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Cancers
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Omics in Ovarian Cancer
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Omics in Ovarian Cancer

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Molecular Cancer Biology".

Deadline for manuscript submissions: closed (15 September 2022) | Viewed by 35754

Special Issue Editors

Dr. Stefania Rizzo

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Guest Editor
1. Clinica di Radiologia Ente Ospedaliero Cantonale (EOC), Istituto Imaging della Svizzera Italiana, Via Tesserete 46, 6900 Lugano, Switzerland
2. Facoltà di Scienze biomediche, Università della Svizzera italiana (USI), Via G. Buffi 13, 6900 Lugano, Switzerland
Interests: oncologic imaging; gynecological imaging; radiomics; radiogenomics
Special Issues, Collections and Topics in MDPI journals
Dr. Lucia Manganaro

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Guest Editor
Department of Radiological, Oncological, and Pathological Sciences, Sapienza, University of Rome, 00161 Rome, Italy
Interests: magnetic resonance imaging; female pelvic imaging; pelvic floor imaging; fetal MRI; endometriosis and chronic pelvic pain; gynaecological pathologies; gynaecological malignancies; oncological imaging; fetal malformations

Special Issue Information

Dear Colleagues,

Omics studies including, but not limited to, proteomics, radiomics, genomics, radiogenomics, transcriptomics, and metabolomics rely on the high-throughput identification and quantification of molecules or imaging biomarkers, as an expression of complex biological mechanisms. Integrative omics in ovarian cancer may represent opportunities for identifying driver genes, as well as molecular signatures, to be used for prognostication and for guiding the path to targeted therapies.

Since a single technology individually cannot capture the entire biological complexity of cancer, integration of multiple technologies has emerged as an approach to provide a more comprehensive view of the biology of the disease. With the vast amount of data to be mined, machine learning tools have become indispensable in the data integration field, which will be necessary to understand, diagnose, and inform treatment of ovarian cancer patients.

This Special Issue will cover the current state-of-the-art of different omics studies in ovarian cancer and will highlight their role for a better understanding of this pathology covering a range of topics from basic to preclinical and clinical aspects.

Dr. Stefania Rizzo
Dr. Lucia Manganaro
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • proteomics
  • radiomics
  • genomics
  • radiogenomics
  • transcriptomics
  • metabolomics
  • ovarian cancer

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Published Papers (9 papers)

