References of "Poulet, Christophe"
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See detailExosomal Long Non-Coding RNAs in Lung Diseases
Poulet, Christophe ULiege; NJOCK, Makon-Sébastien ULiege; MOERMANS, Catherine ULiege et al

in International Journal of Molecular Sciences (2020), 21(10), 3580

Within the non-coding genome landscape, long non-coding RNAs (lncRNAs) and their secretion within exosomes are a window that could further explain the regulation, the sustaining, and the spread of lung ... [more ▼]

Within the non-coding genome landscape, long non-coding RNAs (lncRNAs) and their secretion within exosomes are a window that could further explain the regulation, the sustaining, and the spread of lung diseases. We present here a compilation of the current knowledge on lncRNAs commonly found in Chronic Obstructive Pulmonary Disease (COPD), asthma, Idiopathic Pulmonary Fibrosis (IPF), or lung cancers. We built interaction networks describing the mechanisms of action for COPD, asthma, and IPF, as well as private networks for H19, MALAT1, MEG3, FENDRR, CDKN2B-AS1, TUG1, HOTAIR, and GAS5 lncRNAs in lung cancers. We identified five signaling pathways targeted by these eight lncRNAs over the lung diseases mentioned above. These lncRNAs were involved in ten treatment resistances in lung cancers, with HOTAIR being itself described in seven resistances. Besides, five of them were previously described as promising biomarkers for the diagnosis and prognosis of asthma, COPD, and lung cancers. Additionally, we describe the exosomal-based studies on H19, MALAT1, HOTAIR, GAS5, UCA1, lnc-MMP2-2, GAPLINC, TBILA, AGAP2-AS1, and SOX2-OT. This review concludes on the need for additional studies describing the lncRNA mechanisms of action and confirming their potential as biomarkers, as well as their involvement in resistance to treatment, especially in non-cancerous lung diseases. [less ▲]

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See detailSuitable reference genes determination for real-time PCR using induced sputum samples.
MOERMANS, Catherine ULiege; Deliege, Esteban; Pirottin, Dimitri ULiege et al

in European Respiratory Journal (2019), 54(6),

Induced sputum is a non-invasive method of collecting cells from airways. Gene expression analysis from sputum cells has been used to understand the underlying mechanisms of airway diseases such as asthma ... [more ▼]

Induced sputum is a non-invasive method of collecting cells from airways. Gene expression analysis from sputum cells has been used to understand the underlying mechanisms of airway diseases such as asthma or chronic obstructive pulmonary disease (COPD). Suitable reference genes for normalisation of target mRNA levels between sputum samples have not been defined so far.The current study assessed the expression stability of nine common reference genes in sputum samples from 14 healthy volunteers, 12 asthmatics and 12 COPD patients.Using three different algorithms (geNorm, NormFinder and BestKeeper), we identified HPRT1 and GNB2L1 as the most optimal reference genes to use for normalisation of quantitative reverse transcriptase (RT) PCR data from sputum cells. The higher expression stability of HPRT1 and GNB2L1 were confirmed in a validation set of patients including nine healthy controls, five COPD patients and five asthmatic patients. In this group, the RNA extraction and RT-PCR methods differed, which attested that these genes remained the most reliable whatever the method used to extract the RNA, generate complementary DNA or amplify it.Finally, an example of relative quantification of gene expression linked to eosinophils or neutrophils provided more accurate results after normalisation with the reference genes identified as the most stable compared to the least stable and confirmed our findings. [less ▲]

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See detailTOWARDS AN ACCURATE CANCER DIAGNOSIS MODELIZATION:COMPARISON OF RANDOM FOREST STRATEGIES
Debit, Ahmed ULiege; Poulet, Christophe ULiege; JOSSE, Claire ULiege et al

Poster (2019, March 15)

Machine learning approaches are heavily used to produce models that will one day support clinical decisions. To be reliably used as a medical decision, such diagnosis and prognosis tools have to harbor a ... [more ▼]

