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. Author manuscript; available in PMC: 2024 Mar 1.
Published in final edited form as: Eur J Cancer Prev. 2022 Jun 30;32(2):126–138. doi: 10.1097/CEJ.0000000000000763

Recombinant Human Chorionic Gonadotropin Induces Signaling Pathways Towards Cancer Prevention in the Breast of BRCA1/2 Mutation Carriers

Yanrong Su a,e, Nhi M Dang a,e, Herman Depypere b,e, Julia Santucci-Pereira a, Pedro J Gutiérrez-Díez c, Joice Kanefsky a, Jaak Ph Janssens d, Jose Russo a,f
PMCID: PMC9800649  NIHMSID: NIHMS1836644  PMID: 35881946

Abstract

Background:

Strategies for breast cancer prevention in women with germline BRCA1/2 mutations are limited. We previously showed that recombinant human chorionic gonadotropin (r-hCG) induces mammary gland differentiation and inhibits mammary tumorigenesis in rats. The present study investigated hCG-induced signaling pathways in the breast of young nulliparous women carrying germline BRCA1/2 mutations.

Methods:

We performed RNA-sequencing on breast tissues from 25 BRCA1/2 mutation carriers who received r-hCG treatment for 3 months in a phase II clinical trial, we analyzed the biological processes, reactome pathways, canonical pathways, and upstream regulators associated with genes differentially expressed after r-hCG treatment, and validated genes of interest.

Results:

We observed that r-hCG induces remarkable transcriptomic changes in the breast of BRCA1/2 carriers, especially in genes related to cell development, cell differentiation, cell cycle, apoptosis, DNA repair, chromatin remodeling, and G protein-coupled receptor signaling. We revealed that r-hCG inhibits Wnt/β-catenin signaling, MYC, HMGA1, and HOTAIR, whereas activates TGFB/TGFBR-SMAD2/3/4, BRCA1, TP53, and upregulates BRCA1 protein.

Conclusion:

Our data suggest that use of r-hCG at young age may reduce the risk of breast cancer in BRCA1/2 carriers by inhibiting pathways associated with stem/progenitor cells maintenance and neoplastic transformation, whereas activating genes crucial for breast epithelial differentiation and lineage commitment, and DNA repair.

Keywords: Breast cancer, cancer prevention, human chorionic gonadotropin, Wnt signaling, MYC, TGFB, BRCA1, TP53

Introduction

Germline mutations in the breast cancer susceptibility gene 1 or 2 (BRCA1 or BRCA2) predispose individuals to breast and ovarian cancers at young age (Hall et al. 1990; Miki et al. 1994; Kuchenbaecker et al. 2017). Current cancer risk management for BRCA1 or BRCA2 carrier women (referred to as BRCA1/2 carriers hereafter) includes enhanced screening, prophylactic surgeries, and chemoprevention. Although prophylactic mastectomy decreases the incidence of breast cancer (Rebbeck et al. 2004), the overall uptake is less than 30% (Metcalfe et al. 2019). A recent study showed that only 9.6% of BRCA1/2 carriers underwent the surgery within 5 years of genotyping (Galmor et al. 2021). Chemoprevention has not been validated for the prevention of primary breast cancer in BRCA1 carriers (Calderon-Margalit and Paltiel 2004). There is an urgent need to develop novel and nonsurgical strategies for preventing breast cancer in BRCA1/2 carriers.

Epidemiological studies have identified early full-term pregnancy and breastfeeding as the most significant modifiable factors for preventing breast cancer (MacMahon et al. 1970; Fortner et al. 2019) in the general population. For BRCA1/2 carriers, breastfeeding is still associated with a reduction in breast cancer risk (Kotsopoulos et al. 2012), however, for years it was conflicting whether parity has a protective effect (Milne et al. 2010; Evans et al. 2018; Kotsopoulos et al. 2018). Evans et al. reported that young age at first pregnancy protects BRCA1/2 carriers against early-onset breast cancer (Evans et al. 2018). Several other studies concluded that there is no relationship between the age at first birth and breast cancer risk, while multi-parity is a significant risk-reducing factor in BRCA1/2 carriers (Terry et al. 2018). These findings suggest that the response of the breast tissue in BRCA1/2 carriers to pregnancy is different from that in the general population, the timing and frequency of pregnancy hormone exposure are likely crucial to have a protective effect in BRCA1/2 carriers.

The parity-induced protection against mammary tumorigenesis was observed not only in women, but also in multiple rat strains and mice demonstrated by using experimental models (Thordarson et al. 1995; Sivaraman et al. 1998; Yang et al. 1999; Medina and Smith 1999). Furthermore, use of hormones produced during pregnancy such as 17-β-estradiol and progesterone mimicked the protective effect of pregnancy (Guzman et al. 1999; Rajkumar et al. 2001). It has been postulated this parity-induced protection is mediated by several mechanisms: (1) Gland differentiation induced by hormones produced during pregnancy (Russo et al. 1979; Russo and Russo 1980; Russo et al. 1982; Russo et al. 1990); (2) Decreased proliferation activity of the gland (Sinha and Pazik 1981); (3) Reduction of circulating growth hormones and decrease of estrogen receptor, progesterone receptor alpha, and epidermal growth hormone receptor in the mammary epithelia (Thordarson et al. 1995; Taylor et al. 2009); (4) Change of composition and organization of mammary extracellular matrix (Schedin et al. 2004; Maller et al. 2013); (5) Change in the communication between mammary epithelial cells and the immune microenvironment (Hanasoge Somasundara et al. 2021); and (6) Change in the fate of specific mammary epithelial cells (Wagner et al. 2002; Boulanger et al. 2005; Meier-Abt et al. 2013; Choudhury et al. 2013; Bach et al. 2017). Molecular studies revealed that p53 is a potential mediator of pregnancy-induced protection against breast cancer (Sivaraman et al. 2001; Medina and Kittrell 2003), and reduction of Wnt signaling in mammary epithelium is causally related to parity-induced alterations of stem cell properties (Meier-Abt et al. 2013). Transcriptome analyses displayed upregulation of differentiation genes and alteration of TGFβ signaling and Wnt signaling in parous rats and humans (Blakely et al. 2006; Russo et al. 2012), as well as reduction of hormone signaling response in hormone-responsive luminal cells of parous women (Murrow et al. 2020). In addition, it was reported that pregnancy reprograms the epigenome of mammary epithelial cells and blocks the development of premalignancy lesions (Russo et al. 2012; Feigman et al. 2020).

Our previous studies indicated that the pregnancy hormone hCG also mimics the protective effect of pregnancy, we reported that r-hCG induces mammary gland differentiation and inhibits mammary tumorigenesis in rats (Russo et al. 1990, 1991). Moreover, we demonstrated in vitro that r-hCG prevents human breast epithelial cell transformation (Kocdor et al. 2009). We asked whether r-hCG can be used as a preventative agent against breast cancer in BRCA1/2 carriers. We carried out a phase II clinical trial and demonstrated the r-hCG administration was well tolerated and the treatment induced significant amount of differentially expressed genes (Depypere et al. 2021). We report here our novel findings by RNA-seq analysis. Our findings provide new insights into the molecular mechanisms underlying hCG-induced cancer prevention involving Wnt/β-catenin signaling, TGFβ-miR182-BRCA1 axis, MYC-HMGA1, HOTAIR, and TP53.

Methods

Study design and patient samples collection

The study design and participants have been described in a previous publication (Depypere et al. 2021). In brief, thirty-three women with germline BRCA1/2 mutation but free of breast cancer, at the age of 18 to 29 years old, were included in this study using criteria described in ClinicalTrial.gov (NCT0349569). The characteristics of the participants were shown in Table S1. Participants received a subcutaneous injection of 250 μg r-hCG (OVIDREL, 250 μg/0.5ml; EMD Serono Inc., Rockland, MA, USA) three times a week (Monday, Wednesday and Friday) for 3 months. Breast tissue biopsies were obtained using Spirotome® biopsy before (time point T1) and after 3 months of r-hCG injection (T2), as well as 6 months after the last r-hCG administration (T3) (Fig. 1a).

