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23 pages, 83361 KiB

Open AccessArticle

The G-Protein-Coupled Estrogen Receptor (GPER) Regulates Trimethylation of Histone H3 at Lysine 4 and Represses Migration and Proliferation of Ovarian Cancer Cells In Vitro

by Nan Han, Sabine Heublein, Udo Jeschke, Christina Kuhn, Anna Hester, Bastian Czogalla, Sven Mahner, Miriam Rottmann, Doris Mayr, Elisa Schmoeckel and Fabian Trillsch

Cells 2021, 10(3), 619; https://doi.org/10.3390/cells10030619 - 11 Mar 2021

Cited by 21 | Viewed by 5226

Abstract

Histone H3 lysine 4 trimethylation (H3K4me3) is one of the most recognized epigenetic regulators of transcriptional activity representing, an epigenetic modification of Histone H3. Previous reports have suggested that the broad H3K4me3 domain can be considered as an epigenetic signature for tumor-suppressor genes [...] Read more.

Histone H3 lysine 4 trimethylation (H3K4me3) is one of the most recognized epigenetic regulators of transcriptional activity representing, an epigenetic modification of Histone H3. Previous reports have suggested that the broad H3K4me3 domain can be considered as an epigenetic signature for tumor-suppressor genes in human cells. G-protein-coupled estrogen receptor (GPER), a new membrane-bound estrogen receptor, acts as an inhibitor on cell growth via epigenetic regulation in breast and ovarian cancer cells. This study was conducted to evaluate the relationship of GPER and H3K4me3 in ovarian cancer tissue samples as well as in two different cell lines (Caov3 and Caov4). Silencing of GPER by a specific siRNA and two selective regulators with agonistic (G1) and antagonistic (G15) activity were applied for consecutive in vitro studies to investigate their impacts on tumor cell growth and the changes in phosphorylated ERK1/2 (p-ERK1/2) and H3K4me3. We found a positive correlation between GPER and H3K4me3 expression in ovarian cancer patients. Patients overexpressing GPER as well as H3K4me3 had significantly improved overall survival. Increased H3K4me3 and p-ERK1/2 levels and attenuated cell proliferation and migration were observed in Caov3 and Caov4 cells via activation of GPER by G1. Conversely, antagonizing GPER activity by G15 resulted in opposite effects in the Caov4 cell line. In conclusion, interaction of GPER and H3K4me3 appears to be of prognostic significance for ovarian cancer patients. The results of the in vitro analyses confirm the biological rationale for their interplay and identify GPER agonists, such as G1, as a potential therapeutic approach for future investigations. Full article

(This article belongs to the Special Issue Molecular and Cellular Mechanisms of Cancers: Ovarian Cancer)

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17 pages, 5045 KiB

Open AccessArticle

Small Cell Carcinoma of the Ovary, Hypercalcemic Type (SCCOHT) beyond SMARCA4 Mutations: A Comprehensive Genomic Analysis

by Aurélie Auguste, Félix Blanc-Durand, Marc Deloger, Audrey Le Formal, Rohan Bareja, David C. Wilkes, Catherine Richon, Béatrice Brunn, Olivier Caron, Mojgan Devouassoux-Shisheboran, Sébastien Gouy, Philippe Morice, Enrica Bentivegna, Andrea Sboner, Olivier Elemento, Mark A. Rubin, Patricia Pautier, Catherine Genestie, Joanna Cyrta and Alexandra Leary

Cells 2020, 9(6), 1496; https://doi.org/10.3390/cells9061496 - 19 Jun 2020

Cited by 32 | Viewed by 5990

Abstract

Small cell carcinoma of the ovary, hypercalcemic type (SCCOHT) is an aggressive malignancy that occurs in young women, is characterized by recurrent loss-of-function mutations in the SMARCA4 gene, and for which effective treatments options are lacking. The aim of this study was to [...] Read more.

Small cell carcinoma of the ovary, hypercalcemic type (SCCOHT) is an aggressive malignancy that occurs in young women, is characterized by recurrent loss-of-function mutations in the SMARCA4 gene, and for which effective treatments options are lacking. The aim of this study was to broaden the knowledge on this rare malignancy by reporting a comprehensive molecular analysis of an independent cohort of SCCOHT cases. We conducted Whole Exome Sequencing in six SCCOHT, and RNA-sequencing and array comparative genomic hybridization in eight SCCOHT. Additional immunohistochemical, Sanger sequencing and functional data are also provided. SCCOHTs showed remarkable genomic stability, with diploid profiles and low mutation load (mean, 5.43 mutations/Mb), including in the three chemotherapy-exposed tumors. All but one SCCOHT cases exhibited 19p13.2-3 copy-neutral LOH. SMARCA4 deleterious mutations were recurrent and accompanied by loss of expression of the SMARCA2 paralog. Variants in a few other genes located in 19p13.2-3 (e.g., PLK5) were detected. Putative therapeutic targets, including MAGEA4, AURKB and CLDN6, were found to be overexpressed in SCCOHT by RNA-seq as compared to benign ovarian tissue. Lastly, we provide additional evidence for sensitivity of SCCOHT to HDAC, DNMT and EZH2 inhibitors. Despite their aggressive clinical course, SCCOHT show remarkable inter-tumor homogeneity and display genomic stability, low mutation burden and few somatic copy number alterations. These findings and preliminary functional data support further exploration of epigenetic therapies in this lethal disease. Full article

(This article belongs to the Special Issue Molecular and Cellular Mechanisms of Cancers: Ovarian Cancer)

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Figure 1

Figure 1
An overview of mutational profiles of SCCOHT. (A) Clinical characteristics of the cohort and tests performed. (B) Representative histopathology of a SCCOHT case from this cohort (IGR-04), including rhabdoid features; hematoxylin-eosin-saffron, scale bar: 50 μm. (C) Combined analysis of somatic-only and LOH-related alterations: an overview of the 14 genes altered in at least 50% of samples. (D) Breakdown of variants detected in the 14 recurrently altered genes, including classification as known polymorphisms (Genome Aggregation Database v.2.1.1) and Polyphen-2 functional prediction scores. N/A: not available. P/D: possibly or probably damaging. The variants for which functional impact cannot be ruled out are explicitly listed. (E) Type and localization of the mutations found by WES in the SMARCA4 gene; * indicates that this identical mutation was found in two independent patients. Full article ">Figure 2
SMARCA4 and SMARCA2 expression in SCCOHT. (A) Representative SMARCA4 and SMARCA2 immunohistochemistry in a SMARCA4 mutated SCCOHT and in the one SMARCA4 wild-type. Tumor harboring concomitant ARID1A and ARID1B mutations (IGR-03). (B) Real-time RT-PCR for SMARCA2 in patient tumor samples from this study and in a SCCOHT cell line (BIN-67); expression levels are normalized to three housekeeping genes (YWHAZ/GUSB/HPRT1). (C) Western blot showing expression of several SWI/SNF subunits in SCCOHT cell lines (BIN-67, SCCOHT-1) compared to MRT (G401, MON, TTC709), SMARCA4-mutated lung cancer (H1299), high-grade endometrioid adenocarcinoma of the ovary (SKOV3) and neuroendocrine small cell lung cancer (DMS79) cell lines. (D) Results of Sanger sequencing of the SMARCA2 promoter insertional polymorphism sites, and an example of a heterozygous polymorphism status (−1321 site) in BIN-67 cells. (E) Representative IHC for SOX2 in SCCOHT and a positive control (SOX2-positive MRT) in patient FFPE tumor samples. Full article ">Figure 3
SCCOHT demonstrate remarkable genomic stability and recurrent 19p CN-LOH. (A) LOH regions obtained by WES in each tumor identifies a common “LOH region” on chromosome 19 for all SCCOHTs except IGR03: Chr19:373916-11465316. (B) CGH array profiles for each patient. (C) Zoom on 19p in all tumors fails to show a heterozygous copy number loss, thus suggestive of copy neutral LOH. (D) Artificial representation of the “common LOH region” on chromosome 19 in tumors (source: <a href="http://www.genecards.org" target="_blank">http://www.genecards.org</a>). Full article ">Figure 4
An overview of transcriptomic profiles of SCCOHT. (A) Graphic heatmap representation of rank-normalized expression values for selected, most significantly deregulated genes in the differential expression analysis between SCCOHT and benign ovarian tissue (GTEx). (B) Selected GSEA results for the differential expression analysis between SCCOHT and benign ovarian tissue (GTEx). (C) Selected GSEA results for the differential expression analysis between chemotherapy-exposed SCCOHT samples (IGR-01, IGR-04, IGR-06) and chemotherapy-naïve samples (IGR-02, IGR-05, IGR-08). Full article ">Figure 5
Epigenetic vulnerabilities in SCCOHT A, B. Anti-proliferative effects of 5′-AZAC (A) and TSA (B); − designates protein loss or loss-of-function mutation and/or loss of expression; + designates absence of mutation (wild-type status) and retained expression. (C) Rapid clinical response in SMARCA4-mutated SCCOHT treated with the EZH2 inhibitor EPZ-6438. A CT scan of the tumor at baseline and after four months of EPZ-6438 treatment with 70% decrease in tumor volume (RECIST 1.1). Full article ">

14 pages, 2354 KiB

Open AccessArticle

M2 Macrophages Infiltrating Epithelial Ovarian Cancer Express MDR1: A Feature That May Account for the Poor Prognosis

by Susann Badmann, Sabine Heublein, Doris Mayr, Anna Reischer, Yue Liao, Thomas Kolben, Susanne Beyer, Anna Hester, Christine Zeder-Goess, Alexander Burges, Sven Mahner, Udo Jeschke, Fabian Trillsch and Bastian Czogalla

Cells 2020, 9(5), 1224; https://doi.org/10.3390/cells9051224 - 15 May 2020

Cited by 29 | Viewed by 3617

Abstract

Multi drug resistance protein 1 (MDR1) expression on tumor cells has been widely investigated in context of drug resistance. However, the role of MDR1 on the immune cell infiltrate of solid tumors remains unknown. The aim of this study was to analyze the [...] Read more.

