Literature DB >> 25025175

The three receptor tyrosine kinases c-KIT, VEGFR2 and PDGFRα, closely spaced at 4q12, show increased protein expression in triple-negative breast cancer.

Sara Jansson1, Pär-Ola Bendahl1, Dorthe Aamand Grabau1, Anna-Karin Falck2, Mårten Fernö1, Kristina Aaltonen1, Lisa Rydén3.   

Abstract

BACKGROUND: Triple-negative breast cancer (TNBC) is a heterogeneous subgroup of breast cancer with poor prognosis and no targeted therapy available. Receptor tyrosine kinases (RTKs) are emerging targets in anticancer therapy and many RTK-inhibiting drugs are currently being developed. The aim of this study was to elucidate if there is a correlation between the protein expression of three RTKs c-KIT, VEGFR2 and PDGFRα, their gene copy number, and prognosis in TNBC compared to non-TNBC.
METHODS: Tumor tissue samples from patients diagnosed with primary breast cancer were stained with immunohistochemistry (IHC) for protein assessment, and with fluorescence in situ hybridization (FISH) for gene copy number determination. Breast cancer mortality (BCM), measured from the date of surgery to death, was used as endpoint.
RESULTS: The cohort included 464 patients, out of which 34 (7.3%) had a TNBC. High expression of the three RTKs was more common in TNBC compared to non-TNBC: c-KIT 49% vs. 10% (P<0.001), PDGFRα 32% vs. 19% (P = 0.07) and VEGFR2 32% vs. 6% (P<0.001). The odds ratio (OR) of c-KIT, VEGFR2 and PDGFRα positivity, adjusted for tumor characteristics, was 6.8, 3.6 and 1.3 times higher for TNBC than for non-TNBC. 73.5% of the TNBC had high expression of at least one of the three investigated receptors, compared to 30.0% of the non-TNBC (P<0.001). Survival analysis showed no significant difference in BCM for TNBC patients with high vs. low c-KIT, PDGFRα or VEGFR2 protein expression. 193 (42%) tumors were evaluated with FISH. No correlation was seen between increased gene copy number and TNBC, or between increased gene copy number and high protein expression of the RTK.
CONCLUSION: c-KIT, VEGFR2 and PDGFRα show higher protein expression in TNBC compared to non-TNBC. Further investigation clarifying the importance of these RTKs in TNBC is encouraged, as they are possible targets for anticancer therapy.

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Year:  2014        PMID: 25025175      PMCID: PMC4098911          DOI: 10.1371/journal.pone.0102176

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


Introduction

Breast cancer is a complex heterogeneous disease and it can be classified into several distinctive subgroups based on gene expression profiles [1]–[3]. This classification gives important information about prognosis and cellular molecular aberrations that could serve as targets for novel medical therapy. In clinical practice, immunohistochemical translations of the results of gene expression profiles are being used to allocate the patients to the different subgroups [4]–[6]. Guidelines for classification in the clinical setting have been proposed by St Gallen and are based on IHC analysis of the estrogen receptor (ER), the progesterone receptor (PR) and Ki67, and ISH-analysis of the human epidermal growth factor receptor 2 (HER2). By these analyses breast cancers are classified as luminal A (ER+ and/or PR+, Ki67 low and HER2−), luminal B HER2− (ER+ and/or PR+, Ki67 high and HER2−), luminal B HER2+ (ER+ and/or PR+, any Ki67 and HER2+), HER2-type (ER−, PR− and HER2+) and triple-negative (ER−, PR− and HER2−) [6]. The triple-negative breast cancer (TNBC) subgroup constitute approximately 12–17% of female breast cancers [7] and is associated with a particularly poor prognosis. Patients diagnosed with TNBC are often younger, have tumors with a higher histologic grade and are more frequently BRCA1 mutation carriers than those within the other breast cancer subgroups. They also tend to present with larger tumors at diagnosis [8], [9]. The TNBC group comprises a greater diversity of tumors than the other breast cancer subgroups and it has been proposed that the TNBC group should be further subdivided based on the expression of basal breast cell markers (CK5/6 and/or EGFR) [5], [10]–[13]. Another study further consolidating the heterogeneity of TNBC was performed by Lehnmann et al. where gene expression profiling analysis was performed on a set of 587 TNBC tumors identifying 6 stable TNBC subtypes [14]. At present, the mainstay of adjuvant systemic treatment for TNBC is chemotherapy [7], [9], compared to the other breast cancer subgroups where ER and HER2 provide targets for therapy. Potential targets in the TNBC group are currently being investigated [15] and receptor tyrosine kinases (RTKs) are emerging as such [16]. A RTK is a transmembrane receptor protein that upon binding of its ligand initiate an intracellular signal cascade ultimately leading to changes in the cell's gene expression and phenotype [17]. The best example of a successful RTK-inhibitor is imatinib that inhibits both c-KIT and platelet-derived growth factor receptor alpha (PDGFRα) and it is currently used for treating, amongst others, gastrointestinal stromal tumors (GISTs) and chronic myeloid leukemia (CML) [16]. Two other examples are sunitinib and sorafenib, which are multi-tyrosine kinase inhibitors [18], [19]. In this study, gene copy number and protein expression were evaluated for three RTKs as potential breast cancer drug targets: c-KIT, vascular endothelial growth factor receptor-2 (VEGFR2) and PDGFRα. The genes c-KIT, VEGFR2 and PDGFRα are all adjacently located at the 4q12 chromosomal segment and their involvement in the cancer process have been investigated in various malignancies, such as for example gliomas [20]–[22], malignant peripheral nerve sheath tumors [23] and GISTs [24]. However, to date, their role in breast cancer remains largely unknown. High expression of c-KIT and VEGFR2 has previously been correlated to basal-like breast cancer (BLBC) and TNBC [25]–[28]. Overexpression of PDGFRα in breast cancer has been found to be associated with tumor progression [29] and to be involved in the metastasis process [30]. PDGFRα has recently been associated with basal B like cell lines [31], but to our knowledge, the expression of PDGFRα has not been correlated to a breast cancer subgroup in a clinical cohort before. Gain-of-function mutations in c-KIT leading to pathologic activation are seen in several neoplasms, such as GISTs and acute myeloid leukemia [32]. A previous study has shown that increased gene copy number of c-KIT and VEGFR2 in primary breast cancer is related to an aggressive phenotype and impaired prognosis [27]. The aim of this study was to analyze protein expression and gene copy number for c-KIT, VEGFR2 and PDGFRα in order to elucidate if there is a correlation between the copy number of these genes, their protein expression, and the prognosis of breast cancer in the TNBC subgroup compared to non-TNBC.

