Introduction: Immune checkpoint therapies (ICT) have changed cancer care, yielding robust and durable responses in a subset of patients. Identifying patients who are likely to respond to ICT remains an ongoing challenge. In addition, only a portion of patients with clinical biomarkers respond to therapy. Signatures of RNA expression have been developed to predict response, the majority of which focus on T-cell and cytotoxicity markers, yet have been unable to substantially improve outcome predictions. Here, we present a RNA signature that instead describes tumor-intrinsic immune resistance and a potential mechanism of immunosuppression via tumor signaling on macrophages, derived from single-cell RNA-sequencing (scRNA-seq).

Methods: We performed dimensionality reduction using a Variational Autoencoder (VAE) on 30 scRNA-seq samples from 15 lung adenocarcinoma (LUAD) patients, comprising a total of 183,873 cells from the Cell Ranger pipeline. The VAE model was trained on each sample for 250 iterations, yielding 20 signatures from each patient, for a total of 300 signatures. The relationship of each signature with real-world overall survival (rwOS) across 1,983 bulk RNA-sequenced LUAD patients treated with an FDA approved ICT was assessed via a Cox proportional hazards model with risk set adjustment, using TMB and line of therapy as covariates. The NATMI python package was used to identify putative active ligand-receptor interactions between tumor cells and the immune environment.

Results: Among the VAE encodings, we identified an immune resistance signature significantly associated with decreased rwOS across the patient cohort (HR = 4.2 [2.8-6.3], adjusted p = 4.3e-10). This signature was derived from a small cluster of neoplastic cells (4.4% of cells) in a patient sample that was otherwise dominated by immune cells, including a substantial fraction of cytotoxic CD8 T cells (24.5%). The strongest predicted ligand-receptor interactions were found between the neoplastic cells and macrophages, via the MIF-CD74 interaction, an interaction we found upregulated in multiple single-cell tumor samples. Further, MIF RNA expression alone was significantly associated with rwOS across the 1,983 patient LUAD cohort (HR = 1.5 [1.1-2.1], p = 0.004).

Discussion: MIF-CD74 is a known immunosuppressive interaction and MIF signaling from tumor cells on macrophages has been previously shown to have immunosuppressive effects in a mouse model of melanoma that were largely reversible via MIF-CD74 blockade. Taken together, these results identify a signature of tumor intrinsic immune suppression that can indicate patients likely to experience reduced benefit from ICT. In addition, this signature provides evidence to support blockade of the MIF-CD74 axis as a means to enhance anti-tumor immune responses in LUAD.

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