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From ERα66 to ERα36: a generic method for validating a prognosis marker of breast tumor progression.

Chamard-Jovenin C, Jung AC, Chesnel A, Abecassis J, Flament S, Ledrappier S, Macabre C, Boukhobza T, Dumond H - BMC Syst Biol (2015)

Bottom Line: In vitro, ERalpha36 triggers mitogenic non-genomic signaling and migration ability in response to 17beta-estradiol and tamoxifen.In a retrospective study, we tried to decipher underlying mechanisms of cancer progression by using an original modeling of the relationships between ERalpha36, other estrogen and growth factor receptors and metastatic marker expression.Nonlinear correlation analyses and mutual information computations led to characterize a complex network connecting ERalpha36 to either non-genomic estrogen signaling or to metastatic process.

View Article: PubMed Central - PubMed

Affiliation: CNRS-Université de Lorraine, UMR 7039, Centre de Recherche en Automatique de Nancy, BP70239, F-54506, Vandœuvre-lès-Nancy, France. clemence.jovenin@univ-lorraine.fr.

ABSTRACT

Background: Estrogen receptor alpha36 (ERalpha36), a variant of estrogen receptor alpha (ER) is expressed in about half of breast tumors, independently of the [ER+]/[ER-] status. In vitro, ERalpha36 triggers mitogenic non-genomic signaling and migration ability in response to 17beta-estradiol and tamoxifen. In vivo, highly ERalpha36 expressing tumors are of poor outcome especially as [ER+] tumors are submitted to tamoxifen treatment which, in turn, enhances ERalpha36 expression.

Results: Our study aimed to validate ERalpha36 expression as a reliable prognostic factor for cancer progression from an estrogen dependent proliferative tumor toward an estrogen dispensable metastatic disease. In a retrospective study, we tried to decipher underlying mechanisms of cancer progression by using an original modeling of the relationships between ERalpha36, other estrogen and growth factor receptors and metastatic marker expression. Nonlinear correlation analyses and mutual information computations led to characterize a complex network connecting ERalpha36 to either non-genomic estrogen signaling or to metastatic process.

Conclusions: This study identifies ERalpha36 expression level as a relevant classifier which should be taken into account for breast tumors clinical characterization and [ER+] tumor treatment orientation, using a generic approach for the rapid, cheap and relevant evaluation of any candidate gene expression as a predictor of a complex biological process.

No MeSH data available.


Related in: MedlinePlus

Gene expression network modeling in [ER+] and [ER-] samples. Graphs were designed by computing nonlinear correlation and mutual information between each gene expression pair in either ER-positive (a) or ER-negative (b) samples. The vertices represent genes. The edges linking the vertices indicate that independence between gene expressions is less than 0.05 and links for ERα36 are in bold. P-values are given in Additional file 1: Table S1A and Additional file 2: Table S1B, respectively
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Fig1: Gene expression network modeling in [ER+] and [ER-] samples. Graphs were designed by computing nonlinear correlation and mutual information between each gene expression pair in either ER-positive (a) or ER-negative (b) samples. The vertices represent genes. The edges linking the vertices indicate that independence between gene expressions is less than 0.05 and links for ERα36 are in bold. P-values are given in Additional file 1: Table S1A and Additional file 2: Table S1B, respectively

Mentions: Since breast tumors are usually classified according to their hormone receptor status, tumor samples were first split into two classes according to their respective [ER] status, thus defining a first group of 60 ERα66 expressing samples ([ER+]), and a second group of 58 samples devoid of ERα66 expression ([ER-]). [ER+] breast cancer cell lines such as MCF-7 are considered non metastatic and weakly express ERα36 whereas [ER-] cell lines such as MDA-MB-231 or MDA-MB-235 are highly metastatic and display higher levels of ERα36 expression. In order to assess if such a link between ERα36 expression level and metastatic ability may be observed in vivo, nuclear (ERα66) or membrane-associated estrogen receptors (ERα36, GPER), their counterparts in non-genomic estrogen signaling (EGFR, HER2) as well as metastatic marker (SNAIL1, CXCR4, RANKL, VIM and MMP9) mRNA expression levels were determined by real-time PCR analyses. Among the growing amount of biomarkers related to the ER status (DDB2), the migration/invasion process (MMP9, VIM, CXCR4, RANKL, SNAIL) or the estrogen-response pathways (GPR30, EGFR), those listed above were picked up because they were previously shown to be related to ERα36 [18–20]. Then, we identified the gene networks for each class of tumors by using nonlinear correlation analyses and transfer entropy computation (see Additional file 1: Table S1A and Additional file 2: Table S1B). The processed data obtained from [ER+] samples indicated that ERα36 was a key node of a complex gene network, which involves other steroid and growth factor receptors as well as metastatic markers as a whole (Fig. 1a). On the other hand, ERα36 was connected to the single metastatic marker VIM in the [ER-] network (Fig. 1b). These huge differences displayed by the two networks implied different functioning modes according to the tumor [ER] status and suggested that there could be a quantifiable link between ERα36 position into the network and/or its expression level and tumor metastatic progression.Fig. 1


From ERα66 to ERα36: a generic method for validating a prognosis marker of breast tumor progression.

