Circulating disease-specific CD8 T cells in Merkel cell carcinoma and COVID-19: Prognostic significance and improved approaches for their detection

Abstract

Immune checkpoint inhibition (ICI) that targets the programmed death-1 (PD-1) pathway has made great strides in improving cancer patient outcomes and is the most successful immunotherapy to date, but only a subset of treated patients benefits. Predicting which patients are likely to respond to ICI and understanding the underlying mechanisms are of critical importance to prioritize patients for alternate or synergistic therapies and to avoid toxicities in patients who would not benefit. Because PD-1 pathway blockade targets T cells, it is feasible that patient-specific T cell characteristics could serve as predictive biomarkers of response. However, few studies can directly assess the numerically lower, but more functionally relevant population of cancer-specific T cells. This is because most cancers arise due to unique patient-specific mutations, which makes it extremely difficult to identify infrequent cancer-specific T cells. However, 80% of Merkel cell carcinoma (MCC) cases are virally-driven, persistently express conserved Merkel cell polyomavirus (MCPyV) oncoproteins, and have extraordinarily few amino acid-coding tumor mutations. This allows us to have a common set of target antigens that can be used to study MCPyV-specific T cells and gain insights that can be extended to other cancers. In chapter 1, we review limitations encountered in studying tumor-specific CD8 T cells, propose virus-driven MCC as a model tumor, summarize current knowledge of mechanisms of response and resistance to ICI. In chapter 2, we find that baseline abundance of MCPyV-specific CD8 T cells in blood can predict ICI response in two different clinical trials. However, this does not seem to be the case for intratumoral tumor-specific CD8 T cells. This difference may be attributable to our observation that tumor-specific CD8 T cells in the blood are less dysfunctional than their intratumoral counterparts. In chapter 3, we confirm our finding that baseline frequency of MCPyV-specific CD8 T cells associates with response to ICI in an independent patient cohort. And we additionally find that the frequency of CD39+CLA+ CD8 T cells can predict response to ICI. However, current methods to quantify cancer-specific T cells require freshly acquired (non-archival) tissue, labile expensive custom reagents (peptide-HLA tetramers), and significant technical expertise, and are therefore not well suited for routine clinical care. Thus, in chapters 4 and 5, we explore methods to identify cancer-specific CD8 T cells more easily. In chapter 4, we characterize MCPyV-specific CD8 T cells in blood and identify a set of 98 genes that are enriched in these cells compared to other T cells. We find that this gene set can accurately identify tumor-specific CD8 T cells in both MCC and various mutationally-driven cancers. In chapter 5, we explore the feasibility of generating a library of T cell receptors (TCR) specific to MCPyV oncoproteins and using this library in combination with existing TCR similarity algorithms to identify cancer-specific CD8 T cells. Given that significantly more TCR sequences were publicly available from patients with COVID infection than MCC, we tested the ability of TCR similarity algorithms to identify SARS-CoV-2-specific CD8 T cells in COVID vaccine recipients in chapters 6 and 7. In chapter 6, we compared the phenotype and repertoire of SARS-CoV-2 specific CD8 T cells from the breastmilk and blood of lactating individuals who received a spike-specific mRNA vaccination post-delivery. We used a TCR similarity algorithm, tcrdist3, to identify novel potential SARS-CoV-2-specific CD8 T cells. In chapter 7, we used tcrdist3 to track the frequency of SARS-CoV-2-specific CD8 T cells in longitudinal blood samples from patients with a history of COVID infection, who subsequently received SARS-CoV-2 vaccines. We observed large expansions in response to vaccination and identified novel, functionally-proven SARS-CoV-2-specific CD8 T cells. Being able to accurately track and identify novel COVID-specific CD8 T cells with TCR similarity metrics in these studies increases our confidence in using similar methods for identifying cancer-specific CD8 T cells and justify efforts to create libraries of TCRs that target common oncogenic viruses and mutations. Finally, in chapter 8, we investigate a case of secondary resistance to ICI, despite an abundance of tumor-specific CD8 T cells. We demonstrate that this patient’s tumor lacked HLA-I expression, preventing anti-tumor CD8 T cells from recognizing the tumor cells. Treatment with an intralesional stimulator of interferon genes (STING) agonist partially restored HLA-I expression via induction of an inflammatory tumor microenvironment, allowing to patient to experience an abscopal, partial response. The studies detailed in chapters 2, 3, and 8 have made key advances in identifying mechanisms of response and resistance to PD-(L)1 pathway blockade in Merkel cell carcinoma. Additionally, the studies detailed in chapters 4, 5, 6, and 7 have made important advances in translating the identified mechanisms to more common, mutationally-driven cancers via gene expression profiling and bulk T cell receptor sequencing. We believe that use of these methods will translate to improving outcomes for all patients with cancer.