Reprogramming myeloid cell-mediated immunosuppression in the lung cancer microenvironment

In the lung tumour microenvironment (TME), pro-tumoral, immunosuppressive M2-like macrophages (M2-like Ms) present an obstacle for effective immunotherapy treatment. Pre-clinical research has predominantly used in vivo mouse models to represent the in situ TME. However, such models do not faithfully replicate the human immune system therefore, providing inadequate measures of immunotherapeutic response. Human tumour-derived explants maintain the original 3D tumour architecture and combination of multiple cell types. Therefore, we established an ex vivo tumour explant model of non-small cell lung cancer (NSCLC), incorporating tumourconditioned Ms (TCMs), to determine the role of Ms in mediating response to immune-checkpoint inhibitors (ICIs). Explant outputs were compared to those achieved using an in vitro 3D heterotypic (tumour- and stroma-containing) NSCLC spheroid model in preliminary studies. We hypothesised that successful cellular responses to anti-PDL-1 therapy within the lung TME is M-mediated, and that reprogramming of the immunosuppressive M state, ex vivo, has the potential to bolster ICI efficacy. Using a novel multi-marker analysis approach, we showed that both heterotypic spheroids and NSCLC explants promote M1- (CD206loCD64hi) to M2-like (CD206hiCD64lo) M polarisation. IFN and LPS treatment reversed explant-mediated M2-like skewing, demonstrating the high phenotypic plasticity of Ms in the TME. Transcriptomic analysis of 770 immuno-oncology genes, followed by protein validation of novel targets, identified CD54 and 2 microglobulin (B2M) as novel M1-specific M markers. Further analysis revealed B2M/CD206 as a superior marker combination for defining M1, M2, and intermediate M populations, in vitro. Importantly, NSCLC explants, but not heterotypic spheroids, significantly suppressed T cell responses, revealing patient-derived explants as a more relevant model, than existing 3D human models, for studying tumour-immune interactions within lung cancer. The production of anti-inflammatory factors e.g. IL-10 and arginase from T cell cultures was significantly elevated in the presence of explant-conditioned Ms, but not explants alone, demonstrating that TCMs significantly contribute to the development of an immunosuppressive lung TME. The PDL-1 inhibitor, Atezolizumab significantly improved T cell function and reduced explant-mediated immunosuppression in 1/6 patients, particularly in the presence of M2-like Ms and TCMs. This suggests that response or resistance to anti-PDL-1 therapies may be partially M-dependent. Interestingly, the immune-modifying agents BLZ945 and Zometa, previously shown to reprogram the M2-like M phenotype into an iv M1-like state, significantly released explant-conditioned M-mediated suppression ex vivo. Moreover, a positive trend in improved CD4+ T cell responses was observed from explant-conditioned M co-cultures following Atezolizumab/Zometa combination treatment, compared to treatment with Atezolizumab alone. Overall, the results of this study implicate the NSCLC explant model as a potential tool for predicting patient response to anti-PDL-1 immunotherapy, and exploring combination therapies to improve ICI response, ex vivo. Ongoing research aims to determine the optimal Zometa administration and dosing regimen in an attempt to improve the treatment efficacy of ICIs. Clinical use of the NSCLC explant model may speed up decision making for personalised treatment combinations, in cancers amenable to immune-checkpoint inhibition

The HBP1 tumor suppressor is a negative epigenetic regulator of MYCN driven neuroblastoma through interaction with the PRC2 complex

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An immune-related model based on INHBA, JAG2 and CCL19 to predict the prognoses of colon cancer patients

Abstract Background Colorectal cancer (CRC) is the leading cause of cancer deaths and most common malignant tumors worldwide. Immune-related genes (IRGs) can predict prognoses of patients and the effects of immunotherapy. A series of colon cancer (CCa) samples from The Cancer Genome Atlas (TCGA) were analyzed to provide a new perspective into this field. Methods Differential IRGs and IRGs with significant clinical outcomes (sIRGs) were calculated by the limma algorithm and univariate COX regression analysis. The potential molecular mechanisms of IRGs were detected by PPI, KEGG and GO analysis. Immune-related risk score model (IRRSM) was established based on multivariate COX regression analysis. Based on the median risk score of IRRSM, the high-risk group and low-risk group were distinguished. The expression levels of IHNBA and JAG2 and relationships between IHNBA and clinical features were verified by RT-qPCR. Results 6 differential sIRGs of patients with CCa were selected by univariate COX regression analysis. Based on the sIRGs (INHBA, JAG2 and CCL19), the IRRSM was established to predict survival probability of CCa patients and to explore the potential correlations with clinical features. Furthermore, IRRSM reflected the infiltration status of 22 types of immune cells. The expression levels of IHNBA and JAG2 were higher in CCa tissues than that in adjacent normal tissues. The expression levels of IHNBA and JAG2 were increased in advanced T stages. Conclusion Our results illustrated that some sIRGs showed the latent value of predicting the prognoses of CCa patients and the clinical features. This study could provide a new insight for immune research and treatment strategies in CCa patients

An immune-related model based on INHBA, JAG2 and CCL19 to predict the prognoses of colon cancer patients

Abstract Background Colorectal cancer (CRC) is the leading cause of cancer deaths and most common malignant tumors worldwide. Immune-related genes (IRGs) can predict prognoses of patients and the effects of immunotherapy. A series of colon cancer (CCa) samples from The Cancer Genome Atlas (TCGA) were analyzed to provide a new perspective into this field. Methods Differential IRGs and IRGs with significant clinical outcomes (sIRGs) were calculated by the limma algorithm and univariate COX regression analysis. The potential molecular mechanisms of IRGs were detected by PPI, KEGG and GO analysis. Immune-related risk score model (IRRSM) was established based on multivariate COX regression analysis. Based on the median risk score of IRRSM, the high-risk group and low-risk group were distinguished. The expression levels of IHNBA and JAG2 and relationships between IHNBA and clinical features were verified by RT-qPCR. Results 6 differential sIRGs of patients with CCa were selected by univariate COX regression analysis. Based on the sIRGs (INHBA, JAG2 and CCL19), the IRRSM was established to predict survival probability of CCa patients and to explore the potential correlations with clinical features. Furthermore, IRRSM reflected the infiltration status of 22 types of immune cells. The expression levels of IHNBA and JAG2 were higher in CCa tissues than that in adjacent normal tissues. The expression levels of IHNBA and JAG2 were increased in advanced T stages. Conclusion Our results illustrated that some sIRGs showed the latent value of predicting the prognoses of CCa patients and the clinical features. This study could provide a new insight for immune research and treatment strategies in CCa patients. </jats:sec