLM-based agents rely on heterogeneous interaction capabilities to accomplish complex tasks. Existing approaches often distribute these capabilities across multiple LoRA adapters, which increases adapter storage requirements and introduces routing overhead during inference. A single LoRA avoids this overhead, but learning from diverse agent trajectories under a fixed rank budget presents two challenges. First, trajectories with different interac
Behavior Quotient Learning for Low-Rank Adaptation of LLM Agents
-
Oncogenesis - nature.com science feeds
-
Acrolein- thioredoxin 1- p38 signaling drives neutrophil-mediated immunosuppression and glioblastoma progression
Oncogenesis, Published online: 14 September 2026; doi:10.1038/s41389-026-00655-9Acrolein- thioredoxin 1- p38 signaling drives neutrophil-mediated immunosuppression and glioblastoma progression
Acrolein- thioredoxin 1- p38 signaling drives neutrophil-mediated immunosuppression and glioblastoma progression
Oncogenesis, Published online: 14 September 2026; doi:10.1038/s41389-026-00655-9
Acrolein- thioredoxin 1- p38 signaling drives neutrophil-mediated immunosuppression and glioblastoma progression-
(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
-
NBR1-Mediated Autophagic Degradation of YTHDF1 Curtails <em>FDX1</em> Translation to Drive Concurrent Multikinase Inhibitor Resistance and Cuproptosis Tolerance
Cancer Commun (Lond). 2026 Sep 11;46:0048. doi: 10.34133/cancomm.0048. eCollection 2026.ABSTRACTBackground: Cancer cells frequently acquire adaptive resistance to targeted therapies; however, strategies capable of concurrently overcoming treatment tolerance and reactivating cell death pathways are currently lacking. Here, we investigated the dual role of ferredoxin 1 (FDX1) in modulating both multikinase inhibitor (MKI) sensitivity and cuproptosis susceptibility in hepatocellular carcinoma (HCC)
NBR1-Mediated Autophagic Degradation of YTHDF1 Curtails <em>FDX1</em> Translation to Drive Concurrent Multikinase Inhibitor Resistance and Cuproptosis Tolerance
Cancer Commun (Lond). 2026 Sep 11;46:0048. doi: 10.34133/cancomm.0048. eCollection 2026.
ABSTRACT
Background: Cancer cells frequently acquire adaptive resistance to targeted therapies; however, strategies capable of concurrently overcoming treatment tolerance and reactivating cell death pathways are currently lacking. Here, we investigated the dual role of ferredoxin 1 (FDX1) in modulating both multikinase inhibitor (MKI) sensitivity and cuproptosis susceptibility in hepatocellular carcinoma (HCC), and sought to develop a therapeutic approach for reversing resistance. Methods: HCC models, both in vitro and in vivo, were employed to investigate the role of FDX1 in MKI resistance and cuproptosis evasion. Polysome profiling, SunTag translation reporters, CRISPR-Cas9 mutagenesis, and mass spectrometry were employed to delineate the underlying mechanisms. A codelivery nanoliposome system was engineered and tested in orthotopic HCC models. Results: Prolonged exposure to MKIs led to the down-regulation of FDX1 protein levels, resulting in MKI resistance and cuproptosis tolerance in HCC both in vitro and in vivo. Mechanistically, we found that MKIs inactivated protein kinase B (PKB, also known as AKT)-mechanistic target of rapamycin (mTOR) signaling, thereby suppressing the SET and MYND domain-containing protein 2 (SMYD2)-mediated methylation of YTH domain family protein 1 (YTHDF1) at lysine 515 (K515). Hypomethylated YTHDF1 was degraded via next to BRCA1 gene 1 protein (NBR1)-dependent autophagy, leading to the repression of N6-methyladenosine modification-dependent translation of FDX1 mRNA. FDX1 deficiency drove MKI resistance by reactivating AKT survival signaling while impairing cuproptosis through reduced divalent copper ions (Cu2+) to monovalent copper ions (Cu+) conversion and the loss of protein lipoylation. Additionally, restoring FDX1 expression through NBR1 knockdown or YTHDF1 overexpression overcame MKI resistance and resensitized HCC cells to cuproptosis. Finally, a nanoliposomal system, super cuproptosis detonator liposome, designed for the codelivery of NBR1 small interfering RNA, a copper ionophore, and sorafenib restored FDX1-dependent cuproptosis and exhibited marked anti-HCC efficacy, suppressing HCC growth in vivo. Conclusions: MKIs suppressed SMYD2-mediated YTHDF1 methylation at K515 via the inactivation of AKT-mTOR signaling. This led to the inhibition of FDX1 translation, resulting in AKT signaling reactivation and protein lipoylation impairment, effects that contributed to both MKI resistance and cuproptosis tolerance in HCC. Overcoming MKI resistance and resensitizing cells to cuproptosis by targeting NBR1-mediated YTHDF1 degradation using a nanoliposomal codelivery system represents a promising strategy for HCC treatment.
