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Neuroendocrine pathways and breast cancer progression: a pooled analysis of somatic mutations and gene expression from two large breast cancer cohorts

BMC Cancer volume 22, Article number: 680 (2022) Cite this article

Abstract

Background

Experimental studies indicate that neuroendocrine pathways might play a role in progression of breast cancer. We aim to test the hypothesis that somatic mutations in the genes of neuroendocrine pathways influence breast cancer prognosis, through dysregulated gene expression in tumor tissue.

Methods

We conducted an extreme case–control study including 208 breast cancer patients with poor invasive disease-free survival (iDFS) and 208 patients with favorable iDFS who were individually matched on molecular subtype from the Breast Cancer Cohort at West China Hospital (WCH; N = 192) and The Cancer Genome Atlas (TCGA; N = 224). Whole exome sequencing and RNA sequencing of tumor and paired normal breast tissues were performed. Adrenergic, glucocorticoid, dopaminergic, serotonergic, and cholinergic pathways were assessed for differences in mutation burden and gene expression in relation to breast cancer iDFS using the logistic regression and global test, respectively.

Results

In the pooled analysis, presence of any somatic mutation (odds ratio = 1.66, 95% CI: 1.07–2.58) of the glucocorticoid pathway was associated with poor iDFS and a two-fold increase of tumor mutation burden was associated with 17% elevated odds (95% CI: 2–35%), after adjustment for cohort membership, age, menopausal status, molecular subtype, and tumor stage. Differential expression of genes in the glucocorticoid pathway in tumor tissue (P = 0.028), but not normal tissue (P = 0.701), was associated with poor iDFS. Somatic mutation of the adrenergic and cholinergic pathways was significantly associated with iDFS in WCH, but not in TCGA.

Conclusion

Glucocorticoid pathway may play a role in breast cancer prognosis through differential mutations and expression. Further characterization of its functional role may open new avenues for the development of novel therapeutic targets for breast cancer.

Peer Review reports

Background

Globally, breast cancer is the most common cancer in women [1]. Although the survival rate has improved substantially over the past decades, breast cancer remains the leading cause of cancer death among women [1] and one third of patients with early-stage breast cancer will ultimately develop a metastatic disease [2]. To date, estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) are the most well-established biomarkers to predict prognosis and guide treatment [3]. However, variable clinical courses are not completely captured by these biomarkers [4], and not all tumors respond to the targeted therapy [5]. It is therefore important to advance our understanding of the complex biological mechanisms underlying breast cancer progression and to identify new biomarkers.

Emerging evidence suggests that neuroendocrine pathways play a salient role in the development and progression of many cancers, including breast cancer [6,7,8,9,10,11,12,13]. For instance, animal models showed that β-adrenergic pathway activation facilitated breast cancer metastasis [7]. Glucocorticoid pathway has also been suggested to be involved in multiple processes of breast cancer metastasis, including cell adhesion, chemoresistance, evasion of apoptosis, and angiogenesis [8,9,10,11]. Furthermore, the blockade of cholinergic receptors reduces the proliferation of breast cancer cells [12, 14]. On the other hand, dopamine inhibits tumor angiogenesis [15] and bone metastasis in breast cancer [16], while serotonin stimulates breast tumor proliferation and apoptosis evasion [17, 18].

Data from human studies are however relatively scarce. Cholinergic pathway signaling was shown to be associated with breast cancer recurrence among ER-negative patients [19]. A high expression level of glucocorticoid receptor and its activation-associated genes correlated with shorter relapse-free survival [9, 10]. We found in our previous studies that molecular signatures of adrenergic, glucocorticoid, and serotoninergic pathways were associated with lethal outcome in patients with prostate cancer [20, 21]. As germline variants of neuroendocrine pathways appeared to only explain a small proportion of the dysregulated signaling [20], somatic mutations in neuroendocrine pathways may drive the differential signaling leading to putative biological effects in cancer progression. We, therefore, undertook an integrative molecular approach by pooling data from two large breast cancer cohorts and tested the hypothesis that somatic mutations in the genes of neuroendocrine pathways influence breast cancer prognosis, through dysregulated gene expression in tumor tissue.

Methods

Study populations

The Breast Cancer Cohort at the West China Hospital, Sichuan University, China (the WCH cohort) is a prospective cohort of breast cancer patients diagnosed in the WCH from 2008 onward[22]. As of April 15th, 2018, 7,784 women who were diagnosed with non-metastatic invasive disease were included and prospectively followed for clinical outcomes. Complete information on demographic factors, tumor characteristics, and treatment were collected directly from an interview at baseline and the medical records in WCH. The median follow-up was 4.6 years by April 2018 (Q1 = 2.7, Q3 = 6.9 years). Fresh frozen tumor and/or normal breast tissues were collected at primary surgery whereas blood samples were donated at the time of diagnosis. Patients who have donated both tumor and germline tissues (normal breast tissue or blood) were eligible for the present study (N = 1,462).

