Next Article in Journal
Genetic and Epigenetic Regulation in Lingo-1: Effects on Cognitive Function and White Matter Microstructure in a Case-Control Study for Schizophrenia
Next Article in Special Issue
Cancer-Associated Fibroblasts in Gastrointestinal Cancers: Unveiling Their Dynamic Roles in the Tumor Microenvironment
Previous Article in Journal
Genomic and Reverse Translational Analysis Discloses a Role for Small GTPase RhoA Signaling in the Pathogenesis of Schizophrenia: Rho-Kinase as a Novel Drug Target
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJMS
Volume 24
Issue 21
10.3390/ijms242115625
ijms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Organ-Specificity of Breast Cancer Metastasis

by
Marina K. Ibragimova
1,2,3,*,
Matvey M. Tsyganov
1,3,
Ekaterina A. Kravtsova
1,2,
Irina A. Tsydenova
1,2 and
Nikolai V. Litviakov
1,2,3
1
Department of Experimental Oncology, Cancer Research Institute, Tomsk National Research Medical Center of the Russian Academy of Sciences, Tomsk 634009, Russia
2
Biological Institute, National Research Tomsk State University, Tomsk 634050, Russia
3
Faculty of Medicine and Biology, Siberian State Medical University, Tomsk 634050, Russia
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2023, 24(21), 15625; https://doi.org/10.3390/ijms242115625
Submission received: 28 September 2023 / Revised: 20 October 2023 / Accepted: 23 October 2023 / Published: 26 October 2023
(This article belongs to the Special Issue Discoveries of Genetic Regulations in Onco-Progression)
Browse Figures
Versions Notes

Abstract

:
Breast cancer (BC) remains one of the most common malignancies among women worldwide. Breast cancer shows metastatic heterogeneity with priority to different organs, which leads to differences in prognosis and response to therapy among patients. The main targets for metastasis in BC are the bone, lung, liver and brain. The molecular mechanism of BC organ-specificity is still under investigation. In recent years, the appearance of new genomic approaches has led to unprecedented changes in the understanding of breast cancer metastasis organ-specificity and has provided a new platform for the development of more effective therapeutic agents. This review summarises recent data on molecular organ-specific markers of metastasis as the basis of a possible therapeutic approach in order to improve the diagnosis and prognosis of patients with metastatically heterogeneous breast cancer.

1. Introduction

Breast cancer is the most frequently diagnosed malignant neoplasm in women worldwide and is expected to account for about 25% of all new cancers detected in the female population in the near future [1]. Despite the high incidence rate (2,261,419 cases in 2020 [2]), mortality rates are slowly decreasing in developed countries with the implementation of earlier diagnosis and therapy improvements [3]. Nevertheless, primary disseminated breast cancer is still diagnosed in 10% of women and the 5-year survival rate in these patients is only 25% [4].
It is the development of metastases in breast cancer, not the primary tumour, that is responsible for more than 90% of cancer deaths. According to recent studies, patients with metastatic breast cancer have bone metastases in up to 60–75% of cases, lung metastases in up to 32–37%, liver metastases in up to 32–35%, and brain metastases in up to 10% [5,6]. There is a frequency of metastases to the gastrointestinal tract in breast cancer ranging from 4% to 8% [7], and metastases to the adrenal glands are rare [8].
The detection of ovarian metastases is one of the controversial issues in the specificity of distant metastasis of breast cancer. The incidence of ovarian metastases in breast cancer patients has been shown to be 3–47%, which is mainly demonstrated during autopsies as well as prophylactic and curative oophorectomies [9,10]. Moreover, patients with breast cancer have been shown to be 3–7 times more likely to have primary ovarian cancer than ovarian metastases [11]. In addition, metastatic ovarian lesions in breast cancer sometimes mimic the clinical and histological features of primary ovarian cancer and even lose the characteristic oestrogen receptor (ER) and progesterone receptor (PR) expression levels [12,13].
In turn, the metastatic organ-specific heterogeneity of breast cancer leads to different treatment responses and patient prognosis, in particular, the 5-year overall survival (OS) in the presence of bone metastases is 22.8% [14], in the presence of lung metastasis it is 19% [15], and in the presence of liver metastasis it is 13% [16]. The median OS in the presence of ovarian metastasis is reported to be 16–38 months [17], and the 5-year survival rate is 6–26% [18]. The presence of brain metastasis in patients with breast cancer results in the shortest life expectancy [19].
Different molecular subtypes of breast cancer show different metastatic organotropism. Figure 1 shows the combined literature data on the frequency of metastatic lesions to target organs depending on the molecular subtype of breast tumour [4,20].
Metastasis progression and survival prognosis may depend on specific risk factors such as the extent of lymph node involvement and tumour size. Moreover, in clinical practice, information regarding the risk of metastasis to specific target organs is tentatively determined by the molecular/histopathological subtype of the tumour (Figure 1). All these factors, however, do not allow us to fully predict the specific sites or patterns of metastasis that are characteristic of each tumour.
A hypothesis has been put forward that the primary tumour can provide insight into the organ where metastases will eventually arise. This may have a significant impact on therapeutic and screening strategies for each patient from the time of initial diagnosis. Although organotropism of breast cancer metastasis has a known “statistical” correlation [5], this process remains largely unexplained, and today there is no available diagnostic tool that can accurately predict the risk and target organ preference for each individual patient’s tumour.
The development of targeted systemic treatment has improved median overall survival in metastatic breast cancer (MBC), although many targeted treatments remain expensive and may cause harmful side effects. Promising results have been reported in small cohorts of MBC patients using combination therapy. The CLEOPATRA trial showed that for HER2+ breast cancer that overexpresses HER2 or has ERBB2 (HER2) gene amplification, 16% of patients were progression-free at 8 years and could be effectively treated [21]. The use of a combination of CDK4 and CDK6 inhibitors (CDK4/6) with endocrine therapy for the treatment of HR+ and HER2− breast cancer improves overall survival [22], and increases the proportion of patients with a long-term response [23].
Systematic and in-depth studies of the molecular organ-specific heterogeneity of breast cancer metastasis would allow the identification of more effective agents that target metastasis suppression and contribute to improved patient outcomes.
This review brings together recent data on molecular organ-specific markers of breast cancer metastasis as the basis of a possible therapeutic approach in order to improve the diagnosis and prognosis of patients.

2. Metastatic Breast Cancer Signature

Regardless of the tumour type, dissemination of tumour cells precedes the initial stage of the metastasis cascade. The dissemination process includes the initial steps of the invasion and metastasis cascade, which allow malignant tumour cells to acquire properties that make it possible for them to leave the primary site and migrate to certain distant tissues [24].
One of the most important assumptions that leads scientists to study the organ-specificity of tumour metastasis, and breast cancer in particular, is the assumption that the nature of the primary tumour cell and their spread subsequently determines different metastatic properties, organotropism and response to therapy [25]. In vitro studies demonstrate that metastatic tumour cells migrate individually [26], whereby the spread of metastatic tumour cells in the body has been shown to occur as a cluster of tumour cells moving together [27].
The immediate process of tumour metastasis is a complex process that involves several sequential stages: local invasion with exit from the surrounding tissues of the primary tumour; invasion into blood or lymphatic vessels (intravasation); survival in the bloodstream as circulating tumour cells (CTCs); exit of CTCs from the circulatory system (extravasation); adaptation to the microenvironment in the form of disseminated tumour cells; transformation into cells initiating metastasis with the final formation of macrometastases [28].
Metastatic cancer includes a diverse set of cells with different genetic and phenotypic characteristics that cause differences in progression, metastasis and drug resistance [29]. Hundreds of genes determine invasive potential, with the assumption that a specific metastatic genetic signature can be identified in primary breast tumour cells [30]. Specific mutations may contribute to invasion and metastasis. Clinical genomics studies have shown that TP53, CDKN2A, PTEN, PIK3CA and RB1 are the most predominant genes somatically altered in metastasis [31].
Examination of markers that predict metastatic progression has shown that late-stage cancers arise from different cell types, which influences the possible genetic and epigenetic alterations which contribute to metastatic progression [32]. For example, in colorectal cancer, cells expressing the L1 cell adhesion molecule (L1CAM) have chemoresistance and the ability to initiate metastasis [33].
There has been active work in this direction in the field of breast cancer. In addition to large-scale studies by The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), which have characterised the molecular genetic status of primary breast cancer in detail [34,35], the sequencing of 617 breast cancer samples identified nine genes (TP53, ESR1, GATA3, KMT2C, NCOR1, AKT1, NF1, RIC8A and RB1) that were more frequently mutated in metastatic breast cancer compared to early breast cancer [36]. Genomic comparisons of primary tumour and metastatic tumour samples also found that metastatic clones frequently had a higher mutational load, including driver mutations and copy number aberrations, than primary tumours [37]. In some cancers, driver mutations that are identified in metastases may not be detected in the respective primary tumour [38].
Breast cancer metastases to the brain have been shown to be particularly clonally distinct, with a high number of private mutations compared to other breast cancer metastatic sites [39]. This supports the hypothesis that certain driver mutations may be specific to the organ to which cells metastasise and, in turn, may contribute to heterogeneous responses between distant metastases to different metastatic sites.

3. Organ-Specific Markers of Breast Cancer Metastasis to Distant Organs

To date, these features of breast cancer distant metastasis have been described in detail in the literature: clinical picture, diagnosis, biological mechanism and current approaches in the treatment of metastases to bone [4,40,41], lung [42,43], liver [44,45] and brain [46,47]. The work by Andrea R. Lim presents in detail the current relevant information on this issue [48].
However, there is scattered information on biomarkers of organ-specific metastasis in breast cancer in the literature. This section summarises the current information on biomarkers of metastasis to different target organs in breast cancer (Table 1).

