eISSN: 2084-9869
ISSN: 1233-9687
Polish Journal of Pathology
Current issue Archive Manuscripts accepted About the journal Supplements Editorial board Abstracting and indexing Subscription Contact Instructions for authors Ethical standards and procedures
SCImago Journal & Country Rank
vol. 71
Original paper

Incorporating immunohistochemical markers into screening methods for BRCA1-mutated breast cancer

Agnieszka Tuzimek
Marta M. Fudalej
Aleksandra Sobiborowicz
Michał P. Budzik
Anna Badowska-Kozakiewicz

Students’ Scientific Organization of Cancer Cell Biology, Department of Cancer Prevention, Medical University of Warsaw, Warsaw, Poland
Department of Cancer Prevention, Medical University of Warsaw, Warsaw, Poland
Pol J Pathol 2020; 71 (3): 261-269
Online publish date: 2020/10/25
Article file
- PJP-09-01539.pdf  [0.21 MB]
Get citation
JabRef, Mendeley
Papers, Reference Manager, RefWorks, Zotero


Breast cancer (BC) is the most common type of cancer among women worldwide, as well as the primary cause of female death. According to widespread estimates, over 2 million BC cases were diagnosed in 2018 [1], and approximately 5-10% of them were hereditary [2]. The risk of BC is increased by various modifiable and non-modifiable factors, including genetic mutations [3]. BRCA1 belongs to a large group of genes associated with increased risk of BC. Germline mutations occurring in this gene increase the cumulative risk of BC up to 57% for carriers of the mutation at the age of 70 [4]. BRCA1 is involved in approximately 25% of familial BC cases [2]. As a tumor suppressor protein regulating the cell cycle and DNA repair [5], its dysfunctions play a significant role in carcinogenesis. BRCA1-mutated BC is associated with basal-like phenotype and lack of expression of ER (estrogen receptor), PR (progesterone receptor) and HER2 (human epidermal growth factor receptor 2) in addition to frequent TP53 mutations and poor prognosis [6, 7]. Current criteria for genetic evaluation in BC for BRCA1 (and other) mutations are based mainly on age and personal and family medical history [8], and therefore may be imprecise or incomplete [9, 10]. The insufficient detection of BRCA1 mutation carriers may hinder provision of targeted therapy and impact the patient’s relatives, who – as probable mutation carriers – may require oncological counseling. As there is currently a trend to seek immunohistochemical markers correlating with various clinical data (e.g. overall survival [OS], tumor size, presence of lymph node metastases) [11, 12], this paper summarizes some of the novel approaches to involve immunohistochemistry in the diagnostic work-up in order to increase the sensitivity and specificity of the aforementioned criteria at an acceptable cost.

Promising independent markers of BRCA1 mutation status


Nestin, an intermediate filament protein primarily found in the central nervous system as a stem cell/progenitor cell marker, has also been observed in many other undifferentiated non-neuronal tissues, e.g. the myoepithelial layer of the mammary gland and immature blood vessels [13, 14, 15, 16]. Furthermore, its marked expression has been discovered in many processes, such as neural and muscular regeneration or carcinogenesis [17, 18, 19]. Nestin expression has been observed in malignant tumors as a cancer stem cell marker, and its expression intensity tends to correlate with the tumor grade of malignancy [20, 21, 22]. Positive immunohistochemical nestin staining is an independent factor for poor prognosis and is associated with basal-like differentiation of BC cells [23].
The first link between nestin expression and BRCA1 germline mutation status was based on observation of 8 cases by Li et al. in 2007 [16]. The paper by Krüger et al. (2017) takes that one step further by analyzing the correlation of nestin expression level with multiple variables, such as overall survival, tumor grade of malignancy, presence of lymph node involvement, etc. [24]. When comparing cases with and without a BRCA1 germline mutation, nestin-positive tumors were more likely to be found in the subgroup with a BRCA1 mutation (OR = 8.7, sensitivity 62%, specificity 84%). Nestin expression, among many markers included in the study, was able to convincingly predict BRCA1 germline mutation status in the strongest manner (p < 0.0005) in multivariate analysis. It was also the only protein that significantly predicted the presence of a mutation in patients under 40 years old when including the triple negative (TN) profile.
Although no further studies of analytical and clinical value of nestin have been published yet, it could be applied as a predictor in qualification for BRCA1 germline mutation testing in the future. Statistical data for nestin as well as for other markers described in the present analysis are summarized in Table I.

