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12 pages, 1615 KiB  
Protocol
Establishment of Stable Knockdown of MACC1 Oncogene in Patient-Derived Ovarian Cancer Organoids
by Sophia Hierlmayer, Liliia Hladchenko, Juliane Reichenbach, Christoph Klein, Sven Mahner, Fabian Trillsch, Mirjana Kessler and Anca Chelariu-Raicu
Methods Protoc. 2024, 7(6), 104; https://doi.org/10.3390/mps7060104 - 20 Dec 2024
Viewed by 550
Abstract
High-grade serous ovarian cancer (HGSOC) remains the most lethal gynecological malignancy, and there is still an unmet medical need to deepen basic research on its origins and mechanisms of progression. Patient-derived organoids of high-grade serous ovarian cancer (HGSOC-PDO) are a powerful model to [...] Read more.
High-grade serous ovarian cancer (HGSOC) remains the most lethal gynecological malignancy, and there is still an unmet medical need to deepen basic research on its origins and mechanisms of progression. Patient-derived organoids of high-grade serous ovarian cancer (HGSOC-PDO) are a powerful model to study the complexity of ovarian cancer as they maintain, in vitro, the mutational profile and cellular architecture of the cancer tissue. Genetic modifications by lentiviral transduction allow novel insights into signaling pathways and the potential identification of biomarkers regarding the evolution of drug resistance. Here, we provide an in-depth and detailed protocol to successfully modify the gene expression of HGSOC-PDOs by lentiviral transduction. As an example, we validate our protocol and create a stable knockdown of the MACC1 oncogene with an efficacy of ≥72% in two HGSOC-PDO lines, which remained stable for >3 months in culture. Moreover, we explain step-by-step the sample preparation for the validation procedures on transcriptional (qPCR) and protein (Western Blot) levels. Sustained downregulation of specific genes by lentiviral transduction enables the analysis of the resulting phenotypic and morphological changes. It serves as a valuable in-vitro model to study the mechanisms of ovarian cancer pathogenesis and allows for the evaluation of therapeutic approaches. Full article
(This article belongs to the Section Tissue Engineering and Organoids)
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<p>Overview of the experimental stages of lentiviral transduction of ovarian cancer organoids.</p>
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<p>Depletion of MACC1 affects transcriptional and protein levels in transduced organoid lines. (<b>A</b>) HGS_06 after puromycin treatment. Comparison of the three conditions, shControl, shMACC1-KD, and Mock, after puromycin treatment (1 mg/mL) on Day 10 after lentiviral transduction. (<b>B</b>) HGS_06 shControl and shMACC1 KD after 2 months of organoid cultivation. The mock sample, as expected, underwent complete growth arrest under puromycin treatment and could not be expanded. Scale bar 200 µm. (<b>C</b>) qPCR of relative MACC1 expression levels in the long-term culture of transduced organoid lines. Individual data points represent independent biological replicates. Taken at 1-, 2- and 3-months post-transduction. Fold changes are calculated based on the vddCT values normalized to the control organoid line. Error bars are +-SEM. ** <span class="html-italic">p</span> = 0.0022 and *** <span class="html-italic">p</span> = 0.0006 in paired student <span class="html-italic">t</span>-test. (<b>D</b>) MACC1 knockdown as an effect of shMACC1 introduction was confirmed in both organoid lines by Western Blot analysis.</p>
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12 pages, 2241 KiB  
Article
Molecular Subgroups of HRD Positive Ovarian Cancer and Their Prognostic Significance
by Tatiana Kekeeva, Irina Dudina, Yulia Andreeva, Alexander Tanas, Alexey Kalinkin, Victoria Musatova, Natalia Chernorubashkina, Svetlana Khokhlova, Tatiana Tikhomirova, Mikhail Volkonsky, Sergey Kutsev, Dmitry Zaletaev and Vladimir Strelnikov
Int. J. Mol. Sci. 2024, 25(24), 13549; https://doi.org/10.3390/ijms252413549 - 18 Dec 2024
Viewed by 557
Abstract
Homologous recombination repair deficiency (HRD) is involved in the development of high-grade serous ovarian carcinoma (HGSOC) and its elevated sensitivity to platinum-based chemotherapy. To investigate the heterogeneity of the HRD-positive HGSOC we evaluated the HRD status, including BRCA mutations, genomic scar score, and [...] Read more.
Homologous recombination repair deficiency (HRD) is involved in the development of high-grade serous ovarian carcinoma (HGSOC) and its elevated sensitivity to platinum-based chemotherapy. To investigate the heterogeneity of the HRD-positive HGSOC we evaluated the HRD status, including BRCA mutations, genomic scar score, and methylation status of BRCA1/2 genes in 352 HGSOC specimens. We then divided the HRD-positive cohort into three molecular subgroups, the BRCA mutation cohort (BRCA+), BRCA1 methylation cohort (Meth+), and the rest of the HRD+ cohort (HRD+BRCA-Meth-), and evaluated their first-line chemotherapy response, benefit from olaparib, and progression-free survival (PFS). HRD-positive status was detected in 65% (228/352) of samples. The first group, BRCA+, accounted for 45% (102/228) of HRD positive cases and showed the best outcome in platinum therapy (ORR 96%), the highest olaparib benefit (p = 0.006) and the highest median PFS (46 months). The frequency of the second cohort, Meth+, among HRD-positive patients was 23% (52/228). Patients with Meth+ HGSOC showed a significantly poorer outcome, with a median PFS of 19 months, a significantly lower ORR to platinum therapy (84%) and a modest, but not significant, benefit from olaparib maintenance. The third HRD+BRCA-Meth- group accounted for 32% (74/228) of HRD-positive patients and showed an ORR to platinum therapy similar to that of the BRCA+ group (90%), a higher, but not statistically significant, benefit from olaparib and a median PFS of 23 months. In conclusion, Meth+ subgroup had poor outcomes in terms of chemotherapy response, olaparib benefit, and PFS compared to the other HRD+ subgroups, requiring a more thorough follow-up. Full article
(This article belongs to the Special Issue Biomarkers and Early Detection Strategies of Ovarian Tumors)
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<p>Methylated CpG dinucleotides with positions referring to the translation initiation. (<b>A</b>)—BS sequence of <span class="html-italic">BRCA1</span> (NM_007294.4) exon 1. (<b>B</b>)—BS sequence of <span class="html-italic">BRCA2</span> (NM_000059.3) promoter region.</p>
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<p>(<b>A</b>)—Distribution of patient age (years) in molecular subgroups. (<b>B</b>)—Distribution of individual GSS scores in molecular subgroups. * indicates a significant difference at <span class="html-italic">p</span> &lt; 0.0001. ** indicates a significant difference at <span class="html-italic">p</span> = 0.006. *** indicates a significant difference at <span class="html-italic">p</span> = 0.01.</p>
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<p>Progression free survival. (<b>A</b>)—PFS in patients with HRD- and HRD+ HGSOC. (<b>B</b>)—PFS within HRD positive cohort, which included the BRCA mutation cohort (BRCA+), the <span class="html-italic">BRCA1</span> methylation cohort (Meth+), all the rest of HRD+ cohort (BRCA-Meth-). NE—not estimated.</p>
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<p>Forest plot of hazard ratios comparing progression-free survival (PFS) of patients treated with chemotherapy + olaparib with that of patients treated with chemotherapy only.</p>
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13 pages, 4472 KiB  
Article
The Small GTPase Ran Increases Sensitivity of Ovarian Cancer Cells to Oncolytic Vesicular Stomatitis Virus
by Karen Geoffroy, Mélissa Viens, Emma Mary Kalin, Zied Boudhraa, Dominic Guy Roy, Jian Hui Wu, Diane Provencher, Anne-Marie Mes-Masson and Marie-Claude Bourgeois-Daigneault
Pharmaceuticals 2024, 17(12), 1662; https://doi.org/10.3390/ph17121662 - 10 Dec 2024
Viewed by 701
Abstract
Background/Objectives: Ovarian cancer is the deadliest gynecologic cancer, and with the majority of patients dying within the first five years of diagnosis, new therapeutic options are required. The small guanosine triphosphatase (GTPase) Ras-related nuclear protein (Ran) has been reported to be highly expressed [...] Read more.
