Dataset Information

Combining RANK/RANKL and ERBB-2 targeting as a novel strategy in ERBB-2-positive breast carcinomas.

ABSTRACT:

Background

ERBB-2 is overexpressed in about 20% of breast cancers (BCs), indicating poor prognosis. The receptor activator of nuclear factor-?B (RANK) pathway is implicated in ERBB-2 (+) BC. The purpose of this study was to elucidate the underlying molecular mechanism of this interaction and the beneficial impact of dual targeting of RANK and ERBB-2 pathways.

Methods

We used SKBR3, MCF7, MDA-MB-453, and BT-474 human BC cell lines. We examined RANK and RANKL expression using RT-PCR, Western blot, and immunofluorescence. The evaluation of RANK expression in a cohort of BC patients was performed using immunohistochemistry. The interaction between RANK and ERBB family members was detected using proximity ligation assay (PLA), which enables the visualization of interacting proteins. We used inhibitors of both pathways [trastuzumab (T), pertuzumab (P), denosumab (D)]. NF-?B pathway activation was studied using Western blot. Cell growth and viability was evaluated using XTT, flow cytometry, and clonogenic assay. For cell migration evaluation, scratch assay was performed. Data were analyzed by one-way ANOVA.

Results

Cell lines express RANK and RANKL. RANK immunostaining was also detected in human BC tissue samples. RANK receptor dimerizes with ERBB family members. RANK/ERBB-2 dimer number seems to be associated with ERBB-2 expression (SKBR3, 5.4; BT-474, 8.2; MCF7, 0.7; MDA-MB-453, 0.3). RANK/ERBB-2 dimers were decreased in the presence of the inhibitors D, T, and P, while they were increased after RANKL (R) treatment in SKBR3 (m, 5.4; D, 1.2; T, 1.9; DT, 0.6; TP, 1; DTP, 0.4; R, 11.8) and BT-474 (m, 8.2; D, 3.1; T, 4.3; DT, 0.7; TP, 3.4; DTP, 3.2; R, 11.6). Combination targeting of SKBR3 further decreased NF-?B pathway activation compared to single targeting. In SKBR3, RANKL and ERBB-2 blockage resulted in reduced cell proliferation, increased apoptosis, and lower metastatic potential compared to mock cells (m) and reversed values in RANKL presence. The combination treatment of SKBR3 with D, T, and P had an advantage in functional traits compared to single targeting. Denosumab suppressed NF-?B signaling and diminished proliferation rate in MDA-MB-453 cells. MCF7 did not correspond to inhibitors.

Conclusions

The results indicate a novel physical and molecular association between ERBB-2 and RANK pathways that affects ERBB-2 (+) BC growth. We also present data suggesting that the combination of anti-ERBB-2 agents and RANKL inhibitors have a potential direct anti-tumor effect and should be further tested in certain BC patients.

SUBMITTER: Zoi I

PROVIDER: S-EPMC6892243 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Combining RANK/RANKL and ERBB-2 targeting as a novel strategy in ERBB-2-positive breast carcinomas.

Zoi Ilianna I Karamouzis Michalis V MV Xingi Evangelia E Sarantis Panagiotis P Thomaidou Dimitra D Lembessis Panayiotis P Theocharis Stamatios S Papavassiliou Athanasios G AG

Breast cancer research : BCR 20191203 1

<h4>Background</h4>ERBB-2 is overexpressed in about 20% of breast cancers (BCs), indicating poor prognosis. The receptor activator of nuclear factor-κB (RANK) pathway is implicated in ERBB-2 (+) BC. The purpose of this study was to elucidate the underlying molecular mechanism of this interaction and the beneficial impact of dual targeting of RANK and ERBB-2 pathways.<h4>Methods</h4>We used SKBR3, MCF7, MDA-MB-453, and BT-474 human BC cell lines. We examined RANK and RANKL expression using RT-PCR ...[more]

PMID: 31796128

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Project description:RANKL is important in mammary gland development during pregnancy and mediates the initiation and progression of progesterone-induced breast cancer. No clinical data are available on the effect of pregnancy on RANK/RANKL expression in young breast cancer patients.We used our previously published dataset of 65 pregnant and 130 matched young breast cancer patients with full clinical, pathological, and survival information. 85% of patients had available transcriptomic data as well. RANK/RANKL expression by immunohistochemistry using H-score on the primary tumor and adjacent normal tissue was performed. We examined the difference in expression of RANK/RANKL between pregnant and non-pregnant patients and their association with clinicopathological features and prognosis. We also evaluated genes and pathways associated with RANK/RANKL expression on primary tumors.RANKL but not RANK expression was more prevalent in the pregnant group, both on the tumor and adjacent normal tissue, independent of other clinicopathological factors (both P <0.001). 18.7% of pregnant and 5.3% of non-pregnant patients had tumors showing ?10% of cells with 3+ RANKL expression. RANKL expression was significantly higher in progesterone receptor-positive, and luminal A-like tumors, with negative correlation with Ki-67 (all P <0.001). On the contrary, RANK expression was higher in triple negative tumors (P <0.001). Using false discovery rate <0.05, 151 and 1,207 genes were significantly correlated with tumor-expressed RANKL and RANK expression by immunohistochemistry, respectively. High RANKL expression within primary tumor was associated with pathways related to mammary gland development, bone resorption, T-cell proliferation and regulation of chemotaxis, while RANK expression was associated with immune response and proliferation pathways. At a median follow-up of 65 months, neither RANK nor RANKL expression within tumor was associated with disease free survival in pregnant or non-pregnant group.Pregnancy increases RANKL expression both in normal breast and primary tumors. These results could guide further development of RANKL-targeted therapy.

Dataset Information

Combining RANK/RANKL and ERBB-2 targeting as a novel strategy in ERBB-2-positive breast carcinomas.

Background

Methods

Results

Conclusions

Publications

Combining RANK/RANKL and ERBB-2 targeting as a novel strategy in ERBB-2-positive breast carcinomas.

Similar Datasets

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