Gene expression profile for predicting survival in advanced-stage serous ovarian cancer across two independent datasets

Bevacizumab, olaparib, and durvalumab in patients with relapsed ovarian cancer: a phase II clinical trial from the GINECO group

Most patients with advanced ovarian cancer (AOC) ultimately relapse after platinum-based chemotherapy. Combining bevacizumab, olaparib, and durvalumab likely drives synergistic activity. This open-label phase 2 study (NCT04015739) aimed to assess activity and safety of this triple combination in female patients with relapsed high-grade AOC following prior platinum-based therapy. Patients were treated with olaparib (300 mg orally, twice daily), the bevacizumab biosimilar FKB238 (15 mg/kg intravenously, once-every-3-weeks), and durvalumab (1.12 g intravenously, once-every-3-weeks) in nine French centers. The primary endpoint was the non-progression rate at 3 months for platinum-resistant relapse or 6 months for platinum-sensitive relapse per RECIST 1.1 and irRECIST. Secondary endpoints were CA-125 decline with CA-125 ELIMination rate constant K (KELIM-B) per CA-125 longitudinal kinetics over 100 days, progression free survival and overall survival, tumor response, and safety. Non-progression rates were 69.8% (90%CI 55.9%-80.0%) at 3 months for platinum-resistant relapse patients (N = 41), meeting the prespecified endpoint, and 43.8% (90%CI 29.0%-57.4%) at 6 months for platinum-sensitive relapse (N = 33), not meeting the prespecified endpoint. Median progression-free survival was 4.1 months (95%CI 3.5-5.9) and 4.9 months (95%CI 2.9-7.0) respectively. Favorable KELIM-B was associated with better survival. No toxic deaths or major safety signals were observed. Here we show that further investigation of this triple combination may be considered in AOC patients with platinum-resistant relapse.

Targeting NEAT1 Affects the Sensitivity to PARPi in Serous Ovarian Cancer by Regulating the Homologous Recombination Repair Pathway

Objective: Patients initially sensitive to PARPi (PARP inhibitor) regain resistance because of homologous recombination (HR) restoration, although PARPi has a synthetic lethality effect on serous ovarian cancer cells with BRCA1/2 mutations. This study aimed to investigate the role of NEAT1 in HR function and whether targeting NEAT1 in serous ovarian cancer cells could increase PARPi sensitivity. Methods: Ovarian cancer patients' clinical information and the expression of NEAT1 were collected from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). Ovarian cancer (OC) cells HeyA8 and SKOV3 were silenced by transfecting NEAT1 ASO. QRT-PCR confirmed the mRNA expression of RAD51, FOXM1, NEAT1_1 and NEAT1_2. We assessed the expression of RAD51, FOXM1, and γ-H2AX by Western blotting and Immunofluorescence. Comet Assays were used to detect DNA double-strand damage levels. In OC cells transfected with NEAT1 ASO or co-transfected overexpression RAD51/empty vector and si-NEAT1/si-ctrl, the sensitivity to Olaparib was determined using CCK8 assay. The Kaplan-Meier survival curves assessed the prognostic and predictive roles of NEAT1 in OC. Results: NEAT1 was an independent prognostic marker of ovarian cancer. NEAT1 knockdown reduced the expression of NEAT1_1, NEAT1_2, RAD51, and FOXM1 and increased the expression of γ-H2AX. In addition, Olaparib increased the expression of RAD51, representing HR repair efficiency, which was inhibited by NEAT1 knockdown. Moreover, the knockdown of NEAT1 increased the DNA damage caused by Olaparib, demonstrated by increased nuclear γ-H2AX foci, DNA in the tail, and expression of γ-H2AX. NEAT1 knockdown sensitized ovarian cancer cells to Olaparib by targeting RAD51-HR. NEAT1 expression could predict response to chemotherapy for ovarian cancer. Conclusions: NEAT1 knockdown inhibited HR capacity and increased DNA damage caused by Olaparib in serous ovarian cancer cells, making them more sensitive to Olaparib and providing a crucial therapeutic advantage of increasing sensitivity to Olaparib.

Machine learning-derived identification of prognostic signature for improving prognosis and drug response in patients with ovarian cancer

Clinical assessments relying on pathology classification demonstrate limited effectiveness in predicting clinical outcomes and providing optimal treatment for patients with ovarian cancer (OV). Consequently, there is an urgent requirement for an ideal biomarker to facilitate precision medicine. To address this issue, we selected 15 multicentre cohorts, comprising 12 OV cohorts and 3 immunotherapy cohorts. Initially, we identified a set of robust prognostic risk genes using data from the 12 OV cohorts. Subsequently, we employed a consensus cluster analysis to identify distinct clusters based on the expression profiles of the risk genes. Finally, a machine learning-derived prognostic signature (MLDPS) was developed based on differentially expressed genes and univariate Cox regression genes between the clusters by using 10 machine-learning algorithms (101 combinations). Patients with high MLDPS had unfavourable survival rates and have good prediction performance in all cohorts and in-house cohorts. The MLDPS exhibited robust and dramatically superior capability than 21 published signatures. Of note, low MLDIS have a positive prognostic impact on patients treated with anti-PD-1 immunotherapy by driving changes in the level of infiltration of immune cells. Additionally, patients suffering from OV with low MLDIS were more sensitive to immunotherapy. Meanwhile, patients with low MLDIS might benefit from chemotherapy, and 19 compounds that may be potential agents for patients with low MLDIS were identified. MLDIS presents an appealing instrument for the identification of patients at high/low risk. This could enhance the precision treatment, ultimately guiding the clinical management of OV.

A prognostic prediction model for ovarian cancer using a cross-modal view correlation discovery network

Ovarian cancer is a tumor with different clinicopathological and molecular features, and the vast majority of patients have local or extensive spread at the time of diagnosis. Early diagnosis and prognostic prediction of patients can contribute to the understanding of the underlying pathogenesis of ovarian cancer and the improvement of therapeutic outcomes. The occurrence of ovarian cancer is influenced by multiple complex mechanisms, including the genome, transcriptome and proteome. Different types of omics analysis help predict the survival rate of ovarian cancer patients. Multi-omics data of ovarian cancer exhibit high-dimensional heterogeneity, and existing methods for integrating multi-omics data have not taken into account the variability and inter-correlation between different omics data. In this paper, we propose a deep learning model, MDCADON, which utilizes multi-omics data and cross-modal view correlation discovery network. We introduce random forest into LASSO regression for feature selection on mRNA expression, DNA methylation, miRNA expression and copy number variation (CNV), aiming to select important features highly correlated with ovarian cancer prognosis. A multi-modal deep neural network is used to comprehensively learn feature representations of each omics data and clinical data, and cross-modal view correlation discovery network is employed to construct the multi-omics discovery tensor, exploring the inter-relationships between different omics data. The experimental results demonstrate that MDCADON is superior to the existing methods in predicting ovarian cancer prognosis, which enables survival analysis for patients and facilitates the determination of follow-up treatment plans. Finally, we perform Gene Ontology (GO) term analysis and biological pathway analysis on the genes identified by MDCADON, revealing the underlying mechanisms of ovarian cancer and providing certain support for guiding ovarian cancer treatments.

Identification of lncRNAs Deregulated in Epithelial Ovarian Cancer Based on a Gene Expression Profiling Meta-Analysis

Epithelial ovarian cancer (EOC) is one of the deadliest gynecological cancers worldwide, mainly because of its initially asymptomatic nature and consequently late diagnosis. Long non-coding RNAs (lncRNA) are non-coding transcripts of more than 200 nucleotides, whose deregulation is involved in pathologies such as EOC, and are therefore envisaged as future biomarkers. We present a meta-analysis of available gene expression profiling (microarray and RNA sequencing) studies from EOC patients to identify lncRNA genes with diagnostic and prognostic value. In this meta-analysis, we include 46 independent cohorts, along with available expression profiling data from EOC cell lines. Differential expression analyses were conducted to identify those lncRNAs that are deregulated in (i) EOC versus healthy ovary tissue, (ii) unfavorable versus more favorable prognosis, (iii) metastatic versus primary tumors, (iv) chemoresistant versus chemosensitive EOC, and (v) correlation to specific histological subtypes of EOC. From the results of this meta-analysis, we established a panel of lncRNAs that are highly correlated with EOC. The panel includes several lncRNAs that are already known and even functionally characterized in EOC, but also lncRNAs that have not been previously correlated with this cancer, and which are discussed in relation to their putative role in EOC and their potential use as clinically relevant tools.

