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Spatial evolution of a cachexia-promoting microenvironment in pancreatic cancer

Cell. 2026 Sep 29:S0092-8674(26)01081-0. doi: 10.1016/j.cell.2026.09.012. Online ahead of print.

ABSTRACT

Cachexia is a major cause of morbidity in pancreatic cancer, but the cellular circuitry linking tumor progression to systemic wasting remains incompletely understood. Integrating single-cell RNA sequencing, Xenium spatial transcriptomics, multiplex immunohistochemistry, bulk transcriptomics, and functional studies across human non-cachexia, pre-cachexia, and cachexia samples, together with mouse models, we define a cachexia-associated microenvironmental niche composed of SEMA4A+ tumor cells, AQP9+ macrophages, and LOXL2+ cancer-associated fibroblasts. Mechanistically, SEMA4A-associated signaling promotes bone morphogenetic protein-2 (BMP2)-dependent acquisition of an AQP9-associated macrophage phenotype, and macrophage-derived CXCL8 activates LOXL2+ fibroblasts. LOXL2+ fibroblasts reciprocally enhance tumor cell FOSL1/SEMA4A signaling through exosomal N-glycosylated LOXL2. Spatial analyses demonstrate progressive enrichment of this niche with cachexia severity and association with postoperative development of cachexia in previously non-cachectic patients. These findings provide a framework linking local tumor ecosystem dynamics to cachexia progression.

PMID:42810340 | DOI:10.1016/j.cell.2026.09.012

CoSkill: Joint Reinforcement Learning of Reasoning and Meta-Skill Agents for Hierarchical Skill Evolution

arXiv:2609.04865v2 Announce Type: replace Abstract: Skill libraries improve the sample efficiency of agentic reinforcement learning (RL) by enabling large language model (LLM) agents to reuse procedural knowledge. Yet existing paradigms exhibit structural shortcomings: they either decouple skill evolution from policy optimization or instantiate meta-skills as fixed workflows. Both treat skills as passive objects to be managed, limiting the flexible evolution of skills and their co-adaptation with the reasoning agent. To address the limitations, we propose CoSkill, a unified multi-agent RL framework that recasts the static meta-skill workflow as a learnable Meta-Skill Agent and jointly trains it with a Reasoning Agent over a hierarchical skill library. By modeling the Reasoning and Meta-Skill Agents as a cooperative team sharing a single backbone, CoSkill enables end-to-end co-adaptation: the Reasoning Agent conditions its actions on a retrieved task skill and step skills selected from its child set, while its task performance guides the Meta-Skill Agent in refining those step skills. Experiments on ALFWorld and WebShop show that CoSkill substantially outperforms prior skill-based and RL baselines, achieving success rates of 98.4% and 90.6%, respectively (+3.5 and +6.2 pp). As shown in Figure 1, CoSkill achieves superior early-stage sample efficiency, asymptotic performance, and wall-clock efficiency. Our code is available at https://github.com/jinyuan-cookie/CoSkill.

OA-NBV: Occlusion-Aware Next-Best-View Planning for Human-Centered Active Perception on Mobile Robots

arXiv:2603.11072v2 Announce Type: replace-cross Abstract: We naturally step sideways or lean to see around the obstacle when our view is blocked, and recover a more informative observation. Enabling robots to make the same kind of viewpoint choice is critical for human-centered operations, including search, triage, and disaster response, where cluttered environments and partial visibility frequently degrade downstream perception. However, many Next-Best-View (NBV) methods primarily optimize generic exploration or long-horizon coverage, and do not explicitly target the immediate goal of obtaining a single usable observation of a partially occluded person under real motion constraints. We present Occlusion-Aware Next-Best-View Planning for Human-Centered Active Perception on Mobile Robots (OA-NBV), an occlusion-aware NBV pipeline that autonomously selects the next traversable viewpoint to obtain a more complete view of an occluded human. OA-NBV integrates perception and motion planning by scoring candidate viewpoints using a target-centric visibility model that accounts for occlusion, target scale, and target completeness, while restricting candidates to feasible robot poses. OA-NBV achieves over 90% success rate in both simulation and real-world trials, while baseline NBV methods degrade sharply under occlusion. Beyond success rate, OA-NBV improves observation quality: compared to the strongest baseline, it increases normalized target area by at least 81% and keypoint visibility by at least 58% across settings, making it a drop-in view-selection module for diverse human-centered downstream tasks.

