J Cancer 2018; 9(15):2589-2602. doi:10.7150/jca.23023

Research Paper

Subtyping Of Triple Negative Breast Carcinoma On The Basis Of RTK Expression

Harald Hessel1, Manuela Poignée-Heger2, Sabine Lohmann2, Bianca Hirscher3, Andrea Herold2, Gerald Assmann1,4, Jan Budczies5, Karl Sotlar1,6, Thomas Kirchner1 Corresponding address

1. Institute of Pathology, Faculty of Medicine, LMU Munich, Germany
2. Roche Diagnostics GmbH, Penzberg, Germany
3. Roche Diagnostics International AG, Rotkreuz, Switzerland
4. Pathologiepraxis München, Germany
5. Institute of Pathology, Charité University Hospital, Berlin, Germany
6. University Institute of Pathology, University Hospital Salzburg, Paracelsus Medical University, Austria

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How to cite this article:
Hessel H, Poignée-Heger M, Lohmann S, Hirscher B, Herold A, Assmann G, Budczies J, Sotlar K, Kirchner T. Subtyping Of Triple Negative Breast Carcinoma On The Basis Of RTK Expression. J Cancer 2018; 9(15):2589-2602. doi:10.7150/jca.23023. Available from http://www.jcancer.org/v09p2589.htm


Background: "Triple-negative breast cancers" (TNBC) comprise a heterogeneous group of about 15% of invasive BCs lacking the expression of estrogen and progesterone receptors (ER, PR) and the expression of HER2 (ERBB2) and are therefore no established candidates for targeted treatment options in BC, i.e., endocrine and anti-HER2 therapy. The aim of the present study was to use gene expression profiling and immunohistochemical (IHC) characterization to identify receptor tyrosine kinase (RTK) profiles that would allow patient stratification for the purposes of target-oriented personalized tumor therapy in TNBC.

Methods: Twenty-nine cases of TNBC selected according to routine diagnostic IHC/cytogenetic criteria were examined by reverse transcription polymerase chain reaction (RT-PCR). RTK mRNA expression profiles were generated for a total of 31 tumor-relevant biomarkers, mainly belonging to the IGF- and EGF-receptor families but also including biomarkers related to downstream signaling. Protein expression of selected biomarkers was investigated by IHC.

Results: Hierarchical cluster analysis revealed a dichotomous differentiation pattern amongst TNBCs. A significant difference in gene expression was observed for 16 of the 31 RTK-associated tumor relevant biomarkers between the two newly identified TNBC subgroups. The findings were verified at the posttranslational level by the IHC data. The RTKs HER4, IGF-1R and IGF-2R and the hormone receptors ER and PR below the IHC detection limit play a central role in the differentiation of the two TNBC subgroups. Observed survival was reported as Kaplan-Meier estimates and point towards an improved survival of patients with RTK-high with superior three-year survival rate of 100% compared to RTK-low gene signatures with superior three-year survival rate of 60% (log-rank test, p-value = 0.022).

Conclusion: Gene-expression and IHC analysis of the EGF and IGF receptor families and biomarkers associated with downstream signaling point to the existence of two distinct TNBC subtypes. The RTKs HER4, IGF-1R, IGF-2R and the hormone receptors ER and PR appear to be of particular importance here. Based on survival analysis the differentiation of TNBC with RTK-high and RTK-low gene signatures seems to be of prognostic relevance. Additionally, correlation analysis of the relationship between RTKs and ER suggests co-regulatory mechanisms that may have potential significance in new therapeutic approaches.

Keywords: triple-negative breast cancer, subtyping, personalized tumor therapy, Real-Time RT-PCR, receptor tyrosine kinase (RTK), RTK-high gene signature, RTK-low gene signature


About one in eight women will be diagnosed with breast cancer during their lifetime, making this the most frequent malignant diseases in women [1]. Figures published by the WHO indicate that, worldwide, about 1,000,000 women develop breast cancer every year. Breast cancer comprises a heterogeneous group of malignant diseases that can be differentiated primarily on the basis of histopathological criteria into several distinct sub-groups of varying incidences [2].

Intense research efforts in recent years have provided insight into a large number of the complex molecular mechanisms involved in the pathogenesis of breast cancer [3]. Current attempts to develop individualized tumor therapies are aimed at identifying and treating further distinct subtypes on the basis of therapeutically relevant surrogate markers [4, 5, 6, 7, 8]. In addition to the classical histopathological parameters, the IHC characterization of hormone receptor status and HER2 as well as when indicated the cytogenetic detection of HER2 gene amplification represent the first important parameters upon which individualized tumor-specific therapy for breast carcinoma can be based. About 85% of all breast cancer patients currently receive such individualized therapy, which is aimed at the inhibition of HER2 (Trastuzumab, Lapatinib) and estrogen receptors (Tamoxifen, Letrozol) and is now considered standard treatment [9].

Thus, breast carcinoma can be roughly divided into tumors that are positive for estrogen/progesterone receptors and/or HER2 (triple-positive) which are suited to established, personalized therapy and those that are negative for these markers (triple-negative) without such benefit.

Triple-negative tumors are primarily defined, therefore, by an absence of surrogate markers of relevance for individualized therapy. As has been shown in numerous studies, triple-negative tumors vary in tumor progression, the development of therapeutic resistance, and the clinical course [10, 11, 12, 13, 14, 15], which indicates that they comprise a heterogeneous group. Further characterization and subtyping of triple-negative tumors on the basis of therapeutically relevant biomarkers is therefore strongly necessary.

To this end, a number of international microarray and NGS studies have produced data on the characterization of triple-negative tumors at the molecular level [13, 16, 17, 18]. In addition, further attempts to subtype these tumors have differentiated between basal-like and myoepithelial tumors [19, 20, 21] the findings being primarily of prognostic relevance. However, little therapeutic strategies targeting basal-like tumor cell specific surrogate markers have yet been developed.

