The amino acid transporter SLC7A5 confers a poor prognosis in the highly proliferative breast cancer subtypes and is a key therapeutic target in luminal B tumours

Rokaya El Ansari; Madeleine L. Craze; Islam Miligy; Maria Diez-Rodriguez; Christopher C. Nolan; Ian O. Ellis; Emad A. Rakha; Andrew R. Green

doi:10.1186/s13058-018-0946-6

Research Article

Open Access

The amino acid transporter SLC7A5 confers a poor prognosis in the highly proliferative breast cancer subtypes and is a key therapeutic target in luminal B tumours

Rokaya El Ansari¹,
Madeleine L. Craze¹,
Islam Miligy¹,
Maria Diez-Rodriguez¹,
Christopher C. Nolan¹,
Ian O. Ellis^{1, 2},
Emad A. Rakha^{1, 2} and
Andrew R. Green¹Email author View ORCID ID profile

Breast Cancer Research201820:21

https://doi.org/10.1186/s13058-018-0946-6

Received: 10 October 2017

Accepted: 26 February 2018

Published: 22 March 2018

Abstract

Background

Breast cancer (BC) is a heterogeneous disease characterised by variant biology and patient outcome. The amino acid transporter, SLC7A5, plays a role in BC although its impact on patient outcome in different BC subtypes remains to be validated. This study aimed to determine whether the clinicopathological and prognostic value of SLC7A5 is different within the molecular classes of BC.

Methods

SLC7A5 was assessed at the genomic level, using Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) data (n = 1980), and proteomic level, using immunohistochemical analysis and tissue microarray (TMA) (n = 2664; 1110 training and 1554 validation sets) in well-characterised primary BC cohorts. SLC7A5 expression correlated with clinicopathological and biological parameters, molecular subtypes and patient outcome.

Results

SLC7A5 mRNA and protein expression were strongly correlated with larger tumour size and higher grade. High expression was observed in triple negative (TN), human epidermal growth factor receptor 2 (HER2)+, and luminal B subtypes. SLC7A5 mRNA and protein expression was significantly associated with the expression of the key regulator of tumour cell metabolism, c-MYC, specifically in luminal B tumours only (p = 0.001). High expression of SLC7A5 mRNA and protein was associated with poor patient outcome (p < 0.001) but only in the highly proliferative oestrogen receptor (ER)+/ luminal B (p = 0.007) and HER2+ classes of BC (p = 0.03). In multivariate analysis, SLC7A5 protein was an independent risk factor for shorter breast-cancer-specific survival only in ER+ high-proliferation tumours (p = 0.02).

Conclusions

SLC7A5 appears to play a role in the aggressive highly proliferative ER+ subtype driven by MYC and could act as a potential therapeutic target. Functional assessment is necessary to reveal the specific role played by this transporter in the ER+ highly proliferative subclass and HER2+ subclass of BC.

Keywords

SLC7A5Breast cancerPrognosis

Background

Altered metabolic pathways have been readily accepted as part of the revised hallmarks of cancer where cancer cells are able to regulate their metabolism to provide energy and cellular building blocks required for their unremitting proliferation [1]. Many cancer cells are highly reliant on amino acids for their growth, not only because they are a nitrogen donor for the synthesis of nucleotides and other amino acids, but also because they activate mammalian target of rapamycin complex1 (mTORC1) through nutrient signalling pathways which in turn regulates protein translation and cell growth [2, 3]. There is also increasing evidence that oncogenes and/or tumour-suppressor genes can reprogramme tumour cell metabolism including the direct regulation of the amino acid transporter, solute carrier family 7 member 5 (SLC7A5), by the oncogene MYC [4, 5] and the regulation of expression of the glutamine transporter, SLC1A5, by the tumour suppressor retinoblastoma (Rb) [6].

SLC7A5 is a sodium-independent transporter and acts as an amino acid exchanger by transporting large neutral amino acids such as leucine, phenylalanine and tryptophan by exchange with intracellular glutamine [7]. It therefore functions in supplying amino acids to cancer cells and maintaining intra-cellular leucine, which is considered a master regulator of the mTORC1 signalling pathway [8–10]. For functional expression on the plasma membrane, SLC7A5 must heterodimerise with the heavy chain of SLC3A2 [7, 11].