Multi drug resistance protein 1 (MDR1) expression on tumor cells has been widely investigated in context of drug resistance. However, the role of MDR1 on the immune cell infiltrate of solid tumors remains unknown. The aim of this study was to analyze the prognostic significance of a MDR1+ immune cell infiltrate in epithelial ovarian cancer (EOC) and to identify the MDR1+ leucocyte subpopulation. MDR1 expression was analyzed by immunohistochemistry in 156 EOC samples. In addition to MDR1+ cancer cells, we detected a MDR1+ leucocyte infiltrate (high infiltrate >4 leucocytes per field of view). Correlations and survival analyses were calculated. To identify immune cell subpopulations immunofluorescence double staining was performed. The MDR1+ leucocyte infiltrate was associated with human epidermal growth factor receptor 2 (HER2) (cc = 0.258, p = 0.005) and tumor-associated mucin 1 (TA-MUC1) (cc = 0.202, p = 0.022) expression on cancer cells. A high MDR1+ leucocyte infiltrate was associated with impaired survival, especially in patients whose carcinoma showed either serous histology (median OS 28.80 vs. 50.64 months, p = 0.027, n = 91) or TA-MUC1 expression (median OS 30.60 vs. 63.36 months, p = 0.015, n = 110). Similar findings for PFS suggest an influence of MDR1+ immune cells on the development of chemoresistance. A Cox regression analysis confirmed the independency of a high MDR1+ leucocyte infiltrate as prognostic factor. M2 macrophages were identified as main part of the MDR1+ leucocyte infiltrate expressing MDR1 as well as the M2 marker CD163 and the pan-macrophage marker CD68. Infiltration of MDR1+ leucocytes, mostly M2 macrophages, is associated with poor prognosis of EOC patients. Further understanding of the interaction of M2 macrophages, MDR1 and TA-MUC1 appears to be a key aspect to overcome chemoresistance in ovarian cancer. Full article

(This article belongs to the Special Issue Molecular and Cellular Mechanisms of Cancers: Ovarian Cancer)

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13 pages, 3028 KiB

Open AccessArticle

Ovarian Cancer Translational Activity of the Multicenter Italian Trial in Ovarian Cancer (MITO) Group: Lessons Learned in 10 Years of Experience

by Daniela Califano, Daniela Russo, Giosuè Scognamiglio, Nunzia Simona Losito, Anna Spina, Anna Maria Bello, Anna Capiluongo, Francesca Galdiero, Rossella De Cecio, Simona Bevilacqua, Piera Gargiulo, Edoardo Marchesi, Silvana Canevari, Francesco Perrone, Gennaro Daniele, Loris De Cecco, Delia Mezzanzanica and Sandro Pignata

Cells 2020, 9(4), 903; https://doi.org/10.3390/cells9040903 - 7 Apr 2020

Cited by 10 | Viewed by 3760

Abstract

Ovarian cancer is the most lethal gynecological cancer, and despite years of research, with the exception of a BRCA mutation driving the use of PARP inhibitors, no new prognostic/predictive biomarkers are clinically available. Improvement in biomarker selection and validation may derive from the [...] Read more.

Ovarian cancer is the most lethal gynecological cancer, and despite years of research, with the exception of a BRCA mutation driving the use of PARP inhibitors, no new prognostic/predictive biomarkers are clinically available. Improvement in biomarker selection and validation may derive from the systematic inclusion of translational analyses into the design of clinical trials. In the era of personalized medicine, the prospective centralized collection of high-quality biological material, expert pathological revision, and association to well-controlled clinical data are important or even essential added values to clinical trials. Here, we present the academic experience of the MITO (Multicenter Italian Trial in Ovarian Cancer) group, including gynecologists, pathologists, oncologists, biostatisticians, and translational researchers, whose effort is dedicated to the care and basic/translational research of gynecologic cancer. In our ten years of experience, we have been able to collect and process, for translational analyses, formalin-fixed, paraffin-embedded blocks from more than one thousand ovarian cancer patients. Standard operating procedures for collection, shipping, and processing were developed and made available to MITO researchers through the coordinating center’s web-based platform. Clinical data were collected through dedicated electronic case report forms hosted in a web-based electronic platform and stored in a central database at the trial’s coordinating center, which performed all the analyses related to the proposed translational researches. During this time, we improved our strategies of block management from retrospective to prospective collection, up to the design of a prospective collection with a quality check for sample eligibility before patients’ accrual. The final aim of our work is to share our experience by suggesting a guideline for the process of centralized collection, revision processing, and storing of formalin-fixed, paraffin-embedded blocks for translational purposes. Full article

(This article belongs to the Special Issue Molecular and Cellular Mechanisms of Cancers: Ovarian Cancer)

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16 pages, 5781 KiB

Open AccessArticle

Cholesterol Homeostasis Modulates Platinum Sensitivity in Human Ovarian Cancer

by Daniela Criscuolo, Rosario Avolio, Giovanni Calice, Chiara Laezza, Simona Paladino, Giovanna Navarra, Francesca Maddalena, Fabiana Crispo, Cristina Pagano, Maurizio Bifulco, Matteo Landriscina, Danilo Swann Matassa and Franca Esposito

Cells 2020, 9(4), 828; https://doi.org/10.3390/cells9040828 - 30 Mar 2020

Cited by 46 | Viewed by 7094

Abstract

Despite initial chemotherapy response, ovarian cancer is the deadliest gynecologic cancer, due to frequent relapse and onset of drug resistance. To date, there is no affordable diagnostic/prognostic biomarker for early detection of the disease. However, it has been recently shown that high grade [...] Read more.

Despite initial chemotherapy response, ovarian cancer is the deadliest gynecologic cancer, due to frequent relapse and onset of drug resistance. To date, there is no affordable diagnostic/prognostic biomarker for early detection of the disease. However, it has been recently shown that high grade serous ovarian cancers show peculiar oxidative metabolism, which is in turn responsible for inflammatory response and drug resistance. The molecular chaperone TRAP1 plays pivotal roles in such metabolic adaptations, due to the involvement in the regulation of mitochondrial respiration. Here, we show that platinum-resistant ovarian cancer cells also show reduced cholesterol biosynthesis, and mostly rely on the uptake of exogenous cholesterol for their needs. Expression of FDPS and OSC, enzymes involved in cholesterol synthesis, are decreased both in drug-resistant cells and upon TRAP1 silencing, whereas the expression of LDL receptor, the main mediator of extracellular cholesterol uptake, is increased. Strikingly, treatment with statins to inhibit cholesterol synthesis reduces cisplatin-induced apoptosis, whereas silencing of LIPG, an enzyme involved in lipid metabolism, or withdrawal of lipids from the culture medium, increases sensitivity to the drug. These results suggest caveats for the use of statins in ovarian cancer patients and highlights the importance of lipid metabolism in ovarian cancer treatment. Full article

(This article belongs to the Special Issue Molecular and Cellular Mechanisms of Cancers: Ovarian Cancer)