The search for new approaches to individualized therapy during recent years has revealed biomarkers with predictive relevance for a large number of malignant diseases, which have been used in the development of individualized tumor therapy modalities in clinical trials. Various RTK families are of particular importance here. The treatment success achieved with trastuzumab (Herceptin) in HER2+ breast cancer has stimulated a search for other therapeutic targets including other members of elements of the epidermal growth factor receptor (EGFR) family.

The concept of growth factor receptor based anti-tumor therapy has been investigated and developed in recent years, especially in relation to EGFR. This has produced a series of EGFR inhibitors that have been approved for the treatment of epithelial tumors. In addition to the therapeutic monoclonal antibodies cetuximab and panitumumab, which are of great importance in the treatment of colorectal carcinoma, antineoplastic effects have been demonstrated for the receptor tyrosine kinase inhibitors (RTKI) gefitinib (Iressa) and erlotinib (Rarceva) in non-small cell lung cancer and for lapatinib (Tykerb) in metastatic breast cancer [22, 23, 24].

In addition to the representatives of the EGF receptor family, insulin-like growth factor receptor 1 (IGF-1R) is a further promising candidate for the development of individual therapeutic approaches in oncology. Various specific therapeutic monoclonal antibodies and RTKI against IGF-1R, growth factor antagonists and IGF-1R are currently undergoing clinical trials [25, 26, 27].

The aim of the present study was, by means of gene expression analysis and IHC investigations, to identify various RTK of the EGFR and IGFR families that would enable patient stratification for the purposes of target-oriented individualized tumor therapy in TNBC. In addition, correlation analysis was undertaken to shed light on possible interactions amongst the various RTK and provide a basis for innovative approaches to individualized tumor therapy.

Materials and Methods

Patients and tumors

A total of 29 triple-negative invasive ductal breast carcinomas, which had been diagnosed in the course of routine histopathological investigation at the Institute of Pathology, Ludwig-Maximilians-Universität Munich during the years 2003 to 2005, were selected for investigation. All tumors were re-determined for negative hormone receptor and negative HER2 status according to up-to-date histopathological guidelines and recommendations [28, 29]. ER and PR negativity was defined as positive staining in <1% of tumor cells. HER2 negativity was defined as an IHC score of 0 or 1+; those with an immunoreactive score of 2+ underwent HER2-specific FISH analysis to exclude HER2 gene amplification. Further selection criteria included the high-risk parameters of high tumor grade (grade 3) and low age (cut-off of ≤ 55 years) at primary manifestation of disease (average age 41 years; x = 41.3 years; s = 7.8 years) at the time of diagnosis. Tumor size was documented in relation to the patient specific chemotherapeutic treatment regimen before adjuvant respectively before neoadjuvant chemotherapeutic treatment. The mean tumor size was 3.3 cm (x = 3.3 cm, s = 3.5 cm). Nineteen patients received adjuvant chemotherapy and 9 patients neoadjuvant chemotherapy. Within the observation time of this study recurrence rate was 24% for patients treated with adjuvant chemotherapy and 63% for patients with neoadjuvant treatment. None of the patiens with neoadjuvant chemotherapy showed a pathological complete response (pCR). Tumors in 6 patients showed an infiltration of lymph or blood vessels (lymphangiosis-, hemangiosis carcinomatosa) at the time of diagnosis and one patient had distant metastases. Details of the diagnostically relevant parameters in the study group are given in Table 1.

Material investigated

The investigations were performed on formalin-fixed, paraffin-embedded tissue (FFPET) processed for routine histopathological diagnostic investigation. All analyses were performed at resection specimens.

Deparaffinisation and RNA extraction

For each case, nine 4-µm FFPET whole sections comprising more than 70% tumor were cut and deparaffinized according to standard protocols. Total RNA was isolated by means of a commercial RNA isolation kit (High-Pure RNA Isolation Kit, Roche). RNA samples were analyzed photometric for their qualitative and quantitative content (NanoDrop, Thermo Scientific, USA). The minimum RNA content required for gene expression analysis was 300 ng/µl (total volume: 45 µl). An adequate amount of total RNA could be isolated from a total of 29 of the 31 triple-negative tumors that were initially selected for investigation.

cDNA synthesis

cDNA synthesis was performed by primer-specific reverse transcription of total RNA according to a standard protocol (Transkriptor First Strand cDNA Synthesis Kit, Roche). In brief, 3 µl RNA (50 ng/µl), 1 µl sequence-specific primer (1 µM), 9 µl distilled water, 4 µl 5X buffer, 2 µl nucleotide mix (each 10 mM), 0.5 µl protector RNase inhibitor (40 U/µl) and 0.5 µl transcriptor reverse transcriptase (20 U/µl) per reaction were mixed, preincubated for 10 minutes at 95°C and incubated for one hour at 50°C.

 Table 1 

Diagnostically relevant parameters in the study group

Lymph nodesTumor size (cm)Lymphangiosis carcinomatosa / Angiosis carcinomatosaNeoadjuvant chemo-therapyAdjuvant chemo-therapyDistant metastases

Per 96-well plate a standard dilution series (dilution factor 1:5), calibrator, negative control and 29 tumor samples were processed. A total of 34 biomarkers, including representatives of the EGFR and IGF-1R families and biomarkers related to downstream signaling, and five housekeeping genes (HKG) were analyzed in separate runs.

PCR Amplification

Gene expression data were obtained using sequence-specific detection probes (hydrolysis probes). With the help of the Universal ProbeLibrary Software [30], primer-specific sequences and target-specific hydrolysis probes for 34 biomarkers and 5 HKG were determined on the basis of freely accessible data banks (Table 2).