Materials and Methods

Patients

The patient cohort used in this study was originally assembled for an observational prospective study with the aim of evaluating the presence and prognostic value of disseminated tumor cells in bone marrow. The study was approved by the ethics committee at Lund University, and all the included patients gave a written informed consent (LU699-09, LU75-02). Further information about the patient cohort has been published elsewhere [33], [34]. In summary, patients diagnosed with primary breast cancer in the South Swedish Health Care Region between June 1999 and May 2003, were included in the original cohort. The patients were treated surgically with either mastectomy or breast-conserving therapy based on pre-operatively identified characteristics and staging. Axillary lymph node dissection was performed on patients with lymphatic metastatic spread diagnosed either before surgery or following sentinel node biopsy. Patients were recommended adjuvant therapy according to clinical standards following Regional Guidelines. Data on breast cancer related death was retrieved from the Swedish Register of Causes of Death (Central Statistics Office). The median follow-up time for patients alive and without any breast-cancer related event was 61 months. Detailed information on routine prognostic factors, St Gallen molecular subtype and clinical follow-up data were assembled for all patients as described in Falck et al. [34] In total, 464 patients with known breast cancer subtype and remaining evaluable tumor tissue samples were included in the present study (Figure 1).
Figure 1

Flow-chart of the patient cohort included in this study.

aThese patients were excluded because they did not meet the inclusion criteria (e. g. patients with local breast cancer recurrence, bilateral breast cancer, no breast cancer or too few cancer cells in tissue samples). bTNBC = Triple-negative breast cancer.

Flow-chart of the patient cohort included in this study.

aThese patients were excluded because they did not meet the inclusion criteria (e. g. patients with local breast cancer recurrence, bilateral breast cancer, no breast cancer or too few cancer cells in tissue samples). bTNBC = Triple-negative breast cancer.

Tissue microarray

Formalin-fixed, paraffin embedded tumor tissue samples were retrieved from the Department of Pathology in Lund and Helsingborg, Sweden. Tissue micro arrays (TMAs) were constructed by extracting tissue core biopsies 1.0 mm in diameter from representative areas of invasive breast cancer using a tissue array machine (TMArrayer Pathology Devices, INC.). Two core biopsies were taken from each patient tumor sample. Biopsies were mounted into a recipient block and stored dark at room temperature until glass slide transfer and staining.

Immunohistochemistry (IHC)

Sections between 3 and 4 µm thick were taken from each TMA, transferred to glass slides (Menzel Super frost plus, Thermo Scientific, Germany), dried at room temperature and then baked in a heat chamber for two hours at 60°C. After deparaffinisation and antigen retrieval, staining was performed using an Autostainer Plus (Dako Denmark A/S, Glostrup, Denmark). The following antibodies and dilutions were used: c-KIT (#A4502, Dako Denmark A/S, Glostrup, Denmark, diluted 1∶400), PDGFRα (#3164 Cell Signaling Technology, Inc., Danvers, MA, USA diluted 1∶100) and VEGFR2 (#2479 Cell Signaling Technology, Inc., Danvers, MA, USA diluted 1∶100). To amplify the signal of the primary PDGFRα antibody, a Rabbit Link K8009 (Dako Denmark A/S, Glostrup, Denmark) was used. A visualization kit K801021-2 (Dako Denmark A/S, Glostrup, Denmark) was used for all stainings, and all slides were counterstained with Mayer's Haematoxylin applied for two minutes. Two investigators evaluated the IHC staining independently, SJ and DG for c-KIT, and SJ and KA for PDGFRα and VEGFR2. Stainings were evaluated for intensity 0–3 (0 = negative, 1 = weak, 2 = intermediate and 3 = strong) and percentage of stained tumor cells. Only invasive tumor cells were assessed and only TMA core biopsies with >100 tumor cells were included. Samples with differences in assessment between the two investigators were re-evaluated and a consensus decision taken. The highest value of two cores was used in the statistical analysis. A TMA core biopsy was considered c-KIT positive whenever ≥1% of the cancer cells were stained, according to common practice [25]. No standard IHC assay protocols are available for VEGFR2 and PDGFRα, and assessments were based on previously published histoscore protocols. For VEGFR2, the percentages of stained cancer cells were grouped in 4 groups, (<5% = 0, 5–33% = 1, 34–66% = 2, 67–100% = 3). A score was calculated multiplying the fraction (0–3) with the intensity (0–3) resulting in a product between 0 and 9. A tumor tissue sample was considered VEGFR2 positive if the final score was >6 [35]. For PDGFRα, the percentages of stained cancer cells were grouped in 5 groups, (0% = 0, 1–9% = 1, 10–50% = 2, 51–80% = 3, 81–100% = 4). A score was calculated multiplying the fraction (0–4) with the intensity (0–3) resulting in a product between 0 and 12. A tumor tissue sample was considered PDGFRα positive if the final score was ≥5 [36].