Background/Objectives: Ovarian cancer is the deadliest gynecologic cancer, and with the majority of patients dying within the first five years of diagnosis, new therapeutic options are required. The small guanosine triphosphatase (GTPase) Ras-related nuclear protein (Ran) has been reported to be highly expressed in high-grade serous ovarian cancers (HGSOCs) and associated with poor outcomes. Blocking Ran function or preventing its expression were shown to be promising treatment strategies, however, there are currently no small molecule inhibitors available to specifically inhibit Ran function. Interestingly, a previous study suggested that the Vesicular stomatitis virus (VSV) could inhibit Ran activity. Given that VSV is an oncolytic virus (OV) and, therefore, has anti-cancer activity, we reasoned that oncolytic VSV (oVSV) might be particularly effective against ovarian cancer via Ran inhibition. Methods: We evaluated the sensitivity of patient-derived ovarian cancer cell lines to oVSV, as well as the impact of oVSV on Ran and vice versa, using overexpression systems, small interfering RNAs (siRNAs), and drug inhibition. Results: In this study, we evaluated the interplay between oVSV and Ran and found that, although oVSV does not consistently block Ran, increased Ran activation allows for better oVSV replication and tumor cell killing. Conclusions: Our study reveals a positive impact of Ran on oVSV sensitivity. Given the high expression of Ran in HGSOCs, which are particularly aggressive ovarian cancers, our data suggest that oVSV could be effective against the deadliest form of the disease. Full article
(This article belongs to the Special Issue Oncolytic Viruses: New Cancer Immunotherapy Drugs)
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<p>oVSV infects and kills human ovarian cancer cells. (<b>A</b>) Representative fluorescence pictures of TOV112D, TOV21G, TOV1946, OV1946, TOV3133G, OV2085, TOV2835EP, TOV3041G, TOV3392D, OV3331, and TOV2414 cells infected with oVSV-YFP at an MOI of 0.1 for 24 h. (<b>B</b>) Relative cell viabilities 24, 48, and 72 h post-infection with oVSV at an MOI of 0.1 (n = 6). Dotted lines highlight 100% and 50% viabilities. (<b>C</b>) Ran expression of ovarian cancer cells as measured by Western blot. GAPDH and vinculin are protein-loading controls.</p>
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<p>oVSV infection does not consistently inhibit Ran. Ran activation assay measuring RanGTP (activated Ran) and total Ran (used here as a loading control) by Western blot upon Ran pull-down. A polyclonal antibody against VSV, recognizing multiple viral proteins, was also used to detect infection. Cells were either left untreated or infected with oVSV-YFP at the indicated MOIs for 8 h. MOIs used for each cell line were selected based on the viral sensitivities determined in <a href="#pharmaceuticals-17-01662-f001" class="html-fig">Figure 1</a>. (<b>A</b>–<b>C</b>) show different effects of infection on RanGTP expression. NV = no virus.</p>
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<p>Ran KD decreases VSV replication. (<b>A</b>) Fluorescence intensities of TOV112D, TOV1946, TOV3041G, and TOV2835EP cells transfected with non-targeting (NT) or Ran-targeting siRNAs and infected 16–24 h later with oVSV-YFP at the indicated MOIs (n = 3). (<b>B</b>) Virus outputs (plaque forming units (PFUs)) were measured 24 h post-infection (n = 3). The dotted lines represent viral inputs. Unpaired multiple <span class="html-italic">t</span>-test: *: <span class="html-italic">p</span> ≤ 0.05; **: <span class="html-italic">p</span> ≤ 0.01; ***: <span class="html-italic">p</span> ≤ 0.001; ****: <span class="html-italic">p</span> ≤ 0.0001.</p>
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<p>Ran inhibition impairs VSV replication. (<b>A</b>) Fluorescent intensities of TOV112D, TOV1946, and OV3331 cells treated or not with M36 (40 µM) for 16 h prior to infection with oVSV-YFP at the indicated MOIs (n ≥ 3). (<b>B</b>) Virus outputs were quantified 24 h post-infection (n ≥ 3). The dotted lines represent viral inputs. Statistical analyses by unpaired multiple <span class="html-italic">t</span>-test: ns: <span class="html-italic">p</span> &gt; 0.05; *: <span class="html-italic">p</span> ≤ 0.05; **: <span class="html-italic">p</span> ≤ 0.01; ***: <span class="html-italic">p</span> ≤ 0.001; ****: <span class="html-italic">p</span> ≤ 0.0001.</p>
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<p>Ran KD and inhibition impair oVSV-mediated cancer killing. Relative viability of (<b>A</b>) TOV112D, TOV1946, TOV3041G, and TOV2835EP cells transfected with control non-targeting (NT) or Ran-targeting siRNAs and infected with oVSV-YFP at various MOIs for 24 h (n = 3) or (<b>B</b>) TOV112D, TOV1946, and OV3331 cells pre-treated with M36 (40 µM) for 16 h and infected with oVSV-YFP at various MOIs for 24 h (n = 3). The dotted lines represent viability in non-infected control conditions. Unpaired multiple <span class="html-italic">t</span>-test: *: <span class="html-italic">p</span> ≤ 0.05; **: <span class="html-italic">p</span> ≤ 0.01; ***: <span class="html-italic">p</span> ≤ 0.001; ****: <span class="html-italic">p</span> ≤ 0.0001.</p>
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<p>Ran activation enhances oVSV. (<b>A</b>) Virus outputs and (<b>B</b>) cell viability of TOV1946 and OV3331 cells transfected with constitutive active (CA) or wild type (WT) Ran constructs and infected with oVSV-RFP 24 h later (n ≥ 3). Samples were collected 24 h post-infection. Dotted lines represent viral inputs (<b>A</b>) or viability in non-infected control conditions in (<b>B</b>). Unpaired multiple <span class="html-italic">t</span>-test: ns: <span class="html-italic">p</span> &gt; 0.05; *: <span class="html-italic">p</span> ≤ 0.05, ***: <span class="html-italic">p</span> ≤ 0.001.</p>
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17 pages, 5189 KiB  
Article
Establishment of Novel High-Grade Serous Ovarian Carcinoma Cell Line OVAR79
by Polina V. Shnaider, Irina K. Malyants, Olga M. Ivanova, Veronika S. Gordeeva, Ekaterina A. Svirina, Natalya B. Zakharzhevskaya, Olga Y. Shagaleeva, Oksana V. Selezneva, Alexandra N. Bogomazova, Maria M. Lukina, Olga I. Aleshikova, Nataliya A. Babaeva, Andrey V. Slonov and Victoria O. Shender
Int. J. Mol. Sci. 2024, 25(24), 13236; https://doi.org/10.3390/ijms252413236 - 10 Dec 2024
Viewed by 737
Abstract
High-grade serous ovarian carcinoma (HGSOC) remains the most common and deadly form of ovarian cancer. However, available cell lines usually fail to appropriately represent its complex molecular and histological features. To overcome this drawback, we established OVAR79, a new cell line derived from [...] Read more.
High-grade serous ovarian carcinoma (HGSOC) remains the most common and deadly form of ovarian cancer. However, available cell lines usually fail to appropriately represent its complex molecular and histological features. To overcome this drawback, we established OVAR79, a new cell line derived from the ascitic fluid of a patient with a diagnosis of HGSOC, which adds a unique set of properties to the study of ovarian cancer. In contrast to the common models, OVAR79 expresses TP53 without the common hotspot mutations and harbors the rare combination of mutations in both PIK3CA and PTEN genes, together with high-grade chromosomal instability with multiple gains and losses. These features, together with the high proliferation rate, ease of cultivation, and exceptional transfection efficiency of OVAR79, make it a readily available and versatile tool for various studies in the laboratory. We extensively characterized its growth, migration, and sensitivity to platinum- and taxane-based treatments in comparison with the commonly used SKOV3 and OVCAR3 ovarian cell lines. In summary, OVAR79 is an excellent addition for basic and translational ovarian cancer research and offers new insights into the biology of HGSOC. Full article
(This article belongs to the Special Issue Current Research for Ovarian Cancer Biology and Therapeutics)
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<p>Morphological, growth, and migratory characteristics of the OVAR79 cell line in comparison to SKOV3 and OVCAR3 cell lines. (<b>A</b>) Short tandem repeat (STR) profiling of the OVAR79 cell line. (<b>B</b>) Representative phase-contrast images of OVAR79 cells at low (left image) and high (right image) confluency. (<b>C</b>) Growth curves of OVAR79, SKOV3, and OVCAR3 cell lines. The proliferation rates were measured over 185 h with regular time-point assessments. The data represent the mean ± SD from 3 biologically independent replicates. (<b>D</b>,<b>E</b>) Wound healing assay of OVAR79, SKOV3, and OVCAR3 cell lines. The width of the wound area was measured immediately after scratching (0 h), with wound closure quantified after 8 and 24 h. The bar graph (<b>D</b>) illustrates the average wound closure at each time-point, represented as mean ± SD (<span class="html-italic">n</span> = 3 biologically independent replicates). (<b>F</b>) Fluorescence and phase-contrast images of OVAR79, OVCAR3 and SKOV3 cell lines expressing SRSF2-RFP protein (red).</p>
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<p>Sanger sequencing electropherograms of the OVAR79 cell line showing mutations in the <span class="html-italic">PIK3CA</span> and <span class="html-italic">PTEN</span> genes. (<b>A</b>) A single-nucleotide substitution in exon 1 (c.338T&gt;C) leading to a codon change (CTC&gt;CCC) and an amino acid substitution (Leu113Pro). (<b>B</b>) An indel mutation in exon 9 with a GT deletion and C insertion at position 1658–1659 leading to a codon change (AGT&gt;ACC) and a frameshift. (<b>C</b>) A single-nucleotide variant in intron 2, rs1903858 at chr10:87893929. (<b>D</b>) An indel mutation around exon 4, rs1426397261, due to a duplication of ATACATATT to ATACATATTATACATATT, located at chr10:87931188-87931196.</p>
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<p>Karyotype of the OVAR79 cell line. Regions of gain are indicated in green, regions of loss in red, and copy-neutral losses of heterozygosity (cnLOH) are shown in light blue.</p>
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<p>(<b>A</b>) RT-qPCR analysis of mRNA expression levels of epithelial, mesenchymal, and stemness markers in OVAR79, SKOV3, and OVCAR3 cell lines. Bars represent the relative expression levels of each marker normalized to GAPDH (<span class="html-italic">n</span> = 3 biologically independent experiments). Data represent the mean values ± SEM. (<b>B</b>) Flow cytometry analysis of ovarian cancer markers in OVAR79, SKOV3, and OVCAR3 cell lines. The green color represents the antibody of interest, while the blue color corresponds to the respective isotype control for each antibody. Graphs display marker intensity on the x-axis and the percentage of the cell population on the y-axis (%). (<b>C</b>) Dose–response curves obtained by MTT assay of OVAR79, OVCAR3, and SKOV3 cells that were treated with different concentrations of cisplatin, carboplatin, or paclitaxel for 48 h. The data represent the mean values ± SD (<span class="html-italic">n</span> = 3 biologically independent replicates). IC50 values were determined by fitting a normalized dose–response model to the data using nonlinear regression in GraphPad Prism 8.0 software.</p>
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16 pages, 3274 KiB  
Article
Evaluation of BRIP-1 (FANCJ) and FANCI Protein Expression in Ovarian Cancer Tissue
by Mateusz Kozłowski, Dominika Borzyszkowska, Anna Golara, Damian Durys, Katarzyna Piotrowska, Agnieszka Kempińska-Podhorodecka and Aneta Cymbaluk-Płoska
Biomedicines 2024, 12(12), 2652; https://doi.org/10.3390/biomedicines12122652 - 21 Nov 2024
Viewed by 636
Abstract
Background: Ovarian cancer is one of the most common cancers in women. Markers associated with ovarian cancer are still being sought. The aim of this study was to evaluate the expression of BRIP-1 (FANCJ) and FANCI proteins in ovarian cancer tissue and to [...] Read more.