The Cancer Genome Atlas (TCGA) [23] is an international project that molecularly characterized a large sample of multiple cancers, including breast cancer. Clinical and molecular data have been made publicly available at GDC Data Portal (https://portal.gdc.cancer.gov/projects/TCGA-BLCA). Women who were diagnosed as non-metastatic invasive breast adenocarcinoma and had fresh-frozen samples of primary tumor collected at surgical resection prior to other treatment (chemotherapy or radiotherapy) were enrolled during 2009–2015 (N = 1,063) and followed since diagnosis. The median follow-up was 2.3 years by 2015 (Q1 = 1.2, Q3 = 4.6 years) [24]. Similarly, patients with available data on somatic mutations were eligible for the study (N = 953).

Matched extreme case–control design

To maximize the statistical power and optimize the cost-effectiveness, we employed a matched extreme case–control design which has been successfully implemented to study prognostic biomarkers of cancers by comparing patients with the worst prognosis to those with the most favorable survival [25]. In the present study, patients with any invasive disease-free survival (iDFS) endpoints during the first five years after cancer diagnosis were identified as cases (i.e., with poor prognosis), whereas patients who survived at least five years after diagnosis and had no iDFS endpoints through the last follow-up were considered as controls (i.e., with favorable prognosis). Any local or regional recurrence, distant metastasis, new primary tumors from any sites, cancer-specific death, and death from other causes were defined as iDFS endpoints [26]. One control per case was randomly selected and individually matched to the case on molecular subtype classified according to St Gallen Consensus 2013 [27] (see details in the Supplementary Methods). To avoid introducing potential selection bias, age at diagnosis was not restricted. Finally, we identified 96 cases and 96 controls from the WCH cohort, and 112 cases and 112 controls from TCGA. Among them, 208 cases and 208 controls with somatic mutation data; of which, 202 cases and 205 controls were sequenced for tumor RNA, and 142 normal breast tissues were also sequenced for RNA in the WCH cohort.

Neuroendocrine pathway selection and construction

As previously described [20, 21], we focused on genes in five neuroendocrine pathways with a suggested link to psychological distress. We identified the genes in each pathway from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and based on our previous studies [20, 21]. In total, 544 unique genes were identified, including 236 genes for the adrenergic pathway, 153 for the glucocorticoid pathway, 138 for the dopaminergic pathway, 125 for the serotonergic pathway, and 235 for the cholinergic pathway. The full list and information of each pathway are presented in Table S1.

Whole exome sequencing and RNA-sequencing

In the WCH cohort, DNA was extracted from the tumor tissue and germline-derived samples. Whole Exome Sequencing (WES) was performed on Illumina Novaseq S6000 platform. After quality control, reads were mapped to the reference genome (UCSC hg38) using Burrows-Wheeler Aligner (BWA) software [28]. In TCGA cohort, WES was performed for tumor tissue on Illumina Hi-Seq 2000 platform. Somatic mutation data were downloaded from GDC portal.

In the WCH cohort, RNA sequencing for frozen tumor and normal breast tissue was performed on the Illumina Novaseq S6000 platform. After quality control, reads were mapped to reference genome using Hisat2 v2.0.5 [29]. In TCGA, Raw read counts produced by HT-Seq of tumor RNA were downloaded from the GDC portal. A detailed description of the sample preparation and bioinformatic pipeline for WES and RNA-seq was available in Supplementary methods.

Statistical analysis

Somatic mutations

Tumor mutation burden (TMB) has been established as an important biomarker for cancer survival [30], including breast cancer [31, 32]. Therefore, we calculated somatic TMB for each of the neuroendocrine pathways, defined as the number of mutations in candidate genes per 1 million base pairs of the coding sequence in these genes. TMB was analyzed as a binary variable (any vs. no mutation) and a continuous variable after log2 transformation. Odds ratios (ORs) were estimated using multivariable logistic regression models adjusting for cohort membership, age at diagnosis, menopausal status at diagnosis, molecular subtype, and cancer stage. In addition, we employed a basic model with adjustment for only cohort membership and molecular subtype if one considers that cancer stage is an outline of tumor genome (i.e., mediator) and therefore not necessarily to be adjusted for; and based on the main model, we fitted an advanced model with further adjustment for cancer treatment, which was largely planned according to molecular subtype and tumor characteristics. Estimates from both cohorts were formally compared for heterogeneity using Cochrane’s Q test.