Nonspecific NAD-dependent aldehyde dehydrogenase 1

Aldehyde dehydrogenase 1 (ALDH1) is a cytosolic enzyme responsible for oxidization of exo- and endogenous aldehydes to carboxylic acids. ALDH1, involved in retinoic acid metabolism, contributes to cell differentiation [25], but also to antitumor drug resistance [26, 27, 28], particularly in BC [29, 30].
ALDH1 expression is observed in around 5% of mammary gland cells representing the stem cell population in physiological conditions [31]. In BC, its expression in cancer stem cells (CSC) was found to be associated with decreased OS, high histological grade, ER negativity, PR negativity and HER2 overexpression [32]. Liu et al. (2008) demonstrated that the differentiation of ER(–) stem/progenitor cells to ER(+) luminal cells is conditioned by BRCA1 expression, and its knockdown leads to an increase of ALDH1 expression as well as a decrease of luminal epithelial markers and ER expression in primary breast cells. Since BRCA1 plays a role in DNA repair, it was suggested that BRCA1 loss may result in an increased percentage of genetically unstable breast stem cells susceptible to carcinogenesis [33]. A study by Madjd et al. (2012) revealed a significant inverse correlation between expression of ALDH1 and BRCA1 in BC cells – reduced BRCA1 levels were more often seen in BC cells highly expressing ALDH1 (p = 0.044). In their univariate analysis combined ALDH1(+)/BRCA1(low) phenotype was found to be often present in high grade tumors (p = 0.056) [34]. These results were in line with previous findings by van Heerma Voss et al. (2011), who concluded that ALDH1 expression was significantly higher in both intensity and percentage in BRCA1-related BC, implying that these cases had an enlarged CSC component [35]. Similarly to benign tissues, ALDH1 was expressed in stromal and epithelial cells. When comparing tumoral stromal, peritumoral stromal and epithelial ALDH1 levels in groups of hereditary and sporadic BC cases, only peritumoral and epithelial expression levels were independent predictors of BRCA1 mutation status in multivariate analysis without correlating with each other [36]. In contrast, data provided by Bane et al. (2013) show no statistically significant association between BRCA1 mutation status and ALDH1 expression [37].
To summarize, these conclusions might serve several clinical purposes. Firstly, ALDH1 may be considered as a biomarker for BRCA1 mutation status as well as an additional selection tool for patients requiring genetic testing. Secondly, seeking an established phenotype of BRCA1-related tumors could expedite tracking down new or less frequent types of mutation. Finally, ALDH1 might provide promising new treatment strategies in therapy-resistant BC, as it may enable specific targeting of the BC stem cell population.

Forkhead box class O3 and enhancer of zeste homologue 2

Forkhead box class O3 (FOXO3) is a transcription factor protein of the Forkhead box class O family, which in humans consists of four members: FOXO1, FOXO3, FOXO4 and FOXO6. As one of the downstream effectors of the PI3K/PKB signaling pathway, it is involved in various processes, such as cell cycle regulation, DNA damage repair, apoptosis, oxidative stress, drug response, angiogenesis, glucose metabolism and differentiation, as widely discussed by Myatt and Lam (2007) [38]. Recently, FOXO3 has become a target for potential therapies in BC [39, 40, 41, 42]. Its high expression was found to be associated with a low histological grade, low tumor stage, lymph node negativity and better OS rate in luminal-like BC patients [43, 44]. In addition, its overexpression was proven to suppress estrogen-dependent tumorigenesis in vivo [45].
Enhancer of zeste homologue 2 (EZH2) is a subunit of the polycomb-repressive complex 2 [46] acting as a methyltransferase in the methylation of H3 histone at Lys 23, therefore linking histone methylation and polycomb group-mediated gene silencing [47]. Its overexpression was found to be associated with distant metastases and poor OS in BC patients [48].
A significant relation between FOXO3 promoter methylation and BRCA1 mutation status was shown in a study by Gong et al. (2016) [49]. Lack of BRCA1 protein resulted in reduction of FOXO3 expression level through targeting EZH2. On a molecular basis, this research found that BRCA1-mutated breast tumors had lower levels of FOXO3 protein than BRCA2-mutated or non-mutated cancers, although without statistical significance. Taking into consideration high nuclear EZH2 protein concentration, FOXO3 expression was significantly lower in BRCA1-mutated samples in comparison with BRCA2-mutated and non-mutated tumors. This discovery confirmed that BRCA1 protein positively regulates FOXO3 expression by suppressing EZH2.
Though this method would be constricted by EZH2 levels, its further research may be worth developing in order to increase the specificity and sensitivity of criteria for genetic testing.


Claudins are a group of 24 known proteins involved in the formation of tight junctions between epithelial and endothelial cells. Their ability to determine size of aqueous pores between cells regulates selective paracellular transport of small ions and solutes through tight junctions [50]. Claudin expression and specific physiological functions have been described in a variety of healthy and cancerous tissues, including BC [51], as well as in numerous other pathologies [52]. Aberrant expression levels of certain claudins, frequently observed in neoplasms [52], may result in structural and functional modifications within tight junctions and, as a result, promote tumorigenesis and metastasis through the increase of invasion and survival of tumor cells [53, 54].
The multivariate regression model by Danzinger et al. (2018) revealed that claudin-3 was 22 times more likely to be observed in BRCA1-mutated triple negative breast cancer (TNBC) in comparison to BRCA2 and non-mutated TNBC. What is more, expression of claudin-3 was an independent marker of BRCA1 mutation status in TNBC in a univariate analysis [55]. Van Heerma Voss et al. (2014) described overexpression of claudin-3, -6 and -7 in BRCA1-mutated tumor tissue in comparison to adjacent healthy tissue. In the samples from the BRCA1-mutated group membranous claudin-1 expression was shown to be higher when compared to sporadic BC [56]. The authors proposed claudin-1 and -6 as novel markers of BRCA1 mutation status. Claudin-1 level was the only significant variable when comparing BRCA1-mutated versus non-mutated tumors. High expression of claudin-6 in BRCA1-mutated tumors in comparison to healthy adjacent tissue was independently associated with BRCA1 mutation. Claudin-6 expresses the dedifferentiated state of BRCA1- mutated cancer cells, as BRCA1 regulates mammary stem cell differentiation.
Claudins may have potential not only for BRCA1 mutation typing, but also as a diagnostic strategy and treatment target because of their signaling properties, association with multiple signaling pathways and plenitude of regulatory mechanisms [54]. This could be used for further improvement of understanding and managing BC.

