Association of Copy Number Variation Signature and Survival in Patients With Serous Ovarian Cancer

Deciphering the role of ELAVL1: Insights from pan-cancer multiomics analyses with emphasis on nasopharyngeal carcinoma

Background and Objectives

Cancer continues to be a predominant cause of mortality worldwide, underscoring the critical need to identify and develop novel biomarkers to improve prognostic accuracy and therapeutic approaches. The dysregulation of ELAVL1 is linked to various diseases, including cancer. Nevertheless, its role across different cancer types remains insufficiently investigated.

Methods

We conducted a systematic investigation into the expression patterns, prognostic significance, genomic alterations, modifications, and functional implications of ELAVL1 in pan-cancer types. Besides, we performed in vitro and in vivo experiments to confirm the role of ELAVL1 in nasopharyngeal carcinoma (NPC).

Results

By utilizing multi-omics datasets, we found obvious overexpression of ELAVL1 in various cancer types at both the mRNA and protein levels, with predominant expression in malignant cells. Survival analysis revealed that increased ELAVL1 expression was linked to unfavorable outcomes in certain cancers; however, its effect difers among various cancer types. Additionally, we found that the genomic alterations and modifications of ELAVL1 were related to tumor progression. We discovered that ELAVL1 was elevated in NPC tissues. In addition, survival analysis indicated that NPC patients with higher ELAVL1 expression had worse prognoses. Functional assays demonstrated that ELAVL1 suppression led to decreased proliferation and migration in NPC cell lines. Moreover, ELAVL1 knockdown effectively inhibited NPC progression in the lymph node and lung metastasis models.

Conclusions

In summary, ELAVL1 exhibits diverse and complex involvement in tumor progression. Targeting it might inhibit tumor progression, making it a promising biomarker and therapeutic target for enhancing cancer treatment outcomes.

Characterization of RNF144B and PPP2R2A identified by a novel approach using TCGA data in ovarian cancer

TCGA has identified predominant somatic copy number alterations (SCNA) affecting numerous genes in HGSOC. To identify cancer-driver genes from the regions of SCNA, we have devised a scoring system that integrates information from different genetic alterations. Applying this scoring system to the TCGA-HGSOC dataset (n = 316) we have identified several well-known and novel putative cancer genes in HGSOC. We functionally validated the roles of two previously unknown genes, RNF144B and PPP2R2A. RNF144B, an E3 ubiquitin-ligase is amplified and overexpressed in 16% of HGSOC (TCGA). Overexpression of RNF144B in ovarian cancer cells increased cell proliferation, colony formation, and migration. RNF144B was significantly overexpressed in 50% of primary tumors from patients with HGSOC compared to the ovary. Further, it had significantly reduced expression in tumors after chemotherapy. PPP2R2A, the regulatory subunit of PP2A is deleted and downregulated in 38% of HGSOCs (TCGA). Overexpression of PPP2R2A inhibited cell proliferation, colony-formation, migration, and invasion in ovarian cancer cells. In OVCAR-5, which expresses low levels of PPP2R2A, Niraparib inhibited cell proliferation. PPP2R2A was not expressed in 72% of HGSOCs. This report demonstrates this approach to identifying genes from the TCGA data. Further experiments are required to conclusively prove the role of these genes in the pathogenesis of ovarian cancer.

Genomic instability in ovarian cancer: Through the lens of single nucleotide polymorphisms

Ovarian cancer (OC) is the deadliest gynecological malignancy among all female reproductive cancers. It is characterized by high mortality rate and poor prognosis. Genomic instability caused by mutations, single nucleotide polymorphisms (SNPs), copy number variations (CNVs), microsatellite instability (MSI), and chromosomal instability (CIN) are associated with OC predisposition. SNPs, which are highly prevalent in the general population, show a greater relative risk contribution, particularly in sporadic cancers. Understanding OC etiology in terms of genetic basis can increase the use of molecular diagnostics and provide promising approaches for designing novel treatment modalities. This will help deliver personalized medicine to OC patients, which may soon be within reach. Given the pivotal impact of SNPs in cancers, the primary emphasis of this review is to shed light on their prevalence in key caretaker genes that closely monitor genomic integrity, viz., DNA damage response, repair, cell cycle checkpoints, telomerase maintenance, and apoptosis and their clinical implications in OC. We highlight the current challenges faced in different SNP-based studies. Various computational methods and bioinformatic tools employed to predict the functional impact of SNPs have also been comprehensively reviewed concerning OC research. Overall, this review identifies that variants in the DDR and HRR pathways are the most studied, implying their critical role in the disease. Conversely, variants in other pathways, such as NHEJ, MMR, cell cycle, apoptosis, telomere maintenance, and PARP genes, have been explored the least.