Gene expression

We used TMM normalization [33] to normalize the library size of each sample in both cohorts. Counts per million reads (CPM) in a log2 scale were calculated. We adjusted for batch effect using Combat function in sva package [34] when pooling gene expression data from two cohorts. We used the global test [35] to compare the gene expression between cases and controls at the pathway level. To further shed light on the direction of the association, we analyzed associations between TMB of glucocorticoid pathway and individual gene expression using linear regression; and associations between individual gene expression and iDFS endpoints using logistic regression. We also performed a mediation analysis for the top four significant genes, to estimate in what proportion these individual gene expressions mediated the association between TMB of glucocorticoid pathway and iDFS endpoints. Cohort membership, age at diagnosis, menopausal status at diagnosis, molecular subtype, and cancer stage were adjusted for in these analyses.

To assess the role of ER status on these results, we stratified the analyses for somatic mutations and gene expression by ER status. To test the robustness of these results, we performed additional analyses by excluding the matching pairs of cases with non-breast-cancer death or patients who underwent neoadjuvant chemotherapy, and by removing the overlapping genes across the five pathways.

The statistical analyses were performed in R (version 4.1.2). We used a two-sided P < 0.05 to indicate statistical significance.

Results

Compared with the controls (patients with favorable prognosis), the cases (patients with poor prognosis) were older, more likely to be postmenopausal, and had a more advanced stage at diagnosis (Table 1). The ER, PR, HER2 status and molecular subtypes were largely similar between cases and controls, although the patients of unclassified subtype and unknown HER2 status were fewer in controls due to the matching method. In line with the full cohort, all WCH participants were Asian, mostly Han Chinese (N = 189; 98%); while in TCGA, the majority were white (N = 170; 76%), followed by black or African American (N = 35; 16%). Treatment was similar between cases and controls in the WCH sample. In the TCGA sample, fewer cases than controls received chemotherapy, radiotherapy, or hormonal therapy. These patterns were similar between the WCH cohort and TCGA, except for more similar age distribution between cases and controls and more cases with radiotherapy in the WCH cohort (Table S2). Cases/controls nested from two cohorts were comparable in the distribution of cancer stage, although patients in the WCH cohort were younger, had a higher proportion of Luminal B subtype, and had more mastectomy and chemotherapy, which also represented the discrepancies between the whole WCH and TCGA cohorts (Table S2).

Table 1 Clinical characteristics of breast cancer patients with poor (cases) and favorable (controls) invasive disease-free survival
Full size table

Somatic mutations

Any mutation in the glucocorticoid pathway was associated with a higher risk of iDFS endpoints (OR 1.66, 95% CI 1.07–2.58; Table 2). A two-fold increase of TMB was associated with 17% elevated odds of iDFS endpoints (95% CI 2–35%). The associations were comparable between the WCH and TCGA samples (P for heterogeneity = 0.76). TMB of the adrenergic pathway and cholinergic pathways was associated with a higher risk of iDFS endpoints for the WCH sample, but not in TCGA. TMB of the dopaminergic or serotoninergic pathway was not associated with iDFS endpoints. The association of any mutation and TMB in the glucocorticoid pathway with iDFS was more pronounced among patients with ER-negative tumors (Table S3).

Table 2 The association between tumor mutation burden (TMB) of neuroendocrine pathways and breast cancer prognosis
Full size table

In additional analyses, we observed largely similar associations with overlapping CIs across models with different adjustments (Table S4). The results remained robust for all pathways after excluding the matching pairs of cases with non-breast-cancer death or patients with neoadjuvant chemotherapy (Table S5). After removing the genes shared between multiple neuroendocrine pathways, the estimates remained similar for the glucocorticoid pathway but not for the cholinergic pathway (Table S5).

Gene expression

Pathway analysis showed that, in tumor tissue, differential gene expression of the glucocorticoid (P = 0.028) and serotonergic (P = 0.014) pathways was associated with iDFS (Fig. 1). Although these associations were less significant when separately analyzing the two cohorts, more pronounced results were noted in TCGA which has a larger sample size. Regardless of cohort membership, no association was noted for gene expression of the adrenergic, dopaminergic, or cholinergic pathway in tumor tissue. In contrast, no association between pathways and iDFS was found in normal tissue based on expression data from the WCH sample. In tumor tissue, the associations of all pathways were more pronounced in ER-positive tumors, compared with that in ER-negative tumors, whereas null associations were consistently observed in normal tissue regardless of ER status (Table S6).

Fig. 1
figure 1

Associations of neuroendocrine pathway gene expression in tumor and matched normal breast tissues with prognosis. P value was calculated using Global test, adjusted for cohort membership, age at diagnosis, menopausal status at diagnosis, molecular subtype, and cancer stage

Full size image

Excluding deaths due to causes other than breast cancer or patients with neoadjuvant chemotherapy yielded largely unchanged results for the glucocorticoid and serotonergic pathways (Table S7). The association for the glucocorticoid pathway was somewhat attenuated after removing genes shared between pathways, but the association for the serotonergic pathway remained unchanged.