Deep learning predicts gene rearrangements from histopathology in large B-cell lymphoma

npj Digital Medicine, Published online: 12 September 2026; doi:10.1038/s41746-026-03238-5

Deep learning predicts gene rearrangements from histopathology in large B-cell lymphoma

A clinically derived lipid-endothelial signature links serum multi-omics to immune exclusion and clinical stratification in hepatocellular carcinoma

2 September 2026 at 18:00

Ther Adv Med Oncol. 2026 Aug 31;18:17588359261481797. doi: 10.1177/17588359261481797. eCollection 2026.

ABSTRACT

BACKGROUND: Hepatocellular carcinoma (HCC) is driven by extensive metabolic reprogramming, vascular remodeling, and immune microenvironmental dysfunction. Although numerous stratification signatures have been proposed, few are grounded in clinically derived serum multi-omics and biologically linked to endothelial remodeling, endothelial regulation, and immune exclusion.

OBJECTIVES: This study aimed to identify a serum-derived lipid-endothelial program associated with immune exclusion and clinical stratification in HCC.

DESIGN: A translational multi-omics study integrating clinically collected serum samples, public transcriptomic cohorts, single-cell RNA sequencing, and experimental validation.

METHODS: Proteomic and metabolomic sequencing was performed on serum samples from patients with HCC and normal controls. Dysregulated pathways were integrated with transcriptomic data from TCGA-LIHC and ICGC-LIRI-JP cohorts to identify genes jointly associated with lipid metabolism and leukocyte transendothelial migration. A risk score was calculated using the expression of PON1, TXNRD1, CLDN4, CLDN6, CYP2C9, and CTSA. Higher expression of TXNRD1, CLDN4, CLDN6, and CTSA contributed to a higher risk score, whereas PON1 and CYP2C9 contributed protective coefficients. Immune contexture, tumor mutation burden, and exploratory therapeutic sensitivity patterns were further evaluated using transcriptome-based drug sensitivity prediction, followed by single-cell RNA sequencing and experimental expression validation.

RESULTS: Serum multi-omics analysis revealed prominent dysregulation of lipid metabolic pathways and leukocyte transendothelial migration-related processes in HCC. Integrative analysis identified a six-gene lipid-endothelial signature (PON1, TXNRD1, CLDN4, CLDN6, CYP2C9, and CTSA) that stratified patients into high- and low-risk groups. In the TCGA-LIHC cohort, high-risk patients had significantly poorer overall survival than low-risk patients (log-rank P < 0.0001), and this survival-stratifying association was externally supported in the ICGC-LIRI-JP cohort (log-rank P = 0.016). The high-risk phenotype was associated with immune-excluded features, distinct somatic mutation patterns, and altered predicted sensitivity to several selected anticancer agents. Single-cell analysis and experimental assays further supported the association between the six-gene program, malignant epithelial states, endothelial-related remodeling, and immune microenvironmental heterogeneity.

CONCLUSION: This study defines a clinically derived lipid-endothelial program associated with immune exclusion, adverse prognosis, and potential differences in therapeutic vulnerability in HCC. The proposed signature provides a biologically informed framework for prognostic assessment and may support future evaluation of targeted interventions in HCC.

PMID:42682961 | PMC:PMC13530516 | DOI:10.1177/17588359261481797

POC1A promotes the proliferation, metastasis and stemness of bladder cancer by stabilizing BMI1 via USP7

Oncogene, Published online: 31 August 2026; doi:10.1038/s41388-026-03973-9

POC1A promotes the proliferation, metastasis and stemness of bladder cancer by stabilizing BMI1 via USP7