Background: Ovarian cancer is one of the most common cancers in women. Markers associated with ovarian cancer are still being sought. The aim of this study was to evaluate the expression of BRIP-1 (FANCJ) and FANCI proteins in ovarian cancer tissue and to assess these expressions in differentiating the described clinical features. Methods: The study enrolled 68 patients with ovarian cancer. The cohort was divided into a HGSOC (high-grade serous ovarian cancer) group and a non-HGSOC group, which represented ovarian cancer other than HGSOC. Immunohistochemical evaluation of FANCI and BRIP-1 (FANCJ) protein expression in ovarian cancer tissue samples was performed. All statistical analyses were performed using StatView software (Carry, NC, USA). Results: The FANCI protein mostly showed moderate positive and strong positive expression, while BRIP-1 protein mostly showed no expression or positive expression. Patients with lower expression of FANCI and BRIP-1 showed differences in the clinical stage of HGSOC, which was not observed in patients with higher expression of these proteins. In addition, patients with lower BRIP-1 expression showed differences in menopausal status, which was not observed in patients with higher expression of this protein. Conclusions: This study shows that FANCI protein is a marker associated with lower FIGO stage and histologically high-grade cancer in a group of all ovarian cancers and in non-HGSOC. Full article
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<p>IHC evaluation of FANCI and BRIP: Study and control group samples. 0—negative reaction, 1—positive reaction, 2—moderate positive reaction, 3—strong positive reaction. Objective magnification ×20, scale bars 50 µm.</p>
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<p>ROC curve for all cancers considering FIGO for FANCI protein.</p>
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<p>ROC curve for all cancers considering grade for FANCI protein.</p>
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<p>ROC curve for all cancers considering BMI for FANCI protein.</p>
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<p>ROC curve for non-HGSOC considering FIGO stage for FANCI protein.</p>
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<p>ROC curve for non-HGSOC considering grade for FANCI protein.</p>
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<p>ROC curve for non-HGSOC considering histological type for FANCI protein.</p>
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20 pages, 5538 KiB  
Article
Reduced Levels of miR-145-3p Drive Cell Cycle Progression in Advanced High-Grade Serous Ovarian Cancer
by Eva González-Cantó, Mariana Monteiro, Cristina Aghababyan, Ana Ferrero-Micó, Sergio Navarro-Serna, Maravillas Mellado-López, Sarai Tomás-Pérez, Juan Sandoval, Antoni Llueca, Alejandro Herreros-Pomares, Juan Gilabert-Estellés, Vicente Pérez-García and Josep Marí-Alexandre
Cells 2024, 13(22), 1904; https://doi.org/10.3390/cells13221904 - 18 Nov 2024
Viewed by 1007
Abstract
High-grade serous ovarian cancer (HGSOC) is the most lethal form of gynecologic cancer, with limited treatment options and a poor prognosis. Epigenetic factors, such as microRNAs (miRNAs) and DNA methylation, play pivotal roles in cancer progression, yet their specific contributions to HGSOC remain [...] Read more.
High-grade serous ovarian cancer (HGSOC) is the most lethal form of gynecologic cancer, with limited treatment options and a poor prognosis. Epigenetic factors, such as microRNAs (miRNAs) and DNA methylation, play pivotal roles in cancer progression, yet their specific contributions to HGSOC remain insufficiently understood. In this study, we performed comprehensive high-throughput analyses to identify dysregulated miRNAs in HGSOC and investigate their epigenetic regulation. Analysis of tissue samples from advanced-stage HGSOC patients revealed 20 differentially expressed miRNAs, 11 of which were corroborated via RT-qPCR in patient samples and cancer cell lines. Among these, miR-145-3p was consistently downregulated post-neoadjuvant therapy and was able to distinguish tumoural from control tissues. Further investigation confirmed that DNA methylation controls MIR145 expression. Functional assays showed that overexpression of miR-145-3p significantly reduced cell migration and induced G0/G1 cell cycle arrest by modulating the cyclin D1-CDK4/6 pathway. These findings suggest that miR-145-3p downregulation enhances cell proliferation and motility in HGSOC, implicating its restoration as a potential therapeutic target focused on G1/S phase regulation in the treatment of HGSOC. Full article
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<p>Integrated analysis of miRNA expression profiles in HGSOC: insights from PCA, correlation, and treatment stratification. (<b>A</b>) Principal component analysis of miRNA sequencing results assessing the expression of miRNAs between HGSOC and PCOT groups. (<b>B</b>) miRNA expression results confirm miRNA sequencing results. RT-qPCR validation of miRNA sequencing results of down- and up-regulated miRNAs in HGSOC tissues (<span class="html-italic">n</span> = 20) compared to PCOT (<span class="html-italic">n</span> = 20). miRNA expression levels were normalized to the expression levels of their corresponding control. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001; and Mann–Whitney U test. (<b>C</b>) Correlation between expression levels of miR-145-5p and miR-145-3p, miR-143-5p and miR-145-3p, and miR-143-5p and miR-145-5p. Spearman’s rank correlation. (<b>D</b>) Differential expression analysis between patients who received either neoadjuvant treatment (w/NT) or no treatment before surgery (w/o NT) for up-regulated miRNAs and down-regulated miRNAs. NT, neoadjuvant treatment. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; and Mann–Whitney U test.</p>
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<p><span class="html-italic">MIR145</span> is modulated by DNA methylation. (<b>A</b>) Methylation status of the proximal (200 bp upstream of the transcription starting site) and distal (1500 bp upstream of the transcription starting site) promoter of <span class="html-italic">MIR145</span>. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; and Mann–Whitney U test. (<b>B</b>) Influence of 5-Aza treatment on the relative expression levels of the miR-145-5p and the miR-145-3p in three OC cell lines. ns: not significant; * <span class="html-italic">p</span> &lt; 0.05; *** <span class="html-italic">p</span> &lt; 0.001; and Student <span class="html-italic">t</span>-test.</p>
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<p>(<b>A</b>–<b>C</b>) Differential expression analysis of miR-145-3p in (<b>A</b>) Caov-3, (<b>B</b>) SK-OV-3, and (<b>C</b>) SW-626 cells transfected with the miR-145-3p mimic or with a scramble control. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; and Student <span class="html-italic">t</span>-test. (<b>D</b>–<b>G</b>) Caov-3 (<b>D</b>) and SK-OV-3 (<b>E</b>) cells were transfected with the miR-145-3p mimic or with a scramble control; 48 h after transfection, cells were scratch-wounded and were incubated in their appropriate complete medium for 72 h, and pictures were captured every 2 h post-scratching. Yellow lines indicate the wound borders. For Caov-3 (<b>F</b>) and SK-OV-3 (<b>G</b>): wound confluence (%) represents the fractional area of the wound that is occupied by cells; wound width represents the area of the wound that is not occupied by cells; and the area represents the cell-covered area of the well. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001; and Mann–Whitney U test.</p>
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<p>Effects of miR-145-3p on cell cycle and cyclin D1-CDK4/6 pathway regulation in OC cell lines. (<b>A</b>,<b>B</b>) Cell cycle analysis of: (<b>A</b>) Caov-3 and (<b>B</b>) SK-OV-3 cells without miR-145-3p expression and cells overexpressing miR-145-3p. * <span class="html-italic">p</span> &lt; 0.05; and Student <span class="html-italic">t</span>-test. (<b>C</b>,<b>D</b>) Differential expression analysis of <span class="html-italic">CCND1</span>, <span class="html-italic">CDK4,</span> and <span class="html-italic">CDK6</span> genes between: (<b>C</b>) Caov-3 and (<b>D</b>) SK-OV-3 cells without miR-145-3p expression and cells overexpressing miR-145-3p. ** <span class="html-italic">p</span> &lt; 0.01; and Student <span class="html-italic">t</span>-test. (<b>E</b>,<b>F</b>) Western blot analysis of <span class="html-italic">CCND1</span>, <span class="html-italic">CDK4,</span> and <span class="html-italic">CDK6</span> genes between (<b>E</b>) Caov-3 and (<b>F</b>) SK-OV-3 cells without miR-145-3p expression and cells overexpressing miR-145-3p.</p>
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13 pages, 6113 KiB  
Article
Dynamic Contrast-Enhanced and Diffusion-Weighted Imaging in Magnetic Resonance in the Assessment of Peritoneal Recurrence of Ovarian Cancer in Patients with or Without BRCA Mutation
by Melania Jankowska-Lombarska, Laretta Grabowska-Derlatka, Leszek Kraj and Pawel Derlatka
Cancers 2024, 16(22), 3738; https://doi.org/10.3390/cancers16223738 - 5 Nov 2024
Viewed by 804
Abstract
Background: The aim of this study was to determine the differences in diffusion-weighted imaging (DWI) and dynamic contrast enhancement (DCE) parameters between patients with peritoneal high-grade serous ovarian cancer (HGSOC) recurrence with BRCA mutations (BRCAmut) or BRCA wild type (BRCAwt). Materials and Methods: [...] Read more.