Fluorescence in situ hybridization (FISH)

A custom-made Poseidon quadruple-color DNA probe was ordered from Kreatech (Kreatech, Amsterdam, Netherlands). It was composed of a probe mix to detect c-KIT (labeled with PlatinumBright495, green), VEGFR2 (labeled with PlatinumBright550, gold), PDGFRα (labeled PlatinumBright590, red) and the control region SE4 (labeled with PlatinumBright415, blue). With some minor adjustments, staining was performed according to the manufacturer's manual using chemicals from Tissue Digestion Pretreatment Kit I (Kreatech, Amsterdam, Netherlands). TMA slides were baked for 2 hours in 80°C. They were deparaffinised in xylene for 2×7 min, and rehydrated 3 min each in absolute (ABS), 85% and 70% ethanol. The slides were treated for 15 min in Pretreatment A (97–98°C) and rinsed for 2×2 min in distilled water (dH2O). Pepsin was added to the slides for 40 min and the slides were rinsed in dH2O for 1 min. The slides were put in 2×SSC with pH 7.0 for 5 min, and dehydrated in 70%, 85% and ABS for 1 min each. After drying, 20 µl of Poseidon quadruple-color DNA probe was added and a cover glass was mounted on each TMA slide and sealed with rubber glue. The slides were put on a hot plate (80°C) for 5 min for co-denaturation and incubated in a humid dark chamber to hybridize overnight at 37°C. The next day, rubber glue and cover glass were removed; the TMA slides were dipped in Washbuffer II and rinsed for 2 min in Washbuffer I (73°C). Finally the slides were left in Washbuffer II for 1 min, followed by dehydration in 70%, 85% and ABS for 1 min each. When air-dried, 22 µl of DAPI counterstain (concentration 0.05 µg/ml) and cover glass were added. Only one of the two core biopsies from each patient was examined. 30 invasive cancer cells were evaluated in each sample. The number of gene copies and of chromosome 4 control regions per cell was counted. A gain was considered when a cell contained ≥4 gene copies. If the ratio between gene copies and chromosome 4 control regions was >2 the cell was considered to have an amplification. Any tissue sample containing ≥5 cells with gains and/or amplifications was considered FISH positive. The cut-off point for FISH positivity was chosen based on a comparison of cut-off points used in recent similar studies [20], [21], [27], [37]. Since in this study TMAs of formalin-fixed tissue were used, we used the cut-off point that was closest to the one used by Joensuu et al. [20] who used the same type of tissue material.

Statistical analysis

Breast cancer mortality (BCM) was chosen as endpoint in this study. Survival data was retrieved from the Swedish Register of Causes of Death (Central Statistics Office) and registered events until 31 of December 2010 were recorded. To evaluate differences in the distribution of clinical data and tumor characteristics between the non-TNBC group and the TNBC group a χ2 test was used. Binary logistic regression analysis was performed to quantify the effect of each RTK, with and without adjustment for other tumor characteristics. The effect on survival for high protein expression and increased gene copy number of c-KIT, VEGFR2 and PDGFRα, was described in terms of BCM using cumulative incidence curves. P-values were calculated using the log-rank test and Hazard ratios (HR) using Cox regression. P-values<0.05 were considered statistically significant. The statistical calculations were performed using SPSS Version 21.0 (SPSS, Chicago, IL) and graphs were drawn in Stata Version 12.1 (StataCorp LP, College Station, TX).

Results

Patient cohort and TNBC patient characteristics

34 (7.3%) of the included 464 tumors were diagnosed as triple-negative (Figure 1). The TNBC presented with larger tumors at diagnosis (P = 0.07), higher Nottingham histological grade (NHG) (P<0.001) and the Ki67 index (P<0.001), compared to the non-TNBC (Table 1). Furthermore, the TNBC patients were younger (P = 0.008) and had a 2.7-fold higher BC mortality (95% CI 1.3–5.7, P = 0.009) than those carrying a non-TNBC.
Table 1

Patient and tumor characteristics in relation to triple-negative breast cancer (TNBC).

