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Received today — 7 October 2026 ⏭

Multi-omics-driven personalized management of advanced HCC

✇Omics in Hepatocellular
By: Jianpeng Sheng · Jinyuan Song · Lin Wang · Junlei Zhang · Wei Zhang · Hang Shi · Yaxing Zhao · Chengxiang Guo · Jianghui Tang · Yongtao Ji · Taohong Li · Wei Shao · Xueli Bai · Qing Li · Tingbo Liang
2 October 2026 at 18:00

Cell Rep Med. 2026 Oct 2:103085. doi: 10.1016/j.xcrm.2026.103085. Online ahead of print.

ABSTRACT

Hepatocellular carcinoma (HCC) management is challenging due to its complex tumor microenvironment and poor treatment responses. Here, using tumor specimens from a prospective clinical trial of combined transarterial chemoembolization (TACE) with immune checkpoint blockade (ICB), we perform exhaustive multi-omics analysis including spatial proteomics and transcriptomics, single-cell RNA sequencing, and bulk transcriptomics. These analyses reveal that treatment response is associated with enrichment of anti-tumor T cell regions that are regulated by cGAS-STING activation within immune-suppressive epithelial cells. Conversely, fibrotic processes impede these pro-response processes. Based on these insights, we test triple combination therapy consisting of cGAS activation, immune checkpoint blockade, and anti-fibrosis strategies, which shows improved efficacy over dual therapy. To identify patients who would benefit, we construct a predictive model using a group sparse learning algorithm. Our findings provide a blueprint for crafting personalized HCC therapies using next-generation biomarkers.

PMID:42826719 | DOI:10.1016/j.xcrm.2026.103085

Multi-omics-driven personalized management of advanced HCC

✇(Multiomics OR Omics) AND (Pancreatic)
By: Jianpeng Sheng · Jinyuan Song · Lin Wang · Junlei Zhang · Wei Zhang · Hang Shi · Yaxing Zhao · Chengxiang Guo · Jianghui Tang · Yongtao Ji · Taohong Li · Wei Shao · Xueli Bai · Qing Li · Tingbo Liang
2 October 2026 at 18:00

Cell Rep Med. 2026 Oct 2:103085. doi: 10.1016/j.xcrm.2026.103085. Online ahead of print.

ABSTRACT

Hepatocellular carcinoma (HCC) management is challenging due to its complex tumor microenvironment and poor treatment responses. Here, using tumor specimens from a prospective clinical trial of combined transarterial chemoembolization (TACE) with immune checkpoint blockade (ICB), we perform exhaustive multi-omics analysis including spatial proteomics and transcriptomics, single-cell RNA sequencing, and bulk transcriptomics. These analyses reveal that treatment response is associated with enrichment of anti-tumor T cell regions that are regulated by cGAS-STING activation within immune-suppressive epithelial cells. Conversely, fibrotic processes impede these pro-response processes. Based on these insights, we test triple combination therapy consisting of cGAS activation, immune checkpoint blockade, and anti-fibrosis strategies, which shows improved efficacy over dual therapy. To identify patients who would benefit, we construct a predictive model using a group sparse learning algorithm. Our findings provide a blueprint for crafting personalized HCC therapies using next-generation biomarkers.

PMID:42826719 | DOI:10.1016/j.xcrm.2026.103085

Received — 27 May 2026 ⏭

Targeting cysteinyl leukotriene receptor 1 reprograms tumor-promoting myelopoiesis and overcomes immune checkpoint therapy resistance

✇Nature Cancer
By: Hui Tang · Ping Xie · Jihae Ahn · Mi-Ran Choi · Jie Fan · Siqi Chen · Yi Zhang · Bin Zhang
19 May 2026 at 08:00

Nature Cancer, Published online: 19 May 2026; doi:10.1038/s43018-026-01174-7

Tang et al. identify cysteinyl leukotriene receptor 1 (CysLTR1) as a critical regulator of tumor-induced myelopoiesis, suggesting CysLTR1 targeting to sensitize tumors to immune checkpoint blockade.
Received — 2 April 2026 ⏭

IMAGAgent: Orchestrating Multi-Turn Image Editing via Constraint-Aware Planning and Reflection

✇cs.AI, q-bio.NC updates on arXiv.org
By: Fei Shen · Chengyu Xie · Lihong Wang · Zhanyi Zhang · Xin Jiang · Xiaoyu Du · Jinhui Tang
1 April 2026 at 12:00
arXiv:2603.29602v1 Announce Type: cross Abstract: Existing multi-turn image editing paradigms are often confined to isolated single-step execution. Due to a lack of context-awareness and closed-loop feedback mechanisms, they are prone to error accumulation and semantic drift during multi-turn interactions, ultimately resulting in severe structural distortion of the generated images. For that, we propose \textbf{IMAGAgent}, a multi-turn image editing agent framework based on a "plan-execute-reflect" closed-loop mechanism that achieves deep synergy among instruction parsing, tool scheduling, and adaptive correction within a unified pipeline. Specifically, we first present a constraint-aware planning module that leverages a vision-language model (VLM) to precisely decompose complex natural language instructions into a series of executable sub-tasks, governed by target singularity, semantic atomicity, and visual perceptibility. Then, the tool-chain orchestration module dynamically constructs execution paths based on the current image, the current sub-task, and the historical context, enabling adaptive scheduling and collaborative operation among heterogeneous operation models covering image retrieval, segmentation, detection, and editing. Finally, we devise a multi-expert collaborative reflection mechanism where a central large language model (LLM) receives the image to be edited and synthesizes VLM critiques into holistic feedback, simultaneously triggering fine-grained self-correction and recording feedback outcomes to optimize future decisions. Extensive experiments on our constructed \textbf{MTEditBench} and the MagicBrush dataset demonstrate that IMAGAgent achieves performance significantly superior to existing methods in terms of instruction consistency, editing precision, and overall quality. The code is available at https://github.com/hackermmzz/IMAGAgent.git.
Received — 11 March 2026 ⏭

To Mix or To Merge: Toward Multi-Domain Reinforcement Learning for Large Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haoqing Wang · Xiang Long · Ziheng Li · Yilong Xu · Tingguang Li · Yehui Tang
10 March 2026 at 12:00
arXiv:2602.12566v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) plays a key role in stimulating the explicit reasoning capability of Large Language Models (LLMs). We can achieve expert-level performance in some specific domains via RLVR, such as coding or math. When a general multi-domain expert-level model is required, we need to carefully consider the collaboration of RLVR across different domains. The current state-of-the-art models mainly employ two different training paradigms for multi-domain RLVR: mixed multi-task RLVR and separate RLVR followed by model merging. However, most of the works did not provide a detailed comparison and analysis about these paradigms. To this end, we choose multiple commonly used high-level tasks (e.g., math, coding, science, instruction following, and agent) as our target domains and design extensive qualitative and quantitative experiments using open-source datasets. We find the RLVR across domains exhibits few mutual interferences, and reasoning-intensive domains demonstrate mutually synergistic effects. Furthermore, we analyze the internal mechanisms of mutual gains from the perspectives of weight space geometry, model prediction behavior, information constraints and self-verification. This project is named as M2RL that means Mixed multi-task training or separate training followed by model Merging for Reinforcement Learning, and the homepage is at https://github.com/mosAI25/M2RL.
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