Single-Cell Analysis Portrays the Diversity of Immune States Within Human Melanoma Tumors

Introduction

Checkpoint blockade therapies that aim to reactivate anti-tumor immune responses have revolutionized cancer treatment, resulting in durable responses in a significant proportion of patients with advanced disease. Nevertheless, many patients fail to reach long-term clinical benefit. Recent findings suggest that immune cell infiltrates in tumors are highly heterogeneous, contributing to differential therapy outcomes. Despite its importance, a comprehensive understanding of the diversity of functional states of immune cells within the tumor micro-environment (TMI) is lacking.

Material and method

We characterized myeloid and lymphocyte infiltrates within tumors derived from 28 metastatic melanoma patients by single-cell RNAseq, and parallel T cell receptor (TCR) sequencing, thereby generating an unbiased map of the expression signatures of immune cells and intra- and inter-tumoral clonality of T cells. Last, we performed in-vitro reactivity assays, which allowed us to determine autologous-tumor reactive and non-reactive TCRs, adding the reactive potential of each cell as another layer of information to the single-cell expression signatures, facilitating inference of the functionality of clonally expanded T cell populations in the tumor.

Results and discussion

Different immune cell types and cellular states that were previously described were observed in within the tumor infiltrates. These included naïve, cytotoxic, dysfunctional, and regulatory T cells, NK cells, and the various myeloid subsets such as monocytes, macrophages, and dendritic cells. Notably, even among similar disease stages and treatment background, the composition of the immune cell populations differed considerably between patients, while clonally expanded T cells predominantly adapted similar cell profiles. The high-resolution map demonstrated gradient transitions between activation and dysfunctional states, and pinpointed to novel candidate genes that may serve as targets for checkpoint blockade. Strikingly, we noticed an association between in-vitro reactive T cells and enrichment of dysfunctional T cell signatures within patients. In addition, dysfunctional populations were associated with prevalence of regulatory T cells and follicular helper cells, and a depletion of naïve T cells.

Conclusion

Our findings demonstrate the power of using single-cell RNAseq to determine the immunological make-up in metastatic tumors. The characterization of cell states enabled us to identify candidate genes for targeting of suppressed T cells, expressed in subpopulations of cells with known “exhaustion” signatures previously described. In the future, single-cell analysis may serve as a tool for predicting therapy response, rapid and effective tumor subtype characterization, and ultimately lead to optimization of personalized immunotherapy.