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Figure 1
TRAP1 expression modulates genes involved in inflammation and lipid homeostasis. (A) Volcano plot showing the whole genome gene expression changes in PEA1 cells upon siRNA-mediated TRAP1 silencing (black = p < 0.001, light gray = p< 0.01). Relevant transcripts are highlighted, chosen for belonging to the cholesterol pathway or the inflammatory response. (B) Dot-plot representing hallmarks emerging from gene set enrichment analysis of the differentially expressed genes upon siRNA-mediated TRAP1 silencing in PEA1 cells. (C) Gene set enrichment analysis on the list of genes significantly modulated in expression (p < 0.001) upon TRAP1 silencing in PEA1 cells as for the microarray analysis. (D) Real-time RT-PCR analysis of genes involved in the cholesterol metabolic pathway found differentially expressed upon TRAP1 silencing in PEA1 cells according to microarray analyses. PEA1 and PEO14 cells were transfected with nontargeting control siRNA or TRAP1-directed siRNA (siTRAP1) and collected 72 h after transfection. All data are expressed as mean ± standard error of the mean (S.E.M.) of delta-delta Ct (ddCt) from six (PEA1 siTRAP1), three (PEO14 siTRAP1), or four (PEA2 vs. PEA1) independent experiments with technical triplicates each. Numbers indicate the statistical significance (p-value), based on the Student’s t-test (significant values highlighted in red). Full article ">Figure 2
Drug-resistant cells remodel cholesterol homeostasis. (A) Real-time RT-PCR analysis of expression of genes belonging to the cholesterol pathway in PEA1 and PEO14 upon siRNA-mediated TRAP1 silencing and in PEA2 compared to PEA1. All data are expressed as mean ± S.E.M. of delta-delta Ct (ddCt) from six (PEA1 siTRAP1), three (PEO14 siTRAP1), or four (PEA2 vs. PEA1) independent experiments with technical triplicates each. Numbers indicate the statistical significance (p-value), based on Student’s t-test (significant values highlighted in red). (B) Total lysates obtained from PEA1, PEA2, PEO14, and PEO23 cells or from stable TRAP1 knockdown PEA1 cells (shTRAP1) and their respective nonsilencing controls (shNS) were separated by SDS-PAGE and immunoblotted with the indicated antibodies. Images are representative of three independent experiments. Bar plots below panels show mean ± S.E.M. of relative densitometric band intensity calculated by assuming each band of the control condition (normalized on Actin) = 1 (n = 3). The number above the bars represent the statistical significance (p-value) based on the Student’s t-test (significant values highlighted in red). (C) [14C]-acetate incorporation in neo-synthesized lipids. Chromatogram of non-saponifiable lipids from PEA1 cells following siRNA-mediated TRAP1 silencing and from PEA1 and PEA2 cells, radiolabeled for 2 h. Cholesterol (Cho), diacylglycerols (DAG), fatty acids (FA), triacyglycerides (TG), and cholesteryl esters (CE) are indicated with arrows. Image is representative of two independent experiments. The bar plot shows mean ± S.E.M. of relative cholesterol densitometric band intensity calculated by assuming each band of the control condition = 1. Full article ">Figure 3
Transition to chemoresistance correlates with an increase of cholesterol at the cell surface. (A) Microscopy analysis of unesterified cholesterol through the visualization of filipin fluorescence in the two matched pair of cisplatin-sensitive/resistant cell lines PEA1/PEA2 and PEO14/PEO23. Maximum projection of Z-slices is shown. Scale bar = 20 μm. The bar graph (lower panel) shows the mean fluorescence intensity (arbitrary unit, a.u.) of filipin at the cell surface. Data are expressed as mean ± Standard deviation (SD) (n > 30). Numbers above bars indicate the statistical significance (p-value) based on Student’s t-test. (B) Representative images of double staining, in PEA1 and PEA2 cells, of unesterified cholesterol (Filipin) with Caveolin-1 (CAV-1). Overlay regions between filipin and CAV-1 stainings are indicated with arrowheads. Scale bar = 20 μm. Full article ">Figure 4
Analysis of cholesterol distribution in chemosensitive and chemoresistant cell lines. Representative images of double immunofluorescence assays. Specifically, PEA1 and PEA2 cells were stained with Filipin and the Golgi marker GM130 antibody (A) or the Lysostracker dye (B). Scale bar = 10 μm. Arrows indicate co-localization areas. In b, higher magnification pictures are shown, scale bar = 5 μm. Full article ">Figure 5
Cholesterol synthesis inhibition increases chemoresistance in platinum-sensitive OC cells. (A) PEA1, PEA2, and PEO14 cells were treated with 5 μM Lovastatin and then treated with 20 μM cisplatin for an additional 48 h (when medium was changed, Lovastatin was added again). Subsequently, apoptosis was measured by a luminescent caspase 3/7 activity assay. Data are expressed as mean ± S.E.M. from four (PEA1 and PEA2) or six (PEO14) independent experiments with technical triplicates each. The numbers above the bars indicate the statistical significance (p-value), based on the two-tailed unpaired Student’s t-test (significant values highlighted in red). (B) Twenty-four hours after seeding PEA1 and PEA2 cells in standard culture medium, cells were cultured for additional 48 or 96 h in medium supplemented with lipid-stripped serum. Finally, cell viability was measured by an MTT-based cell viability assay. (C) Immunoblot of total lysates from PEA1 and PEA2 cells 48 h after lipid withdrawal from culture medium. Images are representative of five independent experiments. Bar plots below panels show mean ± S.E.M. of relative densitometric band intensity calculated by assuming each band of the control condition (normalized on Actin) = 1 (n = 5). The numbers above the bars represent the statistical significance (p-value) based on the Student’s t-test (significant values highlighted in red). (D) PEA2 platinum-resistant cells were cultured for 48 h in medium supplemented with lipid-stripped serum and then treated with 40 μM cisplatin for additional 48 h. Apoptosis was measured by a luminescent caspase 3/7 activity assay. Data are expressed as mean ± S.E.M. from four independent experiments with technical triplicates each. The numbers above the bars indicate the statistical significance (p-value), based on the two-tailed unpaired Student’s t-test. (E,F) Kaplan–Meier estimates of the impact of FDPS (E) and LDLR (F) on overall survival in OC, according to The Cancer Genome Atlas (TCGA) database. Full article ">Figure 6
LIPG knockdown sensitizes to cisplatin and oxidative stress induces the expression of genes involved in inflammation. (A) PEA1 cells were transfected with control siRNAs or LIPG-directed siRNAs. Twenty-four hours after transfection, cells were treated with 20 μM cisplatin for additional 48 h. Subsequently, apoptosis was measured by a luminescent caspase 3/7 activity assay. Data are expressed as mean ± S.E.M. from four independent experiments with technical triplicates each. The numbers above the bars indicate the statistical significance (p-value), based on the two-tailed unpaired Student’s t-test. (B) Real-time RT-PCR analysis of expression of indicated genes in PEA1 cells treated with 20 μM H2O2 for 9 h. All data are expressed as mean ± S.E.M. of delta-delta Ct (ddCt) from four independent experiments with technical triplicates each. Numbers indicate the statistical significance (p-value), based on the Student’s t-test (significant values are highlighted in red). Full article ">Figure 7
A proposed model for the remodeling of cholesterol metabolism in the transition from a chemosensitive to a chemoresistant phenotype in human ovarian cancer cells. Drug-sensitive cells show significant activation of the cholesterol biosynthetic pathway and transport of cholesterol through the Golgi apparatus, while uptake of exogenous cholesterol through the LDLR is limited. Drug resistant cells, on the opposite, show reduced levels of FDPS and OSC along the biosynthetic pathway and increased LDL uptake. The inhibition of 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR), the rate-limiting enzyme of the biosynthetic pathway, by statins induces drug resistance, whereas the reduction of LDL recycling and lipid absorption through extracellular lipid withdrawal or LIPG knockdown sensitizes cells to drug-induced apoptosis. Enzymes whose expression has been analyzed in this work are in bold red. IPP = isopentenyl pyrophosphate; FPP = farnesyl pyrophosphate; TCA = tricarboxylic acid. This image was created using images from Servier Medical Art under Creative Commons Attribution 3.0 Unported License (<a href="https://smart.servier.com" target="_blank">https://smart.servier.com</a>). Full article ">

17 pages, 2345 KiB

Open AccessArticle

Non-Phosphorylatable PEA-15 Sensitises SKOV-3 Ovarian Cancer Cells to Cisplatin

by Shahana Dilruba, Alessia Grondana, Anke C. Schiedel, Naoto T. Ueno, Chandra Bartholomeusz, Jindrich Cinatl Jr, Katie-May McLaughlin, Mark N. Wass, Martin Michaelis and Ganna V. Kalayda

Cells 2020, 9(2), 515; https://doi.org/10.3390/cells9020515 - 24 Feb 2020

Cited by 6 | Viewed by 4117

Abstract

The efficacy of cisplatin-based chemotherapy in ovarian cancer is often limited by the development of drug resistance. In most ovarian cancer cells, cisplatin activates extracellular signal-regulated kinase1/2 (ERK1/2) signalling. Phosphoprotein enriched in astrocytes (PEA-15) is a ubiquitously expressed protein, capable of sequestering ERK1/2 [...] Read more.

The efficacy of cisplatin-based chemotherapy in ovarian cancer is often limited by the development of drug resistance. In most ovarian cancer cells, cisplatin activates extracellular signal-regulated kinase1/2 (ERK1/2) signalling. Phosphoprotein enriched in astrocytes (PEA-15) is a ubiquitously expressed protein, capable of sequestering ERK1/2 in the cytoplasm and inhibiting cell proliferation. This and other functions of PEA-15 are regulated by its phosphorylation status. In this study, the relevance of PEA-15 phosphorylation state for cisplatin sensitivity of ovarian carcinoma cells was examined. The results of MTT-assays indicated that overexpression of PEA-15AA (a non-phosphorylatable variant) sensitised SKOV-3 cells to cisplatin. Phosphomimetic PEA-15DD did not affect cell sensitivity to the drug. While PEA-15DD facilitates nuclear translocation of activated ERK1/2, PEA-15AA acts to sequester the kinase in the cytoplasm as shown by Western blot. Microarray data indicated deregulation of thirteen genes in PEA-15AA-transfected cells compared to non-transfected or PEA-15DD-transfected variants. Data derived from The Cancer Genome Atlas (TCGA) showed that the expression of seven of these genes including EGR1 (early growth response protein 1) and FLNA (filamin A) significantly correlated with the therapy outcome in cisplatin-treated cancer patients. Further analysis indicated the relevance of nuclear factor erythroid 2-related factor 2/antioxidant response element (Nrf2/ARE) signalling for the favourable effect of PEA-15AA on cisplatin sensitivity. The results warrant further evaluation of the PEA-15 phosphorylation status as a potential candidate biomarker of response to cisplatin-based chemotherapy. Full article

(This article belongs to the Special Issue Molecular and Cellular Mechanisms of Cancers: Ovarian Cancer)

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17 pages, 6136 KiB

Open AccessArticle

PIK3R1^W624R Is an Actionable Mutation in High Grade Serous Ovarian Carcinoma

by Concetta D’Ambrosio, Jessica Erriquez, Maddalena Arigoni, Sonia Capellero, Gloria Mittica, Eleonora Ghisoni, Fulvio Borella, Dionyssios Katsaros, Silvana Privitera, Marisa Ribotta, Elena Maldi, Giovanna Di Nardo, Enrico Berrino, Tiziana Venesio, Riccardo Ponzone, Marco Vaira, Douglas Hall, Mercedes Jimenez-Linan, Anna L. Paterson, Raffaele A. Calogero, James D. Brenton, Giorgio Valabrega, Maria Flavia Di Renzo and Martina Olivero Show full author list Hide full author list

Cells 2020, 9(2), 442; https://doi.org/10.3390/cells9020442 - 14 Feb 2020

Cited by 10 | Viewed by 3745

Abstract

Identifying cancer drivers and actionable mutations is critical for precision oncology. In epithelial ovarian cancer (EOC) the majority of mutations lack biological or clinical validation. We fully characterized 43 lines of Patient-Derived Xenografts (PDXs) and performed copy number analysis and whole exome sequencing [...] Read more.

Identifying cancer drivers and actionable mutations is critical for precision oncology. In epithelial ovarian cancer (EOC) the majority of mutations lack biological or clinical validation. We fully characterized 43 lines of Patient-Derived Xenografts (PDXs) and performed copy number analysis and whole exome sequencing of 12 lines derived from naïve, high grade EOCs. Pyrosequencing allowed quantifying mutations in the source tumours. Drug response was assayed on PDX Derived Tumour Cells (PDTCs) and in vivo on PDXs. We identified a PIK3R1^W624R variant in PDXs from a high grade serous EOC. Allele frequencies of PIK3R1^W624R in all the passaged PDXs and in samples of the source tumour suggested that it was truncal and thus possibly a driver mutation. After inconclusive results in silico analyses, PDTCs and PDXs allowed the showing actionability of PIK3R1^W624R and addiction of PIK3R1^W624R carrying cells to inhibitors of the PI3K/AKT/mTOR pathway. It is noteworthy that PIK3R1 encodes the p85α regulatory subunit of PI3K, that is very rarely mutated in EOC. The PIK3R1^W624R mutation is located in the cSH2 domain of the p85α that has never been involved in oncogenesis. These data show that patient-derived models are irreplaceable in their role of unveiling unpredicted driver and actionable variants in advanced ovarian cancer. Full article

(This article belongs to the Special Issue Molecular and Cellular Mechanisms of Cancers: Ovarian Cancer)