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Research

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16 pages, 1584 KiB  
Article
CT-Based Radiomics and Deep Learning for BRCA Mutation and Progression-Free Survival Prediction in Ovarian Cancer Using a Multicentric Dataset
by Giacomo Avesani, Huong Elena Tran, Giulio Cammarata, Francesca Botta, Sara Raimondi, Luca Russo, Salvatore Persiani, Matteo Bonatti, Tiziana Tagliaferri, Miriam Dolciami, Veronica Celli, Luca Boldrini, Jacopo Lenkowicz, Paola Pricolo, Federica Tomao, Stefania Maria Rita Rizzo, Nicoletta Colombo, Lucia Manganaro, Anna Fagotti, Giovanni Scambia, Benedetta Gui and Riccardo Manfrediadd Show full author list remove Hide full author list
Cancers 2022, 14(11), 2739; https://doi.org/10.3390/cancers14112739 - 31 May 2022
Cited by 26 | Viewed by 3904
Abstract
Purpose: Build predictive radiomic models for early relapse and BRCA mutation based on a multicentric database of high-grade serous ovarian cancer (HGSOC) and validate them in a test set coming from different institutions. Methods: Preoperative CTs of patients with HGSOC treated at four [...] Read more.
Purpose: Build predictive radiomic models for early relapse and BRCA mutation based on a multicentric database of high-grade serous ovarian cancer (HGSOC) and validate them in a test set coming from different institutions. Methods: Preoperative CTs of patients with HGSOC treated at four referral centers were retrospectively acquired and manually segmented. Hand-crafted features and deep radiomics features were extracted respectively by dedicated software (MODDICOM) and a dedicated convolutional neural network (CNN). Features were selected with and without prior harmonization (ComBat harmonization), and models were built using different machine learning algorithms, including clinical variables. Results: We included 218 patients. Radiomic models showed low performance in predicting both BRCA mutation (AUC in test set between 0.46 and 0.59) and 1-year relapse (AUC in test set between 0.46 and 0.56); deep learning models demonstrated similar results (AUC in the test of 0.48 for BRCA and 0.50 for relapse). The inclusion of clinical variables improved the performance of the radiomic models to predict BRCA mutation (AUC in the test set of 0.74). Conclusions: In our multicentric dataset, representative of a real-life clinical scenario, we could not find a good radiomic predicting model for PFS and BRCA mutational status, with both traditional radiomics and deep learning, but the combination of clinical and radiomic models improved model performance for the prediction of BRCA mutation. These findings highlight the need for standardization through the whole radiomic pipelines and robust multicentric external validations of results. Full article
(This article belongs to the Special Issue Omics in Ovarian Cancer)
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22 pages, 3317 KiB  
Article
Model Cell Lines and Tissues of Different HGSOC Subtypes Differ in Local Estrogen Biosynthesis
by Renata Pavlič, Marija Gjorgoska and Tea Lanišnik Rižner
Cancers 2022, 14(11), 2583; https://doi.org/10.3390/cancers14112583 - 24 May 2022
Cited by 10 | Viewed by 2915
Abstract
Ovarian cancer (OC) is highly lethal and heterogeneous. Several hormones are involved in OC etiology including estrogens; however, their role in OC is not completely understood. Here, we performed targeted transcriptomics and estrogen metabolism analyses in high-grade serous OC (HGSOC), OVSAHO, Kuramochi, COV632, [...] Read more.
Ovarian cancer (OC) is highly lethal and heterogeneous. Several hormones are involved in OC etiology including estrogens; however, their role in OC is not completely understood. Here, we performed targeted transcriptomics and estrogen metabolism analyses in high-grade serous OC (HGSOC), OVSAHO, Kuramochi, COV632, and immortalized normal ovarian epithelial HIO-80 cells. We compared these data with public transcriptome and proteome data for the HGSOC tissues. In all model systems, high steroid sulfatase expression and weak/undetected aromatase (CYP19A1) expression indicated the formation of estrogens from the precursor estrone-sulfate (E1-S). In OC cells, the metabolism of E1-S to estradiol was the highest in OVSAHO, followed by Kuramochi and COV362 cells, and decreased with increasing chemoresistance. In addition, higher HSD17B14 and CYP1A2 expressions were observed in highly chemoresistant COV362 cells and platinum-resistant tissues compared to those in HIO-80 cells and platinum-sensitive tissues. The HGSOC cell models differed in HSD17B10, CYP1B1, and NQO1 expression. Proteomic data also showed different levels of HSD17B10, CYP1B1, NQO1, and SULT1E1 between the four HGSOC subtypes. These results suggest that different HGSOC subtypes form different levels of estrogens and their metabolites and that the estrogen-biosynthesis-associated targets should be further studied for the development of personalized treatment. Full article
(This article belongs to the Special Issue Omics in Ovarian Cancer)
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18 pages, 2030 KiB  
Article
The Long Non-Coding RNA SNHG12 as a Mediator of Carboplatin Resistance in Ovarian Cancer via Epigenetic Mechanisms
by Cecilie Abildgaard, Luisa Matos do Canto, Cláudia Aparecida Rainho, Fabio Albuquerque Marchi, Naiade Calanca, Marianne Waldstrøm, Karina Dahl Steffensen and Silvia Regina Rogatto
Cancers 2022, 14(7), 1664; https://doi.org/10.3390/cancers14071664 - 25 Mar 2022
Cited by 7 | Viewed by 3510
Abstract
Genetic and epigenetic changes contribute to intratumor heterogeneity and chemotherapy resistance in several tumor types. LncRNAs have been implicated, directly or indirectly, in the epigenetic regulation of gene expression. We investigated lncRNAs that potentially mediate carboplatin-resistance of cell subpopulations, influencing the progression of [...] Read more.
Genetic and epigenetic changes contribute to intratumor heterogeneity and chemotherapy resistance in several tumor types. LncRNAs have been implicated, directly or indirectly, in the epigenetic regulation of gene expression. We investigated lncRNAs that potentially mediate carboplatin-resistance of cell subpopulations, influencing the progression of ovarian cancer (OC). Four carboplatin-sensitive OC cell lines (IGROV1, OVCAR3, OVCAR4, and OVCAR5), their derivative resistant cells, and two inherently carboplatin-resistant cell lines (OVCAR8 and Ovc316) were subjected to RNA sequencing and global DNA methylation analysis. Integrative and cross-validation analyses were performed using external (The Cancer Genome Atlas, TCGA dataset, n = 111 OC samples) and internal datasets (n = 39 OC samples) to identify lncRNA candidates. A total of 4255 differentially expressed genes (DEGs) and 14529 differentially methylated CpG positions (DMPs) were identified comparing sensitive and resistant OC cell lines. The comparison of DEGs between OC cell lines and TCGA-OC dataset revealed 570 genes, including 50 lncRNAs, associated with carboplatin resistance. Eleven lncRNAs showed DMPs, including the SNHG12. Knockdown of SNHG12 in Ovc316 and OVCAR8 cells increased their sensitivity to carboplatin. The results suggest that the lncRNA SNHG12 contributes to carboplatin resistance in OC and is a potential therapeutic target. We demonstrated that SNHG12 is functionally related to epigenetic mechanisms. Full article