It has been reported that SLC7A5 is highly expressed in a variety of cancers including oesophageal carcinoma [12], oral cancer [13] and lung adenocarcinoma [14]. SLC7A5 is co-expressed with the glutamine transporter, SLC1A5, in many cancers suggesting a functional coupling of these transporters in supporting tumour progression [15]. In this study, we aimed to assess SLC7A5 gene copy number and mRNA expression, alongside protein expression in large and well-characterised annotated cohorts of BC to determine its biological, clinicopathological and prognostic value in the different BC molecular classes with particular interest in the highly proliferative aggressive subgroups.

Methods

SLC7A5 copy number and gene expression

A cohort of 1980 BC tumours in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) [16] was used to evaluate SLC7A5 gene copy number aberrations (CNA) and gene expression. DNA/RNA was isolated from fresh frozen samples and genomic and transcriptional profiling was obtained using the Affymetrix SNP 6.0 and Illumina HT-12v3 platforms respectively. CNA were considered at the gene level by segments and the Šidák correction [17] was applied for multiple testing. Gene expression data were pre-processed and normalised as described previously [16]. In this cohort, patients included were oestrogen receptor (ER)-positive (ER+) and/or lymph node (LN)-negative (LN-) and did not receive adjuvant chemotherapy, whereas ER- and LN+ patients received adjuvant treatment. X-tile (version 3.6.1, Yale University, USA) was applied to dichotomise SLC7A5 mRNA expression, based on prediction of breast-cancer-specific survival (BCSS). The association between the SLC7A5 mRNA expression and clinicopathological parameters, molecular subtypes, and patient outcome was investigated. The online dataset, Breast Cancer Gene Expression Miner v4.0 (http://bcgenex.centregauducheau.fr) and breast cancer data from The Cancer Genome Atlas (TCGA) [18] were used for external validation of SLC7A5 copy number/or mRNA expression.

Patients and tumours

This study evaluated well-characterised cohorts of patients with early-stage primary operable invasive BC, who presented aged ≤70 years. Patients in the discovery set (n = 1110) presented at Nottingham City Hospital between 1989 and 1998, while the validation set (n = 1554) includes patients who were presented between 1998 and 2006. Patient management was uniform and based on tumour characteristics by Nottingham Prognostic Index (NPI) and hormone receptor status. Patients within the NPI excellent prognostic group (score ≤3.4) received no adjuvant therapy, but those patients with NPI >3.4 received tamoxifen if ER-positive (± goserelin (Zoladex) in case the patients were premenopausal). Conversely, classical cyclophosphamide, methotrexate and 5-flurouracil (CMF) were used if the patients were ER-negative and fit enough to receive chemotherapy. None of the patients in this study received neoadjuvant therapy. Clinical history, tumour characteristics and information on therapy and outcomes are prospectively maintained. Outcome data included development and time to distant metastasis (DM) and breast-cancer-specific survival (BCSS). There was no difference in the distribution of clinicopathological parameters between the discovery and validation cohorts or the METABRIC series of patients (all correlation coefficients ≥0.91, all p < 0.0001) (Additional file 1: Table S1).

Western blotting

The antibody specificity of anti-SLC7A5 (EPR17573, Abcam, UK) was validated using western blotting in human embryonic kidney (HEK) 293 T over expression lysate (Origene Technologies, Rockville, MD, USA) and MDA-MB-175 (luminal B-like), T47D and MCF7 (luminal A) [19] breast cancer lysate (American Type Culture Collection; Rockville, MD, USA). A dilution of 1:200 of the primary antibody and 1:1000 HRP-conjugated (Dako) secondary antibodies were applied: 5% milk /PBS-Tween (0.1%) (Marvel Original Dried Skimmed Milk, Premier Food Groups Ltd., UK) was used for blocking. Mouse monoclonal anti-β-actin primary antibody was used as a marker of endogenously expressed control. SLC7A5 bands were visualised using enhanced chemiluminescence (ECL) showing a single specific band at the correct predicted size (40 kDa) for the SLC7A5 protein.