 Table 2 

Biomarkers / Universal ProbeLibrary Assays

BiomarkerPrimersequence (5' -> 3'-direction)Probe No.Reverse Primer RT
EGFR Fwdcat gtc gat gga ctt cca ga44ttc tcc tct cct gca cc
EGFR Rev1ggg aca gct tgg atc aca ct
HER2 Fwdtgg ctc agt gac ctg ttt tg75tca ggt ttc aca ccg ct
HER2 Rev1ggt cct tat agt ggg cac agg
Her3 Fwdcac aat gcc gac ctc tcc86tgg gca atg gta gag tag ag
Her3 Rev1cac gag gac ata gcc tgt ca
Her4 Fwdttc cac ttt acc aca aca tgc ta78aca gca aat gtc aga ccc
Her4 Rev1cag aat gaa gag ccc acc a
TGF-alpha Fwdtgc tgc cac tca gaa aca gt63agt gct gtc ctg aag aag c
TGF-alpha Rev1atc tgc cac ag tcc acc tg
EGF Fwdtgg ttg tgg ttc atc cat tg68tca cag cct ccg ttt tga ta
EGF Rev1tca cag cct ccg ttt tga ta
beta-cellulin Fwdact gca tca aag gga gat gc49acc ttg ctc caa tgt agc
beta-cellulin Rev1tcc aat gta gcc ttc atc aca
Amphiregulin Fwdcgg aga atg caa ata tat aga gca c38tgt caa tca tgc tgt gag ttt
Amphiregulin Rev1cac cga aat att ctt gct gac a
HB-EGF Fwdtgg ggc ttc tca tgt tta gg55ccg att cct tga gca ca
HB-EGF Rev1tgc cca act tca ctt tct ctt c
NRG-Common Fwdgat cag caa att agg aaa tga cag53tct gaa gac aca tat gct cct
NRG-Common Rev1ggc ata cca gtg atg atc tcg
NRG-GGF2 Fwdgct gcc act act gct gct g70cgg gga cga gta gca cac
NRG-GGF2 Rev1cgg gga cga gta gca cac
NRG-SMDFsecSet Fwdaga acg ccc aag tca gca5atg gct tgt ccc agt g
NRG-SMDF secSetRev1ttg tcc cag tgg tgg atg ta
NRG2 Fwdttc gca tca aat atg gca ac39gac ggt gtc ctt ccc c
NRG2 Rev1ggc ctc gca gac ata ctc c
NRG3 Fwdgcc agt ctg tca aac acc ac26cca cct agc cta ctt cgg
NRG3 Rev1atg gag cat gcc act tct tt
NRG4 Fwdggg gct ttg tta tgt gat acc t23ctg gag cct ggg aga a
NRG4 Rev1cct gta tag ttt tca acg cac ct
BCL2 Fwdagg tgc atc tgg tga tgt ga26cac tcc aac ccc cga tct
BCL2 Rev1cac tcc aac ccc cga tct
G6PDH secSet Fwdaga gac cgt gga tgc tga ag65ctc cgc act gct gac a
G6PDH secSet Rev1tga gga cct ccg tca gat g
beta-Aktin Fwdatt ggc aat gag cgg ttc11atg tcc acg tca cac ttc at
beta-Atkin Revgga tgc cac agg act cca t
ER-Fwdaac cag tgc acc att gat aaa a69att ctc cct cct ctt cgg
ER-Revtcc tct tcg gtc ttt tcg tat c
PGR-Fwdttt aag agg gca atg gaa gg11ttt ttc tgc gga ttt tat caa
PGR-Revcgg att tta tca acg atg cag
PTEN-Fwdgca caa gag gcc cta gat ttc60aat aca cat agc gcc tct ga
PTEN-Revcgc ctc tga ctg gga ata gt
PIK3CA-Fwdcga gat cct ctc tct gaa atc ac2gaa ttt cgg gga tag tta cac aa
PIK3CA-Revgaa ttt cgg gga tag tta cac aa
PIK3R1 Iso 1-Fwdaat gaa cga cag cct gca c16cag tac cat tca gca tct tgt aa
PIK3R1 Iso 1-Revccg ttg ttg gct aca gta gta gg
AKT common-Fwdggc tca ccc agt gac aac tc41agc agc ttc agg tac tca aa
AKT common-Revact caa act cgt tca tgg tca c
SRCcom-Fwdcga gaa agt gag acc acg aa34gtt gag gcc ctt ggc
SRCcom-Revttg gcg ttg tcg aag tca
SCUBE2-Fwdgct gcc atc cac agt aca ag17gac agt gtc ctc tcg ctc caa
SCUBE2-Revgac agt gtc ctc tcg ctc caa
Ki-67-Fwdtga att tcc aag aaa aat acg tga36agc ttt ctc atc agg gtc a
Ki-67-Revtca ggg tca gaa gag aag cta ga
Cyclin B1-Fwdcgc ctg agc cta ttt tgg t34aga aag cct gac aca ggt c
Cyclin B1-Revgca cat cca gat gtt tcc att
HNF3α-Fwdagg gct gga tgg ttg tat tg1atg ttg ctg acc ggg a
HNF3α-Revacc ggg acg gag gag tag
Survivin com-Fwdctt gaa agt ggc acc aga gg29cct cac ttc tca cct ggt aa
Survivin com-Revcaa aaa tga gcc ccc aaa a
TRIP13-Fwdctc atg cgc tgt atg tcc a65gca agc ttc ttt ctc tct tca
TRIP13-Revgtc cac tgc cag aga cag g
HPRT-Fwdtga cct tga ttt att ttg cat acc73aat gtg atg gcc tcc ca
HPRT-Revcga gca aga cgt tca gtc ct
PBGD common-Fwdagc tat gaa gga tgg gca ac25gcc tgc atg gtc tct tgt a
PBGD common-Revttg tat gct atc tga gcc gtc ta
PPP1CA common-Fwdcct ata aga tca agt acc ccg aga15cag ttg aag cag tca gtg aa
PPP1CA common-Revgat gtt gta gcg tct ctt gca c
IGF1 Fwdtgt gga gac agg ggc ttt ta67cac tca tcc acg atg cct
IGF1 Revatc cac gat gcc tgt ctg a
IGF2 Fwdgct ggc aga gga gtg tcc10cat tgg gat tcc cat tgg
IGF2 Revgat tcc cat tgg tgt ctg ga
IGF-1R Fwdttc agc gct gct gat gtg10caa gtt ccc ggc tca tg
IGF-1R Revggc tca tgg tga tct tct cc
IGF-2R Fwdcag acc aca tat acc acg agg a1agt tga gag aaa aga tgg ggt
IGF-2R Revaga aaa gat ggg gtg gct gt
 Table 3 