4. Biomarker Profile of Rare Types of Breast Cancer Metastases to Distant Organs

4.1. Gynaecological Metastases

The work by Kutasovic J.R. (2018) [94] described in detail the clinicopathological and molecular profiling of breast cancer metastasis to gynaecological organs. The study included data from 54 female patients with breast cancer diagnosed with metastasis to gynaecological tissues between 1982 and 2015. A total of 258 metastatic foci (average of five metastases per patient (range 1–11 pcs)) were reported in these 54 patients. The most frequently involved gynaecological organs were the ovaries (46/54; 85.1%), fallopian tubes (29/54; 53.7%) and uterus (20/54; 37%). The median survival of patients was only 1.95 years.
In biomarker expression analysis, FOXA1 and GATA3, key regulators of transcriptional activity, were shown to be highly expressed in primary tumours. Primary tumours also demonstrated CNA with amplification of 1q, 7q, 8q, 11q, 16p and 17q and deletion of 8p, 16q, 22q and Xq (identified in more than 50% of samples). The most frequent alterations in ovarian metastases (CNA identified in more than 50% of samples) included amplifications of 1p/q, 3p, 6p, 7p/q, 8q, 12q, 15q, 17q and 19p/q and deletions on 8p, 13p/q, 16q, 22q and Xq. The most frequent amplifications were detected at loci encoding MDM4, CDK6, FGFR1, MYC, CCND1, CDK4 and MDM2.
In analysis of targeted sequencing data from matched primary tumours and metastases, it was shown that all cases had at least one mutation in common between the primary tumour and metastases, along with unique mutations present either only in the primary tumour (e.g., TBX3 in GM06BR) or only in the metastases (e.g., RB1, TP53 in GM74LO) [94].

4.2. Metastases to the Pancreas

Genetic analyses of breast cancer metastases to the pancreas are very limited due to the rarity of metastasis to this target organ. One study is presented, which is a case report that examined biomarkers of breast cancer metastasis to the pancreas.
GATA3 expression and an ERBB2 mutation (I767M) originating from a breast tumour were detected using immunohistochemistry and molecular diagnostics. The functional significance of this gene mutation has not been determined [95].

5. Genomic Profile of Breast Cancer Organ-Specific Metastasis

Understanding the nature of gene activity involved in metastasis has also been an important goal over the past few decades.
In addition to the development of high-throughput technologies in experimental and clinical oncology, many new prognostic gene markers (gene signatures or differentially expressed genes) that predict the risk of metastasis in patients with breast cancer have emerged [96]. In this section, current information on the study of the genomic profile (expression characteristics, active signalling pathways and CNA) of organ-specific metastasis in breast cancer is compiled.
In 2017, a comprehensive meta-analysis was conducted to investigate the expression of potential marker genes for metastatic breast cancer. In this paper, information on relative gene expression values was collected from 12 studies of primary breast cancer and metastatic breast cancer from the Genevestigator database (Nebion). The results of the data meta-analysis were corroborated with literature data regarding putative markers of metastatic breast cancer, and also the consistency of their reported differential expression was checked [97].
According to the results of the study, VCAM1 seems to be the best potential marker of metastatic breast cancer, but should be validated by gene expression analysis in metastatic tissue samples where contamination by immune cells has been avoided. FZD3 gene expression is high in metastatic tumours compared to primary tumours and this trend is supported by the literature. The high difference in DEPDC1, NUSAP1, FOXM1 and MUC1 gene expression observed between metastatic tissues and primary breast tumours can be considered as a prognostic marker for the development of metastases.
COX2 gene expression is significantly reduced in metastatic tissue compared to both primary tumours and normal tissue, and can be used as a differential marker in the diagnosis of metastatic cancer. RRM2 gene expression is decreased during the progression of metastatic breast cancer and can be proposed as a marker for monitoring progression. This study also revealed that MMP1, VCAM1, FZD3, VEGFC, FOXM1 and MUC1 genes can be considered as markers of breast cancer occurrence because these genes show significant differential expression in breast neoplasms compared to normal tissue [97].
In the same year, an interesting work on the comprehensive identification of molecular biomarkers in breast cancer metastases to the brain was published. In the presented study, the expression profiles of several cases were compared: 3 cases of breast cancer with brain metastasis, 16 cases of non-metastatic breast cancer and 16 cases of primary brain tumour. The genes encoding BCL3, BNIP3, BNIP3P1, BRIP1, CASP14, CDC25A, DMBT1, IDH2, E2F1, MYCN, RAD51, RAD54L and VDR were found to be distinctively overexpressed in mRMR with brain metastases (compared with non-metastatic breast cancer and brain tumours). Network analysis identified key pathways such as Akt, ERK1/2, NFkB and Ras at the predicted stage of activation in MBC. Genes with reduced expression in the dataset that were common to metastatic breast cancer and brain tumours included, for example, the cell line invasion markers JUN, MMP3, TFF1 and HAS2 [98].
In 2019, work on the identification of alternatively-activated pathways between primary and liver-metastatic breast cancer using microarray analysis data was presented. Gene expression microarray data were downloaded from the GEO database: 153 samples were in the primary breast cancer group and 43 samples were in the liver metastasis breast cancer group. Because there was a sampling imbalance between the primary cancer group and the metastatic cancer group, bootstrap analysis was performed: 43 samples were randomly selected from the primary cancer group and compared with 43 samples from the metastatic cancer group. The analysis had a total of 10 repeats.
It was shown that some signalling pathways were active in one condition (primary breast cancer or breast cancer with liver metastases) but not in both. The TnC, PHK, CAMK, NOS, ADCY, FAK2 and IP3-3K pathways were found to be active only in breast cancer metastases to the liver.
Some pathways were significantly active in both primary breast cancer and liver metastases, but the active genes were different. The CALM pathway in calcium signalling is represented by seven genes: CALM1, CALM2, CALM3, CALML3, CALML4, CALML5 and CALML6. CALM2 and CALML5 were found to be significantly active in primary breast cancer, whereas CALML3 and CALML6 were significantly active in liver metastasis of breast cancer. The BMP pathway contained 11 different genes (GDF5, GDF6, GDF7, AMH, BMP2, BMP4, BMP5, BMP6, BMP7, BMP8A and BMP8B). BMP8B was significantly active in primary breast cancer, whereas BMP2, BMP4, BMP5, BMP6 and BMP8B were significantly active in liver metastasis. Consequently, the pathway may be active in both primary cancer and metastatic lesions, but their mechanisms may be different. In the TGF beta-signalling pathway, the DCN gene was active in primary breast cancer and inhibited TGFB expression, but TGFB3 was active in liver metastasis [99].
In 2020, Paul M.R. et al. presented work on the genomic landscape of metastatic breast cancer to identify preferred pathways and targets. The authors performed full-exome and shallow full-genome sequencing to identify genes and pathways preferentially mutated or altered in copy number in metastases compared to the paired primary tumours from which they arose. Seven genes were predominantly mutated in metastases: MYLK, PEAK1, SLC2A4RG, EVC2, XIRP2, PALB2 and ESR1. The copy number of four sites was predominantly altered: deletions of STK11 and CDKN2A/B, and amplifications of PTK6 and PAQR8. Moreover, the presence of PAQR8 amplification was mutually exclusive with mutations in nuclear oestrogen and progesterone receptors, suggesting a role for this marker in treatment resistance. Several pathways were preferentially mutated or altered in metastases, including mTOR, CDK/RB, cAMP/PKA, WNT, HKMT and focal adhesion. By immunohistochemical analysis, pRB was preferentially inactivated and mTORC1 and WNT signalling pathways were enhanced in metastases. These results identify several therapeutic targets that do not undergo significant mutations in primary cancer but are involved in signalling transduction in metastatic recurrence and provide a genomic basis for the efficacy of mTORC1, CDK4/6 and PARP inhibitors in metastatic breast cancer [100].
Breast International Group (BIG) has performed genomic and transcriptomic analysis of primary breast cancer and associated metastases. The AURORA study aims to investigate the processes of the metastatic recurrence of breast cancer by performing multi-omic profiling of paired primary tumours and early metastases. Data on 381 breast cancer patients were included in the work. A driver role of somatic GATA1 and MEN1 mutations was found. Metastases were enriched for ESR1, PTEN, CDH1, PIK3CA and RB1 mutations, MDM4 and MYC amplifications and ARID1A deletion. Clonality changes were observed in ERBB2 and RB1 driver genes [101].
In 2021, a study on the role of TFF1 in the risk of breast cancer metastasising to bone was published. A retrospective analysis of 90 surgically resected breast cancer specimens was performed. TFF1 was identified as a strictly correlated primary tumour marker of bone metastases for ER+ breast cancer. To confirm this observation, analysis of TFF1 function during ER+ breast cancer oncogenesis and metastasis to bone (MCF7 model with enhancement and inhibition of TFF1 function) was performed. It was shown that in primary tumours TFF1 expression can modulate the growth of ER+ breast cancer [102].
Additionally, a study of the gene expression profile of metastatic breast cancer depending on the target organ was published in 2021. This retrospective study included 184 metastatic tumour samples from 176 patients with breast cancer [103].
In the first step, the influence of the target organ on gene expression profiling was assessed. A total of 74 genes were identified, whose high expression was specific to the site of metastasis and independent of subtype, (p < 0.05): 36 bone-specific genes (WIF1, IBSP, MMP9, ITGB3, VIT, HBB, WNT5B, CHAD, BMP2, EYA1, FOXC2, FZD8, OLFML2B, TGFB1, BMP5, ENPP2, NUDT1, FGF7, FOXC1, BMP8A, EYA4, RNASE2, SRPX, MME, LIFR, BAX, SCARA5, EYA2, XRCC3, LEPR, BCL2L1, NCAM1, SMAD3, RAC2, HOXA9, CKB), 18 liver-specific genes (ALDH1A1, CYP4F3, PCK1, RELN, AGT, PPARGC1A, HNF1A, CDH2, APOE, GGH, HGF, MT1G, CLDN1, UBB, HDAC1, EDNRB, GATA4, MARCO), 12 brain-specific genes (CRYAB, NRCAM, FGF1, GDF15, SOX2, GRIN1, RASGRF1, SOX10, CHI3L1, ZIC2, NRXN1, LEFTY2) and 8 skin-specific genes (KRT14, KRT5, S100A7, SERPINB5, MMP3, IL20RB, SFN, TPSAB1). It should be noted that the authors identified three genes from the list of PAM50 genes that are associated with metastasis to bone (FOXC1) and skin (KRT14 and KRT5).
Interestingly, the authors analysed the expression level of the identified 74 genes in 390 primary breast tumours in a publicly available dataset [104], in which three types of metastatic spread were identified: bone and visceral metasynchronous spread, bone spread only, and visceral metastasis only. Visceral metastases included distant metastases to the lungs, liver and brain.
Among the 74 genes, 26 genes (35.1%) were found to be significantly associated with the type of metastatic spread, including 5 bone-specific genes (CHAD, EYA1, TGFB1, BAX and HOXA9) whose high expression was associated only with bone metastasis, 4 bone-specific genes (WIF1, VIT, FOXC2 and MME) whose high expression was associated with bone and visceral metastasis, 2 brain-specific genes (FGF1 and SOX2) whose high expression was associated with bone and visceral metastasis, 2 brain-specific genes (RASGRF1 and CHI3L1) whose high expression was associated with metastasis to internal organs only, and 2 liver-specific genes (GGH and MARCO) whose high expression was associated with visceral metastasis only. This result suggests that certain genes may also indicate an organ-specific type of metastatic spread in the analysis of primary tumours [103].
Full title, acronyms and location of all genes (identified and described) in the review are presented in Table S1.

