Immune checkpoint expression on peripheral cytotoxic lymphocytes in cervical cancer patients: moving beyond the PD-1/PD-L1 axis.

Abstract

Immune checkpoint therapy to reverse natural killer (NK) and T cell exhaustion has emerged as a promising treatment in various cancers. While anti-programmed cell death 1 (PD-1) pembrolizumab has recently gained Food and Drug Administration (FDA) approval for use in recurrent or metastatic cervical cancer, other checkpoint molecules, such as T cell immunoreceptor with immunoglobulin (Ig) and immunoreceptor tyrosine-based inhibition motif (ITIM) domains (TIGIT) and T cell immunoglobulin and mucin-domain containing-3 (Tim-3), have yet to be fully explored in this disease. We report expression of TIGIT, Tim-3 and PD-1 on subsets of peripheral blood NK (CD56dim/neg CD16bright/dim/neg and CD56bright CD16dim/neg ) and T cells. The percentages of these cells were increased in women with cervical cancer and pre-malignant lesions. PD-1+ NK and T cells were likely to co-express TIGIT and/or Tim-3. These cells, with an apparently 'exhausted' phenotype, were augmented in patients. A subset of cells were also natural killer group 2 member D (NKG2D)- and DNAX accessory molecule 1 (DNAM-1)-positive. PD-1int and PD-1high T cells were notably increased in cervical cancer. Soluble programmed cell death ligand 1 (PD-L1) was higher in cancer patient blood versus healthy donors and we observed a positive correlation between sPD-L1 and PD-1+ T cells in women with low-grade lesions. Within the cancer group, there were no significant correlations between sPD-L1 levels and cervical cancer stage. However, when comparing cancer versus healthy donors, we observed an inverse association between sPD-L1 and total T cells and a correlation between sPD-L1 and CD56dim NK cells. Our results may show an overview of the immune response towards pre-cancerous lesions and cervical cancer, perhaps giving an early clue as to whom to administer blocking therapies. The increase of multiple checkpoint markers may aid in identifying patients uniquely responsive to combined antibody therapies.