Antibodies employed in the study

AntigenSourceOrder No.DilutionMethod / SystemPretreatmentIncubation
EGFRVentana/Roche790-2988ready to useVentana /3,3`-diamino-benzidine kit120 min, Protease32 min
HER2Ventana/Roche800-2996ready to useVentana /3,3`-diamino-benzidine kit130min, CC116 min
IGF-1RVentana/Roche790-4346ready to useVentana /3,3`-diamino-benzidine kit160 min, CC128 min
ERVentana/Roche790-4325ready to useVentana /3,3`-diamino-benzidine kit160 min, CC128 min
PRVentana/Roche790-4296ready to useVentana /3,3`-diamino-benzidine kit160 min, CC128 min
CK 5/6Ventana/Roche760-4253ready to useVentana /3,3`-diamino-benzidine kit160 min, CC132 min

1 (XT Ultra View DAB kit, Ventana Medical Systems, Tucson, Arizona)

For quantitative PCR, 3.5 µl cDNA (50ng/µl) were added to 16.5 µl PCR Master Mix according to the manufacturer´s instructions (LightCycler® 480 Probes Master, Roche). For all targets, uniform ThermoCycler running conditions were used. In brief, initial preincubation at 95°C for 5 minutes was followed by 45 cycles of 95°C for 10 seconds, 60°C for 20 seconds and 72°C for 1 second. Gene expression profiles were generated on the LightCycler® 480 Real-Time PCR System (Roche).

A standard curve, consisting of diluted human reference RNA (Universal Human Reference RNA, Stratagene), was included for every RT reaction and subsequent RT-PCR. For a run to be accepted there had to be a valid negative control and an amplification efficiency of between 1.9 and 2.1. Expression data were normalized to five averaged HKG. All experiments were performed in duplicate and the results averaged. Calibrator based calculations were used to determine relative gene expression profiles [31].

Immunohistochemical analysis

IHC investigation for various surrogate markers was performed to substantiate the results of gene expression analysis and to identify basal-like breast carcinomas. Markers investigated included the RTK EGFR, HER2 and IGF-1R, the hormone receptors ER and PR, and the basal-like marker CK5/6 [32]. Whole FFPET sections cut at 3 µm were stained with a Ventana Benchmark XT autostainer (Ventana Medical Systems). Details of the antibodies and methods employed are given in Table 3.

Slides were counterstained with haematoxylin (Ventana). System and isotype controls were included. IHC investigation for ER and PR and for HER2 was performed according to up-to-date histopathological guidelines and recommendations [28, 29].

Immunostaining for EGFR and IGF-1R was evaluated semiquantitatively with a scoring system similar to that established for HER2 (0: no membrane-specific staining, 1+: weak, incomplete cell membrane staining in <10% of cells, 2+: weak or moderate staining of the complete cell membrane in > 10% of cells, 3+: strong, complete membrane staining in > 10% of cells. Immunostaining for CK 5/6 was evaluated according to the criteria of Dabbs et al. [33] (0: no staining, R: single cells stained, 1+: 5-30% of cells stained, 2+: >30-60% of cells stained, 3+: > 60% of cells stained). The scores 0 and R were considered negative, 1+ to 3+ positive.

Statistical analysis

Statistical evaluation was performed with the help of the statistics software SPSS (Chicago, USA) and R [34]. The Kolmogorov Smirnov test was used to test for normal distribution. The Mann-Whitney U test and Student's t-test were used to test for significant differences in gene expression between the postulated subtypes RTK-high and RTK-low. T-test for equality of means and Levene´s test for equality of variances was applied to demonstrate the statistic independence of tumor samples with adjuvant and neoadjuvant chemotherapeutic treatment in context to the gene-expression analyses. The influence of clinical parameters on the gene expression profile was investigated by multivariate analysis on the basis of the Chi-square test and the Mann-Whitney U test. Correlation between variables was observed with Pearson respectively Spearman rank correlation analyses. Hierarchical cluster analysis was performed to identify subtypes within the study cohort. The 3-year survival rate was analyzed with the Kaplan-Meier curve.

 Figure 1 

Pearson correlation - triple-negative breast cancer

J Cancer Image (Click on the image to enlarge.)

Ethics approval and consent to participate

Specimens and data were anonymized, and the need for consent was waived by the institutional ethics committee of the Medical Faculty of the Ludwig Maximilians University of Munich.


Biomarker detection efficiencies within RT-PCR analyses

In the present study 34 tumor associated biomarkers relevant for EGFR and IGF-1R families and related biomarkers of the downstream signaling as well as 5 HKG were analyzed. Expression profiles for all tumor samples could be obtained for 31 of the biomarkers and all five HKG.

