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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 depending on ERα36 expression level. a Network distance characterization as a function of ERα36 expression level (see text for details). Expression level varied between 0 and 20 in the samples expressing ERα36 (x-axis). With step of 0.5 on ERα36 expression level, population was divided into two sub-groups for which networks were computed. A distance between corresponding networks was calculated (y-axis). The ERα36 expression threshold corresponding to the most different gene networks was computed and was equal to 8.35. b–c Graphs were designed by computing nonlinear correlation and mutual information between each gene expression pair in either low ERα36 [ER α36+] expressing (b) or high ERα36 [ERα36++] expressing (c) samples. The vertices represent genes. The edges linking the vertices indicate that independence between gene expressions is less than 0.05. Positive correlations are in blue and negative correlations in red. Correlation values are given in Additional file 3: Table S2A and Additional file 4: Table S2B and P-values in Additional file 5: Table S3A and Additional file 6: Table S3B, respectively
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Fig2: Gene expression network modeling depending on ERα36 expression level. a Network distance characterization as a function of ERα36 expression level (see text for details). Expression level varied between 0 and 20 in the samples expressing ERα36 (x-axis). With step of 0.5 on ERα36 expression level, population was divided into two sub-groups for which networks were computed. A distance between corresponding networks was calculated (y-axis). The ERα36 expression threshold corresponding to the most different gene networks was computed and was equal to 8.35. b–c Graphs were designed by computing nonlinear correlation and mutual information between each gene expression pair in either low ERα36 [ER α36+] expressing (b) or high ERα36 [ERα36++] expressing (c) samples. The vertices represent genes. The edges linking the vertices indicate that independence between gene expressions is less than 0.05. Positive correlations are in blue and negative correlations in red. Correlation values are given in Additional file 3: Table S2A and Additional file 4: Table S2B and P-values in Additional file 5: Table S3A and Additional file 6: Table S3B, respectively

Mentions: According to this metric, we determined the best threshold for ERα36 to subdivide the samples into two populations, in order to obtain the most different networks probably defining the most different tumor phenotypes related to ERα36 expression (Fig. 2a). Among ERα36 expressing samples, the “best” threshold (which leads to the highest network difference score) was ΔC (t) = 8.35 and allowed to segregate a high ERα36 expressing class ([ERα36++]) of 24 tumors and a low ERα36 expressing class ([ERα36+]) of 84 tumors.Fig. 2


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 depending on ERα36 expression level. a Network distance characterization as a function of ERα36 expression level (see text for details). Expression level varied between 0 and 20 in the samples expressing ERα36 (x-axis). With step of 0.5 on ERα36 expression level, population was divided into two sub-groups for which networks were computed. A distance between corresponding networks was calculated (y-axis). The ERα36 expression threshold corresponding to the most different gene networks was computed and was equal to 8.35. b–c Graphs were designed by computing nonlinear correlation and mutual information between each gene expression pair in either low ERα36 [ER α36+] expressing (b) or high ERα36 [ERα36++] expressing (c) samples. The vertices represent genes. The edges linking the vertices indicate that independence between gene expressions is less than 0.05. Positive correlations are in blue and negative correlations in red. Correlation values are given in Additional file 3: Table S2A and Additional file 4: Table S2B and P-values in Additional file 5: Table S3A and Additional file 6: Table S3B, respectively
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig2: Gene expression network modeling depending on ERα36 expression level. a Network distance characterization as a function of ERα36 expression level (see text for details). Expression level varied between 0 and 20 in the samples expressing ERα36 (x-axis). With step of 0.5 on ERα36 expression level, population was divided into two sub-groups for which networks were computed. A distance between corresponding networks was calculated (y-axis). The ERα36 expression threshold corresponding to the most different gene networks was computed and was equal to 8.35. b–c Graphs were designed by computing nonlinear correlation and mutual information between each gene expression pair in either low ERα36 [ER α36+] expressing (b) or high ERα36 [ERα36++] expressing (c) samples. The vertices represent genes. The edges linking the vertices indicate that independence between gene expressions is less than 0.05. Positive correlations are in blue and negative correlations in red. Correlation values are given in Additional file 3: Table S2A and Additional file 4: Table S2B and P-values in Additional file 5: Table S3A and Additional file 6: Table S3B, respectively
Mentions: According to this metric, we determined the best threshold for ERα36 to subdivide the samples into two populations, in order to obtain the most different networks probably defining the most different tumor phenotypes related to ERα36 expression (Fig. 2a). Among ERα36 expressing samples, the “best” threshold (which leads to the highest network difference score) was ΔC (t) = 8.35 and allowed to segregate a high ERα36 expressing class ([ERα36++]) of 24 tumors and a low ERα36 expressing class ([ERα36+]) of 84 tumors.Fig. 2

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