Among the 153 individual genes of the glucocorticoid pathway, we found that gene expression of HSP90AA1, ADCY4, SLC12A5, and GNG7 was associated with both pathway-level TMB and iDFS (empirical P < 0.05 for both associations; Fig. 2). Expression of these top genes mediated (HSP90AA1: 16%, ADCY4: 12%, SLC12A5: -16%, and GNG7: 12%) the association between TMB of glucocorticoid pathway and iDFS, although the mediation analysis was underpowered. The associations of the full list of genes of the glucocorticoid pathway are presented in Table S8.

Fig. 2
figure 2

Top genes of the glucocorticoid pathway expressed in tumor tissue associated with mutation and prognosis. a. Association between glucocorticoid pathway tumor mutation burden (TMB) and individual gene expression by linear regression; b. Association between individual gene expression and breast cancer invasive disease-free survival (iDFS) by logistic regression TMB was classified as mutated or not. Gene expression was used as a continuous variable in log2 scale. Both regression models were adjusted for cohort membership, age at diagnosis, menopausal status at diagnosis, molecular subtype, and cancer stage. The genes with p values < 0.05 for both analyses were presented in this table

Full size image

Discussion

Leveraging omics data from two large clinical cohorts of breast cancer, this study illustrated the contribution of the neuroendocrine pathways to breast cancer prognosis at both the somatic mutation and gene expression levels. We found that a higher somatic mutation burden in the glucocorticoid pathway was associated with an unfavorable iDFS in patients with non-metastatic breast cancer, independent of other known prognostic predictors. Our data further indicated that such association might be mediated through differentially expressed genes of the glucocorticoid pathway in tumor tissue, but not in adjacent normal tissue. We found however limited evidence for the role of other neuroendocrine pathways in breast cancer prognosis.

Glucocorticoid pathway

Upon activation of the hypothalamic–pituitary–adrenal (HPA) axis, glucocorticoids are synthesized, secreted, and combined to the glucocorticoid receptor and could activate multiple downstream processes involved in breast cancer progression, including cell adhesion, inflammation, chemoresistance, evasion of apoptosis, and angiogenesis [8,9,10,11]. Activation of glucocorticoid receptor also regulates the receptor tyrosine kinase ROR1 expression [8] and the Hippo pathway [36], which are importantly involved in cancer progression.

Our data on somatic mutation and gene expression provide further evidence that the glucocorticoid pathway is associated with breast cancer iDFS. A high burden of somatic mutation reflects higher genome instability and chances of altered expression in tumor tissue, whereas no association was found between gene expression in normal breast tissue and iDFS. Moreover, largely similar results were noted in a number of sensitivity analyses, including the exclusion of overlapping genes between the studied neuroendocrine pathways, which argues against the possibility that the association was completely driven by crosstalk between pathways.

We found four top genes in the glucocorticoid pathway which possibly explained the association. These genes have been reported to be associated with cancer survival. Our findings of gene expression and breast cancer prognosis were consistent with the previous evidence for HSP90AA1 [37], ADCY4 [38], and GNG7 [39]. High SLC12A5 expression was suggested to predict poor survival in patients with bladder urothelial [40], colorectal [41] and ovarian [42] cancers, whereas the association direction in breast cancer was found the opposite in our study. However, our results is consistent between the WCH and TCGA samples as well as in line with public data source (e.g. TCGA data analyzed from cBioportal [43], GEO and EGA data from KM plotter [44]).

We further found that the association between somatic mutation burden of glucocorticoid pathway and iDFS was slightly stronger for ER-negative tumors. However, the association of glucocorticoid pathway signaling was more pronounced in ER-positive tumors. This is partly due to the limited statistical power for ER-negative tumors in pathway analysis which is sensitive to sample size. The transcriptional response of glucocorticoid signaling in breast cancer cells is highly heterogeneous [45]. Glucocorticoids were also suggested to interfere with ER signaling pathway and inhibit ER-mediated cell proliferation [11]. Such regulation in ER-positive tumor may bypass the effect of glucocorticoid pathway mutation on iDFS, while the differential expression at the transcriptome level still results in a different prognosis as noted in pathway analysis.

These results may have important clinical implications. Dexamethasone is commonly used in patients with breast cancer to prevent nausea caused by chemotherapy [46] and was suggested to increase the efficacy of cisplatin [47]. However, dexamethasone may also promote breast cancer metastasis by offsetting the response to paclitaxel [8]. Moreover, abnormal diurnal or nocturnal cortisol rhythm has been associated with disease progression and mortality in breast cancer [48, 49]. More research is needed on the benefits of dexamethasone use in breast cancer patients.