Systematic Modeling of Risk-Associated Copy Number Alterations in Cancer

The determination of the cancer prognosis is paramount for patients and medical personnel so that they can devise treatment strategies. Transcriptional-based signatures and subtypes derived from cancer biopsy material have been used in clinical practice for several cancer types to aid in setting the patient prognosis and forming treatment strategies. Other genomic features in cancer biopsies, such as copy number alterations (CNAs), have been underused in clinical practice, and yet they represent a complementary source of molecular information that can add detail to the prognosis, which is supported by recent work in breast, ovarian, and lung cancers. Here, through a systematic strategy, we explored the prognostic power of CNAs in 37 cancer types. In this analysis, we defined two modes of informative features, deep and soft, depending on the number of alleles gained or lost. These informative modes were grouped by amplifications or deletions to form four single-data prognostic models. Finally, the single-data models were summed or combined to generate four additional multidata prognostic models. First, we show that the modes of features are cancer-type dependent, where deep alterations generate better models. Nevertheless, some cancers require soft alterations to generate a feasible model due to the lack of significant deep alterations. Then, we show that the models generated by summing coefficients from amplifications and deletions appear to be more practical for many but not all cancer types. We show that the CNA-derived risk group is independent of other clinical factors. Furthermore, overall, we show that CNA-derived models can define clinically relevant risk groups in 33 of the 37 (90%) cancer types analyzed. Our study highlights the use of CNAs as biomarkers that are potentially clinically relevant to survival in cancer patients.

Benchmarking of Approaches for Gene Copy-Number Variation Analysis and Its Utility for Genetic Aberration Detection in High-Grade Serous Ovarian Carcinomas

Objective: The goal of this study was to compare the results of CNV detection by three different methods using 13 paired carcinoma samples, as well as to perform a statistical analysis of the agreement. Methods: CNV was studied using NanoString nCounter v2 Cancer CN Assay (Nanostring), Illumina Infinium CoreExome microarrays (CoreExome microarrays) and digital droplet PCR (ddPCR). Results: There was a good level of agreement (PABAK score > 0.6) between the CoreExome microarrays and the ddPCR results for finding CNVs. There was a moderate level of agreement (PABAK values ≈ 0.3-0.6) between the NanoString Assay results and microarrays or ddPCR. For 83 out of 87 target genes studied (95%), the agreement between the CoreExome microarrays and NanoString nCounter was characterized by PABAK values < 0.75, except for MAGI3, PDGFRA, NKX2-1 and KDR genes (>0.75). The MET, HMGA2, KDR, C8orf4, PAX9, CDK6, and CCND2 genes had the highest agreement among all three approaches. Conclusions: Therefore, to get a better idea of how to genotype an unknown CNV spectrum in tumor or normal tissue samples that are very different molecularly, it makes sense to use at least two CNV detection methods. One of them, like ddPCR, should be able to quantitatively confirm the results of the other.

PRKDC-Mediated NHEJ May Play a Crucial Role in Aneuploidy of Chromosome 8-Driven Progression of Ovarian Cancer

High malignancy is a prominent characteristic of epithelial ovarian cancer (EOC), emphasizing the necessity for further elucidation of the potential mechanisms underlying cancer progression. Aneuploidy and copy number variation (CNV) partially contribute to the heightened malignancy observed in EOC; however, the precise features of aneuploidy and their underlying molecular patterns, as well as the relationship between CNV and aneuploidy in EOC, remain unclear. In this study, we employed single-cell sequencing data along with The Cancer Genome Atlas (TCGA) to investigate aneuploidy and CNV in EOC. The technique of fluorescence in situ hybridization (FISH) was employed using specific probes. The copy number variation within the genomic region of chromosome 8 (42754568-47889815) was assessed and utilized as a representative measure for the ploidy status of individual cells in chromosome 8. Differential expression analysis was performed between different subgroups based on chromosome 8 ploidy. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein-protein interaction (PPI), and hub-gene analyses were subsequently utilized to identify crucial genes involved. By classifying enriched tumor cells into distinct subtypes based on chromosome 8 ploidy combined with TCGA data integration, we identified key genes driving chromosome 8 aneuploidy in EOC, revealing that PRKDC gene involvement through the mediated non-homologous end-joining pathway may play a pivotal role in disease progression. Further validation through analysis of the GEO and TCGA database and survival assessment, considering both mRNA expression levels and CNV status of PRKDC, has confirmed its involvement in the progression of EOC. Further functional analysis revealed an upregulation of PRKDC in both ovarian EOC cells and tissues, with its expression showing a significant correlation with the extent of copy number variation (CNV) on chromosome 8. Taken together, CNV amplification and aneuploidy of chromosome 8 are important characteristics of EOC. PRKDC and the mediated NHEJ pathway may play a crucial role in driving aneuploidy on chromosome 8 during the progression of EOC.