Agent-as-Peer-Debriefer: A Multi-Agent Framework with Perspective-Based Refinement for Qualitative Analysis

arXiv:2605.24600v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for qualitative data analysis (QDA), yet their outputs often miss the depth and nuance of human analysis. We argue this gap reflects a missing credibility practice from human QDA: peer debriefing, in which an analyst seeks feedback from a disinterested peer and uses it to refine their coding. To bring this practice into LLM-assisted QDA, we propose Agent-as-Peer-Debriefer, a multi-agent QDA framework that builds peer debriefing into key coding steps. In our framework, a Hierarchical Coding Agent follows the standard QDA process to generate codes, sub-themes, and themes, along with self-explanations and reflection memos. It then shares these outputs with three Peer-Debriefing Agents, each applying a distinct analytical perspective (Theory-Driven, Data-Driven, or Applied) and refining the codes by keeping, renaming, reassigning, merging, or splitting them. These perspectives are drawn from established human QDA practices that generalize across domains and datasets. To evaluate the framework, we test it on three datasets across two domains with three LLMs, measuring semantic similarity to human-annotated codes. Across all settings, perspective-based, peer-debriefing refinement aligns more closely with human codes than a single-LLM baseline, and an ablation further shows the gain is not merely from additional refinement. The three perspectives also produce distinct trade-offs, showing that the choice of perspective is a meaningful and controllable design decision. More broadly, these findings suggest that simulating peer debriefing with explicit perspectives is a promising route to more credible LLM-assisted QDA.

GPX8<sup>+</sup> cancer-associated fibroblast-derived lactate contributes to lenvatinib resistance by facilitating BRPF1 expression through histone H3 lysine 18 lactylation in hepatocellular carcinoma

Oncogene, Published online: 22 May 2026; doi:10.1038/s41388-026-03711-1

GPX8+ cancer-associated fibroblast-derived lactate contributes to lenvatinib resistance by facilitating BRPF1 expression through histone H3 lysine 18 lactylation in hepatocellular carcinoma

Applications and challenges of multi-omics approaches in lung cancer research and precision treatment

8 April 2026 at 18:00

Front Genet. 2026 Mar 23;16:1722368. doi: 10.3389/fgene.2025.1722368. eCollection 2025.

ABSTRACT

Lung cancer is one of the most common cancers worldwide and one of the leading causes of cancer death, with a heavy disease burden and severe public health challenges. Multi-omics techniques, such as genomics, proteomics, metabolomics, and radiomics, play a crucial role in the early diagnosis and treatment of lung cancer, revealing the molecular characteristics and mechanisms of lung cancer, and have significant clinical application value. However, it also faces numerous challenges, such as data issues, "black box" problems, and ethical and legal concerns. How to leverage strengths while mitigating weaknesses, achieve clinical translation of technology, and serve patients more effectively deserves our deep reflection. This article reviews the specific applications and challenges of multi-omics methods in lung cancer research and personalized treatment.

PMID:41948518 | PMC:PMC13050793 | DOI:10.3389/fgene.2025.1722368

Engineered immunosuppressive dendritic cells protect against cardiac remodelling

Nature, Published online: 08 April 2026; doi:10.1038/s41586-026-10346-5

Lesion-targeted immune modulation is a feasible strategy to control cardiac fibrosis, and engineered dendritic cells are a promising therapeutic platform for treating cardiac remodelling and heart failure.

RFC4 drives temozolomide resistance in glioblastoma by activating STK38-BECN1-dependent autophagy

Nat Commun. 2026 Mar 23. doi: 10.1038/s41467-026-70798-1. Online ahead of print.

ABSTRACT

Glioblastoma (GBM) remains a lethal brain tumor due to therapy resistance. While autophagy contributes to temozolomide (TMZ) resistance, its regulation is incompletely understood. This study investigates the role of replication factor RFC4, which is associated with poor prognosis and TMZ resistance in GBM. Multi-omics analyses and molecular experiments reveal that TMZ-induced chromatin accessibility enables transcription factor YY1 to bind the RFC4 promoter and upregulate its expression. RFC4, in turn, stabilizes the kinase STK38, which is essential for autophagosome formation. The RFC4-STK38 interaction facilitates BECN1 recruitment, thereby activating autophagy. Phosphorylation of STK38 at T444 stabilizes this complex, whereas a phospho-deficient mutant impairs autophagy. In vivo, RFC4 overexpression confers TMZ resistance, reversible by autophagy inhibition. Thus, our findings identify the RFC4-STK38-BECN1 axis as a mechanism underlying TMZ resistance and a potential target for precision therapy in GBM.

PMID:41872171 | DOI:10.1038/s41467-026-70798-1

Unannotated noncoding transcripts as a source of intratumor heterogeneity in malignant cell states

Sci China Life Sci. 2026 Mar 16. doi: 10.1007/s11427-025-3273-6. Online ahead of print.