Differentiation of triple-negative tumors into two distinct subtypes on the basis of gene expression profiling of RTK associated biomarkers

Hierarchical cluster analysis revealed a dichotomous differentiation pattern amongst the triple-negative tumors (Figure 1).

The two subtypes were characterized by low (subtype RTK-low; N=15) and high (subtype RTK-high; N=14) gene expression of the biomarkers investigated. The corresponding relative gene expression values of each biomarker are shown in Table 4.

Further statistical analysis confirmed significant differences in gene expression between the subgroups RTK-low and RTK-high for 16 of the 31 biomarkers evaluated (Table 4). Of particular interest were significant differences for 3 of the 6 analyzed RTK (HER4, p≤ 0.001; IGF-1R, p < 0,001; IGF-2R, p = 0.015; Figure 2).

 Table 4 

Differential biomarker expression in triple-negative tumors of the subtypes RTK-low and RTK-high

BiomarkerpTNBC subtypeMean95 % CIBiomarkerpTNBC subtypeMean95 % CI
PR0.000RTK-low2.7891.802 - 3.776Amphiregulin0.24RTK-low2.8800.840 - 4.920
RTK-high17.5825.256 - 29.908RTK-high4.6102.122 - 7.278
HER40.000RTK-low1.5100.827 - 2.373NRG-com0.408RTK-low16.995-16.498 - 50.488
RTK-high10.5503.887 - 17.213RTK-high3.3901.669 -5.111
EGF0.043RTK-low3.7281.153 - 6.303Ki-670.407RTK-low1.3901.114 - 1.665
RTK-high18.8254.383 - 33.267RTK-high1.6281.069 - 2.186
PI3K-R1iso0.000RTK-low4.4943.659 - 5.329SRC-com0.254RTK-low17.37513.012 - 21.738
RTK-high12.3928.023 - 16.762RTK-high22.26914.142 - 30.397
SCUBE20.003RTK-low5.6963.953 - 7.439Survivin-com0.119RTK-low0.2620.185 - 0.339
RTK-high22.4979.788 - 35.206RTK-high0.39810.228 - 0.568
BCL20.000RTK-low10.0777.813 - 12.340TRIP130.382RTK-low1.2140.948 - 1.479
RTK-high34.70226.705 - 42.699RTK-high1.0350.690 - 1.381
IGF-1R0.001RTK-low1.4240.876 - 1.971Cyclin B10.425RTK-low0.920.738 - 1.102
RTK-high4.3322.159 - 6.504RTK-high1.0820.696 - 1.467
IGF-2R0.015RTK-low2.5451.827 - 3.263HER20.794RTK-low6.3535.173 - 7.533
RTK-high3.4932.980 - 4.007RTK-high6.0934.299 - 7.888
IGF20.004RTK-low0.3760.224 - 0.976HER30.798RTK-low6.3245.258 - 7.389
RTK-high0.4730.069-1.015RTK-high8.6505.070 - 12.231
HNF3-α0.001RTK-low0.1420.025-0.308β-Cellulin0.889RTK-low63.35437.120 - 89.588
RTK-high0.3060.135 - 0.477RTK-high60.97335.728 - 86.218
IGF10.000RTK-low31.94325.742 - 38.143HBEGF0.446RTK-low3.4081.849 - 4.968
RTK-high113.49373.942 - 153.044RTK-high4.1892.689 - 5.688
ER0.000RTK-low0.8330.520 - 1.146NRG-20.727RTK-low5.4161.366 - 9.467
RTK-high2.5541.452 - 3.656RTK-high4.6512.572 - 6.730
PTEN0.000RTK-low3.7042.278 - 5.129AKT-com0.311RTK-low4.6063.578 - 5.633
RTK-high6.7864.883 - 8.689RTK-high5.6753.640 - 7.710
Pi3K-CA0.043RTK-low5.4153.521 - 7.310EGFR0.257RTK-low23.781-12.782 - 60.344
RTK-high8.4816.383 - 10.579RTK-high3.6452.668 - 4.621
LEF10.003RTK-low0.8710.684 - 0.106TGF-α0.219RTK-low3.5871.112 - 6.062
RTK-high0.25610.131 - 0.381RTK-high5.8652.817 - 8.913
PI3K R1 iso0.000RTK-low4.4943.659 - 5.329
RTK-high12.3928.023 - 16.762
 Figure 2 

Differential gene expression by the triple-negative subtypes RTK-low und RTK-high in relation to PR, ER, HER4, IGF-1R and IGF-2R

J Cancer Image (Click on the image to enlarge.)

Interestingly, significant differences in gene expression patterns for ER (p = 0.001) and PR (p < 0.001) were also found between the two subtypes (Figure 2). By contrast, all cases of triple-negative tumors, by definition lacked IHC staining for ER and PR.

Correlation analysis was performed to determine whether the RTK and hormone receptors investigated are subject to functional interaction (Table 5).

 Table 5 

Correlation coefficients for RTK, ER and PR gene expression


** p<0.005; * p<0.05

Increased correlation, in particular for HER4 with ER (r = 0.675; p≤0.001), HER4 with PR (r=0.623, p≤0.001), as well as IGF-1R with ER (r = 0.419; p=0.024) and IGF-1R with PR (r=0.555; p=0.002) was found (Figure 3).

 Figure 3 

Increased correlation between analyzed RTK and hormone receptors pictured in scatter plot charts A) HER4 and ER, B) HER4 and PR, C) IGF-1R and ER, D) IGF-1R and PR

J Cancer Image (Click on the image to enlarge.)

Additionally, we compared the patient survival based on the RTK-high and RTK-low gene signature. Kaplan-Meier analysis demonstrated superior three-year survival in the RTK-high group as opposed to the patients in the RTK-low group (three-year survival rate of 100% v. 60%; log-rank test, p-value = 0.022; Figure 4).

