RNA Immune Signatures from Pan-Cancer Analysis Are Prognostic for High-Grade Serous Ovarian Cancer and Other Female Cancers

Polyclonal lymphoid expansion drives paraneoplastic autoimmunity in neuroblastoma

Neuroblastoma is a lethal childhood solid tumor of developing peripheral nerves. Two percent of children with neuroblastoma develop opsoclonus myoclonus ataxia syndrome (OMAS), a paraneoplastic disease characterized by cerebellar and brainstem-directed autoimmunity but typically with outstanding cancer-related outcomes. We compared tumor transcriptomes and tumor-infiltrating T and B cell repertoires from 38 OMAS subjects with neuroblastoma to 26 non-OMAS-associated neuroblastomas. We found greater B and T cell infiltration in OMAS-associated tumors compared to controls and showed that both were polyclonal expansions. Tertiary lymphoid structures (TLSs) were enriched in OMAS-associated tumors. We identified significant enrichment of the major histocompatibility complex (MHC) class II allele HLA-DOB∗01:01 in OMAS patients. OMAS severity scores were associated with the expression of several candidate autoimmune genes. We propose a model in which polyclonal auto-reactive B lymphocytes act as antigen-presenting cells and drive TLS formation, thereby supporting both sustained polyclonal T cell-mediated anti-tumor immunity and paraneoplastic OMAS neuropathology.

Identification of a tumor immune-inflammation signature predicting prognosis and immune status in breast cancer

Background

Breast cancer has become the malignancy with the highest mortality rate in female patients worldwide. The limited efficacy of immunotherapy as a breast cancer treatment has fueled the development of research on the tumor immune microenvironment.

Methods

In this study, data on breast cancer patients were collected from The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohorts. Differential gene expression analysis, univariate Cox regression analysis, and least absolute shrinkage and selection operator (LASSO) Cox regression analysis were performed to select overall survival (OS)-related, tumor tissue highly expressed, and immune- and inflammation-related genes. A tumor immune-inflammation signature (TIIS) consisting of 18 genes was finally screened out in the LASSO Cox regression model. Model performance was assessed by time-dependent receiver operating characteristic (ROC) curves. In addition, the CIBERSORT algorithm and abundant expression of immune checkpoints were utilized to clarify the correlation between the risk signature and immune landscape in breast cancer. Furthermore, the association of IL27 with the immune signature was analyzed in pan-cancer and the effect of IL27 on the migration of breast cancer cells was investigated since the regression coefficient of IL27 was the highest.

Results

A TIIS based on 18 genes was constructed via LASSO Cox regression analysis. In the TCGA-BRCA training cohort, 10-year AUC reached 0.89, and prediction performance of this signature was also validated in the METABRIC set. The high-risk group was significantly correlated with less infiltration of tumor-killing immune cells and the lower expression level of the immune checkpoint. Furthermore, we recommended some small-molecule drugs as novel targeted drugs for new breast cancer types. Finally, the relationship between IL27, a significant prognostic immune and inflammation cytokine, and immune status was analyzed in pan-cancer. Expression of IL27 was significantly correlated with immune regulatory gene expression and immune cell infiltration in pan-cancer. Furthermore, IL27 treatment improved breast cancer cell migration.

Conclusion

The TIIS represents a promising prognostic tool for estimating OS in patients with breast cancer and is correlated with immune status.

PLK3 amplification and tumor immune microenvironment of metastatic tumors are linked to adjuvant treatment outcomes in uterine serous cancer

Uterine serous carcinoma (USC), an aggressive variant of endometrial cancer representing approximately 10% of endometrial cancer diagnoses, accounts for ∼39% of endometrial cancer-related deaths. We examined the role of genomic alterations in advanced-stage USC associated with outcome using paired primary-metastatic tumors (n = 29) treated with adjuvant platinum and taxane chemotherapy. Comparative genomic analysis of paired primary-metastatic patient tumors included whole exome sequencing and targeted gene expression. Both PLK3 amplification and the tumor immune microenvironment (TIME) in metastatic tumors were linked to time-to-recurrence (TTR) risk without any such association observed with primary tumors. TP53 loss was significantly more frequent in metastatic tumors of platinum-resistant versus platinum-sensitive patients and was also associated with increased recurrence and mortality risk. Increased levels of chr1 breakpoints in USC metastatic versus primary tumors co-occur with PLK3 amplification. PLK3 and the TIME are potential targets for improving outcomes in USC adjuvant therapy.

Prognostic immunologic signatures in epithelial ovarian cancer

Epithelial Ovarian Cancer (EOC) is a deadly gynecologic malignancy in which patients frequently develop recurrent disease following initial platinum-taxane chemotherapy. Analogous to many other cancer subtypes, EOC clinical trials have centered upon immunotherapeutic approaches, most notably programmed cell death 1 (PD-1) inhibitors. While response rates to these immunotherapies in EOC patients have been low, evidence suggests that ovarian tumors are immunogenic and that immune-related genomic profiles can serve as prognostic markers. This review will discuss recent advances in the development of immune-based prognostic signatures in EOC that predict patient clinical outcomes, as well as emphasize specific research areas that need to be addressed to drive this field forward.