PMID:42729649 | PMC:PMC13562797 | DOI:10.34133/cancomm.0048
-
Cell
-
Targeting peripheral 5-HT2AR enhances antitumor immunity in colorectal cancer
By selectively targeting peripheral 5-HT2AR without inducing psychedelic effects, a non-brain-penetrant agonist boosts antitumor CD8+ T cell immunity and improves immunotherapy responses in preclinical models of colorectal cancer.
Targeting peripheral 5-HT2AR enhances antitumor immunity in colorectal cancer
-
cs.AI, q-bio.NC updates on arXiv.org
-
DRIVE: Modeling Skills at the Reasoning and Interaction Levels for Web Agents under Continual Learning
arXiv:2605.23939v1 Announce Type: new Abstract: Web agents require both high-level reasoning (for task decomposition) and low-level interactions (for page elements manipulation) to conduct different tasks. However, these knowledge types differ fundamentally: reasoning knowledge (e.g., booking a flight requires first searching for routes) is abstract and transferable across websites, while interaction knowledge (e.g., clicking the Search button at a specific coordinate on Site A) depends heavily
DRIVE: Modeling Skills at the Reasoning and Interaction Levels for Web Agents under Continual Learning
-
Omics in Hepatocellular
-
Fatty Acid Degradation (FAD) Subtype-Informed Treatment Allocation in Unresectable Hepatocellular Carcinoma (FAD-HCC-01): Protocol for a Prospective Multicentre Proof-of-Concept Study
J Hepatocell Carcinoma. 2026 May 15;13:608436. doi: 10.2147/JHC.S608436. eCollection 2026.ABSTRACTBACKGROUND: Hepatocellular carcinoma (HCC) exhibits substantial biological and metabolic heterogeneity, contributing to variable therapeutic responses in unresectable disease. Although immune checkpoint inhibitors combined with anti-angiogenic agents have improved outcomes, treatment selection remains largely empirical because validated predictive biomarkers are lacking. Recent multi-omics studies h
Fatty Acid Degradation (FAD) Subtype-Informed Treatment Allocation in Unresectable Hepatocellular Carcinoma (FAD-HCC-01): Protocol for a Prospective Multicentre Proof-of-Concept Study
J Hepatocell Carcinoma. 2026 May 15;13:608436. doi: 10.2147/JHC.S608436. eCollection 2026.
ABSTRACT
BACKGROUND: Hepatocellular carcinoma (HCC) exhibits substantial biological and metabolic heterogeneity, contributing to variable therapeutic responses in unresectable disease. Although immune checkpoint inhibitors combined with anti-angiogenic agents have improved outcomes, treatment selection remains largely empirical because validated predictive biomarkers are lacking. Recent multi-omics studies have identified fatty acid degradation (FAD)-related transcriptional signatures that classify HCC into distinct metabolic subtypes with different immune microenvironment characteristics and therapeutic vulnerabilities. Retrospective analyses suggest that F1/F2 subtypes may derive greater benefit from immune checkpoint inhibitor-based systemic therapy, whereas F3 tumours may be more responsive to transarterial chemoembolisation (TACE). However, whether FAD-based metabolic stratification can prospectively inform treatment allocation remains unknown.
METHODS: FAD-HCC-01 is a prospective, multicentre, open-label proof-of-concept Phase II study designed to evaluate the feasibility and preliminary clinical activity of FAD-informed treatment allocation in patients with unresectable HCC. Eligible patients with Barcelona Clinic Liver Cancer stage B or C disease and no prior systemic therapy will undergo baseline tumour transcriptomic profiling to determine FAD subtype. Patients with F1/F2 tumours will receive camrelizumab plus rivoceranib, whereas patients with F3 tumours will receive TACE combined with camrelizumab and rivoceranib. Eighty-six patients will be enrolled, with 43 in each biomarker-defined cohort. The primary endpoint is objective response rate according to RECIST version 1.1. Secondary endpoints include objective response rate by mRECIST, disease control rate, progression-free survival, overall survival, duration of response, conversion to curative treatment, and safety. Exploratory analyses will assess concordance between MRI-derived proton density fat fraction and transcriptomic FAD classification.