Fig. 1:

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R-hCG treatment induces significant changes in transcriptomic profile of the breast tissue of BRCA1/2 carriers. (a) Scheme of the r-hCG treatment and three time points for tissue collection. (b) Venn diagram representing the number of DEGs found up and down-regulated (FC>1.5) at T2 and T3 compared to the control sample of the same patient at T1, and the common DEGs between 2 time points. The 25 women were divided into two groups according to the contraceptives use: 11 women who never used contraceptives, or stopped oral contraceptives more than 30 days prior to r-hCG treatment were named responders, and 14 women who stopped oral contraceptives less than 30 days prior to r-hCG treatment, or used contraceptives during the study, were named low-responders. (c) Volcano plots of pairwise comparisons for DEGs with FC>1.5. The y-axis is the negative log10 of FDR-adjusted p values (-log10(p value)), a higher value indicates greater significance and the x-axis is the difference in expression between the two time points as measured in log2 fold change (log2FC). Orange dots represent genes showing statistically significant changes (FDRp<0.05) and log2FC<0.58, blue dots represent genes showing statistically significant changes and log2FC>0.58 (FC>1.5). Black dots represent non-significant genes. (d) Expression heatmap for overall normalized gene expression of 2135 DEGs obtained from 11 responders at three time points. Color code: yellow for over-expression, black for intermediate expression, and blue for under-expression. (e) Biological processes of DEGs (show top 30 up and down-process) in the responders. (f) Changes in DEGs number related to DNA repair, chromatin, and cell cycle over time.

RNA-sequencing (RNA-seq) and analysis

Statistical power analysis for RNA-seq was described in the previous article (Depypere et al. 2021). RNA samples from 25 women whose biopsies yielded enough material were used for RNA-seq. Library construction and sequencing were carried out by the BGI Company in Hong Kong. Details for RNA-seq and analysis see Additional file 1. We conducted pairwise comparisons for each woman: T2 vs. T1 and T3 vs. T1 (T1 was the expression before hCG treatment). Genes with fold change (FC) larger than 1.5 (FC>1.5) and a false discovery rate (FDR)-adjusted p-value less than 0.05 (FDRp<0.05) were considered as differentially expressed genes (DEGs).

Gene enrichment analysis

Gene Ontology (GO) Enrichment Analysis for the DEGs were analyzed via the Reactome Knowledgebase (www.reactome.org) (Fabregat et al. 2016), ShinyGO v0.61 (Ge et al. 2020), DAVID toolkit (Huang et al. 2007), and Benjamini-Hochberg correction with a cutoff of p<0.05. Comparison between our DEGs and database of GO consortium (http://geneontology.org/) (Ashburner et al. 2000) were performed to obtain all known genes associated with DNA repairs, chromatin remodeling, G protein-coupled receptor (GPCR) and cell cycle. Signaling pathways and biological processes with FDRp<0.05 were considered significant. Canonical pathways and upstream regulators were analyzed using Ingenuity Pathway Analysis (IPA, Qiagen, USA) with adjusted p value <0.05 and Z-score>2.0 for activated pathway/regulator and Z-score <−2.0 for inhibited pathway/regulator (Krämer et al. 2014). Interactive networks of target genes and related regulator/pathway were built using IPA. Venn diagram, volcano plots and heatmaps were generated using R version 4.0.3 (https://www.r-project.org/) with package VennDiagram, ggplot2, and pheatmap. Chord diagrams for relationships between target genes and related signaling pathways at different time points for each group of women were generated using Circos (Krzywinski et al. 2009).

Analysis of GPCR signaling related genes

For 75 GPCR signaling related genes, the mean expression for responders and low-responders was presented in log2 value. The change of gene expression was calculated by the formula: log2 (fold change) = mean expression at T2 or T3 - mean expression at T1.

Quantitative RT-PCR (qRT-PCR) validation

TaqMan gene expression assays were used for the analysis of genes of interest, details see Additional file 1. Data were analyzed by using ddCt method. Results are expressed as fold changes (log2 scale). The two-sided Fisher’s exact test was used for comparison of proportions. P<0.05 was considered as statistically significant. Data were presented as Mean±SEM.

Immunohistochemical analysis (IHC)

Paraffin sections of breast tissues were used for IHC analysis of BRCA1 protein expression. Details see Additional file 1.

Results

R-hCG treatment induces remarkable transcriptomic changes in the breast tissue of BRCA1/2 carriers

To identify the transcriptomic changes induced by r-hCG, we performed RNA-seq on breast tissues from 25 women. Our initial analysis showed that the response to r-hCG treatment of each woman did not vary with the BRCA1 or BRCA2 status, or the menstrual cycle at the day performing the first biopsy (T1), but was strikingly associated to the use of hormonal contraceptives during the clinical trial (Table S2).

Venn Diagrams (Fig.1b) show the number of DEGs with fold change above 1.5 (FC1.5). There were 1907 DEGs at T2 and 1065 DEGs at T3 for 11 women without contraceptives (named as responders) while there were almost no changes at T2 and only 260 DEGs at T3 for 14 women with contraceptives (named as low-responders) (Table S3–5). There were 112 common up-regulated genes between responders and low-responders at T3 (Table S6), whereas the down-regulated genes were different, suggesting that the contraceptives prompted a delayed and reduced response to r-hCG, and might induce a distinct effect.

Volcano plots show the fold change and significance of the DEGs (Fig. 1c). The heatmap was used to visualize the gene expression across the samples. There were 704 common up-regulated genes between T2 and T3 in responders, accounting for 68.3% and 78.5% of up-regulated genes at T2 and T3, respectively. For down-regulated genes, there were 132 common genes between T2 and T3 in responders, accounting for 78.6% of down-regulated genes at T3 (Fig. 1b, 1d; Table S7). Since hCG treatment was withdrawn at T2, existence of these common genes suggests a persistent effect of r-hCG on the transcriptomic profile. For low-responders, gene expression changes were only observed at T3 (Fig. S1).

GO enrichment analysis of the DEGs revealed that r-hCG greatly affected (Fig. 1e, Fig. S2a, Table S8, S9) cellular developmental process, cell differentiation, and anatomic structure morphogenesis at both T2 and T3 in responders and at T3 in low-responders. Furthermore, DEGs related to cell cycle and apoptotic process were mainly observed in responders (Fig. 1f, Fig. S3). Notably, the processes of stem cell development, stem cell proliferation, and differentiation were over-represented among the genes down-regulated by r-hCG at T2 in responders only (Fig. 2a). Reactome pathway analysis showed extracellular matrix (ECM) organization and collagen formation were enriched with up-regulated genes in both responders and low-responders, suggesting a role of r-hCG in remodeling ECM. In addition, signaling by ERBB2 and ERBB4 were down-regulated in responders at T2 (Fig. S2b, 2c, Table S10), suggesting r-hCG may have an impact on regulating ERBB signaling.

Fig. 2:

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DEGs related to stem cell, DNA repair, and chromatin. (a) Table shows DEGs related to stem cell proliferation and maintenance in the responders. (b) Chord diagrams show the association between DEGs with their significant biological process at T2 and T3 in the responders. DEGs related to DNA repair and chromatin are analyzed here.

In summary, r-hCG has a remarkable effect on the transcriptomic profile of breast tissue from BRCA1/2 carriers who did not use contraceptives, whereas the use of contraceptives interfered with hCG’s effects, exhibiting a delay in the gene expression response and a dramatic decrease in the number of DEGs.

R-hCG induces expression changes in genes related to DNA repair, chromatin organization and remodeling, and GPCR only in women without contraceptive exposure

We reported previously that pregnancy-induced chromatin remodeling in the breast epithelial cells might contribute, in part, to the reduced breast cancer risk associated with pregnancy and lactation (Russo et al. 2012; Gutiérrez-Díez et al. 2021). Here, we identified over 40 DEGs associated with DNA repair, chromatin remodeling and organization at both T2 and T3 (Fig. 1f) only in responders. Analysis of gene ontology showed that these DEGs are mainly associated with chromatin modification, organization, and remodeling, transcription, cell differentiation, cell cycle, apoptosis, double-strand break repair, DNA replication, etc. at T2 (Fig. 2b, Fig. S4, Table S11). At T3, these DEGs were involved in not only the processes observed at T2 but also tissue development (Fig. S4). These data implicate r-hCG in regulating genes in chromatin remodeling and DNA repair.

We also reported that the biological process related to GPCR is a possible confounding factor for the genes whose statistical distribution from nulliparous to parous women were significantly modified (Gutiérrez-Díez et al. 2021). In the present study, 75 genes related to GPCR signaling were up-regulated at T2 and/or T3 in responders compared to only 2 up-regulated genes at T3 in low-responders (Fig. S5). A more detailed analysis was run to inspect differences in these DEGs between responders and low-responders (Table S12–15). A general tendency to up-regulation relative to the expression at T1 was identified both for responders and low-responders, whereas the changes in responders were more striking and significant. The use of contraceptives was associated with a slight increase of expression in these genes at base level (T1) in low-responders, and then to smaller expression increases (T2 vs. T1 and T3 vs. T1) of these genes in low-responders compared to responders. The results suggest that in responders GPCR signaling was strongly and immediately activated under the effect of r-hCG, the use of contraceptives might interfere with GPCR signaling via its influence on the initial gene expression as described above or the binding of r-hCG with its receptor. The study of this signaling in more detail is warranted.