Topoisomerase 1 type IB and placental cadherin

Topoisomerase 1 type IB (TOP1) is a nuclear enzyme involved in DNA replication, discovered in 1971 by Wang [57]. It supports the progression of the replication fork by relaxing transcription-related supercoils forming ahead [58]. Its necessity in cell division and development of multicellular organisms was proven by Lee et al. in 1993 [59] and later utilized as a treatment target, leading to the FDA’s approval of two TOP1 inhibitors: irinotecan and topotecan [60, 61].
Placental cadherin (CDH3), a product of the CDH3 gene [62], is a calcium-dependent cell-to-cell adhesion protein involved in the formation of adherens junctions [63]. It is primarily expressed in the basal layer of epithelia, including the mammary gland [64, 65]. Upregulation of this protein is correlated with tumor aggressiveness and poor prognosis in BC [66, 67, 68].
CDH3 was proposed to be a serum marker in basal-like BC [69], as well as a poor prognostic marker in BRCA1-deficient BC by Arnes et al. (2005) [70], who correlated positive CDH3 status with significantly worse BC-specific survival in univariate analysis, poor prognosis in univariate analysis and presence of a BRCA1 mutation (p < 0.0001). This relation was later confirmed by a preclinical study by Gorski et al. (2010), who provided a biological explanation of this phenomenon – BRCA1 protein represses transcription of the CDH3 gene [71].
Warmoes et al. (2016) identified extracellular protein biomarkers of BRCA1-deficient BC murine models in secretomes and exosome-like extracellular vesicles. Identifying well over 2 thousand proteins in BRCA1-deficient secretomes, two abundant proteins – CDH3 and TOP1 – were chosen for validation through immunohistochemical staining of BC specimens. Separately, both proteins presented a significant difference in expression level between BRCA1-mutated (higher expression) and sporadic BC (lower expression). For both proteins, their expression was found to be an independent predictor of BRCA1/2-related BC, as well as together – TOP1 and CDH3 positivity was significantly correlated with BRCA1/2 mutation independently of age and TN profile.
These findings suggest a substantial improvement of prediction of BRCA1/2 mutation if assessment of TOP1 and/or CDH3 was performed routinely, especially in HER2 or ER positive breast carcinomas in women over 45 years old [72].

Epidermal growth factor receptor

Epidermal growth factor receptor (EGFR) is a transmembrane tyrosine kinase, one of four members of the epithelial growth factor receptor family [73]. It is involved in a complicated network of signaling pathways, including the Ras/Raf/MEK/ERK pathway mediating cell proliferation, the PI3K/PDK/AKT pathway regulating cell survival and many others, participating in cell adhesion, motility, angiogenesis and organogenesis [74]. Due to its pleiotropic effects on cell metabolism, it became a promising target in anticancer therapy [75].
In BC, EGFR overexpression correlates with reduced disease-free survival and OS rates [76]. Although many analyses have been conducted, there is no consensus about its utility in prediction of the BRCA1 mutation in BC. Danziger et al. (2018) found that the lack of expression of EGFR in tumor tissue was associated with BRCA1 mutation status when compared to wild-type TNBC despite common expression of this protein in basal-like BC [55], the most common phenotype in BRCA1-related breast tumors [77, 78, 79]. In contrast, Collins et al. (2009) found no statistically significant difference in EGFR expression between BRCA1 mutation carriers and negative cases of TNBC [80]. Van der Groep et al. (2006) found that EGFR expression was more frequent in BRCA1-mutated cases [81]. Arnes et al. (2009) reported that EGFR in multiple regression analysis was the strongest predictor of BRCA1 mutation in models adjusted for age, histological grade and ER status, especially when EGFR expression was measured with the Dako criteria (EGFR-DA) or presented strong staining (EGFR-HI) [82].
Considering the inconsistency in statistical methods throughout the described papers and/or relatively small number of cases, no definite conclusions can be made at this point – additional research should be considered.