A deep-learning-based genomic status estimating framework for homologous recombination deficiency detection from low-pass whole genome sequencing

Genome-wide sequencing allows for prediction of clinical treatment responses and outcomes by estimating genomic status. Here, we developed Genomic Status scan (GSscan), a long short-term memory (LSTM)-based deep-learning framework, which utilizes low-pass whole genome sequencing (WGS) data to capture genomic instability-related features. In this study, GSscan directly surveys homologous recombination deficiency (HRD) status independent of other existing biomarkers. In breast cancer, GSscan achieved an AUC of 0.980 in simulated low-pass WGS data, and obtained a higher HRD risk score in clinical BRCA-deficient breast cancer samples (p = 1.3 × 10-4, compared with BRCA-intact samples). In ovarian cancer, GSscan obtained higher HRD risk scores in BRCA-deficient samples in both simulated data and clinical samples (p = 2.3 × 10-5 and p = 0.039, respectively, compared with BRCA-intact samples). Moreover, HRD-positive patients predicted by GSscan showed longer progression-free intervals in TCGA datasets (p = 0.0011) treated with platinum-based adjuvant chemotherapy, outperforming existing low-pass WGS-based methods. Furthermore, GSscan can accurately predict HRD status using only 1 ng of input DNA and a minimum sequencing coverage of 0.02 × , providing a reliable, accessible, and cost-effective approach. In summary, GSscan effectively and accurately detected HRD status, and provide a broadly applicable framework for disease diagnosis and selecting appropriate disease treatment.

Comprehensive analysis of single cell and bulk data develops a promising prognostic signature for improving immunotherapy responses in ovarian cancer

The tumor heterogeneity is an important cause of clinical therapy failure and yields distinct prognosis in ovarian cancer (OV). Using the advantages of integrated single cell RNA sequencing (scRNA-seq) and bulk data to decode tumor heterogeneity remains largely unexplored. Four public datasets were enrolled in this study, including E-MTAB-8107, TCGA-OV, GSE63885, and GSE26193 cohorts. Random forest algorithm was employed to construct a multi-gene prognostic panel and further evaluated by receiver operator characteristic (ROC), calibration curve, and Cox regression. Subsequently, molecular characteristics were deciphered, and treatments strategies were explored to deliver precise therapy. The landscape of cell subpopulations and functional characteristics, as well as the dynamic of macrophage cells were detailly depicted at single cell level, and then screened prognostic candidate genes. Based on the expression of candidate genes, a stable and robust cell characterized gene associated prognosis signature (CCIS) was developed, which harbored excellent performance at prognosis assessment and patient stratification. The ROC and calibration curves, and Cox regression analysis elucidated CCIS could serve as serve as an independent factor for predicting prognosis. Moreover, a promising clinical tool nomogram was also constructed according to stage and CCIS. Through comprehensive investigations, patients in low-risk group were charactered by favorable prognosis, elevated genomic variations, higher immune cell infiltrations, and superior antigen presentation. For individualized treatment, patients in low-risk group were inclined to better immunotherapy responses. This study dissected tumor heterogeneity and afforded a promising prognostic signature, which was conducive to facilitating clinical outcomes for patients with OV.

Evolutionary mode and timing of dissemination of high-grade serous carcinomas

Dissemination within the peritoneal cavity is a main determinant of poor patient outcomes from high-grade serous carcinomas (HGSCs). The dissemination process is poorly understood from a cancer evolutionary perspective. We reconstructed the evolutionary trajectories across a median of 5 tumor sites and regions from each of 23 patients based on deep whole-exome sequencing. Polyclonal cancer origin was detected in 1 patient. Ovarian tumors had more complex subclonal architectures than other intraperitoneal tumors in each patient, which indicated that tumors developed earlier in the ovaries. Three common modes of dissemination were identified, including monoclonal or polyclonal dissemination of monophyletic (linear) or polyphyletic (branched) subclones. Mutation profiles of initial or disseminated clones varied greatly among cancers, but recurrent mutations were found in 7 cancer-critical genes, including TP53, BRCA1, BRCA2, and DNMT3A, and in the PI3K/AKT1 pathway. Disseminated clones developed late in the evolutionary trajectory models of most cancers, in particular in cancers with DNA damage repair deficiency. Polyclonal dissemination was predicted to occur predominantly as a single and rapid wave, but chemotherapy exposure was associated with higher genomic diversity of disseminated clones. In conclusion, we described three common evolutionary dissemination modes across HGSCs and proposed factors associated with dissemination diversity.