R-hCG treatment inhibits Wnt/β-catenin canonical pathway in the breast tissue of BRCA1/2 carriers

Ingenuity Pathway Analysis (IPA) was performed to identify the enriched canonical pathways of the DEGs, we observed activation or inhibition of many pathways that are implicated in breast development and tumorigenesis, of which, Wnt/β-catenin and PPAR signaling pathway were inhibited while p38 MAPK signaling and cAMP-mediated signaling were activated in responders at both T2 and T3 (Fig. 3a, Table S16). In addition, ErbB2-ErbB3 signaling, Wnt/Ca+ pathway, and mouse embryonic stem cell pluripotency were inhibited whereas prolactin signaling was activated at T2, and TGFβ signaling was activated at T3 in responders. For the network of Wnt/β-catenin signaling, positive regulators including SOXE family (SOX9, SOX10) and frizzled receptors (FZD1, FZD7) were down-regulated, while negative regulators including SOXF family (SOX7, SOX17, and SOX18) and SFRP family (SFRP2, SFRP4) were up-regulated (Fig. 3b) at T2 and/or T3 in responders. IPA depicted the DEGs involved in canonical Wnt/β-catenin signaling at T3 in responders (Fig. S6a). A similar change was observed in low-responders at T3 with the up-regulation of SFRP2, SFRP4 and SOX18 to a less extent, also resulting in Wnt/β-catenin signaling inhibition (Fig. S6b, 6c). Validation by qRT-PCR (Fig. 3c) confirmed the changes of selected genes in Wnt signaling. Overall, the results strongly indicate that r-hCG treatment inhibited Wnt/β-catenin signaling pathway in the breast of the responders both at the end of r-hCG treatment and six months later. Whereas in low-responders, the inhibition was delayed, and the extent of inhibition was decreased too.

Fig. 3:

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Impacts of r-hCG treatment on canonical pathways. (a) Enriched canonical pathways of the DEGs at T2 and T3 in responders. Significant pathways were determined to be activated with positive z-score and inhibited with negative z-score and FDRp<0.05 (q value), in which z-score is the statistical measure of correlation between relationship direction and gene expression. The most significant 73 pathways at T2, and all significant pathways at T3 are shown. Red arrows indicate activated pathways while blue arrows indicate inhibited pathways discussed in the present study. (b) Change of the expression in genes related to Wnt/β-catenin signaling pathway. Bubble graphs representing involvements of the canonical pathway genes determined by IPA (Qiagen, USA) and visualized by R 4.1.0. (c) Validation of selected DEGs by qRT-PCR in two groups of women over time. Data was analyzed by pairwise comparison between each time point after treatment versus the baseline (before treatment); error bars representing for the Mean±SEM; *p<0.05, **p<0.01, ***p<0.001. n=10 for responders, n=14 for low-responders.

R-hCG treatment activates upstream regulators TGFB/TGFBR-SMAD2/3/4, TP53 and BRCA1, whereas inhibits MYC, and induces BRCA1 protein in the breast of BRCA1/2 carriers

We performed upstream regulator analysis and selected eight upstream regulators that are related to breast development and carcinogenesis and have the highest Z-score. We identified (Fig. 4a, Fig. S7, Table S17, 18) that TGFB1, TGFBR1, and TP53 were predicted activated whereas MYC was strongly inhibited at T2 and T3 in responders. Moreover, TGFB2 and TGFBR2 were activated at T2 and still had an increased activity at T3 in responders. There was a similar impact at T3 to these regulators in low-responders with a lower Z-score except for TGFB2 and TGFB3. Notably, BRCA1 was predicted activated at T3 in responders only. In addition, SMAD2/3/4, the down-stream regulator of TGFBR1/2, were predicted activated over time in both groups, while HMGA1, a target gene of MYC, was observed in responders only. IPA revealed that the number of DEGs as target genes of the upstream regulators in responders was much greater than that in low-responders (Fig. 4b, Fig. S8), suggesting the activation or inhibition of these upstream regulators was more pronounced in responders than low-responders. Chord diagrams (Fig. 4d) show the relationship between regulators and target DEGs. We confirmed the expression changes of ID4 (TGFBR1 target), KIT (BRCA1 target), HMOX1 (BRCA1 and TP53 target), and HMGA1 (MYC target) by qRT-PCR (Fig. 4c). We also determined the expression of long non-coding RNA HOTAIR, a MYC-activated driver of malignancy implicated in breast carcinogenesis (Mozdarani et al. 2020). Consistently, HOTAIR was significantly down-regulated at T3 in 9/9 (100%) responders and 11/14 (78.6%) low-responders, suggesting the inhibition of MYC.

Fig. 4:

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Impacts of r-hCG treatment on the upstream regulators TGFB, TGFBR, TP53, BRCA1, and MYC. (a) Activation Z-scores for the selected upstream regulators. All activated upstream regulators had Z-score≥2.0, while inhibited regulators had Z-score≤−2. (b) IPA network showing the interaction between TGFBR1 and its target genes along with other regulators. Orange and yellow lines show the interaction in the responders while grey lines show the interaction in the low-responders. (c) Validation of selected DEGs by qRT-PCR in the two groups over time. Pairwise comparison between each time point after treatment versus the baseline (before treatment); error bars representing for Mean±SEM; *p<0.05, **p<0.01, ***p<0.001. (d) Chord diagrams show the relationship between the regulators and target DEGs in the responders and low-responders at T2 and T3. (e) IHC analysis of BRCA1 expression on the breast tissue of BRCA1/2 carriers before and after r-hCG treatment. Pictures show one representative example of BRCA1 carriers from each group. Magnification, 40× objective. Scale bar, 20 μm. The quantification is shown on the right panel. Each line represents for one subject. One sample T-test was used for the statistical analysis.

It has been reported that TGFβ upregulates BRCA1 through miR182 (Martinez-Ruiz et al. 2016). We examined the expression of miR182 and BRCA1 by qRT-PCR, there was no significant change in BRCA1 (using primers located on exons 22–23) although miR182 was significantly decreased at T2 in both groups and T3 in low-responders (Fig. 4c). We then performed IHC on breast tissues with an antibody recognizing the N-terminal BRCA1 and detecting total BRCA1 protein. Consistent with the finding that BRCA1 was activated at T3 in responders, r-hCG treatment significantly induced total BRCA1 protein at T3 in both BRCA1 and BRCA2 carriers only in responders (Fig. 4e).

Taken together, the findings strongly suggest that r-hCG significantly activates TGFB/TFGBR-SMAD2/3/4 and TP53, whereas inhibits oncogene MYC and its target genes HMGA1 and HOTAIR in the responders. These effects were reduced and delayed in the low-responders. Additionally, r-hCG activates BRCA1 in the responders only, and induces BRCA1 protein expression might partially through TGFβ-miR182-BRCA1 axis.

Discussion

This study reported signaling pathways induced by r-hCG treatment in the breast tissue of BRCA1/2 carriers. First, r-hCG induces significant expression changes in genes related to development, cell differentiation, cell cycle, apoptosis, stem cell proliferation, DNA repair, chromatin organization and remodeling, and GPCR signaling; Second, r-hCG inhibits Wnt/β-catenin signaling pathway; Third, r-hCG activates TGFB/TGFBR-SMAD2/3/4, TP53, and BRCA1, whereas inhibits MYC; Fourth, r-hCG inhibits the expression of HOTAIR and miR182; and (5) r-hCG increases BRCA1 protein expression.

We observed a clear difference in the response to r-hCG treatment, the serum progesterone level (Depypere et al. 2021), and changes in GPCR signaling between the responders and low-responders. There was no relationship between the menstrual cycles at the day of the first biopsy and the response to r-hCG (Table S2). Further study is needed to investigate the interference of contraceptives on the response to r-hCG.

BRCA1/2 play an important role in regulating the differentiation of mammary epithelial cells (Rajan et al. 1997; Liu et al. 2008). The breast tissue of BRCA carriers is less developed compared to that of control woman (Russo et al. 2001). In this study, we observed over 300 genes up-regulated by r-hCG are involved in cell development and differentiation, suggesting r-hCG may induce breast development in BRCA1/2 carriers.