The pattern of alternative splicing in lung adenocarcinoma shows novel events correlated with tumorigenesis and immune microenvironment

Lung adenocarcinoma (LUAD) is the leading cause of cancer deaths worldwide due to the lack of early diagnostic markers and specific drugs. Previous studies have shown the association of LUAD growth with aberrant alternative splicing (AS). Herein, clinical data of 535 tumor tissues and 59 normal tissues were extracted from The Cancer Genome Atlas (TCGA) database. Each sample was analyzed using the ESTIMATE algorithm; a comparison between higher and lower score groups (stromal or immune) was made to determine the overall- and progression-free survival-related differentially expressed AS (DEAS) events. We then performed unsupervised clustering of these DEASs, followed by determining their relationship with survival rate, immune cells, and the tumor microenvironment (TME). Next, two prognostic signatures were developed using bioinformatics tools to explore the prognosis of cases with LUAD. Five OS- and six PFS-associated DEAS events were implemented to establish a prognostic risk score model. When compared to the high-risk group (HRG), the PFS and OS of the low-risk group (LRG) were found to be considerable. Additionally, a better prognosis was found considerably associated with the ESTIMATE score of the patients as well as immune cells infiltration. Our analysis of AS events in LUAD not only helps to clarify the tumorigenesis mechanism of AS but also provides ideas for revealing potential prognostic biomarkers and therapeutic targets.

Stromal infiltrating mast cells identify immunoevasive subtype high-grade serous ovarian cancer with poor prognosis and inferior immunotherapeutic response

Tumor infiltrating mast cells (TIMs), with pro- or anti-tumorigenic role in different types of malignancies, have been implicated in resistance to anti-PD1 therapy. Here, we aimed to identify the relevance of TIMs with the prognosis, immune contexture, and immunotherapy in high-grade serous ovarian cancer (HGSOC). Tissue microarrays containing 197 HGSOC patients were assessed by immunohistochemistry (IHC) for detecting the expression of mast cell tryptase and other immune markers. Kaplan-Meier curve, log-rank test, and Cox regression model were applied to perform survival analysis. Single-cell RNA-seq analysis and flow cytometric analysis were selected to characterize TIMs. Furthermore, short-term HGSOC organoids were employed to validate the effect of TIMs on anti-PD1 therapy. Abundance of stromal TIMs (sTIMs) predicted dismal prognosis and linked to immunoevasive subtype of HGSOC, characterized by increased infiltration of pro-tumor cells (Treg cells, M2-polarized macrophages, and neutrophils) and impaired anti-tumor immune functions. Intensive inter-cell interactions between TIMs and other immune cells were identified, suggesting potential cross-talks to foster an immunosuppressive microenvironment. Organoids derived from sTIMs-low patients were associated with increased response to anti-PD-1 treatment other than the presence of high sTIMs infiltration. A nomogram, constructed by combining FIGO stage, sTIMs, and PD-L1, with an area under the curve (AUC) for predicting 5-year overall survival of 0.771 was better than that of FIGO staging system of 0.619. sTIMs/PD-L1-based classifier has potential clinical application in predicting prognosis of patients with HGSOC. sTIMs-high tumors correlate with immunosuppressive tumor microenvironment (TME) and possess potential insensitivity to immunotherapy.

Identification of CXCL10-Relevant Tumor Microenvironment Characterization and Clinical Outcome in Ovarian Cancer

Background

Chemokines are implicated in tumor microenvironment (TME) cell infiltration. Development of ovarian cancer involves heterologous cells together with the adjacent microenvironment. Nonetheless, our understanding of the chemokine-related TME characteristics in ovarian cancer remains obscure.

Methods

In this large-scale multi-platform study of 10 microarray datasets consisting of 1,673 ovarian cancer patients, we comprehensively evaluated CXCL10 and CXCL9 expression risk classifications for predicting overall survival (OS) and TME immune characteristics. The cross-validation between a standard cohort (TCGA: The Cancer Genome Atlas) and three test cohorts (GEO: Gene-Expression Omnibus) was applied. We investigated differences in the biological functions and the underlying mechanisms between high- and low-risk classifications.

Results

We identified that evaluation of CXCL10 expression could predict the tumor development, immune cell infiltration, TME signature, genetic alteration, and patient prognosis in ovarian cancer. Low-risk classification was characterized by high CXCL10 expression and prolonged prognosis, which was positively associated with specific immune cell infiltration (i.e., T cells, DCs, aDC, and Th2 cells) and TME immune-relevant signatures. Meanwhile, the high-risk classification was defined by lower CXCL10/CXCL9 expression and relevant poor prognosis and immune infiltrations. The CXCL10-based low-risk classification was also linked to antitumor biological function of specific immune gene sets, such as IL2-STAT5 signaling. Additionally, a mutational pattern featured by enrichment of C > T transition was further identified to be associated with immune cell infiltration.

Conclusions

This work proposed a promising biomarker for evaluating TME immune characteristics and clinical outcomes in patients with ovarian cancer. Estimation of CXCL10 risk pattern sheds a novel insight on ovarian cancer TME immune characteristics and provides strategies for ovarian cancer immunotherapy.