Sets of markers useful in selecting probable BRCA-1 mutation carriers

Apart from searching for independent biomarkers useful in selecting patients for genetic testing, it is also proposed to combine multiple factors and evaluate them simultaneously in a single procedure to assess the probability of BRCA1 mutation. There are different approaches: from the most basic clinical data, i.e. patient’s age and family history [83, 84, 85], adding less or more common immunohistochemical markers [78, 86, 87, 88, 89] to molecular models based on whole-genome sequencing [90], gene expression profiling [91], copy number analysis [92, 93] or array comparative genomic hybridization [94].
One of the most recent suggestions by Vos et al. (2018) comprises 14 markers chosen with Lasso logistic regression analysis: age, cyclin D1, ERα, ERβ, FGFR2, FGFR3, FGFR4, GLUT1, IGFR, Ki-67, MAI, MLH1, p120 and TOP2A. This model was able to differentiate BRCA1-related tumors with sensitivity never dropping below 80% depending on the chosen probability threshold. The second model proposed in that paper, including age, BCL2, CK5/6, CK8/18, cyclin D1, E-cadherin, ERα, HER2, Ki-67, MAI, MLH1, p16, PMS2, PR and vimentin, was developed with an emphasis on immunochemical markers, clinical and morphological data that are commonly available in pathology laboratories, achieving sensitivity of 78% [87]. Both of these methods have a very good to excellent discriminative performance.
Another attempt to find the best fitting multivariate model was made by Hassanein et al. (2013) [88]. Their paper investigated morphological parameters and 21 immunohistochemical markers to arrange the minimum of markers providing the best possible performance. The final model included grade 3, MS110, Lys27H3, vimentin and KI67, achieving specificity of 81% and sensitivity of 83% on a validation set.
Miolo et al. (2009) focused on markers used in molecular subtyping of BCs – ER, PR and HER2 [89]. The sensitivity and specificity were 100% and 83.3%, respectively. Adding age to these markers, Spurdle et al. (2014) provided very robust insight into likelihood ratios for ER alone, grade alone, combined ER and grade stratified by age and ER/PR/HER2 TN status in a large number of cases: 4477 BRCA1 mutation carriers, 2565 BRCA2 mutation carriers and 47 565 assumed BRCA1 and BRCA2 mutation-negative BC patients to assess the pathogenicity of BRCA1 or BRCA2-mutated variants [86]. They found that ER positivity predicted lack of a BRCA1 mutation regardless of tumor grade. Moreover, an ER-negative grade 3 result was better at predicting the presence of a BRCA1 mutation in women over 50 years old than under that age. Apart from that, TN status had a very high value in predicting BRCA1 mutation irrespectively of age.
La Cruz et al. (2012) also reported that ER negativity was associated with BRCA1 mutation. They proposed a test comprising ER status and mitotic activity [95]. ER-negative phenotype with a high mitotic rate had specificity and sensitivity of 99% and 43% respectively, in prediction of BRCA1 mutation. The presence of at least one of those factors decreased specificity to 79%, but increased sensitivity to 67%.
Lakhani et al. (2005) focused on basal markers expressed in BRCA1- and BRCA2-mutated tumors: CK14, CK5/6, CK17, EGFR and osteonectin [78]. Despite all markers being more prevalent in mutated tumors, only CK14, CK5/6 and ER remained significant in prediction of BRCA1 carrier status. Two models were proposed: the first one was based on ER-negative and CK5/6 positive status with specificity and sensitivity of 97% and 56%, respectively, with the area under the receiver operating characteristic (ROC) curve of 0.77. The second test with 3 factors – ER, CK5/6 and CK14 status – resulted in the area under the ROC curve rising to 0.87.
Mavaddat et al. (2010) also highlighted a potentially important role of ER, CK5/6 and CK14 in prediction of BRCA1-mutated cases [96]. On the other hand, Danzinger et al. (2018) found no significant correlation between expression levels of CK5 and CK14 among the BRCA1, BRCA2 and non-mutated group in TNBC. In the performed ROC analysis they found that claudin-3 and EGFR expression levels were able to predict BRCA1 mutation status in TNBC with fairly high sensitivity and specificity (area under curve 0.802, p < 0.001) [55].
Quite an interesting approach was proposed by van der Groep et al. (2006) – they found that age, Ki-67 and EGFR are the best predictors of BRCA1 mutation status, and created a decision tree consisting of those factors in order to sort cases into four groups with rising risk of BRCA1-mutated BC [81]. This analysis was aimed at those cases where standard screening methods, e.g. based on family history and age, failed to give a definite answer to whether genetic testing should be performed.
BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) is a risk prediction model for breast and ovarian cancer. It is used to compute BRCA1 and BRCA2 mutation carrier probability based on age, a polygenic component (a large number of genes each contributing in a small part to increase the risk of cancer) and a set of families identified through population-based studies of BC consisting of multiple individuals screened for BRCA1 and BRCA2 mutations [97]. In 2010 Mavaddat et al. combined BOADICEA with well-known distinctive pathological features of BRCA1-related tumors – ER-negative status, TN status and expression of basal markers – in order to achieve improved discrimination of BRCA1- and BRCA2-related as well as sporadic BC. They achieved that by subdividing the overall disease into different end points, implementing it in BOADICEA and incorporating the aforementioned markers [96]. In 2012 the model base of families was increased to 2785 via a collaborative data set from the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA), which distinguished additional differences among BRCA1, BRCA2 and non-mutated tumors. Among many comprehensive analyses, they confirmed that the majority of BRCA1-related tumors are ER-negative and TN [98]. In 2008 BOADICEA was validated on 1934 families in the United Kingdom [99] and currently figures as a recommended risk assessment tool in the National Institute for Health and Care Excellence clinical guideline CG164 [100]. BOADICEA is widely accessible to healthcare professionals and members of the public, as its implemented web application is constantly modified to simplify use in the clinical environment [97].
All aforementioned sets of markers are summarized in Table II. Extensive data comparison of selected papers, with clinical and molecular methods taken into account, was also included in a paper by Vos et al. (2018) [87].
In conclusion, immunohistochemical staining is a basic method used for assignment of breast cancer to differential histological and molecular subtypes. Therefore, searching for correlations between well-known and widely used tumor division methods and clinically valuable data is a natural consequence. In the case of a hereditary BC, quick and confident qualification for genetic testing is crucial as it results in better therapeutic decisions and facilitates oncological and genetic counselling. The variety of described approaches – validated or not – demonstrates the possibility to further upgrade the accuracy of criteria for further genetic evaluation. There seem to be a few good candidates so far: nestin, ALDH1, claudins, TOP1 with CDH3 as well as sets, especially those which consist of markers used on a regular basis. Though most of them have not been validated yet, they could have advantages over those based on clinical types, as clinical data obtained from a patient could be imprecise or incomplete, particularly in the case of family history. On the other hand, they are burdened with the imperfection of a clinical model and therefore may result in false negatives. Molecular methods, although more precise, are currently more expensive than other methods, and this cannot be expected to change in the near future. Moreover, immunohistochemistry-based methods can be widely used by pathology laboratories with limited access to molecular techniques. Therefore, it is beneficial to utilize immunohistochemistry as a screening test before genetic analyses.