Machine learning approaches are heavily used to produce models that will one day support clinical decisions. To be reliably used as a medical decision, such diagnosis and prognosis tools have to harbor a high-level of precision. Random Forests have been already used in cancer diagnosis, prognosis, and screening. Numerous Random Forests methods have been derived from the original random forest algorithm from Breiman et al. in 2001. Nevertheless, the precision of their generated models remains unknown when facing biological data. The precision of such models can be therefore too variable to produce models with the same accuracy of classification, making them useless in daily clinics. Here, we perform an empirical comparison of Random Forest based strategies, looking for their precision in model accuracy and overall computational time. An assessment of 15 methods is carried out for the classification of paired normal -tumor patients, from 3 TCGA RNA-Seq datasets: BRCA (Breast Invasive Carcinoma), LUSC (Lung Squamous Cell Carcinoma), and THCA (Thyroid Carcinoma). Results demonstrate noteworthy differences in the precisions of the model accuracy and the overall time processing, between the strategies for one dataset, as well as between datasets for one strategy. Therefore, we highly recommend to test each random forest strategy prior to modelization. This will certainly improve the precision in model accuracy while revealing the method of choice for the candidate data. [less ▲]

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See detailTowards an accurate cancer diagnosis modelization: Comparison of Random Forest strategies
Debit, Ahmed ULiege; Poulet, Christophe ULiege; JOSSE, Claire ULiege et al

Poster (2018, October 05)

Machine learning approaches are heavily used to produce models that will one day support clinical decisions. To be reliably used as a medical decision, such diagnosis and prognosis tools have to harbor a ... [more ▼]

Machine learning approaches are heavily used to produce models that will one day support clinical decisions. To be reliably used as a medical decision, such diagnosis and prognosis tools have to harbor a high-level of precision. Random Forests have been already used in cancer diagnosis, prognosis, and screening. Numerous Random Forests methods have been derived from the original random forest algorithm from Breiman et al. in 2001. Nevertheless, the precision of their generated models remains unknown when facing biological data. The precision of such models can be therefore too variable to produce models with the same accuracy of classification, making them useless in daily clinics. Here, we perform an empirical comparison of Random Forest based strategies, looking for their precision in model accuracy and overall computational time. An assessment of 15 methods is carried out for the classification of paired normal - tumor patients, from 3 TCGA RNA-Seq datasets: BRCA (Breast Invasive Carcinoma), LUSC (Lung Squamous Cell Carcinoma), and THCA (Thyroid Carcinoma). Results demonstrate noteworthy differences in the precisions of the model accuracy and the overall time processing, between the strategies for one dataset, as well as between datasets for one strategy. Therefore, we highly recommend to test each random forest strategy prior to modelization. This will certainly improve the precision in model accuracy while revealing the method of choice for the candidate data. [less ▲]

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See detailPredictive and prognostic role of peripheral blood eosinophil count in triple-negative and hormone receptor-negative/HER2-positive breast cancer patients undergoing neoadjuvant treatment
ONESTI, Concetta Elisa ULiege; JOSSE, Claire ULiege; Poncin, Aurélie ULiege et al

in Oncotarget (2018)

In current clinical practices, up to 27% of all breast cancer patients receive neoadjuvant chemotherapy. High pathological complete response rate is frequently associated with tumor-infiltrating ... [more ▼]