(This article belongs to the Special Issue Omics in Ovarian Cancer)
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17 pages, 2539 KiB  
Article
The Role of the Extracellular Matrix and Tumor-Infiltrating Immune Cells in the Prognostication of High-Grade Serous Ovarian Cancer
by Yuri Belotti, Elaine Hsuen Lim and Chwee Teck Lim
Cancers 2022, 14(2), 404; https://doi.org/10.3390/cancers14020404 - 14 Jan 2022
Cited by 11 | Viewed by 3723
Abstract
Ovarian cancer is the eighth global leading cause of cancer-related death among women. The most common form is the high-grade serous ovarian carcinoma (HGSOC). No further improvements in the 5-year overall survival have been seen over the last 40 years since the adoption [...] Read more.
Ovarian cancer is the eighth global leading cause of cancer-related death among women. The most common form is the high-grade serous ovarian carcinoma (HGSOC). No further improvements in the 5-year overall survival have been seen over the last 40 years since the adoption of platinum- and taxane-based chemotherapy. Hence, a better understanding of the mechanisms governing this aggressive phenotype would help identify better therapeutic strategies. Recent research linked onset, progression, and response to treatment with dysregulated components of the tumor microenvironment (TME) in many types of cancer. In this study, using bioinformatic approaches, we identified a 19-gene TME-related HGSOC prognostic genetic panel (PLXNB2, HMCN2, NDNF, NTN1, TGFBI, CHAD, CLEC5A, PLXNA1, CST9, LOXL4, MMP17, PI3, PRSS1, SERPINA10, TLL1, CBLN2, IL26, NRG4, and WNT9A) by assessing the RNA sequencing data of 342 tumors available in the TCGA database. Using machine learning, we found that specific patterns of infiltrating immune cells characterized each risk group. Furthermore, we demonstrated the predictive potential of our risk score across different platforms and its improved prognostic performance compared with other gene panels. Full article
(This article belongs to the Special Issue Omics in Ovarian Cancer)
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22 pages, 6170 KiB  
Article
OTX015 Epi-Drug Exerts Antitumor Effects in Ovarian Cancer Cells by Blocking GNL3-Mediated Radioresistance Mechanisms: Cellular, Molecular and Computational Evidence
by Francesca Megiorni, Simona Camero, Paola Pontecorvi, Lucrezia Camicia, Francesco Marampon, Simona Ceccarelli, Eleni Anastasiadou, Nicola Bernabò, Giorgia Perniola, Antonio Pizzuti, Pierluigi Benedetti Panici, Vincenzo Tombolini and Cinzia Marchese
Cancers 2021, 13(7), 1519; https://doi.org/10.3390/cancers13071519 - 25 Mar 2021
Cited by 9 | Viewed by 2474
Abstract
Ovarian cancer (OC) is the most aggressive gynecological tumor worldwide and, notwithstanding the increment in conventional treatments, many resistance mechanisms arise, this leading to cure failure and patient death. So, the use of novel adjuvant drugs able to counteract these pathways is urgently [...] Read more.
Ovarian cancer (OC) is the most aggressive gynecological tumor worldwide and, notwithstanding the increment in conventional treatments, many resistance mechanisms arise, this leading to cure failure and patient death. So, the use of novel adjuvant drugs able to counteract these pathways is urgently needed to improve patient overall survival. A growing interest is focused on epigenetic drugs for cancer therapy, such as Bromodomain and Extra-Terminal motif inhibitors (BETi). Here, we investigate the antitumor effects of OTX015, a novel BETi, as a single agent or in combination with ionizing radiation (IR) in OC cellular models. OTX015 treatment significantly reduced tumor cell proliferation by triggering cell cycle arrest and apoptosis that were linked to nucleolar stress and DNA damage. OTX015 impaired migration capacity and potentiated IR effects by reducing the expression of different drivers of cancer resistance mechanisms, including GNL3 gene, whose expression was found to be significantly higher in OC biopsies than in normal ovarian tissues. Gene specific knocking down and computational network analysis confirmed the centrality of GNL3 in OTX015-mediated OC antitumor effects. Altogether, our findings suggest OTX015 as an effective option to improve therapeutic strategies and overcome the development of resistant cancer cells in patients with OC. Full article
(This article belongs to the Special Issue Omics in Ovarian Cancer)
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24 pages, 6657 KiB  
Article
Evaluation of the Role of ITGBL1 in Ovarian Cancer
by Alexander Jorge Cortez, Katarzyna Aleksandra Kujawa, Agata Małgorzata Wilk, Damian Robert Sojka, Joanna Patrycja Syrkis, Magdalena Olbryt and Katarzyna Marta Lisowska
Cancers 2020, 12(9), 2676; https://doi.org/10.3390/cancers12092676 - 19 Sep 2020
Cited by 9 | Viewed by 3938
Abstract
In our previous microarray study we identified two subgroups of high-grade serous ovarian cancers with distinct gene expression and survival. Among differentially expressed genes was an Integrin beta-like 1 (ITGBL1), coding for a poorly characterized protein comprised of ten EGF-like repeats. [...] Read more.
In our previous microarray study we identified two subgroups of high-grade serous ovarian cancers with distinct gene expression and survival. Among differentially expressed genes was an Integrin beta-like 1 (ITGBL1), coding for a poorly characterized protein comprised of ten EGF-like repeats. Here, we have analyzed the influence of ITGBL1 on the phenotype of ovarian cancer (OC) cells. We analyzed expression of four putative ITGBL1 mRNA isoforms in five OC cell lines. OAW42 and SKOV3, having the lowest level of any ITGBL1 mRNA, were chosen to produce ITGBL1-overexpressing variants. In these cells, abundant ITGBL1 mRNA expression could be detected by RT-PCR. Immunodetection was successful only in the culture media, suggesting that ITGBL1 is efficiently secreted. We found that ITGBL1 overexpression affected cellular adhesion, migration and invasiveness, while it had no effect on proliferation rate and the cell cycle. ITGBL1-overexpressing cells were significantly more resistant to cisplatin and paclitaxel, major drugs used in OC treatment. Global gene expression analysis revealed that signaling pathways affected by ITGBL1 overexpression were mostly those related to extracellular matrix organization and function, integrin signaling, focal adhesion, cellular communication and motility; these results were consistent with the findings of our functional studies. Overall, our results indicate that higher expression of ITGBL1 in OC is associated with features that may worsen clinical course of the disease. Full article
(This article belongs to the Special Issue Omics in Ovarian Cancer)
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Review