The authors declare no conflict of interest.
1. Bray F, Ferlay J, Soerjomataram I, et al. 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.
2. Larsen MJ, Thomassen M, Gerdes A-M, et al. Hereditary breast cancer: clinical, pathological and molecular characteristics. Breast Cancer (Auckl) 2014; 8: 145-155.
3. Rojas K, Stuckey A. Breast Cancer Epidemiology and risk factors. Clin Obstet Gynecol 2016; 59: 651-672.
4. Chen S, Parmigiani G. Meta-analysis of BRCA1 and BRCA2 penetrance. J Clin Oncol 2007; 25: 1329-1333.
5. Somasundaram K. Breast cancer gene 1 (BRCA1): role in cell cycle regulation and DNA repair – perhaps through transcription. J Cell Biochem 2003; 88: 1084-1091.
6. Honrado E, Benítez J, Palacios J. Histopathology of BRCA1- and BRCA2-associated breast cancer. Crit Rev Oncol Hematol 2006; 59: 27-39.
7. Schmidt MK, van den Broek AJ, Tollenaar RAEM, et al. Breast cancer survival of BRCA1/BRCA2 mutation carriers in a hospital-based cohort of young women. J Natl Cancer Inst 2017; 109.
8. National Comprehensive Cancer Network. NCCN Clinical Practice Guidelines in Oncology: Genetic/Familial High-Risk Assessment: Breast and Ovarian. https://www.nccn.org/professionals/physician_gls/pdf/genetics_screening.pdf. Accessed 05 Mar 2019.
9. Hartman A-R, Kaldate RR, Sailer LM, et al. Prevalence of BRCA mutations in an unselected population of triple-negative breast cancer. Cancer 2012; 118: 2787-2795.
10. Grindedal EM, Heramb C, Karsrud I, et al. Current guidelines for BRCA testing of breast cancer patients are insufficient to detect all mutation carriers. BMC Cancer 2017; 17: 438.
11. Stolnicu S, Bauer O, Naznean A, et al. ER–/PR+ subset of invasive breast carcinoma (IBC): a distinct phenotype with good prognosis. Pol J Pathol 2018; 69: 311-313.
12. Adamkov M, Drahošová S, Chylíková J, et al. Survivin in breast lesions: immunohistochemical analysis of 196 cases. Pol J Pathol 2017; 68: 297-305.
13. Mokrý J, Cízková D, Filip S, et al. Nestin expression by newly formed human blood vessels. Stem Cells Dev 2004; 13: 658-664.
14. Lendahl U, Zimmerman LB, McKay RDG. CNS stem cells express a new class of intermediate filament protein. Cell 1990; 60: 585-595.
15. Sejersen T, Lendahl U. Transient expression of the intermediate filament nestin during skeletal muscle development. J Cell Sci 19993; 106 (Pt 4): 1291-1300.
16. Li H, Cherukuri P, Li N, et al. Nestin is expressed in the basal/myoepithelial layer of the mammary gland and is a selective marker of basal epithelial breast tumors. Cancer Res 2007; 67: 501-510.
17. Krum JM, Rosenstein JM. Transient coexpression of nestin, GFAP, and vascular endothelial growth factor in mature reactive astroglia following neural grafting or brain wounds. Exp Neurol 1999; 160: 348-360.
18. Vaittinen S, Lukka R, Sahlgren C, et al. The expression of intermediate filament protein Nestin as related to vimentin and desmin in regenerating skeletal muscle. J Neuropathol Exp Neurol 2001; 60: 588-597.
19. Ishiwata T, Matsuda Y, Naito Z. Nestin in gastrointestinal and other cancers: Effects on cells and tumor angiogenesis. World J Gastroenterol 2011; 17: 409-418.
20. Yang XH, Wu QL, Yu XB, et al. Nestin expression in different tumours and its relevance to malignant grade. J Clin Pathol 2008; 61: 467-473.
21. Neradil J, Veselska R. Nestin as a marker of cancer stem cells. Cancer Sci 2015; 106: 803-811.
22. Ehrmann J, Kolár Z, Mokry J. Nestin as a diagnostic and prognostic marker: Immunohistochemical analysis of its expression in different tumours. J Clin Pathol 2005; 58: 222-223.
23. Asleh K, Won JR, Gao D, et al. Nestin expression in breast cancer: Association with prognosis and subtype on 3641 cases with long-term follow-up. Breast Cancer Res Treat 2018; 168: 107-115.
24. Krüger K, Wik E, Knutsvik G, et al. Expression of Nestin associates with BRCA1 mutations, a basal-like phenotype and aggressive breast cancer. Sci Rep 2017; 7: 1089.
25. Ginestier C, Wicinski J, Cervera N, et al. Retinoid signaling regulates breast cancer stem cell differentiation. Cell Cycle 2009; 8: 3297-3302.
26. Lindahl R. Aldehyde dehydrogenases and their role in carcinogenesis. Crit Rev Biochem Mol Biol 1992; 27: 283-335.
27. Januchowski R, Wojtowicz K, Zabel M. The role of aldehyde dehydrogenase (ALDH) in cancer drug resistance. Biomed Pharmacother 2013; 67: 669-680.