Background: The aim of this study was to determine the differences in diffusion-weighted imaging (DWI) and dynamic contrast enhancement (DCE) parameters between patients with peritoneal high-grade serous ovarian cancer (HGSOC) recurrence with BRCA mutations (BRCAmut) or BRCA wild type (BRCAwt). Materials and Methods: We retrospectively analyzed the abdominal and pelvic magnetic resonance (MR) images of 43 patients suspected of having recurrent HGSOC, of whom 18 had BRCA1/2 gene mutations. Patients underwent MRI examination via a 1.5 T MRI scanner, and the analyzed parameters were as follows: apparent diffusion coefficient (ADC), time to peak (TTP) and perfusion maximum enhancement (Perf. Max. En.). Results: The mean ADC in patients with BRCAwt was lower than that in patients with BRCAmut: 788.7 (SD: 139.5) vs. 977.3 (SD: 103), p-value = 0.00002. The average TTP value for patients with BRCAwt was greater than that for patients with mutations: 256.3 (SD: 50) vs. 160.6 (SD: 35.5), p-value < 0.01. The Perf. Max. En. value was lower in the BRCAwt group: 148.6 (SD: 12.3) vs. 233.6 (SD: 29.2), p-value < 0.01. Conclusion: Our study revealed a statistically significant correlation between DWI and DCE parameters in examinations of peritoneal metastasis in patients with BRCA1/2 mutations. Adding DCE perfusion to the MRI protocol for ovarian cancer recurrence in patients with BRCAmut may be a valuable tool. Full article
(This article belongs to the Special Issue Radiomics in Gynaecological Cancers)
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<p>MRI of a 65-year-old patient with HGSOC recurrence BRCAwt. (<b>A</b>) Midline large peritoneal metastases on the STIR sequence. (Short tau inversion recovery) (<b>B</b>) ADC map image of the same peritoneal implant with small ROIs placed in the areas that qualitatively have the lowest signal.</p>
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<p>Images of the same 46-year-old patient with HGSOC recurrence BRCAmut. (<b>A</b>) Large peritoneal metastases in the right iliac fossa on the STIR sequence. (<b>B</b>) ADC map. Magnified image of the same peritoneal implant with small ROIs placed in the areas that qualitatively have the lowest signal.</p>
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<p>Images from the 65-year-old patient with HGSOC recurrence BRCAwt, contrast enhancement maps and curves.</p>
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<p>MRI of a 46-year-old patient with HGSOC recurrence with a BRCAmut. Contrast en-hancement maps and curves.</p>
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17 pages, 1972 KiB  
Article
Dual Targeting of CX3CR1 and PARP in Models of High-Grade Serous Ovarian Carcinoma
by Jia Xie and Maria V. Barbolina
Cancers 2024, 16(22), 3728; https://doi.org/10.3390/cancers16223728 - 5 Nov 2024
Viewed by 842
Abstract
Background/Objectives: Clinical use of poly(ADP-ribose) polymerase inhibitors (PARPis) against metastatic high-grade serous ovarian carcinoma (HGSOC) is limited to cases with deficient a homologous recombination (HR). Our objective was to determine whether the impairment of the fractalkine receptor (CX3CR1) could sensitize HR-proficient [...] Read more.
Background/Objectives: Clinical use of poly(ADP-ribose) polymerase inhibitors (PARPis) against metastatic high-grade serous ovarian carcinoma (HGSOC) is limited to cases with deficient a homologous recombination (HR). Our objective was to determine whether the impairment of the fractalkine receptor (CX3CR1) could sensitize HR-proficient cases to PARPis. Methods: The efficacy of a dual drug combination, including AZD8797, an inhibitor of CX3CR1, and several PARPis was examined using cell lines and xenograft models. Results: The effectiveness of PARPis and AZD8797 drug combinations ranged from additive to strongly synergistic. Olaparib was synergistic with AZD8797 in OVCAR-4, Caov-3, and OHSAHO. Niraparib and AZD8797 produced synergy in OVCAR-4 and ES2. Rucaparib and AZD8797 were strongly synergistic in Caov-3 and OVSAHO. Veliparib was strongly synergistic with AZD8797 in OVCAR-4 and Caov-3. Notably, a combination of veliparib and AZD8797 produced a strong synergistic effect in a xenograft model. Conclusions: While the exact mechanisms determining the nature of the PARPis and AZD8797 interaction remain to be uncovered, our data indicate that, in a subset of models, selected PARPis strongly synergize with the inhibition of CX3CR1, suggesting a potential therapeutic opportunity. Full article
(This article belongs to the Special Issue Advances in Ovarian Cancer Research and Treatment)
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<p>Downregulation of CX<sub>3</sub>CR1 synergizes with X-ray radiation to reduce clone formation. Caov-3 was transiently transfected with either CX<sub>3</sub>CR1-specific (designated “CX<sub>3</sub>CR1si”) or control (designated “Ctrl si”) si RNAs or the vehicle (designated “NT”) and subjected to 0, 1, 2, or 3 gray X-ray radiation, 10 µM olaparib, or DMSO, as indicated, on the 3rd day following transfection. CX<sub>3</sub>CR1 expression was determined using Western blot; GAPDH was used as the loading control. Normalized expression of CX<sub>3</sub>CR1 was calculated using digital densitometry. Original image of Western blot can be found at <a href="#app1-cancers-16-03728" class="html-app">supplementary file</a>. Survival curves are the average of five independent experiments. * <span class="html-italic">p</span> &lt; 0.05; two-way ANOVA test. Radiosensitization shown in the table was determined if both SF2<sub>control</sub>/SF2<sub>CX3CR1</sub> and D10<sub>control</sub>/D10<sub>CX3CR1</sub> were &gt;1.1; SF2—surviving fraction at 2 gray, D10—radiation dose required to kill 90% of the cell population.</p>
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<p>Drug interaction between AZD8797 and PARPis in OVCAR-4. OVCAR-4 cells were treated with olaparib (<b>A</b>), rucaparib (<b>B</b>), niraparib (<b>C</b>), and veliparib (<b>D</b>) at indicated concentrations for 48 h and examined with a clonogenic assay. Percent of the effect was calculated using the equation: effect = (number of colonies in control − number of colonies in the experiment)/(number of colonies in control) × 100%. An average of six independent experiments is shown. The data between two groups (individual drugs vs combinations) were analyzed using the Mann–Whitney U test; * <span class="html-italic">p</span> &lt; 0.05. The tables summarize the data for all tested combinations.</p>
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<p>Drug interaction between AZD8797 and PARPis in HGSOC cell lines. Caov-3 (<b>A</b>), OVSAHO (<b>B</b>), and ES2 (<b>C</b>) cells were treated with drugs at the indicated concentrations for 48 h and examined with a clonogenic assay. An average of six independent experiments is shown. The data between two groups (individual drugs vs combinations) were analyzed using the Mann–Whitney U test; * <span class="html-italic">p</span> &lt; 0.05. The tables summarize the data for all tested combinations. (<b>D</b>) Expression of CX<sub>3</sub>CR1 was examined with Western blot in all tested cell lines. Original image of Western blot can be found at <a href="#app1-cancers-16-03728" class="html-app">supplementary file</a>. Expression of β-actin was used as a loading control. (<b>E</b>) The table summarizes the nature of AZD8797’s and PARPis’ interaction in all tested cell lines.</p>
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<p>Combining niraparib and AZD8797 significantly reduces tumor weight at parietal peritoneum sites in the ES2 xenograft model. (<b>A</b>) The scheme represents an experimental design where 10<sup>6</sup> ES2 cells were i.p. injected into athymic nude mice (<span class="html-italic">n</span> = 10/group), allowed to form tumors for 3 days, followed by the treatment with either AZD8797 (0.625 mg/mouse) or niraparib (0.625 mg/mouse) or their combination that was delivered by oral gavage on four consecutive days followed by three consecutive drug-free days for three weeks and sacrificed. The images were created in BioRender. (<b>B</b>) At the time of sacrifice, the ascites were collected, measured, plotted with BoxPlotR software (<a href="http://chemgrid.org" target="_blank">chemgrid.org</a>), and analyzed using the Mann–Whitney U test. The overall survival was plotted using GraphPad Prism 8 software and analyzed using the log-rank (Mantel–Cox) test. (<b>C</b>) The tumor burden at parietal and visceral peritoneum sites for each animal in all groups was found by weighing the excised tumor specimens, then plotted as box plots using BoxPlotR software and statistically analyzed with the Mann–Whitney U test between vehicle (hpbcd)-treated and drug-treated groups. * <span class="html-italic">p</span> &lt; 0.05. The combination index (CI) was quantified using the Bliss Independence model. Percent of tumor reduction is indicated on the graphs.</p>
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<p>Daily administration of olaparib and AZD8797 significantly reduces tumor weight in the OVCAR-4 xenograft model. (<b>A</b>) The scheme represents an experimental design where 10<sup>6</sup> OVCAR-4 cells were i.p. injected into athymic nude mice (n = 10/group), allowed to form tumors for 7 days, followed by the treatment with either AZD8797 (0.625 mg/mouse) or olaparib (0.25 mg/mouse) or their combination that was delivered by oral gavage daily for three weeks, followed by a two-month drug-free period and sacrificed. The images were created in BioRender. (<b>B</b>) At the time of sacrifice, the total tumor mass was collected, measured, and plotted with BoxPlotR software (<a href="http://chemgrid.org" target="_blank">chemgrid.org</a>), and analyzed using the Mann–Whitney U test between the vehicle-treated and drug-treated groups. * <span class="html-italic">p</span> &lt; 0.05. The combination index (CI) was quantified using the Bliss Independence model. Percent of tumor reduction in drug-treated groups in comparison to the vehicle (hpbcd)-treated control group is indicated on the graphs.</p>
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<p>Combining veliparib and AZD8797 robustly and synergistically reduces tumor weight in the OVCAR-4 xenograft model. (<b>A</b>) The scheme represents an experimental design in which 10<sup>6</sup> OVCAR-4 cells were i.p. injected into athymic nude mice (n = 10/group), allowed to form tumors for 1 month, followed by treatment with either AZD8797 (0.33 mg/mouse) or veliparib (0.33 mg/mouse) or their combination that was delivered by oral gavage once weekly for thirteen weeks, and sacrificed. The images were created in BioRender. (<b>B</b>) At the time of sacrifice, the tumor was collected, measured, plotted with BoxPlotR software (<a href="http://chemgrid.org" target="_blank">chemgrid.org</a>), and analyzed using the Mann–Whitney U test by comparing the tumor weight in the vehicle-treated group to drug-treated groups. * <span class="html-italic">p</span> &lt; 0.05. The combination index (CI) was quantified using the Bliss Independence model. Percent of tumor reduction is indicated on the graphs. (<b>C</b>) Phosphorylation of H2AX in vehicle- and drug-treated cells was examined with immunofluorescence staining. Cells were treated with DMSO, 1 µM veliparib, 5 µM AZD8797, and a combination of 1 µM veliparib and 5 µM AZD8797 for 48 h, fixed, immunostained with ɣH2AX-specific antibodies, incubated with DAPI, and imaged. Bar: 25 micron. ɣH2AX foci (indicated with a yellow arrow) were quantified using AxioVision 4.8 software in 100 random cells from three independent experiments, averaged, plotted, and statistically analyzed between the control and drug-treated groups with the Mann–Whitney U test. **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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19 pages, 5819 KiB  
Article
Serous Ovarian Carcinoma: Detailed Analysis of Clinico-Pathological Characteristics as Prognostic Factors
by Lamia Sabry Aboelnasr, Hannah Meehan, Srdjan Saso, Ernesto Yagüe and Mona El-Bahrawy
Cancers 2024, 16(21), 3611; https://doi.org/10.3390/cancers16213611 - 25 Oct 2024
Viewed by 1337
Abstract
Background/Objectives: Serous ovarian carcinoma (SOC) is the most common subtype of epithelial ovarian cancer, with high-grade (HGSOC) and low-grade (LGSOC) subtypes presenting distinct clinical behaviours. This study aimed to evaluate histopathologic features in SOC, correlating these with prognostic outcomes, and explore the potential [...] Read more.