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Graphical abstract

Graphical abstract
Full article ">Figure 1
Histological characterization of PDX lines. Representative images of TMAs of PDX lines compared to sections of the corresponding source tumours. Numbers on the left are those of PDX lines as catalogued by the PROFILING approved protocol, while the numbers of the used FFPE block of samples of source tumours are shown on the top left of each panel. The complete characterization of these and the other thirty-eight PDX lines is reported in <a href="#app1-cells-09-00442" class="html-app">Table S1</a>. Full article ">Figure 2
Single Nucleotide Variants (SNVs) in cancer genes found in PDX lines derived from naïve HGS-EOC. Variants with an allele frequency (AF) ≥ 0.1 are listed. Only SNVs not classified as SNPs based on the SNP database are shown in this <a href="#cells-09-00442-f002" class="html-fig">Figure 2</a>. All the variants, including those classified as SNPs, are reported in the related <a href="#app1-cells-09-00442" class="html-app">Table S4</a>. Legend to boxes is shown on the right. Full article ">Figure 3
Identification of the PIK3R1W624R mutation as truncal mutation in parallel and serial passages of the PDX line #475 and in the corresponding source tumour. (A) The W624R mutation in PIK3R1 is one of several public mutations found in seven parallel and serial passages of the PDX line#475. (B–E) Pyrosequencing analysis confirmed the presence of the TP53 and the PIK3R1 mutations found in the #475 PDX line in two FFPE samples from distinct blocks of the source tumour. The TP53 and the PIK3R1 mutated sequences showed the same allele frequency (AF) in each sample. The AF of the PDX line-specific TP53 mutation was considered as a proxy of the percentage of tumour cells in the human tumour samples. (B) Sequence of PIK3R1 in Control Reference Genome; (C) percentage of PIK3R1W624R in FFPE sample A1 from the paraffin block 2998 of the source tumour; (D) percentage of PIK3R1W624R in FFPE sample A4 from the paraffin block 2304 of the source tumour; (E) percentage of mutated sequences of TP53 and PIK3R1 in the two above FFPE samples. Full article ">Figure 4
Crystal structures showing interaction of the p110 isoforms with the p85 isoforms in human and mouse PI3K. (A) Domain organization of p85α. (B) Available crystal structure (PDB ID 4L1B) of human p110α isoform with catalytic activity (grey) complexed with nSH2 (blue) and iSH2 (green) domains of human p85α; (C) available crystal structure (PDB ID 2Y3A) of mouse p110β in complex with iSH2 (green) and cSH2 (pink) domains of mouse p85β. (D) Alignment of the cSH2 domains of the human p85α and mouse p85β; homology is shown in the middle: W624 of the human p85α protein is conserved and corresponds to the W616 of the mouse p85β protein (red box in D). Full article ">Figure 5
Response of PIK3R1W624R carrying PDTCs to inhibitors of the PI3K/AKT/mTOR pathway. In each experiment, control cell lines were assayed, too. (A,C,E,G): Dose-response curves in CellTiterGlo 72 h viability assays. Normalized growth rate (GR value) inhibition metrics of three replicate experiments is shown to take into account cell division rates. The sign of GR values relates directly to response phenotype: positive for partial growth inhibition, zero for complete cytostatic effect and negative for cytotoxicity. The x axis shows drug concentration on a log10 (Log) scale. (B,D,F,H): Western blot analysis of the phosphorylation of the AKT signal transducer in response to drugs, as a proxy of PI3K activation status. (A) The PIK3R1W624R PDTCs showed susceptibility to the pan-class I PI3K inhibitor buparlisib (BKM120), comparable to that of the highly responsive A2780 cells; (C) the PIK3R1W624R PDTCs showed susceptibility to the dual PI3K/mTOR inhibitor dactolisib (BEZ235), comparable to that of the most sensitive (OVCAR-8) of the above cell lines; (E) the PIK3R1W624R PDTCs were also highly susceptible to the p110α specific PI3K inhibitor alpelisib (BYL719) as well as the A2780 cells; and (G) resistant to the p110β specific inhibitor GSK2636771 to which the LNCaP cells are exquisitely susceptible. Full article ">Figure 6
In vivo response of the PIK3R1W624R PDXs to buparlisib. Randomized mice were divided into two cohorts and treated with 20 mg/kg buparlisib, administered as described in the Methods section. (A) Growth curves of treated (black solid lines) and control (green solid lines) animals. (B) Western blot analysis of the phosphorylation of the AKT signal transducer in response to drugs, as a proxy of PI3K activation status in response to drugs, in the individual treated (T2 and T3) and control (C1) PDXs indicated in panel (A). Full article ">Figure 7
Immunohistochemical detection of proliferation index and decreased activation of PI3K in PIK3R1W624R PDXs, treated with buparlisib as shown in <a href="#cells-09-00442-f006" class="html-fig">Figure 6</a>. (A) Representative images of Ki67 positive cells detected in treated and control PDXs grown as shown in <a href="#cells-09-00442-f006" class="html-fig">Figure 6</a> panel A; (B) representative images of P-S6 positive cells detected in treated and control PDXs grown as shown in <a href="#cells-09-00442-f006" class="html-fig">Figure 6</a> panel A. (C) Quantification of Ki67 positive nuclei evaluated as a percentage of positive area versus total nuclei area. (D) Quantification of P-S6 positive cells evaluated as a percentage of positive area versus total area. The p value has been calculated using unpaired t-Student test. Full article ">

18 pages, 4061 KiB

Open AccessFeature PaperArticle

Identification and Characterization of a New Platinum-Induced TP53 Mutation in MDAH Ovarian Cancer Cells

by Ilaria Lorenzon, Ilenia Pellarin, Ilenia Pellizzari, Sara D’Andrea, Barbara Belletti, Maura Sonego, Gustavo Baldassarre and Monica Schiappacassi

Cells 2020, 9(1), 36; https://doi.org/10.3390/cells9010036 - 21 Dec 2019

Cited by 10 | Viewed by 3566

Abstract

Platinum-based chemotherapy is the therapy of choice for epithelial ovarian cancer (EOC). Acquired resistance to platinum (PT) is a frequent event that leads to disease progression and predicts poor prognosis. To understand possible mechanisms underlying acquired PT-resistance, we have recently generated and characterized [...] Read more.

Platinum-based chemotherapy is the therapy of choice for epithelial ovarian cancer (EOC). Acquired resistance to platinum (PT) is a frequent event that leads to disease progression and predicts poor prognosis. To understand possible mechanisms underlying acquired PT-resistance, we have recently generated and characterized three PT-resistant isogenic EOC cell lines. Here, we more deeply characterize several PT-resistant clones derived from MDAH-2774 cells. We show that, in these cells, the increased PT resistance was accompanied by the presence of a subpopulation of multinucleated giant cells. This phenotype was likely due to an altered progression through the M phase of the cell cycle and accompanied by the deregulated expression of genes involved in M phase progression known to be target of mutant TP53. Interestingly, we found that PT-resistant MDAH cells acquired in the TP53 gene a novel secondary mutation (i.e., S185G) that accompanied the R273H typical of MDAH cells. The double p53^S185G/R273H mutant increases the resistance to PT in a TP53 null EOC cellular model. Overall, we show how the selective pressure of PT is able to induce additional mutation in an already mutant TP53 gene in EOC and how this event could contribute to the acquisition of novel cellular phenotypes. Full article

(This article belongs to the Special Issue Molecular and Cellular Mechanisms of Cancers: Ovarian Cancer)

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16 pages, 4038 KiB

Open AccessArticle

TIMP-1 Is Overexpressed and Secreted by Platinum Resistant Epithelial Ovarian Cancer Cells

by Maura Sonego, Evelina Poletto, Eliana Pivetta, Milena S. Nicoloso, Rosanna Pellicani, Gian Luca Rampioni Vinciguerra, Francesca Citron, Roberto Sorio, Maurizio Mongiat and Gustavo Baldassarre

Cells 2020, 9(1), 6; https://doi.org/10.3390/cells9010006 - 18 Dec 2019

Cited by 20 | Viewed by 3436

Abstract

Epithelial Ovarian Cancer (EOC) is the most lethal gynecological cancer in developed countries, and the development of new strategies to overcome chemoresistance is an awaited clinical need. Angiogenesis, the development of new blood vessels from pre-existing vasculature, has been validated as a therapeutic [...] Read more.

Epithelial Ovarian Cancer (EOC) is the most lethal gynecological cancer in developed countries, and the development of new strategies to overcome chemoresistance is an awaited clinical need. Angiogenesis, the development of new blood vessels from pre-existing vasculature, has been validated as a therapeutic target in this tumor type. The aim of this study is to verify if EOC cells with acquired resistance to platinum (PT) treatment display an altered angiogenic potential. Using a proteomic approach, we identified the tissue inhibitor of metalloproteinases 1 (TIMP-1) as the only secreted factor whose expression was up-regulated in PT-resistant TOV-112D and OVSAHO EOC cells used as study models. We report that TIMP-1 acts as a double-edged sword in the EOC microenvironment, directly affecting the response to PT treatment on tumor cells and indirectly altering migration and proliferation of endothelial cells. Interestingly, we found that high TIMP-1 levels in stage III–IV EOC patients associate with decreased overall survival, especially if they were treated with PT or bevacizumab. Taken together, these results pinpoint TIMP-1 as a key molecule involved in the regulation of EOC PT-resistance and progression disclosing the possibility that it could be used as a new biomarker of PT-resistance and/or therapeutic target. Full article

(This article belongs to the Special Issue Molecular and Cellular Mechanisms of Cancers: Ovarian Cancer)

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19 pages, 4294 KiB

Open AccessFeature PaperArticle

Rewiring of Lipid Metabolism and Storage in Ovarian Cancer Cells after Anti-VEGF Therapy

by Matteo Curtarello, Martina Tognon, Carolina Venturoli, Micol Silic-Benussi, Angela Grassi, Martina Verza, Sonia Minuzzo, Marica Pinazza, Valentina Brillo, Giovanni Tosi, Ruggero Ferrazza, Graziano Guella, Egidio Iorio, Adrien Godfroid, Nor Eddine Sounni, Alberto Amadori and Stefano Indraccolo

Cells 2019, 8(12), 1601; https://doi.org/10.3390/cells8121601 - 9 Dec 2019

Cited by 30 | Viewed by 5053

Abstract

Anti-angiogenic therapy triggers metabolic alterations in experimental and human tumors, the best characterized being exacerbated glycolysis and lactate production. By using both Liquid Chromatography-Mass Spectrometry (LC-MS) and Nuclear Magnetic Resonance (NMR) analysis, we found that treatment of ovarian cancer xenografts with the anti-Vascular [...] Read more.