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19 pages, 3460 KiB  
Review
Maintenance Treatment of Newly Diagnosed Advanced Ovarian Cancer: Time for a Paradigm Shift?
by Paul DiSilvestro, Nicoletta Colombo, Philipp Harter, Antonio González-Martín, Isabelle Ray-Coquard and Robert L. Coleman
Cancers 2021, 13(22), 5756; https://doi.org/10.3390/cancers13225756 - 17 Nov 2021
Cited by 14 | Viewed by 6448
Abstract
Recent data have demonstrated substantial efficacy with poly (ADP-ribose) polymerase (PARP) inhibitors as treatment and/or maintenance therapy in patients with newly diagnosed advanced epithelial ovarian cancer (EOC). Here, we review efficacy and safety results from four recent Phase III trials in newly diagnosed [...] Read more.
Recent data have demonstrated substantial efficacy with poly (ADP-ribose) polymerase (PARP) inhibitors as treatment and/or maintenance therapy in patients with newly diagnosed advanced epithelial ovarian cancer (EOC). Here, we review efficacy and safety results from four recent Phase III trials in newly diagnosed EOC: SOLO1 (olaparib), PAOLA-1 (olaparib in combination with bevacizumab), PRIMA (niraparib), and VELIA (veliparib). The implications of these data for current clinical practice and areas for future research are discussed, including ongoing studies of targeted agents in the newly diagnosed setting. Data from SOLO1, PAOLA-1, PRIMA, and VELIA confirm the benefit of PARP inhibitors (olaparib, niraparib, veliparib) for women with newly diagnosed EOC. The greatest benefit was seen in patients with a BRCA1 and/or BRCA2 mutation or in the homologous recombination deficiency (HRD)-test positive subgroup. These four well-conducted studies have generated practice-changing data. However, deciding how to apply these results in clinical practice is challenging, and substantial differences in trial design impede cross-trial comparisons. Recent PARP inhibitor approvals (olaparib, niraparib) in the newly diagnosed EOC setting have provided new maintenance treatment options for a broader patient population. The results of these studies call for personalized medicine based on biomarker profile and other factors, including tolerability, cost considerations, and physician and patient preference. Important areas for future research include appropriate use of both BRCA mutation and HRD testing to inform magnitude of PARP inhibitor benefit as well as exploring further options for patients who are HRD-test negative and for those who become PARP inhibitor resistant. Full article
(This article belongs to the Special Issue Omics in Ovarian Cancer)
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16 pages, 680 KiB  
Review
Leveraging Genomics, Transcriptomics, and Epigenomics to Understand the Biology and Chemoresistance of Ovarian Cancer
by Sandra Muñoz-Galván and Amancio Carnero
Cancers 2021, 13(16), 4029; https://doi.org/10.3390/cancers13164029 - 10 Aug 2021
Cited by 13 | Viewed by 3346
Abstract
Ovarian cancer is a major cause of fatality due to a gynecological malignancy. This lethality is largely due to the unspecific clinical manifestations of ovarian cancer, which lead to late detection and to high resistance to conventional therapies based on platinum. In recent [...] Read more.
Ovarian cancer is a major cause of fatality due to a gynecological malignancy. This lethality is largely due to the unspecific clinical manifestations of ovarian cancer, which lead to late detection and to high resistance to conventional therapies based on platinum. In recent years, we have advanced our understanding of the mechanisms provoking tumor relapse, and the advent of so-called omics technologies has provided exceptional tools to evaluate molecular mechanisms leading to therapy resistance in ovarian cancer. Here, we review the contribution of genomics, transcriptomics, and epigenomics techniques to our knowledge about the biology and molecular features of ovarian cancers, with a focus on therapy resistance. The use of these technologies to identify molecular markers and mechanisms leading to chemoresistance in these tumors is discussed, as well as potential further applications. Full article
(This article belongs to the Special Issue Omics in Ovarian Cancer)
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Other