Characteristicsa All patients N (%)non-TNBC N (%)TNBC Nb (%) P-value
N = 464N = 430N = 34
Age
Median (range)58 (26–88)58 (26–88)52 (29–86)
<5095 (20)82 (19)13 (38)0.01c
≥50369 (80)348 (81)21 (62)
Histopathologic type
Ductal329 (71)301 (70)28 (82)<0.001c
Lobular85 (18)85 (20)0
Tubular18 (4)18 (4)0
Medullary11 (2)6 (1)5 (15)
Mainly DCIS12 (3)12 (3)0
Other9 (2)8 (2)1 (3)
Unknown000
Tumor size
≤20 mm310 (67)292 (68)18 (53)0.09c
>20 mm153 (33)137 (32)16 (47)
Unknown110
Node status
N+190 (42)175 (42)15 (44)0.86c
N0264 (58)245 (58)19 (56)
Unknown10100
NHG
1101 (22)101 (24)0<0.001e
2243 (53)235 (55)8 (24)
3115 (25)89 (21)26 (77)
Unknown550
Ki67
High (>20%)157 (34)131 (31)26 (77)<0.001c
Low (≤20%)307 (66)299 (70)8 (24)
Unknown000

DCIS = ductal cancer in situ; N0 = node negative; N+ = node positive; NHG = Nottingham histological grade.

Percentages are shown despite the small number of patients in this group (N<50).

P-value from Fisher's Exact Test.

P-value from Linear-by-Linear Association test.

DCIS = ductal cancer in situ; N0 = node negative; N+ = node positive; NHG = Nottingham histological grade. Percentages are shown despite the small number of patients in this group (N<50). P-value from Fisher's Exact Test. P-value from Linear-by-Linear Association test. 32 (94%) of the 34 TNBCs had a Core Basal phenotype (defined as being negative for ER, PR and HER2, and positive for CK5/6 and/or EGFR).

Protein expression in TNBC compared to non-TNBC

Examples of IHC staining are shown in Figure 2A–2F and the results are summarized in Table 2. Test for correlations between the expressions of the three proteins are shown in Table S1. Table S2 a, b and c presents the distribution of the tumors between the percentage groups (c-KIT) and the IHC scores (VEGFR2 and PDGFRα) for TNBC and non-TNBC. Cut off points has been marked by a separating space in each table to demonstrate the rationale behind the cut off points. Significantly higher expression of c-KIT and VEGFR2 was found in TNBC compared to non-TNBC tumors (P<0.001) and PDGFRα showed the same tendency (P = 0.07).
Figure 2

The left panels show examples of positive immunohistochemical staining with strong intensity for c-KIT (A), VEGFR2 (C) and PDGFRα (E).

Negative controls are shown to the right, c-KIT (B), VEGFR2 (D) and PDGFRα (F). Original magnification ×40.

Table 2

Protein expression and gene copy number of c-KIT, VEGFR2 and PDGFRα in relation to triple-negative breast cancer (TNBC).

IHCa and FISHa markerAll patients N (%)Luminal A N (%)Luminal B HER2− N (%)Luminal B HER2+ N (%)HER2+ N (%)TNBC N (%) P-value for St Gallen subgroup P-value TNBC vs non-TNBC
c-KIT IHC
Positive57 (12)20 (8)13 (16)8 (11)0 (0)16 (49)<0.001b <0.001b
Negative404 (88)233 (92)67 (84)66 (89)17 (100)17 (52)
Unknown320001
VEGFR2 IHC
Positive43 (10)18 (7)6 (8)7 (10)1 (6)11 (32)<0.001b <0.001b
Negative412 (90)230 (93)73 (92)67 (91)15 (94)23 (68)
Unknown971010
PDGFRα IHC
Positive94 (20)38 (15)24 (30)15 (20)3 (18)11 (32)0.02b 0.07b
Negative367 (80)215 (85)55 (70)59 (80)14 (82)23 (68)
Unknown321000
c-KIT FISH
Positive21 (11)11 (11)4 (10)3 (10)1 (14)2 (11)1b 1b
Negative172 (89)86 (89)36 (90)27 (90)6 (86)16 (89)
Unknown27115840441016
VEGFR2 FISH
Positive22 (11)10 (10)5 (13)4 (13)1 (14)2 (11)1b 1b
Negative171 (89)87 (90)35 (88)26 (87)6 (86)16 (89)
Unknown27115840441016
PDGFRα FISH
Positive24 (12)12 (12)5 (13)4 (13)1 (14)2 (11)1b 1b
Negative169 (88)85 (88)35 (88)26 (87)6 (86)16 (89)
Unknown27115840441016

IHC = immunohistochemistry. FISH = fluorescence in situ hybridization.

P-value from Fisher's Exact Test.

The left panels show examples of positive immunohistochemical staining with strong intensity for c-KIT (A), VEGFR2 (C) and PDGFRα (E).