Anti-angiogenic therapy triggers metabolic alterations in experimental and human tumors, the best characterized being exacerbated glycolysis and lactate production. By using both Liquid Chromatography-Mass Spectrometry (LC-MS) and Nuclear Magnetic Resonance (NMR) analysis, we found that treatment of ovarian cancer xenografts with the anti-Vascular Endothelial Growth Factor (VEGF) neutralizing antibody bevacizumab caused marked alterations of the tumor lipidomic profile, including increased levels of triacylglycerols and reduced saturation of lipid chains. Moreover, transcriptome analysis uncovered up-regulation of pathways involved in lipid metabolism. These alterations were accompanied by increased accumulation of lipid droplets in tumors. This phenomenon was reproduced under hypoxic conditions in vitro, where it mainly depended from uptake of exogenous lipids and was counteracted by treatment with the Liver X Receptor (LXR)-agonist GW3965, which inhibited cancer cell viability selectively under reduced serum conditions. This multi-level analysis indicates alterations of lipid metabolism following anti-VEGF therapy in ovarian cancer xenografts and suggests that LXR-agonists might empower anti-tumor effects of bevacizumab. Full article

(This article belongs to the Special Issue Molecular and Cellular Mechanisms of Cancers: Ovarian Cancer)

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Figure 1

Figure 1
Effects of anti-VEGF therapy on tumor growth, microvessels density and necrosis. (A) Kinetics of tumor development in NOD/SCID mice s.c. injected with ovarian cancer cell lines (IGROV-1, SKOV3, OVCAR3, and OC316) and the effects of multiple injections of the anti-VEGF mAb bevacizumab (arrows, 100 µg/dose, administered bi-or three-weekly) on tumor size compared to controls (n = 6 mice for group), * p < 0.05, ** p < 0.01, t-test. Tumors were collected and analyzed two days after the last dose of anti-VEGF mAb or PBS for bevacizumab and control groups, respectively. (B) Microvessels density (MVD) evaluation by staining with anti-CD31 mAb. Columns show mean ± SD values (n = 5–10 fields for tumor; n = 6 tumors for group), *** p < 0.001, t-test. (C) Columns indicate quantitative analysis of necrotic areas in n = 6 different tumors for each group, ** p < 0.01, *** p < 0.001, t-test. Full article ">Figure 2
Increase of intra-tumor lipid amount following anti-VEGF therapy. (A) Evaluation of the tumor lipidome component by LC-MS in IGROV-1 and SKOV3 tumor xenografts following anti-VEGF therapy. Total lipids signal is normalized to 1,2-dilauroyl-sn-glycero-3-phosphocoline (DLPC) signal and to tumor volume (mm3). Columns show mean ± SD values (n = 6 tumors for group), * p < 0.05, ** p < 0.01, t-test. (B) Representative images of immunofluorescence staining for adipophilin (ADRP) protein, a specific marker of lipid accumulation, in control and bevacizumab-treated tumors (left). Quantification of ADRP expression normalized to DAPI staining density. Columns show mean ± SD values (n = 6 tumors/group) * p < 0.05, Mann–Whitney test, (right). Full article ">Figure 3
Relative abundances of different lipid classes in bevacizumab-treated and control tumors. (A) Evaluation of relative amounts of lipid classes in bevacizumab-treated IGROV-1 tumors compared to the control. Dot blots show mean ± SD values (6 tumors/group). Most of the results are statistically significant (adj p-value < 0.05). (B) The same analysis showed only marginal differences between bevacizumab-treated and control tumors in the SKOV3 model. Full article ">Figure 3 Cont.
Relative abundances of different lipid classes in bevacizumab-treated and control tumors. (A) Evaluation of relative amounts of lipid classes in bevacizumab-treated IGROV-1 tumors compared to the control. Dot blots show mean ± SD values (6 tumors/group). Most of the results are statistically significant (adj p-value < 0.05). (B) The same analysis showed only marginal differences between bevacizumab-treated and control tumors in the SKOV3 model. Full article ">Figure 4
LD accumulation under hypoxia condition and serum starvation in IGROV-1 and SKOV3 ovarian cancer cells. (A) Quantification of LD in cancer cells, cultured under normoxia (N) or hypoxia (H) for 48 h, by flow cytometry analysis following staining with BODIPY 493/503 dye. X-mean values are normalized to normoxia condition. Columns show mean ± SD values of three experimental replicates (* p < 0.05, ** p < 0.01, t-test) (B) LD content in cancer cells cultured under normoxia and serum starvation for 24 h. X-mean values are normalized to 1% FBS condition. Columns show mean ± SD values of three experimental replicates (* p < 0.05, ** p < 0.01, *** p < 0.001, t-test). (C) LD content in cancer cells cultured under normoxia and 1% FBS condition with supplementation of oleic acid in three different concentrations (0.3 mM, 1.8 mM and 15 mM) for 24 h. Columns show mean ± SD values of three experimental replicates (* p < 0.05, ** p < 0.01, *** p < 0.001, t-test). Full article ">Figure 5
LD content and cell viability modulation by lipid metabolism’s inhibitors in IGROV-1 and SKOV3 ovarian cancer cells. (A) Evaluation of LD content in cancer cells cultured in hypoxia for 48h following treatment with the FASN inhibitor C75 and the LXR agonist GW3965 used at IC50 concentrations (C75 IC50 46 µM and 50 µM for IGROV-1 and SKOV3, respectively. GW3965 IC50 22.5 µM and 36.5 µM for IGROV-1 and SKOV3, respectively). X-mean values are normalized to hypoxia values. Columns show mean ± SD values of three experimental replicates (* p < 0.05, ** p < 0.01, t-test). (B) Effects of hypoxia and serum starvation on the expression of FAS and CD36 in ovarian cancer cells. Left panel: Western Blot analysis of FAS and CD36 expression in one representative experiment. Tubulin was used as loading control. Right panel: quantitative analysis of FAS and CD36 expression. Columns show mean ± SD values of three experimental replicates. N = normoxia, H = hypoxia. (C) GW3965 treatment decreased proliferation of tumor cells both under normoxia and hypoxia conditions. Columns show mean ± SD values of three experimental replicates (*** p < 0.001, t-test). Full article ">Figure 6
GW3965 effect on IGROV-1 and SKOV3 ovarian cancer cell death. Apoptosis/necrosis evaluation in cancer cells following GW3965 treatment at IC50 concentrations and under serum starvation (1% FBS) with supplementation of oleic acid (1.8 mM). Top panel: representative dot blots of cell populations with the X-axis for Annexin V positivity and the Y-axis for propidium iodide (PI) positivity. Bottom panel: Quantitative evaluation of necrosis. Data are normalized to PI positivity cells percentage under normal serum condition (10% FBS). Columns report the mean values ± SD of four different replicates for each condition (** p < 0.01, *** p < 0.001, t-test). Full article ">

17 pages, 1219 KiB

Open AccessArticle

Transcriptional Characterization of Stage I Epithelial Ovarian Cancer: A Multicentric Study

by Enrica Calura, Matteo Ciciani, Andrea Sambugaro, Lara Paracchini, Giuseppe Benvenuto, Salvatore Milite, Paolo Martini, Luca Beltrame, Flaminia Zane, Robert Fruscio, Martina Delle Marchette, Fulvio Borella, Germana Tognon, Antonella Ravaggi, Dionyssios Katsaros, Eliana Bignotti, Franco Odicino, Maurizio D’Incalci, Sergio Marchini and Chiara Romualdi

Cells 2019, 8(12), 1554; https://doi.org/10.3390/cells8121554 - 1 Dec 2019

Cited by 11 | Viewed by 4262

Abstract

Stage I epithelial ovarian cancer (EOC) represents about 10% of all EOCs. It is characterized by a complex histopathological and molecular heterogeneity, and it is composed of five main histological subtypes (mucinous, endometrioid, clear cell and high, and low grade serous), which have [...] Read more.

Stage I epithelial ovarian cancer (EOC) represents about 10% of all EOCs. It is characterized by a complex histopathological and molecular heterogeneity, and it is composed of five main histological subtypes (mucinous, endometrioid, clear cell and high, and low grade serous), which have peculiar genetic, molecular, and clinical characteristics. As it occurs less frequently than advanced-stage EOC, its molecular features have not been thoroughly investigated. In this study, using in silico approaches and gene expression data, on a multicentric cohort composed of 208 snap-frozen tumor biopsies, we explored the subtype-specific molecular alterations that regulate tumor aggressiveness in stage I EOC. We found that single genes rather than pathways are responsible for histotype specificities and that a cAMP-PKA-CREB1 signaling axis seems to play a central role in histotype differentiation. Moreover, our results indicate that immune response seems to be, at least in part, involved in histotype differences, as a higher immune-reactive behavior of serous and mucinous samples was observed with respect to other histotypes. Full article

(This article belongs to the Special Issue Molecular and Cellular Mechanisms of Cancers: Ovarian Cancer)