Many abnormal cellular and molecular events are found in the breast of BRCA1/2 carriers prior to tumorigenesis. Breast tissue/epithelial cells from BRCA1 carriers exhibit defects in progenitor cell lineage commitment and display amplification of luminal progenitors (Lim et al. 2009; Molyneux et al. 2010; Proia et al. 2011; Hu et al. 2021). The BRCA1 carrier breast epithelial cells show inadequate DNA repair, cell trans-differentiation, telomere dysfunction, and genomic instability (Sedic et al. 2015; Wang et al. 2019; Chiang et al. 2019). Moreover, Wnt pathway enriched with up-regulated genes was identified in BRCA1mut/+ signature (Proia et al. 2011).

Wnt signaling is critical for stem cell self-renewal and tumorigenesis (Mohammed et al. 2016). Enrichment of Wnt/β-catenin signaling is evident in triple negative breast cancer (Khramtsov et al. 2010). Specifically, the Wnt signaling components including SOX9, SOX10, FZD7, and MYC are implicated in maintaining human breast luminal progenitor and cancer stem cells (Moumen et al. 2013; Chakrabarti et al. 2014; Domenici et al. 2019) and positively correlated with triple negative status of the breast cancer (Wang et al. 2015; Wang et al. 2017). These genes were down-regulated by r-hCG treatment in our study. The inhibition of WNT/β-catenin signaling in the breast of BRCA carriers by r-hCG is in concordance with the finding that parity is associated with a reduction of Wnt signaling in mammary gland (Meier-Abt et al. 2013; Russo et al. 2014; Muenst et al. 2017). Altogether, these findings suggest that r-hCG treatment may protect BRCA1/2 carriers from breast cancer partially by suppressing stemness and inducing differentiation of breast epithelial cells mediated by Wnt signaling inhibition.

P53 is important in mediating pregnancy and r-hCG-induced resistance to mammary tumorigenesis, as well as in blocking MYC-driving oncogenesis in parous murine mammary epithelial cells (Srivastava et al. 1997; Sivaraman et al. 2001; Feigman et al. 2020). P53 protein is higher in breast tissue from early-parity women compared to that from late-parity or nulliparous women (Gutierrez et al. 2015), and elevated in mouse mammary gland during pregnancy and involution (Strange et al. 1992). Activation of TP53 in the breast by r-hCG suggest that r-hCG may induce breast cancer prevention in BRCA1/2 carriers.

Tgfbs regulate development and functional differentiation of mouse mammary gland (Daniel et al. 2001; Ewan et al. 2002), modulate BRCA1 and direct mammary epithelial cell fate via a TGFβ-miR182-BRCA1 axis (Martinez-Ruiz et al. 2016). TGFβ signaling engages oncogene-induced senescence and prevents malignant transformation in human mammary epithelial cells (Cipriano et al. 2011). TGFB1 (Bellacosa et al. 2010), TGFB2 (Ding et al. 2019), and TGFBR2 (Proia et al. 2011) are significantly down-regulated in the breast of BRCA1 carriers, suggesting a defect in TGFβ signaling in these women. Our study showed that TGFB1/2, TGFBR1/2, and BRCA1 were predicted activated, TGFB1 and TGFB3 RNA, and BRCA1 protein were up-regulated, while MYC was predicated inhibited and ID4, miR182, Kit were down-regulated by r-hCG. ID4 controls luminal lineage commitment in normal mammary epithelium and inhibits BRCA1 function (Baker et al. 2016). MiR182 targets a network of genes involved in DNA repair (Krishnan et al. 2013). Kit is required for growth and survival of the cells of origin of BRCA1-associated breast cancers (Regan et al. 2012). BRCA1 and p53 are important in maintaining the normal population of luminal progenitor cells in breast tissues (Hu et al. 2021). BRCA1 can bind MYC and inhibit its transcriptional and transformation activity (Wang et al. 1998). Collectively, these results strongly suggest that r-hCG treatment regulates the signaling pathways that are crucial for breast epithelial differentiation and lineage commitment, DNA repair, and prevention of neoplastic transformation. In addition, r-hCG may be used as a hormonal regulator to rescue BRCA1 haploinsufficiency for BRCA1 carriers.

Epigenetics offers new horizons for cancer prevention. In this study, we observed that r-hCG induced a long-lasting change in gene expression. We propose that epigenetic mechanisms are involved in this change. Expression of genes involved in chromatin remodeling, including HMGA1 were changed by r-hCG. HMGA1 can directly induce SOX9 expression, amplify Wnt signaling (Xian et al. 2017), suppress BRCA1 promoter (Baldassarre et al. 2003), and inhibit the function of p53 family members (Frasca et al. 2006). In addition, researches showed that SOX7 (Stovall et al. 2013) and SOX17 (Fu et al. 2010) are epigenetically inactivated by promoter methylation in human breast cancer. These data suggest that the inhibition of Wnt signaling may be mediated by epigenetic mechanisms. Furthermore, we also observed decrease of HOTAIR and miR182 after r-hCG treatment. Taken together, our findings suggest that r-hCG may be used as an epigenetic modulator for breast cancer prevention.

We have previously conducted microarray analysis using RNA extracted from mammary gland tissue of rats treated with either r-hCG or control (Santucci-Pereira et al. 2013), we observed the biological processes such as development, cell differentiation, response to stress, and response to stimulus were overrepresented among the DEGs in rat mammary gland and breast of BRCA1/2 women after r-hCG treatment, suggesting that r-hCG might have a protective effect against breast cancer in BRCA1/2 carriers, similar to the effect we demonstrated in rat experimental model (Russo et al. 1991). The participants in this study are being followed up for additional years for assessment of their risk to develop breast cancer.

In conclusion, our study suggests that administration of r-hCG inhibits genes/signaling pathways involved in stem/progenitor cell maintenance and neoplastic transformation, whereas activates pathways important for differentiation, mammary epithelial cell commitment, and genomic stability in the breast of BRCA1/2 carriers, and may subsequently lead to reduce the risk of developing breast cancer (Fig. 5). Potentially, this protection can also expand to other women at risk for breast cancer or to the general population. Future r-hCG clinic trial studies performed in a larger group of BRCA1/2 carriers and followed longer after r-hCG treatment may provide further insight into the translation of r-hCG to the clinic.

Fig. 5:

Fig. 5:

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Proposed working model of r-hCG induced cancer prevention. Activated genes/regulators are in warm color while inhibited genes are in cool color shapes. (1) R-hCG actions on the LHCGR and activates GPCR signaling, resulting in cAMP production, ERK and AKT activation. AKT phosphorylation stabilizes BRCA1 protein; (2) R-hCG inhibits WNT signaling through up-regulating negative regulators and down-regulating positive regulators, suppressing stemness of mammary progenitor cells; (3) R-hCG activates TGFβ signaling, which can induce BRCA1 through downregulating miR182 and ID4, maintaining mammary gland lineage commitment and genome stability; (4) R-hCG induces expression changes in chromatin remodeling genes, including HMGA1, which can positively regulate WNT signaling, downregulate BRCA1 expression, and repress TP53 activity; (5) R-hCG activates TP53 and apoptosis process, conserving genome stability; (6) R-hCG inhibits MYC activity, suppresses HMGA1 and HOTAIR expression, preventing cell transformation. In addition, there are crosstalks and feedback loops among these pathways. For example, TGFβ inhibit WNT signaling through WNT5a. SOX9 and β-catenin regulate MYC expression. BRCA1 binds MYC and inhibits its transcriptional and transforming activity. HMGA1 is a target of MYC, HMGA1 not only activates WNT target genes but also amplifies Wnt signals. β-catenin and MYC stimulate HOTAIR expression, HOTAIR activates Wnt signaling. These crosstalks are not depicted here.

Supplementary Material

Figure S1

Additional file 3: Figure S1. R-hCG treatment induces significant changes in transcriptomic profile of the breast tissue of BRCA1/2 carriers. Expression heatmap is shown for overall normalized gene expression of 260 DEGs obtained from 14 low-responders. Color code: yellow for over-expression, black for intermediate expression, and blue for under-expression. The expression of these 260 DEGs in responders are also shown for the comparison between responders and low-responders. Of the 260 DEGs, 54 up-regulated genes in low-responders at T3 overlap with up-regulated genes in responders at both T2 and T3, whereas only 5 down-regulated genes in low-responders are in common with that of the responders.

Figure S2

Additional file 4: Figure S2. Biological processes and Reactome pathways affected by r-hCG treatment. (a) Biological process of DEGs at T3 in low-responders. (b) Reactome pathways of DEGs at T3 in low-responders. (c) Reactome pathways of DEGs at T2 and T3 in responders.

Figure S3

Additional file 5: Figure S3. DEGs related to apoptosis. Tables show gene ontology categories and DEGs related to apoptosis in responders and low-responders.

