A diversity outbred F1 mouse model identifies host-intrinsic genetic regulators of response to immune checkpoint inhibitors

多样性杂交 F1 小鼠模型可识别出对免疫检查点抑制剂反应的宿主内在遗传调节因子

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作者:Justin B Hackett, James E Glassbrook, Maria C Muñiz, Madeline Bross, Abigail Fielder, Gregory Dyson, Nasrin Movahhedin, Jennifer McCasland, Claire McCarthy-Leo, Heather M Gibson

Abstract

Immune checkpoint inhibitors (ICI) have improved outcomes for a variety of malignancies; however, many patients fail to benefit. While tumor-intrinsic mechanisms are likely involved in therapy resistance, it is unclear to what extent host genetic background influences response. To investigate this, we utilized the Diversity Outbred (DO) and Collaborative Cross (CC) mouse models. DO mice are an outbred stock generated by crossbreeding eight inbred founder strains, and CC mice are recombinant inbred mice generated from the same eight founders. We generated 207 DOB6F1 mice representing 48 DO dams and demonstrated that these mice reliably accept the C57BL/6-syngeneic B16F0 tumor and that host genetic background influences response to ICI. Genetic linkage analysis from 142 mice identified multiple regions including one within chromosome 13 that associated with therapeutic response. We utilized 6 CC strains bearing the positive (NZO) or negative (C57BL/6) driver genotype in this locus. We found that 2/3 of predicted responder CCB6F1 crosses show reproducible ICI response. The chromosome 13 locus contains the murine prolactin family, which is a known immunomodulating cytokine associated with various autoimmune disorders. To directly test whether prolactin influences ICI response rates, we implanted inbred C57BL/6 mice with subcutaneous slow-release prolactin pellets to induce mild hyperprolactinemia. Prolactin augmented ICI response against B16F0, with increased CD8 infiltration and 5/8 mice exhibiting slowed tumor growth relative to controls. This study highlights the role of host genetics in ICI response and supports the use of F1 crosses in the DO and CC mouse populations as powerful cancer immunotherapy models.

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