Adrenergic, dopaminergic, serotonergic, and cholinergic pathways

Our data suggested that TMB of the adrenergic and cholinergic pathways were associated with breast cancer iDFS, although the results were primarily driven by the WCH samples and not shown in the gene expression analysis. The differential expression of the serotonergic pathway in tumor tissue associated with breast cancer iDFS is consistent with findings from a previous report indicating that the poor prognosis of breast cancer was featured by active serotonin production [50]. However, we did not observe an association between the dopaminergic pathway and breast cancer survival, similar to the findings from our earlier studies on prostate cancer [20, 21].

The major strengths of this study include the large cohorts of breast cancer patients with rich information on somatic mutations, gene expression, clinical and follow-up data. The extreme case–control design provides good efficiency and reserves the power from a full cohort [25]. Consistent results from patients with diverse race/ethnical backgrounds in our pooled analysis may help to better generalize the findings across populations. However, several limitations should be noted. First, the two cohorts have heterogeneity. For example, the two cohorts differ in the distributions of race, age, molecular subtype, and length of follow-up. The estimates for glucocorticoid pathway were however consistent between the cohorts, supporting the validity of this finding. Second, only patients with available biospecimens were included in this study. However, the clinical characteristics were largely comparable between patients included in the present analysis and the whole cohort. Third, treatment information was differentially missed between cases and controls in the TCGA sample, resulting in fewer treatment records in cases. This is because cases have a fast-progressing disease and shorter survival and therefore their information is more likely to be incomplete, due to known reasons such as changing health centers and delayed data collection[24]. However, the adjustment for treatment does not change the results substantially in WCH. Another limitation is the multiple tests for five pathways without adjusting for multiplicity. Reassuringly, our claim was restricted to the pathway which showed robust estimates in both cohorts and at both DNA and RNA levels, which diminishes the possibility of a false-positive result. Lastly, the present study aimed to provide biological insights into the link between neuroendocrine pathways and breast cancer prognosis rather than making any claims on functional roles. Future studies are warranted to confirm the biological roles of neuroendocrine pathways in breast cancer prognosis.

Conclusions

The glucocorticoid pathway may play a role in breast cancer prognosis through differential mutations and expression in tumor tissue. These findings may have implications for the development of novel therapeutic targets for breast cancer, if confirmed in independent investigations.

Availability of data and materials

The Somatic mutation and gene expression data from TCGA were downloaded from GDC portal (https://portal.gdc.cancer.gov). The data from WCH cannot be shared publicly due to the privacy of individuals that participated in the study. The data that support the findings of this study are available on request from the corresponding author.

References

  1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;caac.21660.

  2. O’Shaughnessy J. Extending survival with chemotherapy in metastatic breast cancer. Oncologist. 2005;10(S3):20–9. https://doi.org/10.1634/theoncologist.10-90003-20.

  3. Mamounas EP, Mitchell MP, Woodward WA. Molecular predictive and prognostic markers in locoregional management. J Clin Oncol. 2020;38(20):2310–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Parise CA, Caggiano V. Breast cancer survival defined by the ER/PR/HER2 subtypes and a surrogate classification according to tumor grade and immunohistochemical biomarkers. J Cancer Epidemiol. 2014;2014:469251.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Loi S, Dafni U, Karlis D, Polydoropoulou V, Young BM, Willis S, et al. Effects of estrogen receptor and human epidermal growth factor receptor-2 levels on the efficacy of trastuzumab: a secondary analysis of the HERA trial. JAMA Oncol. 2016;2(8):1040–7.

    Article  PubMed  Google Scholar 

  6. Lutgendorf SK, Andersen BL. Biobehavioral approaches to cancer progression and survival: mechanisms and interventions. Am Psychol. 2015;70(2):186–97.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Sloan EK, Priceman SJ, Cox BF, Yu S, Pimentel MA, Tangkanangnukul V, et al. The sympathetic nervous system induces a metastatic switch in primary breast cancer. Cancer Res. 2010;70(18):7042–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Obradović MMS, Hamelin B, Manevski N, Couto JP, Sethi A, Coissieux MM, et al. Glucocorticoids promote breast cancer metastasis. Nature. 2019;567:540 (Nature Publishing Group).