Background/Objectives: Serous ovarian carcinoma (SOC) is the most common subtype of epithelial ovarian cancer, with high-grade (HGSOC) and low-grade (LGSOC) subtypes presenting distinct clinical behaviours. This study aimed to evaluate histopathologic features in SOC, correlating these with prognostic outcomes, and explore the potential clinical implications. Methods: We analysed 51 SOC cases for lymphovascular space invasion (LVSI), tumour border configuration (TBC), microvessel density (MVD), tumour budding (TB), the tumour–stroma ratio (TSR), the stromal type, tumour-infiltrating lymphocytes (TILs), and tertiary lymphoid structures (TLSs). A validation cohort of 54 SOC cases from The Cancer Genome Atlas (TCGA) was used for comparison. Results: In the discovery set, significant predictors of aggressive behaviour included LVSI, high MVD, high TB, and low TILs. These findings were validated in the validation set where the absence of TLSs, lower peritumoural TILs, immature stromal type, and low TSR were associated with worse survival outcomes. The stromal type was identified as an independent prognostic predictor in SOC across both datasets. Inter-observer variability analysis demonstrated substantial to almost perfect agreement for these features, ensuring the reproducibility of the findings. Conclusions: The histopathological evaluation of immune and stromal features, such as TILs, TLSs, TB, TSR, and stromal type, provides critical prognostic information for SOC. Incorporating these markers into routine pathological assessments could enhance risk stratification and guide treatment, offering practical utility, particularly in low-resource settings when molecular testing is not feasible. Full article
(This article belongs to the Special Issue Tumor-Associated Microenvironments and Inflammation)
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<p>The histopathological features in high-grade serous ovarian carcinoma (HGSOC). HGSOC cases showing (<b>A</b>) a high density of peritumoural tumour-infiltrating lymphocytes (TILs) at the tumour front (H and E, ×100), (<b>B</b>) a high density of intratumoural TILs in the stroma between tumour clusters (H and E, ×100), (<b>C</b>) an immature tertiary lymphoid structure (TLS) at the invasive front of a metastatic omental lesion (H and E, ×100), (<b>D</b>) a mature TLS at the invasive front of a primary ovarian lesion (H and E, ×200), (<b>E</b>) tumour-associated tissue eosinophilia (TATE) in the centre of the tumoural component (H and E, ×400), and (<b>F</b>) Lymphovascular space invasion (LVSI) at the invasive tumour front (H and E, ×100).</p>
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<p>The histopathological features of epithelial–mesenchymal transition in high-grade serous ovarian carcinoma (HGSOC). HGSOC cases showing (<b>A</b>) peritumoural tumour buds (yellow circles) at the tumour front (H and E, ×400), (<b>B</b>) intratumoural tumour buds (yellow circles) in the tumour centre (H and E, ×200), (<b>C</b>) high microvessel density (MVD) (red arrows) at the invasive tumour front (H and E, ×200), and (<b>D</b>) a low tumour–stroma ratio, an intermediate stromal type with myxoid areas, and an infiltrative tumour border (H and E, ×100).</p>
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<p>The histopathological features in primary and metastatic low-grade serous ovarian carcinoma (LGSOC). LGSOC primary lesions showing (<b>A</b>) a pushing tumour border at the tumour front (H and E, ×40), (<b>B</b>) a low density of peritumoural tumour-infiltrating lymphocytes (H and E, ×200), and (<b>C</b>) a high tumour stroma ratio (H and E, ×200). (<b>D</b>) A metastatic HGSOC lesion showing infiltrative tumour border, immature myxoid stroma, and a low density of TILs (H and E, ×100).</p>
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<p>Kaplan–Meier curves for serous ovarian carcinoma (SOC) patients in the discovery set. (<b>A</b>) Overall survival (OS) stratified by the presence of lymph node (LN) metastasis (<span class="html-italic">p</span> = 0.0017). (<b>B</b>) Disease-free survival (DFS) stratified by peritumoural tumour budding (TB) (<span class="html-italic">p</span> = 0.024). (<b>C</b>) DFS stratified by tumour–stroma ratio (TSR) (<span class="html-italic">p</span> = 0.014). (<b>D</b>) DFS stratified by stromal type (<span class="html-italic">p</span> = 0.00037).</p>
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<p>Kaplan–Meier curves for serous ovarian carcinoma (SOC) patients in the validation set. (<b>A</b>) Overall survival (OS) stratified by the presence of tertiary lymphoid structures (TLSs) (<span class="html-italic">p</span> = 0.0034). (<b>B</b>) Disease-free survival (DFS) stratified by the tumour–stroma ratio (TSR) (<span class="html-italic">p</span> = 0.041). (<b>C</b>) DFS stratified by the stromal type (<span class="html-italic">p</span> = 0.025). (<b>D</b>) DFS stratified by peritumoural tumour-infiltrating lymphocytes (TILs) (<span class="html-italic">p</span> = 0.045).</p>
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31 pages, 12504 KiB  
Article
Metabolomic Analysis of Histological Composition Variability of High-Grade Serous Ovarian Cancer Using 1H HR MAS NMR Spectroscopy
by Agnieszka Skorupa, Mateusz Klimek, Mateusz Ciszek, Sławomir Pakuło, Tomasz Cichoń, Bartosz Cichoń, Łukasz Boguszewicz, Andrzej Witek and Maria Sokół
Int. J. Mol. Sci. 2024, 25(20), 10903; https://doi.org/10.3390/ijms252010903 - 10 Oct 2024
Viewed by 1109
Abstract
In this work, the HR MAS NMR (high-resolution magic-angle spinning nuclear magnetic resonance) spectroscopy technique was combined with standard histological examinations to investigate the metabolic features of high-grade serous ovarian cancer (HGSOC) with a special focus on the relation between a metabolic profile [...] Read more.