6. Conclusions

Metastatic progression represents a major therapeutic challenge, whereby unpredictable tumour heterogeneity both between patients and within each tumour becomes a major obstacle in the search for a rational therapeutic approach.
The accumulated knowledge on the genomics of breast cancer over the last decade has significantly increased the understanding of intratumoural heterogeneity, which is now considered to be a driving force for cancer progression. In this context, the knowledge and understanding of metastatic breast cancer is somewhat behind that of primary cancer [105].
According to the available literature, the present review summarises information on biomarkers of metastasis to different target organs in breast cancer. Figure 2 shows schematically the markers of organ-specificity of metastases development in breast cancer.
In turn, understanding the mechanism of heterogeneity, including in the context of the organospecificity of metastatic potential in breast cancer, is crucial for the development of new effective diagnostic and prognostic strategies. However, additional studies are needed to further validate the identified genes and molecular mechanisms for future clinical applications.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms242115625/s1.

Author Contributions

Conceptualization, M.K.I. and N.V.L.; methodology, M.K.I. and M.M.T.; software, I.A.T.; investigation, M.K.I.; resources, M.M.T., E.A.K. and I.A.T.; data curation, M.K.I.; writing—original draft preparation, M.K.I.; writing—review and editing, N.V.L.; supervision, M.K.I.; project administration, M.K.I.; funding acquisition, M.K.I. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Russian Science Foundation grant No. 22-25-00499.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Bray, F.; Ferlay, J.; Soerjomataram, I.; Siegel, R.L.; Torre, L.A.; Jemal, A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 2018, 68, 394–424. [Google Scholar] [CrossRef]
  2. Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 2021, 71, 209–249. [Google Scholar] [CrossRef] [PubMed]
  3. Zielonke, N.; Gini, A.; Jansen, E.E.L.; Anttila, A.; Segnan, N.; Ponti, A.; Veerus, P.; de Koning, H.J.; van Ravesteyn, N.T.; Heijnsdijk, E.A.M. Evidence for reducing cancer-specific mortality due to screening for breast cancer in Europe: A systematic review. Eur. J. Cancer 2020, 127, 191–206. [Google Scholar] [CrossRef] [PubMed]
  4. Lourenço, C.; Conceição, F.; Jerónimo, C.; Lamghari, M.; Sousa, D.M. Stress in Metastatic Breast Cancer: To the Bone and Beyond. Cancers 2022, 14, 1881. [Google Scholar] [CrossRef] [PubMed]
  5. Wu, Q.; Li, J.; Zhu, S.; Wu, J.; Chen, C.; Liu, Q.; Wei, W.; Zhang, Y.; Sun, S. Breast cancer subtypes predict the preferential site of distant metastases: A SEER based study. Oncotarget 2017, 8, 27990–27996. [Google Scholar] [CrossRef] [PubMed]
  6. Buonomo, O.C.; Caredda, E.; Portarena, I.; Vanni, G.; Orlandi, A.; Bagni, C.; Petrella, G.; Palombi, L. New insights into the metastatic behavior after breast cancer surgery, according to well-established clinicopathological variables and molecular subtypes. PLoS ONE 2017, 12, e0184680. [Google Scholar] [CrossRef] [PubMed]
  7. Almubarak, M.M.; Laé, M.; Cacheux, W.; de Cremoux, P.; Pierga, J.-Y.; Reyal, F.; Bennett, S.P.; Falcou, M.-C.; Salmon, R.J.; Baranger, B.; et al. Gastric metastasis of breast cancer: A single centre retrospective study. Dig. Liver Dis. 2011, 43, 823–827. [Google Scholar] [CrossRef] [PubMed]
  8. Demirci, U.; Buyukberber, S.; Cakir, T.; Poyraz, A.; Baykara, M.; Karakus, E.; Tufan, G.; Benekli, M.; Coskun, U. Isolated mucinous adrenal metastasis in a breast cancer patient. J. Oncol. Pharm. Pract. 2011, 17, 444–447. [Google Scholar] [CrossRef]
  9. Bastings, L.; Beerendonk, C.C.; Westphal, J.R.; Massuger, L.F.; Kaal, S.E.; van Leeuwen, F.E.; Braat, D.D.M.; Peek, R. Autotransplantation of cryopreserved ovarian tissue in cancer survivors and the risk of reintroducing malignancy: A systematic review. Hum. Reprod. Update 2013, 19, 483–506. [Google Scholar] [CrossRef]
  10. Peters, I.T.; van Zwet, E.W.; Smit, V.T.; Liefers, G.J.; Kuppen, P.J.; Hilders, C.G.; Trimbos, J.B. Prevalence and risk factors of ovarian metastases in breast cancer patients < 41 years of age in the Netherlands: A nationwide retrospective cohort study. PLoS ONE 2017, 12, e0168277. [Google Scholar]
  11. Tian, W.; Zhou, Y.; Wu, M.; Yao, Y.; Deng, Y. Ovarian metastasis from breast cancer: A comprehensive review. Clin. Transl. Oncol. 2019, 21, 819–827. [Google Scholar] [CrossRef]
  12. Yadav, B.S.; Sharma, S.C.; Robin, T.P.; Sams, S.; Elias, A.D.; Kaklamani, V.; Marcom, P.K.; Schaefer, S.; Morris, G.J. Synchronous primary carcinoma of breast and ovary versus ovarian metastases. Semin. Oncol. 2015, 42, e13–e24. [Google Scholar] [CrossRef]
  13. Tamas, J.; Vereczkey, I.; Toth, E. Metastatic tumors in the ovary, difficulties of histologic diagnosis. Magyar Onkol. 2015, 59, 205–213. [Google Scholar]
  14. Xiong, Z.; Deng, G.; Huang, X.; Li, X.; Xie, X.; Wang, J.; Shuang, Z.; Wang, X. Bone metastasis pattern in initial metastatic breast cancer: A population-based study. Cancer Manag. Res. 2018, 10, 287–295. [Google Scholar] [CrossRef] [PubMed]
  15. Caswell-Jin, J.L.; Plevritis, S.K.; Tian, L.; Cadham, C.J.; Xu, C.; Stout, N.K.; Sledge, G.W.; Mandelblatt, J.S.; Kurian, A.W. Change in Survival in Metastatic Breast Cancer with Treatment Advances: Meta-Analysis and Systematic Review. JNCI Cancer Spectr. 2018, 2, pky062. [Google Scholar] [CrossRef] [PubMed]
  16. Bale, R.; Putzer, D.; Schullian, P. Local Treatment of Breast Cancer Liver Metastasis. Cancers 2019, 11, 1341. [Google Scholar] [CrossRef] [PubMed]
  17. Sal, V.; Demirkiran, F.; Topuz, S.; Kahramanoglu, I.; Yalcin, I.; Bese, T.; Sozen, H.; Tokgozoglu, N.; Salihoglu, Y.; Turan, H.; et al. Surgical treatment of metastatic ovarian tumors from extragenital primary sites. Int. J. Gynecol. Cancer 2016, 26, 688–696. [Google Scholar] [CrossRef] [PubMed]
  18. Rabban, J.T.; Barnes, M.; Chen, L.-M.; Powell, C.B.; Crawford, B.; Zaloudek, C.J. Ovarian pathology in risk-reducing salpingo-oophorectomies from women with BRCA mutations, emphasizing the differential diagnosis of occult primary and metastatic carcinoma. Am. J. Surg. Pathol. 2009, 33, 1125–1136. [Google Scholar] [CrossRef] [PubMed]
  19. Wang, R.; Zhu, Y.; Liu, X.; Liao, X.; He, J.; Niu, L. The Clinicopathological features and survival outcomes of patients with different metastatic sites in stage IV breast cancer. BMC Cancer 2019, 19, 1091. [Google Scholar] [CrossRef]
  20. Harbeck, N.; Penault-Llorca, F.; Cortes, J.; Gnant, M.; Houssami, N.; Poortmans, P.; Ruddy, K.; Tsang, J.; Cardoso, F. Breast cancer. Nat. Rev. Dis. Primers 2019, 5, 66. [Google Scholar] [CrossRef]
  21. Swain, S.M.; Miles, D.; Kim, S.-B.; Im, Y.-H.; Im, S.-A.; Semiglazov, V.; Ciruelos, E.; Schneeweiss, A.; Loi, S.; Monturus, E.; et al. Pertuzumab, trastuzumab, and docetaxel for HER2-positive metastatic breast cancer (CLEOPATRA): End-of-study results from a double-blind, randomised, placebo-controlled, phase 3 study. Lancet Oncol. 2020, 21, 519–530. [Google Scholar] [CrossRef]
  22. Sledge, G.W., Jr.; Toi, M.; Neven, P.; Sohn, J.; Inoue, K.; Pivot, X.; Burdaeva, O.; Okera, M.; Masuda, N.; Kaufman, P.A.; et al. The effect of abemaciclib plus fulvestrant on overall survival in hormone receptor-positive, ERBB2-negative breast cancer that progressed on endocrine therapy—MONARCH 2: A randomized clinical trial. JAMA Oncol. 2019, 6, 116–124. [Google Scholar] [CrossRef] [PubMed]