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Figure 1
Subtypes classification of stage I Epithelial ovarian cancer (EOC) samples as defined by Tothill et al. along with their clinical information: (i) immunoreactive (green)—associated with infiltration of immune cells, (ii) proliferative (yellow)—a low stromal expression subtype with high levels of circulating CA125, (iii) differentiated (orange)—a poor prognosis subtype displaying strong stromal response, correlating with extensive desmoplasia, and (iv) mesenchymal (light blue)—with high expression of N- and P-cadherins. Full article ">Figure 2
Immune cell composition and behavior in stage I EOC samples. (A) absolute fractions of immune cell types per sample obtained using a CIBERSORT deconvolution method; (B) patient’s immunophenograms grouped by histotypes and ordered by grade; (C) cluster analysis of patients using major histocompatibility complex (MHC), checkpoints immunomodulators (CP), immune effector cells (EC) and suppressor cells (SC) values along with histo-pathological annotations and immunophenoscores (IPS). (D) box-plots of IPS along with its four components (MHC, CP, SC and EC) across histotypes and tumor grades. Sample sizes for each boxplot is <math display="inline"><semantics> <mrow> <msub> <mi>n</mi> <mrow> <mi>C</mi> <mi>c</mi> </mrow> </msub> <mo>=</mo> <mn>16</mn> <mo>,</mo> <mo> </mo> <msub> <mi>n</mi> <mrow> <mi>E</mi> <mi>n</mi> <mi>d</mi> </mrow> </msub> <mo>=</mo> <mn>19</mn> <mo>,</mo> <mo> </mo> <msub> <mi>n</mi> <mrow> <mi>M</mi> <mi>u</mi> <mi>c</mi> </mrow> </msub> <mo>=</mo> <mn>17</mn> <mo>,</mo> <mo> </mo> <msub> <mi>n</mi> <mrow> <mi>S</mi> <mi>e</mi> <mi>r</mi> <mi>H</mi> <mi>i</mi> <mi>g</mi> <mi>h</mi> </mrow> </msub> <mo>=</mo> <mn>16</mn> <mo>,</mo> <mo> </mo> <msub> <mi>n</mi> <mrow> <mi>S</mi> <mi>e</mi> <mi>r</mi> <mi>L</mi> <mi>o</mi> <mi>w</mi> </mrow> </msub> <mo>=</mo> <mn>7</mn> </mrow> </semantics></math>. (E) Kaplan–Meier survival curves of patients grouped according to cluster 1 and 2 (see panel c) stratified by grade. Full article ">Figure 3
Pathways involved in stage I EOC histotype specificity (A) schematic overview of histotype pathway analyses’ results. A complete network is detailed in <a href="#app1-cells-08-01554" class="html-app">Supplementary Material 4</a>. Colored points reflect the histotype involvement as described in the legend; (B) box-plot of qRT-PCR expression values of selected elements of the network in the training and in the validation set. Full article ">

15 pages, 4600 KiB

Open AccessArticle

Detection of Abundant Non-Haematopoietic Circulating Cancer-Related Cells in Patients with Advanced Epithelial Ovarian Cancer

by Juhi Kumar, Dimple Chudasama, Charlotte Roberts, Mikael Kubista, Robert Sjöback, Jayanta Chatterjee, Vladimir Anikin, Emmanouil Karteris and Marcia Hall

Cells 2019, 8(7), 732; https://doi.org/10.3390/cells8070732 - 17 Jul 2019

Cited by 6 | Viewed by 4008

Abstract

Background: Current diagnosis and staging of advanced epithelial ovarian cancer (aEOC) has important limitations and better biomarkers are needed. We investigate the performance of non-haematopoietic circulating cells (CCs) at the time of disease presentation and relapse. Methods: Venous blood was collected [...] Read more.

Background: Current diagnosis and staging of advanced epithelial ovarian cancer (aEOC) has important limitations and better biomarkers are needed. We investigate the performance of non-haematopoietic circulating cells (CCs) at the time of disease presentation and relapse. Methods: Venous blood was collected prospectively from 37 aEOC patients and 39 volunteers. CCs were evaluated using ImageStream Technology™ and specific antibodies to differentiate epithelial cells from haematopoetic cells. qRT-PCR from whole blood of relapsed aEOC patients was carried out for biomarker discovery. Results: Significant numbers of CCs (CK⁺/WT1⁺/CD45⁻) were identified, quantified and characterised from aEOC patients compared to volunteers. CCs are abundant in women with newly diagnosed aEOC, prior to any treatment. Evaluation of RNA from the CCs in relapsed aEOC patients (n = 5) against a 79-gene panel revealed several differentially expressed genes compared to volunteers (n = 14). Size differentiation of CCs versus CD45⁺ haematopoietic cells was not reliable. Conclusion: CCs of non-haematopoetic origin are prevalent, particularly in patients with newly diagnosed aEOC. Exploiting a CC-rich population in aEOC patients offers insights into a part of the circulating microenvironment. Full article

(This article belongs to the Special Issue Molecular and Cellular Mechanisms of Cancers: Ovarian Cancer)

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Figure 1
Details of patients enlisted in the study. (A) Neoadjuvant chemotherapy and interval surgery; NACT cohort (n = 13) and Primary surgery, PDS cohort (n = 9); (B) Relapse treatment aEOC patients (n = 15). Full article ">Figure 2
Circulating cell (CC) integrity over 6 days in EDTA tubes (A; 2 days), Streck tubes (B; 3 days), PAXgene tubes (C, 6 days) and Roche (D, 6 days) as assessed by Imagestream™. Chanel 1: brightfield, Channel 5: DRAQ5™ nuclear staining (red). Full article ">Figure 3
Circulating cells from an ovarian cancer patient blood sample based on staining in a scatter image generated by the Imagestream™. The micrograph shows images of single cells from ovarian cancer patients with: (A): positive staining for CK and nuclear staining (DRAQ5) identifying a potential circulating ovarian cell (CC), (B): negative staining for CK but positive for DRAQ5 identifying a potential white blood cell (WBC), (C): combination of 2 potential WBCs (CK−) with a circulating ovarian CC (CK+); all three were stained positive for DRAQ5, (D): positive staining for CK, negative for CD45 and nuclear staining (DRAQ5) identifying a CC, (E): negative staining for CK, positive for CD45 and nuclear staining (DRAQ5) identifying a WBC, (F): a combination of 2 cells; one WT1 positive and one negative, both negative for CD45, but positive for nuclear staining (DRAQ5) identifying two potentially different CCs, but not WBCs. Full article ">Figure 4
Enumeration of CCs in controls against ovarian cancer (OC) patients: (A): CK+; * p < 0.05, and (B) WT1+, * p < 0.05. ROC curve analysis was used to measure sensitivity and specificity, an AUC of 0.78 (*** p < 0.0001) was calculated for CK+ (C), and an AUC of 0.82 (*** p = 0.0006) was calculated for WT1+ (D). Enumeration of CCs in OC patients prior to any treatment at all (NACT), following primary surgery only (post-PDS), or relapse and comparison to healthy controls. (E): CK+/CD45−/DRAQ5™+ and (F) WT1+/CD45−/DRAQ5™+, ** p < 0.001. Full article ">Figure 5
Depiction of CC levels (blue markers) and CA125 (orange markers) for 4 aEOC patients over time (measured in days), in a cohort treated with chemotherapy (arrows). Full article ">Figure 6
Different sizes of WBCs (A: 8µM; B: 10µM), CK+ CCs (C: 7µM, D: 8.5 µM), WT1+ CCs (E: 7µM; F: 8µM). (G): No apparent differences in size were detectable when combined CK+ and WT1+ CCs were measured and compared to controls. However, WT1+ CCs were larger compared to CK+ CCs; but not by a great margin (H; * p < 0.05). Full article ">Figure 7
Oncomine analysis for VEGFA (A), HJURP (B), CCNE2 (C) and RAD51 (D) mRNA expression, using the Bonome ovarian dataset; Human Genome U133A Array (Normal benign controls (Lane 0) n = 10, and Ovarian Carcinoma (Lane 1) n = 185), demonstrate a significant upregulation for all four genes in the cancer cohort compared to controls: VEGFA (210512_s_at): (p = 1.24 × 10−13; fold change = 1.670), HJURP (218726_at): (p = 9.10 × 10−10; fold change = 2.347), CCNE2 (211814_s_at): (p = 5.80 × 10−6; fold change = 1.286) and RAD51 (205023_at): (p = 1.08 × 10−4; fold change = 1.475). Overall Survival (OS) plotted as a Kaplan Meier, shows poorer overall survival in the high expression group of OC patients for all 4 genes tested. More specifically: VEGFA (210512_s_at; (E): (p = 0.027, Low = 482 High = 1174), HJURP (218726_at; (F): (p = 0.015, Low = 459 High = 1197), CCNE2 (205034_at; (G): (p = 0.00046, Low = 686 High = 970), and RAD51 (205023_at; (H): (p = 0.023, Low = 554 High = 1102). Full article ">

19 pages, 2607 KiB

Open AccessArticle

Discovery and Validation of Novel Biomarkers for Detection of Epithelial Ovarian Cancer

by Hagen Kulbe, Raik Otto, Silvia Darb-Esfahani, Hedwig Lammert, Salem Abobaker, Gabriele Welsch, Radoslav Chekerov, Reinhold Schäfer, Duska Dragun, Michael Hummel, Ulf Leser, Jalid Sehouli and Elena Ioana Braicu

Cells 2019, 8(7), 713; https://doi.org/10.3390/cells8070713 - 12 Jul 2019

Cited by 29 | Viewed by 6479

Abstract

Detection of epithelial ovarian cancer (EOC) poses a critical medical challenge. However, novel biomarkers for diagnosis remain to be discovered. Therefore, innovative approaches are of the utmost importance for patient outcome. Here, we present a concept for blood-based biomarker discovery, investigating both epithelial [...] Read more.