28. Sládek NE. Human aldehyde dehydrogenases: potential pathological, pharmacological, and toxicological impact. J Biochem Mol Toxicol 2003; 17: 7-23.
29. Sládek NE, Kollander R, Sreerama L, et al. Cellular levels of aldehyde dehydrogenases (ALDH1A1 and ALDH3A1) as predictors of therapeutic responses to cyclophosphamide-based chemotherapy of breast cancer: a retrospective study. Rational individualization of oxazaphosphorine-based cancer chemotherapeutic regimens. Cancer Chemother Pharmacol 2002; 49: 309-321.
30. Tanei T, Morimoto K, Shimazu K, et al. Association of breast cancer stem cells identified by aldehyde dehydrogenase 1 expression with resistance to sequential Paclitaxel and epirubicin-based chemotherapy for breast cancers. Clin Cancer Res 2009; 15: 4234-4241.
31. Ginestier C, Hur MH, Charafe-Jauffret E, et al. ALDH1 is a marker of normal and malignant human mammary stem cells and a predictor of poor clinical outcome. Cell Stem Cell 2007; 1: 555-567.
32. Zhou L, Jiang Y, Yan T, et al. The prognostic role of cancer stem cells in breast cancer: a meta-analysis of published literatures. Breast Cancer Res Treat 2010; 122: 795-801.
33. Liu S, Ginestier C, Charafe-Jauffret E, et al. BRCA1 regulates human mammary stem/progenitor cell fate. Proc Natl Acad Sci U S A 2008; 105: 1680-1685.
34. Madjd Z, Ramezani B, Molanae S, et al. High Expression of stem cell marker ALDH1 is associated with reduced BRCA1 in invasive breast carcinomas. Asian Pac J Cancer Prev 2012; 13: 2973-2978.
35. van Heerma Voss MR, van der Groep P, Bart J, et al. Expression of the stem cell marker ALDH1 in BRCA1 related breast cancer. Cell Oncol (Dordr) 2011; 34: 3-10.
36. van Heerma Voss MR, van der Groep P, Bart J, et al. Expression of the stem cell marker ALDH1 in the normal breast of BRCA1 mutation carriers. Breast Cancer Res 2010; Treat 123: 611-612.
37. Bane A, Viloria-Petit A, Pinnaduwage D, et al. Clinical-pathologic significance of cancer stem cell marker expression in familial breast cancers. Breast Cancer Res Treat 2013; 140: 195-205.
38. Myatt SS, Lam EW-F. The emerging roles of forkhead box (Fox) proteins in cancer. Nat Rev Cancer 2007; 7: 847-859.
39. McGovern UB, Francis RE, Peck B, et al. Gefitinib (Iressa) represses FOXM1 expression via FOXO3a in breast cancer. Mol Cancer Ther 2009; 8: 582-591.
40. Park S-H, Chung YM, Ma J, et al. Pharmacological activation of FOXO3 suppresses triple-negative breast cancer in vitro and in vivo. Oncotarget 2016; 7: 42110-42125.
41. Hu T, Chung YM, Guan M, et al. Reprogramming ovarian and breast cancer cells into non-cancerous cells by low-dose metformin or SN-38 through FOXO3 activation. Sci Rep 2014; 4: 5810.
42. Wolfe AR, Debeb BG, Lacerda L, et al. Simvastatin prevents triple-negative breast cancer metastasis in pre-clinical models through regulation of FOXO3a. Breast Cancer Res Treat 2015; 154: 495-508.
43. Jiang Y, Zou L, Lu W-Q, et al. Foxo3a expression is a prognostic marker in breast cancer. PLoS One 2013; 8: e70746.
44. Habashy HO, Rakha EA, Aleskandarany M, et al. FOXO3a nuclear localisation is associated with good prognosis in luminal-like breast cancer. Breast Cancer Res Treat 2011; 129: 11-21.
45. Zou Y, Tsai W-B, Cheng C-J, et al. Forkhead box transcription factor FOXO3a suppresses estrogen-dependent breast cancer cell proliferation and tumorigenesis. Breast Cancer Res 2008; 10: R21.
46. Sauvageau M, Sauvageau G. Polycomb group proteins: multi-faceted regulators of somatic stem cells and cancer. Cell Stem Cell 2010; 7: 299-313.
47. Cao R, Wang L, Wang H, et al. Role of histone H3 lysine 27 methylation in Polycomb-group silencing. Science 2002; 298: 1039-1043.
48. Kleer CG, Cao Q, Varambally S, et al. EZH2 is a marker of aggressive breast cancer and promotes neoplastic transformation of breast epithelial cells. Proc Natl Acad Sci U S A 2003; 100: 11606-11611.
49. Gong C, Yao S, Gomes AR, et al. BRCA1 positively regulates FOXO3 expression by restricting FOXO3 gene methylation and epigenetic silencing through targeting EZH2 in breast cancer. Oncogenesis 2016; 5: e214.
50. Angelow S, Ahlstrom R, Yu ASL. Biology of claudins. Am J Physiol Renal Physiol 2008; 295: F867-F876.
51. Lanigan F, McKiernan E, Brennan DJ, et al. Increased claudin-4 expression is associated with poor prognosis and high tumour grade in breast cancer. Int J Cancer 2009; 124: 2088-2097.
