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Cytomegalovirus (CMV) infection is associated with graft rejection in renal transplantation. Memory-like natural killer (NK) cells expressing NKG2C and lacking FcεRIγ are established during CMV infection. Additionally, CD8+ T cells expressing NKG2C have been observed in some CMV-seropositive patients. However, in vivo kinetics detailing the development and differentiation of these lymphocyte subsets during CMV infection remain limited. Here, we interrogated the in vivo kinetics of lymphocytes in CMV-infected renal transplant patients using longitudinal samples compared with those of nonviremic (NV) patients. Recipient CMV-seropositive (R+) patients had preexisting memory-like NK cells (NKG2C+CD57+FcεRIγ-) at baseline, which decreased in the periphery immediately after transplantation in both viremic and NV patients. We identified a subset of prememory-like NK cells (NKG2C+CD57+FcεRIγlow-dim) that increased during viremia in R+ viremic patients. These cells showed a higher cytotoxic profile than preexisting memory-like NK cells with transient up-regulation of FcεRIγ and Ki67 expression at the acute phase, with the subsequent accumulation of new memory-like NK cells at later phases of viremia. Furthermore, cytotoxic NKG2C+CD8+ T cells and γδ T cells significantly increased in viremic patients but not in NV patients. These three different cytotoxic cells combinatorially responded to viremia, showing a relatively early response in R+ viremic patients compared with recipient CMV-seronegative viremic patients. All viremic patients, except one, overcame viremia and did not experience graft rejection. These data provide insights into the in vivo dynamics and interplay of cytotoxic lymphocytes responding to CMV viremia, which are potentially linked with control of CMV viremia to prevent graft rejection.

The success of immunotherapy has led to a myriad of clinical trials accompanied by efforts to gain mechanistic insight and identify predictive signatures for personalization. However, many immune monitoring technologies face investigator bias, missing unanticipated cellular responses in limited clinical material. We present here a mass cytometry (CyTOF) workflow for standardized, systems-level biomarker discovery in immunotherapy trials. To broadly enumerate immune cell identity and activity, we established and extensively assessed a reference panel of 33 antibodies to cover major cell subsets, simultaneously quantifying activation and immune checkpoint molecules in a single assay. This assay enumerates ≥98% of peripheral immune cells with ≥4 positively identifying antigens. Robustness and reproducibility are demonstrated on multiple samples types, across two research centers and by orthogonal measurements. Using automated analysis, we identify stratifying immune signatures in bone marrow transplantation-associated graft-versus-host disease. Together, this validated workflow ensures comprehensive immunophenotypic analysis and data comparability and will accelerate biomarker discovery.

The success of immunotherapy has led to a myriad of clinical trials accompanied by efforts to gain mechanistic insight and identify predictive signatures for personalization. However, many immune monitoring technologies face investigator bias, missing unanticipated cellular responses in limited clinical material. We present here a mass cytometry (CyTOF) workflow for standardized, systems-level biomarker discovery in immunotherapy trials. To broadly enumerate immune cell identity and activity, we established and extensively assessed a reference panel of 33 antibodies to cover major cell subsets, simultaneously quantifying activation and immune checkpoint molecules in a single assay. This assay enumerates ≥98% of peripheral immune cells with ≥4 positively identifying antigens. Robustness and reproducibility are demonstrated on multiple samples types, across two research centers and by orthogonal measurements. Using automated analysis, we identify stratifying immune signatures in bone marrow transplantation-associated graft-versus-host disease. Together, this validated workflow ensures comprehensive immunophenotypic analysis and data comparability and will accelerate biomarker discovery.