Detection of epithelial ovarian cancer (EOC) poses a critical medical challenge. However, novel biomarkers for diagnosis remain to be discovered. Therefore, innovative approaches are of the utmost importance for patient outcome. Here, we present a concept for blood-based biomarker discovery, investigating both epithelial and specifically stromal compartments, which have been neglected in search for novel candidates. We queried gene expression profiles of EOC including microdissected epithelium and adjacent stroma from benign and malignant tumours. Genes significantly differentially expressed within either the epithelial or the stromal compartments were retrieved. The expression of genes whose products are secreted yet absent in the blood of healthy donors were validated in tissue and blood from patients with pelvic mass by NanoString analysis. Results were confirmed by the comprehensive gene expression database, CSIOVDB (Ovarian cancer database of Cancer Science Institute Singapore). The top 25% of candidate genes were explored for their biomarker potential, and twelve were able to discriminate between benign and malignant tumours on transcript levels (p < 0.05). Among them T-cell differentiation protein myelin and lymphocyte (MAL), aurora kinase A (AURKA), stroma-derived candidates versican (VCAN), and syndecan-3 (SDC), which performed significantly better than the recently reported biomarker fibroblast growth factor 18 (FGF18) to discern malignant from benign conditions. Furthermore, elevated MAL and AURKA expression levels correlated significantly with a poor prognosis. We identified promising novel candidates and found the stroma of EOC to be a suitable compartment for biomarker discovery. Full article

(This article belongs to the Special Issue Molecular and Cellular Mechanisms of Cancers: Ovarian Cancer)

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Figure 1
Overview of the biomarker identification concept. Three independent studies for genes over-expressed in malignant tissue were interrogated (Gene Expression Omnibus (GEO) series GSE29156, GSE40595 and GSE14407). Genes found to be over-expressed in both studies while simultaneously being secreted into the bloodstream were defined as biomarker candidates using the secretome database (DB). The candidates’ expression signatures in tissue and blood were measured by NanoString analysis and enzyme-linked immunosorbent assay (ELISA), respectively and compared to the reported signatures in the CSIOVDB database (Ovarian cancer database of Cancer Science Institute Singapore) to determine whether the measured signatures could be independently replicated. Versican (VCAN), syndecan-3 (SDC3), aurora kinase A (AURKA) and T-cell differentiation protein myelin and lymphocyte (MAL) were confirmed as potential biomarkers, but not claudin-6 (CLDN6) by this analysis. Full article ">Figure 2
Principal component analysis (PCA) of patient-derived malignant and benign samples. Data from malignant and benign samples supported the pathological sample classification as malignant or benign since the sample were separable along the principal component 1 (PC1) of a principal component analysis (PCA) of their pairwise correlation. Their separability allowed identification of differentially expressed biomarker candidates to distinguish between benign and malignant samples. Full article ">Figure 3
Validation of biomarker candidates in tissue and blood. (A) This plot depicts the Log–Fold changes and P-values of differential biomarker expression values between malignant (positive values) and benign tissue (negative values). The top 10 significant (P-value significance ≥ ~1.3) candidate biomarkers are labelled. (B) The distribution of gene expression levels of biomarker candidates matrix metalloproteinase 15 (MMP15), bone morphogenetic protein 7 (BMP7), denticleless E3 ubiquitin protein ligase (DTL), maternal embryonic leucine zipper kinase (MELK), complement factor B (CFB), nuclear orphan receptor (NR2F6), galactoside 2-alpha-L-fucosyltransferase-2 (FUT2), claudin-6 (CLDN6), aurora kinase A (AURKA), interferon-stimulated gene 15 (ISG15), myelin and lymphocyte protein (MAL), fibroblast growth factor 18 (FGF18) in benign and ovarian cancer tissues are shown (p < 0.05). The expression data were obtained by NanoString analysis using the mRNA from tissue samples of patients with benign (N = 10) disease or ovarian cancer (N = 10). Full article ">Figure 4
Reported biomarker expression. Log–Fold change of biomarker candidates are shown for two sets of cohorts; (A) healthy ovarian surface epithelium (OSE) versus ovarian cancer (OVCA) and (B) healthy versus cancerous stromal tissue. P-values higher than 1.3 are significant (horizontal line). Genes have been ranked according to their P-values in the OSE versus OVCA comparison from highest to lowest statistical power. Data have been procured from the CSIOVDB database [<a href="#B39-cells-08-00713" class="html-bibr">39</a>]. Both plots show the same genes but are differently ordered by increasing the P-value. Differentially expressed biomarker candidates that distinguish malignant from healthy tissues are clearly present in plot A. By comparison, significantly fewer biomarkers that distinguish malignant from benign tissue are identifiable on plot B. In particular, the P-values for differential expression are significantly higher on plot B, although VCAN, ISG15, and MAL show a comparable Log–Fold change, which indicates a higher variance, i.e., expression heterogeneity within the groups. Full article ">Figure 5
Gene-expression in ovarian cancer. Gene expression profiles of (A) AURKA and (C) MAL in normal tissue, including ovarian surface epithelium (OSE), stroma and fallopian tube epithelium (FTE), and the ovarian cancer disease state are shown. The correlation of gene expression with the PFS and OS of ovarian cancer patients is presented in (B,D), respectively. Kaplan–Meier plots were generated with samples of low (blue) and high (red) gene expression levels within the CSIOVDB dataset. Full article ">

23 pages, 1926 KiB

Open AccessArticle

New Challenges in Tumor Mutation Heterogeneity in Advanced Ovarian Cancer by a Targeted Next-Generation Sequencing (NGS) Approach

by Marica Garziera, Rossana Roncato, Marcella Montico, Elena De Mattia, Sara Gagno, Elena Poletto, Simona Scalone, Vincenzo Canzonieri, Giorgio Giorda, Roberto Sorio, Erika Cecchin and Giuseppe Toffoli

Cells 2019, 8(6), 584; https://doi.org/10.3390/cells8060584 - 14 Jun 2019

Cited by 27 | Viewed by 5371

Abstract

Next-generation sequencing (NGS) technology has advanced knowledge of the genomic landscape of ovarian cancer, leading to an innovative molecular classification of the disease. However, patient survival and response to platinum-based treatments are still not predictable based on the tumor genetic profile. This retrospective [...] Read more.

Next-generation sequencing (NGS) technology has advanced knowledge of the genomic landscape of ovarian cancer, leading to an innovative molecular classification of the disease. However, patient survival and response to platinum-based treatments are still not predictable based on the tumor genetic profile. This retrospective study characterized the repertoire of somatic mutations in advanced ovarian cancer to identify tumor genetic markers predictive of platinum chemo-resistance and prognosis. Using targeted NGS, 79 primary advanced (III–IV stage, tumor grade G2-3) ovarian cancer tumors, including 64 high-grade serous ovarian cancers (HGSOCs), were screened with a 26 cancer-genes panel. Patients, enrolled between 1995 and 2011, underwent primary debulking surgery (PDS) with optimal residual disease (RD < 1 cm) and platinum-based chemotherapy as first-line treatment. We found a heterogeneous mutational landscape in some uncommon ovarian histotypes and in HGSOC tumor samples with relevance in predicting platinum sensitivity. In particular, we identified a poor prognostic signature in patients with HGSOC harboring concurrent mutations in two driver actionable genes of the panel. The tumor heterogeneity described, sheds light on the translational potential of targeted NGS approach for the identification of subgroups of patients with distinct therapeutic vulnerabilities, that are modulated by the specific mutational profile expressed by the ovarian tumor. Full article

(This article belongs to the Special Issue Molecular and Cellular Mechanisms of Cancers: Ovarian Cancer)

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Figure 1
Mutational landscape in advanced ovarian tumors (n = 79) by NGS. (a) Somatic profile in HGSOCs (n = 64). (b) Somatic profile in non-HGSOCs (n = 15). (c) A bar graph represents the single-nucleotide variants (SNVs) found in advanced ovarian tumors. VAF: variant allele frequency; INDEL: insertion or deletion leading to in-frame or frameshift change; HGSOC: high-grade serous ovarian cancer; Uncl: unclassified; Endom: endometrioid; Undif: undifferentiated; Trans: transitional; Clear: clear cells; Mucin: mucinous. Full article ">Figure 2
Mutation frequencies by ovarian cancer histological subtypes (n = 79) patients with advanced ovarian cancer. Mutation frequency was calculated as the number of variant occurrences within each gene divided for the total number of patients in the following ovarian cancer histological subtypes: (a) HGSOCs; (b) Endometrioid; (c) Mixed; (d) Undifferentiated (e) Mucinous; (f) Transitional; (g) Clear cells; (h) Unclassified. HGSOC: high-grade serous ovarian cancer. Full article ">Figure 3
Number of concurrent mutations identified in driver actionable genes of the panel in seven patients with high-grade serous ovarian cancer (HGSOC). Full article ">Figure 4
TP53 mutational landscape and correlation with platinum sensitivity. (a) A bar graph represents the SNVs in TP53 found in advanced ovarian tumors (All, n = 79), including HGSOCs (HGSOC, n = 64). (b) Distribution of GOF, LOF and Uncl mutations in TP53 according to platinum sensitivity in HGSOCs. HGSOC: high-grade serous ovarian cancer. GOF: gain-of-function; LOF: loss-of-function; Uncl: unclassified. *p value < 0.05. Full article ">

Review

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15 pages, 706 KiB

Open AccessReview

Vitamin D and Ovarian Cancer: Systematic Review of the Literature with a Focus on Molecular Mechanisms

by Andraž Dovnik and Nina Fokter Dovnik

Cells 2020, 9(2), 335; https://doi.org/10.3390/cells9020335 - 1 Feb 2020

Cited by 25 | Viewed by 6368

Abstract

Vitamin D is a lipid soluble vitamin involved primarily in calcium metabolism. Epidemiologic evidence indicates that lower circulating vitamin D levels are associated with a higher risk of ovarian cancer and that vitamin D supplementation is associated with decreased cancer mortality. A vast [...] Read more.

Vitamin D is a lipid soluble vitamin involved primarily in calcium metabolism. Epidemiologic evidence indicates that lower circulating vitamin D levels are associated with a higher risk of ovarian cancer and that vitamin D supplementation is associated with decreased cancer mortality. A vast amount of research exists on the possible molecular mechanisms through which vitamin D affects cancer cell proliferation, cancer progression, angiogenesis, and inflammation. We conducted a systematic review of the literature on the effects of vitamin D on ovarian cancer cell. Full article

(This article belongs to the Special Issue Molecular and Cellular Mechanisms of Cancers: Ovarian Cancer)

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22 pages, 4119 KiB A Mutation in Pre- and Post- Neo-Adjuvant Chemotherapy (NACT) Tumor Samples in High-Grade Serous Ovarian Cancer (HGSOC)" data-journal="cells">

Open AccessCase Report

Clonal Evolution of TP53 c.375+1G>A Mutation in Pre- and Post- Neo-Adjuvant Chemotherapy (NACT) Tumor Samples in High-Grade Serous Ovarian Cancer (HGSOC)

by Marica Garziera, Erika Cecchin, Giorgio Giorda, Roberto Sorio, Simona Scalone, Elena De Mattia, Rossana Roncato, Sara Gagno, Elena Poletto, Loredana Romanato, Fabrizio Ecca, Vincenzo Canzonieri and Giuseppe Toffoli

Cells 2019, 8(10), 1186; https://doi.org/10.3390/cells8101186 - 1 Oct 2019

Cited by 12 | Viewed by 4793

Abstract

Carboplatin/paclitaxel is the reference regimen in the treatment of advanced high-grade serous ovarian cancer (HGSOC) in neo-adjuvant chemotherapy (NACT) before interval debulking surgery (IDS). To identify new genetic markers of platinum-resistance, next-generation sequencing (NGS) analysis of 26 cancer-genes was performed on paired matched [...] Read more.

