Construction of a new tumor immunity-related signature to assess and classify the prognostic risk of ovarian cancer

Leveraging various extracellular matrix levels to assess prognosis and sensitivity to immunotherapy in patients with ovarian cancer

Background

Ovarian cancer (OC) is the fifth leading cause of cancer-related deaths among women. Late diagnosis and heterogeneous treatment result in a poor prognosis for patients with OC. Therefore, we aimed to develop new biomarkers to predict accurate prognoses and provide references for individualized treatment strategies.

Methods

We constructed a co-expression network applying the "WGCNA" package and identified the extracellular matrix-associated gene modules. We figured out the best model and generated the extracellular matrix score (ECMS). The ECMS' ability to predict accurate OC patients' prognoses and responses to immunotherapy was evaluated.

Results

The ECMS was an independent prognostic factor in the training [hazard ratio (HR) = 3.132 (2.068-4.744), p< 0.001] and testing sets [HR = 5.514 (2.084-14.586), p< 0.001]. The receiver operating characteristic curve (ROC) analysis showed that the AUC values for 1, 3, and 5 years were 0.528, 0.594, and 0.67 for the training set, respectively, and 0.571, 0.635, and 0.684 for the testing set, respectively. It was found that the high ECMS group had shorter overall survival than the low ECMS group [HR = 2 (1.53-2.61), p< 0.001 in the training set; HR = 1.62 (1.06-2.47), p = 0.021 in the testing set; HR = 1.39 (1.05-1.86), p = 0.022 in the training set]. The ROC values of the ECMS model for predicting immune response were 0.566 (training set) and 0.572 (testing set). The response rate to immunotherapy was higher in patients with low ECMS.

Conclusion

We created an ECMS model to predict the prognosis and immunotherapeutic benefits in OC patients and provided references for individualized treatment of OC patients.

Development and validation of a novel anoikis-related gene signature for predicting prognosis in ovarian cancer

Anoikis plays a critical role in variable cancer types. However, studies that focus on the prognostic values of anoikis-related genes (ANRGs) in OV are scarce. Cohorts with transcriptome data and corresponding clinicopathologic data of OV patients were collected and consolidated from public databases. Multiple bioinformatics approaches were used to screen key genes from 446 anoikis-related genes, including Cox regression analysis, random survival forest analysis, and Kaplan-Meier analysis of best combinations. A five-gene signature was constructed in the discovery cohort (TCGA) and validated in four validation cohorts (GEO). Risk score of the signature stratified patients into high-risk (HRisk) and low-risk (LRisk) subgroups. Patients in the HRisk group were associated with worse OS than those in the LRisk group in both the TCGA cohort (p<0.0001, HR=2.718, 95%CI:1.872-3.947) and the four GEO cohorts (p<0.05). Multivariate Cox regression analyses confirmed that the risk score served as an independent prognostic factor in both cohorts. The signature's predictive capacity was further demonstrated by the nomogram analysis. Pathway enrichment analysis revealed that immunosuppressive and malignant progression-related pathways were enriched in the HRisk group, including TGF-β, WNT and ECM pathways. The LRisk group was characterized by immune-active signaling pathways (interferon-gamma, T cell activation, etc.) and higher proportions of anti-tumor immune cells (NK, M1, etc.) while HRisk patients were associated with higher stromal scores and less TCR richness. In conclusion, the signature reveals a close relationship between the anoikis and prognosis and may provide a potential therapeutic target for OV patients.

Adaptive transcriptomic and immune infiltrate responses in the tumor immune microenvironment following neoadjuvant chemotherapy in high grade serous ovarian cancer reveal novel prognostic associations and activation of pro-tumorigenic pathways