In current clinical practices, up to 27% of all breast cancer patients receive neoadjuvant chemotherapy. High pathological complete response rate is frequently associated with tumor-infiltrating lymphocytes. Additionally, circulating immune cells are also often linked to chemotherapy response. We performed a retrospective analysis on a cohort of 112 breast cancer patients (79 triple-negative, 33 hormone receptor-negative/HER2-positive) treated with standard neoadjuvant chemotherapy. Eosinophil and lymphocyte counts were collected from whole blood at baseline and during follow-ups and their associations with pathological complete response, relapse, disease-free and breast cancer-specific survival were analyzed. We observed a higher pathological complete response rate in patients who presented at baseline a relative eosinophil count ≥ 1.5% (55.6%) than in those with a relative eosinophil count < 1.5% (36.2%)(p = 0.04). An improvement in breast cancerspecific survival in patients with high relative eosinophil count (p = 0.05; HR = 0.336; 95% CI = 0.107–1.058) or with high relative lymphocyte count (threshold = 17.5%, p = 0.01; HR = 0.217; 95% CI = 0.060–0.783) were also observed. Upon combining the two parameters into the eosinophil x lymphocyte product with a threshold at 35.8, associations with pathological complete response (p = 0.002), relapse (p = 0.028), disease-free survival (p = 0.012) and breast cancer-specific survival (p = 0.001) were also recorded. In conclusion, the relative eosinophil count and eosinophil x lymphocyte product could be promising, affordable and accessible new biomarkers that are predictive for neoadjuvant chemotherapy response and prognostic for longer survival in triplenegative and hormone receptors-negative/HER2-positive breast cancers. Confirmation of these results in a larger patient population is needed. [less ▲]

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See detailTowards an accurate cancer diagnosis modelization:Comparison of Random Forest strategies
Debit, Ahmed ULiege; JOSSE, Claire ULiege; JERUSALEM, Guy ULiege et al

Poster (2018, September 13)

Machine learning approaches are heavily used to produce models that will one day support clinical decisions. To be reliably used as a medical decision, such diagnosis and prognosis tools have to harbor a ... [more ▼]

Machine learning approaches are heavily used to produce models that will one day support clinical decisions. To be reliably used as a medical decision, such diagnosis and prognosis tools have to harbor a high-level of precision. Random Forests have been already used in cancer diagnosis, prognosis, and screening. Numerous Random Forests methods have been derived from the original random forest algorithm from Breiman et al. in 2001. Nevertheless, the precision of their generated models remains unknown when facing biological data. The precision of such models can be therefore too variable to produce models with the same accuracy of classification, making them useless in daily clinics. Here, we perform an empirical comparison of Random Forest based strategies, looking for their precision in model accuracy and over all computational time. An assessment of 15 methods is carried out for the classification of paired normal - tumor patients, from 3 TCGA RNA-Seq datasets: BRCA (Breast Invasive Carcinoma), LUSC (Lung Squamous Cell Carcinoma), and THCA (Thyroid Carcinoma).Results demonstrate noteworthy differences in the precision of the model accuracy and the overall time processing, between the strategies for one dataset, as well as between datasets for one strategy. Therefore, we highly recommend to test each random forest strategy prior to modelization. This will certainly improve the precision in model accuracy while revealing the method of choice for the candidate data. [less ▲]

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See detailRole of DNA methylation in INK4a-ARF-INK4b locus expression in breast cancer
Latgé, Guillaume ULiege; JOSSE, Claire ULiege; Poulet, Christophe ULiege et al

Poster (2018, September 13)

Breast cancer is a public health problem : one woman in 9 will suffer of it during her lifetime. The estrogen receptor expressing sub-type (ER+) is the most frequent, with 75 % of the cases. Those tumors ... [more ▼]

Breast cancer is a public health problem : one woman in 9 will suffer of it during her lifetime. The estrogen receptor expressing sub-type (ER+) is the most frequent, with 75 % of the cases. Those tumors frequently become resistant to hormonotherapy and spread as metastasis. In this case, chemotherapy needs to be administrated. The CDK4/6 inhibitors in combination with hormonotherapy appears as the new standard treatment for metastatic disease and allows to postpone the chemotherapy. Those drugs play the same role as the endogenous p16-p15 proteins, and it is expected that the patients who have lost their protein expression are also those who will present the best response to treatment. However, none of the currently tested biomarkers turns out to be predictive of treatment response. The INK locus, where p16/p14-p15 proteins are encoded, is often altered in cancers and is involved in cell cycle regulation. The p16/p14-p15 expression is negatively regulated by a non-coding RNA called ANRIL. My aim is to explore the molecular mechanisms linked to the non-coding RNA of the INK locus and involved in the expression regulation of the proteins p16/p14-p15, and to highlight potential biomarkers of the CDK4/6 inhibitors treatment response in ER+ breast cancer. To this end, I already collected In Vitro expression data by RT-qPCR in some cell lines with different expression patterns. DNA methylation are investigated thanks to a Deoxyazacytidine treatment, a DNA methylation inhibitor then by ImmunoPrecipitation of methylated DNA. So, the first links between the expression of the locus and its methylation can be done. In future, other treamtents, protein and RNA interactions will be studied to explain the potential links and molecular mechanisms. [less ▲]