SpiderLearner: An ensemble approach to Gaussian graphical model estimation

Gaussian graphical models (GGMs) are a popular form of network model in which nodes represent features in multivariate normal data and edges reflect conditional dependencies between these features. GGM estimation is an active area of research. Currently available tools for GGM estimation require investigators to make several choices regarding algorithms, scoring criteria, and tuning parameters. An estimated GGM may be highly sensitive to these choices, and the accuracy of each method can vary based on structural characteristics of the network such as topology, degree distribution, and density. Because these characteristics are a priori unknown, it is not straightforward to establish universal guidelines for choosing a GGM estimation method. We address this problem by introducing SpiderLearner, an ensemble method that constructs a consensus network from multiple estimated GGMs. Given a set of candidate methods, SpiderLearner estimates the optimal convex combination of results from each method using a likelihood-based loss function.K $$ K $$ -fold cross-validation is applied in this process, reducing the risk of overfitting. In simulations, SpiderLearner performs better than or comparably to the best candidate methods according to a variety of metrics, including relative Frobenius norm and out-of-sample likelihood. We apply SpiderLearner to publicly available ovarian cancer gene expression data including 2013 participants from 13 diverse studies, demonstrating our tool's potential to identify biomarkers of complex disease. SpiderLearner is implemented as flexible, extensible, open-source code in the R package ensembleGGM at https://github.com/katehoffshutta/ensembleGGM.

Sensitivity Analysis for Survival Prognostic Prediction with Gene Selection: A Copula Method for Dependent Censoring

Prognostic analysis for patient survival often employs gene expressions obtained from high-throughput screening for tumor tissues from patients. When dealing with survival data, a dependent censoring phenomenon arises, and thus the traditional Cox model may not correctly identify the effect of each gene. A copula-based gene selection model can effectively adjust for dependent censoring, yielding a multi-gene predictor for survival prognosis. However, methods to assess the impact of various types of dependent censoring on the multi-gene predictor have not been developed. In this article, we propose a sensitivity analysis method using the copula-graphic estimator under dependent censoring, and implement relevant methods in the R package “compound.Cox”. The purpose of the proposed method is to investigate the sensitivity of the multi-gene predictor to a variety of dependent censoring mechanisms. In order to make the proposed sensitivity analysis practical, we develop a web application. We apply the proposed method and the web application to a lung cancer dataset. We provide a template file so that developers can modify the template to establish their own web applications.

The EMT-activator ZEB1 is unrelated to platinum drug resistance in ovarian cancer but is predictive of survival

The IGROVCDDP cisplatin-resistant ovarian cancer cell line is an unusual model, as it is also cross-resistant to paclitaxel. IGROVCDDP, therefore, models the resistance phenotype of serous ovarian cancer patients who have failed frontline platinum/taxane chemotherapy. IGROVCDDP has also undergone epithelial-mesenchymal transition (EMT). We aim to determine if alterations in EMT-related genes are related to or independent from the drug-resistance phenotypes. EMT gene and protein markers, invasion, motility and morphology were investigated in IGROVCDDP and its parent drug-sensitive cell line IGROV-1. ZEB1 was investigated by qPCR, Western blotting and siRNA knockdown. ZEB1 was also investigated in publicly available ovarian cancer gene-expression datasets. IGROVCDDP cells have decreased protein levels of epithelial marker E-cadherin (6.18-fold, p = 1.58e-04) and higher levels of mesenchymal markers vimentin (2.47-fold, p = 4.43e-03), N-cadherin (4.35-fold, p = 4.76e-03) and ZEB1 (3.43-fold, p = 0.04). IGROVCDDP have a spindle-like morphology consistent with EMT. Knockdown of ZEB1 in IGROVCDDP does not lead to cisplatin sensitivity but shows a reversal of EMT-gene signalling and an increase in cell circularity. High ZEB1 gene expression (HR = 1.31, n = 2051, p = 1.31e-05) is a marker of poor overall survival in high-grade serous ovarian-cancer patients. In contrast, ZEB1 is not predictive of overall survival in high-grade serous ovarian-cancer patients known to be treated with platinum chemotherapy. The increased expression of ZEB1 in IGROVCDDP appears to be independent of the drug-resistance phenotypes. ZEB1 has the potential to be used as biomarker of overall prognosis in ovarian-cancer patients but not of platinum/taxane chemoresistance.

Integrated bioinformatics analysis of validated and circulating miRNAs in ovarian cancer

Recent studies have focused on the early detection of ovarian cancer (OC) using tumor materials by liquid biopsy. The mechanisms of microRNAs (miRNAs) to impact OC and signaling pathways are still unknown. This study aims to reliably perform functional analysis of previously validated circulating miRNAs' target genes by using pathfindR. Also, overall survival and pathological stage analyses were evaluated with miRNAs' target genes which are common in the The Cancer Genome Atlas and GTEx datasets. Our previous studies have validated three downregulated miRNAs (hsa-miR-885-5p, hsa-miR-1909-5p, and hsalet7d-3p) having a diagnostic value in OC patients' sera, with high-throughput techniques. The predicted target genes of these miRNAs were retrieved from the miRDB database (v6.0). Active-subnetwork-oriented Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was conducted by pathfindR using the target genes. Enrichment of KEGG pathways assessed by the analysis of pathfindR indicated that 24 pathways were related to the target genes. Ubiquitin-mediated proteolysis, spliceosome and Notch signaling pathway were the top three pathways with the lowest p-values (p < 0.001). Ninety-three common genes were found to be differentially expressed (p < 0.05) in the datasets. No significant genes were found to be significant in the analysis of overall survival analyses, but 24 genes were found to be significant with pathological stages analysis (p < 0.05). The findings of our study provide in-silico evidence that validated circulating miRNAs' target genes and enriched pathways are related to OC and have potential roles in theranostics applications. Further experimental investigations are required to validate our results which will ultimately provide a new perspective for translational applications in OC management.

Dynamic Risk Prediction via a Joint Frailty-Copula Model and IPD Meta-Analysis: Building Web Applications

Clinical risk prediction formulas for cancer patients can be improved by dynamically updating the formulas by intermediate events, such as tumor progression. The increased accessibility of individual patient data (IPD) from multiple studies has motivated the development of dynamic prediction formulas accounting for between-study heterogeneity. A joint frailty-copula model for overall survival and time to tumor progression has the potential to develop a dynamic prediction formula of death from heterogenous studies. However, the process of developing, validating, and publishing the prediction formula is complex, which has not been sufficiently described in the literature. In this article, we provide a tutorial in order to build a web-based application for dynamic risk prediction for cancer patients on the basis of the R packages joint.Cox and Shiny. We demonstrate the proposed methods using a dataset of breast cancer patients from multiple clinical studies. Following this tutorial, we demonstrate how one can publish web applications available online, which can be manipulated by any user through a smartphone or personal computer. After learning this tutorial, developers acquire the ability to build an online web application using their own datasets.

Copy number and expression of CEP89, a protein required for ciliogenesis, are increased and predict poor prognosis in patients with ovarian cancer

CEP89 (centrosomal protein 89) is required for ciliogenesis and mitochondrial metabolism, but its role in cancer has yet to be clarified. We report that CEP89 is overexpressed in ovarian cancer (OC) compared to normal ovaries. Likewise, its expression is higher in malignant ovarian tumors than in borderline ovarian tumors with low malignant potential. More than a quarter of patients with OC have copy number gains in the CEP89 gene, and patients with high expression have more than a year shorter overall survival compared to those with low expression. Moreover, we found that CEP89 can be considered as a prognostic marker for poor overall survival in patients with OC, after adjusting for tumor stage and residual tumor. Nine out of the top 10 protein interactors of CEP89 have the highest percentage of total copy number variation (CNV) events in OC among all other cancer types. Furthermore, CEP89 messenger RNA (mRNA) levels are higher in OC patients with disease recurrence compared to those with no recurrence. We also analyzed CEP89 levels in OC cell lines in terms of CNV, mRNA, and protein levels; and observed that the FUOV-1 cell line has the highest levels among cell lines that originated from primary sites. Our study suggests that CEP89 may be a valuable prognostic predictor for the overall survival of patients with OC, and it could also be a novel therapeutic target in this malignancy.

A Combined Feature Screening Approach of Random Forest and Filter-based Methods for Ultra-high Dimensional Data

Background:

Various feature (variable) screening approaches have been proposed in the past decade to mitigate the impact of ultra-high dimensionality in classification and regression problems, including filter based methods such as sure indepen¬dence screening, and wrapper based methods such random forest. However, the former type of methods rely heavily on strong modelling assumptions while the latter ones requires an adequate sample size to make the data speak for themselves. These require¬ments can seldom be met in biochemical studies in cases where we have only access to ultra-high dimensional data with a complex structure and a small number of observations.