    Article  PubMed  CAS  Google Scholar 

  9. Pan D, Kocherginsky M, Conzen SD. Activation of the glucocorticoid receptor is associated with poor prognosis in estrogen receptor-negative breast cancer. Cancer Res. 2011;71(20):6360–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. West DC, Kocherginsky M, Tonsing-Carter EY, Dolcen DN, Hosfield DJ, Lastra RR, et al. Discovery of a glucocorticoid receptor (gr) activity signature using selective gr antagonis in er-negative breast cancer. Clin Cancer Res. 2018;24(14):3433–46.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Karmakar S, Jin Y, Nagaich AK. Interaction of glucocorticoid receptor (GR) with estrogen receptor (ER) α and activator protein 1 (AP1) in dexamethasone-mediated interference of ERα activity. J Biol Chem. 2013;288(33):24020–34.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Koker SC, Jahja E, Shehwana H, Keskus AG, Konu O. Cholinergic receptor nicotinic alpha 5 (CHRNA5) RNAi is associated with cell cycle inhibition, apoptosis DNA damage response and drug sensitivity in Breast cancer. PLoS One. 2018;13(12):e0208982.

    Article  CAS  Google Scholar 

  13. Salem AR, Martínez Pulido P, Sanchez F, Sanchez Y, Español AJ, Sales ME. Effect of low dose metronomic therapy on MCF-7 tumor cells growth and angiogenesis. Role of muscarinic acetylcholine receptors. Int Immunopharmacol. 2020;84:106514.

    Article  CAS  PubMed  Google Scholar 

  14. Sun Z, Bao J, Zhangsun M, Dong S, Zhangsun D, Luo S. ΑO-conotoxin GexIVa inhibits the growth of breast cancer cells via interaction with α9 nicotine acetylcholine receptors. Mar Drugs. 2020;18(4):195.

    Article  CAS  PubMed Central  Google Scholar 

  15. Sarkar C, Chakroborty D, Chowdhury UR, Dasgupta PS, Basu S. Dopamine increases the efficacy of anticancer drugs in breast and colon cancer preclinical models. Clin Cancer Res. 2008;14(8):2502–10.

    Article  CAS  PubMed  Google Scholar 

  16. Minami K, Liu S, Liu Y, Chen A, Wan Q, Na S, et al. Inhibitory effects of dopamine receptor D 1 agonist on mammary tumor and bone metastasis. Sci Rep. 2017;7:45686.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Pai VP, Marshall AM, Hernandez LL, Buckley AR, Horseman ND. Altered serotonin physiology in human breast cancers favors paradoxical growth and cell survival. Breast Cancer Res. 2009;11(6):R81.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Gwynne WD, Hallett RM, Girgis-Gabardo A, Bojovic B, Dvorkin-Gheva A, Aarts C, et al. Serotonergic system antagonists target breast tumor initiating cells and synergize with chemotherapy to shrink human breast tumor xenografts. Oncotarget. 2017;8(19):32101–16.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Gao QG, Li ZM, Wu KQ. Partial least squares based analysis of pathways in recurrent breast cancer. Eur Rev Med Pharmacol Sci. 2013;17(16):2159–65.

    PubMed  Google Scholar 

  20. Lu D, Carlsson J, Penney KL, Davidsson S, Andersson S-O, Mucci LA, et al. Expression and genetic variation in neuroendocrine signaling pathways in lethal and nonlethal prostate cancer among men diagnosed with localized disease. Cancer Epidemiol Biomarkers Prev. 2017;26(12):1781–7.

    Article  CAS  PubMed  Google Scholar 

  21. Lu D, Sinnott JA, Valdimarsdóttir U, Fang F, Gerke T, Tyekucheva S, et al. Stress-related signaling pathways in lethal and nonlethal prostate cancer. Clin Cancer Res. 2016;22(3):765–72.

    Article  CAS  PubMed  Google Scholar 

  22. Xie Y, Valdimarsdóttir UA, Wang C, Zhong X, Gou Q, Zheng H, et al. Public health insurance and cancer-specific mortality risk among patients with breast cancer: a prospective cohort study in China. Int J Cancer. 2021;148(1):28–37.

    Article  CAS  PubMed  Google Scholar 

  23. The Cancer Genome Atlas Program - National Cancer Institute. https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga.

  24. Liu J, Lichtenberg T, Hoadley KA, Poisson LM, Lazar AJ, Cherniack AD, et al. An Integrated TCGA pan-cancer clinical data resource to drive high-quality survival outcome analytics. Cell. 2018;173(2):400-416.e11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Salim A, Ma X, Fall K, Andrén O, Reilly M. Analysis of incidence and prognosis from “extreme” case-control designs. Stat Med. 2014;33(30):5388–98.

    Article  PubMed  Google Scholar 

  26. Gourgou-Bourgade S, Cameron D, Poortmans P, Asselain B, Azria D, Cardoso F, et al. Guidelines for time-to-event end point definitions in breast cancer trials: results of the DATECAN initiative (Definition for the Assessment of Time-to-event Endpoints in CANcer trials)†. Ann Oncol. 2015;26(5):873–9.