Negative controls are shown to the right, c-KIT (B), VEGFR2 (D) and PDGFRα (F). Original magnification ×40. IHC = immunohistochemistry. FISH = fluorescence in situ hybridization. P-value from Fisher's Exact Test. Binary logistic regression showed that the unadjusted odds ratio (OR) of c-KIT positivity was 8.9 times higher for TNBC cases than for non-TNBC cases (95% CI 4.2–19, P<0.001). The corresponding unadjusted OR of VEGFR2 positivity was 5.8 (95% CI 2.6–13, P<0.001), and of PDGFRα positivity 2.0 (95% CI 0.9–4.2, P = 0.08). The OR of c-KIT positivity, adjusted for histopathological type, grade, tumor size >20 mm, and lymph node engagement, was 6.8 times higher for TNBC cases than for non-TNBC cases (95% CI 2.9–16, P<0.001) and the corresponding adjusted OR of VEGFR2 positivity was 3.6 (95% CI 1.4–9.3, P = 0.007), and of PDGFRα positivity 1.3 (95% CI 0.6–3.1, P = 0.5). To further investigate the connection between these three RTKs and TNBC we also analyzed how many tumors were positive for at least one, and at least two, of the three receptors. 25 (73.5%) of the 34 TNBC tumors had a high expression of at least one of the three receptors compared to 129 (30.0%) of the 430 non-TNBC (P<0.001). 12 (35.3%) of the 34 TNBC tumors showed a high expression of at least two of the three receptors compared to 25 (5.8%) of the 429 non-TNBC (P<0.001). Only three tumors in total were positive for all three RTKs, one of them was a TNBC. OR for a TNBC patient compared to a non-TNBC patient to have high expression of at least one, or at least two, of the three RTKs vs fewer, adjusted for the above mentioned factors was 4.3 (95% CI 1.8–9.9, P = 0.001), and 5.3 (95% CI 2.0–13, P = 0.001) respectively.

Gene copy number increase and comparison of high protein expression and increased gene copy number

FISH staining was evaluated in 193 (42%) patient tissue samples. The quality of the staining in the rest of the samples was too low to evaluate, or no staining was seen at all. A possible explanation for this is that we used tissue micro arrays of paraffin embedded tissue and a custom made quadruple probe, two factors known to complicate the FISH procedure [38]. Examples of normal FISH staining pattern and positive FISH staining pattern are shown in Figure 3A and 3B. 21 (11%) of the 193 evaluated patient tumors were c-KIT FISH positive, 22 (11%) were VEGFR2 FISH positive and 24 (12%) were PDGFRα FISH positive (Table 2). There was no difference in the percentage FISH positive tumors in the TNBC group compared to the non-TNBC group. No correlation was seen between the c-KIT, PDGFRα and VEGFR2 IHC positive tumors, and the respective marker FISH positive tumors (Table S3).
Figure 3

Examples of FISH. A shows a normal cell with two copies of each gene (green = c-KIT, yellow/gold = VEGFR2 and red = PDGFRα), and two centromeres (blue).

B shows a cell with a gene and centromere copy number gain. Original magnification ×63.

Examples of FISH. A shows a normal cell with two copies of each gene (green = c-KIT, yellow/gold = VEGFR2 and red = PDGFRα), and two centromeres (blue).

B shows a cell with a gene and centromere copy number gain. Original magnification ×63.

Correlation between high protein expression, increased gene copy number and survival

Cumulative incidence curves were calculated for patients with tumors having a high protein expression of c-KIT (Figure 4A, B and C), VEGFR2 (Figure 4D, E and F) and PDGFRα (Figure 4G, H and I) in both the non-TNBC (Figure 4B, E and H) and the TNBC group (Figure 4C, F and I). For c-KIT and PDGFRα, no statistically significant difference in BCM was seen for the IHC positive versus the IHC negative groups, neither for TNBC patients nor for non-TNBC patients. For VEGFR2 there was moderate evidence (P = 0.03) for lower BCM for the IHC positive patients in the non-TNBC group (Figure 4E), but no difference in the TNBC group (Figure 4F).
Figure 4

Breast cancer mortality (BCM) stratified for positive vs negative status of c-KIT (A, B and C), VEGFR2 (D, E and F) and PDGFRα (G, H and I) for all (A, D, G), non triple-negative (B, E, H) and triple-negative patients (C, F, I).

Cox regression showed no significant influence on mortality in the TNBC group for the tumors with high marker expression of c-KIT HR = 0.7 (95% CI 0.2–2.2), PDGFRα HR = 2.0 (95% CI 0.6–6.4), VEGFR2 HR = 1.4 (95% CI 0.4–4.8). In the non-TNBC group, a HR indicating notably lower breast cancer mortality was seen for the VEGFR2 positive patients, HR = 0.04 (95% CI 0.001–3.3), but this result was not significant (P = 0.16). For c-KIT and PDGFRα, the HRs showed no significant mortality influence in the non-TNBC group (c-KIT HR = 1.0, 95% CI 0.4–2.6 and PDGFRα HR = 0.4, 95% CI 0.2–1.0). No difference in BCM was seen for FISH positive versus FISH negative patients, neither in the TNBC nor in the non-TNBC group (data not shown).