Chamard-Jovenin C, Jung AC, Chesnel A, Abecassis J, Flament S, Ledrappier S, Macabre C, Boukhobza T, Dumond H - BMC Syst Biol (2015)

Gene expression network modeling in [ER+] and [ER-] samples. Graphs were designed by computing nonlinear correlation and mutual information between each gene expression pair in either ER-positive (a) or ER-negative (b) samples. The vertices represent genes. The edges linking the vertices indicate that independence between gene expressions is less than 0.05 and links for ERα36 are in bold. P-values are given in Additional file 1: Table S1A and Additional file 2: Table S1B, respectively
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4469423&req=5

Fig1: Gene expression network modeling in [ER+] and [ER-] samples. Graphs were designed by computing nonlinear correlation and mutual information between each gene expression pair in either ER-positive (a) or ER-negative (b) samples. The vertices represent genes. The edges linking the vertices indicate that independence between gene expressions is less than 0.05 and links for ERα36 are in bold. P-values are given in Additional file 1: Table S1A and Additional file 2: Table S1B, respectively
Mentions: Since breast tumors are usually classified according to their hormone receptor status, tumor samples were first split into two classes according to their respective [ER] status, thus defining a first group of 60 ERα66 expressing samples ([ER+]), and a second group of 58 samples devoid of ERα66 expression ([ER-]). [ER+] breast cancer cell lines such as MCF-7 are considered non metastatic and weakly express ERα36 whereas [ER-] cell lines such as MDA-MB-231 or MDA-MB-235 are highly metastatic and display higher levels of ERα36 expression. In order to assess if such a link between ERα36 expression level and metastatic ability may be observed in vivo, nuclear (ERα66) or membrane-associated estrogen receptors (ERα36, GPER), their counterparts in non-genomic estrogen signaling (EGFR, HER2) as well as metastatic marker (SNAIL1, CXCR4, RANKL, VIM and MMP9) mRNA expression levels were determined by real-time PCR analyses. Among the growing amount of biomarkers related to the ER status (DDB2), the migration/invasion process (MMP9, VIM, CXCR4, RANKL, SNAIL) or the estrogen-response pathways (GPR30, EGFR), those listed above were picked up because they were previously shown to be related to ERα36 [18–20]. Then, we identified the gene networks for each class of tumors by using nonlinear correlation analyses and transfer entropy computation (see Additional file 1: Table S1A and Additional file 2: Table S1B). The processed data obtained from [ER+] samples indicated that ERα36 was a key node of a complex gene network, which involves other steroid and growth factor receptors as well as metastatic markers as a whole (Fig. 1a). On the other hand, ERα36 was connected to the single metastatic marker VIM in the [ER-] network (Fig. 1b). These huge differences displayed by the two networks implied different functioning modes according to the tumor [ER] status and suggested that there could be a quantifiable link between ERα36 position into the network and/or its expression level and tumor metastatic progression.Fig. 1

Bottom Line: In vitro, ERalpha36 triggers mitogenic non-genomic signaling and migration ability in response to 17beta-estradiol and tamoxifen.In a retrospective study, we tried to decipher underlying mechanisms of cancer progression by using an original modeling of the relationships between ERalpha36, other estrogen and growth factor receptors and metastatic marker expression.Nonlinear correlation analyses and mutual information computations led to characterize a complex network connecting ERalpha36 to either non-genomic estrogen signaling or to metastatic process.

View Article: PubMed Central - PubMed

Affiliation: CNRS-Université de Lorraine, UMR 7039, Centre de Recherche en Automatique de Nancy, BP70239, F-54506, Vandœuvre-lès-Nancy, France. clemence.jovenin@univ-lorraine.fr.

ABSTRACT

Background: Estrogen receptor alpha36 (ERalpha36), a variant of estrogen receptor alpha (ER) is expressed in about half of breast tumors, independently of the [ER+]/[ER-] status. In vitro, ERalpha36 triggers mitogenic non-genomic signaling and migration ability in response to 17beta-estradiol and tamoxifen. In vivo, highly ERalpha36 expressing tumors are of poor outcome especially as [ER+] tumors are submitted to tamoxifen treatment which, in turn, enhances ERalpha36 expression.

Results: Our study aimed to validate ERalpha36 expression as a reliable prognostic factor for cancer progression from an estrogen dependent proliferative tumor toward an estrogen dispensable metastatic disease. In a retrospective study, we tried to decipher underlying mechanisms of cancer progression by using an original modeling of the relationships between ERalpha36, other estrogen and growth factor receptors and metastatic marker expression. Nonlinear correlation analyses and mutual information computations led to characterize a complex network connecting ERalpha36 to either non-genomic estrogen signaling or to metastatic process.

Conclusions: This study identifies ERalpha36 expression level as a relevant classifier which should be taken into account for breast tumors clinical characterization and [ER+] tumor treatment orientation, using a generic approach for the rapid, cheap and relevant evaluation of any candidate gene expression as a predictor of a complex biological process.

No MeSH data available.


Related in: MedlinePlus