In summary, two subtypes of TNBC could be identified on the basis of gene expression profiles of 16 biomarkers of the EGF and IGF receptor families and biomarkers of their downstream signaling as well as the hormone receptors ER and PR (Table 4). Of special interest are the RTK HER4 and IGF-1R, as well as the hormone receptors ER and PR, for which the findings of correlation analysis indicate functional interaction in cell signaling.

Comparison of IHC and gene expression findings

In order to determine to what extent the differences in gene expression between the subtypes RTK-high and RTK-low as detected by RT-PCR are also reflected at the protein level, IHC investigations were carried out for certain key proteins from the biomarker panel (IGF-1R and EGFR: study findings, HER2, ER and PR: findings of routine diagnostic work-up). For IGF-1R and EGFR the IHC findings were consistent with the gene expression data. Accordingly, differences in IGF-1R expression at both transcriptional and translational levels were found between the subtypes RTK-high and RTK-low (IHC: p Mann-Whitney U test = 0.005, RT-PCR: p Mann-Whitney U test < 0.001; r Spearman-Rho = 0.706, p ≤ 0.001). The expression profile of EGFR in both subtypes of triple-negative tumors could also be confirmed at the IHC level (IHC: p Mann-Whitney U test = 0.377, RT-PCR: p t-test = 0.257; r Spearman-Rho = 0.311, p = 0.100).

 Figure 4 

Overall survival of patients with RTK-high and RTK-low gene signature

J Cancer Image (Click on the image to enlarge.)

As shown in Table 1, each case in the study group exhibited a triple negative phenotype according to established histopathological criteria. However, there were highly significant differences in the gene expression profiles for both ER (p Mann-Whitney U test = 0.001) and PR (p Mann-Whitney U test ≤ 0.001) amongst the triple-negative tumors in relation to the postulated subtypes RTK-high and RTK-low. As with the IHC findings, no significant difference was found for HER2 at the transcriptional level between the postulated subtypes RTK-high and RTK-low (p t-test = 0.794; r Spearman-Rho = -0.087, p = 0.655).

Immunohistochemical characterization of basal-like subtypes

In relation to the much debated question as to the extent to which triple-negative tumors correspond to the basal-like breast cancer subtype, and to determine to what extent one of the two triple-negative subtypes defined in the study correlates with the basal-like subtype, IHC staining for the basal-like tumor associated biomarker CK5/6 and EGFR was performed. The IHC analyses revealed a basal-like phenotype within 79% of the samples. Statistical analysis did show no significant difference between the triple-negative subtypes defined in this study on the basis of gene expression analyses and the basal-like and non-basal-like TNBC subtypes (p Pearson Chi-Square test = 0.080).

Comparison of clinical parameters and genetic expression of tumor-related biomarkers

By means of multivariate analysis the variables nodal status, age, tumor size and angiosis carcinomatosa were tested for a possible relationship to biomarker expression and subtype (RTK-high and RTK-low). The Chi-square test and Mann-Whitney U test failed to show a significant relationship between any of the clinicopathological parameters and the subtypes.

T-test for equality of means and Levene's test for equality of variances demonstrated the statistic independence of tumor samples with adjuvant and neoadjuvant chemotherapeutic treatment in context to the RTK gene-expression analyses (Table 6). None of the changes was significant after Bonferroni correction.


Characterization and subtyping of triple-negative tumors based on therapeutically relevant RTK

The search for individualized treatment strategies in recent years has revealed biomarkers with predictive relevance for a large number of malignancies. This information has been applied to the development of individualized tumor therapies. The various RTK are of special importance here, particularly representatives of the EGF and IGF receptor families which, in the case of overexpression, represent target proteins for individualized tumor therapy for an increasing number of tumor entities.

In the case of breast cancer, too, gene expression data and IHC findings have characterized patients for whom target-specific therapy is suitable. A number of clinical trials are concerned with the question as to what extent inhibition of EGFR or IGF-1R by specific monoclonal antibodies or RTKI can benefit patients with advanced breast cancer [35, 36, 37, 38, 39]. The combination of chemotherapy with EGFR inhibition appears so far to have mixed results. Adequate results of an individual therapeutic IGF-1R inhibition are not yet available.

 Table 6 

Statistic independence of tumor samples with adjuvant and neoadjuvant chemotherapeutic treatment in context to the RTK gene-expression analyses

BiomarkerFold Change
(neoadjuvant versus adjuvant)
p-value (Welch)p-value (Levene)BiomarkerFold Change
(neoadjuvant versus adjuvant)
p-value (Welch)p-value (Levene)
HB_EGF1.1830.5730.924Survivin com-1.6000.1170.765
NRG_com-1.5060.5310.962TRIP 131.0440.7940.256

At the same time, it has been shown in recent years that despite the importance of single tumor-relevant biomarkers, the neoplastic behaviour of tumors is influenced by numerous interactions in downstream signaling. The crosstalk of various overexpressed RTK, hormonal transcriptional regulation (ER) or mutational, constitutive activation in downstream signaling (EGFR, KRAS) represent multifactorial mechanisms involved in tumor growth. Because of the complexity of the situation, more recent clinical trials have focused increasingly on combination treatment strategies in narrowly defined patient groups. The characterization of breast tumors using a panel of tumor-specific surrogate markers not only contributes to a better understanding of functional mechanisms involved in tumor differentiation, growth and invasion, but also represents a basis for new combination treatment strategies. In addition, exact subtyping of breast cancer cases such as luminal A, luminal B, normal breast-like, ERBB2-overexpressing and basal-like molecular subtypes of breast cancer is still used for optimal patient selection in clinical trials.