Objective:

In this research, we want to investigate the possibility of combing both filter based screening methods and random forest based screening methods in the regression context.

Method:

We have combined four state-of-art filter approaches, namely, sure independence screening (SIS) , robust rank corre¬lation based screening (RRCS), high dimensional ordinary least squares projection (HOLP) and a model free sure independence screening procedure based on the distance correlation (DCSIS) from the statistical community with a random forest based Boruta screening method from the machine learning community for regression problems.

Result:

Among all combined methods, RF-DCSIS performs better than the other methods in terms of screening accuracy and prediction capability on the simulated scenarios and real benchmark datasets.

Conclusion:

By empirical study from both extensive simulation and real data, we have shown that both filter based screening and random forest based screening have their pros and cons while a combination of both may lead to a better feature screening result and prediction capability

Keywords:

feature screening, filter-based method, ultra-high dimensional data, variable selection, random forest,RF-DCSIS

Identification of key gene signatures for the overall survival of ovarian cancer

BACKGROUND

The five-year overall survival (OS) of advanced-stage ovarian cancer remains nearly 25-35%, although several treatment strategies have evolved to get better outcomes. A considerable amount of heterogeneity and complexity has been seen in ovarian cancer. This study aimed to establish gene signatures that can be used in better prognosis through risk prediction outcome for the survival of ovarian cancer patients. Different studies' heterogeneity into a single platform is presented to explore the penetrating genes for poor or better survival. The integrative analysis of multiple data sets was done to determine the genes that influence poor or better survival. A total of 6 independent data sets was considered. The Cox Proportional Hazard model was used to obtain significant genes that had an impact on ovarian cancer patients. The gene signatures were prepared by splitting the over-expressed and under-expressed genes parallelly by the variable selection technique. The data visualisation techniques were prepared to predict the overall survival, and it could support the therapeutic regime.

RESULTS

We preferred to select 20 genes in each data set as upregulated and downregulated. Irrespective of the selection of multiple genes, not even a single gene was found common among data sets for the survival of ovarian cancer patients. However, the same analytical approach adopted. The chord plot was presented to make a comprehensive understanding of the outcome.

CONCLUSIONS

This study helps us to understand the results obtained from different studies. It shows the impact of the heterogeneity from one study to another. It shows the requirement of integrated studies to make a holistic view of the gene signature for ovarian cancer survival.

Two novel prognostic models for ovarian cancer respectively based on ferroptosis and necroptosis

Background

Platinum-resistant cases account for 25% of ovarian cancer patients. Our aim was to construct two novel prognostic models based on gene expression data respectively from ferroptosis and necroptosis, for predicting the prognosis of advanced ovarian cancer patients with platinum treatment.

Methods

According to the different overall survivals, we screened differentially expressed genes (DEGs) from 85 ferroptosis-related and 159 necroptosis-related gene expression data in the GSE32062 cohort, to establish two ovarian cancer prognostic models based on calculating risk factors of DEGs, and log-rank test was used for statistical significance test of survival data. Subsequently, we validated the two models in the GSE26712 cohort and the GSE17260 cohort. In addition, we took gene enrichment and microenvironment analyses respectively using limma package and GSVA software to compare the differences between high- and low-risk ovarian cancer patients.

Results

We constructed two ovarian cancer prognostic models: a ferroptosis-related model based on eight-gene expression signature and a necroptosis-related model based on ten-gene expression signature. The two models performed well in the GSE26712 cohort, but the performance of necroptosis-related model was not well in the GSE17260 cohort. Gene enrichment and microenvironment analyses indicated that the main differences between high- and low- risk ovarian cancer patients occurred in the immune-related indexes, including the specific immune cells abundance and overall immune indexes.

Conclusion

In this study, ovarian cancer prognostic models based on ferroptosis and necroptosis have been preliminarily validated in predicting prognosis of advanced patients treated with platinum drugs. And the risk score calculated by these two models reflected immune microenvironment. Future work is needed to find out other gene signatures and clinical characteristics to affect the accuracy and applicability of the two ovarian cancer prognostic models.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-09166-9.

OSov: An Interactive Web Server to Evaluate Prognostic Biomarkers for Ovarian Cancer

Simple Summary

The OSov web server incorporates gene expression profiles with clinical risk factors to estimate the ovarian cancers patients’ survival, and provides a tool for multiple analysis, such as forest-plot, uni/multi-variate survival analysis, Kaplan-Meier plot and nomogram construction.

Abstract

Ovarian cancer is one of the most aggressive and highly lethal gynecological cancers. The purpose of our study is to build a free prognostic web server to help researchers discover potential prognostic biomarkers by integrating gene expression profiling data and clinical follow-up information of ovarian cancer. We construct a prognostic web server OSov (Online consensus Survival analysis for Ovarian cancer) based on RNA expression profiles. OSov is a user-friendly web server which could present a Kaplan–Meier plot, forest plot, nomogram and survival summary table of queried genes in each individual cohort to evaluate the prognostic potency of each queried gene. To assess the performance of OSov web server, 163 previously published prognostic biomarkers of ovarian cancer were tested and 72% of them had their prognostic values confirmed in OSov. It is a free and valuable prognostic web server to screen and assess survival-associated biomarkers for ovarian cancer.

Myeloid antigen-presenting cell niches sustain antitumor T cells and license PD-1 blockade via CD28 costimulation

The mechanisms regulating exhaustion of tumor-infiltrating lymphocytes (TIL) and responsiveness to PD-1 blockade remain partly unknown. In human ovarian cancer, we show that tumor-specific CD8+ TIL accumulate in tumor islets, where they engage antigen and upregulate PD-1, which restrains their functions. Intraepithelial PD-1+CD8+ TIL can be, however, polyfunctional. PD-1+ TIL indeed exhibit a continuum of exhaustion states, with variable levels of CD28 costimulation, which is provided by antigen-presenting cells (APC) in intraepithelial tumor myeloid niches. CD28 costimulation is associated with improved effector fitness of exhausted CD8+ TIL and is required for their activation upon PD-1 blockade, which also requires tumor myeloid APC. Exhausted TIL lacking proper CD28 costimulation in situ fail to respond to PD-1 blockade, and their response may be rescued by local CTLA-4 blockade and tumor APC stimulation via CD40L.

Construction of a Macrophage Infiltration Regulatory Network and Related Prognostic Model of High-Grade Serous Ovarian Cancer

Background

High-grade serous ovarian cancer (HGSOC) carries the highest mortality in the gynecological cancers; however, therapeutic outcomes have not significantly improved in recent decades. Macrophages play an essential role in the occurrence and development of ovarian cancer, so the mechanisms of macrophage infiltration should be elucidated.

Method

We downloaded transcriptome data of ovarian cancers from the Gene Expression Omnibus and The Cancer Genome Atlas. After rigorous screening, 1566 HGSOC were used for data analysis. CIBERSORT was used to estimate the level of macrophage infiltration and WGCNA was used to identify macrophage-related modules. We constructed a macrophage-related prognostic model using machine learning LASSO algorithm and verified it using multiple HGSOC cohorts.

Results

In the GPL570-OV cohort, high infiltration level of M1 macrophages was associated with a good outcome, while high infiltration level of M2 macrophages was associated with poor outcomes. We used WGCNA to select genes correlated with macrophage infiltration. These genes were used to construct protein-protein interaction maps of macrophage infiltration. IFL44L, RSAD2, IFIT3, MX1, IFIH1, IFI44, and ISG15 were the hub genes in the network. We then constructed a macrophage-related prognostic model composed of CD38, ACE2, BATF2, HLA-DOB, and WARS. The model had the ability to predict the overall survival rate of HGSOC patients in GPL570-OV, GPL6480-OV, TCGA-OV, GSE50088, and GSE26712. In exploring the immune microenvironment, we found that CD4 memory T cells and activated mast cells showed that the degree of infiltration was higher in the high-risk group, while M1 macrophages were the opposite, and HLA molecules were overexpressed in the high-risk group.

Conclusion

We constructed a macrophage infiltration-related protein interaction network that provides a basis for studying macrophages in HGSOC. Our macrophage-related prognostic model is robust and widely applicable. It predicts overall survival in HGSOC patients and may improve HGSOC treatment.

Cytotoxic lymphocytes-related gene ITK from a systematic CRISPR screen could predict prognosis of ovarian cancer patients with distant metastasis

Background

Ovarian cancer, a highly metastatic malignancy, has benefited tremendously from advances in modern human genomics. However, the genomic variations related to the metastasis remains unclear.

Methods

We filtered various significant genes (n = 6722) associated with metastasis within a large-scale functional genomic CRISPR/Cas9 knock-out library including 122,756 single guide RNAs, and identified ITK (IL2 Inducible T Cell Kinase) as a potential cancer suppressor gene for ovarian cancer metastasis. Downstream bioinformatic analysis was performed for ITK using public databases.

