Immunogenomic Analyses of Advanced Serous Ovarian Cancer Reveal Immune Score is a Strong Prognostic Factor and an Indicator of Chemosensitivity

Development and validation of a hypoxia-immune-based microenvironment gene signature for risk stratification in gastric cancer

Background

Increasing evidences have found that the clinical importance of the interaction between hypoxia and immune status in gastric cancer microenvironment. However, reliable prognostic signatures based on combination of hypoxia and immune status have not been well-established. This study aimed to develop a hypoxia-immune-based gene signature for risk stratification in gastric cancer.

Methods

Hypoxia and immune status was estimated with transcriptomic profiles for a discovery cohort from GEO database using the t-SNE and ESTIMATE algorithms, respectively. The Cox regression model with the LASSO method was applied to identify prognostic genes and to develop a hypoxia-immune-based gene signature. The TCGA cohort and two independent cohorts from GEO database were used for external validation.

Results

Low hypoxia status (p < 0.001) and high immune status (p = 0.005) were identified as favorable factors for patients’ overall survival. By using the LASSO model, four genes, including CXCR6, PPP1R14A and TAGLN, were identified to construct a gene signature for risk stratification. In the discovery cohort (n = 357), patients with low risk yielded better outcomes than those with high risk regarding overall survival across and within TNM stage subgroups. Multivariate analysis identified the hypoxia-immune-based gene signature as an independent prognostic factor (p < 0.001). A nomogram integrating the gene signature and known risk factors yielded better performance and net benefits in calibration and decision curve analyses. Similar results were validated in the TCGA (n = 321) and two independent GEO (n = 300 and n = 136, respectively) cohorts.

Conclusions

The hypoxia-immune-based gene signature represents a promising tool for risk stratification tool in gastric cancer. It might serve as a prognostic classifier for clinical decision-making regarding individualized prognostication and treatment, and follow-up scheduling.

Gene networks and expression quantitative trait loci associated with adjuvant chemotherapy response in high-grade serous ovarian cancer

BACKGROUND

A major impediment in the treatment of ovarian cancer is the relapse of chemotherapy-resistant tumors, which occurs in approximately 25% of patients. A better understanding of the biological mechanisms underlying chemotherapy resistance will improve treatment efficacy through genetic testing and novel therapies.

METHODS

Using data from high-grade serous ovarian carcinoma (HGSOC) patients in the Cancer Genome Atlas (TCGA), we classified those who remained progression-free for 12 months following platinum-taxane combination chemotherapy as "chemo-sensitive" (N = 160) and those who had recurrence within 6 months as "chemo-resistant" (N = 110). Univariate and multivariate analysis of expression microarray data were used to identify differentially expressed genes and co-expression gene networks associated with chemotherapy response. Moreover, we integrated genomics data to determine expression quantitative trait loci (eQTL).

RESULTS

Differential expression of the Valosin-containing protein (VCP) gene and five co-expression gene networks were significantly associated with chemotherapy response in HGSOC. VCP and the most significant co-expression network module contribute to protein processing in the endoplasmic reticulum, which has been implicated in chemotherapy response. Both univariate and multivariate analysis findings were successfully replicated in an independent ovarian cancer cohort. Furthermore, we identified 192 cis-eQTLs associated with the expression of network genes and 4 cis-eQTLs associated with BRCA2 expression.

CONCLUSION

This study implicates both known and novel genes as well as biological processes underlying response to platinum-taxane-based chemotherapy among HGSOC patients.

A gene signature for immune subtyping of desert, excluded, and inflamed ovarian tumors

PROBLEM

The current tumor immunology paradigm emphasizes the role of the immune tumor microenvironment and distinguishes several histologically and transcriptionally different immune tumor subtypes. However, the experimental validation of such classification is so far limited to selected cancer types. Here, we aimed to explore the existence of inflamed, excluded, and desert immune subtypes in ovarian cancer, as well as investigate their association with the disease outcome.

METHOD OF STUDY

We used the publicly available ovarian cancer dataset from The Cancer Genome Atlas for developing subtype assignment algorithm, which was next verified in a cohort of 32 real-world patients of a known tumor subtype.

RESULTS

Using clinical and gene expression data of 489 ovarian cancer patients in the publicly available dataset, we identified three transcriptionally distinct clusters, representing inflamed, excluded, and desert subtypes. We developed a two-step subtyping algorithm with COL5A2 serving as a marker for separating excluded tumors, and CD2, TAP1, and ICOS for distinguishing between inflamed and desert tumors. The accuracy of gene expression-based subtyping algorithm in a real-world cohort was 75%. Additionally, we confirmed that patients bearing inflamed tumors are more likely to survive longer.

CONCLUSION

Our results highlight the presence of transcriptionally and histologically distinct immune subtypes among ovarian tumors and emphasize the potential benefit of immune subtyping as a clinical tool for treatment tailoring.