Tissue arrays and immunohistochemical analysis

The discovery set (n = 1110) were arrayed as previously described using a single 0.6-mm core sampled from the periphery of each invasive tumour [20]. The validation set (n = 1554) were similarly arrayed using a tissue microarray (TMA) GrandMaster (3D Histech). Immunohistochemical (IHC) staining was performed on 4-μm TMA sections using the Novolink polymer detection system (Leica Biosystems, RE7150-K). Briefly, tissue slides were deparaffinised with xylene and rehydrated through three changes of alcohol. Heat-induced antigen epitope retrieval was performed in citrate buffer (pH 6.0) for 20 min using a microwave oven (Whirpool JT359 Jet Chef 1000 W). Endogenous peroxidase activity was blocked by peroxidase block for 5 min. Slides were washed with Tris-buffered saline (TBS, pH 7.6), followed by application of protein block for 5 min. Following another TBS wash, sections were incubated with the primary SLC7A5 antibody diluted at 1:50 in Leica antibody diluent (RE AR9352, Lieca, Biosysytems, UK) overnight at 4 °C. Slides were washed with TBS followed by incubation with post primary block for 30 min followed by a TBS wash. Novolink polymer was applied for 30 min: 3,3′-diaminobenzidine (DAB) chromogen was applied for 5 min. Slides were counterstained with Novolink haematoxylin for 6 min, dehydrated and coverslipped.

Stained TMA sections were scored using high resolution digital images (NanoZoomer; Hamamatsu Photonics, Welwyn Garden City, UK), at × 20 magnification. Evaluation of staining for SLC7A5 was based on a semi-quantitative assessment of digital images of the cores using a modified histochemical score (H-score) which includes an assessment of both the intensity and the percentage of stained cells [21]. Staining intensity was assessed as follows: 0, negative; 1, weak; 2, medium; 3, strong, and the percentage of the positively stained tumour cells was estimated subjectively. The final H-score was calculated by multiplying the percentage of positive cells (0–100) by the intensity (0–3), producing a total range of 0–300. Dichotomisation of protein expression in predicting BCSS was determined using x-tile software.

Immunhistochemical staining and dichotomisation of the other biomarkers included in this study were as per previous publications [20, 22–30]. ER and progesterone receptor (PgR) positivity was defined as ≥ 1% staining. Immunoreactivity of HER2 in TMA cores was scored using standard HercepTest guidelines (Dako). Chromogenic in situ hybridisation (CISH) was used to quantify HER2 gene amplification in borderline cases using the HER2 FISH pharmDx™ plus HER2 CISH pharmDx™ kit (Dako) and was assessed according to the American Society of Clinical Oncology guidelines. BC molecular subtypes were defined based on tumour IHC profile and the Elston-Ellis [31] mitotic score as: ER+/HER2- low proliferation (mitotic score 1), ER+/HER2- high proliferation (mitotic score 2 and 3); HER2-positive class: HER2+ regardless of ER status; triple negative: ER-, PgR- and HER2- [32]. Basal-like phenotype was defined as tumours expressing cytokeratin (Ck) 5/6, and/or Ck14 and/or Ck17.

Statistical analysis

Statistical analysis was performed using SPSS 22.0 statistical software (SPSS Inc., Chicago, IL, USA). Spearman’s correlation coefficient was calcualted to examine the association between continuous variables. The chi-square test was performed to analyse relationships between categorical variables. For the continuous variables, differences between three or more groups were assessed using one-way analysis of variance (ANOVA) with the post-hoc Tukey multiple comparison test (for normally distributed data) or Kruskal-Wallis test (for non-normal distribution). Differences between two groups were assessed using the t test (normally distributed data) or Mann-Whitney test (non-normal distribution). Survival curves were analysed by the Kaplan-Meier and log rank test. Cox’s proportional hazard method was performed for multivariate analysis to identify the independent prognostic/predictive factors. The p values were adjusted for multiple testing using the Bonferroni correction. A p value ˂0.05 was considered significant. The study endpoints were 10-year BCSS or distant metastasis-free survival (DMFS).

