Combination immuno-oncology therapy with immune checkpoint blockers targeting PD-L1, PD-1 or CTLA4 and epigenetic drugs targeting MYC and immune evasion for precision medicine

Using Machine Learning Modeling to Explore New Immune-Related Prognostic Markers in Non-Small Cell Lung Cancer

OBJECTIVE

To find new immune-related prognostic markers for non-small cell lung cancer (NSCLC).

METHODS

We found GSE14814 is related to NSCLC in GEO database. The non-small cell lung cancer observation (NSCLC-OBS) group was evaluated for immunity and divided into high and low groups for differential gene screening according to the score of immune evaluation. A single factor COX regression analysis was performed to select the genes related to prognosis. A prognostic model was constructed by machine learning, and test whether the model has a test efficacy for prognosis. A chip-in-chip non-small cell lung cancer chemotherapy (NSCLC-ACT) sample was used as a validation dataset for the same validation and prognostic analysis of the model. The coexpression genes of hub genes were obtained by pearson analysis and gene enrichment, function enrichment and protein interaction analysis. The tumor samples of patients with different clinical stages were detected by immunohistochemistry and the expression difference of prognostic genes in tumor tissues of patients with different stages was compared.

RESULTS

By screening, we found that LYN, C3, COPG2IT1, HLA.DQA1, and TNFRSF17 is closely related to prognosis. After machine learning, we constructed the immune prognosis model from these 5 genes, and the model AUC values were greater than 0.9 at three time periods of 1, 3, and 5 years; the total survival period of the low-risk group was significantly better than that of the high-risk group. The results of prognosis analysis in ACT samples were consistent with OBS groups. The coexpression genes are mainly involved B cell receptor signaling pathway and are mainly enriched in apoptotic cell clearance. Prognostic key genes are highly correlated with PDCD1, PDCD1LG2, LAG3, and CTLA4 immune checkpoints. The immunohistochemical results showed that the expression of COPG2IT1 and HLA.DQA1 in stage III increased significantly and the expression of LYN, C3, and TNFRSF17 in stage III decreased significantly compared with that of stage I. The experimental results are consistent with the previous analysis.

CONCLUSION

LYN, C3, COPG2IT1, LA.DQA1, and NFRSF17 may be new immune markers to judge the prognosis of patients with non-small cell lung cancer.

Immune checkpoint-based therapy in myeloid malignancies: a promise yet to be fulfilled

INTRODUCTION

Immune system evasion is essential for tumor cell survival and is mediated by the immunosuppressive tumor microenvironment and the activation of inhibitory immune checkpoints. While immune checkpoint-based therapy yielded impressive results in several advanced solid malignancies such as melanoma and non-small cell lung cancer, its role in acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS) is still evolving. Areas covered: Here we review the immunology in the tumor microenvironment in the bone marrow and discuss the current preclinical and clinical data for immune checkpoint-based therapy in myeloid neoplasms. Expert commentary: Clinical trials of immune checkpoint inhibitors (ICI) in AML and MDS are still in early stages and reported results so far have been modest especially for monotherapy use in the refractory settings. However, there are preliminary data for synergistic effects for combination of multiple ICI with hypomethylating agents and conventional chemotherapy. ICI might also be effective in eradicating minimal residual disease and to prevent relapse following induction chemotherapy or hematopoietic stem cell transplant. Additional trials to provide insight into the efficacy and safety profile of immune checkpoint-based therapy, its optimal timing and potential combination with other types of therapy as well as identification of predictive biomarkers are needed.