Rescue of p53 functions by in vitro-transcribed mRNA impedes the growth of high-grade serous ovarian cancer

BACKGROUND

The cellular tumor protein p53 (TP53) is a tumor suppressor gene that is frequently mutated in human cancers. Among various cancer types, the very aggressive high-grade serous ovarian carcinoma (HGSOC) exhibits the highest prevalence of TP53 mutations, present in >96% of cases. Despite intensive efforts to reactivate p53, no clinical drug has been approved to rescue p53 function. In this study, our primary objective was to administer in vitro-transcribed (IVT) wild-type (WT) p53-mRNA to HGSOC cell lines, primary cells, and orthotopic mouse models, with the aim of exploring its impact on inhibiting tumor growth and dissemination, both in vitro and in vivo.

METHODS

To restore the activity of p53, WT p53 was exogenously expressed in HGSOC cell lines using a mammalian vector system. Moreover, IVT WT p53 mRNA was delivered into different HGSOC model systems (primary cells and patient-derived organoids) using liposomes and studied for proliferation, cell cycle progression, apoptosis, colony formation, and chromosomal instability. Transcriptomic alterations induced by p53 mRNA were analyzed using RNA sequencing in OVCAR-8 and primary HGSOC cells, followed by ingenuity pathway analysis. In vivo effects on tumor growth and metastasis were studied using orthotopic xenografts and metastatic intraperitoneal mouse models.

RESULTS

Reactivation of the TP53 tumor suppressor gene was explored in different HGSOC model systems using newly designed IVT mRNA-based methods. The introduction of WT p53 mRNA triggered dose-dependent apoptosis, cell cycle arrest, and potent long-lasting inhibition of HGSOC cell proliferation. Transcriptome analysis of OVCAR-8 cells upon mRNA-based p53 reactivation revealed significant alterations in gene expression related to p53 signaling, such as apoptosis, cell cycle regulation, and DNA damage. Restoring p53 function concurrently reduces chromosomal instability within the HGSOC cells, underscoring its crucial contribution in safeguarding genomic integrity by moderating the baseline occurrence of double-strand breaks arising from replication stress. Furthermore, in various mouse models, treatment with p53 mRNA reduced tumor growth and inhibited tumor cell dissemination in the peritoneal cavity in a dose-dependent manner.

CONCLUSIONS

The IVT mRNA-based reactivation of p53 holds promise as a potential therapeutic strategy for HGSOC, providing valuable insights into the molecular mechanisms underlying p53 function and its relevance in ovarian cancer treatment.

Integration of genomics and transcriptomics highlights the crucial role of chromosome 5 open reading frame 34 in various human malignancies

Although some data suggest that chromosome 5 open reading frame 34 (C5orf34) plays a pivotal part in the onset and disease progression of various cancers, there is no pan-cancer investigation of C5orf34 at present. This study sought to establish the predictive importance of C5orf34 in a variety of human malignancies and to understand its fundamental immunological function. In our research, we applied a combination of several bioinformatics techniques and basic experiments to investigate the differential expression of C5orf34, and its relationship with prognosis, methylation, single nucleotide variant, clinical characteristics, microsatellite instability, tumor mutational burden, copy number variation, and immune cell infiltration of several cancers from the database that is publicly available with the aim of identifying the potential prognostic markers. In this study we found that C5orf34 expression differed significantly among cancers types, according to the findings. The expression level of C5orf34 is markedly increased in the majority of malignancies when compared to normal tissues, which is significantly correlated with an unfavorable prognosis of patients. Immunohistochemical staining confirmed the findings that C5orf34 expression was remarkably up-regulated in a variety of gynecologic cancers. Moreover, C5orf34 expression was shown to be correlated with the clinical features of patients. C5orf34 was also found to be expressed with genes that code for the major immune suppressors, chemokines, immune activators, chemokine receptors, and histocompatibility complex. Finally, our study shows that C5orf34 has the potential to be employed as a prognostic biomarker. Moreover, it might regulate the immune microenvironment in a variety of malignancies.

Impact of 2 years of COVID-19 pandemic on ovarian cancer treatment in IRCCS-AUSL of Reggio Emilia

OBJECTIVE

To assess compliance with the 2019 regional recommendation to centralize epithelial ovarian cancer (EOC) patients and to assess whether the COVID-19 pandemic has affected the quality of care for EOC patients.