Discussion

TNBC is a subgroup of breast cancer with poor prognosis and no targeted therapy available. In this study we show that TNBC is associated with high protein expression of the three RTKs and potential drug targets, c-KIT, VEGFR2 and PDGFRα, closely spaced at 4q12. The strongest evidence for correlation was seen for c-KIT and VEGFR2 while PDGFRα showed a somewhat weaker association. When RTK expression was adjusted for known tumor characteristics, the significant correlations for c-KIT and VEGFR2 expression were retained whereas the association between PDGFRα and TNBC was not significant. The low number of TNBC patients in the cohort can be one explanation for this. However, linking PDGFRα to TNBC is an interesting finding because PDGFRα has been detected as a key protein in one of the central epithelial to mesenchymal transition (EMT) processes, the invadopodia formation. EMT is a process where tumor cells lose epithelial characteristics and conversely gain invasive mesenchymal and stem cell-like features, and it has been related to breast cancer with a basal-like phenotype [39]. In invadopodia formation, PDGFRα is up-regulated and activated downstream of Twist1, and it is known that blocking of PDGFRα strongly decreases invadopodia formation [30]. Also, a recent study showed that mRNAs encoding PDGFRα, PDGFRβ and their ligand PDGF-C were highly expressed in basal B subtype of breast cancer cell lines with mesenchymal properties but not in luminal-like cell counterparts with more epithelial features [31]. High PDGFRα expression might thus be a sign of an active EMT process in the TNBC tumors. Interestingly, Lehmann et al. have recently found that genes involved in the PDGFR and VEGF pathways are upregulated in the mesenchymal and mesenchymal stem-like TNBC subtypes [14]. It would thus be intriguing to investigate the expression of c-KIT, VEGFR2 and PDGFRα in different genomic TNBC subtypes. However the number of TNBC patients in the present cohort was too low for additional subdivision. To further test the association between TNBC and the three RTKs encoded at 4q12, we analyzed how many tumors had a high expression of at least one, or at least two, of the three RTKs. We noticed a remarkably elevated frequency of high expression of either one of three markers (73.5% compared to 30.0%, P<0.001) or two of three markers (35.3% compared to 5.8%, P<0.001) in the TNBC compared to the non-TNBC. Also, the ORs for TNBC and high expression of at least one, or at least two, of the three RTKs were 4.3 and 5.3, respectively, when compared to non-TNBC, with P-values of 0.001. These results support a connection between TNBC and high expression of c-KIT, VEGFR2 and PDGFRα. Survival analysis did not show any correlation between high protein expression of c-KIT, VEGFR2 or PDGFRα and changes in BCM in the TNBC subgroup. Previous studies have shown varying results; Nielsen et al., 2004 reported no difference in survival for c-KIT positive BLBC patients compared to control [40], whereas Kashiwagi et al., 2012 found a poorer outcome for c-KIT positive BLBC patients with a hazard ratio of 2.29 [26]. One explanation for these opposing results is differences in cut-point for categorizing a sample as positive; Nielsen et al., 2004 used ≥25% stained cells as cut-point while Kashiwagi et al., 2012 set their limit to ≥10%. In this study we used ≥1% stained cells as cut-point, since it is a standard cut-point for c-KIT assessment [25]. VEGFR2 has in one study been found to be significantly correlated to decreased breast cancer specific survival (BCSS) in TNBC patients [28]. In the present study, no such correlation was found. However, we found that VEGFR2 positive non-TNBC patients had a statistically significant lower mortality than VEGFR2 negative non-TNBC patients. A possible explanation for these differing results is that the previous study [28] included only premenopausal women with stage II breast cancer, while the present study had no upper age limit and a cohort weighted towards a low risk profile. Also, treatment regimens differed between the two studies. PDGFRα has been found to be associated with breast cancer progression and metastasis [29], [30]. In this study we did not find any association between poor survival and high PDGFRα expression. 193 (42%) of the 464 FISH stained tissue samples were assessable for gene copy number. No difference was seen in tissue samples with increased gene copy number between the TNBC and the non-TNBC group. Nor was any correlation seen between high protein expression and increased gene copy number. Survival analysis showed no correlation between increased gene copy number and differences in BCM. c-KIT, VEGFR2 and PDGFRα have previously been found to be amplified in 15–33% of primary glioblastomas [20], [41] and amplification of c-KIT and PDGFRα to be associated with poor survival of glioblastoma patients [21]. In a recent study, an increased copy number of the genes c-KIT and VEGFR2 was found in the TNBC subgroup, and increased gene copy number was related to an aggressive phenotype and impaired prognosis [27]. These results were not confirmed in this study. Staining methods differed between the two studies where Johansson et al., used fresh frozen tumor tissue, and we used formalin-fixed TMAs in the present study. The results from this study suggest that a multi-targeting RTK inhibitor, such as for example sunitinib or sorafenib, would be a possible treatment option for TNBC patients. We found that as many as 73.5% of TNBC patients have a high expression of at least one of the three RTKs c-KIT, VEGFR2 and PDGFRα. A few pilot studies have been conducted evaluating sunitinib as treatment for metastatic breast cancer and in those studies TNBC patients showed promising response rates [42], [43]. However, a larger randomized phase III study on sunitinib as single treatment for metastatic breast cancer was aborted ahead of schedule since preliminary data showed a lower progression free survival amongst the sunitinib treated patients [44]. Important to notice is that the inclusion criteria in these studies were metastatic breast cancer regardless of subgroup or RTK expression (with the exception of the phase III randomized trial where HER2 positive patients were excluded). Since TNBC patients seemed to benefit from the anti-RTK treatment in pilot studies [42], [43], it is possible that only TNBC or BLBC patients should be included in a future study of anti-RTK treatment. In addition to the RTK inhibitors described above, there are also drugs aimed at the RTK ligands such as bevacizumab targeting VEGF-A. In a recent study it was shown that addition of bevacizumab to TNBC patients in the neoadjuvant setting could increase the rate of pathologic complete response [45]. In conclusion, we have found that the proteins c-KIT, VEGFR2 and PDGFRα, encoded by genes at 4q12, are associated to the St Gallen breast cancer subgroup TNBC. No correlation was seen between high protein expression, increased gene copy number and BCM in the TNBC group. A remarkably high expression of at least one, and at least two, of the three investigated markers was seen in the majority of TNBC patients compared to non-TNBC, which suggests that anti-RTK therapy could be useful in this patient group in the future. Presents correlations between c-KIT, VEGFR2 and PDGFRα. (DOCX) Click here for additional data file. A, b and c presents the distribution of the tumors between the percentage groups (c-KIT) and the IHC scores (VEGFR2 and PDGFRα) for TNBC and non-TNBC. (DOCX) Click here for additional data file. Shows how IHC and FISH results relate. (DOCX) Click here for additional data file.
  45 in total