  23. O’Leary, B.; Cutts, R.J.; Liu, Y.; Hrebien, S.; Huang, X.; Fenwick, K.; André, F.; Loibl, S.; Loi, S.; Garcia-Murillas, I.; et al. The genetic landscape and clonal evolution of breast cancer resistance to palbociclib plus fulvestrant in the PALOMA-3 trial. Cancer Discov. 2018, 8, 1390–1403. [Google Scholar] [CrossRef] [PubMed]
  24. Lambert, A.W.; Pattabiraman, D.R.; Weinberg, R.A. Emerging biological principles of metastasis. Cell 2017, 168, 670–691. [Google Scholar] [CrossRef] [PubMed]
  25. Tabassum, D.P.; Polyak, K. Tumorigenesis: It takes a village. Nat. Rev. Cancer 2015, 15, 473–483. [Google Scholar] [CrossRef] [PubMed]
  26. Clark, A.G.; Vignjevic, D.M. Modes of cancer cell invasion and the role of the microenvironment. Curr. Opin. Cell Biol. 2015, 36, 13–22. [Google Scholar] [CrossRef] [PubMed]
  27. Cheung, K.J.; Ewald, A.J. A collective route to metastasis: Seeding by tumor cell clusters. Science 2016, 352, 167–169. [Google Scholar] [CrossRef]
  28. Lin, D.; Shen, L.; Luo, M.; Zhang, K.; Li, J.; Yang, Q.; Zhu, F.; Zhou, D.; Zheng, S.; Chen, Y.; et al. Circulating tumor cells: Biology and clinical significance. Signal Transduct. Target. Ther. 2021, 6, 404. [Google Scholar] [CrossRef]
  29. Lawson, D.A.; Kessenbrock, K.; Davis, R.T.; Pervolarakis, N.; Werb, Z. Tumour heterogeneity and metastasis at single-cell resolution. Nat. Cell Biol. 2018, 20, 1349–1360. [Google Scholar] [CrossRef]
  30. Kimbung, S.; Loman, N.; Hedenfalk, I. Clinical and molecular complexity of breast cancer metastases. Semin. Cancer Biol. 2015, 35, 85–95. [Google Scholar] [CrossRef]
  31. Birkbak, N.J.; McGranahan, N. Cancer genome evolutionary trajectories in metastasis. Cancer Cell 2020, 37, 8–19. [Google Scholar] [CrossRef] [PubMed]
  32. Yang, D.; Denny, S.K.; Greenside, P.G.; Chaikovsky, A.C.; Brady, J.J.; Ouadah, Y.; Granja, J.M.; Jahchan, N.S.; Lim, J.S.; Kwok, S.; et al. Intertumoral heterogeneity in SCLC is influenced by the cell type of origin. Cancer Discov. 2018, 8, 1316–1331. [Google Scholar] [CrossRef] [PubMed]
  33. Ganesh, K.; Basnet, H.; Kaygusuz, Y.; Laughney, A.M.; He, L.; Sharma, R.; O’Rourke, K.P.; Reuter, V.P.; Huang, Y.-H.; Turkekul, M.; et al. L1CAM defines the regenerative origin of metastasis-initiating cells in colorectal cancer. Nat. Cancer 2020, 1, 28–45. [Google Scholar] [CrossRef] [PubMed]
  34. The Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 2012, 490, 61–70. [Google Scholar] [CrossRef] [PubMed]
  35. International Cancer Genome Consortium. International network of cancer genome projects. Nature 2010, 464, 993–998. [Google Scholar] [CrossRef] [PubMed]
  36. Bertucci, F.; Ng, C.K.Y.; Patsouris, A.; Droin, N.; Piscuoglio, S.; Carbuccia, N.; Soria, J.C.; Dien, A.T.; Adnani, Y.; Kamal, M.; et al. Genomic characterization of metastatic breast cancers. Nature 2019, 569, 560–564. [Google Scholar] [CrossRef]
  37. Brown, D.; Smeets, D.; Székely, B.; Larsimont, D.; Szász, A.M.; Adnet, P.-Y.; Rothé, F.; Rouas, G.; Nagy, Z.I.; Faragó, Z.; et al. Phylogenetic analysis of metastatic progression in breast cancer using somatic mutations and copy number aberrations. Nat. Commun. 2017, 8, 14944. [Google Scholar] [CrossRef]
  38. Yates, L.R.; Knappskog, S.; Wedge, D.; Farmery, J.H.R.; Gonzalez, S.; Martincorena, I.; Alexandrov, L.B.; Van Loo, P.; Haugland, H.K.; Lilleng, P.K.; et al. Genomic evolution of breast cancer metastasis and relapse. Cancer Cell 2017, 32, 169–184. [Google Scholar] [CrossRef]
  39. De Mattos-Arruda, L.; Sammut, S.J.; Ross, E.M.; Bashford-Rogers, R.; Greenstein, E.; Markus, H.; Morganella, S.; Teng, Y.; Maruvka, Y.; Pereira, B.; et al. The genomic and immune landscapes of lethal metastatic breast cancer. Cell Rep. 2019, 27, 2690–2708. [Google Scholar] [CrossRef]
  40. Tahara, R.K.; Brewer, T.M.; Theriault, R.L.; Ueno, N.T. Bone Metastasis of Breast Cancer. In Breast Cancer Metastasis and Drug Resistance; Ahmad, A., Ed.; Advances in Experimental Medicine and Biology; Springer: Cham, Switzerland, 2019; Volume 1152. [Google Scholar]
  41. Leto, G. Current status and future directions in the treatment of bone metastases from breast cancer. Clin. Exp. Pharmacol. Physiol. 2019, 46, 968–971. [Google Scholar] [CrossRef]
  42. Medeiros, B.; Allan, A.L. Molecular Mechanisms of Breast Cancer Metastasis to the Lung: Clinical and Experimental Perspectives. Int. J. Mol. Sci. 2019, 20, 2272. [Google Scholar] [CrossRef] [PubMed]
  43. Zou, Y.; Ye, F.; Kong, Y.; Hu, X.; Deng, X.; Xie, J.; Song, C.; Ou, X.; Wu, S.; Wu, L.; et al. The Single-Cell Landscape of Intratumoral Heterogeneity and The Immunosuppressive Microenvironment in Liver and Brain Metastases of Breast Cancer. Adv. Sci. 2023, 10, 2203699. [Google Scholar] [CrossRef] [PubMed]
  44. Horn, S.R.; Stoltzfus, K.C.; Lehrer, E.J.; Dawson, L.A.; Tchelebi, L.; Gusani, N.J.; Sharma, N.K.; Chen, H.; Trifiletti, D.M.; Zaorsky, N.G.; et al. Epidemiology of liver metastases. Cancer Epidemiol. 2020, 67, 101760. [Google Scholar] [CrossRef] [PubMed]
  45. Tsilimigras, D.I.; Brodt, P.; Clavien, P.A.; Muschel, R.J.; D’Angelica, M.I.; Endo, I.; Parks, R.W.; Doyle, M.; de Santibañes, E.; Pawlik, T.M. Liver metastases. Nat. Rev. Dis. Primers 2021, 7, 27. [Google Scholar] [CrossRef] [PubMed]
  46. Hosonaga, M.; Saya, H.; Arima, Y. Molecular and cellular mechanisms underlying brain metastasis of breast cancer. Cancer Metastasis Rev. 2020, 39, 711–720. [Google Scholar] [CrossRef] [PubMed]
  47. Corti, C.; Antonarelli, G.; Criscitiello, C.; Lin, N.U.; Carey, L.A.; Cortés, J.; Poortmans, P.; Curigliano, G. Targeting brain metastases in breast cancer. Cancer Treat. Rev. 2022, 103, 102324. [Google Scholar] [CrossRef] [PubMed]
  48. Lim, A.R.; Ghajar, C.M. Thorny ground, rocky soil: Tissue-specific mechanisms of tumor dormancy and relapse. Semin. Cancer Biol. 2022, 78, 104–123. [Google Scholar] [CrossRef] [PubMed]
  49. Brown, J.; Rathbone, E.; Hinsley, S.; Gregory, W.; Gossiel, F.; Marshall, H.; Burkinshaw, R.; Shulver, H.; Thandar, H.; Bertelli, G.; et al. Associations between serum bone biomarkers in early breast cancer and development of bone metastasis: Results from the AZURE (BIG01/04) trial. J. Natl. Cancer Inst. 2018, 110, 871–879. [Google Scholar] [CrossRef]
  50. Holen, I.; Lefley, D.V.; Francis, S.E.; Rennicks, S.; Bradbury, S.; Coleman, R.E.; Ottewell, P. IL-1 drives breast cancer growth and bone metastasis in vivo. Oncotarget 2016, 7, 75571–75584. [Google Scholar] [CrossRef]
  51. Westbrook, J.A.; Cairns, D.A.; Peng, J.; Speirs, V.; Hanby, A.M.; Holen, I.; Wood, S.L.; Ottewell, P.D.; Marshall, H.; Banks, R.E.; et al. CAPG and GIPC1: Breast cancer biomarkers for bone metastasis development and treatment. J. Natl. Cancer Inst. 2016, 108, djv360. [Google Scholar] [CrossRef]
  52. Sutherland, A.; Forsyth, A.; Cong, Y.; Grant, L.; Juan, T.-H.; Lee, J.K.; Klimowicz, A.; Petrillo, S.K.; Hu, J.; Chan, A.; et al. The role of prolactin in bone metastasis and breast cancer cell-mediated osteoclast differentiation. J. Natl. Cancer Inst. 2016, 108, djv338. [Google Scholar] [CrossRef] [PubMed]
  53. Rafiei, S.; Tiedemann, K.; Tabaries, S.; Siegel, P.M.; Komarova, S.V. Peroxiredoxin 4: A novel secreted mediator of cancer induced osteoclastogenesis. Cancer Lett. 2015, 361, 262–270. [Google Scholar] [CrossRef] [PubMed]
  54. Li, Y.; Zhang, H.; Zhao, Y.; Wang, C.; Cheng, Z.; Tang, L.; Gao, Y.; Liu, F.; Li, J.; Li, Y.; et al. A mandatory role of nuclear PAK4–LIFR axis in breast-to-bone metastasis of ERα-positive breast cancer cells. Oncogene 2019, 38, 808–821. [Google Scholar] [CrossRef] [PubMed]
  55. Pavlovic, M.; Arnal-Estape, A.; Rojo, F.; Bellmunt, A.; Tarragona, M.; Guiu, M.; Planet, E.; Garcia-Albéniz, X.; Morales, M.; Urosevic, J.; et al. Enhanced MAF oncogene expression and breast cancer bone metastasis. J. Natl. Cancer Inst. 2015, 107, djv256. [Google Scholar] [CrossRef] [PubMed]