METHODS

We compared data from EOC patients treated before the introduction of the 2019 regional recommendation (2018-2019) with data obtained from EOC patients treated after the regional recommendation was adopted during the first 2 years of the COVID-19 pandemic (2020-2021). Data were retrieved from the Optimal Ovarian Cancer Pathway records. R software version 4.1.2 (the R Foundation for Statistical Computing, Vienna, Austria) was used for the statistical analysis.

RESULTS

251 EOC patients were centralized. The number of EOC patients centralized increased from 2% to 49% despite the COVID-19 pandemic. During the COVID-19 pandemic, there was an increase in the use of neoadjuvant chemotherapy and interval debulking surgery. There was an improvement in the percentage of Stage III patients without gross residual disease following both primary and interval debulking surgery. The percentage of EOC cases discussed by the multidisciplinary tumor board (MTB) increased from 66% to 89% of cases.

CONCLUSION

Despite the COVID-19 pandemic, centralization has increased and the quality of care has been preserved thanks to the MTB.

Spatial and temporal intra-tumoral heterogeneity in advanced HGSOC: Implications for surgical and clinical outcomes

Limited evidence exists on the impact of spatial and temporal heterogeneity of high-grade serous ovarian cancer (HGSOC) on tumor evolution, clinical outcomes, and surgical operability. We perform systematic multi-site tumor mapping at presentation and matched relapse from 49 high-tumor-burden patients, operated up front. From SNP array-derived copy-number data, we categorize dendrograms representing tumor clonal evolution as sympodial or dichotomous, noting most chemo-resistant patients favor simpler sympodial evolution. Three distinct tumor evolutionary patterns from primary to relapse are identified, demonstrating recurrent disease may emerge from pre-existing or newly detected clones. Crucially, we identify spatial heterogeneity for clinically actionable homologous recombination deficiency scores and for poor prognosis biomarkers CCNE1 and MYC. Copy-number signature, phenotypic, proteomic, and proliferative-index heterogeneity further highlight HGSOC complexity. This study explores HGSOC evolution and dissemination across space and time, its impact on optimal surgical cytoreductive effort and clinical outcomes, and its consequences for clinical decision-making.

Machine learning-based integration develops an immune-related risk model for predicting prognosis of high-grade serous ovarian cancer and providing therapeutic strategies

BACKGROUND

High-grade serous ovarian cancer (HGSOC) is a highly lethal gynecological cancer that requires accurate prognostic models and personalized treatment strategies. The tumor microenvironment (TME) is crucial for disease progression and treatment. Machine learning-based integration is a powerful tool for identifying predictive biomarkers and developing prognostic models. Hence, an immune-related risk model developed using machine learning-based integration could improve prognostic prediction and guide personalized treatment for HGSOC.

METHODS

During the bioinformatic study in HGSOC, we performed (i) consensus clustering to identify immune subtypes based on signatures of immune and stromal cells, (ii) differentially expressed genes and univariate Cox regression analysis to derive TME- and prognosis-related genes, (iii) machine learning-based procedures constructed by ten independent machine learning algorithms to screen and construct a TME-related risk score (TMErisk), and (iv) evaluation of the effect of TMErisk on the deconstruction of TME, indication of genomic instability, and guidance of immunotherapy and chemotherapy.

RESULTS

We identified two different immune microenvironment phenotypes and a robust and clinically practicable prognostic scoring system. TMErisk demonstrated superior performance over most clinical features and other published signatures in predicting HGSOC prognosis across cohorts. The low TMErisk group with a notably favorable prognosis was characterized by BRCA1 mutation, activation of immunity, and a better immune response. Conversely, the high TMErisk group was significantly associated with C-X-C motif chemokine ligands deletion and carcinogenic activation pathways. Additionally, low TMErisk group patients were more responsive to eleven candidate agents.

CONCLUSION

Our study developed a novel immune-related risk model that predicts the prognosis of ovarian cancer patients using machine learning-based integration. Additionally, the study not only depicts the diversity of cell components in the TME of HGSOC but also guides the development of potential therapeutic techniques for addressing tumor immunosuppression and enhancing the response to cancer therapy.

Can circulating cell free DNA be a promising marker in ovarian cancer? - a genome-scale profiling study in a single institution

BACKGROUND

Cell-free DNA (cfDNA) is emerging as a potential biomarker for the detection of ovarian cancer (OC). Recently, we reported a method based upon cfDNA whole-genome sequencing data including the nucleosome distribution (nucleosome footprinting NF), terminal signature sequence (motif), DNA fragmentation (fragment), and copy number variation (CNV).In the present study, we explored whether multiomics early screening technology in cfDNA can be applied for early screening of ovarian cancer.