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See detailPredictive and prognostic role of peripheral blood eosinophil count in triple negative and hormone receptor negative/HER2 positive breast cancers patients undergoing neoadjuvant treatment.
ONESTI, Concetta Elisa ULiege; JOSSE, Claire ULiege; PONCIN, Aurélie ULiege et al

Scientific conference (2018, September 13)

Introduction: In clinical practices, up to 27% of breast cancer (BC) patients receive neoadjuvant chemotherapy (NAC). In this context, a pathological complete response (pCR) is the most commonly used end ... [more ▼]

Introduction: In clinical practices, up to 27% of breast cancer (BC) patients receive neoadjuvant chemotherapy (NAC). In this context, a pathological complete response (pCR) is the most commonly used end-point. High pCR rate is frequently associated with tumor infiltrating lymphocytes. Besides, circulating immune cells are also often linked to chemotherapy response. Materials and methods: We performed a retrospective analysis on 112 BC patients (79 triple negative, 33 HR-/HER2+), treated with standard NAC. The median follow-up was 37.5 months (range 9-156). Eosinophil and lymphocyte count were collected at baseline, after surgery, at 1 year of follow-up and at relapse. The primary end-point is the association between the relative eosinophil count (REC) and pCR. The secondary end-points are the associations of REC, relative lymphocyte count (RLC) and eosinophil/lymphocyte product (ELP) with relapse, disease free (DFS) and breast cancer specific (BCSS) survival and to study the variation of REC and RLC during follow-up. Results: We observed a higher pCR rate in patients with REC≥1.5% vs patients with REC <1.5% (55.6% vs 36.2%, p = 0.04), and a higher median REC in patients with pCR (1.9% vs 1.2%, p 0.042). No statistically significant associations were detected with relapse, nor between RLC with pCR or relapse. We observed a 3-year BCSS of 91% vs 80% for high and low REC respectively (p 0.05; HR 0.336, 95% CI 0.107-1.058) and of 88% vs 49% in RLC≥17.5% and <17.5% respectively (p 0.01; HR 0.217, 95% CI 0.060-0.783). No significant differences were detected for DFS. Combining the two parameters in the ELP, we observed an association with pCR (59.6% in ELP≥35.8 vs 30.9% in ELP<35.8, p 0.002), relapse (12.3% vs 29.1% in high and low ELP, p 0.028), DFS (3-year DFS 90% vs 69% in high and low ELP, p 0.012; HR 0.337, 95% CI 0.138-0.823) and BCSS (3-year BCSS 95% vs 75% in high and low ELP, p 0.001; HR 0.129, 95% CI 0.029-0.573). Moreover, we observed a raise of REC after surgery from 1.4% to 2.6% (p 0.0001) and a significant reduction at relapse from 2.8% to 1.7% (p 0.021). Conversely, a reduction of RLC from 26.9% at baseline to 20.45% after surgery (p 0.0001), without significant variation at relapse, was detected. Conclusion: REC, RLC and ELP could be new promising, affordable and accessible biomarkers predictive for NAC response and prognostic for longer survival in TNBC and HR-/HER2+ BC. Confirmation in a larger cohort is needed. [less ▲]