52. Krause G, Winkler L, Mueller SL, et al. Structure and function of claudins. Biochim Biophys Acta 2008; 1778: 631-645.
53. Tabariès S, Siegel PM. The role of claudins in cancer metastasis. Oncogene 2017; 36: 1176-1190.
54. Osanai M, Takasawa A, Murata M, et al. Claudins in cancer: bench to bedside. Pflugers Arch 2017; 469: 55-67.
55. Danzinger S, Tan YY, Rudas M, et al. Differential claudin 3 and EGFR expression predicts BRCA1 mutation in triple-negative breast cancer. Cancer Invest 2018; 36: 378-388.
56. van Heerma Voss MR, van Diest PJ, Smolders YHCM, et al.
57. Distinct claudin expression characterizes BRCA1-related breast cancer. Histopathology 2014; 65: 814-827.
58. Wang JC. Interaction between DNA and an Escherichia coli protein ω. J Mol Biol 1971; 55: 523-533.
59. Champoux JJ. DNA topoisomerases: structure, function, and mechanism. Annu Rev Biochem 2001; 70: 369-413.
60. Lee MP, Brown SD, Chen A, et al. DNA topoisomerase I is essential in Drosophila melanogaster. Proc Natl Acad Sci U S A 1993; 90: 6656-6660.
61. Kollmannsberger C, Mross K, Jakob A, et al. Topotecan – A novel topoisomerase I inhibitor: pharmacology and clinical experience. Oncology 1999; 56: 1-12.
62. Ewesuedo RB, Ratain MJ. Topoisomerase I inhibitors. Oncologist 1997; 2: 359-364.
63. Full report on CDH3 gene. https://www.ncbi.nlm.nih.gov/gene?Db=gene&Cmd=ShowDetailView&TermToSearch=1001. Accessed 05 Mar 2019.
64. Stemmler MP. Cadherins in development and cancer. Mol Biosyst 2008; 4: 835-850.
65. Shimoyama Y, Hirohashi S, Hirano S, et al. Cadherin cell-adhesion molecules in human epithelial tissues and carcinomas. Cancer Res 1989; 49: 2128-2133.
66. Kovács A, Walker RA. P-cadherin as a marker in the differential diagnosis of breast lesions. J Clin Pathol 2003; 56: 139-141.
67. Paredes J, Stove C, Stove V, et al. P-cadherin is up-regulated by the antiestrogen ICI 182,780 and promotes invasion of human breast cancer cells. Cancer Res 2004; 64: 8309-8317.
68. Paredes J, Albergaria A, Oliveira JT, et al. P-cadherin overexpression is an indicator of clinical outcome in invasive breast carcinomas and is associated with CDH3 promoter hypo-methylation. Clin Cancer Res 2005; 11: 5869-5877.
69. Turashvili G, McKinney SE, Goktepe O, et al. P-cadherin expression as a prognostic biomarker in a 3992 case tissue microarray series of breast cancer. Mod Pathol 2011; 24: 64-81.
70. Mannello F, Tonti GAM, Medda V, et al. Increased shedding of soluble fragments of P-cadherin in nipple aspirate fluids from women with breast cancer. Cancer Sci 2008; 99: 2160-2169.
71. Arnes JB, Brunet J-S, Stefansson I, et al. Placental cadherin and the basal epithelial phenotype of BRCA1-related breast cancer. Clin Cancer Res 2005; 11: 4003-4011.
72. Gorski JJ, James CR, Quinn JE, et al. BRCA1 transcriptionally regulates genes associated with the basal-like phenotype in breast cancer. Breast Cancer Res Treat 2010; 122: 721-731.
73. Warmoes M, Lam SW, van der Groep P, et al. Secretome proteomics reveals candidate non-invasive biomarkers of BRCA1 deficiency in breast cancer. Oncotarget 2016; 7: 63537-63548.
74. Roskoski R. The ErbB/HER family of protein-tyrosine kinases and cancer. Pharmacol Res 2014; 79: 34-74.
75. Avraham R, Yarden Y. Feedback regulation of EGFR signalling: decision making by early and delayed loops. Nat Rev Mol Cell Biol 2011; 12: 104-117.
76. Seshacharyulu P, Ponnusamy MP, Haridas D, et al. Targeting the EGFR signaling pathway in cancer therapy. Expert Opin Ther Targets 2012; 16: 15-31.
77. Gonzalez-Conchas GA, Rodriguez-Romo L, Hernandez-Barajas D, et al. Epidermal growth factor receptor overexpression and outcomes in early breast cancer: A systematic review and a meta-analysis. Cancer Treat Rev 2018; 62: 1-8.
78. Sorlie T, Tibshirani R, Parker J, et al. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci U S A 2003; 100: 8418-8423.
79. Lakhani SR, Reis-Filho JS, Fulford L, et al. Prediction of BRCA1 status in patients with breast cancer using estrogen receptor and basal phenotype. Clin Cancer Res 2005; 11: 5175-5180.
80. Foulkes WD, Stefansson IM, Chappuis PO, et al. Germline BRCA1 mutations and a basal epithelial phenotype in breast cancer. J Natl Cancer Inst 2003; 95: 1482-1485.
81. Collins LC, Martyniak A, Kandel MJ, et al. Basal cytokeratin and epidermal growth factor receptor expression are not predictive of BRCA1 mutation status in women with triple-negative breast cancers. Am J Surg Pathol 2009; 33: 1093-1097.