Figure S4

Additional file 6: Figure S4. R-hCG induces expression changes in genes related to DNA repair and chromatin only in responders. Graphs show biological processes regulated by group of DEGs related to DNA repair, chromatin remodeling in responders. Arrows indicate processes mentioned in the text.

Figure S5

Additional file 7: Figure S5. DEGs associated with G protein-coupled receptor signaling. Tables show function categories and up-regulated genes in responders and low-responders.

Figure S6

Additional file 8: Figure S6. Impacts of r-hCG treatment on canonical pathways. (a) IPA network depicted the DEGs involved in canonical Wnt/β-catenin signaling at T3 in responders. DEGs found in this study are shown in green (down-regulation) or pink circle(up-regulation). (b) Canonical signaling pathways regulated by DEGs at T3 in low-responders. Significant pathways or regulator enrichment were determined activated with positive z-score and inhibited with negative z-score and the FDRp<0.05 (q value), in which z-score is the statistical measure of correlation between relationship direction and gene expression. Blue arrow indicates inhibited pathway discussed in the result. (c) Heatmap of 10 up-regulated and 9 down-regulated DEGs in Wnt/β-catenin signaling pathway in the responders and low-responders across three time points. Color code: yellow for over-expression, white for intermediate expression, and blue for under-expression.

Figure S7

Additional file 9: Figure S7. Upstream regulators TGFRB2, TGFBR1, and BRCA1 are predicted activated in the responders. Tables show upstream regulators regulated by r-hCG treatment in the responders by IPA analysis.

Figure S8

Additional file 10: Figure S8. Networks of upstream regulators TGFBR2, MYC, BRCA1, and TP53 and targets in responders and low-responders. IPA networks depicted the activation of TGFBR2 at T2 and increased activity at T3 in the responders (a), the inhibition of the MYC at T2 and T3 in the responders, and decreased activity at T3 in the low-responders (b), the activation of BRCA1 at T3 in the responders (c), and the activation of TP53 at T2 and T3 in the responders and increased activity at T3 in the low-responders (d). Upstream relationship between regulators and targets at T3 in the low-responders are in grey lines, showing the number of DEGs as target genes was much smaller than that in the responders.

Supplemental methods

Additional file 1: Supplemental methods.

Supplemental tables

Additional file 2: Supplemental tables.

Table S1. Characteristics

Table S2. RNA-seq response

Table S3. DEGs T2. Responders

Table S4. DEGs T3. Responders

Table S5. DEGs T3. Low-responders

Table S6. Common DEGs, T3

Table S7. Common DEGs, Responders

Table S8. Biological processes

Table S9. Cell developmental DEGs

Table S10. Reactome pathways

Table S11 DNA, chromatin DEGs

Table S12. GPCR genes FC at T2

Table S13. GPCR genes FC at T3

Table S14. GPCR, T2

Table S15. GPCR, T3

Table S16. Canonical pathways

Table S17. DEGs, TP53 activation

Table S18. MYC inhibition

Acknowledgements

The authors thank the participants in this trial for their willing contribution to the project. In addition, the authors thank the Biostatistics and Bioinformatics Facility at Fox Chase Cancer Center and Drexel university interns. The authors are grateful to Dr. Eric A. Ross for assisting on statistical analysis.

Funding

The hCG clinical trial was supported by a grant of Think Pink, Belgium and ECP to Dr. Depypere H. The RNA-seq study was supported by the Barbara and Joseph Breitman donation, the Flyers wives donation, and the NCI/NIH Cancer Center Support Grant P30-CA006927 to Dr. Russo J. Resources needed for the collaboration of Dr. Gutiérrez-Díez P were financed by the University of Valladolid (Spain) and the Spanish Ministry of Science, grant PID2020-113554GB-I00.

Abbreviations

FC

fold change

FDRp

false discovery rate (FDR)-adjusted p-value

GO

Gene Ontology

GPCR

G protein-coupled receptor

IHC

Immunohistochemical analysis

IPA

Ingenuity Pathway Analysis

r-hCG

recombinant human chorionic gonadotropin

qRT-PCR

quantitative RT-PCR

SEM

standard error of mean

DEGs

differentially expressed genes

Footnotes

Competing interests

The authors declare no conflicts of interest

Ethics approval and consent to participate

The breast tissue biopsies of the patients were obtained from Dr. Herman Depypere at the Ghent University Hospital, after obtaining the consent of the participants and the approval of the ethical board of the Ghent University Hospital in Belgium (EudraCT number: 2015-001720-36; EC number: 2015/0588).

Consent for publication

All subjects have written informed consent.

Availability of data and materials

All data are provided in the paper. The raw RNA-seq data will be available upon a reasonable request to Dr. Herman Depypere.

