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Single cell analysis reveals distinct immune landscapes in transplant and primary sarcomas that determine response or resistance to immunotherapy

ABSTRACT: Abstract: Despite impressive responses in some patients, immunotherapy fails to cure most cancer patients. Preclinical studies indicate that radiotherapy synergizes with immunotherapy, promoting radiation-induced antitumor immunity. Nearly all preclinical immunotherapy studies utilize transplant tumor models, but cure rates of transplant tumors treated with immunotherapy overestimate patient responses. Here, we show that transplant sarcomas are cured by PD-1 blockade and radiotherapy, but identical treatment fails in autochthonous sarcomas, which demonstrate tumor-specific immune tolerance. We characterize tumor-infiltrating immune cells from transplant and primary tumors and reveal striking differences in their immune landscapes. Although radiotherapy remodels myeloid cells in primary and transplant sarcomas, only transplant tumors are enriched for activated CD8+ T cells associated with tumor clearance. By CIBERSORTx, the immune microenvironment of primary sarcomas in mice transcriptionally resembles most human sarcomas, while transplant sarcomas model the most inflamed sarcomas in patients. These results identify distinct microenvironments in murine sarcomas that coevolve with the immune system and suggest that patients whose sarcomas have an immune phenotype similar to transplant tumors may benefit most from PD-1 blockade and radiotherapy.

ORGANISM(S): Mus musculus

PROVIDER: GSE148856 | GEO | 2020/10/21

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.

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Single cell analysis reveals distinct immune landscapes in transplant and primary sarcomas that determine response or resistance to immunotherapy

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