In the study presented, we examined gene expression levels of various RTK and related biomarkers in TNBC without individualized treatment options with the aim of defining heterogeneous expression patterns and possible TNBC subtypes. Hierarchical cluster analysis was able to identify a panel of biomarkers that allows two distinct subtypes of TNBC to be distinguished. Further analysis of the genetic cluster emphasizes in particular the significance of the two RTK IGF-1R and HER4, as well as the hormone receptors ER and PR. Cases with overexpression of these markers described above (RTK-high) represent a potentially interesting group for clinical investigations. In addition, the possibility to identify those breast cancer patients who are potential responders for personalized medicine targeting IGF-1R or ER is of particular significance.

Differentiation of triple-negative tumors into basal-like positive and negative subtypes

Several microarray-based studies have demonstrated a differentiation of breast cancer into at least 5 different subtypes [40, 41, 42, 43]. Selected gene sets for around 500 biomarkers enable subtyping of breast carcinoma into luminal A, luminal B, normal breast-like, ERBB2-overexpressing and basal-like tumors. Tumors with the basal-like phenotype which are characterized by, among other features, by the absence of IHC ER and HER2 expression, are of particular clinical interest. Scientific studies employing IHC techniques and gene expression analysis have shown that up to 15% of all breast cancers exhibit the basal-like subtype [32, 33]. Numerous studies have attempted to equate triple-negative tumors with basal-like breast cancer, and indeed an accumulation of the basal-like phenotype amongst triple-negative tumors has been documented. Nevertheless, it has been shown that the two are not identical with each other [44]. Comparative analysis has shown that, although triple-negative tumors (immunohistochemically defined) often show a basal-like phenotype, and tumors diagnosed as basal-like breast cancer (on the basis of gene expression) are predominantly triple-negative, the two categories are up to 30% discordant [19, 45].

Methods for the identification of the basal-like phenotype rely mainly on the detection of expression of proliferation-specific biomarkers by gene expression analysis and immuno-histochemical techniques. Our study utilized IHC staining for CK 5/6. Eighty-one percent of the tumor samples were found to express a basal-like phenotype, a prevalence consistent with figures quoted in the literature.

In our study, comparison of the subtypes RTK-high and RTK-low and the group classed as CK 5/6 positive revealed no significant correlation. Therefore, on the basis of our findings it can be assumed that this is a newly defined and independent gene expression signature that permits further subtyping of TNBC into RTK-high and RTK-low genotypes.

Gene expression of ER in triple-negative breast cancer

IHC analysis of the hormone receptor status, is one of the standardized clinical investigations used in the diagnostic work-up of patients with breast cancer for treatment prediction. Selection of the cases for our study was based upon the findings of routine histopathological investigations. By definition, all exhibited a negative (<1%) IHC score for ER and PR. By contrast, our gene expression analysis revealed over-expression of ER and PR in some cases, there being a highly significant difference between the subtypes RTK-high and RTK-low. The discrepancy may be due to the increased sensitivity of RT-PCR. Differences in regulation of protein expression at the translational level are also conceivable.

The results of correlation analysis pertaining to IGF-1R, HER4 and ER in tumors with the RTK-high genotype suggest that these receptors interact with each other through regulatory mechanisms. Hence, further in-vivo investigations, especially into the potential of new immunotherapeutic approaches using combined immunotherapeutic approaches in TNBC patients with the RTK-high genotype, would be of great interest [46, 47, 48].

Crosstalk between IGF-1R and ER signaling

Recent studies have shown that there is crosstalk in cell signaling between RTK and ER. Various different trans-activating mechanisms with stimulating effects on the downstream signaling of adjacent RTK and ER were generally found [49, 50].

IGF-1R is one of the most important representatives of the IGF receptor family and, like ER, is associated with neoplastic properties when overexpressed [51, 52, 53].

In our study we found increased expression of IGF-1R and ER in TNBC of the RTK-high subtype. In addition, strong correlation between IGF-1R and ER was seen, which suggests functional interaction between the two receptors. As shown in recent studies, IGF-1R and ER are able to trans-activate each other and lead to activation or enhancement of downstream signaling [52]. In addition, in vivo experiments have demonstrated their combined significance in tumorigenesis [54].

In studies of IGF-1R-mediated ER activation [55, 56, 57] it has been shown that tyrosine kinases involved in IGF-1R downstream signaling, such as MAPK, RSK and AKT, are able to phosphorylate and activate ER at the AF-1 domain (Serin118, Serin167). Furthermore, in vivo experiments showed that IGF-1 is able to increase the expression of PR, an estrogen response element.

In addition to IGF-1R-mediated ER phosphorylation and activation, effects of ER on IGF-1R signaling have also been demonstrated [47]. ER is able, by means of various different regulatory mechanisms, to impact on IGF-1R downstream signaling. Studies have shown that anti-estrogens like tamoxifen can inhibit IGF-mediated growth [58]. Thus, it has been demonstrated that estrogens are able to regulate IGF-1R at the transcriptional level and to transactivate IGF-1R by phosphorylation. ER belongs to the steroid hormone receptor family and functions primarily as a transcription factor. IGF-1R is one of the most important estrogen response elements [58].

Besides the ability of ER to inactivate inhibitory elements of IGFR signaling, such as IGFBP-3, binding of ER to Shc with subsequent phosphorylation of MAPK have also been described. In addition to the singular importance of hormone receptors as a therapeutic target, several studies have demonstrated the existence of complex regulatory mechanisms and interactions in downstream signaling between ER/PR and growth factors [59, 60]. Several clinical trials concerning the combined use of IGF-1R and ER-inhibitors are under investigation [61, 62].