1.  Neoadjuvant chemotherapy and bevacizumab for HER2-negative breast cancer.

Authors:  Gunter von Minckwitz; Holger Eidtmann; Mahdi Rezai; Peter A Fasching; Hans Tesch; Holm Eggemann; Iris Schrader; Kornelia Kittel; Claus Hanusch; Rolf Kreienberg; Christine Solbach; Bernd Gerber; Christian Jackisch; Georg Kunz; Jens-Uwe Blohmer; Jens Huober; Maik Hauschild; Tanja Fehm; Berit Maria Müller; Carsten Denkert; Sibylle Loibl; Valentina Nekljudova; Michael Untch
Journal:  N Engl J Med       Date:  2012-01-26       Impact factor: 91.245

2.  Protein kinase C α is a central signaling node and therapeutic target for breast cancer stem cells.

Authors:  Wai Leong Tam; Haihui Lu; Joyce Buikhuisen; Boon Seng Soh; Elgene Lim; Ferenc Reinhardt; Zhenhua Jeremy Wu; Jordan A Krall; Brian Bierie; Wenjun Guo; Xi Chen; Xiaole Shirley Liu; Myles Brown; Bing Lim; Robert A Weinberg
Journal:  Cancer Cell       Date:  2013-09-09       Impact factor: 31.743

3.  Biomarker expression and St Gallen molecular subtype classification in primary tumours, synchronous lymph node metastases and asynchronous relapses in primary breast cancer patients with 10 years' follow-up.

Authors:  Anna-Karin Falck; Pär-Ola Bendahl; Gunilla Chebil; Hans Olsson; Mårten Fernö; Lisa Rydén
Journal:  Breast Cancer Res Treat       Date:  2013-06-27       Impact factor: 4.872

4.  Molecular portraits of human breast tumours.

Authors:  C M Perou; T Sørlie; M B Eisen; M van de Rijn; S S Jeffrey; C A Rees; J R Pollack; D T Ross; H Johnsen; L A Akslen; O Fluge; A Pergamenschikov; C Williams; S X Zhu; P E Lønning; A L Børresen-Dale; P O Brown; D Botstein
Journal:  Nature       Date:  2000-08-17       Impact factor: 49.962

5.  Increased gene copy number of KIT and VEGFR2 at 4q12 in primary breast cancer is related to an aggressive phenotype and impaired prognosis.

Authors:  Ida Johansson; Kristina E Aaltonen; Anna Ebbesson; Dorthe Grabau; Caroline Wigerup; Ingrid Hedenfalk; Lisa Rydén
Journal:  Genes Chromosomes Cancer       Date:  2011-12-14       Impact factor: 5.006

6.  c-Kit expression as a prognostic molecular marker in patients with basal-like breast cancer.

Authors:  S Kashiwagi; M Yashiro; T Takashima; N Aomatsu; H Kawajiri; Y Ogawa; N Onoda; T Ishikawa; K Wakasa; K Hirakawa
Journal:  Br J Surg       Date:  2013-01-14       Impact factor: 6.939

7.  Inference of the Basal epithelial phenotype in breast carcinoma from differential marker expression, using tissue microarrays in triple negative breast cancer and women younger than 35.

Authors:  Aziza Nassar; Zachary M Sussman; Diane Lawson; Cynthia Cohen
Journal:  Breast J       Date:  2012-08-10       Impact factor: 2.431

8.  Comparison of basal-like triple-negative breast cancer defined by morphology, immunohistochemistry and transcriptional profiles.

Authors:  Patrycja Gazinska; Anita Grigoriadis; John P Brown; Rosemary R Millis; Anca Mera; Cheryl E Gillett; Lars H Holmberg; Andrew N Tutt; Sarah E Pinder
Journal:  Mod Pathol       Date:  2013-02-08       Impact factor: 7.842

9.  Distinct phenotypic differences associated with differential amplification of receptor tyrosine kinase genes at 4q12 in glioblastoma.

Authors:  Anna Burford; Suzanne E Little; Alexa Jury; Sergey Popov; Ross Laxton; Lawrence Doey; Safa Al-Sarraj; Juliane M Jürgensmeier; Chris Jones
Journal:  PLoS One       Date:  2013-08-21       Impact factor: 3.240

10.  Analysis of and prognostic information from disseminated tumour cells in bone marrow in primary breast cancer: a prospective observational study.