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See detailNatural Antisense Transcripts: Molecular Mechanisms and Implications in Breast Cancers.
Latge, Guillaume ULiege; Poulet, Christophe ULiege; Bours, Vincent ULiege et al

in International Journal of Molecular Sciences (2018), 19(1),

Natural antisense transcripts are RNA sequences that can be transcribed from both DNA strands at the same locus but in the opposite direction from the gene transcript. Because strand-specific high ... [more ▼]

Natural antisense transcripts are RNA sequences that can be transcribed from both DNA strands at the same locus but in the opposite direction from the gene transcript. Because strand-specific high-throughput sequencing of the antisense transcriptome has only been available for less than a decade, many natural antisense transcripts were first described as long non-coding RNAs. Although the precise biological roles of natural antisense transcripts are not known yet, an increasing number of studies report their implication in gene expression regulation. Their expression levels are altered in many physiological and pathological conditions, including breast cancers. Among the potential clinical utilities of the natural antisense transcripts, the non-coding|coding transcript pairs are of high interest for treatment. Indeed, these pairs can be targeted by antisense oligonucleotides to specifically tune the expression of the coding-gene. Here, we describe the current knowledge about natural antisense transcripts, their varying molecular mechanisms as gene expression regulators, and their potential as prognostic or predictive biomarkers in breast cancers. [less ▲]

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See detailCellular and genomic disease signature of peripheral blood mononuclear cells in patients with malignant pleural mesothelioma
Mallon, Zachary R.; Poulet, Christophe ULiege; Enstrom, Amanda et al

Poster (2017)

Background: Recent data on the incidence malignant pleural mesothelioma (MPM) and the continued large-scale use of asbestos throughout the developing world portends an epidemic of asbestos-related disease ... [more ▼]

Background: Recent data on the incidence malignant pleural mesothelioma (MPM) and the continued large-scale use of asbestos throughout the developing world portends an epidemic of asbestos-related disease. MPM is an aggressive and fatal cancer with few treatment options. Recent advances in large scale genomic and high throughput cellular analyses now provide the tools to more easily attain markers of disease status and potential responsiveness to immunotherapeutics. Materials and Methods: Here we present pre-treatment cellular and genomic biomarker data on a cohort of chemotherapy-naïve MPM patients, and demographically matched healthy donors (HD). MPM patients were enrolled in a Phase 1b study utilizing CRS-207, a live, attenuated Listeria monocytogenes strain engineered to express the tumor-associated antigen, mesothelin. Four different multi-color flow cytometry panels were used to provide resolution on major immune cell populations of T cells, γδ T cells, B Cells, dendritic cells, monocytes, and natural killer cells. Together, these panels provided deeper resolution on 39 distinct subpopulations of major immune cell subsets. RNA from these cells was used to perform multiplex gene expression analysis on 770 genes using the Nanostring nCounter PanCancer Pathway Panel. Results: FACS analysis yielded numerous subpopulations with statistically significant differences between MPM patients and healthy controls. Differences in immune populations were analyzed by median and significant findings included populations of CD4+ T cells, CD8+ T cells, B cells, classical monocytes, and monocytic myeloid derived suppressor cells*. Class comparison and hierarchical clustering of gene expression data revealed genomic markers that were significantly expressed in MPM compared to healthy controls. Immune subset deconvolution of gene expression data provided similar findings as FACS analysis and corroborated this disease signature across experimental platforms. Conclusions: Understanding a patient’s biological disease signature can aide in diagnosis, as well as in making informed choices about therapies amidst the complex and broadening immunotherapeutic landscape. Until recently, existing biomarker data in MPM has been limited to a small number of serological markers and limited immune analysis. Here, we present the first comprehensive report of a MPM disease signature from the cellular and genomic perspectives. Correlation of patient baseline disease signatures with treatment outcome may yield biomarkers predictive of treatment efficacy. Predictive signatures are being investigated in the on-going Phase 1b study of CRS-207 and chemotherapy, as well as in the Phase 2 study of CRS-207 with pembrolizumab in MPM patients who failed prior treatment. *Inclusion of additional subjects confirmed the significance of all immune cell subsets except for MDSCs. [less ▲]