In this work, the HR MAS NMR (high-resolution magic-angle spinning nuclear magnetic resonance) spectroscopy technique was combined with standard histological examinations to investigate the metabolic features of high-grade serous ovarian cancer (HGSOC) with a special focus on the relation between a metabolic profile and a cancer cell fraction. The studied group consisted of 44 patients with HGSOC and 18 patients with benign ovarian tumors. Normal ovarian tissue was also excised from 13 control patients. The metabolic profiles of 138 tissue specimens were acquired on a Bruker Avance III 400 MHz spectrometer. The NMR spectra of the HGSOC samples could be discriminated from those acquired from the non-transformed tissue and were shown to depend on tumor purity. The most important features that differentiate the samples with a high fraction of cancer cells from the samples containing mainly fibrotic stroma are the increased intensities in the spectral regions corresponding to phosphocholine/glycerophosphocholine, phosphoethanolamine/serine, threonine, uridine nucleotides and/or uridine diphosphate (UDP) nucleotide sugars. Higher levels of glutamine, glutamate, acetate, lysine, alanine, leucine and isoleucine were detected in the desmoplastic stroma within the HGSOC lesions compared to the stroma of benign tumors. The HR MAS NMR analysis of the metabolic composition of the epithelial and stromal compartments within HGSOC contributes to a better understanding of the disease’s biology. Full article
(This article belongs to the Special Issue Metabolomics in Oncology)
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<p>Median <sup>1</sup>H CPMG HR-MAS NMR spectra [region 0.7–4.8 ppm (<b>a</b>), region 2.9–4.8 ppm (<b>b</b>), region 0.7–3.1 ppm (<b>c</b>), and region 5.5–8.4 ppm (<b>d</b>)] of the cancer compartment, fibrotic compartment within malignant tumors, fibrotic compartment within benign tumors, and normal ovary tissue (samples collected from control group). Assignment of signals: Lip—lipids, Val—valine, Ile—isoleucine, Leu—leucine, IPA—isopropanol, 3-HB—3-hydroxybutyrate, Lac—lactate, Ala—alanine, Lys—lysine, Ace—acetate, NAA—N-acetylaspartate, Glu—glutamate, Gln—glutamine, Met—methionine, Ac—acetone, Suc—succinate, hTau—hipotaurine, Asp—aspartate, Cre—creatine, Eth—ethanolamine, Cho—choline, PCho—phosphocholine, GPCho—glycerophosphocholine, SI—scylloinositol, Tau—tauryna, Gly—glycine, MI—Myo-inositol, PE—phosphoethanolamine, Ser—serine, Asc—ascorbate, Thre—threonine, GSH—glutathione, Glc—glucose, Ura—uracil, Ur—uridine, UDP—uridine-5′-diphosphate, UTP—Uridine-5′-triphosphate, UMP—Uridine 5′-monophosphate, Ino—inosine, Fum—fumarate, Tyr—tyrosine, Phe—phenylalanine, HX—hypoxantine, N?—unassigned signal.</p>
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<p>The scheme of the multivariate analysis. PCA—principal component analysis, OPLSR—orthogonal partial least squares regression, OPLS-DA—orthogonal partial least squares discriminant analysis.</p>
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<p>The scores (<b>a</b>) and loadings (<b>b</b>) plots obtained from the PCA model 2. The presented projection plane represents 57.6% of the total variation in the dataset. The scores and loadings for the i-th principal component are denoted as t[i] and p[i]. C 0%—the samples containing no cancer cells, C 1–19%—the samples characterized by a cancer content of 1–19%, C 20–100%—the samples characterized by a cancer content of 20–100%.</p>
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<p>The scores plot obtained from PCA model 3 presenting the distribution of the samples belonging to the following groups: C 0% (the samples containing no cancer cells), C 1–19% (the samples characterized by a cancer content of 20–100%), C 20–100% (the samples characterized by a cancer content of 20–100%) (<b>a</b>) and colored according to a cancer cell fraction (<b>b</b>) and a normal ovary tissue fraction (<b>c</b>). The presented projection plane represents 55.6% of the total variation in the dataset. The scores for the i-th principal component are denoted as t[i].</p>
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<p>The scores plot obtained from PCA model 4 presenting the distribution of the samples belonging to the following groups: C 0% (the samples containing no cancer cells), C 1–19% (the samples characterized by a cancer content of 20–100%), C 20–100% (the samples characterized by a cancer content of 20–100%) (<b>a</b>) and colored according to the fraction of cancer cells (<b>c</b>) necrosis (<b>d</b>), fibrosis (<b>e</b>), vessels (<b>f</b>), and inflammation (<b>g</b>). The loadings plot obtained from PCA model 4 (<b>b</b>). The presented projection plane represents 62.7% of the total variation in the dataset. The scores for the i-th principal component are denoted as t[i]. TME—tumor microenvironment.</p>
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<p>The scores plot obtained from (<b>a</b>) PCA model 5 including all the samples fulfilling the criteria presented in <a href="#ijms-25-10903-t003" class="html-table">Table 3</a> and (<b>b</b>) PCA model 5a computed based on a dataset reduced in order to limit the number of the samples per patient. The scores for the i-th principal component are denoted as t[i].</p>
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<p>The cross-validated scores (<b>a</b>) and loading plots [aliphatic region—(<b>b</b>), aromatic region—(<b>c</b>)] obtained from the OPLS-DA 1<sub>CPMG</sub> model. The cross-validated scores for the predictive component are denoted as tcv[1], whereas for the orthogonal one, they are designated as tocv[1]. The loadings for the predictive component are denoted as p[1]. The signals in the loadings plots are colored according to the p(corr)[1] values (the loadings scaled as correlation coefficients between the original data and the scores obtained for the first component). The abbreviations for metabolites are the same as in the legend of <a href="#ijms-25-10903-f001" class="html-fig">Figure 1</a>.</p>
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<p>The cross-validated scores (<b>a</b>) and loadings plots [aliphatic region—(<b>b</b>), aromatic region—(<b>c</b>)] obtained from OPLS-DA 2<sub>CPMG</sub> model. The cross-validated scores for the predictive component are denoted as tcv[1], while for the orthogonal one, they are denoted as tocv[1]. The loadings for the predictive component are denoted as p[1]. The signals in the loadings plots are colored according to the p(corr)[1] values (the loadings scaled as correlation coefficients between the original data and the scores obtained for the first component). The abbreviations for metabolites are the same as in the legend of <a href="#ijms-25-10903-f001" class="html-fig">Figure 1</a>.</p>
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<p>The predicted scores plots obtained from the OPLS-DA 1<sub>CPMG</sub> (<b>a</b>) and OPLS-DA 1<sub>J-res</sub> (<b>b</b>) models. The predicted scores plots for the predictive and orthogonal components are denoted as tPS[1] and toPS[1], respectively. C 0%—the samples containing no cancer cells, C 1–19%—the samples characterized by a cancer content of 1–19%, and C 20–100%—the samples characterized by a cancer content of 20–100%.</p>
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<p>The cross-validated scores (tcv[1]) (<b>a</b>) and loading (p[1]) plots [aliphatic region—(<b>b</b>), aromatic region—(<b>c</b>)] obtained from the OPLS-DA 3<sub>CPMG</sub> model. The signals in the loadings plots are colored according to the p(corr)[1] values (the loadings scaled as correlation coefficients between the original data and the scores obtained for the first component). The abbreviations for metabolites are the same as in the legend of <a href="#ijms-25-10903-f001" class="html-fig">Figure 1</a>.</p>
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<p>The cross-validated scores (<b>a</b>) and loading plots [aliphatic region—(<b>b</b>), aromatic region—(<b>c</b>)] obtained from the OPLS-DA 4<sub>J-Res</sub> model. The cross-validated scores for the predictive component are denoted as tcv[1], whereas for the orthogonal one, they are denoted as tocv[1]. The loadings for the predictive component are denoted as p[1]. The signals in the loadings plots are colored according to the p(corr)[1] values (the loadings scaled as correlation coefficients between the original data and the scores obtained for the first component). The abbreviations for metabolites are the same as in the legend of <a href="#ijms-25-10903-f001" class="html-fig">Figure 1</a>.</p>
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<p>The scores (<b>a</b>) and loadings plots [aliphatic region—(<b>b</b>), aromatic region—(<b>c</b>)] obtained from the OPLSR <sub>CPMG</sub> model. The X-scores for the predictive component are denoted as t[1], whereas the Y-scores are denoted as u[1]. The loadings for the first predictive component are denoted as p[1]. The signals in the loadings plots are colored according to the p(corr)[1] values (the loadings scaled as correlation coefficients between the original data and the scores obtained for the first component). The abbreviations for metabolites are the same as in the legend of <a href="#ijms-25-10903-f001" class="html-fig">Figure 1</a>.</p>
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<p>The cancer cells fraction predicted by the OPLS<sub>CPMG</sub> model (based on cross-validation) vs. the actual cancer content determined from histological examinations. r—Pearson correlation coefficient.</p>
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<p>The scores plots obtained from the PCA analysis of the CPMG spectra acquired from the samples forming the ‘heterogeneity datasets’ (PCA model 6): t[1] vs. t[2] scores colored according to the patient identification number (<b>a</b>), cancer cells content (<b>c</b>), fibrosis fraction (<b>e</b>), inflammation fraction (<b>g</b>), necrosis fraction (<b>i</b>); t[1] vs. t[3] scores colored according to the patient identification number (<b>b</b>), cancer cells content (<b>d</b>), fibrosis fraction (<b>f</b>), inflammation fraction (<b>h</b>), necrosis fraction (<b>j</b>); the coordinate system formed by the first three principal components explains 73% of the total variation in the dataset. The scores for the i-th principal component are denoted as t[i].</p>
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19 pages, 11038 KiB  
Article
YKL40/Integrin β4 Axis Induced by the Interaction between Cancer Cells and Tumor-Associated Macrophages Is Involved in the Progression of High-Grade Serous Ovarian Carcinoma
by Keitaro Yamanaka, Yu-ichiro Koma, Satoshi Urakami, Ryosuke Takahashi, Satoshi Nagamata, Masaki Omori, Rikuya Torigoe, Hiroki Yokoo, Takashi Nakanishi, Nobuaki Ishihara, Shuichi Tsukamoto, Takayuki Kodama, Mari Nishio, Manabu Shigeoka, Hiroshi Yokozaki and Yoshito Terai
Int. J. Mol. Sci. 2024, 25(19), 10598; https://doi.org/10.3390/ijms251910598 - 1 Oct 2024
Viewed by 1278
Abstract
Macrophages in the tumor microenvironment, termed tumor-associated macrophages (TAMs), promote the progression of various cancer types. However, many mechanisms related to tumor–stromal interactions in epithelial ovarian cancer (EOC) progression remain unclear. High-grade serous ovarian carcinoma (HGSOC) is the most malignant EOC subtype. Herein, [...] Read more.