Results

We found that patients in low-ITK group had poor prognosis and more distant metastasis than those in high-ITK group in TCGA and GEO databases. We also demonstrated that ITK combined with the clinical factors could accurately predict prognosis through multiple Cox regression analysis and ROC analysis. Moreover, alterations correlated with distant metastasis emereged with significantly increased expression in SAMRCD1 in low-ITK group, but CD244 and SOCS1 in high-ITK group. Integrated analysis revealed dysregulated molecular processes including predominantly oncogenic signaling pathways in low-ITK group but immune related pathways in high-ITK group, which suggested ITK might inhibit distant metastasis in ovarian cancer. Furtherly, deconvolution of the cellular composition of all samples validated the close correlation between ITK and immune related function especially for cytotoxic lymphocytes.

Conclusions

Together, these data provide insights into the potential role of ITK, with implications for the future development of tansformative ovarian cancer therapeutics.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-03119-3.

Four Prognosis-Associated lncRNAs Serve as Biomarkers in Ovarian Cancer

Long non-coding RNAs (lncRNAs) play crucial roles in ovarian cancer (OC) development. However, prognosis-associated lncRNAs (PALs) for OC have not been completely elucidated. Our study aimed to identify the PAL signature of OC. A total of 663 differentially expressed lncRNAs were identified in the databases. According to the weighted gene coexpression analysis, the highly correlated genes were clustered into seven modules related to the clinical phenotype of OC. A total of 25 lncRNAs that were significantly related to overall survival were screened based on univariate Cox regression analysis. The prognostic risk model constructed contained seven PALs based on the parameter λ_min, which could stratify OC patients into two risk groups. The results showed that the risk groups had different overall survival rates in both The Cancer Genome Atlas (TCGA) and two verified Gene Expression Omnibus (GEO) databases. Univariate and multivariate Cox regression analyses confirmed that the risk model was an independent risk factor for OC. Gene enrichment analysis revealed that the identified genes were involved in some pathways of malignancy. The competitive endogenous RNA (ceRNA) network included five PALs, of which four were selected for cell function assays. The four PALs were downregulated in 33 collected OC tissues and 3 OC cell lines relative to the control. They were shown to regulate the proliferative, migratory, and invasive potential of OC cells via Cell Counting Kit-8 (CCK-8) and transwell assays. Our study fills the gaps of the four PALs in OC, which are worthy of further study.

A Liquid-Liquid Phase Separation-Related Gene Signature as Prognostic Biomarker for Epithelial Ovarian Cancer

Objective

The aim of the present study was to construct and test a liquid-liquid phase separation (LLPS)-related gene signature as a prognostic tool for epithelial ovarian cancer (EOC).

Materials and Methods

The data set GSE26712 was used to screen the differentially expressed LLPS-related genes. Functional enrichment analysis was performed to reveal the potential biological functions. GSE17260 and GSE32062 were combined as the discovery to construct an LLPS-related gene signature through a three-step analysis (univariate Cox, least absolute shrinkage and selection operator, and multivariate Cox analyses). The EOC data set from The Cancer Genome Atlas as the test set was used to test the LLPS-related gene signature.

Results

The differentially expressed LLPS-related genes involved in several cancer-related pathways, such as MAPK signaling pathway, cell cycle, and DNA replication. Eleven genes were selected to construct the LLPS-related gene signature risk index as prognostic biomarker for EOC. The risk index could successfully divide patients with EOC into high- and low-risk groups. The patients in high-risk group had significantly shorter overall survival than those with in low-risk group. The LLPS-related gene signature was validated in the test set and may be an independent prognostic factor compared to routine clinical features.

Conclusion

We constructed and validated an LLPS-related gene signature as a prognosis tool in EOC through integrated analysis of multiple data sets.

Loss of lncRNA SNHG8 promotes epithelial-mesenchymal transition by destabilizing CDH1 mRNA

Long non-coding RNAs (lncRNAs) are widely involved in a variety of biological processes, including epithelial-mesenchymal transition (EMT). In the current study, we found that lncRNA small nucleolar RNA host gene 8 (SNHG8) was tightly correlated with EMT-associated gene signatures, and was down-regulated by Zinc finger E-box-binding homeobox 1 (ZEB1) during EMT progress. Functionally, knockdown of SNHG8 induced EMT in epithelial cells, through destabilizing the CDH1 mRNA dependent on a 17-nucleotide sequence shared by SNHG8 and CDH1. In addition, analysis with public database showed that SNHG8 tended to be down-regulated in different cancer types and the lower expression of SNHG8 predicted poorer prognosis. Taken together, our study reports a ZEB1-repressed lncRNA SNHG8 which is important for stabilizing CDH1 mRNA, thereby maintaining the epithelial status of epithelial cells.

CD96 Correlates With Immune Infiltration and Impacts Patient Prognosis: A Pan-Cancer Analysis

Background

Immunotherapy has significantly improved patient outcomes, but encountered obstacles recently. CD96, a novel immune checkpoint expressed on T cells and natural killer (NK) cells, is essential for regulating immune functions. However, how CD96 correlating with immune infiltration and patient prognosis in pan-cancer remains unclear.

Methods

HPA, TCGA, GEO, GTEx, Oncomine, TIMER2.0, PrognoScan, Linkedomics, Metascape, and GEPIA2 databases were used to analyze CD96 in cancers. Visualization of data was mostly achieved by R language, version 4.0.2.

Results

In general, CD96 was differentially expressed between most cancer and adjacent normal tissues. CD96 significantly impacted the prognosis of diverse cancers. Especially, high CD96 expression was associated with poorer overall survival (OS) and disease-specific survival (DSS) in the TCGA lower grade glioma (LGG) cohort (OS, HR = 2.18, 95% CI = 1.79–2.66, P < 0.001). The opposite association was significantly observed in skin cutaneous melanoma (SKCM) cohort (OS, HR = 0.96, 95% CI = 0.94–0.98, P < 0.001). Notably, SKCM samples demonstrated the highest CD96 mutation frequency among all cancer types. Furthermore, in most cancers, CD96 expression level was significantly correlated with expression levels of recognized immune checkpoints and abundance of multiple immune infiltrates including CD8+ T cells, dendric cells (DCs), macrophages, monocytes, NK cells, neutrophils, regulatory T cells (Tregs), and follicular helper T cells (Tfh). CD96 was identified as a risk factor, protective factor, and irrelevant variable in LGG, SKCM and adrenocortical carcinoma (ACC), respectively. CD96 related genes were involved in negative regulation of leukocyte in LGG, however, involved in multiple positive immune processes in SKCM. Furthermore, CD96 was significantly associated with particular immune marker subsets. Importantly, it strongly correlated with markers of type 1 helper T cell (Th1) in SKCM, but not in LGG or ACC either.

Conclusions

CD96 participates in diverse immune responses, governs immune cell infiltration, and impacts malignant properties of various cancer types, thus standing as a potential biomarker for determining patient prognosis and immune infiltration in multiple cancers, especially in glioma and melanoma.

Development and Validation of an Immune-Related Gene-Pair Model of High-Grade Serous Ovarian Cancer After Platinum-Based Chemotherapy

Background

High-grade serous ovarian cancer (HGSOC) is a common cause of death from gynecological cancer, with an overall survival rate that has not significantly improved in decades. Reliable bio-markers are needed to identify high-risk HGSOC to assist in the selection and development of treatment options.

Method

The study included ten HGSOC cohorts, which were merged into four separate cohorts including a total of 1,526 samples. We used the relative expression of immune genes to construct the gene-pair matrix, and the least absolute shrinkage and selection operator regression was performed to build the prognosis model using the training set. The prognosis of the model was verified in the training set (363 cases) and three validation sets (of 251, 354, and 558 cases). Finally, the differences in immune cell infiltration and gene enrichment pathways between high and low score groups were identified.

Results

A prognosis model of HGSOC overall survival rate was constructed in the training set, and included data for 35 immune gene-related gene pairs and the regression coefficients. The risk stratification of HGSOC patients was successfully performed using the training set, with a p-value of Kaplan-Meier of < 0.001. A score from this model is an independent prognostic factor of HGSOC, and prognosis was evaluated in different clinical subgroups. This model was also successful for the other three validation sets, and the results of Kaplan-Meier analysis were statistically significant (p < 0.05). The model can also predict patient progression-free survival with HGSOC to reflect tumor growth status. There was a lower infiltration level of M1 macrophages in the high-risk group compared to that in the low-risk group (p < 0.001). Finally, the immune-related pathways were enriched in the low-risk group.

