(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
A programmed cell death learning signature predicts immunotherapy response and identifies AP1S1 as a regulator of immune exclusion in breast cancer Guixin Wang · Junming Cao · Parhat Kaysar · Chenglu Lu · Ziyi Chen · Zhaohui Chen · Han Zhang · Yue Yu · Yongjie Xie · Shan Cheng · Pengpeng Zhang · Xin Wang
Chin J Cancer Res. 2026 Aug 30;38(4):480-500. doi: 10.21147/j.issn.1000-9604.2026.04.08.ABSTRACTOBJECTIVE: Breast cancer remains a leading cause of global cancer mortality, characterized by profound heterogeneity. While immune checkpoint blockade (ICB) has transformed oncology, its efficacy in breast cancer is often hindered by "immune-cold" microenvironments and immune exclusion. Programmed cell death (PCD) is a critical regulator of tumor immune microenvironment (TIME). However, its role in th
9 September 2026 at 18:00

A programmed cell death learning signature predicts immunotherapy response and identifies AP1S1 as a regulator of immune exclusion in breast cancer

(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)

By: Guixin Wang · Junming Cao · Parhat Kaysar · Chenglu Lu · Ziyi Chen · Zhaohui Chen · Han Zhang · Yue Yu · Yongjie Xie · Shan Cheng · Pengpeng Zhang · Xin Wang

9 September 2026 at 18:00

Chin J Cancer Res. 2026 Aug 30;38(4):480-500. doi: 10.21147/j.issn.1000-9604.2026.04.08.

ABSTRACT

OBJECTIVE: Breast cancer remains a leading cause of global cancer mortality, characterized by profound heterogeneity. While immune checkpoint blockade (ICB) has transformed oncology, its efficacy in breast cancer is often hindered by "immune-cold" microenvironments and immune exclusion. Programmed cell death (PCD) is a critical regulator of tumor immune microenvironment (TIME). However, its role in the breast cancer immune microenvironment remains poorly understood.

METHODS: We integrated multi-omics data from six breast cancer cohorts (N=3,764) to develop a programmed cell death learning signature (PCDsig) using over 100 machine learning combinations. The model was benchmarked against 29 published signatures. Single-cell transcriptomic analysis decoded the immune landscape and cellular crosstalk. The role of adaptor-related protein complex 1 subunit sigma 1 (AP1S1) was validated through a clinical cohort, in vitro functional assays, and in vivo syngeneic mouse models.

RESULTS: PCDsig significantly stratified patient prognosis across all cohorts, consistently outperforming 29 existing models. High PCDsig scores correlated with immune-excluded phenotypes, reduced CD8+ T cell infiltration, and lower immunophenoscores. Single-cell analysis revealed that high-PCDsig tumors utilize vascular endothelial growth factor A (VEGFA) signaling to foster an immunosuppressive microenvironment. AP1S1 was identified as the core driver of immune exclusion. And our clinical cohort supported the immune exclusion effect of AP1S1. AP1S1 knockdown impaired tumor progression in vitro and fundamentally remodeled the tumor immune ecosystem in vivo. Combining AP1S1 inhibition with anti-programmed cell death ligand 1 (anti-PD-L1) therapy exerted profound synergistic effects, driven by massive infiltration and functional activation of cytotoxic Granzyme B (GZMB)+CD8+ T cells.

CONCLUSIONS: Our study establishes the PCDsig we developed is a potential prognostic and predictive biomarker for breast cancer. We provide the first evidence of AP1S1 as a core immunomodulatory oncogene that mediates immune exclusion. Targeting AP1S1 represents a highly promising strategy to sensitize cold breast tumors to ICB, offering a new perspective for precision immunotherapy.

PMID:42712842 | PMC:PMC13551362 | DOI:10.21147/j.issn.1000-9604.2026.04.08

(Multiomics OR Omics) AND (Pancreatic)
A programmed cell death learning signature predicts immunotherapy response and identifies AP1S1 as a regulator of immune exclusion in breast cancer Guixin Wang · Junming Cao · Parhat Kaysar · Chenglu Lu · Ziyi Chen · Zhaohui Chen · Han Zhang · Yue Yu · Yongjie Xie · Shan Cheng · Pengpeng Zhang · Xin Wang
Chin J Cancer Res. 2026 Aug 30;38(4):480-500. doi: 10.21147/j.issn.1000-9604.2026.04.08.ABSTRACTOBJECTIVE: Breast cancer remains a leading cause of global cancer mortality, characterized by profound heterogeneity. While immune checkpoint blockade (ICB) has transformed oncology, its efficacy in breast cancer is often hindered by "immune-cold" microenvironments and immune exclusion. Programmed cell death (PCD) is a critical regulator of tumor immune microenvironment (TIME). However, its role in th
9 September 2026 at 18:00

A programmed cell death learning signature predicts immunotherapy response and identifies AP1S1 as a regulator of immune exclusion in breast cancer

(Multiomics OR Omics) AND (Pancreatic)

By: Guixin Wang · Junming Cao · Parhat Kaysar · Chenglu Lu · Ziyi Chen · Zhaohui Chen · Han Zhang · Yue Yu · Yongjie Xie · Shan Cheng · Pengpeng Zhang · Xin Wang

9 September 2026 at 18:00

Chin J Cancer Res. 2026 Aug 30;38(4):480-500. doi: 10.21147/j.issn.1000-9604.2026.04.08.