Macrophages in the tumor microenvironment, termed tumor-associated macrophages (TAMs), promote the progression of various cancer types. However, many mechanisms related to tumor–stromal interactions in epithelial ovarian cancer (EOC) progression remain unclear. High-grade serous ovarian carcinoma (HGSOC) is the most malignant EOC subtype. Herein, immunohistochemistry was performed on 65 HGSOC tissue samples, revealing that patients with a higher infiltration of CD68+, CD163+, and CD204+ macrophages had a poorer prognosis. We subsequently established an indirect co-culture system between macrophages and EOC cells, including HGSOC cells. The co-cultured macrophages showed increased expression of the TAM markers CD163 and CD204, and the co-cultured EOC cells exhibited enhanced proliferation, migration, and invasion. Cytokine array analysis revealed higher YKL40 secretion in the indirect co-culture system. The addition of YKL40 increased proliferation, migration, and invasion via extracellular signal-regulated kinase (Erk) signaling in EOC cells. The knockdown of integrin β4, one of the YKL40 receptors, suppressed YKL40-induced proliferation, migration, and invasion, as well as Erk phosphorylation in some EOC cells. Database analysis showed that high-level expression of YKL40 and integrin β4 correlated with a poor prognosis in patients with serous ovarian carcinoma. Therefore, the YKL40/integrin β4 axis may play a role in ovarian cancer progression. Full article
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<p>Number of infiltrating macrophages correlates with poor prognosis in ovarian cancer. (<b>A</b>): Immunohistochemistry for the detection of pan-macrophage marker CD68 and tumor-associated macrophage (TAM) markers CD163 and CD204 was performed in 65 human high-grade serous ovarian carcinoma (HGSOC) tissues, and the respective positive cells were counted in high-power fields (three images/case). Patients were subsequently categorized into high or low groups based on the median of the average count of infiltrating macrophages. (<b>B</b>,<b>C</b>): Kaplan–Meier analysis of (<b>B</b>) overall survival (OS) in 65 patients with HGSOC with low (<span class="html-italic">n</span> = 32) and high (<span class="html-italic">n</span> = 33) counts and of (<b>C</b>) progression-free survival (PFS) in 62 patients with HGSOC with low (<span class="html-italic">n</span> = 32) and high (<span class="html-italic">n</span> = 30) counts, based on low- and high-level CD68, CD163 and CD204 expression. <span class="html-italic">p</span>-values were determined using the log-rank test. Scale bar: 20 µm (<b>A</b>).</p>
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<p>Co-culture of macrophages and epithelial ovarian cancer (EOC) cells promotes polarization of macrophages into TAMs and enhances malignant phenotypes of EOC cells. (<b>A</b>): Indirect co-culture of EOC cells (KURAMOCHI, SKOV3 and OVCAR3) in the lower chamber and macrophages in the transwell assay with 0.4 µm pores was performed for 48 h. For comparison, EOC cells and macrophages were also cultured alone for 48 h as monocultured controls. (<b>B</b>): Expression of TAM markers CD163 and CD204 was compared using real-time quantitative PCR (qPCR) between monocultured and co-cultured macrophages. (<b>C</b>): Expression of extracellular signal-regulated kinase (Erk) and phosphorylated Erk (p-Erk; Thr202/Tyr204) in monocultured and co-cultured EOC cells was evaluated using Western blotting. β-actin was used as a control. (<b>D</b>): MTS assays were performed to compare the proliferation of monocultured and co-cultured EOC cells. (<b>E</b>): Transwell migration assays were performed to compare monocultured and co-cultured EOC cells. After 48 h of incubation, migrating cells into the lower surface were counted in five random fields per chamber. Representative images are shown in <a href="#app1-ijms-25-10598" class="html-app">Figure S2A</a>. (<b>F</b>): Transwell invasion assays were performed to compare monocultured and co-cultured EOC cells. After 48 h of incubation, invading cells into the lower surface were counted in five random fields per chamber. Representative images are shown in <a href="#app1-ijms-25-10598" class="html-app">Figure S2B</a>. Mϕ, macrophages. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Increased YKL40 resulting from indirect co-culture of macrophages and EOC cells promotes malignant phenotypes of EOC cells. (<b>A</b>): Macrophages and EOC cells were each monocultured for 48 h, and macrophages and EOC cells were co-cultured for 48 h. Cell supernatants were collected. (<b>B</b>): Cytokine arrays were performed on each collected cell supernatant. Colored boxes indicate spots of enhanced signal in the supernatants of the co-culture compared with the KURAMOCHI and macrophage monocultures. (<b>C</b>): ELISA was performed to investigate the secretion of YKL40 in three types of cell supernatants for each EOC cell line. (<b>D</b>–<b>F)</b>: Each EOC cell line was treated with recombinant human YKL40 (rhYKL40) at concentrations of 0, 250 and 500 ng/mL, which was performed for (<b>D</b>) MTS assays, (<b>E</b>) transwell migration assays, and (<b>F</b>) transwell invasion assays after 48 h. (<b>E</b>) Migrating cells and (<b>F</b>) invading cells were counted in five random fields per chamber. Representative images of transwell migration and invasion assays are shown in <a href="#app1-ijms-25-10598" class="html-app">Figure S2C,D</a>, respectively. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001. N.S., not significant.</p>
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<p>YKL40 promotes malignant phenotypes of EOC cells via the Erk pathway. (<b>A</b>): Time-dependent expression levels of Erk and p-Erk in EOC cells treated with rhYKL40 (250 ng/mL) were analyzed using Western blotting, with β-actin as control. (<b>B</b>–<b>D</b>): Changes in (<b>B</b>) proliferation, (<b>C</b>) migration and (<b>D</b>) invasion of EOC cells following rhYKL40 (250 ng/mL) treatment with or without MEK1/2 inhibitor (PD98059; 10 µM) were assessed using (<b>B</b>) MTS assay, (<b>C</b>) transwell migration assay, and (<b>D</b>) transwell invasion assay, respectively. Representative images of transwell migration and invasion assays are shown in <a href="#app1-ijms-25-10598" class="html-app">Figure S2E,F</a>, respectively. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001. N.S., not significant. Furthermore, the original Western blotting images are provided as <a href="#app1-ijms-25-10598" class="html-app">Figure S5A–D</a>.</p>
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<p>Enhanced effects of malignant phenotypes of EOC cells by YKL40 mediated through integrin β4 (ITGB4). (<b>A</b>,<b>B</b>): EOC cells were transfected with siRNA targeting ITGB4 (siITGB4, 20 nM). The knockdown efficiency of ITGB4 in EOC cells was assessed using (<b>A</b>) qPCR and (<b>B</b>) Western blotting. Negative control siRNA (siNC, 20 nM) was used as a negative control. (<b>C</b>–<b>E</b>): EOC cells transfected with either siITGB4 or siNC were treated with or without rhYKL40 (250 ng/mL), and the proliferation, migration, and invasion were compared using (<b>C</b>) MTS assay, (<b>D</b>) transwell migration assay, and (<b>E</b>) transwell invasion assay, respectively. Representative images of transwell migration and invasion assays are shown in <a href="#app1-ijms-25-10598" class="html-app">Figure S2G,H</a>, respectively. (<b>F</b>): EOC cells transfected with either siNC or siITGB4 were treated with rhYKL40 (250 ng/mL) and time-dependent expression levels of Erk and p-Erk were analyzed using Western blotting, with β-actin as control. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001. N.S., not significant.</p>
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<p>The YKL40/ITGB4 axis may act as an indicator of poor prognosis in EOC. (<b>A</b>,<b>B</b>): Immunohistochemistry for (<b>A</b>) YKL40 and (<b>B</b>) ITGB4 was performed in 65 human HGSOC tissues, and respective positive cells were evaluated in high-power fields (three images/case). Patients were subsequently categorized into high and low groups. (<b>C</b>): Kaplan–Meier analysis of OS in 65 patients with HGSOC with low (<span class="html-italic">n</span> = 17) and high (<span class="html-italic">n</span> = 48) expression and PFS in 62 patients with HGSOC with low (<span class="html-italic">n</span> = 16) and high (<span class="html-italic">n</span> = 46) expression, based on low- and high-level YKL40 expression. (<b>D</b>): Kaplan–Meier analysis of OS in 65 patients with HGSOC with low (<span class="html-italic">n</span> = 37) and high (<span class="html-italic">n</span> = 28) expression and PFS in 62 patients with HGSOC with low (<span class="html-italic">n</span> = 36) and high (<span class="html-italic">n</span> = 26) expression, based on low- and high-level ITGB4 expression. <span class="html-italic">p</span>-values were determined using the log-rank test. (<b>E</b>–<b>H</b>): Database analyses for YKL40 and ITGB4 were performed using TNMplot and Kaplan–Meier plotter. TNMplot was used to evaluate the difference in expression levels of (<b>E</b>) YKL40 between normal and tumor tissues in EOC tissues. Kaplan–Meier plotter was used to assess the prognosis of OS and PFS by comparing low- and high-level expression groups of (<b>F</b>) YKL40. TNMplot was used to evaluate the difference in expression levels of (<b>G</b>) ITGB4 between normal and tumor tissues in EOC tissues. Kaplan–Meier plotter was used to assess the prognosis of OS and PFS by comparing low- and high-level expression groups of (<b>H</b>) ITGB4. Scale bar: 20 µm (<b>A</b>,<b>B</b>).</p>
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<p>Schematic diagram illustrating the role of YKL40 in the EOC microenvironment. Indirect interactions with EOC cells lead to polarization of macrophages into TAMs. YKL40 is mainly secreted by TAMs. This activates the Erk pathway via ITGB4, promoting the migration and invasion of EOC cells.</p>
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12 pages, 920 KiB  
Article
The Predictive Value of the Fibrinogen–Albumin-Ratio Index on Surgical Outcomes in Patients with Advanced High-Grade Serous Ovarian Cancer
by Magdalena Postl, Melina Danisch, Fridolin Schrott, Paul Kofler, Patrik Petrov, Stefanie Aust, Nicole Concin, Stephan Polterauer and Thomas Bartl
Cancers 2024, 16(19), 3295; https://doi.org/10.3390/cancers16193295 - 27 Sep 2024
Viewed by 699
Abstract
Background/Objectives: The present study evaluates predictive implications of the pretherapeutic Fibrinogen–Albumin-Ratio Index (FARI) in high-grade serous ovarian cancer (HGSOC) patients undergoing primary cytoreductive surgery. Methods: This retrospective study included 161 patients with HGSOC International Federation of Gynecology and Obstetrics (FIGO) stage ≥ IIb, [...] Read more.