    Article  CAS  PubMed  Google Scholar 

  27. Goldhirsch A, Winer EP, Coates AS, Gelber RD, Piccart-Gebhart M, Thürlimann B, et al. Personalizing the treatment of women with early breast cancer: highlights of the St Gallen international expert consensus on the primary therapy of early breast cancer 2013. Ann Oncol. 2013;24(9):2206.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25(14):1754–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Kim D, Paggi JM, Park C, Bennett C, Salzberg SL. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol. 2019;37(8):907–15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Wu H-X, Wang Z-X, Zhao Q, Chen D-L, He M-M, Yang L-P, et al. Tumor mutational and indel burden: a systematic pan-cancer evaluation as prognostic biomarkers. Ann Transl Med. 2019;7(22):640–640.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Ding H, Zhao J, Zhang Y, Wang G, Cai S, Qiu F. Tumor mutational burden and prognosis across pan-cancers. Ann Oncol. 2018;29(viii16):7.

    Google Scholar 

  32. Barroso-Sousa R, Keenan TE, Pernas S, Exman P, Jain E, Garrido-Castro AC, et al. Tumor mutational burden and pten alterations as molecular correlates of response to pd-1/l1 blockade in metastatic triple-negative breast cancer. Clin Cancer Res. 2020;26(11):2565–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Robinson MD, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 2010;11(3):R25.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012;28(6):882.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Goeman JJ, Van de Geer S, De Kort F, van Houwellingen HC. A global test for groups of genes: testing association with a clinical outcome. Bioinformatics. 2004;20(1):93–9.

    Article  CAS  PubMed  Google Scholar 

  36. He L, Yuan L, Sun Y, Wang P, Zhang H, Feng X, et al. Glucocorticoid receptor signaling activates TEAD4 to promote breast cancer progression. Cancer Res. 2019;79(17):4399–411.

    Article  CAS  PubMed  Google Scholar 

  37. Klimczak M, Biecek P, Zylicz A, Zylicz M. Heat shock proteins create a signature to predict the clinical outcome in breast cancer. Sci Reports. 2019;9(1):1–15.

    CAS  Google Scholar 

  38. Fan Y, Mu J, Huang M, Imani S, Wang Y, Lin S, et al. Epigenetic identification of ADCY4 as a biomarker for breast cancer: an integrated analysis of adenylate cyclases. Epigenomics. 2019;11(14):1561–79.

    Article  CAS  PubMed  Google Scholar 

  39. Mei J, Wang T, Zhao S, Zhang Y. Osthole inhibits breast cancer progression through upregulating tumor suppressor GNG7. J Oncol. 2021;2021:6610511.

    PubMed  PubMed Central  Google Scholar 

  40. Liu JY, Dai YB, Li X, Cao K, Xie D, Tong ZT, et al. Solute carrier family 12 member 5 promotes tumor invasion/metastasis of bladder urothelial carcinoma by enhancing NF-κB/MMP-7 signaling pathway. Cell Death Dis. 2017;8(3):e2691–e2691.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Xu L, Li X, Cai M, Chen J, Li X, Wu WKK, et al. Original article: Increased expression of Solute carrier family 12 member 5 via gene amplification contributes to tumour progression and metastasis and associates with poor survival in colorectal cancer. Gut. 2016;65(4):635.

    Article  CAS  PubMed  Google Scholar 

  42. Yang G-P, He W-P, Tan J-F, Yang Z-X, Fan R-R, Ma N-F, et al. Overexpression of SLC12A5 is associated with tumor progression and poor survival in ovarian carcinoma. Int J Gynecol Cancer. 2019;29:1280–4.

    Article  PubMed  Google Scholar 

  43. Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2(5):401–4.

    Article  PubMed  Google Scholar 

  44. Győrffy B. Survival analysis across the entire transcriptome identifies biomarkers with the highest prognostic power in breast cancer. Comput Struct Biotechnol J. 2021;19:4101–9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Hoffman JA, Papas BN, Trotter KW, Archer TK. Single-cell RNA sequencing reveals a heterogeneous response to Glucocorticoids in breast cancer cells. Commun Biol. 2020;3(1):126.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Cassileth PA, Lusk EJ, Torri S, DiNubile N, Gerson SL. Antiemetic efficacy of dexamethasone therapy in patients receiving cancer chemotherapy. Arch Intern Med. 1983;143(7):1347–9.

    Article  CAS  PubMed  Google Scholar 

  47. Martin JD, Panagi M, Wang C, Khan TT, Martin MR, Voutouri C, et al. Dexamethasone increases cisplatin-loaded nanocarrier delivery and efficacy in metastatic breast cancer by normalizing the tumor microenvironment. ACS Nano. 2019;13(6):6396–408.

    Article  CAS  PubMed  Google Scholar 

  48. Sephton SE, Sapolsky RM, Kraemer HC, Spiegel D. Diurnal cortisol rhythm as a predictor of breast cancer survival. J Natl Cancer Inst. 2000;92:994–1000.