Conclusion

The prognostic model based on immune-related gene pairs developed is a potential prognostic marker for high-grade serous ovarian cancer treated with platinum. The model has robust prognostic ability and wide applicability. More prospective studies will be needed to assess the practical application of this model for precision therapy.

Tumor-intrinsic and -extrinsic (immune) gene signatures robustly predict overall survival and treatment response in high grade serous ovarian cancer patients

In the present study, we developed a transcriptomic signature capable of predicting prognosis and response to primary therapy in high grade serous ovarian cancer (HGSOC). Proportional hazard analysis was performed on individual genes in the TCGA RNAseq data set containing 229 HGSOC patients. Ridge regression analysis was performed to select genes and develop multigenic models. Survival analysis identified 120 genes whose expression levels were associated with overall survival (OS) (HR = 1.49-2.46 or HR = 0.48-0.63). Ridge regression modeling selected 38 of the 120 genes for development of the final Ridge regression models. The consensus model based on plurality voting by 68 individual Ridge regression models classified 102 (45%) as low, 23 (10%) as moderate and 104 patients (45%) as high risk. The median OS was 31 months (HR = 7.63, 95% CI = 4.85-12.0, P < 1.0-10) and 77 months (HR = ref) in the high and low risk groups, respectively. The gene signature had two components: intrinsic (proliferation, metastasis, autophagy) and extrinsic (immune evasion). Moderate/high risk patients had more partial and non-responses to primary therapy than low risk patients (odds ratio = 4.54, P < 0.001). We concluded that the overall survival and response to primary therapy in ovarian cancer is best assessed using a combination of gene signatures. A combination of genes which combines both tumor intrinsic and extrinsic functions has the best prediction. Validation studies are warranted in the future.

M2-like macrophages dictate clinically relevant immunosuppression in metastatic ovarian cancer

Background

The immunological microenvironment of primary high-grade serous carcinomas (HGSCs) has a major impact on disease outcome. Conversely, little is known on the microenvironment of metastatic HGSCs and its potential influence on patient survival. Here, we explore the clinical relevance of the immunological configuration of HGSC metastases.

Methods

RNA sequencing was employed on 24 paired primary tumor microenvironment (P-TME) and metastatic tumor microenvironment (M-TME) chemotherapy-naive HGSC samples. Immunohistochemistry was used to evaluate infiltration by CD8⁺ T cells, CD20⁺ B cells, DC-LAMP⁺ (lysosomal-associated membrane protein 3) dendritic cells (DCs), NKp46⁺ (natural killer) cells and CD68⁺CD163⁺ M2-like tumor-associated macrophages (TAMs), abundance of PD-1⁺ (programmed cell death 1), LAG-3⁺ (lymphocyte-activating gene 3) cells, and PD-L1 (programmed death ligand 1) expression in 80 samples. Flow cytometry was used for functional assessments on freshly resected HGSC samples.

Results

1468 genes were differentially expressed in the P-TME versus M-TME of HGSCs, the latter displaying signatures of extracellular matrix remodeling and immune infiltration. M-TME infiltration by immune effector cells had little impact on patient survival. Accordingly, M-TME-infiltrating T cells were functionally impaired, but not upon checkpoint activation. Conversely, cytokine signaling in favor of M2-like TAMs activity appeared to underlie inhibited immunity in the M-TME and poor disease outcome.

Conclusions

Immunosuppressive M2-like TAM infiltrating metastatic sites limit clinically relevant immune responses against HGSCs.

Bioinformatics analysis of gene expression profile of serous ovarian carcinomas to screen key genes and pathways

Background

Serous ovarian carcinomas (SCA) are the most common and most aggressive ovarian carcinoma subtype which etiology remains unclear. To investigate the prospective role of mRNAs in the tumorigenesis and progression of SCA, the aberrantly expressed mRNAs were calculated based on the NCBI-GEO RNA-seq data.

Results

Of 21,755 genes with 89 SCA and SBOT cases from 3 independent laboratories, 59 mRNAs were identified as differentially expressed genes (DEGs) (|log₂Fold Change| > 1.585, also |FoldChange| > 3 and adjusted P < 0.05) by DESeq R. There were 26 up-regulated DEGs and 33 down-regulated DEGs screened. The hierarchical clustering analysis, functional analysis and pathway enrichment analysis were performed on all DEGs and found that Polo-like kinase (PLK) signaling events are important. PPI network constructed with different filtration conditions screened out 4 common hub genes (KIF11, CDC20, PBK and TOP2A). Mutual exclusivity or co-occurrence analysis of 4 hub genes identified a tendency towards co-occurrence between KIF11 and CDC20 or TOP2A in SCA (p < 0.05). To analyze further the potential role of KIF11 in SCA, the co-expression profiles of KIF11 in SCA were identified and we found that CDC20 co-expressed with KIF11 also is DEG that we screened out before. To verify our previous results in this paper, we assessed the expression levels of 4 hub DEGs (all up-regulated) and 4 down-regulated DEGs in Oncomine database. And the results were consistent with previous conclusions obtained from GEO series. The survival curves showed that KIF11, CDC20 and TOP2A expression are significantly related to prognosis of SCA patients.

Conclusions

From all the above results, we speculate that KIF11, CDC20 and TOP2A played an important role in SCA.

Impact of PD-L1 and T-cell inflamed gene expression profile on survival in advanced ovarian cancer

OBJECTIVE

Programmed death ligand 1 (PD-L1) expression affects tumor evasion of immune surveillance. The prognostic value and relationship of PD-L1 expression to T-cell-inflamed immune signatures in ovarian cancer are unclear. The purpose of this study is to evaluate the impact of PD-L1 on overall survival and its correlation with an immune-mediated gene expression profile in patients with advanced ovarian cancer.

METHODS

PD-L1 expression in tumor and immune cells was assessed by immunohistochemistry, and PD-L1-positive expression was defined as a combined positive score ≥1; a T-cell-inflamed gene expression profile containing interferon γ response genes was evaluated using extracted RNA from surgical samples. Associations between PD-L1 expression, gene expression profile status, and overall survival were analyzed using the Kaplan-Meier method, log-rank test, and multivariate Cox proportional hazards regression models.

RESULTS

A total of 376 patients with advanced epithelial ovarian, primary peritoneal, or fallopian tube cancer treated by cytoreductive surgery and platinum-based therapy were included. PD-L1-positive expression was observed in 50.5% of patients and associated with more advanced stage (p=0.047), more aggressive histologic subtype (p=0.001), and platinum sensitivity defined by increasing treatment-free interval from first platinum-based chemotherapy to next systemic treatment (p=0.027). PD-L1-positive expression was associated with longer overall survival in multivariate analyses (adjusted HR 0.72, 95% CI 0.56 to 0.93). In subgroup analyses, this association was most pronounced in patients with partially platinum-sensitive disease (treatment-free interval ≥6 to <12 months). T-cell-inflamed gene expression profile status correlated with PD-L1 expression (Spearman, ρ=0.712) but was not an independent predictor of overall survival.

CONCLUSION

PD-L1 expression is associated with longer overall survival among advanced ovarian cancer patients. PD-L1 expression may be an independent prognostic biomarker.

Links between cancer metabolism and cisplatin resistance

Cisplatin is one of the most potent and widely used chemotherapeutic agent in the treatment of several solid tumors, despite the high toxicity and the frequent relapse of patients due to the onset of drug resistance. Resistance to chemotherapeutic agents, either intrinsic or acquired, is currently one of the major problems in oncology. Thus, understanding the biology of chemoresistance is fundamental in order to overcome this challenge and to improve the survival rate of patients. Studies over the last 30 decades have underlined how resistance is a multifactorial phenomenon not yet completely understood. Recently, tumor metabolism has gained a lot of interest in the context of chemoresistance; accumulating evidence suggests that the rearrangements of the principal metabolic pathways within cells, contributes to the sensitivity of tumor to the drug treatment. In this review, the principal metabolic alterations associated with cisplatin resistance are highlighted. Improving the knowledge of the influence of metabolism on cisplatin response is fundamental to identify new possible metabolic targets useful for combinatory treatments, in order to overcome cisplatin resistance.

The Repertoire of Serous Ovarian Cancer Non-genetic Heterogeneity Revealed by Single-Cell Sequencing of Normal Fallopian Tube Epithelial Cells

The inter-differentiation between cell states promotes cancer cell survival under stress and fosters non-genetic heterogeneity (NGH). NGH is, therefore, a surrogate of tumor resilience but its quantification is confounded by genetic heterogeneity. Here we show that NGH in serous ovarian cancer (SOC) can be accurately measured when informed by the molecular signatures of the normal fallopian tube epithelium (FTE) cells, the cells of origin of SOC. Surveying the transcriptomes of ∼6,000 FTE cells, predominantly from non-ovarian cancer patients, identified 6 FTE subtypes. We used subtype signatures to deconvolute SOC expression data and found substantial intra-tumor NGH. Importantly, NGH-based stratification of ∼1,700 tumors robustly correlated with survival. Our findings lay the foundation for accurate prognostic and therapeutic stratification of SOC.