Authors:  Anna-Karin Falck; Pär-Ola Bendahl; Christian Ingvar; Jorma Isola; Per-Ebbe Jönsson; Pia Lindblom; Kristina Lövgren; Karin Rennstam; Mårten Fernö; Lisa Rydén
Journal:  BMC Cancer       Date:  2012-09-11       Impact factor: 4.430

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  19 in total

1.  Predictive value of vascular endothelial growth factor receptor type 2 in triple-negative breast cancer patients treated with neoadjuvant chemotherapy.

Authors:  Nataliya Babyshkina; Marina Zavyalova; Natalia Tarabanovskaya; Tatyana Dronova; Nadejda Krakhmal; Elena Slonimskaya; Julia Kzhyshkowska; Evgeny Choynzonov; Nadejda Cherdyntseva
Journal:  Mol Cell Biochem       Date:  2017-12-11       Impact factor: 3.396

2.  The Relationship between KIT Copy Number Variation, Protein Expression, and Angiogenesis in Sporadic Breast Cancer.

Authors:  Maryam Rahimi; Farkhondeh Behjati; Khorram Khorshid Hamid Reza; Masoud Karimlou; Elahe Keyhani
Journal:  Rep Biochem Mol Biol       Date:  2020-04

3.  Combined kinase inhibitors of MEK1/2 and either PI3K or PDGFR are efficacious in intracranial triple-negative breast cancer.

Authors:  Amanda E D Van Swearingen; Maria J Sambade; Marni B Siegel; Shivani Sud; Robert S McNeill; Samantha M Bevill; Xin Chen; Ryan E Bash; Louisa Mounsey; Brian T Golitz; Charlene Santos; Allison Deal; Joel S Parker; Naim Rashid; C Ryan Miller; Gary L Johnson; Carey K Anders
Journal:  Neuro Oncol       Date:  2017-10-19       Impact factor: 12.300

Review 4.  Current advances in biomarkers for targeted therapy in triple-negative breast cancer.

Authors:  Brett Fleisher; Charlotte Clarke; Sihem Ait-Oudhia
Journal:  Breast Cancer (Dove Med Press)       Date:  2016-10-06

5.  Phase II, randomized, placebo-controlled study of dovitinib in combination with fulvestrant in postmenopausal patients with HR+, HER2- breast cancer that had progressed during or after prior endocrine therapy.

Authors:  Antonino Musolino; Mario Campone; Patrick Neven; Neelima Denduluri; Carlos H Barrios; Javier Cortes; Kimberly Blackwell; Hatem Soliman; Zsuzsanna Kahan; Hervé Bonnefoi; Matthew Squires; Yong Zhang; Stephanie Deudon; Michael M Shi; Fabrice André
Journal:  Breast Cancer Res       Date:  2017-02-10       Impact factor: 6.466

6.  Microenvironmental control of breast cancer subtype elicited through paracrine platelet-derived growth factor-CC signaling.

Authors:  Pernilla Roswall; Matteo Bocci; Michael Bartoschek; Hong Li; Glen Kristiansen; Sara Jansson; Sophie Lehn; Jonas Sjölund; Steven Reid; Christer Larsson; Pontus Eriksson; Charlotte Anderberg; Eliane Cortez; Lao H Saal; Christina Orsmark-Pietras; Eugenia Cordero; Bengt Kristian Haller; Jari Häkkinen; Ingrid J G Burvenich; Elgene Lim; Akira Orimo; Mattias Höglund; Lisa Rydén; Holger Moch; Andrew M Scott; Ulf Eriksson; Kristian Pietras
Journal:  Nat Med       Date:  2018-03-12       Impact factor: 53.440

Review 7.  Contributions of Thyroid Hormone to Cancer Metastasis.

Authors:  Shaker A Mousa; Gennadi V Glinsky; Hung-Yun Lin; Osnat Ashur-Fabian; Aleck Hercbergs; Kelly A Keating; Paul J Davis
Journal:  Biomedicines       Date:  2018-08-22

8.  Prognostic significance of CD117 expression and TP53 missense mutations in triple-negative breast cancer.

Authors:  Yanli Luo; Wentao Huang; Huizhen Zhang; Guang Liu
Journal:  Oncol Lett       Date:  2018-02-22       Impact factor: 2.967

9.  The PDGF pathway in breast cancer is linked to tumour aggressiveness, triple-negative subtype and early recurrence.

Authors:  Sara Jansson; Kristina Aaltonen; Pär-Ola Bendahl; Anna-Karin Falck; Maria Karlsson; Kristian Pietras; Lisa Rydén
Journal:  Breast Cancer Res Treat       Date:  2018-01-29       Impact factor: 4.872

10.  Dual Mechanisms of LYN Kinase Dysregulation Drive Aggressive Behavior in Breast Cancer Cells.

Authors:  Giusy Tornillo; Catherine Knowlson; Howard Kendrick; Joe Cooke; Hasan Mirza; Iskander Aurrekoetxea-Rodríguez; Maria D M Vivanco; Niamh E Buckley; Anita Grigoriadis; Matthew J Smalley
Journal:  Cell Rep       Date:  2018-12-26       Impact factor: 9.423

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