Clinical responses to adoptive T-cell transfer can be modeled in an autologous immune-humanized mouse model

Henrik Jespersen, Mattias F. Lindberg, Marco Donia, Elin M. V. Söderberg, Rikke Andersen, Ulrich Keller, Lars Ny, Inge Marie Svane, Lisa M. Nilsson and Jonas A. Nilsson ()
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Henrik Jespersen: Sahlgrenska Cancer Center at University of Gothenburg and Sahlgrenska University Hospital
Mattias F. Lindberg: Sahlgrenska Cancer Center at University of Gothenburg and Sahlgrenska University Hospital
Marco Donia: University of Copenhagen
Elin M. V. Söderberg: Sahlgrenska Cancer Center at University of Gothenburg and Sahlgrenska University Hospital
Rikke Andersen: University of Copenhagen
Ulrich Keller: Technische Universität München
Lars Ny: Sahlgrenska Cancer Center at University of Gothenburg and Sahlgrenska University Hospital
Inge Marie Svane: University of Copenhagen
Lisa M. Nilsson: Sahlgrenska Cancer Center at University of Gothenburg and Sahlgrenska University Hospital
Jonas A. Nilsson: Sahlgrenska Cancer Center at University of Gothenburg and Sahlgrenska University Hospital

Nature Communications, 2017, vol. 8, issue 1, 1-10

Abstract: Abstract Immune checkpoint inhibitors and adoptive cell transfer (ACT) of autologous tumor-infiltrating T cells have shown durable responses in patients with melanoma. To study ACT and immunotherapies in a humanized model, we have developed PDXv2.0 — a melanoma PDX model where tumor cells and tumor-infiltrating T cells from the same patient are transplanted sequentially in non-obese diabetic/severe combined immune-deficient/common gamma chain (NOG/NSG) knockout mouse. Key to T-cell survival/effect in this model is the continuous presence of interleukin-2 (IL-2). Tumors that grow in PDXv2.0 are eradicated if the autologous tumor cells and T cells come from a patient that exhibited an objective response to ACT in the clinic. However, T cells from patients that are non-responders to ACT cannot kill tumor cells in PDXv2.0. Taken together, PDXv2.0 provides the potential framework to further model genetically diverse human cancers for assessing the efficacy of immunotherapies as well as combination therapies.

Date: 2017
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DOI: 10.1038/s41467-017-00786-z

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