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See detailGenomic Studies of Multiple Myeloma Reveal an Association between X Chromosome Alterations and Genomic Profile Complexity.
Sticca, Tiberio ULiege; CABERG, Jean-Hubert ULiege; Wenric, Stéphane ULiege et al

in Genes, Chromosomes and Cancer (2017), 56

The genomic profile of multiple myeloma (MM) has prognostic value by dividing patients into a good prognosis hyperdiploid group and a bad prognosis non-hyperdiploid group with a higher incidence of IgH ... [more ▼]

The genomic profile of multiple myeloma (MM) has prognostic value by dividing patients into a good prognosis hyperdiploid group and a bad prognosis non-hyperdiploid group with a higher incidence of IgH translocations. This classification, however, is inadequate and many other parameters like mutations, epigenetic modifications and genomic heterogeneity may influence the prognosis. We performed a genomic study by array-based comparative genomic hybridization (aCGH) on a cohort of 162 patients to evaluate the frequency of genomic gains and losses. We identified a high frequency of X chromosome alterations leading to partial Xq duplication, often associated with Xi deletion in female patients. This partial X duplication could be a cytogenetic marker of aneuploidy as it is correlated with a high number of chromosomal breakages. Patient with high level of chromosomal breakage had reduced survival regardless the region implicated. A higher transcriptional level was shown for genes with potential implication in cancer and located in this altered region. Among these genes, IKBKG and IRAK1 are members of the NFKB pathway which plays an important role in MM and is a target for specific treatments. [less ▲]

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See detailInvolvement of Ikbζ in glioblastomas and its potential implication in radioresistance
Dubois, Nadège ULiege; Willems, Marie; Kroonen, Jérôme et al

Poster (2016, November 29)

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See detailInvolvement of Ikbζ in glioblastomas and its potential implication in radioresistance
Dubois, Nadège ULiege; Willems, Marie; Kroonen, Jérôme et al

Poster (2016, September 09)

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See detailPrognostic relevance of epilepsy at presentation in glioblastoma patients.
Berendsen, Sharon; Varkila, Meri; Kroonen, Jerome et al

in Neuro-Oncology (2016)

BACKGROUND: Epileptogenic glioblastomas are thought to convey a favorable prognosis, either due to early diagnosis or potential antitumor effects of antiepileptic drugs. We investigated the relationship ... [more ▼]

BACKGROUND: Epileptogenic glioblastomas are thought to convey a favorable prognosis, either due to early diagnosis or potential antitumor effects of antiepileptic drugs. We investigated the relationship between survival and epilepsy at presentation, early diagnosis, and antiepileptic drug therapy in glioblastoma patients. METHODS: Multivariable Cox regression was applied to survival data of 647 consecutive patients diagnosed with de novo glioblastoma between 2005 and 2013 in order to investigate the association between epilepsy and survival in glioblastoma patients. In addition, we quantified the association between survival and valproic acid (VPA) treatment. RESULTS: Epilepsy correlated positively with survival (HR: 0.75 (95% CI: 0.61-0.92), P < .01). This effect is independent of age, sex, performance status, type of surgery, adjuvant therapy, tumor location, and tumor volume, suggesting that this positive correlation cannot be attributed solely to early diagnosis. For patients who presented with epilepsy, the use of the antiepileptic drug VPA did not associate with survival when compared with patients who did not receive VPA treatment. CONCLUSION: Epilepsy is an independent prognostic factor for longer survival in glioblastoma patients. This prognostic effect is not solely explained by early diagnosis, and survival is not associated with VPA treatment. [less ▲]

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See detailInvolvement of Ikbζ in glioblastomas and its potential implication in radioresistance
Dubois, Nadège ULiege; Willems, Marie; Kroonen, Jérôme et al

Poster (2016)