References

  1. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. 2000. ‘Gene ontology: tool for the unification of biology. The Gene Ontology Consortium’, Nat Genet, 25: 25–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Bach K, Pensa S, Grzelak M, Hadfield J, Adams DJ, Marioni JC, et al. 2017. ‘Differentiation dynamics of mammary epithelial cells revealed by single-cell RNA sequencing’, Nat Commun, 8: 2128. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Baker LA, Holliday H, and Swarbrick A. 2016. ‘ID4 controls luminal lineage commitment in normal mammary epithelium and inhibits BRCA1 function in basal-like breast cancer’, Endocr Relat Cancer, 23: R381–92. [DOI] [PubMed] [Google Scholar]
  4. Baldassarre G, Battista S, Belletti B, Thakur S, Pentimalli F, Trapasso F, et al. 2003. ‘Negative regulation of BRCA1 gene expression by HMGA1 proteins accounts for the reduced BRCA1 protein levels in sporadic breast carcinoma’, Mol Cell Biol, 23: 2225–38. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Bellacosa A, Godwin AK, Peri S, Devarajan K, Caretti E, Vanderveer L, et al. 2010. ‘Altered gene expression in morphologically normal epithelial cells from heterozygous carriers of BRCA1 or BRCA2 mutations’, Cancer Prev Res (Phila), 3: 48–61. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Blakely CM, Stoddard AJ, Belka GK, Dugan KD, Notarfrancesco KL, Moody SE, et al. 2006. ‘Hormone-induced protection against mammary tumorigenesis is conserved in multiple rat strains and identifies a core gene expression signature induced by pregnancy’, Cancer Res, 66: 6421–31. [DOI] [PubMed] [Google Scholar]
  7. Boulanger CA, Wagner KU, and Smith GH. 2005. ‘Parity-induced mouse mammary epithelial cells are pluripotent, self-renewing and sensitive to TGF-beta1 expression’, Oncogene, 24: 552–60. [DOI] [PubMed] [Google Scholar]
  8. Calderon-Margalit R, and Paltiel O. 2004. ‘Prevention of breast cancer in women who carry BRCA1 or BRCA2 mutations: a critical review of the literature’, Int J Cancer, 112: 357–64. [DOI] [PubMed] [Google Scholar]
  9. Chakrabarti R, Wei Y, Hwang J, Hang X, Andres Blanco M, Choudhury A, et al. 2014. ‘ΔNp63 promotes stem cell activity in mammary gland development and basal-like breast cancer by enhancing Fzd7 expression and Wnt signalling’, Nat Cell Biol, 16: 1004–15, 1–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Chiang HC, Zhang X, Li J, Zhao X, Chen J, Wang HT, et al. 2019. ‘BRCA1-associated R-loop affects transcription and differentiation in breast luminal epithelial cells’, Nucleic Acids Res, 47: 5086–99. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Choudhury S, Almendro V, Merino VF, Wu Z, Maruyama R, Su Y, et al. 2013. ‘Molecular profiling of human mammary gland links breast cancer risk to a p27(+) cell population with progenitor characteristics’, Cell stem cell, 13: 117–30. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Cipriano R, Kan CE, Graham J, Danielpour D, Stampfer M, and Jackson MW. 2011. ‘TGF-beta signaling engages an ATM-CHK2-p53-independent RAS-induced senescence and prevents malignant transformation in human mammary epithelial cells’, Proc Natl Acad Sci U S A, 108: 8668–73. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Daniel Charles W., Robinson Stephen, and Silberstein Gary B.. 2001. ‘The Transforming Growth Factors Beta in Development and Functional Differentiation of the Mouse Mammary Gland.’ in Newburg David S. (ed.), Bioactive Components of Human Milk (Springer US: Boston, MA: ). [DOI] [PubMed] [Google Scholar]
  14. Depypere H, Su Y, Dang N, Poppe B, Stanczyk F, Janssens J, et al. 2021. ‘Prolonged recombinant pregnancy hormone use in BRCA1 and BRCA2 mutation carriers’, Eur J Cancer Prev, 30: 195–203. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Ding L, Su Y, Fassl A, Hinohara K, Qiu X, Harper NW, et al. 2019. ‘Perturbed myoepithelial cell differentiation in BRCA mutation carriers and in ductal carcinoma in situ’, Nat Commun, 10: 4182. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Domenici G, Aurrekoetxea-Rodríguez I, Simões BM, Rábano M, Lee SY, Millán JS, et al. 2019. ‘A Sox2-Sox9 signalling axis maintains human breast luminal progenitor and breast cancer stem cells’, Oncogene, 38: 3151–69. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Evans DG, Harkness EF, Howel S, Woodward ER, Howell A, and Lalloo F. 2018. ‘Young age at first pregnancy does protect against early onset breast cancer in BRCA1 and BRCA2 mutation carriers’, Breast Cancer Res Treat, 167: 779–85. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Ewan KB, Shyamala G, Ravani SA, Tang Y, Akhurst R, Wakefield L, et al. 2002. ‘Latent transforming growth factor-beta activation in mammary gland: regulation by ovarian hormones affects ductal and alveolar proliferation’, Am J Pathol, 160: 2081–93. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Fabregat A, Sidiropoulos K, Garapati P, Gillespie M, Hausmann K, Haw R, et al. 2016. ‘The Reactome pathway Knowledgebase’, Nucleic Acids Res, 44: D481–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Feigman MJ, Moss MA, Chen C, Cyrill SL, Ciccone MF, Trousdell MC, et al. 2020. ‘Pregnancy reprograms the epigenome of mammary epithelial cells and blocks the development of premalignant lesions’, Nat Commun, 11: 2649. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Fortner RT, Sisti J, Chai B, Collins LC, Rosner B, Hankinson SE, et al. 2019. ‘Parity, breastfeeding, and breast cancer risk by hormone receptor status and molecular phenotype: results from the Nurses’ Health Studies’, Breast Cancer Res, 21: 40. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Frasca F, Rustighi A, Malaguarnera R, Altamura S, Vigneri P, Del Sal G, et al. 2006. ‘HMGA1 inhibits the function of p53 family members in thyroid cancer cells’, Cancer Res, 66: 2980–9. [DOI] [PubMed] [Google Scholar]
  23. Fu DY, Wang ZM, Li Chen, Wang BL, Shen ZZ, Huang W, et al. 2010. ‘Sox17, the canonical Wnt antagonist, is epigenetically inactivated by promoter methylation in human breast cancer’, Breast Cancer Res Treat, 119: 601–12. [DOI] [PubMed] [Google Scholar]
  24. Galmor L, Bernstein-Molho R, Sklair-Levy M, Madoursky-Feldman D, Zippel D, Laitman Y, et al. 2021. ‘Time trends in uptake rates of risk-reducing mastectomy in Israeli asymptomatic BRCA1 and BRCA2 mutation carriers’, Breast Cancer Res Treat, 185: 391–99. [DOI] [PubMed] [Google Scholar]
  25. Ge SX, Jung D, and Yao R. 2020. ‘ShinyGO: a graphical gene-set enrichment tool for animals and plants’, bioinformatics, 36: 2628–29. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Gutiérrez-Díez PJ, Gomez-Pilar J, Hornero R, Martínez-Rodríguez J, López-Marcos MA, and Russo J. 2021. ‘The role of gene to gene interaction in the breast’s genomic signature of pregnancy’, Sci Rep, 11: 2643. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Gutierrez CM, Lopez-Valdez R, Subramani R, Arumugam A, Nandy S, Rajamanickam V, et al. 2015. ‘A Breast Tissue Protein Expression Profile Contributing to Early Parity-Induced Protection Against Breast Cancer’, Cell Physiol Biochem, 37: 1671–85. [DOI] [PubMed] [Google Scholar]
  28. Guzman RC, Yang J, Rajkumar L, Thordarson G, Chen X, and Nandi S. 1999. ‘Hormonal prevention of breast cancer: mimicking the protective effect of pregnancy’, Proc Natl Acad Sci U S A, 96: 2520–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Hall JM, Lee MK, Newman B, Morrow JE, Anderson LA, Huey B, et al. 1990. ‘Linkage of early-onset familial breast cancer to chromosome 17q21’, Science, 250: 1684–9. [DOI] [PubMed] [Google Scholar]
  30. Hanasoge Somasundara AV, Moss MA, Feigman MJ, Chen C, Cyrill SL, Ciccone MF, et al. 2021. ‘Parity-induced changes to mammary epithelial cells control NKT cell expansion and mammary oncogenesis’, Cell Rep, 37: 110099. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Hu L, Su L, Cheng H, Mo C, Ouyang T, Li J, et al. 2021. ‘Single-Cell RNA Sequencing Reveals the Cellular Origin and Evolution of Breast Cancer in BRCA1 Mutation Carriers’, Cancer Res, 81: 2600–11. [DOI] [PubMed] [Google Scholar]
  32. Huang DW, Sherman BT, Tan Q, Collins JR, Alvord WG, Roayaei J, et al. 2007. ‘The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists’, Genome Biol, 8: R183. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Khramtsov AI, Khramtsova GF, Tretiakova M, Huo D, Olopade OI, and Goss KH. 2010. ‘Wnt/beta-catenin pathway activation is enriched in basal-like breast cancers and predicts poor outcome’, Am J Pathol, 176: 2911–20. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Kocdor Hilal, Kocdor Mehmet A, Russo Jose, Snider Kara E, Vanegas Johana E, Russo Irma H, et al. 2009. ‘Human chorionic gonadotropin (hCG) prevents the transformed phenotypes induced by 17 β-estradiol in human breast epithelial cells’, Cell biology international, 33: 1135–43. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Kotsopoulos J, Gronwald J, Lynch HT, Eisen A, Neuhausen SL, Tung N, et al. 2018. ‘Age at first full-term birth and breast cancer risk in BRCA1 and BRCA2 mutation carriers’, Breast Cancer Res Treat, 171: 421–26. [DOI] [PubMed] [Google Scholar]
  36. Kotsopoulos J, Lubinski J, Salmena L, Lynch HT, Kim-Sing C, Foulkes WD, et al. 2012. ‘Breastfeeding and the risk of breast cancer in BRCA1 and BRCA2 mutation carriers’, Breast Cancer Res, 14: R42. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Krämer A, Green J, Pollard J Jr., and Tugendreich S. 2014. ‘Causal analysis approaches in Ingenuity Pathway Analysis’, bioinformatics, 30: 523–30. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Krishnan K, Steptoe AL, Martin HC, Wani S, Nones K, Waddell N, et al. 2013. ‘MicroRNA-182–5p targets a network of genes involved in DNA repair’, Rna, 19: 230–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Krzywinski M, Schein J, Birol I, Connors J, Gascoyne R, Horsman D, et al. 2009. ‘Circos: an information aesthetic for comparative genomics’, Genome Res, 19: 1639–45. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Kuchenbaecker KB, Hopper JL, Barnes DR, Phillips KA, Mooij TM, Roos-Blom MJ, et al. 2017. ‘Risks of Breast, Ovarian, and Contralateral Breast Cancer for BRCA1 and BRCA2 Mutation Carriers’, Jama, 317: 2402–16. [DOI] [PubMed] [Google Scholar]
  41. Lim E, Vaillant F, Wu D, Forrest NC, Pal B, Hart AH, et al. 2009. ‘Aberrant luminal progenitors as the candidate target population for basal tumor development in BRCA1 mutation carriers’, Nat Med, 15: 907–13. [DOI] [PubMed] [Google Scholar]
  42. Liu S, Ginestier C, Charafe-Jauffret E, Foco H, Kleer CG, Merajver SD, et al. 2008. ‘BRCA1 regulates human mammary stem/progenitor cell fate’, Proc Natl Acad Sci U S A, 105: 1680–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. MacMahon B, Cole P, Lin TM, Lowe CR, Mirra AP, Ravnihar B, et al. 1970. ‘Age at first birth and breast cancer risk’, Bull World Health Organ, 43: 209–21. [PMC free article] [PubMed] [Google Scholar]