  56. Coleman, R.E.; Collinson, M.; Gregory, W.; Marshall, H.; Bell, R.; Dodwell, D.; Keane, M.; Gil, M.; Barrett-Lee, P.; Ritchie, D.; et al. Benefits and risks of adjuvant treatment with zoledronic acid in stage II/III breast cancer. 10 years follow-up of the AZURE randomized clinical trial (BIG 01/04). J. Bone Oncol. 2018, 13, 123–135. [Google Scholar] [CrossRef] [PubMed]
  57. Westbrook, J.A.; Wood, S.L.; Cairns, D.A.; McMahon, K.; Gahlaut, R.; Thygesen, H.; Shires, M.; Roberts, S.; Marshall, H.; Oliva, M.R.; et al. Identification and validation of DOCK4 as a potential biomarker for risk of bone metastasis development in patients with early breast cancer. J. Pathol. 2019, 247, 381–391. [Google Scholar] [CrossRef] [PubMed]
  58. Sun, J.; Huang, J.; Lan, J.; Zhou, K.; Gao, Y.; Song, Z.; Deng, Y.; Liu, L.; Dong, Y.; Liu, X. Overexpression of CENPF correlates with poor prognosis and tumor bone metastasis in breast cancer. Cancer Cell Int. 2019, 19, 264. [Google Scholar] [CrossRef]
  59. Zhang, Y.; He, W.; Zhang, S. Seeking for Correlative Genes and Signaling Pathways with Bone Metastasis from Breast Cancer by Integrated Analysis. Front. Oncol. 2019, 9, 138. [Google Scholar] [CrossRef]
  60. Bahrami, A.; Aledavood, A.; Anvari, K.; Hassanian, S.M.; Maftouh, M.; Yaghobzade, A.; Salarzaee, O.; ShahidSales, S.; Avan, A. The prognostic and thera-peutic application of microRNAs in breast cancer: Tissue and circu-lating microRNAs. J. Cell Physiol. 2018, 233, 774–786. [Google Scholar] [CrossRef]
  61. Fan, X.; Chen, W.; Fu, Z.; Zeng, L.; Yin, Y.; Yuan, H. MicroRNAs, a sub-population of regulators, are involved in breast cancer progres-sion through regulating breast cancer stem cells. Oncol. Lett. 2017, 14, 5069–5076. [Google Scholar]
  62. Yuan, X.; Qian, N.; Ling, S.; Li, Y.; Sun, W.; Li, J.; Du, R.; Zhong, G.; Liu, C.; Yu, G.; et al. Breast cancer exosomes contribute to pre-metastatic niche formation and promote bone metastasis of tumor cells. Theranostics 2021, 11, 1429–1445. [Google Scholar] [CrossRef] [PubMed]
  63. Romero-Moreno, R.; Curtis, K.J.; Coughlin, T.R.; Miranda-Vergara, M.C.; Dutta, S.; Natarajan, A.; Facchine, B.A.; Jackson, K.M.; Nystrom, L.; Li, J.; et al. The CXCL5/CXCR2 axis is sufficient to promote breast cancer colonization during bone metastasis. Nat. Commun. 2019, 10, 4404. [Google Scholar] [CrossRef]
  64. Sousa, S.; Gineyts, E.; Geraci, S.; Croset, M.; Clézardin, P. RANK-RANKL signaling inhibition delays early breast cancer bone metastasis formation. AACR Cancer Res. 2018, 78, 29. [Google Scholar] [CrossRef]
  65. Devignes, C.S.; Aslan, Y.; Brenot, A.; Devillers, A.; Schepers, K.; Fabre, S.; Chou, J.; Casbon, A.-J.; Werb, Z.; Provot, S. HIF signaling in osteoblast-lineage cells promotes systemic breast cancer growth and metastasis in mice. Proc. Natl. Acad. Sci. USA 2018, 115, E992–E1001. [Google Scholar] [CrossRef] [PubMed]
  66. Bartels, S.; Christgen, M.; Luft, A.; Persing, S.; Jödecke, K.; Lehmann, U.; Kreipe, H. Estrogen receptor (ESR1) mutation in bone metastases from breast cancer. Mod. Pathol. 2018, 31, 56–61. [Google Scholar] [CrossRef] [PubMed]
  67. Masuda, T.; Endo, M.; Yamamoto, Y.; Odagiri, H.; Kadomatsu, T.; Nakamura, T.; Tanoue, H.; Ito, H.; Yugami, M.; Miyata, K.; et al. ANGPTL2 increases bone metastasis of breast cancer cells through enhancing CXCR4 signaling. Sci. Rep. 2015, 5, 9170. [Google Scholar] [CrossRef] [PubMed]
  68. Schrijver, W.A.M.E.; van Diest, P.J.; Dutch Distant Breast Cancer Metastases Consortium; Moelans, C.B. Unravelling site-specific breast cancer metastasis: A microRNA expression profiling study. Oncotarget 2017, 8, 3111–3123. [Google Scholar] [CrossRef]
  69. Wang, Z.; Li, T.-E.; Chen, M.; Pan, J.-J.; Shen, K.-W. MiR-106b-5p contributes to the lung metastasis of breast cancer via targeting CNN1 and regulating Rho/ROCK1 pathway. Aging 2020, 12, 1867–1887. [Google Scholar] [CrossRef]
  70. Tang, X.; Shi, L.; Xie, N.; Liu, Z.; Qian, M.; Meng, F.; Xu, Q.; Zhou, M.; Cao, X.; Zhu, W.-G.; et al. SIRT7 antagonizes TGF-beta signaling and inhibits breast cancer metastasis. Nat. Commun. 2017, 8, 318. [Google Scholar] [CrossRef]
  71. Pascual, G.; Avgustinova, A.; Mejetta, S.; Martín, M.; Castellanos, A.; Attolini, C.S.O.; Berenguer, A.; Prats, N.; Toll, A.; Hueto, J.A.; et al. Targeting metastasis-initiating cells through the fatty acid receptor CD36. Nature 2017, 541, 41–45. [Google Scholar] [CrossRef]
  72. Aleckovic, M.; Wei, Y.; LeRoy, G.; Sidoli, S.; Liu, D.D.; Garcia, B.A.; Kang, Y. Identification of Nidogen 1 as a lung metastasis protein through secretome analysis. Genes Dev. 2017, 31, 1439–1455. [Google Scholar] [CrossRef] [PubMed]
  73. Liu, X.; Adorno-Cruz, V.; Chang, Y.-F.; Jia, Y.; Kawaguchi, M.; Dashzeveg, N.K.; Taftaf, R.; Ramos, E.K.; Schuster, E.J.; El-Shennawy, L.; et al. EGFR inhibition blocks cancer stem cell clustering and lung metastasis of triple negative breast cancer. Theranostics 2021, 11, 6632–6643. [Google Scholar] [CrossRef] [PubMed]
  74. Cao, H.; Zhang, Z.; Zhao, S.; He, X.; Yu, H.; Yin, Q.; Zhang, Z.; Gu, W.; Chen, L.; Li, Y. Hydrophobic interaction mediating self-assembled nanoparticles of succinobucol suppress lung metastasis of breast cancer by inhibition of VCAM-1 expression. J. Control. Release 2015, 205, 162–171. [Google Scholar] [CrossRef] [PubMed]
  75. Zhuang, X.; Zhang, H.; Li, X.; Li, X.; Cong, M.; Peng, F.; Yu, J.; Zhang, X.; Yang, Q.; Hu, G. Differential effects on lung and bone metastasis of breast cancer by Wnt signalling inhibitor DKK1. Nat. Cell Biol. 2017, 19, 1274–1285. [Google Scholar] [CrossRef] [PubMed]
  76. Kazan, J.M.; El-Saghir, J.; Saliba, J.; Shaito, A.; Jalaleddine, N.; El-Hajjar, L.; Al-Ghadban, S.; Yehia, L.; Zibara, K.; El-Sabban, M. Cx43 Expression Correlates with Breast Cancer Metastasis in MDA-MB-231 Cells In Vitro, In a Mouse Xenograft Model and in Human Breast Cancer Tissues. Cancers 2019, 11, 460. [Google Scholar] [CrossRef]
  77. Chen, I.X.; Chauhan, V.P.; Posada, J.; Ng, M.R.; Wu, M.W.; Adstamongkonkul, P.; Huang, P.; Lindeman, N.; Langer, R.; Jain, R.K. Blocking CXCR4 alleviates desmoplasia, increases T-lymphocyte infiltration, and improves immunotherapy in metastatic breast cancer. Proc. Natl. Acad. Sci. USA 2019, 116, 4558–4566. [Google Scholar] [CrossRef] [PubMed]
  78. Dupuy, F.; Tabariès, S.; Andrzejewski, S.; Dong, Z.; Blagih, J.; Annis, M.G.; Omeroglu, A.; Gao, D.; Leung, S.; Amir, E.; et al. PDK1-dependent metabolic reprogramming dictates metastatic potential in breast cancer. Cell Metab. 2015, 22, 577–589. [Google Scholar] [CrossRef]
  79. Liu, P.; Wang, Z.; Ou, X.; Wu, P.; Zhang, Y.; Wu, S.; Xiao, X.; Li, Y.; Ye, F.; Tang, H. The FUS/circEZH2/KLF5/feedback loop contributes to CXCR4-induced liver metastasis of breast cancer by enhancing epithelial-mesenchymal transition. Mol. Cancer 2022, 21, 198. [Google Scholar] [CrossRef]
  80. Wang, Z.; Yang, L.; Wu, P.; Li, X.; Tang, Y.; Ou, X.; Zhang, Y.; Xiao, X.; Wang, J.; Tang, H. The circROBO1/KLF5/FUS feedback loop regulates the liver metastasis of breast cancer by inhibiting the selective autophagy of afadin. Mol. Cancer 2022, 21, 29. [Google Scholar] [CrossRef]
  81. Tabariès, S.; Annis, M.G.; Hsu, B.E.; Tam, C.E.; Savage, P.; Park, M.; Siegel, P.M. Lyn modulates Claudin-2 expression and is a therapeutic target for breast cancer liver metastasis. Oncotarget 2015, 6, 9476–9487. [Google Scholar] [CrossRef]