ABSTRACT

Phenotypic diversity of malignant cells within a tumor underlies intratumor heterogeneity (ITH), a key determinant of cancer metastasis and treatment failure. However, the molecular mechanisms driving this heterogeneity are poorly understood. Here, we curated and analyzed a cohort of 3' tag-based single-cell RNA-seq covering 12 common cancer types. We identified thousands of poly(A) site (PAS) peaks representing the 3' ends of previously unannotated transcripts, whose expression is widely associated with diverse malignant cellular states. By integrating multi-omics data, we characterized the expression patterns and epigenetic landscape of these unannotated PAS peak-associated transcripts (UPTs). The expression heterogeneity of UPTs was supported by multi-region sampling bulk RNA-seq data and recapitulated within cancer cell lines. As proof of principle validation, functional experiments confirmed that two noncoding UPTs promoted the proliferation and migration of lung cancer cells. Our results suggest that epigenetic activation of unannotated noncoding transcripts might represent a previously unrecognized mechanism contributing to transcriptomic ITH.

PMID:41870780 | DOI:10.1007/s11427-025-3273-6

Unannotated noncoding transcripts as a source of intratumor heterogeneity in malignant cell states

Sci China Life Sci. 2026 Mar 16. doi: 10.1007/s11427-025-3273-6. Online ahead of print.

ABSTRACT

Phenotypic diversity of malignant cells within a tumor underlies intratumor heterogeneity (ITH), a key determinant of cancer metastasis and treatment failure. However, the molecular mechanisms driving this heterogeneity are poorly understood. Here, we curated and analyzed a cohort of 3' tag-based single-cell RNA-seq covering 12 common cancer types. We identified thousands of poly(A) site (PAS) peaks representing the 3' ends of previously unannotated transcripts, whose expression is widely associated with diverse malignant cellular states. By integrating multi-omics data, we characterized the expression patterns and epigenetic landscape of these unannotated PAS peak-associated transcripts (UPTs). The expression heterogeneity of UPTs was supported by multi-region sampling bulk RNA-seq data and recapitulated within cancer cell lines. As proof of principle validation, functional experiments confirmed that two noncoding UPTs promoted the proliferation and migration of lung cancer cells. Our results suggest that epigenetic activation of unannotated noncoding transcripts might represent a previously unrecognized mechanism contributing to transcriptomic ITH.

PMID:41870780 | DOI:10.1007/s11427-025-3273-6

Low-dose intestinal irradiation enhances the efficacy and prognosis of PD-1 blockade in metastatic non-small cell lung cancer

Clin Cancer Res. 2026 Mar 18. doi: 10.1158/1078-0432.CCR-25-4153. Online ahead of print.

ABSTRACT

PURPOSE: Intestinal low-dose irradiation (ILDR) may enhance immunotherapy efficacy by modulating the gut microbiota and metabolism; however, its role in metastatic non-small cell lung cancer (mNSCLC), particularly in the first-line setting, remains unclear.

EXPERIMENTAL DESIGN: This multicenter retrospective and prospective study included mNSCLC patients receiving first- and second-line programmed cell death protein 1 (PD-1) inhibitors along with abdominopelvic radiotherapy between 2018 and 2025. Patients were stratified by the mean intestinal radiation dose into <1 Gy, 1-3 Gy, and >3 Gy groups and treatment outcomes were compared. The blood and fecal samples were subjected to multi-omics profiling.

RESULTS: g>309 patients were included in the retrospective analysis. Optimal efficacy was observed with a small intestinal mean radiation dose (SIMRD) of 1-3 Gy, showing longer progression-free survival (PFS, 10.2 months) and overall survival (OS, 22.8 months) (P < 0.01), which was consistent across subgroups. Compared with 1-3 Gy, SIMRD >3 Gy (Hazard ratio [HR] = 4.87, P < 0.001) and <1 Gy (HR = 1.85, P < 0.001) independently predicted worse OS. Prospective results confirmed the best disease control rate (P = 0.041) and PFS (P = 0.046) with SIMRD of 1-3 Gy. Responders were enriched in Bacillota, Clostridia, and indole derivatives, particularly indole-3-carboxylic acid. Moreover, the 1-3 Gy group exhibited increased circulating macrophage inflammatory protein-3α and reduced circulating α4β7+ regulatory T cells.