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11 pages, 600 KiB  
Systematic Review
Computed Tomography Based Radiomics as a Predictor of Survival in Ovarian Cancer Patients: A Systematic Review
by Stefania Rizzo, Lucia Manganaro, Miriam Dolciami, Maria Luisa Gasparri, Andrea Papadia and Filippo Del Grande
Cancers 2021, 13(3), 573; https://doi.org/10.3390/cancers13030573 - 2 Feb 2021
Cited by 29 | Viewed by 3828
Abstract
The objective of this systematic review was to assess the results of radiomics for prediction of overall survival (OS) and progression free survival (PFS) in ovarian cancer (OC) patients. A secondary objective was to evaluate the findings of papers that based their analyses [...] Read more.
The objective of this systematic review was to assess the results of radiomics for prediction of overall survival (OS) and progression free survival (PFS) in ovarian cancer (OC) patients. A secondary objective was to evaluate the findings of papers that based their analyses on inter-site heterogeneity. This systematic review was conducted according to the PRISMA statement. After the initial retrieval of 145 articles, the final systematic review comprised six articles. Association between radiomic features and OS was evaluated in 3/6 studies (50%); all articles showed a significant association between radiomic features and OS. Association with PFS was evaluated in 5/6 (83%) articles; the period of follow-up ranged between six and 36 months. All the articles showed significant association between radiomic models and PFS. Inter-site textural features were used for analysis in 2/6 (33%) articles. They demonstrated that high levels of inter-site textural heterogeneity were significantly associated with incomplete surgical resection in breast cancer gene-negative patients, and that lower heterogeneity was associated with complete resectability. There were some differences among papers in methodology; for example, only 3/6 (50%) articles included validation cohorts. In conclusion, radiomic models have demonstrated promising results as predictors of survival in OC patients, although larger studies are needed to allow clinical applicability. Full article
(This article belongs to the Special Issue Omics in Ovarian Cancer)
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