  82. Yang, J.; Wu, N.N.; Huang, D.J.; Luo, Y.C.; Huang, J.Z.; He, H.Y.; Lu, H.L.; Song, W.L. PPFIA1 is upregulated in liver metastasis of breast cancer and is a potential poor prognostic indicator of metastatic relapse. Tumour Biol. 2017, 39, 1010428317713492. [Google Scholar] [CrossRef] [PubMed]
  83. Tian, C.; Liu, S.; Wang, Y.; Song, X. Prognosis and Genomic Landscape of Liver Metastasis in Patients with Breast Cancer. Front. Oncol. 2021, 11, 588136. [Google Scholar] [CrossRef] [PubMed]
  84. Blazquez, R.; Wlochowitz, D.; Wolff, A.; Seitz, S.; Wachter, A.; Perera-Bel, J.; Bleckmann, A.; Beißbarth, T.; Salinas, G.; Riemenschneider, M.J.; et al. PI3K: A master regulator of brain metastasis-promoting macrophages/microglia. Glia 2018, 66, 2438–2455. [Google Scholar] [CrossRef]
  85. Hohensee, I.; Chuang, H.N.; Grottke, A.; Werner, S.; Schulte, A.; Horn, S.; Lamszus, K.; Bartkowiak, K.; Witzel, I.; Westphal, M.; et al. PTEN mediates the cross talk between breast and glial cells in brain metastases leading to rapid disease progression. Oncotarget 2017, 8, 6155–6168. [Google Scholar] [CrossRef] [PubMed]
  86. Anders, C.K.; Rhun, E.L.; Bachelot, T.D.; Yardley, D.A.; Awada, A.; Conte, P.F.; Conte, P.F.; Kabos, P.; Bear, M.; Yang, Z.; et al. A phase II study of abemaciclib in patients (pts) with brain metastases (BM) secondary to HR+, HER2- metastatic breast cancer (MBC). J. Clin. Oncol. 2019, 37, 1017. [Google Scholar] [CrossRef]
  87. Priego, N.; Zhu, L.; Monteiro, C.; Mulders, M.; Wasilewski, D.; Bindeman, W.; Doglio, L.; Martínez, L.; Martínez-Saez, E.; Ramón y Cajal, S.; et al. STAT3 labels a subpopulation of reactive astrocytes required for brain metastasis. Nat. Med. 2018, 24, 1024–1035. [Google Scholar] [CrossRef] [PubMed]
  88. Ghoochani, A.; Schwarz, M.A.; Yakubov, E.; Engelhorn, T.; Doerfler, A.; Buchfelder, M.; Bucala, R.; Savaskan, N.E.; Eyüpoglu, I.Y. MIF-CD74 signaling impedes microglial M1 polarization and facilitates brain tumorigenesis. Oncogene 2016, 35, 6246–6261. [Google Scholar] [CrossRef]
  89. Wang, S.; Liang, K.; Hu, Q.; Li, P.; Song, J.; Yang, Y.; Yao, J.; Mangala, L.S.; Li, C.; Yang, W.; et al. JAK2-binding long noncoding RNA promotes breast cancer brain metastasis. J. Clin. Investig. 2017, 127, 4498–4515. [Google Scholar] [CrossRef]
  90. Wu, K.; Fukuda, K.; Xing, F.; Zhang, Y.; Sharma, S.; Liu, Y.; Chan, M.D.; Zhou, X.; Qasem, S.A.; Pochampally, R.; et al. Roles of the cyclooxygenase 2 matrix metalloproteinase 1 pathway in brain metastasis of breast cancer. J. Biol. Chem. 2015, 290, 9842–9854. [Google Scholar] [CrossRef]
  91. Cordero, A.; Kanojia, D.; Miska, J.; Panek, W.K.; Xiao, A.; Han, Y.; Bonamici, N.; Zhou, W.; Xiao, T.; Wu, M.; et al. FABP7 is a key metabolic regulator in HER2+ breast cancer brain metastasis. Oncogene 2019, 38, 6445–6460. [Google Scholar] [CrossRef]
  92. Sato, J.; Shimomura, A.; Kawauchi, J.; Matsuzaki, J.; Yamamoto, Y.; Takizawa, S.; Sakamoto, H.; Ohno, M.; Narita, Y.; Ochiya, T.; et al. Brain metastasis-related microRNAs in patients with advanced breast cancer. PLoS ONE 2019, 14, e0221538. [Google Scholar] [CrossRef]
  93. Figueira, I.; Galego, S.; Custódio-Santos, T.; Vicente, R.; Molnár, K.; Haskó, J.; Malhó, R.; Videira, M.; Wilhelm, I.; Krizbai, I.; et al. Picturing Breast Cancer Brain Metastasis Development to Unravel Molecular Players and Cellular Crosstalk. Cancers 2021, 13, 910. [Google Scholar] [CrossRef] [PubMed]
  94. Kutasovic, J.R.; McCart Reed, A.E.; Males, R.; Sim, S.; Saunus, J.M.; Dalley, A.; McEvoy, C.R.; Dedina, L.; Miller, G.; Peyton, S.; et al. Breast cancer metastasis to gynaecological organs: A clinico-pathological and molecular profiling study. J. Pathol. Clin. Res. 2019, 5, 25–39. [Google Scholar] [CrossRef] [PubMed]
  95. Shee, K.; Strait, A.M.; Liu, X. Biomarkers to diagnose metastatic breast carcinoma to the pancreas: A case report and update. Diagn. Cytopathol. 2019, 47, 912–917. [Google Scholar] [CrossRef] [PubMed]
  96. Karagiannis, G.S.; Goswami, S.; Jones, J.G.; Oktay, M.H.; Condeelis, J.S. Signatures of breast cancer metastasis at a glance. J. Cell Sci. 2016, 129, 1751–1758. [Google Scholar] [CrossRef] [PubMed]
  97. Bell, R.; Barraclough, R.; Vasieva, O. Gene Expression Meta-Analysis of Potential Metastatic Breast Cancer Markers. Curr. Mol. Med. 2017, 17, 200–210. [Google Scholar] [CrossRef]
  98. Schulten, H.J.; Bangash, M.; Karim, S.; Dallol, A.; Hussein, D.; Merdad, A.; Al-Thoubaity, F.K.; Al-Maghrabi, J.; Jamal, A.; Al-Ghamdi, F.; et al. Comprehensive molecular biomarker identification in breast cancer brain metastases. J. Transl. Med. 2017, 15, 269. [Google Scholar] [CrossRef] [PubMed]
  99. Wang, L.; Li, J.; Liu, E.; Kinnebrew, G.; Zhang, X.; Stover, D.; Huo, Y.; Zeng, Z.; Jiang, W.; Cheng, L.; et al. Identification of Alternatively-Activated Pathways between Primary Breast Cancer and Liver Metastatic Cancer Using Microarray Data. Genes 2019, 10, 753. [Google Scholar] [CrossRef] [PubMed]
  100. Paul, M.R.; Pan, T.-C.; Pant, D.K.; Shih, N.N.; Chen, Y.; Harvey, K.L.; Solomon, A.; Lieberman, D.; Morrissette, J.J.; Soucier-Ernst, D. Genomic landscape of metastatic breast cancer identifies preferentially dysregulated pathways and targets. J. Clin. Investig. 2020, 130, 4252–4265. [Google Scholar] [CrossRef]
  101. Aftimos, P.; Oliveira, M.; Irrthum, A.; Fumagalli, D.; Sotiriou, C.; Gal-Yam, E.N.; Robson, M.E.; Ndozeng, J.; Di Leo, A.; Ciruelos, E.M.; et al. Genomic and Transcriptomic Analyses of Breast Cancer Primaries and Matched Metastases in AURORA, the Breast International Group (BIG) Molecular Screening Initiative. Cancer Discov. 2021, 11, 2796–2811. [Google Scholar] [CrossRef]
  102. Spadazzi, C.; Mercatali, L.; Esposito, M.; Wei, Y.; Liverani, C.; De Vita, A.; Miserocchi, G.; Carretta, E.; Zanoni, M.; Cocchi, C.; et al. Trefoil factor-1 upregulation in estrogen-receptor positive breast cancer correlates with an increased risk of bone metastasis. Bone 2021, 144, 115775. [Google Scholar] [CrossRef]
  103. Brasó-Maristany, F.; Paré, L.; Chic, N.; Martínez-Sáez, O.; Pascual, T.; Mallafré-Larrosa, M.; Schettini, F.; González-Farré, B.; Sanfeliu, E.; Martínez, D.; et al. Gene expression profiles of breast cancer metastasis according to organ site. Mol. Oncol. 2021, 16, 69–87. [Google Scholar] [CrossRef]
  104. Lawler, K.; Papouli, E.; Naceur-Lombardelli, C.; Mera, A.; Ougham, K.; Tutt, A.; Kimbung, S.; Hedenfalk, I.; Zhan, J.; Zhang, H.; et al. Gene expression modules in primary breast cancers as risk factors for organotropic patterns of first metastatic spread: A case control study. Breast Cancer Res. 2017, 19, 113. [Google Scholar] [CrossRef]
  105. Cioce, M.; Sacconi, A.; Donzelli, S.; Bonomo, C.; Perracchio, L.; Carosi, M.; Telera, S.; Fazio, V.M.; Botti, C.; Strano, S.; et al. Breast cancer metastasis: Is it a matter of OMICS and proper ex-vivo models? Comput. Struct. Biotechnol. J. 2022, 20, 4003–4008. [Google Scholar] [CrossRef]
Ijms 24 15625 g001
Figure 1. Frequency of metastasis to target organs depending on the molecular subtype of breast tumour. Note: subtypes are grouped into four categories based on the immunohistochemical expression of hormone receptors: oestrogen receptor positive (ER+), progesterone receptor positive (PR+), human epidermal growth factor receptor positive (HER2+) and triple-negative.
Figure 1. Frequency of metastasis to target organs depending on the molecular subtype of breast tumour. Note: subtypes are grouped into four categories based on the immunohistochemical expression of hormone receptors: oestrogen receptor positive (ER+), progesterone receptor positive (PR+), human epidermal growth factor receptor positive (HER2+) and triple-negative.
Ijms 24 15625 g001
Ijms 24 15625 g002