METHODS

Fifty-nine patients with OC and 100 healthy controls were included in this prospective study. Cell-free DNA was extracted from plasma and analyzed by low-pass whole-genome sequencing. Genomic features were obtained for all samples of the cohort, including copy number variation (CNV), 5'-end motifs, fragmentation profiles, and nucleosome footprinting (NF). An integrated scoring system termed the OC score was developed based on the performance of these four features.

RESULTS

All four features showed diagnostic potential for OC. Based on the unique genome features of cfDNA, the OC score has high accuracy in distinguishing OC patients from healthy controls (AUC 97.7%; sensitivity 94.7%; specificity 98.0%) as a new comprehensive diagnostic method for OC. The OC score showed a gradual trend from healthy controls to OC patients with different stages, especially for early OC monitoring of concern, which achieved a satisfactory sensitivity (85.7%) at a high specificity.

CONCLUSIONS

This is the first study evaluating the potential of cell-free DNA for the diagnosis of primary OC using multidimensional early screening technology. We present a promising method to increase the accuracy of prediction in patients with OC.

Necroptosis-Related Modification Patterns Depict the Tumor Microenvironment, Redox Stress Landscape, and Prognosis of Ovarian Cancer

Necroptosis is one of programmed cell death discovered recently, which involves in tumorigenesis, cancer metastasis, and immune reaction. We studied the necroptosis-related genes (NRGs) in ovarian cancer (OV) tissues using data from public databases, which separated into two NRGclusters. Patients in cluster A would have severe clinical characteristics, poor prognosis, and worse tumor microenvironment infiltration characteristics. The NRG score was achieved through the Cox analysis, along with a construction of a prognostic model. People with lower risk score would have better prognosis, lower expression of redox related genes, higher immunogenicity, and better effect on immunotherapy. In addition, the NRG score was closely related to cancer stem cell index, copy number variations, tumor mutation load, and chemosensitivity. We built a nomogram to enhance clinical application of the signature. These outcomes can help use know the function of NRGs in OV and provide new ideas for evaluating clinical outcome and developing more effective treatment protocols.

Pathway-level mutation analysis in primary high-grade serous ovarian cancer and matched brain metastases

Brain metastases (BMs) in ovarian cancer (OC) are a rare event. BMs occur most frequently in high-grade serous (HGS) OC. The molecular features of BMs in HGSOC are poorly understood. We performed a whole-exome sequencing analysis of ten matched pairs of formalin-fixed paraffin-embedded samples from primary HGSOC and corresponding BMs. Enrichment significance (p value; false discovery rate) was computed using the Reactome, the Kyoto Encyclopedia of Genes and Genomes pathway collections, and the Gene Ontology Biological Processes. Germline DNA damage repair variants were found in seven cases (70%) and involved the BRCA1, BRCA2, ATM, RAD50, ERCC4, RPA1, MLHI, and ATR genes. Somatic mutations of TP53 were found in nine cases (90%) and were the only stable mutations between the primary tumor and BMs. Disturbed pathways in BMs versus primary HGSOC constituted a complex network and included the cell cycle, the degradation of the extracellular matrix, cell junction organization, nucleotide metabolism, lipid metabolism, the immune system, G-protein-coupled receptors, intracellular vesicular transport, and reaction to chemical stimuli (Golgi vesicle transport and olfactory signaling). Pathway analysis approaches allow for a more intuitive interpretation of the data as compared to considering single-gene aberrations and provide an opportunity to identify clinically informative alterations in HGSOC BM.

Spontaneous Transformation of a p53 and Rb-Defective Human Fallopian Tube Epithelial Cell Line after Long Passage with Features of High-Grade Serous Carcinoma

Ovarian cancer is one of the most lethal gynecological cancers, and 80% are high-grade serous carcinomas (HGSOC). Despite advances in chemotherapy and the development of targeted therapies, the survival rate of HGSOC has only moderately improved. Therefore, a cell model that reflects the pathogenesis and clinical characteristics of this disease is urgently needed. We previously developed a human fallopian tube epithelial cell line (FE25) with p53 and Rb deficiencies. After long-term culture in vitro, cells at high-passage numbers showed spontaneous transformation (FE25L). This study aimed to compare FE25 cells cultured in vitro for low (passage 16-31) and high passages (passage 116-139) to determine whether these cells can serve as an ideal cell model of HGSOC. Compared to the cells at low passage, FE25L cells showed increased cell proliferation, clonogenicity, polyploidy, aneuploidy, cell migration, and invasion. They also showed more resistance to chemotherapy and the ability to grow tumors in xenografts. RNA-seq data also showed upregulation of hypoxia, epithelial-mesenchymal transition (EMT), and the NF-κB pathway in FE25L compared to FE25 cells. qRT-PCR confirmed the upregulation of EMT, cytokines, NF-κB, c-Myc, and the Wnt/β-catenin pathway. Cross-platform comparability found that FE25L cells could be grouped with the other most likely HGSOC lines, such as TYKNU and COV362. In conclusion, FE25L cells showed more aggressive malignant behavior than FE25 cells and hence might serve as a more suitable model for HGSOC research.