    Article  CAS  PubMed  Google Scholar 

  49. Zeitzer JM, Nouriani B, Rissling MB, Sledge GW, Kaplan KA, Aasly L, et al. Aberrant nocturnal cortisol and disease progression in women with breast cancer. Breast Cancer Res Treat. 2016;158(1):43.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Leoncikas V, Wu H, Ward LT, Kierzek AM, Plant NJ. Generation of 2,000 breast cancer metabolic landscapes reveals a poor prognosis group with active serotonin production. Sci Rep. 2016;6:19771.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank the West China Biobank, Department of Clinical Research Management, West China Hospital, Sichuan University for the bio-sample storage. We thank Dr. Jianming Zeng (University of Macau) and his team biotrainee for generously sharing their experiences and codes. The results shown here are in part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga.

This work was presented as an e-Poster (215P) in ESMO Congress 2021, 16-21 September 2021.

Funding

Open access funding provided by Karolinska Institute. This work was supported by grants awarded to KH by the China Scholarship Council (No. 201806240005); to FF by the Swedish Cancer Society (20 0846 PjF); to DL by the National Natural Science Foundation of China (No. 8187111500) and the Swedish Research Council (2018–00648). The funding bodies did not play any role in the design of the study and collection, analysis, or interpretation of data or in writing the manuscript.

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Authors and Affiliations

  1. Laboratory of Molecular Diagnosis of Cancer, Clinical Research Center for Breast, West China Hospital, Sichuan University, Chengdu, China

    Kejia Hu, Chengshi Wang, Chuanxu Luo, Hong Zheng, Ting Luo & Donghao Lu

  2. Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden

    Kejia Hu, Jacob Bergstedt, Katja Fall, Fang Fang & Donghao Lu

  3. Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China

    Chengshi Wang

  4. West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China

    Huan Song

  5. Center of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavík, Iceland

    Huan Song & Unnur A. Valdimarsdóttir

  6. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden

    Jacob Bergstedt, Kamila Czene & Unnur A. Valdimarsdóttir

  7. Clinical Epidemiology and Biostatistics, School of Medical Sciences, Örebro Universitet, Örebro, Sweden

    Katja Fall

  8. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Unnur A. Valdimarsdóttir & Donghao Lu

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  1. Kejia Hu
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  12. Donghao Lu
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Contributions

DL, FF, KC, KF, and UV contributed to conceptualization. CL, CW, DL, JB, and KH contributed to data curation, formal analysis, investigation, methodology, software, validation and visualization. HZ and TL contributed to resources. HS, DL, FF also contributed to project administration and supervision. All authors drafted and reviewed the manuscript.

Corresponding author

Correspondence to Donghao Lu.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the West China Hospital of Sichuan University Biomedical Research Ethics Committee (No. 2019-16) and the Regional Ethics Review Board in Stockholm, Sweden (Dnr 2020-04828). In the WCH cohort, written informed consent has been obtained from each participant. All patients in TCGA provided informed consent based on the guidelines from the TCGA Ethics, Law and Policy Group. All procedures performed involving human participants were in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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The authors declare that they have no competing interests.

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Supplementary Information

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Additional file 1: Table S1.

List of genes included in the five candidate neuroendocrine pathways. Table S2. Clinical characteristics of breast cancer patients from TCGA and WCH cohort, separately. Table S3. The associations between tumor mutation burden (TMB) of neuroendocrine pathways and prognosis, stratification analysis by ER status. Table S4. The associations between tumor mutation burden (TMB) of neuroendocrine pathways and prognosis with different adjustment. Table S5. The associations between tumor mutation burden (TMB) of neuroendocrine pathways and prognosis, in subsets of cohorts or exclusive gene list. Table S6. The associations between neuroendocrine pathway gene expression in tumor and normal breast tissue and prognosis, stratification analysis by ER status. Table S7. The associations between neuroendocrine pathway gene expression in tumor tissue and prognosis, in subsets of the cohorts or exclusive gene list. Table S8. The full list of genes of the glucocorticoid pathway expressed in tumor tissue associated with mutation and prognosis. Supplementary Methods include Matching of cases and controls, Whole exome sequencing and data processing and RNA-sequencing and data processing.

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Hu, K., Wang, C., Luo, C. et al. Neuroendocrine pathways and breast cancer progression: a pooled analysis of somatic mutations and gene expression from two large breast cancer cohorts. BMC Cancer 22, 680 (2022). https://doi.org/10.1186/s12885-022-09779-8

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Keyword

  • Breast cancer
  • Somatic mutation
  • Differential expression
  • Pathway

BMC Cancer

ISSN: 1471-2407

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