Figure 2. Biomarkers of organ-specificity of distant metastasis occurrence in breast cancer. Note: experimentally confirmed metastasis markers are highlighted in light blue, primary tumour metastasis markers are highlighted in light yellow.
Figure 2. Biomarkers of organ-specificity of distant metastasis occurrence in breast cancer. Note: experimentally confirmed metastasis markers are highlighted in light blue, primary tumour metastasis markers are highlighted in light yellow.
Ijms 24 15625 g002
Table 1. Markers of organ-specific metastasis in breast cancer.
Table 1. Markers of organ-specific metastasis in breast cancer.
MarkerDescriptionSource
BONES
P1NP, CTX, 1-CTPPatients with high serum levels of P1NP, CTX and 1-CTP have been shown to have a high risk of metastasising to bone soon after diagnosis (p = 0.006, p = 0.009, p = 0.008, respectively).[49]
IL-1βIn preclinical experimental mouse models, IL-1β inhibitors have been shown to prevent the development of bone metastases.[50]
CAPG/GIPC1The identification of CAPG and GIPC1 in primary tumour samples (by IHC) was a strong prognostic indicator for the development of bone metastases of breast cancer.
Cox regression analysis showed that control patients were more likely to develop first distant recurrence in bone (hazard ratio [HR] = 4.5, 95% confidence interval [CI] = 2.1 to 9.8, p < 0.001) and die (HR for overall survival = 1.8, 95% CI = 1.01 to 3.24, p = 0.045) if both proteins were highly expressed in the primary tumour.		[51]
PRLRHigh PRLR expression in primary breast tumour is associated with shorter time to metastasis (p = 0.03).[52]
PRDX4High expression of PRDX4 in primary breast tumour is associated with metastasis within 5 years.[53]
PAK4PAK4 enhances the invasive potential of ERα-positive breast cancer cells in vitro and promotes metastasis in vivo. The status of the nuclear PAK4 (nPAK4) scores was significantly higher in the bone metastatic breast cancer group than in the non-bone metastatic breast cancer group (p = 2.22 × 10−9).[54]
MAFMAF is a molecular target for the prevention or treatment of bone metastases because MAF accumulation (16q23 amplification) plays a role in bone colonisation.
16q23 gain copy number alterations (CNA) encoding the transcription factor MAF mediate breast cancer bone metastasis through PTHrP control. 16q23 gain (hazard ratio (HR) for bone metastasis = 14.5, 95% confidence interval (CI) = 6.4 to 32.9, p < 0.001) as well as MAF overexpression (HR for bone metastasis = 2.5, 95% CI = 1.7 to 3.8, p < 0.001) in primary breast tumours were specifically associated with risk of metastasis to bone but not to other organs.		[55,56]
DOCK4In a triple-negative MDA-MB-231 cell line model, DOCK4 was identified as a biomarker of bone metastasis in early stages of breast cancer.
Adjusted Cox regression analyses showed that high DOCK4 expression in the control arm was significantly prognostic for first recurrence in bone (HR 2.13, 95%CI 1.06–4.30, p = 0.034) (a clinical validation). High DOCK4 expression was not associated with metastasis to non-skeletal sites when these were assessed collectively.		[57]
CENPFCENPF promotes breast cancer metastasis to bone by activating PI3K-AKT-mTORC1 signalling and represents a novel therapeutic target for breast cancer treatment.[58]
MMP9, MMP13, TNFAIP6, CD200, DHRS3, ASS1, VIMTogether, they can be considered as specific prognostic markers of metastasis to bone in primary breast cancer.
The relative expression of MMP9, MMP13, TNFAIP6 and CD200 were significantly up-regulated (p < 0.05), while DHRS3, ASS1 and VIM were significantly down-regulated in the bone metastasis compared with lung and liver metastasis (p < 0.05).		[59]
miR-200, -128, -99a, -29b, -600, -34, -30, let-7 miRNAThese miRs act as tumour suppressors and inhibit breast cancer metastasis to bone.[60,61]
miR-21Exosomal miR-21 derived from SCP28 cells promotes osteoclastogenesis through regulation of PDCD4 protein levels. The level of miR-21 is significantly higher in serum exosomes of breast cancer patients with bone metastases than in other subpopulations.[62]
CXCL5/CXCR2CXCL5 stimulates proliferation of breast cancer cells and their colonisation in bone. Inhibition of its CXCR2 receptor with an antagonist blocks the proliferation of metastatic cells. CXCL5 and CXCR2 inhibitors may be effective in the treatment of tumours with metastasis to bone.[63]
RANKL/RANKRANKL/RANK regulates breast cancer cell migration. RANKL acts as a chemoattractive agent on tumour cells which overexpress one of its receptors. Blocking signalling by AMG161 (IgG1) reduces micrometastasis formation in bone marrow in vivo. Daily subcutaneous injections of 1.5 mg/kg AMG161 antibody to MDA-MB231RANK tumour-bearing animals reduced bone micrometastases and early bone marrow colonization without affecting lung micrometastasis.[64]
CXCL-12HIF signalling transduction in osteoporosis precursor cells increases blood levels of CXCL-12, promoting metastasis to bone.[65]
ESR1Mutations in the ESR1 gene have been observed in bone metastases, suggesting a potential causative role.
In this study, bone metastases from breast cancer (n = 231) were analysed for ESR1 mutation. Activating ESR1 mutations were identified in 27 patients (12%). The most frequent mutation was p.D538G (53%), no mutations were found in exon 4 (K303) or 7 (S463). Metastatic breast cancer with activating mutations of ESR1 had a higher Ki67 labelling index than primary luminal cancers (median 30%, ranging from 5 to 60% with 85% of cases revealing ≥ 20% Ki67-positive cells).		[66]
ANGPTL2ANGPTL2 increases breast cancer cell metastasis to bone by enhancing CXCR4 signal transduction.[67]
LUNGS
miR-106b-5pIt is an independent predictor of lung metastases (based on the expression level in the primary tumour). MiR-106b-5p promotes lung metastasis by suppressing CNN1 and activating the Rho/ROCK1 pathway.[68,69]
SIRT7SIRT7 counteracts TGFβ signalling and inhibits breast cancer metastases to the lung.[70]
Tumour stem cells (TSCs) (CD44hi CD36+)The formation of lung metastases is associated with TSC function, metabolic changes and immune response. Lung metastasis can be mediated by TSCs with CD44hi CD36+ phenotype.[71]
NID1Secretome analysis of lung metastases of breast cancer has shown that Nidogen 1 (NID1) is associated with poor treatment outcomes. NID1 promotes lung metastasis of breast cancer by increasing the motility of tumour cells and promoting their adhesion to the endothelium, thereby compromising its integrity and promoting angiogenesis.[72]
EGFREGFR inhibition successfully blocks circulating tumour cells (by immunohistochemistry) clustering and triple-negative breast cancer metastasis to the lung.[73]
VCAM-1VCAM-1 can be considered as a potential therapeutic target in lung metastasis of breast cancer. Selective inhibition of VCAM-1 has been successfully used to suppress the development of metastases.
The experimental results showed that the SCB-loaded nanoparticles (SN) could greatly improve the oral delivery and suppress breast cancer metastasis to the lung. The cell migration and invasion abilities of metastatic 4T1 breast cancer cells were obviously inhibited by SN. Moreover, the VCAM-1 expression on 4T1 cells was significantly reduced by SN, and the binding ratio of RAW 264.7 cells to 4T1 cells was significantly decreased from 47.4% to 3.2%. Furthermore, the oral bioavailability of SCB was greatly increased 13-fold under the effect of SN, and the biodistribution in major organs was markedly improved.		[74]
DKK1In patients with breast cancer, low serological levels of DKK1 are associated with the risk of developing lung metastases.[75]
