Multi-omics-driven personalized management of advanced HCC

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

Multimodal Continual Learning with MLLMs from Multi-scenario Perspectives

cs.AI, q-bio.NC updates on arXiv.org

By: Kai Jiang · Siqi Huang · Xiangyu Chen · Jiawei Shao · Hongyuan Zhang · Ping Luo · Xuelong Li

16 March 2026 at 12:00

arXiv:2511.18507v3 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) deployed on devices must adapt to continuously changing visual scenarios such as variations in background and perspective, to effectively perform complex visual tasks. To investigate catastrophic forgetting under real-world scenario shifts, we construct a multimodal visual understanding dataset (MSVQA), covering four distinct scenarios and perspectives: high-altitude, underwater, low-altitude, and indoor environments. Furthermore, we propose UNIFIER (mUltimodal coNtInual learning with MLLMs From multi-scenarIo pERspectives), a continual learning (CL) framework designed to address visual discrepancies while learning different scenarios. Compared to existing CL methods, UNIFIER enables knowledge accumulation within the same scenario and mutual enhancement across different scenarios via Vision Representation Expansion (VRE) and Vision Consistency Constraint (VCC). Experimental results show that UNIFIER improves the last-step VQA scores by 2.70%~10.62% and the last-step F1 scores by 3.40%~7.69% compared to the state-of-the-art method, QUAD, in 20-step cross-scenario continual learning tasks. MSVQA dataset is available at https://huggingface.co/datasets/Kaij00/MSVQA.

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