Identification of Tumor Microenvironment-Related Alternative Splicing Events to Predict the Prognosis of Endometrial Cancer

Background

Endometrial cancer (EC) is one of the most common female malignant tumors. The immunity is believed to be associated with EC patients’ survival, and growing studies have shown that aberrant alternative splicing (AS) might contribute to the progression of cancers.

Methods

We downloaded the clinical information and mRNA expression profiles of 542 tumor tissues and 23 normal tissues from The Cancer Genome Atlas (TCGA) database. ESTIMATE algorithm was carried out on each EC sample, and the OS-related different expressed AS (DEAS) events were identified by comparing the high and low stromal/immune scores groups. Next, we constructed a risk score model to predict the prognosis of EC patients. Finally, we used unsupervised cluster analysis to compare the relationship between prognosis and tumor immune microenvironment.

Results

The prognostic risk score model was constructed based on 16 OS-related DEAS events finally identified, and then we found that compared with high-risk group the OS in the low-risk group was notably better. Furthermore, according to the results of unsupervised cluster analysis, we found that the better the prognosis, the higher the patient’s ESTIMATE score and the higher the infiltration of immune cells.

Conclusions

We used bioinformatics to construct a gene signature to predict the prognosis of patients with EC. The gene signature was combined with tumor microenvironment (TME) and AS events, which allowed a deeper understanding of the immune status of EC patients, and also provided new insights for clinical patients with EC.

Tumor Microenvironmental Competitive Endogenous RNA Network and Immune Cells Act as Robust Prognostic Predictor of Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is malignant hematologic tumors with frequent recurrence and cause high mortality. Its fate is determined by abnormal intracellular competitive endogenous RNA (ceRNA) network and extracellular tumor microenvironment (TME). This study aims to build a ceRNA network related to AML TME to explore new prognostic and therapeutic targets. The RNA expression data of AML were obtained from The Cancer Genome Atlas (TCGA) database. First, we used the ESTIMATE algorithm to calculate the immune cells and stromal cells infiltration scores in the TME and found that all scores were highly correlated with AML’s prognostic characteristics. Subsequently, differentially expressed mRNAs and lncRNAs between high and low score groups were identified to construct a TME-related ceRNA network. Further, the Cox-lasso survival model was employed to screen out the hub prognostic ceRNA network composed of two mRNAs (EPB41L3, COL2A1), three miRNAs (hsa-mir-26a-5p, hsa-mir-148b-3p, hsa-mir-148a-3p), and two lncRNAs (CYP1B1-AS1, C9orf106), and construct nomograms. Finally, we used CIBERSORT algorithm and Kaplan-Meier survival analysis to identify the prognostic TME immune cells and found that naive B cells, M2-type macrophages, and helper follicular T cells were related to prognosis, and the hub ceRNAs were highly correlated with immune cell infiltration. This study provided a new perspective to elucidate how TME regulates AML process and put forward the new therapy strategies combining targeting tumor cells with disintegrating TME.

Identification of an immune-related long noncoding RNA signature that predicts prognosis in breast cancer patients

Aim: The purpose of this study was to identify an immune-related long noncoding RNA (lncRNA) signature that predicts the prognosis of breast cancer. Materials & methods: The expression profiles of breast cancer were downloaded from The Cancer Genome Atlas. Cox regression analysis was used to identify an immune-related lncRNA signature. Results: The five immune-related lncRNAs could be used to construct a breast cancer survival prognosis model. The receiver operating characteristic curve evaluation found that the accuracy of the model for predicting the 1-, 3- and 5-year prognosis of breast cancer was 0.688, 0.708 and 0.686. Conclusion: This signature may have an important clinical significance for improving predictive results and guiding the treatment of breast cancer patients.

Identification of Immune-Related Prognostic Biomarkers Based on the Tumor Microenvironment in 20 Malignant Tumor Types With Poor Prognosis

Cancer, especially malignant tumors with poor prognosis, has become a major hazard to human life and health. The tumor microenvironment is gaining increasing attention from researchers, as it offers a new focus for tumor diagnosis, therapy, and prognosis. The numbers of immune and stromal cells, which are major components of the tumor microenvironment, could be determined from RNA-seq data with the Estimation of STromal and Immune cells in Malignant Tumors using Expression data (ESTIMATE) algorithm. To explore the effects of immune and stromal cells on tumor prognosis, we analyzed associations between overall survival and immune/stromal scores for 20 malignant tumor types based on The Cancer Genome Atlas (TCGA) data. For six of the 20 tumor types, we observed statistically significant associations. Furthermore, to better explain the predictive ability of these scores, differentially expressed genes (DEGs) were identified in groups of cases with high or low immune or stromal scores for each of these six malignant tumor types. In addition, a list of immune-related genes was screened to identify prognostic predictors for one or more tumor types. Thus, multi-database joint analysis can provide a new approach to the assessment of tumor prognosis and allow the identification of related genes that may be new biomarkers for tumor metastasis and prognosis.