CONCLUSIONS: ILDR influences the efficacy of PD-1 blockade in patients with mNSCLC, particularly when SIMRD is maintained within the 1-3 Gy range, likely through modulation of the gut microbiota-metabolite-immune axis.

PMID:41849236 | DOI:10.1158/1078-0432.CCR-25-4153

Continual Learning in Large Language Models: Methods, Challenges, and Opportunities

arXiv:2603.12658v1 Announce Type: cross Abstract: Continual learning (CL) has emerged as a pivotal paradigm to enable large language models (LLMs) to dynamically adapt to evolving knowledge and sequential tasks while mitigating catastrophic forgetting-a critical limitation of the static pre-training paradigm inherent to modern LLMs. This survey presents a comprehensive overview of CL methodologies tailored for LLMs, structured around three core training stages: continual pre-training, continual fine-tuning, and continual alignment.Beyond the canonical taxonomy of rehearsal-, regularization-, and architecture-based methods, we further subdivide each category by its distinct forgetting mitigation mechanisms and conduct a rigorous comparative analysis of the adaptability and critical improvements of traditional CL methods for LLMs. In doing so, we explicitly highlight core distinctions between LLM CL and traditional machine learning, particularly with respect to scale, parameter efficiency, and emergent capabilities. Our analysis covers essential evaluation metrics, including forgetting rates and knowledge transfer efficiency, along with emerging benchmarks for assessing CL performance. This survey reveals that while current methods demonstrate promising results in specific domains, fundamental challenges persist in achieving seamless knowledge integration across diverse tasks and temporal scales. This systematic review contributes to the growing body of knowledge on LLM adaptation, providing researchers and practitioners with a structured framework for understanding current achievements and future opportunities in lifelong learning for language models.

OffTopicEval: When Large Language Models Enter the Wrong Chat, Almost Always!

arXiv:2509.26495v3 Announce Type: replace Abstract: Large Language Model (LLM) safety is one of the most pressing challenges for enabling wide-scale deployment. While most studies and global discussions focus on generic harms, such as models assisting users in harming themselves or others, enterprises face a more fundamental concern: whether LLM-based agents are safe for their intended use case. To address this, we introduce operational safety, defined as an LLM's ability to appropriately accept or refuse user queries when tasked with a specific purpose. We further propose OffTopicEval, an evaluation suite and benchmark for measuring operational safety both in general and within specific agentic use cases. Our evaluations on six model families comprising 20 open-weight LLMs reveal that while performance varies across models, all of them remain highly operationally unsafe. Even the strongest models - Qwen-3 (235B) with 77.77% and Mistral (24B) with 79.96% - fall far short of reliable operational safety, while GPT models plateau in the 62-73% range, Phi achieves only mid-level scores (48-70%), and Gemma and Llama-3 collapse to 39.53% and 23.84%, respectively. While operational safety is a core model alignment issue, to suppress these failures, we propose prompt-based steering methods: query grounding (Q-ground) and system-prompt grounding (P-ground), which substantially improve OOD refusal. Q-ground provides consistent gains of up to 23%, while P-ground delivers even larger boosts, raising Llama-3.3 (70B) by 41% and Qwen-3 (30B) by 27%. These results highlight both the urgent need for operational safety interventions and the promise of prompt-based steering as a first step toward more reliable LLM-based agents.

SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks

arXiv:2602.12670v3 Announce Type: replace Abstract: Agent Skills are structured packages of procedural knowledge that augment LLM agents at inference time. Despite rapid adoption, there is no standard way to measure whether they actually help. We present SkillsBench, a benchmark of 86 tasks across 11 domains paired with curated Skills and deterministic verifiers. Each task is evaluated under three conditions: no Skills, curated Skills, and self-generated Skills. We test 7 agent-model configurations over 7,308 trajectories. Curated Skills raise average pass rate by 16.2 percentage points(pp), but effects vary widely by domain (+4.5pp for Software Engineering to +51.9pp for Healthcare) and 16 of 84 tasks show negative deltas. Self-generated Skills provide no benefit on average, showing that models cannot reliably author the procedural knowledge they benefit from consuming. Focused Skills with 2--3 modules outperform comprehensive documentation, and smaller models with Skills can match larger models without them.
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