Identification of Malignant Cell Populations Associated with Poor Prognosis in High-Grade Serous Ovarian Cancer Using Single-Cell RNA Sequencing

Simple Summary

Ovarian cancer has a high recurrence rate (~75%), and tumor heterogeneity is associated with such tumor recurrence. However, it is still poorly understood in ovarian cancer. To reveal tumor heterogeneity, we performed single-cell RNA sequencing (RNA-seq) of serous ovarian cancer cells from four different patients: two with primary carcinoma, one with recurrent carcinoma, and one with carcinoma treated with interval debulking surgery. As a result, we found two malignant tumor cell subtypes associated with poor prognosis. One malignant population included the earliest cancer cells and cancer stem-like cells. SLC3A1 and PEG10 were identified as the marker genes of cancer-initiating cells. The other malignant population expressing CA125 (MUC16), the most common biomarker for ovarian cancer, is associated with a decrease in the number of tumor-infiltrating cytotoxic T lymphocytes (CTLs). Our findings will offer new markers for diagnosis and choosing treatments targeting the malignant populations in ovarian cancer.

Abstract

To reveal tumor heterogeneity in ovarian cancer, we performed single-cell RNA sequencing (RNA-seq). We obtained The Cancer Genome Atlas (TCGA) survival data and TCGA gene expression data for a Kaplan–Meier plot showing the association of each tumor population with poor prognosis. As a result, we found two malignant tumor cell subtypes associated with poor prognosis. Next, we performed trajectory analysis using scVelo and Monocle3 and cell–cell interaction analysis using CellphoneDB. We found that one malignant population included the earliest cancer cells and cancer stem-like cells. Furthermore, we identified SLC3A1 and PEG10 as the marker genes of cancer-initiating cells. The other malignant population expressing CA125 (MUC16) is associated with a decrease in the number of tumor-infiltrating cytotoxic T lymphocytes (CTLs). Moreover, cell–cell interaction analysis implied that interactions mediated by LGALS9 and GAS6, expressed by this malignant population, caused the CTL suppression. The results of this study suggest that two tumor cell populations, including a cancer-initiating cell population and a population expressing CA125, survive the initial treatment and suppress antitumor immunity, respectively, and are associated with poor prognosis. Our findings offer a new understanding of ovarian cancer heterogeneity and will aid in the development of diagnostic tools and treatments.

Identification of the Immune Signatures for Ovarian Cancer Based on the Tumor Immune Microenvironment Genes

Ovarian cancer (OV) is a deadly gynecological cancer. The tumor immune microenvironment (TIME) plays a pivotal role in OV development. However, the TIME of OV is not fully known. Therefore, we aimed to provide a comprehensive network of the TIME in OV. Gene expression data and clinical information from OV patients were obtained from the Cancer Genome Atlas Program (TCGA) database. Non-negative Matrix Factorization, NMFConsensus, and nearest template prediction algorithms were used to perform molecular clustering. The biological functions of differentially expressed genes (DEGs) were identified using Metascape, gene set enrichment analysis (GSEA), gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The copy number variations (CNVs), single nucleotide polymorphisms (SNPs) and tumor mutation burden were analyzed using Gistic 2.0, R package maftools, and TCGA mutations, respectively. Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data and CIBERSORT were utilized to elucidate the TIME. Moreover, external data from the International Cancer Genome Consortium (ICGC) and ArrayExpress databases were used to validate the signature. All 361 samples from the TCGA OV dataset were classified into Immune Class and non-Immune Class with immune signatures. By comparing the two classes, we identified 740 DEGs that accumulated in immune-related, cancer-related, inflammation-related biological functions and pathways. There were significant differences in the CNVs between the Immune and non-Immune Classes. The Immune Class was further divided into immune-activated and immune-suppressed subtypes. There was no significant difference in the top 20 genes in somatic SNPs among the three groups. In addition, the immune-activated subtype had significantly increased proportions of CD4 memory resting T cells, T cells, M1 macrophages, and M2 macrophages than the other two groups. The qRT-PCR results indicated that the mRNA expression levels of RYR2, FAT3, MDN1 and RYR1 were significantly down-regulated in OV compared with normal tissues. Moreover, the signatures of the TIME were validated using ICGC cohort and the ArrayExpress cohort. Our study clustered the OV patients into an immune-activated subtype, immune-suppressed subtype, and non-Immune Class and provided potential clues for further research on the molecular mechanisms and immunotherapy strategies of OV.

