Identification of immune microenvironment subtypes that predicted the prognosis of patients with ovarian cancer

Integration of local and systemic immunity in ovarian cancer: Implications for immunotherapy

Cancer is a disease that induces many local and systemic changes in immunity. The difficult nature of ovarian cancer stems from the lack of characteristic symptoms that contributes to a delayed diagnosis and treatment. Despite the enormous progress in immunotherapy, its efficacy remains limited. The heterogeneity of tumors, lack of diagnostic biomarkers, and complex immune landscape are the main challenges in the treatment of ovarian cancer. Integrative approaches that combine the tumor microenvironment - local immunity - together with periphery - systemic immunity - are urgently needed to improve the understanding of the disease and the efficacy of treatment. In fact, multiparametric analyses are poised to improve our understanding of ovarian tumor immunology. We outline an integrative approach including local and systemic immunity in ovarian cancer. Understanding the nature of both localized and systemic immune responses will be crucial to boosting the efficacy of immunotherapies in ovarian cancer patients.

miR‑382 inhibits breast cancer progression and metastasis by affecting the M2 polarization of tumor‑associated macrophages by targeting PGC‑1α

Macrophages are principal immune cells with a high plasticity in the human body that can differentiate under different conditions in the tumor microenvironment to adopt two polarized phenotypes with opposite functions. Therefore, converting macrophages from the immunosuppressive phenotype (M2) to the inflammatory phenotype (M1) is considered a promising therapeutic strategy for cancer. However, the molecular mechanisms underlying this conversion process have not yet been completely elucidated. In recent years, microRNAs (miRNAs or miRs) have been shown to play key roles in regulating macrophage polarization through their ability to modulate gene expression. In the present study, it was found that miR‑382 expression was significantly downregulated in tumor‑associated macrophages (TAMs) and M2‑polarized macrophages in breast cancer. In vitro, macrophage polarization toward the M2 phenotype and M2‑type cytokine release were inhibited by transfection with miR‑382‑overexpressing lentivirus. Similarly, the overexpression of miR‑382 inhibited the ability of TAMs to promote the malignant behaviors of breast cancer cells. In addition, peroxisome proliferator‑activated receptor γ coactivator‑1α (PGC‑1α) was identified as the downstream target of miR‑382 and it was found that PGC‑1α affected macrophage polarization by altering the metabolic status. The ectopic expression of PGC‑1α restored the phenotype and cytokine secretion of miR‑382‑overexpressing macrophages. Furthermore, PGC‑1α expression reversed the miR‑382‑induced changes in the metabolic state of TAMs and the effects of TAMs on breast cancer cells. Of note, the in vivo growth and metastasis of 4T1 cells were inhibited by miR‑382‑overexpressing TAMs. Taken together, the results of the present study suggest that miR‑382 may alter the metabolic status of macrophages by targeting PGC‑1α, thereby decreasing the proportion of TAMs with the M2 phenotype, and inhibiting the progression and metastasis of breast cancer.

The Immune Subtype Contributes to Distinct Overall Survival for Ovarian Cancer Patients With Platinum-Based Adjuvant Therapy

Objective

Nowadays, platinum-based therapy has been widely used as the first-line therapy of ovarian cancer. However, the effect of the tumor microenvironment on platinum-based therapy remains unclear. In this study, we aim to investigate the relationship between immune microenvironment subtypes and the prognosis of platinum-based therapy in ovarian cancer.

Methods

We integrated 565 ovarian cancer samples from two datasets and obtained the immune subtypes (ISs) by consistent clustering of 1190 immune-related gene expressions. The proportional hazards regression model was used to assess the relationship between ISs and the prognosis of platinum-based adjuvant therapy including progression-free survival (PFS) and overall survival (OS). The prognostic contribution of ISs was validated in three additional cohorts. Non-parametric tests were used to assess genomic characteristics, the proportion of immune cells, and immune-related signature differences among ISs.