Differential expression of immune related genes in high-grade ovarian serous carcinoma

OBJECTIVE

To identify novel immunologic targets and biomarkers associated with overall survival (OS) in high-grade serous ovarian cancer (HGSC).

METHODS

In this retrospective study, microarray data from 51 HGSC specimens were analyzed (Affymetrix HG-U133A). A panel of 183 immune/inflammatory response related genes linked to 279 probe sets was constructed a priori and screened. Associations between gene expression and OS were assessed using logrank tests. Multiple testing was addressed within the False Discovery Rate (FDR) framework. For external validation, TCGA Ovarian dataset and five GSE publicly available HGSC datasets were evaluated.

RESULTS

In Duke data, 110 probe sets linked to 83 immunologic/inflammatory-related genes were differentially expressed in tumors from long versus short-term HGSC survivors (adjusted p < 0.05). In TCGA, concordant with the results from the Duke discovery cohort, high expression of one probe (IL6R) demonstrated a consistent significance and concordant association with higher expression in long-term HGSC survivors (Duke q-value = 0.022) and improved OS in the TCGA dataset (p-value = 0.015, HR = 0.8). Thirteen genes in GSE14764 (N = 4) and GSE26712 (N = 9) datasets had significant p-values and consistent concordant with Duke Data. Despite the significant associations of gene expression and OS in the individual GSE datasets, in the GSE meta-analysis no genes were consistently concordant and significantly associated with survival.

CONCLUSIONS

Evaluation of IL6R expression may be warranted based on higher expression in long-term survivors and association with improved survival in advanced HGSC. The other candidate genes may also be of worthy of further exploration to enhance immuno-oncology drug discovery.

Integrated pan-cancer gene expression and drug sensitivity analysis reveals SLFN11 mRNA as a solid tumor biomarker predictive of sensitivity to DNA-damaging chemotherapy

Background

Precision oncology seeks to integrate multiple layers of data from a patient’s cancer to effectively tailor therapy. Conventional chemotherapies are sometimes effective but accompanied by adverse events, warranting the identification of a biomarker of chemosensitivity.

Objective

Identify an mRNA biomarker that predicts chemosensitivity across solid tumor subtypes.

Methods

We performed a pan-solid tumor analysis integrating gene expression and drug sensitivity profiles from 3 cancer cell line datasets to identify transcripts correlated with sensitivity to a panel of chemotherapeutics. We then tested the ability of an mRNA biomarker to predictive clinical outcomes in cohorts of patients with breast, lung, or ovarian cancer.

Results

Expression levels of several mRNA transcripts were significantly correlated with sensitivity or resistance chemotherapeutics in cancer cell line datasets. The only mRNA transcript significantly correlated with sensitization to multiple classes of DNA-damaging chemotherapeutics in all 3 cell line datasets was encoded by Schlafen Family Member 11 (SLFN11). Analyses of multiple breast, lung, and ovarian cancer patient cohorts treated with chemotherapy confirmed SLFN11 mRNA expression as a predictive biomarker of longer overall survival and improved tumor response.

Conclusions

Tumor SLFN11 mRNA expression is a biomarker of sensitivity to an array of DNA-damaging chemotherapeutics across solid tumor subtypes.

Novel Model for Comprehensive Assessment of Robust Prognostic Gene Signature in Ovarian Cancer Across Different Independent Datasets

Different analytical methods or models can often find completely different prognostic biomarkers for the same cancer. In the study of prognostic molecular biomarkers of ovarian cancer (OvCa), different studies have reported a variety of prognostic gene signatures. In the current study, based on geometric concepts, the linearity-clustering phase diagram with integrated P-value (LCP) method was used to comprehensively consider three indicators that are commonly employed to estimate the quality of a prognostic gene signature model. The three indicators, namely, concordance index, area under the curve, and level of the hazard ratio were determined via calculation of the prognostic index of various gene signatures from different datasets. As evaluation objects, we selected 13 gene signature models (Cox regression model) and 16 OvCa genomic datasets (including gene expression information and follow-up data) from published studies. The results of LCP showed that three models were universal and better than other models. In addition, combining the three models into one model showed the best performance in all datasets by LCP calculation. The combination gene signature model provides a more reliable model and could be validated in various datasets of OvCa. Thus, our method and findings can provide more accurate prognostic biomarkers and effective reference for the precise clinical treatment of OvCa.

A novel risk score system for assessment of ovarian cancer based on co-expression network analysis and expression level of five lncRNAs

Background

Ovarian cancer (OC) is the most deadly gynaecological cancer, contributing significantly to female cancer-related deaths worldwide. Improving the outlook for OC patients depends on the identification of more reliable prognostic biomarkers for early diagnosis and survival prediction. The various roles of long non-coding RNAs (lncRNAs) in OC have attracted increasing attention. This study aimed to identify a lncRNA-based signature for survival prediction in OC patients.

Methods

RNA expression data and clinical information from a large number of OC patients were downloaded from a public database. These data were regarded as a training set to construct a weighed gene co-expression network analysis (WGCNA) network, mine stable modules, and screen differentially expressed lncRNAs. The prognostic lncRNAs were screened using univariate Cox regression analysis and the optimal prognosis lncRNA combination was screened using a Cox-PH model. The finalised lncRNA combination was used to construct the risk score system, which was validated and assessed for effectiveness using other independent datasets. Further functional pathway enrichment was performed using gene set enrichment analysis (GSEA).

Results

A co-expression network was constructed and four stable modules with OC-related biological functions were obtained. A total of 19 lncRNAs significantly related to prognosis of ovarian cancer were obtained using univariate Cox regression analysis, and the 5 prognostic signature lncRNAs GAS5, HCP5, PART1, SNHG11, and SNHG5 were used to establish a risk assessment system. The reliability of the prognostic scoring system was further confirmed using validation sets, which indicated that the risk assessment system could be used as an independent prognostic factor. Pathway enrichment analysis revealed that the network modules related to the above five prognostic genes were significantly associated with cell local adhesion, cancer signaling pathways, JAK-STAT signalling, and endogenous cell receptor interaction.

Conclusions

The risk score system established in this study could provide a novel reliable method to identify individuals at high risk of OC. In addition, the five prognostic lncRNAs identified here are promising potential prognostic biomarkers that could help to elucidate the pathogenesis of OC.

Overexpression of CCDC69 activates p14ARF/MDM2/p53 pathway and confers cisplatin sensitivity

OBJECTIVES

The aim of the study is to explore the relationship between CCDC69 expression and resistance of ovarian cancer cells to cisplatin and reveal the underlying mechanism.

METHODS

One hundred thirty five ovarian cancer patients with intact chemo-response information from The Cancer Genome Atlas (TCGA) database were included and analyzed. Stable CCDC69 overexpressing 293 and ovarian cancer A2780 cell lines were established and subjected to examine cell apoptosis and cell cycle distribution using CCK-8 assay and flow cytometry. Cell cycle and apoptosis pathway were evaluated by immunoblots. Stability of p14^ARF/MDM2/p53 pathway related proteins were determined by half-life analysis and ubiquitination experiments.

RESULTS

We found that CCDC69 expression was significantly higher in chemo-sensitive groups compared with chemo-resistant groups from TCGA database. High CCDC69 expression was associated longer survival. CCDC69 overexpressing 293 and A2780 cells with wildtype p53 and contributes to cisplatin sensitivity following treatment with cisplatin. We further found over-expression of CCDC69 activated p14^ARF/MDM2/p53 pathway. Importantly, we also demonstrated that CCDC69 expression extended p53 and p14^ARF protein half-life and shortened MDM2 protein half-life. Ubiquitination assay revealing a decrease in p14 ubiquitination in CCDC69 over-expression cells comparing to cells expressing empty vector.

CONCLUSIONS

It is tempting to conclude that targeting CCDC69 may play a role in cisplatin resistance.