Alterations in TP53 Are a Potential Biomarker of Bladder Cancer Patients Who Benefit From Immune Checkpoint Inhibition

In recent years, immune checkpoint inhibitors (ICIs) targeting CTLA-4 or PD1/PDL1 have achieved remarkable success in the treatment of bladder cancer (BLCA), but only a few patients have shown durable clinical benefits. The prognostic role of a mutant form of the tumor suppressor gene TP53 (TP53-MT) in predicting the efficacy of ICIs is highly controversial; therefore, in this study, we obtained data for 210 patients from an immunotherapy cohort, 412 patients from The Cancer Genome Atlas (TCGA)-BLCA cohort and 18 BLCA cell lines from Genomics of Drug Sensitivity in Cancer (GDSC), and we performed integrated bioinformatic analysis to explore the relationships between TP53-MT and clinical benefits derived from ICI treatment and the underlying mechanisms. We conclude that TP53-MT is a potential indicator of a relatively good response to ICIs and associated with prolonged overall survival (OS) (log-rank test, hazard ratio (HR) = 0.65 [95% confidence interval (CI), 0.44-0.99], p = 0.041). Through integrated analysis with several platforms, we found that TP53-MT patients were more likely to benefit from ICIs than wild-type P53 (TP53-WT) patients, which may be the result of 2 major mechanisms. First, the patients with TP53-MT showed stronger tumor antigenicity and tumor antigen presentation, as indicated by a higher tumor mutational load, a higher neoantigen load and increased expression of MHC; second, the antitumor immunity preexisting in tumors was stronger in samples with TP53-MT than in those with TP53-WT, including enrichment of interferon-gamma, positive regulation of TNF secretion pathways and increased expression of some immunostimulatory molecules, such as CXCL9 and CXCL10. This study provided some clues for identifying patients who would potentially benefit from ICIs at the somatic genomic level, developing new indications for targeted second-generation sequencing and promoting the development of precision medicine.

Clinical Relevance and Immunosuppressive Pattern of Circulating and Infiltrating Subsets of Myeloid-Derived Suppressor Cells (MDSCs) in Epithelial Ovarian Cancer

Myeloid-derived suppressor cells (MDSCs) expansion is a hallmark of cancer. Three major MDSC subsets defined as monocytic (M)-MDSCs, polymorphonuclear (PMN)-MDSCs and early stage (e)MDSCs can be revealed in human diseases. However, the clinical relevance and immunosupressive pattern of these cells in epithelial ovarian cancer (EOC) are unknown. Therefore, we performed a comprehensive analysis of each MDSC subset and immunosupressive factors in the peripheral blood (PB), peritoneal fluid (PF), and the tumor tissue (TT) samples from EOC and integrated this data with the patients' clinicopathological characteristic. MDSCs were analyzed using multicolor flow cytometry. Immunosuppressive factors analysis was performed with ELISA and qRT-PCR. The level of M-MDSCs in the PB/PF/TT of EOC was significantly higher than in healthy donors (HD); frequency of PMN-MDSCs was significantly greater in the TT than in the PB/PF and HD; while the level of eMDSCs was greater in the PB compared with the PF and HD. Elevated abundance of tumor-infiltrating M-MDSCs was associated with advanced stage and high grade of EOC. An analysis of immunosuppressive pattern showed significantly increased blood-circulating ARG/IDO/IL-10-expressing M- and PMN-MDSCs in the EOC patients compared with HD and differences in the accumulation of these subsets in the three tumor immune microenvironments (TIME). This accumulation was positively correlated with levels of TGF-β and ARG1 in the plasma and PF. Low level of blood-circulating and tumor-infiltrating M-MDSCs, but neither PMN-MDSCs nor eMDSCs was strongly associated with prolonged survival in ovarian cancer patients. Our results highlight M-MDSCs as the subset with potential the highest clinical significance.

A robust panel based on tumour microenvironment genes for prognostic prediction and tailoring therapies in stage I-III colon cancer

Background

Tumour microenvironment (TME) is critical for the regulation of cancer development as well as therapy. The objective of the current study was the development of a robust prognostic model based on TME-relevant genes.

Methods

Five public microarray datasets providing clinical information were obtained. The least absolute shrinkage and selection operator regression method was used to reduce the dimensionality of robust prognostic genes identified via the bootstrap method.

Findings

We established a prognostic panel, designated as tumour microenvironment risk score (TMRS), consisting of 100 genes. With specific risk score formulae, the TMRS panel possesses a strong ability to predict relapse-free survival and overall survival through both univariate and multivariate analyses. Compared with the TNM stage, the TMRS panel showed much higher predictive accuracy. Further analysis revealed that patients with higher TMRS scores exhibited no therapeutic benefits from adjuvant chemotherapy, probably due to the activation of stromal relevant pathways and infiltration of stromal cells. Besides colon cancer, the TMRS panel was also revealed to be a reliable tool for prognostic prediction and chemotherapeutic decision-making in gastric cancer. Its value in predicting immunotherapy outcomes was also confirmed in two other cohorts consisting of metastatic urothelial carcinoma patients and melanoma patients.

Interpretation

Our TMRS panel may be an effective tool for survival prediction and treatment guidance in patients with stage I–III colon cancer.

Fund

This work was supported by the National Natural Science Foundation of China (No. 81772580) and Guangzhou Planed Project of Science and Technology (No. 201803010070).