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See detailConnexin 30 expression inhibits growth of human malignant gliomas but protects them against radiation therapy
Artesi, Maria ULiege; Kroonen, Jerome; Poulet, Christophe ULiege et al

in Neuro-Oncology (2015)

Connexins (Cxs) are potentially key players in the control of tumor cell proliferation. Normal astrocytes express large amounts of connexin30 and we have previously demonstrated its underexpression in rat ... [more ▼]

Connexins (Cxs) are potentially key players in the control of tumor cell proliferation. Normal astrocytes express large amounts of connexin30 and we have previously demonstrated its underexpression in rat glioma cells. Here we report that a Cx30 gene deletion occurs in approximately 30% of human glioblastomas (GBMs). Cx30 mRNA levels are decreased in these tumors compared to glial tumors of lower grade and to non tumoral tissues. At the protein level, Cx30 is lost in approximately one third of the tumors in our cohort of 230 glioblastoma biopsies. Restoration of Cx30 in U87 glioma cell lines and in glioma primary cultures (GM1), effectively retards cell proliferation in vitro and inhibits tumor growth in vivo. This correlates with a decrease in TCF, CK2 and NFkappaB activity and with altered expression levels of cell cycle control proteins such as cyclin D1, P27, P21 and c-myc. In patient biopsies, Cx30 expression was found in tumor regions displaying a lower Ki-67 proliferative index. Interestingly, in our cohort of malignant glioma patients, Cx30 expression proved to be an adverse prognostic factor. Here, we demonstrate its role in mediating cell survival and a hypermetabolic state after radiation treatment in human glioma cells. Mechanistically, we propose that a HSP90- mediated Cx30 translocation into mitochondria occurs after radiation treatment. The inhibition of the HSP90 molecular chaperone with geldamycin inhibits the radiation- induced translocation of Cx30 into mitochondria, decreasing the hypermetabolic state and restoring radiation sensitivity in those cells. These results demonstrate the central regulatory role of connexin30 in the biology of human gliomas. [less ▲]

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See detailConnexin 30 expression inhibits growth of human malignant gliomas but protects them against radiation therapy.
Artesi, Maria ULiege; Kroonen, Jerome; Bredel, Markus et al

in Neuro-Oncology (2014)

BACKGROUND: Glioblastomas remain ominous tumors that almost invariably escape treatment. Connexins are a family of transmembrane, gap junction-forming proteins, some members of which were reported to act ... [more ▼]

BACKGROUND: Glioblastomas remain ominous tumors that almost invariably escape treatment. Connexins are a family of transmembrane, gap junction-forming proteins, some members of which were reported to act as tumor suppressors and to modulate cellular metabolism in response to cytotoxic stress. METHODS: We analyzed the copy number and expression of the connexin (Cx)30 gene gap junction beta-6 (GJB6), as well as of its protein immunoreactivity in several public and proprietary repositories of glioblastomas, and their influence on patient survival. We evaluated the effect of the expression of this gap junction protein on the growth, DNA repair and energy metabolism, and treatment resistance of these tumors. RESULTS: The GJB6 gene was deleted in 25.8% of 751 analyzed tumors and mutated in 15.8% of 158 tumors. Cx30 immunoreactivity was absent in 28.9% of 145 tumors. Restoration of Cx30 expression in human glioblastoma cells reduced their growth in vitro and as xenografts in the striatum of immunodeficient mice. Cx30 immunoreactivity was, however, found to adversely affect survival in 2 independent retrospective cohorts of glioblastoma patients. Cx30 was found in clonogenic assays to protect glioblastoma cells against radiation-induced mortality and to decrease radiation-induced DNA damage. This radioprotection correlated with a heat shock protein 90-dependent mitochondrial translocation of Cx30 following radiation and an improved ATP production following this genotoxic stress. CONCLUSION: These results underline the complex relationship between potential tumor suppressors and treatment resistance in glioblastomas and single out GJB6/Cx30 as a potential biomarker and target for therapeutic intervention in these tumors. [less ▲]

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