Ovarian Cancer Prognostic Prediction Model Using RNA Sequencing Data

Ovarian cancer is one of the leading causes of cancer-related deaths in gynecologic malignancies. Over 70 % of ovarian cancer cases are high-grade serous ovarian cancers (HGSC) and have high death rates due to their resistance to chemotherapy. Despite advances in surgical and pharmaceutical therapies, overall survival rates are not good and accurate prediction of prognosis is not easy because of the highly heterogeneous nature of ovarian cancer. To improve patient's prognosis through proper treatment, we present a prognostic prediction model by integrating the high dimensional RNA sequencing data with their clinical data through the following steps: (1) gene filtration, (2) pre-screening, (3) gene marker selection (4) integrated study of selected gene markers and prediction model building. These steps of the prognostic prediction model can be applied to other types of cancer besides ovarian cancer.

compound.Cox: Univariate feature selection and compound covariate for predicting survival

BACKGROUND AND OBJECTIVE

Univariate feature selection is one of the simplest and most commonly used techniques to develop a multigene predictor for survival. Presently, there is no software tailored to perform univariate feature selection and predictor construction.

METHODS

We develop the compound.Cox R package that implements univariate significance tests (via the Wald tests or score tests) for feature selection. We provide a cross-validation algorithm to measure predictive capability of selected genes and a permutation algorithm to assess the false discovery rate. We also provide three algorithms for constructing a multigene predictor (compound covariate, compound shrinkage, and copula-based methods), which are tailored to the subset of genes obtained from univariate feature selection. We demonstrate our package using survival data on the lung cancer patients. We examine the predictive capability of the developed algorithms by the lung cancer data and simulated data.

RESULTS

The developed R package, compound.Cox, is available on the CRAN repository. The statistical tools in compound.Cox allow researchers to determine an optimal significance level of the tests, thus providing researchers an optimal subset of genes for prediction. The package also allows researchers to compute the false discovery rate and various prediction algorithms.

Decoding transcriptomic intra-tumour heterogeneity to guide personalised medicine in ovarian cancer

The evaluation of intra-tumour heterogeneity (ITH) from a transcriptomic point of view is limited. Single-cell cancer studies reveal significant genomic and transcriptomic ITH within a tumour and it is no longer adequate to employ single-subtype assignment as this does not acknowledge the ITH that exists. Molecular assessment of subtype heterogeneity (MASH) was developed to comprehensively report on the composition of all transcriptomic subtypes within a tumour lesion. Using MASH on 3431 ovarian cancer samples, correlation and association analyses with survival, metastasis and clinical outcomes were performed to assess the impact of subtype composition as a surrogate for ITH. The association was validated on two independent cohorts. We identified that 30% of ovarian tumours consist of two or more subtypes. When biological features of the subtype constituents were examined, we identified significant impact on clinical outcomes with the presence of poor prognostic subtypes (Mes or Stem-A). Poorer outcomes correlated with having higher degrees of poor prognostic subtype populations within the tumour. Subtype prediction in several independent datasets reflected a similar prognostic trend. In addition, paired analysis of primary and recurrent/metastatic tumours demonstrated Mes and/or Stem-A subtypes predominated in recurrent and metastatic tumours regardless of the original primary subtype. Given the biological and prognostic value in delineating individual subtypes within a tumour, a clinically applicable MASH assay using NanoString^® technology was developed as a classification tool to comprehensively describe constituents of molecular subtypes. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Consensus on Molecular Subtypes of High-Grade Serous Ovarian Carcinoma

Purpose: The majority of ovarian carcinomas are of high-grade serous histology, which is associated with poor prognosis. Surgery and chemotherapy are the mainstay of treatment, and molecular characterization is necessary to lead the way to targeted therapeutic options. To this end, various computational methods for gene expression-based subtyping of high-grade serous ovarian carcinoma (HGSOC) have been proposed, but their overlap and robustness remain unknown.Experimental Design: We assess three major subtype classifiers by meta-analysis of publicly available expression data, and assess statistical criteria of subtype robustness and classifier concordance. We develop a consensus classifier that represents the subtype classifications of tumors based on the consensus of multiple methods, and outputs a confidence score. Using our compendium of expression data, we examine the possibility that a subset of tumors is unclassifiable based on currently proposed subtypes.Results: HGSOC subtyping classifiers exhibit moderate pairwise concordance across our data compendium (58.9%-70.9%; P < 10^-5) and are associated with overall survival in a meta-analysis across datasets (P < 10^-5). Current subtypes do not meet statistical criteria for robustness to reclustering across multiple datasets (prediction strength < 0.6). A new subtype classifier is trained on concordantly classified samples to yield a consensus classification of patient tumors that correlates with patient age, survival, tumor purity, and lymphocyte infiltration.Conclusions: A new consensus ovarian subtype classifier represents the consensus of methods and demonstrates the importance of classification approaches for cancer that do not require all tumors to be assigned to a distinct subtype. Clin Cancer Res; 24(20); 5037-47. ©2018 AACR.

Consensus on Molecular Subtypes of High-Grade Serous Ovarian Carcinoma

Purpose: The majority of ovarian carcinomas are of high-grade serous histology, which is associated with poor prognosis. Surgery and chemotherapy are the mainstay of treatment, and molecular characterization is necessary to lead the way to targeted therapeutic options. To this end, various computational methods for gene expression-based subtyping of high-grade serous ovarian carcinoma (HGSOC) have been proposed, but their overlap and robustness remain unknown.Experimental Design: We assess three major subtype classifiers by meta-analysis of publicly available expression data, and assess statistical criteria of subtype robustness and classifier concordance. We develop a consensus classifier that represents the subtype classifications of tumors based on the consensus of multiple methods, and outputs a confidence score. Using our compendium of expression data, we examine the possibility that a subset of tumors is unclassifiable based on currently proposed subtypes.Results: HGSOC subtyping classifiers exhibit moderate pairwise concordance across our data compendium (58.9%-70.9%; P < 10-5) and are associated with overall survival in a meta-analysis across datasets (P < 10-5). Current subtypes do not meet statistical criteria for robustness to reclustering across multiple datasets (prediction strength < 0.6). A new subtype classifier is trained on concordantly classified samples to yield a consensus classification of patient tumors that correlates with patient age, survival, tumor purity, and lymphocyte infiltration.Conclusions: A new consensus ovarian subtype classifier represents the consensus of methods and demonstrates the importance of classification approaches for cancer that do not require all tumors to be assigned to a distinct subtype. Clin Cancer Res; 24(20); 5037-47. ©2018 AACR.

Programmed cell removal by calreticulin in tissue homeostasis and cancer

Macrophage-mediated programmed cell removal (PrCR) is a process essential for the clearance of unwanted (damaged, dysfunctional, aged, or harmful) cells. The detection and recognition of appropriate target cells by macrophages is a critical step for successful PrCR, but its molecular mechanisms have not been delineated. Here using the models of tissue turnover, cancer immunosurveillance, and hematopoietic stem cells, we show that unwanted cells such as aging neutrophils and living cancer cells are susceptible to "labeling" by secreted calreticulin (CRT) from macrophages, enabling their clearance through PrCR. Importantly, we identified asialoglycans on the target cells to which CRT binds to regulate PrCR, and the availability of such CRT-binding sites on cancer cells correlated with the prognosis of patients in various malignancies. Our study reveals a general mechanism of target cell recognition by macrophages, which is the key for the removal of unwanted cells by PrCR in physiological and pathophysiological processes.

Establishment of a SVM classifier to predict recurrence of ovarian cancer

Gene expression data using retrieved ovarian cancer (OC) samples were used to identify genes of interest and a support vector machine (SVM) classifier was subsequently established to predict the recurrence of OC. Three datasets (GSE17260, GSE44104 and GSE51088) investigating OC gene expression were downloaded from the Gene Expression Omnibus. Differentially expressed genes (DEGs) in samples from patients with non-recurrent and recurrent OC were revealed via a homogeneity test and quality control analysis. A protein-protein interaction (PPI) network was subsequently established for the DEGs using data from Biological General Repository for Interaction Datasets, Human Protein Reference Database and Database of Interacting Proteins. Degrees of interaction and betweenness centrality (BC) scores were calculated for each node in the PPI network. The top 100 genes ranked by BC scores were selected to identify feature genes via recursive feature elimination using the GSE17260 dataset. Following this, a SVM classifier was constructed and further validated using the GSE44104 and GSE51088 datasets and independent gene expression data obtained from the Cancer Genome Atlas (TCGA). A total of 639 DEGs were identified from the three gene expression datasets, and a PPI network including 249 nodes and 354 edges was constructed. A SVM classifier consisting of 39 feature genes (including cullin 3, mouse double minute 2 homolog, aurora kinase A, WW domain containing oxidoreducatase, large tumor suppressor kinase 2, sirtuin 6, staphylococcal nuclease and tudor domain containing 1, leucine rich repeats and immunoglobulin like domains 1 and aurora kinase 1 interacting protein 1) was subsequently constructed. The prediction accuracies of the SVM classifier for GSE17260, GSE44104 and GSE51088 datasets as well as data downloaded from TCGA were revealed to be 92.7, 93.3, 96.6 and 90.4%, respectively. Furthermore, the results of the present study revealed that patients with predicted non-recurrent OC survived significantly longer compared with the patients with predicted recurrent OC (P=6.598×10⁻⁶). A SVM classifier consisting of 39 feature genes was established for predicting the recurrence and prognosis of OC. Therefore, the results of the present study suggested that the 39 feature genes may serve important roles in the development of OC and may represent therapeutic biomarkers of OC.