Toward More Comprehensive Homologous Recombination Deficiency Assays in Ovarian Cancer, Part 1: Technical Considerations

Simple Summary

High-grade serous ovarian cancer (HGSOC) is the most frequent and lethal form of ovarian cancer and is associated with homologous recombination deficiency (HRD) in 50% of cases. This specific alteration is associated with sensitivity to PARP inhibitors (PARPis). Despite vast prognostic improvements due to PARPis, current molecular assays assessing HRD status suffer from several limitations, and there is an urgent need for a more accurate evaluation. In these companion reviews (Part 1: Technical considerations; Part 2: Medical perspectives), we develop an integrative review to provide physicians and researchers involved in HGSOC management with a holistic perspective, from translational research to clinical applications.

Abstract

High-grade serous ovarian cancer (HGSOC), the most frequent and lethal form of ovarian cancer, exhibits homologous recombination deficiency (HRD) in 50% of cases. In addition to mutations in BRCA1 and BRCA2, which are the best known thus far, defects can also be caused by diverse alterations to homologous recombination-related genes or epigenetic patterns. HRD leads to genomic instability (genomic scars) and is associated with PARP inhibitor (PARPi) sensitivity. HRD is currently assessed through BRCA1/2 analysis, which produces a genomic instability score (GIS). However, despite substantial clinical achievements, FDA-approved companion diagnostics (CDx) based on GISs have important limitations. Indeed, despite the use of GIS in clinical practice, the relevance of such assays remains controversial. Although international guidelines include companion diagnostics as part of HGSOC frontline management, they also underscore the need for more powerful and alternative approaches for assessing patient eligibility to PARP inhibitors. In these companion reviews, we review and present evidence to date regarding HRD definitions, achievements and limitations in HGSOC. Part 1 is dedicated to technical considerations and proposed perspectives that could lead to a more comprehensive and dynamic assessment of HR, while Part 2 provides a more integrated approach for clinicians.

Prognostic Value of Long Noncoding RNA DLEU2 and Its Relationship with Immune Infiltration in Kidney Renal Clear Cell Carcinoma and Liver Hepatocellular Carcinoma

Background

DLEU2 is a long noncoding RNA considered important in the progression of many cancers. However, correlations between DLEU2 and kidney renal clear cell carcinoma (KIRC) and liver hepatocellular carcinoma (LIHC) have rarely been reported.

Methods

We first analysed the expression of DLEU2 across cancers and the correlation between DLEU2 and the clinical features of KIRC and LIHC by using the “ggplot2” package in R and searched the Oncomine database and Timer website platform. We verified the expression of DLEU2 in the GEO dataset (GSE105261 and GSE45267). Receiver operating characteristic (ROC) curves were drawn using the “pROC” and “ggplot2” packages in R, and we constructed a DLEU2-based prognostic nomogram for KIRC and LIHC by using the “survival” and “rms” packages in R. Then, we analysed the correlation between DLEU2 expression and prognosis in R as well as the correlation between DLEU2 and immune cell infiltration in the TIMER database. Finally, we explored the causes of DLEU2 upregulation in the UCSC Xena and UALCAN databases.

Results

We found that DLEU2 was upregulated in many cancers, including KIRC and LIHC. Expression of DLEU2 is associated with tumour stage, grade, lymphatic metastasis, and distant metastasis in KIRC as well as alpha-fetoprotein (AFP), tumour stage, grade, lymphatic metastasis, and distant metastasis in LIHC. DLEU2 is an adverse factor for the prognosis of KIRC and LIHC. In addition, DLEU2 has moderate accuracy in diagnosing KIRC and LIHC and predicting their prognosis. Moreover, we found that expression of DLEU2 correlated positively with immune cell infiltration in KIRC and LIHC, and upregulation of DLEU2 in KIRC and LIHC suggests a poor prognosis based on immune cells analysis. Genetic and epigenetic analyses of DLEU2 indicate that copy number variations (CNVs) and methylation contribute to the upregulation of DLEU2.

Conclusion

The long noncoding RNA DLEU2 has the potential to predict the prognosis and immune infiltration of KIRC and LIHC.