{
  "abstract": "Background Only 23% of advanced melanoma patients treated with PD-1 blockade have a ten-year progression free survival, 1 and it remains unknown as to why many patients do not respond to treatment. This variable response to PD-1 blockade is also seen in mice.2 CD8+ T cells, regulatory T cells (Tregs), and antigen-presenting dendritic cells (DCs) play an important role in the immune response to cancer; however, unraveling the effects of multimodal interactions between tumor and immune cells and their contributions to tumor control using an experimental approach alone is time- and resource-intensive. Thus, to identify critical immunological features associated with variable response to αPD-1 immunotherapy, we built a mathematical model of the interactions between CD8+ T cells, Tregs, DCs, and tumor cells rooted in current biological concepts and parameterized using murine experiments.Methods Our mathematical model comprises five ordinary differential equations and 32 immunological parameters including tumor immunogenicity, the effects of IL-2 prolonging T cell lifespan, 2 Treg suppression of antitumor immune response through CTLA-4,3 recruitment of immune cells into the tumor,4 5 and expression of PD-L1 on DCs and tumor cells to deactivate T cells.6 The model successfully fits data on D4M-S melanoma growth in mice and immune cell dynamics. Employing Latin Hypercube Sampling,7 we generated 1000 parameter sets, each of which represents a unique ‘virtual’ mouse. We performed parameter sensitivity analysis to gain an insight into which mechanisms modulate the success of αPD-1 immunotherapy.Results The model captures the sensitivity to αPD-1 immunotherapy seen in experimental data and predicts that the Treg influx into the tumor is the most important determinant of resistance to αPD-1. The model further suggests the tumor and immune states before therapy are key limiting factors. Increasing the initial number of CD8 + T cells alone doesn’t always result in better outcomes; instead, the model implies there exists an optimal initial ratio of immune cells that will enhance response to treatment. These results are being experimentally validated.Conclusions The mathematical model we developed accurately captures tumor and immune dynamics, simulates responses to immunotherapy, and predicts mechanisms of resistance to αPD-1. While we focus on melanoma and PD-1 blockade, this model can be applied to a variety of tumors and immunotherapies. This integrated approach of modeling and experimental validation can identify crucial determinants of resistance to immunotherapy and guide the development of more effective therapeutic strategies with significant economic and temporal efficiency.Acknowledgements This study was funded by pilot grants from the Center for Complex Biological Systems and the Chao Family Comprehensive Cancer Center (University of California Irvine). R.S.S. is supported by a R01 Diversity Supplement and an NSF-GRFP fellowship.References Wolchok JD, Chiarion-Sileni V, Rutkowski P, Cowey CL, Schadendorf D, Wagstaff J, Queirolo P, Dummer R, Butler MO, Hill AG, Postow MA, Gaudy-Marqueste C, Medina T, Lao CD, Walker J, Márquez-Rodas I, Haanen JBAG, Guidoboni M, Maio M, Schöffski P, Carlino MS, Sandhu S, Lebbé C, Ascierto PA, Long GV, Ritchings C, Nassar A, Askelson M, Benito MP, Wang W, Hodi FS, Larkin J; CheckMate 067 Investigators. Final, 10-year outcomes with nivolumab plus ipilimumab in advanced melanoma. N Engl J Med. 2025;392(1):11–22.Geels SN, Moshensky A, Sousa RS, Murat C, Bustos MA, Walker BL, Singh R, Harbour SN, Gutierrez G, Hwang M, Mempel TR, Weaver CT, Nie Q, Hoon DSB, Ganesan AK, Othy S, Marangoni F. Interruption of the intratumor CD8+ T cell:treg crosstalk improves the efficacy of PD-1 immunotherapy. Cancer Cell. 2024;42(6):1051–1066.Marangoni F, Zhakyp A, Corsini M, Geels SN, Carrizosa E, Thelen M, Mani V, Prüßmann JN, Warner RD, Ozga AJ, Di Pilato M, Othy S, Mempel TR. Expansion of tumor-associated Treg cells upon disruption of a CTLA-4-dependent feedback loop. Cell. 2021;184(15):3998–4015.Spranger S, Bao R, Gajewski TF. Melanoma-intrinsic β-catenin signalling prevents anti-tumour immunity. Nature. 2015;523(7559):231–5.Mempel TR, Marangoni F. Guidance factors orchestrating regulatory T cell positioning in tissues during development, homeostasis, and response. Immunol Rev. 2019;289(1):129–141.Tumeh PC, Harview CL, Yearley JH, Shintaku IP, Taylor EJ, Robert L, Chmielowski B, Spasic M, Henry G, Ciobanu V, West AN, Carmona M, Kivork C, Seja E, Cherry G, Gutierrez AJ, Grogan TR, Mateus C, Tomasic G, Glaspy JA, Emerson RO, Robins H, Pierce RH, Elashoff DA, Robert C, Ribas A. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature. 2014;515(7528):568–71.Helton JC, Davis FJ. Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems. Reliability Engineering & Systems Safety. 2003;81(1):23–69.Ethics Approval Mouse studies were approved under IACUC protocol AUP22-092.",
  "authors": [
    {
      "affiliations": [
        "University of California Irvine, Irvine, CA, USA"
      ],
      "name": "Rachel S Sousa"
    },
    {
      "affiliations": [
        "University of California Irvine, Irvine, CA, USA"
      ],
      "name": "Shannon N Geels"
    },
    {
      "affiliations": [
        "University of California Irvine, Irvine, CA, USA"
      ],
      "name": "Alexander Moshensky"
    },
    {
      "affiliations": [
        "University of California Irvine, Irvine, CA, USA"
      ],
      "name": "Claire Murat"
    },
    {
      "affiliations": [
        "University of California Irvine, Irvine, CA, USA"
      ],
      "name": "John Lowengrub"
    },
    {
      "affiliations": [
        "University of California Irvine, Irvine, CA, USA"
      ],
      "name": "Francesco Marangoni"
    }
  ],
  "title": "1122 Identifying critical features of tumor responsiveness to PD-1 immunotherapy using mathematical and biological models",
  "uid": "36595797-4298-5a15-965e-e8e21a6e7963"
}