Results

SLC7A5 genomic profiling

SLC7A5 was amplified in 0.3% and 0.6% of BC cases in the METABRIC and TCGA datasets, respectively, while deletion (deep and shallow) was detected in 56% and 68% of cases in the same cohorts respectively. Point mutations in SLC7A5 were extremely rare, where TCGA data reported just one case with a missense mutation (Additional file 2: Figure S4A) [33, 34]. SLC7A5 is situated on chromosome 16 (16q24.2); all the annotated genes, which were located on 16q [35] were selected to determine their CNV in relation to SLC7A5 and assess whether these aberrations were locus-specific or involved large chromosomal segments. There was significant positive correlation between SLC7A5 deletion and the deletion of all genes (p < 0.001, Additional file 3: Table S2) in both the METABRIC and TCGA data. However, amplification of three genes (FANCA, CBFA2T3 and CDT1) showed significant association with the amplified SLC7A5 (p ≤ 0.03, data not shown) in the afore-mentioned datasets together.

SLC7A5 expression in breast cancer

SLC7A5 protein expression was observed, predominantly in the membrane of invasive breast cancer cells, with expression levels varying from absent to high (Fig. 1b and c). The distribution of the SLC7A5 protein expression was unimodal and left-skewed. The SLC7A5 mRNA expression had a normal distribution. Expression of SLC7A5 mRNA and protein were dichotomised using cut points derived from prediction of patient survival using X-tile (https://medicine.yale.edu/lab/rimm/research/software.aspx; Yale University). Positive SLC7A5 expression (>15 H-score) was observed in 191/1110 (17%) and 268/1554 (17%) of cases in the discovery and validation sets, respectively, while high SLC7A5 mRNA expression (log2 intensity >8) was observed in 1019/1923 (53%) of the METABRIC breast cancer cases. A total of 49/1980 (2.4%) of cases had a copy number (CN) gain of SLC7A5 and 530/1980 (26.7%) a CN loss. Significant association was observed between SLC7A5 copy number variation (CNV) and SLC7A5 mRNA expression (p < 0.001, Fig. 2).

Fig. 1
SLC7A5 protein expression in invasive breast cancer cores. a Negative immunohistochemical (IHC) expression. b Positive IHC expression

Fig. 2
*SLC7A5* copy number aberrations and relationship with mRNA expression in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohort using one-way analysis of variance and the post-hoc Tukey test

SLC7A5 and clinicopathological parameters

Table 1 summarises the associations between SLC7A5 protein expression including larger tumour size, high tumour grade, and poor Nottingham Prognostic Index (NPI) (all p < 0.001). Regarding BC metastatic sites, high SLC7A5 protein levels were associated with the development of distant metastases (DM) in the brain (p < 0.001) and lung (p = 0.04), while there was no association with development of DM in the bone or liver.

Table 1

Clinicopathological associations of SLC7A5 protein expression in the discovery and validation breast cancer series

SLC7A5 protein
Discovery set						Validation set
	Number (%)	Mean		p value	Adjusted p value	Number (%)	Mean		p value	Adjusted p value
Patient’s age (years)
≥50	395 (36)	582.20		0.003	0.018	469 (30)	859.46		7.0 × 10⁻¹²	<0.0001
<50	714 (64)	539.95		0.003	0.018	1070 (70)	730.79		7.0 × 10⁻¹²	<0.0001
Tumour size
≥2.0 cm	532 (48)	521.22		0.000002	<0.0001	939 (61)	729.47		6.5 × 10⁻⁹	<0.0001
˂2.0 cm	577 (52)	586.14		0.000002	<0.0001	599 (39)	832.24		6.5 × 10⁻⁹	<0.0001
Lympho-vascular invasion
Negative	722 (65)	545.17		0.138	0.55	1086 (71)	743.95		0.000007	<0.0001
Positive	382 (35)	566.35		0.138	0.55	451 (29)	829.31		0.000007	<0.0001
Site of distant metastasis
Brain
No	1044 (94)	546.62		0.0001	0.0008	N/A
Yes	61 (6)	662.27		0.0001	0.0008	N/A
Lung
No	1003 (91)	547.12		0.007	0.04	N/A
Yes	102 (9)	610.80		0.007	0.04	N/A
Bone
No	876 (79)	554.57		0.651	1.30	N/A
Yes	229 (21)	547.00		0.651	1.30	N/A
Liver
No	949 (86)	551.43		0.568	1.70	N/A
Yes	156 (14)	562.57		0.568	1.70	N/A
	Number (%)	Mean	χ2	p value	Adjusted p value	Number (%)	Mean	χ2	p value	Adjusted p value
Tumour grade
1	190 (17)	450.77	171.5	5.6 × 10⁻³⁸	<0.0001	231 (15)	585.30	723.48	7.7 × 10⁻⁷²	<0.0001
2	366 (33)	473.19				622 (40)	647.46
3	550 (50)	642.43				685 (45)	942.43
Lymph node stage
1	674 (61)	542.19	4.811	0.09	0.45	955 (62)	754.30	12.56	0.002	0.004
2	341 (31)	574.98				428 (28)	767.94
3	91 (8)	556.75				153 (10)	858.69
Nottingham Prognostic Index
Good	332 (30)	458.21	102.4	5.6 × 10⁻²³	<0.0001	521 (34)	620.66	156.60	9.8 × 10⁻³⁵	<0.0001
Moderate	593 (53)	578.27				768 (50)	828.08
Poor	184 (17)	654.64				246 (16)	892.49
IHC subtypes
ER+/HER2- low proliferation	250 (27)	391.92	178.4	1.8 × 10⁻³⁸	<0.0001	N/A
ER+/HER2- high proliferation	351 (38)	419.54
Triple negative	191 (20)	617.95
HER2+	143 (15)	519.69
Histological type
Ductal (including mixed)	922 (83)	563.83	69.05	3.5 × 10⁻¹⁴	<0.0001	1335 (87)	782.19	77.07	7.2 × 10⁻¹⁶	<0.0001
Lobular	101 (9)	454.17				120 (8)	584.17
Medullary	26 (2)	832.02				13 (0.8)	1257.27
Miscellaneous	7 (0.6)	440.50				9 (0.6)	1037.78
Special type	53 (5.4)	472.75				57 (3.6)	655.12