Carboplatin/paclitaxel is the reference regimen in the treatment of advanced high-grade serous ovarian cancer (HGSOC) in neo-adjuvant chemotherapy (NACT) before interval debulking surgery (IDS). To identify new genetic markers of platinum-resistance, next-generation sequencing (NGS) analysis of 26 cancer-genes was performed on paired matched pre- and post-NACT tumor and blood samples in a patient with stage IV HGSOC treated with NACT-IDS, showing platinum-refractory/resistance and poor prognosis. Only the TP53 c.375+1G>A somatic mutation was identified in both tumor samples. This variant, associated with aberrant splicing, was in trans configuration with the 72Arg allele of the known germline polymorphism TP53 c.215C>G (p. Pro72Arg). In the post-NACT tumor sample we observed the complete expansion of the TP53 c.375+1G>A driver mutant clone with somatic loss of the treatment-sensitive 72Arg allele. NGS results were confirmed with Sanger method and immunostaining for p53, BRCA1, p16, WT1, and Ki-67 markers were evaluated. This study showed that (i) the splice mutation in TP53 was present as an early driver mutation at diagnosis; (ii) the mutational profile was shared in pre- and post-NACT tumor samples; (iii) the complete expansion of a single dominant mutant clone through loss of heterozygosity (LOH) had occurred, suggesting a possible mechanism of platinum-resistance in HGSOC under the pressure of NACT. Full article

(This article belongs to the Special Issue Molecular and Cellular Mechanisms of Cancers: Ovarian Cancer)

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Figure 1
Graphical representation of CA-125 levels across the disease/treatment course of the patient diagnosed with HGSOC. The two time points, at D-LPS (pre-NACT) and at IDS (post-NACT), in which blood and ovarian tumor tissue samples were collected, are highlighted. CA-125: cancer antigen 125; D-LPS: diagnostic laparoscopy; NACT: neo-adjuvant chemotherapy; IDS: interval debulking surgery; HGSOC: high-grade serous ovarian cancer. Full article ">Figure 2
Graphic representation of VAF observed for SNVs in TP53 identified by NGS in blood (reference) and tumor tissue samples, collected at pre-therapy (pre-NACT) at the D-LPS and post-therapy (post-NACT) at the IDS, from a patient with HGSOC. (a) Bar-graph shows VAF decrease in pre- (27.71%) and post-NACT (4.21%) tumor samples, indicating prevalence of the WT “C” allele (blue bar) compared to the Alt “G” allele (yellow bar) at IDS for the TP53 c.215C>G (p.P72R) SNP, present in heterozygosis (57.31%) in the blood reference sample; (b) Bar-graph shows VAF increase in pre- (50.13%) and post-NACT (94.33%) tumor samples highlighting prevalence of the Alt “A” allele (red bar) compared to the WT “G” allele (green bar) in the HGSOC tissue sample collected at IDS of TP53 c.375+1G>A mutation, absent in the blood reference sample (only WT “G” allele, green bar). VAF: variant allele frequency; NACT: neo-adjuvant chemotherapy; HGSOC: high-grade serous ovarian cancer; D-LPS: diagnostic laparoscopy; IDS: interval debulking surgery; Alt: alternative; WT: wild-type. Full article ">Figure 3
Confirmatory Sanger electropherograms of TP53 variants identified by NGS in a patient with HGSOC, in tumor samples collected at D-LPS (pre-NACT) and at IDS (post-NACT), and in reference (germline) blood sample. (a) Sequencing result in the reference blood sample showing polymorphism TP53 c.215C>G in heterozygosis (blue box) and the WT “G” allele at the TP53 position c.375+1G>G (black box); (b) Sequencing result in the tumor sample collected at the D-LPS (pre-NACT) of the polymorphism TP53 c.215C>G (increased peak for WT “C” allele, blue box) and mutation TP53 c.375+1G>A (heterozygosis, red box), in trans configuration; (c) Sequencing result in the tumor sample collected at the IDS (post-NACT) of the polymorphism TP53 c.215C>G indicating homozygosis of the WT “C” allele or loss of the Alt “G” allele (blue box) and mutation TP53 c.375+1G>A indicating homozygosis of the Alt “A” mutated allele or loss of the WT “G” allele (red box), in trans configuration. HGSOC: high-grade serous ovarian cancer; D-LPS: diagnostic laparoscopy; IDS: interval debulking surgery; NACT: neo-adjuvant chemotherapy; SNP: single nucleotide polymorphism; NGS: next-generation sequencing; WT: wild-type. Full article ">Figure 4
Representative model of double TP53 SNVs in trans configuration identified in the pre-NACT/chemo-naïve HGSOC sample at D-LPS, with clone expansion and somatic LOH in the post-NACT tumor sample collected at IDS. In normal cells (Blood) at the germline level, only the TP53 SNP c.215C>G was detected; in the chemo-naïve/untreated (Tumor pre-NACT) sample, the splice mutation TP53 c.375+1G>A was present with the minor Alt allele in trans to the WT allele of polymorphism TP53 c.215C>G; in the post-therapy/treated (Tumor post-NACT) sample, the complete clone expansion with somatic LOH was observed with loss of the minor Alt allele for TP53 c.215C>G SNP and loss of the WT allele for TP53 c.375+1G>A mutation. Alt allele of the mutation TP53 c.375+1G>A is represented in red, the reference WT in green; Alt allele of the polymorphism TP53 c.215C>G is colored in orange, the reference WT allele in blue; the minor Alt alleles are in lower-case letters, the WT alleles are in upper-case letters. VAF: variant allele frequency; NACT: neo-adjuvant chemotherapy; HGSOC: high-grade serous ovarian cancer; D-LPS: diagnostic laparoscopy; IDS: interval debulking surgery; Alt: alternative; WT: wild-type; LOH: loss of heterozygosity. Full article ">Figure 5
Hematoxilin and eosin (H&E, 5×, 10× and 20× magnification) of HGSOC sections collected at D-LPS (pre-NACT) and at IDS (post-NACT) and immunohistochemical staining for p53 (10×, 20× and 40× magnification). (a–c) H&E of bouin-fixed, paraffin-embedded tumor tissue collected at D-LPS showing the serous ovarian carcinoma architecture of HGSOC case with somatic mutation in TP53 (a: 5×; b: 10×; c: 20×); (d–f) Absent (-) nuclear p53 expression on tumor tissue collected at D-LPS of HGSOC case with somatic mutation in TP53 (d: 10×; e: 20×; f: 40×); (g–i) H&E of formalin-fixed, paraffin-embedded tumor tissue collected at IDS showing the serous ovarian carcinoma architecture of HGSOC case with somatic mutation in TP53 (g: 5×; h: 10×; i: 20×); (l–n) Absent (-) nuclear p53 expression on tumor tissue collected at IDS of HGSOC case with somatic mutation in TP53 (l: 10×; m: 20×; n: 40×). No cytoplasmic staining was observed. NACT: neo-adjuvant chemotherapy; HGSOC: high-grade serous ovarian cancer; D-LPS: diagnostic laparoscopy; IDS: interval debulking surgery. Full article ">Figure 6
Immunohistochemical staining for BRCA1, p16, WT1, and Ki-67 of HGSOC sections collected at D-LPS from a patient diagnosed of HGSOC, before NACT (20× and 40× magnification). (a,b) Positive nuclear staining for BRCA1 observed in ~95% of tumor cells (a: 20×, b: 40×); (c,d) Diffuse nuclear and cytoplasmic staining of p16 exhibited by ~95% of tumor cells (c: 20×, d: 40×); (e,f) Nuclear and cytoplasmic staining for WT1 observed in ~70% of tumor cells (e: 20×, f: 40×);(g,h) Diffuse nuclear staining of Ki-67 observed in ~95% of tumor cells (g: 20×, h: 40×). NACT: neo-adjuvant chemotherapy; HGSOC: high-grade serous ovarian cancer; D-LPS: diagnostic laparoscopy; BRCA1: BRCA1 DNA repair associated; WT1: Wilms tumor 1. Full article ">

14 pages, 777 KiB

Open AccessFeature PaperPerspective

Gynecological Cancers Translational, Research Implementation, and Harmonization: Gynecologic Cancer InterGroup Consensus and Still Open Questions

by Marina Bagnoli, Ting Yan Shi, Charlie Gourley, Paul Speiser, Alexander Reuss, Hans W. Nijman, Carien L. Creutzberg, Suzy Scholl, Anastassia Negrouk, Mark F. Brady, Kosei Hasegawa, Katsutoshi Oda, Iain A. McNeish, Elise C. Kohn, Amit M. Oza, Helen MacKay, David Millan, Katherine Bennett, Clare Scott and Delia Mezzanzanica

Cells 2019, 8(3), 200; https://doi.org/10.3390/cells8030200 - 26 Feb 2019

Cited by 7 | Viewed by 4820

Abstract

In the era of personalized medicine, the introduction of translational studies in clinical trials has substantially increased their costs, but provides the possibility of improving the productivity of trials with a better selection of recruited patients. With the overall goal of creating a [...] Read more.

In the era of personalized medicine, the introduction of translational studies in clinical trials has substantially increased their costs, but provides the possibility of improving the productivity of trials with a better selection of recruited patients. With the overall goal of creating a roadmap to improve translational design for future gynecological cancer trials and of defining translational goals, a main discussion was held during a brainstorming day of the Gynecologic Cancer InterGroup (GCIG) Translational Research Committee and overall conclusions are here reported. A particular emphasis was dedicated to the new frontier of the immunoprofiling of gynecological cancers. The discussion pointed out that to maximize patients’ benefit, translational studies should be integral to clinical trial design with standardization and optimization of procedures including a harmonization program of Standard Operating Procedures. Pathology-reviewed sample collection should be mandatory and ensured by dedicated funding. Biomarker validation and development should be made public and transparent to ensure rapid progresses with positive outcomes for patients. Guidelines/templates for patients’ informed consent are needed. Importantly for the public, recognized goals are to increase the involvement of advocates and to improve the reporting of translational data in a forum accessible to patients. Full article

(This article belongs to the Special Issue Molecular and Cellular Mechanisms of Cancers: Ovarian Cancer)

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