Deciphering the molecular mechanisms orchestrating myeloid-derived immunosuppression in cancer and therapy

Immunotherapy has revolutionized the treatment of several human cancers, including non-small cell lung cancer (NSCLC). However, treatment with immune checkpoint blockade (ICB) is not beneficial for the majority of patients, as many of them exhibit partial clinical responses, as well as adverse effects. One of the great challenges of cancer care is to reconcile the high therapeutic efficacy of immunotherapy with low toxicity, deciphering the molecular network controlling cancer-related immune alterations and identifying putative prognostic markers and therapeutic targets. During cancer progression, myeloid-derived suppressor cells (MDSCs) abundantly expand, resulting in hampered anti-tumor adaptive immune responses and thus contributing to tumor progression and resistance to immunotherapy. Hence, understanding the molecular basis underlying the immunosuppressive features of cancer associated myeloid cells, is a crucial step for the identification of potential markers of tumor progression and response to therapy. By flow cytometry analysis we demonstrated a significant expansion of MDSCs related to cancer stage, suggesting an active role of these cells in fueling tumor progression. Moreover, in an cohort of advanced NSCLC patients, we observed a negative correlation between the M-MDSC/Tcell, monocyte/Tcell ratios and patient's response to ICB treatment. Since the myeloid compartment in the tumor tissue has specific correlations with the corresponding one in peripheral blood, we investigated by RNA-sequencing the transcriptional profile of circulating monocytes and M-MDSCS of NSCLC patients. Our results revealed a tumor-stage dependent evolution of blood monocyte and M-MDSC transcriptional signatures, which is reflected in metabolic alterations occurring in these cells during cancer development. Accordingly, transcriptional landscapes of monocytes and M-MDSCs of advanced NSCLC, detected prior to ICB treatment, appear to be predictive of the patient's clinical response.