  44. Maller O, Hansen KC, Lyons TR, Acerbi I, Weaver VM, Prekeris R, et al. 2013. ‘Collagen architecture in pregnancy-induced protection from breast cancer’, J Cell Sci, 126: 4108–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Martinez-Ruiz H, Illa-Bochaca I, Omene C, Hanniford D, Liu Q, Hernando E, et al. 2016. ‘A TGFbeta-miR-182-BRCA1 axis controls the mammary differentiation hierarchy’, Sci Signal, 9: ra118. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Medina D, and Smith GH. 1999. ‘Chemical carcinogen-induced tumorigenesis in parous, involuted mouse mammary glands’, J Natl Cancer Inst, 91: 967–9. [DOI] [PubMed] [Google Scholar]
  47. Medina Daniel, and Kittrell Frances S. 2003. ‘p53 function is required for hormone-mediated protection of mouse mammary tumorigenesis’, Cancer Research, 63: 6140–43. [PubMed] [Google Scholar]
  48. Meier-Abt F, Milani E, Roloff T, Brinkhaus H, Duss S, Meyer DS, et al. 2013. ‘Parity induces differentiation and reduces Wnt/Notch signaling ratio and proliferation potential of basal stem/progenitor cells isolated from mouse mammary epithelium’, Breast Cancer Res, 15: R36. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Metcalfe K, Eisen A, Senter L, Armel S, Bordeleau L, Meschino WS, et al. 2019. ‘International trends in the uptake of cancer risk reduction strategies in women with a BRCA1 or BRCA2 mutation’, Br J Cancer, 121: 15–21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Miki Y, Swensen J, Shattuck-Eidens D, Futreal PA, Harshman K, Tavtigian S, et al. 1994. ‘A strong candidate for the breast and ovarian cancer susceptibility gene BRCA1’, Science, 266: 66–71. [DOI] [PubMed] [Google Scholar]
  51. Milne RL, Osorio A, Ramón y Cajal T, Baiget M, Lasa A, Diaz-Rubio E, et al. 2010. ‘Parity and the risk of breast and ovarian cancer in BRCA1 and BRCA2 mutation carriers’, Breast Cancer Res Treat, 119: 221–32. [DOI] [PubMed] [Google Scholar]
  52. Mohammed MK, Shao C, Wang J, Wei Q, Wang X, Collier Z, et al. 2016. ‘Wnt/β-catenin signaling plays an ever-expanding role in stem cell self-renewal, tumorigenesis and cancer chemoresistance’, Genes Dis, 3: 11–40. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Molyneux Gemma, Geyer Felipe C., Magnay Fiona-Ann, Afshan McCarthy Howard Kendrick, Natrajan Rachael, et al. 2010. ‘BRCA1 basal-like breast cancers originate from luminal epithelial progenitors and not from basal stem cells’, Cell stem cell, 7: 403–17. [DOI] [PubMed] [Google Scholar]
  54. Moumen M, Chiche A, Decraene C, Petit V, Gandarillas A, Deugnier MA, et al. 2013. ‘Myc is required for β-catenin-mediated mammary stem cell amplification and tumorigenesis’, Mol Cancer, 12: 132. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Mozdarani H, Ezzatizadeh V, and Rahbar Parvaneh R. 2020. ‘The emerging role of the long non-coding RNA HOTAIR in breast cancer development and treatment’, J Transl Med, 18: 152. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Muenst S, Mechera R, Däster S, Piscuoglio S, Ng CKY, Meier-Abt F, et al. 2017. ‘Pregnancy at early age is associated with a reduction of progesterone-responsive cells and epithelial Wnt signaling in human breast tissue’, Oncotarget, 8: 22353–60. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Murrow Lyndsay M., Weber Robert J., Caruso Joseph, McGinnis Christopher S., Phong Kiet, Gascard Philippe, et al. 2020. ‘Changes in epithelial proportions and transcriptional state underlie major premenopausal breast cancer risks’, bioRxiv: 430611. [Google Scholar]
  58. Proia TA, Keller PJ, Gupta PB, Klebba I, Jones AD, Sedic M, et al. 2011. ‘Genetic predisposition directs breast cancer phenotype by dictating progenitor cell fate’, Cell stem cell, 8: 149–63. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Rajan JV, Marquis ST, Gardner HP, and Chodosh LA. 1997. ‘Developmental expression of Brca2 colocalizes with Brca1 and is associated with proliferation and differentiation in multiple tissues’, Dev Biol, 184: 385–401. [DOI] [PubMed] [Google Scholar]
  60. Rajkumar L, Guzman RC, Yang J, Thordarson G, Talamantes F, and Nandi S. 2001. ‘Short-term exposure to pregnancy levels of estrogen prevents mammary carcinogenesis’, Proc Natl Acad Sci U S A, 98: 11755–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Rebbeck TR, Friebel T, Lynch HT, Neuhausen SL, van ‘t Veer L, Garber JE, et al. 2004. ‘Bilateral prophylactic mastectomy reduces breast cancer risk in BRCA1 and BRCA2 mutation carriers: the PROSE Study Group’, J Clin Oncol, 22: 1055–62. [DOI] [PubMed] [Google Scholar]
  62. Regan JL, Kendrick H, Magnay FA, Vafaizadeh V, Groner B, and Smalley MJ. 2012. ‘c-Kit is required for growth and survival of the cells of origin of Brca1-mutation-associated breast cancer’, Oncogene, 31: 869–83. [DOI] [PubMed] [Google Scholar]
  63. Russo IH, Koszalka M, and Russo J. 1990. ‘Effect of human chorionic gonadotropin on mammary gland differentiation and carcinogenesis’, Carcinogenesis, 11: 1849–55. [DOI] [PubMed] [Google Scholar]
  64. Russo IH, Koszalka M, and Russo J. 1991. ‘Comparative study of the influence of pregnancy and hormonal treatment on mammary carcinogenesis’, Br J Cancer, 64: 481–4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Russo J, Lynch H, and Russo IH. 2001. ‘Mammary gland architecture as a determining factor in the susceptibility of the human breast to cancer’, Breast J, 7: 278–91. [DOI] [PubMed] [Google Scholar]
  66. Russo J, and Russo IH. 1980. ‘Susceptibility of the mammary gland to carcinogenesis. II. Pregnancy interruption as a risk factor in tumor incidence’, Am J Pathol, 100: 497–512. [PMC free article] [PubMed] [Google Scholar]
  67. Russo J, Santucci-Pereira J, de Cicco RL, Sheriff F, Russo PA, Peri S, et al. 2012. ‘Pregnancy-induced chromatin remodeling in the breast of postmenopausal women’, Int J Cancer, 131: 1059–70. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Russo J, Tay LK, and Russo IH. 1982. ‘Differentiation of the mammary gland and susceptibility to carcinogenesis’, Breast Cancer Res Treat, 2: 5–73. [DOI] [PubMed] [Google Scholar]
  69. Russo J, Wilgus G, and Russo IH. 1979. ‘Susceptibility of the mammary gland to carcinogenesis: I Differentiation of the mammary gland as determinant of tumor incidence and type of lesion’, Am J Pathol, 96: 721–36. [PMC free article] [PubMed] [Google Scholar]
  70. Russo Jose, Julia Santucci-Pereira, and Russo Irma H. 2014. ‘The genomic signature of breast cancer prevention’, Genes, 5: 65–83. [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Santucci-Pereira J, George C, Armiss D, Russo IH, Vanegas JE, Sheriff F, et al. 2013. ‘Mimicking pregnancy as a strategy for breast cancer prevention’, Breast Cancer Manag, 2: 283–94. [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Schedin P, Mitrenga T, McDaniel S, and Kaeck M. 2004. ‘Mammary ECM composition and function are altered by reproductive state’, Mol Carcinog, 41: 207–20. [DOI] [PubMed] [Google Scholar]
  73. Sedic M, Skibinski A, Brown N, Gallardo M, Mulligan P, Martinez P, et al. 2015. ‘Haploinsufficiency for BRCA1 leads to cell-type-specific genomic instability and premature senescence’, Nat Commun, 6: 7505. [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Sinha DK, and Pazik JE. 1981. ‘Tumorigenesis of mammary gland by 7,12-dimethylbenz(a)anthracene during pregnancy: relationship with DNA synthesis’, Int J Cancer, 27: 807–10. [DOI] [PubMed] [Google Scholar]
  75. Sivaraman L, Conneely OM, Medina D, and O’Malley BW. 2001. ‘p53 is a potential mediator of pregnancy and hormone-induced resistance to mammary carcinogenesis’, Proc Natl Acad Sci U S A, 98: 12379–84. [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Sivaraman L, Stephens LC, Markaverich BM, Clark JA, Krnacik S, Conneely OM, et al. 1998. ‘Hormone-induced refractoriness to mammary carcinogenesis in Wistar-Furth rats’, Carcinogenesis, 19: 1573–81. [DOI] [PubMed] [Google Scholar]
  77. Srivastava Pramod, Russo Jose, and Russo Irma H. 1997. ‘Chorionic gonadotropin inhibits rat mammary carcinogenesis through activation of programmed cell death’, Carcinogenesis, 18: 1799–808. [DOI] [PubMed] [Google Scholar]
  78. Stovall DB, Wan M, Miller LD, Cao P, Maglic D, Zhang Q, et al. 2013. ‘The regulation of SOX7 and its tumor suppressive role in breast cancer’, Am J Pathol, 183: 1645–53. [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Strange R, Feng Li, Saurer S, Burkhardt A, and RR %J Development Friis. 1992. ‘Apoptotic cell death and tissue remodelling during mouse mammary gland involution’, Development, 115: 49–58. [DOI] [PubMed] [Google Scholar]
  80. Taylor D, Pearce CL, Hovanessian-Larsen L, Downey S, Spicer DV, Bartow S, et al. 2009. ‘Progesterone and estrogen receptors in pregnant and premenopausal non-pregnant normal human breast’, Breast Cancer Res Treat, 118: 161–8. [DOI] [PubMed] [Google Scholar]
  81. Terry MB, Liao Y, Kast K, Antoniou AC, McDonald JA, Mooij TM, et al. 2018. ‘The Influence of Number and Timing of Pregnancies on Breast Cancer Risk for Women With BRCA1 or BRCA2 Mutations’, JNCI Cancer Spectr, 2: pky078. [DOI] [PMC free article] [PubMed] [Google Scholar]
  82. Thordarson G, Jin E, Guzman RC, Swanson SM, Nandi S, and Talamantes F. 1995. ‘Refractoriness to mammary tumorigenesis in parous rats: is it caused by persistent changes in the hormonal environment or permanent biochemical alterations in the mammary epithelia?’, Carcinogenesis, 16: 2847–53. [DOI] [PubMed] [Google Scholar]
  83. Wagner KU, Boulanger CA, Henry MD, Sgagias M, Hennighausen L, and Smith GH. 2002. ‘An adjunct mammary epithelial cell population in parous females: its role in functional adaptation and tissue renewal’, Development, 129: 1377–86. [DOI] [PubMed] [Google Scholar]
  84. Wang H, Xiang D, Liu B, He A, Randle HJ, Zhang KX, et al. 2019. ‘Inadequate DNA Damage Repair Promotes Mammary Transdifferentiation, Leading to BRCA1 Breast Cancer’, Cell, 178: 135–51.e19. [DOI] [PMC free article] [PubMed] [Google Scholar]
  85. Wang J, Li M, Chen D, Nie J, Xi Y, Yang X, et al. 2017. ‘Expression of C-myc and β-catenin and their correlation in triple negative breast cancer’, Minerva Med, 108: 513–17. [DOI] [PubMed] [Google Scholar]
  86. Wang Q, Zhang H, Kajino K, and Greene MI. 1998. ‘BRCA1 binds c-Myc and inhibits its transcriptional and transforming activity in cells’, Oncogene, 17: 1939–48. [DOI] [PubMed] [Google Scholar]
  87. Wang YL, Overstreet AM, Chen MS, Wang J, Zhao HJ, Ho PC, et al. 2015. ‘Combined inhibition of EGFR and c-ABL suppresses the growth of triple-negative breast cancer growth through inhibition of HOTAIR’, Oncotarget, 6: 11150–61. [DOI] [PMC free article] [PubMed] [Google Scholar]
  88. Xian L, Georgess D, Huso T, Cope L, Belton A, Chang YT, et al. 2017. ‘HMGA1 amplifies Wnt signalling and expands the intestinal stem cell compartment and Paneth cell niche’, Nat Commun, 8: 15008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  89. Yang J, Yoshizawa K, Nandi S, and Tsubura A. 1999. ‘Protective effects of pregnancy and lactation against N-methyl-N-nitrosourea-induced mammary carcinomas in female Lewis rats’, Carcinogenesis, 20: 623–8. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Figure S1

