cs.AI, q-bio.NC updates on arXiv.org
A Taxonomy of Architecture Options for Foundation Model-based Agents: Analysis and Decision Model Jingwen Zhou · Qinghua Lu · Jieshan Chen · Liming Zhu · Xiwei Xu · Zhenchang Xing · Stefan Harrer
arXiv:2408.02920v2 Announce Type: replace-cross Abstract: The rapid advancement of AI technology has led to widespread applications of agent systems across various domains. However, the need for detailed architecture design poses significant challenges in designing and operating these systems. This paper introduces a taxonomy focused on the architectures of foundation-model-based agents, addressing critical aspects such as functional capabilities and non-functional qualities. We also discuss th
10 September 2026 at 12:00

A Taxonomy of Architecture Options for Foundation Model-based Agents: Analysis and Decision Model

By: Jingwen Zhou · Qinghua Lu · Jieshan Chen · Liming Zhu · Xiwei Xu · Zhenchang Xing · Stefan Harrer

10 September 2026 at 12:00

arXiv:2408.02920v2 Announce Type: replace-cross Abstract: The rapid advancement of AI technology has led to widespread applications of agent systems across various domains. However, the need for detailed architecture design poses significant challenges in designing and operating these systems. This paper introduces a taxonomy focused on the architectures of foundation-model-based agents, addressing critical aspects such as functional capabilities and non-functional qualities. We also discuss the operations involved in both design-time and run-time phases, providing a comprehensive view of architectural design and operational characteristics. By unifying and detailing these classifications, our taxonomy aims to improve the design of foundation-model-based agents. Additionally, the paper establishes a decision model that guides critical design and runtime decisions, offering a structured approach to enhance the development of foundation-model-based agents. Our contributions include providing a structured architecture design option and guiding the development process of foundation-model-based agents, thereby addressing current fragmentation in the field.

(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