82. van der Groep P, Bouter A, van der Zanden R, et al. Distinction between hereditary and sporadic breast cancer on the basis of clinicopathological data. J Clin Pathol 2006; 59: 611-617.
83. Arnes JB, Bégin LR, Stefansson I, et al. (2009) Expression of epidermal growth factor receptor in relation to BRCA1 status, basal-like markers and prognosis in breast cancer. J Clin Pathol 2009; 62: 139-146.
84. Kang HH, Williams R, Leary J, et al. Evaluation of models to predict BRCA germline mutations. Br J Cancer 2006; 95: 914-920.
85. Kang E, Kim S-W. The Korean hereditary breast cancer study: review and future perspectives. J Breast Cancer 2013; 16: 245-253.
86. Biswas S, Atienza P, Chipman J, et al. A two-stage approach to genetic risk assessment in primary care. Breast Cancer Res Treat 2016; 155: 375-383.
87. Spurdle AB, Couch FJ, Parsons MT, et al. Refined histopathological predictors of BRCA1 and BRCA2 mutation status: A large-scale analysis of breast cancer characteristics from the BCAC, CIMBA, and ENIGMA consortia. Breast Cancer Res 2014; 16: 3419.
88. Vos S, Elias SG, van der Groep P, et al. Comprehensive proteomic profiling-derived immunohistochemistry-based prediction models for BRCA1 and BRCA2 germline mutation-related breast carcinomas. Am J Surg Pathol 2018; 42: 1262-1272.
89. Hassanein M, Huiart L, Bourdon V, et al. Prediction of BRCA1 germ-line mutation status in patients with breast cancer using histoprognosis grade, MS110, Lys27H3, vimentin, and KI67. Pathobiology 2013; 80: 219-227.
90. Miolo G, Canzonieri V, Giacomi C de, et al. Selecting for BRCA1 testing using a combination of homogeneous selection criteria and immunohistochemical characteristics of breast cancers. BMC Cancer 2009; 9: 360.
91. Davies H, Glodzik D, Morganella S, et al. HRDetect is a predictor of BRCA1 and BRCA2 deficiency based on mutational signatures. Nat Med 2017; 23: 517-525.
92. Hedenfalk I, Duggan D, Chen Y, et al. Gene-expression profiles in hereditary breast cancer. N Engl J Med 2001; 344: 539-548.
93. Lips EH, Mulder L, Hannemann J, et al. Indicators of homologous recombination deficiency in breast cancer and association with response to neoadjuvant chemotherapy. Ann Oncol 2011; 22: 870-876.
94. Branham MT, Campoy E, Laurito S, et al. Epigenetic regulation of ID4 in the determination of the BRCAness phenotype in breast cancer. Breast Cancer Res Treat 2016; 155: 13-23.
95. Wessels LFA, van Welsem T, Hart AAM, et al. Molecular classification of breast carcinomas by comparative genomic hybridization: a specific somatic genetic profile for BRCA1 tumors. Cancer Res 2002; 62: 7110-7117.
96. La Cruz J de, Andre F, Harrell RK, et al. Tissue-based predictors of germ-line BRCA1 mutations: Implications for triaging of genetic testing. Hum Pathol 2012; 43: 1932-1939.
97. Mavaddat N, Rebbeck TR, Lakhani SR, et al. Incorporating tumour pathology information into breast cancer risk prediction algorithms. Breast Cancer Res 2010; 12: R28.
98. Lee AJ, Cunningham AP, Kuchenbaecker KB, et al. BOADICEA breast cancer risk prediction model: updates to cancer incidences, tumour pathology and web interface. Br J Cancer 2014; 110: 535-545.
99. Mavaddat N, Barrowdale D, Andrulis IL, et al. Pathology of breast and ovarian cancers among BRCA1 and BRCA2 mutation carriers: results from the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA). Cancer Epidemiol Biomarkers Prev 2012; 21: 134-147.
100. Antoniou AC, Hardy R, Walker L, et al. Predicting the likelihood of carrying a BRCA1 or BRCA2 mutation: validation of BOADICEA, BRCAPRO, IBIS, Myriad and the Manchester scoring system using data from UK genetics clinics. J Med Genet 2008; 45: 425-431.
101. National Institute for Health and Care Excellence. Familial breast cancer: classification, care and managing breast cancer and related risks in people with a family history of breast cancer: Clinical guideline. 2013.
Copyright: © 2020 Polish Association of Pathologists and the Polish Branch of the International Academy of Pathology This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License (http://creativecommons.org/licenses/by-nc-sa/4.0/), allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material, provided the original work is properly cited and states its license.
Quick links
© 2022 Termedia Sp. z o.o. All rights reserved.
Developed by Bentus.