Crosstalk between HER4 and ER signaling

The c-erbB-4 gene (HER4) belongs to the EGFR family. Little is known about the functional significance of HER4 in the pathogenesis of breast cancer. In normal breast tissue, its functions include the differentiation of myocardial cells and mammary epithelial cells and the development of the central nervous system [63, 64]. Compared to the most important representatives of the EGFR family, EGFR and HER2, the prognostic impact of HER4 expression in breast cancer are basically unclear and controversially discussed [65, 66]. Unlike the remaining members of the EGF receptor family, which act primarily as mitogenic effectors in breast cancer cells, HER4 appears to have a large number of different functions in normal and neoplastic breast tissue. With regard to the overexpression of HER4, experimental studies have shown both oncogenic and tumor suppressive functions [67, 68]. Other investigations have also shown that HER4 is generally associated with differentiated and less aggressive tumors, and that it correlates with a better prognosis and a longer disease-free interval [66, 69, 70]. In addition, it has been shown that breast cancer patients who exhibit coexpression of HER4 and ER have a lower recurrence rate [71] and a higher survival rate [72] than those who express ER alone.

Interestingly, several in vivo and in vitro studies have shown a strong correlation and crosstalk between HER4 and ER expression in breast cancer [68, 73, 74]. Intense efforts to elucidate mechanism of crosstalk between HER4 and ER could identify an autocrine HER4/ER signaling pathway where the factor HER4 intracellular domain (4ICD), a cleavage product of HER4, acts as transcriptional coregulator of ER [73, 75, 76, 77, 78].

Our results point to an increased rate of coexpression of HER4 and ER in triple-negative tumors with the RTK-high phenotype. The coexpression of HER4 and ER suggests the existence of crosstalk in downstream-signaling within defined subpopulations of breast cancer patients.

ER / RTK crosstalk- development of intrinsic and acquired resistance

Antihormonal therapy (tamoxifen, anastrozole) and HER2 inhibition (trastuzumab, lapatinib) represent important cornerstones in personalized therapy for the treatment of breast cancer in women with overexpression of the corresponding receptors. Further promising targets for individualized therapy the RTK EGFR and IGF-1R and targeting the PI3K-Akt pathway are currently subject of several studies and clinical trials [79, 80, 81, 82]. However, follow-up studies did show the development of resistance in some patients receiving antihormonal therapy and/or RTK inhibitors.

Studies have demonstrated intrinsic or acquired resistance to treatment with tamoxifen in 20-30% of hormone-receptor-positive breast cancer patients [83]. Although the mechanisms involved have not yet been clarified in detail, it appears that, in addition to coregulatory and epigenetic mechanisms, transactivating processes between RTK and ER could be responsible [80, 84]. Further investigations have revealed complex co-operation of genomic and non-genomic/fast ER signaling and their crosstalk with growth factor receptors, such as EGFR, HER2, HER4 or IGF-1R, in the development of endocrine resistance [85, 86, 87]. In brief, activated cytoplasmic and membrane-bound ER show the ability to stimulate RTK, either directly via adaptor proteins like Shc, or indirectly by nuclear ER through increased release of RTK-specific ligands, for example HB-EGF. Following subsequent activation of the downstream signaling kinases MAPK and AKT, phosphorylation and thus activation of nuclear ER occurs, which leads to increased gene expression, including the expression of RTK-related genes. Thus, innovative treatment strategies aim at inhibition of both RTK and ER.

Current indications for hormone receptor therapy are based on standardized IHC findings of routine diagnostic investigations. According to the histopathological guidelines and recommendations [28], antihormonal treatment is indicated when more than 1% of the tumor cells are ER and/or PR positive. Large overviews of randomized clinical trials have confirmed the therapeutic value of antihormonal treatment only in immunreactive ER-positive breast cancers.

Our investigations demonstrated a significant increase in ER gene expression level in a newly characterized subtype (RTK-high) of TNBC. In addition, correlation analysis suggests functional interaction between IGF-1R, HER4 and ER. It is not yet clear to what extent antihormonal therapy would be effective in tumors in which hormone receptor status is negative by immunohistochemistry but gene expression is elevated. However, on the basis of crosstalk between ER and RTK, it is conceivable that a combination of inhibition of both ER and corresponding RTK, like IGF-1R or HER4, could be of benefit.


RTK-associated gene expression profiles generated in this study revealed dichotomous differentiation within the triple-negative study group. IHC analysis verified this at the posttranslational level. Correlation analysis of the biomarkers investigated suggests a functional connection between IGF-1R, IGF-2R, HER4, PR and ER. Based on survival analysis the differentiation of triple-negative tumors with RTK-high and RTK-low gene signature seems to be from prognostic relevance.


TNBC: Triple-negative breast cancers; ER: estrogen receptor; PR: progesterone receptors; IHC: immunohistochemical; RTK: receptor tyrosine kinase; RT-PCR: reverse transcription polymerase chain reaction; EGFR: epidermal growth factor receptor; RTKI: receptor tyrosine kinase inhibitors; IFF-1R: insulin-like growth factor receptor 1; pCR: pathological complete response; FFPET: formalin-fixed, paraffin-embedded tissue; HKG: housekeeping genes; 4ICD: HER4 intracellular domain.


We thank Annegret Schäfer of the IHC laboratory of the Institute of Pathology of the Ludwig Maximilians University of Munich for their dedication and contributions.

Competing Interests

The authors have declared that no competing interest exists.


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Author contact

Corresponding address Corresponding author: Dr. Harald Hessel, Institute of Pathology, Faculty of Medicine, LMU Munich, Thalkirchner Strasse 36, 80337 München, Germany. harald.hesseluni-muenchen.de; Phone: +49 89 2180 73680; Fax: +49 89 2180 73742

Received 2017-9-26
Accepted 2018-4-16
Published 2018-6-23