Use of causal modeling to uncover cell-cell communication dynamics in the tumor microenvironment during anti-PD-1 therapy in breast cancer patients.
Qiu, Aodong Zhang, Han Ramsey, Joseph Daniel Sun, Boyang Andrews, Bryan Zhang, Kun Cooper, Gregory F. Chen, Lujia Lu, Xinghua
Qiu, Aodong
Zhang, Han
Ramsey, Joseph Daniel
Sun, Boyang
Andrews, Bryan
Zhang, Kun
Cooper, Gregory F.
Chen, Lujia
Lu, Xinghua
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Department
Machine Learning
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Journal article
Date
2025
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English
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Abstract
Background: Immune checkpoint blockade (ICB) targeting PD-1/PD-L1 is crucial in treating breast cancer. Despite clinical success, the mechanisms by which ICB therapies reshape the tumor microenvironment (TME) and enhance anti-tumor activity remain unclear. Recent studies reveal that anti-PD-1 therapies broadly alter TME cells beyond PD-1+ T cells, highlighting extensive cell-cell communication (CCC) as a key factor. Understanding CCC changes in response to anti-PD-1 therapy can illuminate its mechanisms of action (MOA) and TME dynamics. This study applies causal inference methodology to investigate CCC between T cells and non-T cells in the TME of breast cancer patients receiving anti-PD-1 therapy. Methods: We analyzed single-cell RNA-seq data from 31 breast cancer patients pre- and on-treatment with anti-PD-1 (pembrolizumab) reported by Bassez et al., 2021. We identified differentially expressed genes (DEGs) across cell types. We applied the instrumental variable method to identify causal relationships between signals in T-cell and non-T-cell DEGs to uncover treatment-induced CCC. We further searched for ligand-receptor pairs mediating CCC and identified gene expression modules (GEMs) regulated by these ligand-receptors. Finally, we constructed a CCC network from T to non-T cells. Results: Anti-PD1 treatment induced broad gene expression changes in diverse cell populations. Major T cell pathways influenced by anti-PD-1 therapy include NF-κB, interferon-γ and interleukins. CD4+ and CD8+ exhausted cells, expressing high levels of PDCD1 (encoding PD-1), exhibited distinct activated pathways. We identified the CCC network from T cells to non-T cells via ligand-receptor interactions. For example, anti-PD1 treatment activated cellular stress, apoptosis, and pro-inflammatory cytokine signaling pathways in T cells, altering the expression of a GEM that included TSC22D3 and TXNIP. This initiated signaling to myeloid cells via RPS19-C5AR1, leading to NF-κB activation. CD4+ exhausted T cells primarily signaled to monocytes, enhancing helper functions to recruit and activate monocytes, thereby promoting immune regulation and amplification. In contrast, CD8+ exhausted T cells primarily interacted with macrophages, intensifying cytotoxic responses that facilitated effective tumor-cell killing. Conclusions: Our analyses provide insights into the dynamic interplay of cells within the TMEs during anti-PD-1 therapy. These findings could facilitate the identification of new biomarkers for predicting heterogeneous treatment responses to anti-PD-1 regimens, potentially enhancing the design and customization of immunotherapeutic strategies for breast cancer patients. Finally, this study demonstrated the utility of causal inference methodologies for mechanistic studies of CCC.
Citation
A. Qiu et al., “Use of causal modeling to uncover cell-cell communication dynamics in the tumor microenvironment during anti-PD-1 therapy in breast cancer patients.,” Journal of Clinical Oncology, vol. 43, no. 16_suppl, Jun. 2025, doi: 10.1200/JCO.2025.43.16_SUPPL.E14585
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Journal of Clinical Oncology
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Wolters Kluwer