Results

We identified and validated five ISs associated with different clinical outcomes of the platinum-based adjuvant therapy in ovarian cancer patients. These differences were only found in OS rather than PFS. An immune subtype had the worst OS. Those patients mainly derived from the mesenchymal subtype had the lowest tumor purity with a high leukocyte fraction as well as stromal fraction and had the highest TGF-β response signaling. By contrast, an immune subtype characterized by immunoreactive status with the highest CD8+T cell infiltration and elevated IFN-γ response signaling had the best prognosis. Other subtypes with more diverse immunologic features such as lowest macrophage regulation signaling showed intermediate prognoses. Notably, the contribution of ISs to OS was independent of the clinical response to platinum-based drugs.

Conclusion

Our analysis revealed the association between different immune characteristics and platinum-based adjuvant therapy, indicating the combination of ISs and chemotherapy could optimize the treatment strategy of OC patients.

Increased co-expression of MEST and BRCA1 is associated with worse prognosis and immune infiltration in ovarian cancer

OBJECTIVE

The crosstalk between tumor microenvironment (TME) and cancer cells plays a critical role in the occurrence and development of ovarian cancer. Imprinted gene MEST is a tumor-promoting factor that modulates several carcinogenic signaling pathways. This study aimed to investigate the expression pattern of MEST and its association with immune cell infiltration.

METHODS

The transcriptome data of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database was utilized, and the expression and immune characteristics of MEST were verified by immunohistochemistry of ovarian cancer specimens. Kaplan-Meier Plotter was used to assess the prognostic value in patients with ovarian cancer.

RESULTS

We found that high expression of MEST was associated with diminished immune cell infiltration and worse prognosis of ovarian cancer patients in independent cohorts. There was a positive correlation between MEST and BRCA1 expression. The MEST^highBRCA1^high ovarian cancer group was correlated with lower infiltration of CD4⁺ cells, CD57⁺ cells, CD68⁺ cells and MPO⁺ cells, had worse overall survival (OS) in TCGA (HR = 1.57, p = 0.0004) and GSE27651 (HR = 4.27, p = 0.0002) cohorts, and predicted poor progress free survival (PFS) in GSE9891 (HR = 1.76, p = 0.0098) and GSE15622 (HR = 4.80, p = 0.0121) cohorts. Moreover, the expression of PD-L1 predicted unfavorable OS (HR = 2.48, p = 0.0415) and PFS (HR = 2.36, p = 0.0215) in MEST^lowBRCA1^low ovarian cancer group in GSE9891 cohort.

CONCLUSION

These findings suggest that the co-expression of MEST and BRCA1 may be an ideal combination for predicting the prognosis and response to immunotherapy in patients with ovarian cancer.

Identification of immune microenvironment subtypes that predicted the prognosis of patients with ovarian cancer

Ovarian cancer (OC) is associated with high mortality rate. However, the correlation between immune microenvironment and prognosis of OC remains unclear. This study aimed to explore prognostic significance of OC tumour microenvironment. The OC data set was selected from the cancer genome atlas (TCGA), and 307 samples were collected. Hierarchical clustering was performed according to the expression of 756 genes. The immune and matrix scores of all immune subtypes were determined, and Kruskal-Wallis test was used to analyse the differences in the immune and matrix scores between OC samples with different immune subtypes. The model for predicting prognosis was constructed based on the expression of immune-related genes. TIDE platform was applied to predict the effect of immunotherapy on patients with OC of different immune subtypes. The 307 OC samples were classified into three immune subtypes A-C. Patients in subtype B had poorer prognosis and lower survival rate. The infiltration of helper T cells and macrophages in microenvironment indicated significant differences between immune subtypes. Enrichment analyses of immune cell molecular pathways showed that JAK-STAT3 pathway changed significantly in subtype B. Furthermore, predictive response to immunotherapy in subtype B was significantly higher than that in subtype A and C. Immune subtyping can be used as an independent predictor of the prognosis of OC patients, which may be related to the infiltration patterns of immune cells in tumour microenvironment. In addition, patients in immune subtype B have superior response to immunotherapy, suggesting that patients in subtype B are suitable for immunotherapy.