Chemotherapy is much more effective in immunocompetent mice than in immunodeficient ones, and it is now acknowledged that an efficient immune system is necessary to optimize chemotherapy activity and efficacy. Furthermore, chemotherapy itself may reinvigorate immune response in different ways: by targeting cancer cells through the induction of cell stress, the release of damage signals and the induction of immunogenic cell death, by targeting immune cells, inhibiting immune suppressive cells and/or activating immune effector cells; and by targeting the host physiology through changes in the balance of gut microbiome. All these effects acting on immune and non-immune components interfere with the tumor microenvironment, leading to the different activity and efficacy of treatments. This article describes the correlation between chemotherapy and the immune changes induced in the tumor microenvironment. Our ultimate aim is to pave the way for the identification of the best drugs or combinations, the doses, the schedules and the right sequences to use when chemotherapy is combined with immunotherapy.

Cancer induces immune suppression to overcome its recognition and eradication by the immune system. Cytokines are messengers able to modulate immune response or suppression. There is great interest in the evaluation of their changes during treatment in order to identify their relationship with clinical outcome. We evaluated 18 cytokines in breast cancer patients treated with eribulin before starting treatment (T0) and after four courses of therapy (T1). Longitudinal modifications were considered and cytokine clusters through PCA and HCPC correlated to patients’ outcomes were identified. Forty-one metastatic breast cancer patients and fifteen healthy volunteers were included. After clustering, we identified at T0 six patient clusters with different risk of relapse and death. At T1, only four clusters were identified, and three of them accounted for thirty-eight of forty-one patients, suggesting a possible role of treatment in reducing heterogeneity. The cluster with the best survival at T1 was characterized by low levels of IL-4, IL-6, IL-8, IL-10, CCL-2, CCL-4, and TGF-β. The cluster showing the worst survival encompassed high levels of IL-4, IL-6, IL-8, IL-10, CCL-2, and IFN-γ. A subgroup of patients with short progression-free survival (PFS) and long overall survival (OS) was comprised in the cluster characterized by low levels of CCL-2, IL-6, IL-8, IL-10, and IL-12 at T0. Our data support the prognostic significance of longitudinal serum cytokine analysis. This approach may help identify patients for whom early treatment stop avoids needless toxicity or might justify treatment beyond early progression. Further investigations are required to validate this hypothesis.

Published data suggest that immunotherapy plays a role even in patients with very advanced tumours. We investigated the immune profile of end-stage cancer patients treated with immunotherapy to identify changes induced by treatment. Breast, colon, renal and prostate cancer patients were eligible. Treatment consisted of metronomic cyclophosphamide, low-dose interleukin-2 (IL-2) and a single radiation shot. A panel of 16 cytokines was assessed using automated ELISA before treatment (T0), after radiation (RT; T1), at cycle 2 (T2) and at disease progression (TPD). Receiving operating characteristic (ROC) analysis was used to identify cytokine cut-off related to overall survival (OS). Principal component analysis (PCA) was used to identify the immune profile correlating better with OS and progression-free survival. Twenty-three patients were enrolled. High IL-2, low IL-8 and CCL-2 correlated with OS. The PCA identified a cluster of patients, with high IL-2, IL-12 and IFN-γ levels at T0 having longer PFS and OS. In all cohorts, IL-2 and IL-5 increased from T0 to T2; a higher CCL-4 level compared to T2 and a higher IL-8 level compared to T0 were found at TPD. The progressive increase of the IL-10 level during treatment negatively correlated with OS. Our data suggested that baseline cytokine levels may predict patients’ outcome and that the treatment may affect their kinetic even in end-stage patients. Cytokine profiling of end-stage patients might offer a tool for medical decisions (EUDRACT: 2016-000578-39).