The high rate of ovarian cancer recurrence and chemoresistance necessitates further research into how chemotherapy affects the tumor immune microenvironment (TIME). While studies have shown that immune infiltrate increases following neoadjuvant (NACT) chemotherapy, there lacks a comprehensive understanding of chemotherapy-induced effects on immunotranscriptomics and cancer-related pathways and their relationship with immune infiltrate and patient responses. In this study, we performed NanoString nCounter^® PanCancer IO360 analysis of 31 high grade serous ovarian cancer (HGSOC) patients with matched pre-treatment biopsy and post-NACT tumor. We observed increases in pro-tumorigenic and immunoregulatory pathways and immune infiltrate following NACT, with striking increases in a cohort of genes centered on the transcription factors ATF3 and EGR1. Using quantitative PCR, we analyzed several of the top upregulated genes in HGSOC cell lines, noting that two of them, ATF3 and AREG, were consistently upregulated with chemotherapy exposure and significantly increased in platinum resistant cells compared to their sensitive counterparts. Furthermore, we observed that pre-NACT immune infiltrate and pathway scores were not strikingly related to platinum free interval (PFI), but post-NACT immune infiltrate, pathway scores, and gene expression were. Finally, we found that higher levels of a cohort of proliferative and DNA damage-related genes was related to shorter PFI. This study underscores the complex alterations in the ovarian TIME following chemotherapy exposure and begins to untangle how immunologic factors are involved in mediating chemotherapy response, which will allow for the future development of novel immunologic therapies to combat chemoresistance.

Construction of a New Tumor Immunity-Related Signature to Assess and Classify the Prognostic Risk of Colorectal Cancer

Purpose

Although immunotherapy and checkpoint inhibitors contribute to the treatment of colorectal cancer (CRC), few patients can benefit from these treatments. Therefore, our goal was to develop a marker based on immune-related genes to predict the prognosis of patients with CRC to guide treatment strategies.

Methods

Gene expression data from colorectal cancer patients in the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas were analyzed systematically. We used Cox regression to identify immune-related genes with potential prognostic value. The expression of immune genes, infiltration level of immune cells, and several immune-related molecules were further compared between the high-risk and low-risk groups. Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes pathway analyses were used for functional analysis.

Results

Five GEO datasets were integrated into a merged GEO dataset, which showed obvious survival in StromalScore and ESTIMATEScore. WGCNA showed that 749 genes of the pink module are related to immunity, 95 of which are related to prognosis, correlating with cytokine–cytokine receptor interaction and natural killer cell-mediated cytotoxicity. Among these genes, an 11-gene signature was developed through stability selection and LASSO Cox regression. Univariate and multifactorial Cox regression analyses demonstrated that gene signature was an independent prognostic factor for predicting survival in patients with colorectal cancer. Samples from the low-risk group may be more sensitive to immunotherapy. In addition, the nomogram risk prediction model effectively predicted the prognosis of CRC patients by appropriately stratifying the risk scores.

Conclusion

In conclusion, we developed a novel immune-related gene signature that may be useful in predicting cancer progression and prognosis, thus contributing to the individualized management of colorectal cancer patients.

Immune Modeling Analysis Reveals Immunologic Signatures Associated With Improved Outcomes in High Grade Serous Ovarian Cancer

Epithelial ovarian cancer (EOC) is the most lethal gynecologic malignancy worldwide, as patients are typically diagnosed at a late stage and eventually develop chemoresistant disease following front-line platinum-taxane based therapy. Only modest results have been achieved with PD-1 based immunotherapy in ovarian cancer patients, despite the fact that immunological responses are observed in EOC patients. Therefore, the goal of this present study was to identify novel immune response genes and cell subsets significantly associated with improved high grade serous ovarian cancer (HGSOC) patient prognosis. A transcriptomic-based immune modeling analysis was employed to determine levels of 8 immune cell subsets, 10 immune escape genes, and 22 co-inhibitory/co-stimulatory molecules in 26 HGSOC tumors. Multidimensional immune profiling analysis revealed CTLA-4, LAG-3, and Tregs as predictive for improved progression-free survival (PFS). Furthermore, the co-stimulatory receptor ICOS was also found to be significantly increased in patients with a longer PFS and positively correlated with levels of CTLA-4, PD-1, and infiltration of immune cell subsets. Both ICOS and LAG-3 were found to be significantly associated with improved overall survival in The Cancer Genome Atlas (TCGA) ovarian cancer cohort. Finally, PVRL2 was identified as the most highly expressed transcript in our analysis, with immunohistochemistry results confirming its overexpression in HGSOC samples compared to normal/benign. Results were corroborated by parallel analyses of TCGA data. Overall, this multidimensional immune modeling analysis uncovers important prognostic immune factors that improve our understanding of the unique immune microenvironment of ovarian cancer.