CONCLUSION: This proof-of-concept study will prospectively assess whether FAD-based metabolic subtyping can inform treatment allocation in unresectable HCC. The results may provide early evidence supporting metabolism-informed precision therapy and the design of future biomarker-guided clinical trials.
PMID:42164571 | PMC:PMC13186218 | DOI:10.2147/JHC.S608436
-
Omics In Lung
-
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.ABSTRACTCancer 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 limit
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
-
(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
-
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.ABSTRACTCancer 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 p
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
-
cs.AI, q-bio.NC updates on arXiv.org
-
CN-Buzz2Portfolio: A Chinese-Market Dataset and Benchmark for LLM-Based Macro and Sector Asset Allocation from Daily Trending Financial News
arXiv:2603.22305v1 Announce Type: cross Abstract: Large Language Models (LLMs) are rapidly transitioning from static Natural Language Processing (NLP) tasks including sentiment analysis and event extraction to acting as dynamic decision-making agents in complex financial environments. However, the evolution of LLMs into autonomous financial agents faces a significant dilemma in evaluation paradigms. Direct live trading is irreproducible and prone to outcome bias by confounding luck with skill,
CN-Buzz2Portfolio: A Chinese-Market Dataset and Benchmark for LLM-Based Macro and Sector Asset Allocation from Daily Trending Financial News
-
cs.AI, q-bio.NC updates on arXiv.org
-
\$OneMillion-Bench: How Far are Language Agents from Human Experts?
arXiv:2603.07980v1 Announce Type: cross Abstract: As language models (LMs) evolve from chat assistants to long-horizon agents capable of multi-step reasoning and tool use, existing benchmarks remain largely confined to structured or exam-style tasks that fall short of real-world professional demands. To this end, we introduce \$OneMillion-Bench \$OneMillion-Bench, a benchmark of 400 expert-curated tasks spanning Law, Finance, Industry, Healthcare, and Natural Science, built to evaluate agents a
\$OneMillion-Bench: How Far are Language Agents from Human Experts?
-
cs.AI, q-bio.NC updates on arXiv.org
-
DSH-Bench: A Difficulty- and Scenario-Aware Benchmark with Hierarchical Subject Taxonomy for Subject-Driven Text-to-Image Generation
arXiv:2603.08090v1 Announce Type: cross Abstract: Significant progress has been achieved in subject-driven text-to-image (T2I) generation, which aims to synthesize new images depicting target subjects according to user instructions. However, evaluating these models remains a significant challenge. Existing benchmarks exhibit critical limitations: 1) insufficient diversity and comprehensiveness in subject images, 2) inadequate granularity in assessing model performance across different subject d
DSH-Bench: A Difficulty- and Scenario-Aware Benchmark with Hierarchical Subject Taxonomy for Subject-Driven Text-to-Image Generation
-
cs.AI, q-bio.NC updates on arXiv.org
-
Unveiling Downstream Performance Scaling of LLMs: A Clustering-Based Perspective
arXiv:2502.17262v4 Announce Type: replace-cross Abstract: The escalating scale and cost of Large Language Models (LLMs) training necessitate accurate pre-training prediction of downstream task performance for comprehensive understanding of scaling properties. This is challenged by: 1) the emergence phenomenon, where unpredictable capabilities appearing suddenly at critical model scales; and 2) uneven task difficulty and inconsistent performance scaling patterns, leading to high metric variabili
Unveiling Downstream Performance Scaling of LLMs: A Clustering-Based Perspective
-
cs.AI, q-bio.NC updates on arXiv.org
-
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
Not All Candidates are Created Equal: A Heterogeneity-Aware Approach to Pre-ranking in Recommender Systems
-
cs.AI, q-bio.NC updates on arXiv.org
-
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 trans
From Medical Records to Diagnostic Dialogues: A Clinical-Grounded Approach and Dataset for Psychiatric Comorbidity
-
cs.AI, q-bio.NC updates on arXiv.org
-
ROMA: Recursive Open Meta-Agent Framework for Long-Horizon Multi-Agent Systems
arXiv:2602.01848v2 Announce Type: replace Abstract: Current agentic frameworks underperform on long-horizon tasks. As reasoning depth increases, sequential orchestration becomes brittle, context windows impose hard limits that degrade performance, and opaque execution traces make failures difficult to localize or debug. We introduce ROMA (Recursive Open Meta-Agents), a domain-agnostic framework that addresses these limitations through recursive task decomposition and structured aggregation. ROM