Melanoma is the most serious form of skin cancer and more likely to grow and metastasize. It has a high response rate to checkpoint inhibitor therapy compared to other cancers; however, about 60% of patients treated do not respond well or relapse. Immune checkpoints are proteins expressed on T cells and function to ensure self-tolerance, but they are also used by tumor cells to limit anti-tumor immune function.
Understanding the factors that influence whether a patient could respond well or not to checkpoint therapy can help researchers identify biomarkers for predicting which patients would be candidates for checkpoint therapy. Researchers from the Cancer Genome Computational Analysis group at the Broad Institute conducted RNA sequencing experiments at the single-cell level to profile the transcriptome of immune cells, focusing on cytotoxic T cells from patients treated with checkpoint inhibitors. Tumor samples from patients with metastatic melanoma were used to perform sequencing and biomarker detection experiments.
They identified states/subsets of cytotoxic T cells, the main players in checkpoint inhibitor function, that had patterns of gene expression specific for a given tumor response to therapy. Tumors that responded to treatment had more cytotoxic T cells with characteristics of memory cytotoxic T cells. However, in tumors that did not respond to therapy, an exhausted, or dysfunctional, cytotoxic T cell phenotype was predominant.
A potential marker that can be used to predict which patients may respond favorably to checkpoint inhibitor therapy is TCF7, a transcription factor necessary for the persistence of memory T cells. Immunofluorescence studies with tumor specimens showed that there were more cytotoxic T cells expressing TCF7 in responding samples and more TCF7-negative T cells in non-responding samples.
These study results show how cytotoxic T cell subsets/states and markers are associated with tumor fate subsequent to checkpoint inhibitor therapy. The findings may be used as a basis for the development of biomarkers to predict which patients may respond to checkpoint inhibitor immunotherapy, as well as methods that can increase the level of TCF7-expressing cells to bolster immunotherapy effectiveness.