Connexin43 (Cx43)Mice injected with Cx43-shCx43-inhibited tumour cells exhibited more lung metastases compared to parental MDA-MB-231 cells. This observation was confirmed by qPCR analysis of human 18S RNA levels in secondary metastatic sites in the lungs. Higher levels of human 18S RNA were found in the lungs of mice injected with shCx43 cells compared to the lungs of mice injected with parental MDA-MB-231 cells. This observation indicates that suppression of Cx43 increases the metastatic potential of MDA-MB-231 cells.[76]
LIVER
Connexin43 (Cx43)Metastatic foci in the liver were almost absent in mice inoculated with parental MDA-MB-231 cells or Cx43D cells by week 9, compared to those clearly observed in mice inoculated with shCx43 cells. This result is consistent with the increased levels of human 18S RNA in the livers of mice inoculated with shCx43 cells.
Inhibition of Cx43 induced metastasis of MDA-MB-231 cells to lung and liver at week 9, when the original MDA-MB-231 cells had not yet metastasised. These findings correlate with increased tumour volume and decreased survival of xenograft mice in vivo.		[76]
CXCR4/CXCL12CXCR4 inhibition doubles the response to immune checkpoint blockers in mice with metastatic triple-negative breast cancer (TNBC). CXCL12/CXCR4-mediated desmoplasia in metastatic breast cancer promotes immunosuppression and is a potential target to overcome therapeutic resistance to immune checkpoint blockade in MBC patients.[77]
PDK1PDK1-dependent metabolic reprogramming is a key regulation of metabolism and metastasis to the liver in breast cancer. PDK1 is particularly required for metabolic adaptation to nutrient restriction and hypoxia as a HIF1α target of metastatic cells in the liver.[78]
circRNA hsa_circ_0008324 (circEZH2)CircEZH2 enhances oncogenesis and metastasis in vitro and in vivo by activating KLF5 protein expression, which in turn activates CXCR4 transcription, leading to the initiation of the EMT programme in breast cancer.[79]
circRNA hsa_circ_0124696 (circROBO1)Increased expression of circROBO1 was found in liver metastases in breast cancer and correlated with poor prognosis. Knockdown of circROBO1 strongly inhibited proliferation, migration and invasion of RRM cells, whereas circROBO1 overexpression showed opposite effects. circROBO1 overexpression promoted tumour growth and metastasis to the liver in vivo.[80]
Lyn (Src-family kinase)The Lyn-selective kinase inhibitor, bafetinib (INNO-406), reduces claudin-2 expression and suppresses breast cancer metastasis to the liver.[81]
PPFIA1PPFIA1 is activated in breast cancer metastasis to the liver and is a potentially unfavourable prognostic sign of metastases development.
Kaplan–Meier plotter results showed that although high PPFIA1 expression was generally associated with reduced distant metastasis-free survival in oestrogen receptor+ patients, subgroup analysis only confirmed significant association in an oestrogen receptor+/N− (node-negative) group (median survival, high PPFIA1 group vs. low PPFIA1 cohort: 191.21 vs. 236.22 months, hazard ratio: 2.23, 95% confidence interval: 1.42–3.5, p < 0.001), but not in an oestrogen receptor+/N+ (nodal positive) group (hazard ratio: 1.63, 95% confidence interval: 0.88–3.03, p = 0.12). In oestrogen receptor patients, there was no association between PPFIA1 expression and distant metastasis-free survival, regardless of Nm (mixed nodal status), N− or N+ subgroups. In bc-GenExMiner 4.0 programme using the Nottingham Prognostic Index and Adjuvant! Online-adjusted analysis validated the independent prognostic value of PPFIA1 in relation to the risk of metastasis in patients with oestrogen receptor+/N−.		[82]
ESR1, AKT1, ERBB2, FGFR4ESR1 (20%), AKT1 (8%), ERBB2 (7%) and FGFR4 (4%) were identified as driver genes for breast cancer metastasis.[83]
BRAIN
PI3KActivation of PI3K was found in a large proportion (77%) of brain metastases in patients with breast cancer, and activation of PI3K-Akt signalling in such metastases was associated with poor outcomes.
Pharmacological inhibition of PI3K activity was found to attenuate the expression of PD-L1, CTLA4 and CSF1 genes, as well as the infiltration of metastatic breast cancer cells into the brains of mice.		[84,85]
CDK4 u CDK6Abemaciclib, an inhibitor of the cyclin-dependent kinases CDK4 and CDK6, has shown potential for the treatment of brain metastases in patients with breast cancer. The combination of abemaciclib with endocrine therapy was effective in patients with HER2-negative breast cancer and brain metastases, and 38% of patients had a reduction in metastatic tumour burden.[86]
STAT3The STAT3 inhibitor silibinin, which penetrates the blood–brain barrier, impairs the viability of brain metastases in both mice and humans. This inhibitor is thought to block the growth of brain metastases by targeting STAT3 in tumour-associated astrocytes, thereby weakening their interaction with tumour cells and microglia.[87]
JAK, JAK2The JAK inhibitor ruxolitinib limits the growth of primary brain tumours and also reduces the number of tumour-associated astrocytes in mice.
JAK2/STAT3 signal transduction is hyperactivated when breast cancer metastasises to the brain. Inhibition of JAK2 results in reduced brain metastasis in vivo, suggesting that JAK2 may be a promising therapeutic target.		[88,89]
COX2COX2 can promote MMP1 expression, which is significantly correlated with brain metastasis. In addition, COX2 and prostaglandin activate astrocytes to release chemokine ligand, promoting self-renewal of tumour stem cells or tumour-initiating cells in the brain.[90]
FABP7FABP7 is a key regulator of metabolism in HER2+ breast cancer metastasis to the brain. FABP7 has been shown to be required for the activation of key metastatic genes and pathways, such as integrins-Src and VEGFA, as well as for the growth of HER2+ breast cancer cells in the brain microenvironment in vivo.[91]
miR-4428, miR-4480In a study of microRNAs in patients with advanced breast cancer with brain metastases, it was shown that the determination of miR-4428 and miR-4480 in serum may be useful as prognostic biomarkers.
A total of 51 serum samples from patients with breast cancer and brain metastasis, and 28 serum samples from controls without brain metastasis were obtained. Two miRNAs, miR-4428 and miR-4480 could significantly distinguish patients with brain metastasis, with area under the receiver operating characteristic curve (AUC) values of 0.779 and 0.781, respectively, while a combination of miR-4428 and progesterone receptor had an AUC value of 0.884.		[92]
PLVAPPLVAP staining was observed not only in isolated brain microvessels but also in brain metastases in breast cancer. Immune labelling for PLVAP was performed in 4T1 TNBC culture, where clear expression of this protein was observed.[93]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Ibragimova, M.K.; Tsyganov, M.M.; Kravtsova, E.A.; Tsydenova, I.A.; Litviakov, N.V. Organ-Specificity of Breast Cancer Metastasis. Int. J. Mol. Sci. 2023, 24, 15625. https://doi.org/10.3390/ijms242115625

AMA Style

Ibragimova MK, Tsyganov MM, Kravtsova EA, Tsydenova IA, Litviakov NV. Organ-Specificity of Breast Cancer Metastasis. International Journal of Molecular Sciences. 2023; 24(21):15625. https://doi.org/10.3390/ijms242115625

Chicago/Turabian Style

Ibragimova, Marina K., Matvey M. Tsyganov, Ekaterina A. Kravtsova, Irina A. Tsydenova, and Nikolai V. Litviakov. 2023. "Organ-Specificity of Breast Cancer Metastasis" International Journal of Molecular Sciences 24, no. 21: 15625. https://doi.org/10.3390/ijms242115625

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop