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Monitoring systemic immune responses to checkpoint inhibition in breast cancer reveals host responses and mechanisms of resistance

ABSTRACT: 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.

ORGANISM(S): Mus musculus

PROVIDER: GSE293819 | GEO | 2025/12/17

REPOSITORIES: GEO

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Project description:This ordinary differential equation model simulating the mechanisms that govern cancer-immune dynamics and their role in tumor responses to immunotherapy is described by the publication: Creemers JHA, Lesterhuis WJ, Mehra N, et al. "A tipping point in cancer-immune dynamics leads to divergent immunotherapy responses and hampers biomarker discovery." Journal for ImmunoTherapy of Cancer 2021;9:e002032. doi:10.1136/jitc-2020-002032 Comment: Simulation parameters in supplementary table 1 mismatch with figure 1 in manuscript. Therefore, to clarify, the following parameter values were used: Reproduction of Fig. 1(C), xi = 0.0005 Reproduction of Fig. 1(D), xi = 0.00025 Abstract: Background: Predicting treatment response or survival of cancer patients remains challenging in immuno-oncology. Efforts to overcome these challenges focus, among others, on the discovery of new biomarkers. Despite advances in cellular and molecular approaches, only a limited number of candidate biomarkers eventually enter clinical practice. Methods: A computational modeling approach based on ordinary differential equations was used to simulate the fundamental mechanisms that dictate tumor-immune dynamics and to investigate its implications on responses to immune checkpoint inhibition (ICI) and patient survival. Using in silico biomarker discovery trials, we revealed fundamental principles that explain the diverging success rates of biomarker discovery programs. Results: Our model shows that a tipping point—a sharp state transition between immune control and immune evasion—induces a strongly non-linear relationship between patient survival and both immunological and tumor-related parameters. In patients close to the tipping point, ICI therapy may lead to long-lasting survival benefits, whereas patients far from the tipping point may fail to benefit from these potent treatments. Conclusion: These findings have two important implications for clinical oncology. First, the apparent conundrum that ICI induces substantial benefits in some patients yet completely fails in others could be, to a large extent, explained by the presence of a tipping point. Second, predictive biomarkers for immunotherapy should ideally combine both immunological and tumor-related markers, as a patient’s distance from the tipping point can typically not be reliably determined from solely one of these. The notion of a tipping point in cancer-immune dynamics helps to devise more accurate strategies to select appropriate treatments for patients with cancer.

Project description:Claret2009 - Predicting phase III overall survival in colorectal cancer This model is described in the article: Model-based prediction of phase III overall survival in colorectal cancer on the basis of phase II tumor dynamics. Claret L, Girard P, Hoff PM, Van Cutsem E, Zuideveld KP, Jorga K, Fagerberg J, Bruno R. J. Clin. Oncol. 2009 Sep; 27(25): 4103-4108 Abstract: PURPOSE: We developed a drug-disease simulation model to predict antitumor response and overall survival in phase III studies from longitudinal tumor size data in phase II trials. METHODS: We developed a longitudinal exposure-response tumor-growth inhibition (TGI) model of drug effect (and resistance) using phase II data of capecitabine (n = 34) and historical phase III data of fluorouracil (FU; n = 252) in colorectal cancer (CRC); and we developed a parametric survival model that related change in tumor size and patient characteristics to survival time using historical phase III data (n = 245). The models were validated in simulation of antitumor response and survival in an independent phase III study (n = 1,000 replicates) of capecitabine versus FU in CRC. RESULTS: The TGI model provided a good fit of longitudinal tumor size data. A lognormal distribution best described the survival time, and baseline tumor size and change in tumor size from baseline at week 7 were predictors (P < .00001). Predicted change of tumor size and survival time distributions in the phase III study for both capecitabine and FU were consistent with observed values, for example, 431 days (90% prediction interval, 362 to 514 days) versus 401 days observed for survival in the capecitabine arm. A modest survival improvement of 39 days (90% prediction interval, -21 to 110 days) versus 35 days observed was predicted for capecitabine. CONCLUSION: The modeling framework successfully predicted survival in a phase III trial on the basis of capecitabine phase II data in CRC. It is a useful tool to support end-of-phase II decisions and design of phase III studies. This model is hosted on BioModels Database and identified by: MODEL1708310001. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Monitoring systemic immune responses to checkpoint inhibition in breast cancer reveals host responses and mechanisms of resistance

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