GEO series
Monitoring systemic immune responses to checkpoint inhibition in breast cancer reveals host responses and mechanisms of resistance
GSE293819
Mus musculus
Expression profiling by high throughput sequencing
142 samples
2025/12/17
GPL24247
Summary
Immune checkpoint inhibitors (ICI) have improved survival in various cancers, but their success in breast cancer, specifically triple-negative breast cancer (TNBC), remains limited, benefiting less than 10% of patients. We modeled ICI response in vivo to unravel the mechanisms underlying immunotherapy efficacy, identify mechanisms of resistance in non-responsive tumors, and ascertain the therapeutic benefits of different chemotherapeutic combinations with ICI in breast cancer. We investigated the impact of ICI as monotherapy and in combination with other therapeutics in mouse models of mammary cancer, which we found robustly suppressed primary tumor growth and extended survival. Interestingly, even within a single model, responses to ICI were highly variable. Resistance was not reliably retained by transplantation into syngeneic hosts, suggesting a role for systemic host immunity rather than tumor-autonomous mechanisms. Transcriptomic analysis of the primary tumor landscape by fine-needle aspiration revealed that upregulated cytotoxic T cell response and inflammatory interferon signaling (both at baseline and post anti-PD-L1 administration) corresponded to favorable response to ICI. Longitudinal analysis of the peripheral blood uncovered enhanced myeloid cell recruitment in resistant mice, prior to therapy initiation. Similar effects were observed through longitudinal assessment of peripheral blood in ICI-treated human breast cancer patients. Blocking myeloid cell recruitment with navaraxin (CXCR1/2 inhibitor) improved ICI responses, further suppressing tumor growth and improving survival. These findings provide insight into resistance mechanisms and suggest the potential for minimally invasive strategies (sampling of systemic immune cells from peripheral blood) to identify patients likely to respond to ICI. This approach may help inform de-escalation strategies to mitigate therapeutic toxicities and limit unnecessary treatments.
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Paper (PMID 41371907) ↗
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