ABSTRACT

OBJECTIVE: Breast cancer remains a leading cause of global cancer mortality, characterized by profound heterogeneity. While immune checkpoint blockade (ICB) has transformed oncology, its efficacy in breast cancer is often hindered by "immune-cold" microenvironments and immune exclusion. Programmed cell death (PCD) is a critical regulator of tumor immune microenvironment (TIME). However, its role in the breast cancer immune microenvironment remains poorly understood.

METHODS: We integrated multi-omics data from six breast cancer cohorts (N=3,764) to develop a programmed cell death learning signature (PCDsig) using over 100 machine learning combinations. The model was benchmarked against 29 published signatures. Single-cell transcriptomic analysis decoded the immune landscape and cellular crosstalk. The role of adaptor-related protein complex 1 subunit sigma 1 (AP1S1) was validated through a clinical cohort, in vitro functional assays, and in vivo syngeneic mouse models.

RESULTS: PCDsig significantly stratified patient prognosis across all cohorts, consistently outperforming 29 existing models. High PCDsig scores correlated with immune-excluded phenotypes, reduced CD8+ T cell infiltration, and lower immunophenoscores. Single-cell analysis revealed that high-PCDsig tumors utilize vascular endothelial growth factor A (VEGFA) signaling to foster an immunosuppressive microenvironment. AP1S1 was identified as the core driver of immune exclusion. And our clinical cohort supported the immune exclusion effect of AP1S1. AP1S1 knockdown impaired tumor progression in vitro and fundamentally remodeled the tumor immune ecosystem in vivo. Combining AP1S1 inhibition with anti-programmed cell death ligand 1 (anti-PD-L1) therapy exerted profound synergistic effects, driven by massive infiltration and functional activation of cytotoxic Granzyme B (GZMB)+CD8+ T cells.

CONCLUSIONS: Our study establishes the PCDsig we developed is a potential prognostic and predictive biomarker for breast cancer. We provide the first evidence of AP1S1 as a core immunomodulatory oncogene that mediates immune exclusion. Targeting AP1S1 represents a highly promising strategy to sensitize cold breast tumors to ICB, offering a new perspective for precision immunotherapy.

PMID:42712842 | PMC:PMC13551362 | DOI:10.21147/j.issn.1000-9604.2026.04.08

cs.AI, q-bio.NC updates on arXiv.org
Countering Catastrophic Forgetting of Large Language Models for Better Instruction Following via Weight-Space Model Merging Mengxian Lyu · Cheng Peng · Ziyi Chen · Mengyuan Zhang · Jieting Li Lu · Yonghui Wu
arXiv:2604.01538v1 Announce Type: cross Abstract: Large language models have been adopted in the medical domain for clinical documentation to reduce clinician burden. However, studies have reported that LLMs often "forget" a significant amount of instruction-following ability when fine-tuned using a task-specific medical dataset, a critical challenge in adopting general-purpose LLMs for clinical applications. This study presents a model merging framework to efficiently adapt general-purpose LLM
3 April 2026 at 12:00

Countering Catastrophic Forgetting of Large Language Models for Better Instruction Following via Weight-Space Model Merging

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

By: Mengxian Lyu · Cheng Peng · Ziyi Chen · Mengyuan Zhang · Jieting Li Lu · Yonghui Wu

3 April 2026 at 12:00

arXiv:2604.01538v1 Announce Type: cross Abstract: Large language models have been adopted in the medical domain for clinical documentation to reduce clinician burden. However, studies have reported that LLMs often "forget" a significant amount of instruction-following ability when fine-tuned using a task-specific medical dataset, a critical challenge in adopting general-purpose LLMs for clinical applications. This study presents a model merging framework to efficiently adapt general-purpose LLMs to the medical domain by countering this forgetting issue. By merging a clinical foundation model (GatorTronLlama) with a general instruct model (Llama-3.1-8B-Instruct) via interpolation-based merge methods, we seek to derive a domain-adapted model with strong performance on clinical tasks while retaining instruction-following ability. Comprehensive evaluation across medical benchmarks and five clinical generation tasks (e.g., radiology and discharge summarization) shows that merged models can effectively mitigate catastrophic forgetting, preserve clinical domain expertise, and retain instruction-following ability. In addition, our model merging strategies demonstrate training efficiency, achieving performance on par with fully fine-tuned baselines under severely constrained supervision (e.g., 64-shot vs. 256-shot). Consequently, weight-space merging constitutes a highly scalable solution for adapting open-source LLMs to clinical applications, facilitating broader deployment in resource-constrained healthcare environments.

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