Additional file 3: Figure S1. R-hCG treatment induces significant changes in transcriptomic profile of the breast tissue of BRCA1/2 carriers. Expression heatmap is shown for overall normalized gene expression of 260 DEGs obtained from 14 low-responders. Color code: yellow for over-expression, black for intermediate expression, and blue for under-expression. The expression of these 260 DEGs in responders are also shown for the comparison between responders and low-responders. Of the 260 DEGs, 54 up-regulated genes in low-responders at T3 overlap with up-regulated genes in responders at both T2 and T3, whereas only 5 down-regulated genes in low-responders are in common with that of the responders.

NIHMS1836644-supplement-Figure_S1.tif (1.9MB, tif)
Figure S2

Additional file 4: Figure S2. Biological processes and Reactome pathways affected by r-hCG treatment. (a) Biological process of DEGs at T3 in low-responders. (b) Reactome pathways of DEGs at T3 in low-responders. (c) Reactome pathways of DEGs at T2 and T3 in responders.

NIHMS1836644-supplement-Figure_S2.jpg (1.5MB, jpg)
Figure S3

Additional file 5: Figure S3. DEGs related to apoptosis. Tables show gene ontology categories and DEGs related to apoptosis in responders and low-responders.

NIHMS1836644-supplement-Figure_S3.tif (1.1MB, tif)
Figure S4

Additional file 6: Figure S4. R-hCG induces expression changes in genes related to DNA repair and chromatin only in responders. Graphs show biological processes regulated by group of DEGs related to DNA repair, chromatin remodeling in responders. Arrows indicate processes mentioned in the text.

NIHMS1836644-supplement-Figure_S4.tif (846.2KB, tif)
Figure S5

Additional file 7: Figure S5. DEGs associated with G protein-coupled receptor signaling. Tables show function categories and up-regulated genes in responders and low-responders.

NIHMS1836644-supplement-Figure_S5.jpg (1.4MB, jpg)
Figure S6

Additional file 8: Figure S6. Impacts of r-hCG treatment on canonical pathways. (a) IPA network depicted the DEGs involved in canonical Wnt/β-catenin signaling at T3 in responders. DEGs found in this study are shown in green (down-regulation) or pink circle(up-regulation). (b) Canonical signaling pathways regulated by DEGs at T3 in low-responders. Significant pathways or regulator enrichment were determined activated with positive z-score and inhibited with negative z-score and the FDRp<0.05 (q value), in which z-score is the statistical measure of correlation between relationship direction and gene expression. Blue arrow indicates inhibited pathway discussed in the result. (c) Heatmap of 10 up-regulated and 9 down-regulated DEGs in Wnt/β-catenin signaling pathway in the responders and low-responders across three time points. Color code: yellow for over-expression, white for intermediate expression, and blue for under-expression.

NIHMS1836644-supplement-Figure_S6.jpg (685KB, jpg)
Figure S7

Additional file 9: Figure S7. Upstream regulators TGFRB2, TGFBR1, and BRCA1 are predicted activated in the responders. Tables show upstream regulators regulated by r-hCG treatment in the responders by IPA analysis.

NIHMS1836644-supplement-Figure_S7.jpg (1.4MB, jpg)
Figure S8

Additional file 10: Figure S8. Networks of upstream regulators TGFBR2, MYC, BRCA1, and TP53 and targets in responders and low-responders. IPA networks depicted the activation of TGFBR2 at T2 and increased activity at T3 in the responders (a), the inhibition of the MYC at T2 and T3 in the responders, and decreased activity at T3 in the low-responders (b), the activation of BRCA1 at T3 in the responders (c), and the activation of TP53 at T2 and T3 in the responders and increased activity at T3 in the low-responders (d). Upstream relationship between regulators and targets at T3 in the low-responders are in grey lines, showing the number of DEGs as target genes was much smaller than that in the responders.

NIHMS1836644-supplement-Figure_S8.jpg (1.5MB, jpg)
Supplemental methods

Additional file 1: Supplemental methods.

NIHMS1836644-supplement-Supplemental_methods.docx (22.3KB, docx)
Supplemental tables

Additional file 2: Supplemental tables.

Table S1. Characteristics

Table S2. RNA-seq response

Table S3. DEGs T2. Responders

Table S4. DEGs T3. Responders

Table S5. DEGs T3. Low-responders

Table S6. Common DEGs, T3

Table S7. Common DEGs, Responders

Table S8. Biological processes

Table S9. Cell developmental DEGs

Table S10. Reactome pathways

Table S11 DNA, chromatin DEGs

Table S12. GPCR genes FC at T2

Table S13. GPCR genes FC at T3

Table S14. GPCR, T2

Table S15. GPCR, T3

Table S16. Canonical pathways

Table S17. DEGs, TP53 activation

Table S18. MYC inhibition

NIHMS1836644-supplement-Supplemental_tables.xlsx (1.2MB, xlsx)

Data Availability Statement

All data are provided in the paper. The raw RNA-seq data will be available upon a reasonable request to Dr. Herman Depypere.

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