Background/Objectives: The present study evaluates predictive implications of the pretherapeutic Fibrinogen–Albumin-Ratio Index (FARI) in high-grade serous ovarian cancer (HGSOC) patients undergoing primary cytoreductive surgery. Methods: This retrospective study included 161 patients with HGSOC International Federation of Gynecology and Obstetrics (FIGO) stage ≥ IIb, who underwent primary cytoreductive surgery followed by platinum-based chemotherapy. Associations between the FARI and complete tumor resection status were described by receiver operating characteristics, and binary logistic regression models were fitted. Results: Higher preoperative FARI values correlated with higher ascites volumes (r = 0.371, p < 0.001), and higher CA125 levels (r = 0.271, p = 0.001). A high FARI cut at its median (≥11.06) was associated with lower rates of complete tumor resection (OR 3.13, 95% CI [1.63–6.05], p = 0.001), and retrained its predictive value in a multivariable model independent of ascites volumes, CA125 levels, FIGO stage, and Charlson Comorbidity Index (CCI). Conclusions: The FARI appears to act as a surrogate for higher intra-abdominal tumor load. After clinical validation, FARI could serve as a readily available serologic biomarker to complement preoperative patient assessment, helping to identify patients who are likely to achieve complete tumor resection during primary cytoreductive surgery. Full article
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<p>Consort diagram depicting all patients with ovarian cancer undergoing primary treatment who were not included into final analysis (<span class="html-italic">n</span> = 1013/1174, 86.3%). HGSOC, high-grade serous ovarian cancer; FIGO, International Federation of Gynaecology and Obstetrics.</p>
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<p>(<b>a</b>) A high FARI ≥ 11.06 is associated with impaired progression-free survival in patients with advanced high-grade serous ovarian cancer. The yellow line depicts patients with a FARI &lt; 11.06 and the blue line depicts patients with a FARI ≥ 11.06, with 95% confidence intervals, respectively. (<b>b</b>) A high FARI ≥ 11.06 is associated with impaired disease-specific survival in patients with advanced high-grade serous ovarian cancer. The yellow line depicts patients with a FARI &lt; 11.06 and the blue line depicts patients with a FARI ≥ 11.06, with 95% confidence intervals, respectively.</p>
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17 pages, 314 KiB  
Review
Novel Targeted Agents in Advanced and Recurrent Low-Grade Serous Ovarian Cancer: A Silver Lining in the Therapy of a Chemoresistant Disease?
by Arina Onoprienko, Thomas Bartl, Christoph Grimm, Nicole Concin and Stephan Polterauer
Cancers 2024, 16(19), 3268; https://doi.org/10.3390/cancers16193268 - 26 Sep 2024
Cited by 1 | Viewed by 1433
Abstract
Low-grade serous ovarian carcinoma (LGSOC) is a rare subtype of epithelial ovarian cancer, characterized by a unique molecular background and specific clinical behavior. A growing body of molecular data underscores LGSOC as a distinct disease entity; however, clinical evidence on the optimal treatment [...] Read more.
Low-grade serous ovarian carcinoma (LGSOC) is a rare subtype of epithelial ovarian cancer, characterized by a unique molecular background and specific clinical behavior. A growing body of molecular data underscores LGSOC as a distinct disease entity; however, clinical evidence on the optimal treatment regimens for LGSOC remains limited due to the low incidence of the disease. Consequently, treatment recommendations for LGSOC are still often derived from findings on the more common high-grade serous ovarian carcinoma (HGSOC) and typically focus on radical cytoreductive surgery and platinum-based chemotherapy. Since LGSOCs typically exhibit only limited responsiveness to platinum-based chemotherapy, the clinical management of advanced and recurrent LGSOCs remains a significant therapeutic challenge and often results in limited treatment options and suboptimal outcomes. Recent advances in molecular profiling and the identification of new, promising targets, such as the mitogen-activated protein kinase (MAPK) pathway, offer hope for improving both the prognosis and health-related quality of life in affected patients. Given the high unmet clinical need to establish new therapeutic standards beyond cytotoxic chemotherapy, this review aims to summarize the most promising molecular targets and emerging targeted agents. Full article
(This article belongs to the Special Issue Feature Review for Cancer Therapy)
18 pages, 601 KiB  
Article
Association of the Single Nucleotide Polymorphisms rs11556218, rs4778889, rs4072111, and rs1131445 of the Interleukin-16 Gene with Ovarian Cancer
by Rafał Watrowski, Eva Schuster, Toon Van Gorp, Gerda Hofstetter, Michael B. Fischer, Sven Mahner, Stefan Polterauer, Robert Zeillinger and Eva Obermayr
Int. J. Mol. Sci. 2024, 25(19), 10272; https://doi.org/10.3390/ijms251910272 - 24 Sep 2024
Viewed by 938
Abstract
Single nucleotide polymorphisms (SNPs) of the IL-16 gene have been reported to influence the risk of several cancers, but their role in ovarian cancer (OC) has not been studied. Using the restriction fragment length polymorphism (PCR-RFLP) method, we examined four IL-16 SNPs: rs11556218 [...] Read more.
Single nucleotide polymorphisms (SNPs) of the IL-16 gene have been reported to influence the risk of several cancers, but their role in ovarian cancer (OC) has not been studied. Using the restriction fragment length polymorphism (PCR-RFLP) method, we examined four IL-16 SNPs: rs11556218 (T > G), rs4778889 (T > C), rs4072111 (C > T), and rs1131445 (T > C) in blood samples from 413 women of Central European descent, including 200 OC patients and 213 healthy controls. Among the patients, 62% were postmenopausal, 84.5% were diagnosed in late stages (FIGO IIb-IV), and 73.5% had high-grade serous OC (HGSOC). Minor allele frequencies in controls were 9.2% for rs11556218 (G allele), 13.7% for rs4778889 (C allele), 10.4% for rs4072111 (T allele), and 32.3% for rs1131445 (C allele). We found significant associations of rs11556218 (G vs. T allele: OR 2.76, 95% CI 1.84–4.14, p < 0.0001) with elevated OC risk in the whole cohort (p < 0.001) and in both premenopausal (p < 0.001) and postmenopausal (p = 0.001) subgroups. These associations remained significant across heterozygote (p < 0.001), dominant (p < 0.001), and overdominant (p < 0.001) models. IL-16 rs4778889 was associated with OC risk predominantly in premenopausal women (p < 0.0001 in almost all models). In the whole cohort, the C allele was associated with OC risk (OR 1.54, CI 95% 1.06–2.23, p = 0.024), and the association of rs4778889 was significant in dominant (p = 0.019), overdominant (p = 0.033), and heterozygote (p = 0.027) models. Furthermore, rs4778889 was linked with HGSOC (p = 0.036) and endometriosis-related OC subtypes (p = 0.002). No significant associations were found for rs4072111 or rs1131445 (p = 0.81 or 0.47, respectively). In conclusion, rs11556218 and rs4778889 SNPs are associated with OC risk, especially in premenopausal women. Full article
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<p>Age at initial diagnosis in cases and controls.</p>
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<p>Menopausal status (cutoff point 51 years) of cases and controls.</p>
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29 pages, 3725 KiB  
Review
Targeted Nanocarrier-Based Drug Delivery Strategies for Improving the Therapeutic Efficacy of PARP Inhibitors against Ovarian Cancer
by Patrycja Gralewska, Arkadiusz Gajek, Agnieszka Marczak and Aneta Rogalska
Int. J. Mol. Sci. 2024, 25(15), 8304; https://doi.org/10.3390/ijms25158304 - 30 Jul 2024
Viewed by 2096
Abstract
The current focus of ovarian cancer (OC) research is the improvement of treatment options through maximising drug effectiveness. OC remains the fifth leading cause of cancer-induced mortality in women worldwide. In recent years, nanotechnology has revolutionised drug delivery systems. Nanoparticles may be utilised [...] Read more.
The current focus of ovarian cancer (OC) research is the improvement of treatment options through maximising drug effectiveness. OC remains the fifth leading cause of cancer-induced mortality in women worldwide. In recent years, nanotechnology has revolutionised drug delivery systems. Nanoparticles may be utilised as carriers in gene therapy or to overcome the problem of drug resistance in tumours by limiting the number of free drugs in circulation and thereby minimising undesired adverse effects. Cell surface receptors, such as human epidermal growth factor 2 (HER2), folic acid (FA) receptors, CD44 (also referred to as homing cell adhesion molecule, HCAM), and vascular endothelial growth factor (VEGF) are highly expressed in ovarian cancer cells. Generation of active targeting nanoparticles involves modification with ligands that recognise cell surface receptors and thereby promote internalisation by cancer cells. Several poly(ADP-ribose) polymerase (PARP) inhibitors (PARPi) are currently used for the treatment of high-grade serous ovarian carcinomas (HGSOC) or platinum-sensitive relapsed OC. However, PARP resistance and poor drug bioavailability are common challenges, highlighting the urgent need to develop novel, effective strategies for ovarian cancer treatment. This review evaluates the utility of nanoparticles in ovarian cancer therapy, with a specific focus on targeted approaches and the use of PARPi nanocarriers to optimise treatment outcomes. Full article
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<p>A simplified graphical illustration of various types of nanoparticles used in ovarian cancer treatment.</p>
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<p>The controlled release of drugs from nanoparticles that respond to tumour tissue microenvironment.</p>
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<p>Schematic representation of the mechanism of action of ovarian cancer nanoparticles. Free drugs accumulate at both normal and tumour tissue sites, whereas drugs encapsulated in nanocarriers are located in cancer tissue using the EPR effect. Receptor-mediated active targeting promotes drug accumulation predominantly in the tumour tissue because of the specific ligands present on the surface, leading to improved selectiveness and therapeutic responses.</p>
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<p>Types of liposomes used for chemotherapy and gene therapy in ovarian cancer: cationic liposomes; neutral liposomes; pegylated liposomes- PEG and ligands such as CD44, VEGFR, FR, or HER2 targeted liposomes.</p>
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<p>Proposed mechanism of action of PARP inhibitors.</p>
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