Impact of chemotactic factors and receptors on the cancer immune infiltrate: a bioinformatics study revealing homogeneity and heterogeneity among patient cohorts

Multiple soluble factors including proteins (in particular chemokines), non-proteinaceous factors released by dead cells, as well as receptors for such factors (in particular chemokine receptors, formyl peptide receptors and purinergic receptors), influence the recruitment of distinct cell subsets into the tumor microenvironment. We performed an extensive bioinformatic analysis on tumor specimens from 5953 cancer patients to correlate the mRNA expression levels of chemotactic factors/receptors with the density of immune cell types infiltrating the malignant lesions. This meta-analysis, which included specimens from breast, colorectal, lung, ovary and head and neck carcinomas as well as melanomas, revealed that a subset of chemotactic factors/receptors exhibited a positive and reproducible correlation with several infiltrating cell types across various solid cancers, revealing a universal pattern of association. Hence, this meta-analysis distinguishes between homogeneous associations that occur across different cancer types and heterogeneous correlations, that are specific of one organ. Importantly, in four out of five breast cancer cohorts for which clinical data were available, the levels of expression of chemotactic factors/receptors that exhibited universal (rather than organ-specific) positive correlations with the immune infiltrate had a positive impact on the response to neoadjuvant chemotherapy. These results support the notion that general (rather than organ-specific) rules governing the recruitment of immune cells into the tumor bed are particularly important in determining local immunosurveillance and response to therapy.

HOXA4/HOXB3 gene expression signature as a biomarker of recurrence in patients with high-grade serous ovarian cancer following primary cytoreductive surgery and first-line adjuvant chemotherapy

OBJECTIVES

Aberrant homeobox (HOX) gene expression is reported in high-grade serous ovarian carcinoma (HGSOC), however, its prognostic significance remains unclear.

METHODS

HOX genes associated with progression-free survival (PFS) in a discovery cohort of primary HGSOC samples with RNA sequencing data, and those previously reported to be associated with clinical outcomes, were selected for qPCR testing in an independent training cohort of primary HGSOC samples (n=71). A prognostic model for PFS was developed using univariate and multivariate Cox regression. Patients were stratified into risk groups that optimized the test statistic. The model was tested in an independent HGSOC cohort from The Cancer Genome Atlas (TCGA) (n=320). The effect of selected HOX genes on drug sensitivity and reactive oxygen species (ROS) accumulation was examined in vitro.

RESULTS

Of 23 HOX genes tested in the training cohort, HOXA4 (HR=1.20, 95% CI=1.07-1.34, P=0.002) and HOXB3 (HR=1.09, 95% CI=1.01-1.17, P=0.027) overexpression were significantly associated with shorter PFS in multivariate analysis. Based on the optimal cutoff of the HOXA4/HOXB3 risk score, median PFS was 16.9months (95% CI=14.6-21.2months) and not reached (>80months) for patients with high and low risk scores, respectively (HR=8.89, 95% CI=2.09-37.74, P<0.001). In TCGA, the HOXA4/HOXB3 risk score was significantly associated with disease-free survival (HR=1.44, 95% CI=1.00-2.09, P=0.048). HOXA4 or HOXB3 overexpression in ovarian cancer cells decreased sensitivity to cisplatin and attenuated the generation of cisplatin-induced ROS (P<0.05).

CONCLUSIONS

HOXA4/HOXB3 gene expression-based risk score may be useful for prognostic risk stratification and warrants prospective validation in HGSOC patients.

Deconstruction of a Metastatic Tumor Microenvironment Reveals a Common Matrix Response in Human Cancers

We have profiled, for the first time, an evolving human metastatic microenvironment by measuring gene expression, matrisome proteomics, cytokine and chemokine levels, cellularity, extracellular matrix organization, and biomechanical properties, all on the same sample. Using biopsies of high-grade serous ovarian cancer metastases that ranged from minimal to extensive disease, we show how nonmalignant cell densities and cytokine networks evolve with disease progression. Multivariate integration of the different components allowed us to define, for the first time, gene and protein profiles that predict extent of disease and tissue stiffness, while also revealing the complexity and dynamic nature of matrisome remodeling during development of metastases. Although we studied a single metastatic site from one human malignancy, a pattern of expression of 22 matrisome genes distinguished patients with a shorter overall survival in ovarian and 12 other primary solid cancers, suggesting that there may be a common matrix response to human cancer.Significance: Conducting multilevel analysis with data integration on biopsies with a range of disease involvement identifies important features of the evolving tumor microenvironment. The data suggest that despite the large spectrum of genomic alterations, some human malignancies may have a common and potentially targetable matrix response that influences the course of disease. Cancer Discov; 8(3); 304-19. ©2017 AACR.This article is highlighted in the In This Issue feature, p. 253.

Comprehensive analysis of lncRNA expression profiles reveals a novel lncRNA signature to discriminate nonequivalent outcomes in patients with ovarian cancer

There is growing evidence of dysregulated long non-coding RNAs (lncRNAs) serving as potential biomarkers for cancer prognosis. However, systematic efforts of searching for an expression-based lncRNA signature for prognosis prediction in ovarian cancer (OvCa) have not been made yet. Here, we performed comprehensive analysis for lncRNA expression profiles and clinical data of 544 OvCa patients from The Cancer Genome Atlas (TCGA), and identified an eight-lncRNA signature with ability to classify patients of the training cohort into high-risk group showing poor outcome and low-risk group showing significantly improved outcome, which was further validated in the validation cohort and entire TCGA cohort. Multivariate Cox regression analysis and stratified analysis demonstrated that the prognostic value of this signature was independent of other clinicopathological factors. Associating the outcome prediction with BRCA1 and/or BRCA2 mutation revealed a superior prognosis performance both in BRCA1/2-mutated and BRCA1/2 wild-type tumors. Finally, a significantly correlation was found between the lncRNA signature and the complete response rate of chemotherapy, suggesting that this eight-lncRNA signature may be a measure to predict chemotherapy response and identify platinum-resistant patients who might benefit from other more efficacious therapies. With further prospective validation, this eight-lncRNA signature may have important implications for outcome prediction and therapy decisions.

Altered glutamine metabolism in platinum resistant ovarian cancer

Ovarian cancer is characterized by an increase in cellular energy metabolism, which is predominantly satisfied by glucose and glutamine. Targeting metabolic pathways is an attractive approach to enhance the therapeutic effectiveness and to potentially overcome drug resistance in ovarian cancer. In platinum-sensitive ovarian cancer cell lines the metabolism of both, glucose and glutamine was initially up-regulated in response to platinum treatment. In contrast, platinum-resistant cells revealed a significant dependency on the presence of glutamine, with an upregulated expression of glutamine transporter ASCT2 and glutaminase. This resulted in a higher oxygen consumption rate compared to platinum-sensitive cell lines reflecting the increased dependency of glutamine utilization through the tricarboxylic acid cycle. The important role of glutamine metabolism was confirmed by stable overexpression of glutaminase, which conferred platinum resistance. Conversely, shRNA knockdown of glutaminase in platinum resistant cells resulted in re-sensitization to platinum treatment. Importantly, combining the glutaminase inhibitor BPTES with platinum synergistically inhibited platinum sensitive and resistant ovarian cancers in vitro. Apoptotic induction was significantly increased using platinum together with BPTES compared to either treatment alone. Our findings suggest that targeting glutamine metabolism together with platinum based chemotherapy offers a potential treatment strategy particularly in drug resistant ovarian cancer.

The rise of genomic profiling in ovarian cancer

INTRODUCTION

Next-generation sequencing and advances in 'omics technology have rapidly increased our understanding of the molecular landscape of epithelial ovarian cancers. Areas covered: Once characterized only by histologic appearance and clinical behavior, we now understand many of the molecular phenotypes that underlie the different ovarian cancer subtypes. While the current approach to treatment involves standard cytotoxic therapies after cytoreductive surgery for all ovarian cancers regardless of histologic or molecular characteristics, focus has shifted beyond a 'one size fits all' approach to ovarian cancer. Expert commentary: Genomic profiling offers potentially 'actionable' opportunities for development of targeted therapies and a more individualized approach to treatment with concomitant improved outcomes and decreased toxicity.