IHC immunohistochemical analysis, ER oestrogen receptor, HER2 human epidermal growth factor receptor 2, NA Not applicable

p value in bold in these tables means statistically significant associations

High SLC7A5 mRNA expression was significantly associated with larger tumour size (Fig. 3a, p < 0.001), higher grade (Fig. 3b, p < 0.001), positive nodal metastasis (Fig. 3c, p< 0.001) and poor NPI (Fig. 3d, p < 0.001). Both SLC7A5 mRNA and SLC7A5 protein were associated with medullary-like tumours. Where data were available, these associations were confirmed using the Breast Cancer Gene-Expression Miner v4.0 (Additional file 4: Figure S1A, B) and the TCGA data (Additional file 2: Figure S4B). In addition SLC7A5 copy number loss was significantly associated with good prognostic parameters including, lower grade and good NPI (Table 2, p < 0.001). There was positive association between SLC7A5 copy number gain and MYC gain (p < 0.001, Table 2).

Fig. 3
SLC7A5 mRNA expression and its association with clinicopathological parameters and molecular subtypes. a SLC7A5 and tumour size. b SLC7A5 and tumour grade. c SLC7A5 and lymph node stage. d SLC7A5 and Nottingham Prognostic Index. e SLC7A5 and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) integrative clusters. f SLC7A5 and prediction analysis of microarray 50 (PAM50) subtypes, g SLC7A5 and SMCGENE subtypes in the METABRIC cohort using one-way analysis of variance with the post-hoc Tukey test

Table 2

Copy number aberrations of SLC7A5 in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) breast cancer series and their association with clinicopathological parameters, MYC copy number aberrations and breast cancer subtypes

SLC7A5 copy number
Gain				Loss
	No, number (%)	Yes, number (%)	χ² (p value)	Adjusted p value	No, number (%)	Yes, number (%)	χ² (p value)	Adjusted p value
Age (years)
≥50	1520 (97.7)	36 (2.3)	0.405 (0.524)	1.572	1098 (70.6)	458 (29.4)	27.479 (1.5 × 10^{− 7})	<0.0001
˂50	372 (97.1)	11 (2.9)	0.405 (0.524)	1.572	321 (83.8)	62 (16.2)	27.479 (1.5 × 10^{− 7})	<0.0001
Tumour size
≥2.0 cm	1291 (97.0)	40 (3.0)	5.226 (0.022)	0.132	976 (73.3)	355 (26.7)	0.094 (0.759)	0.282
˂2.0 cm	614 (98.7)	8 (1.3)	5.226 (0.022)	0.132	452 (72.7)	170 (27.3)	0.094 (0.759)	0.282
Tumour grade
1	170 (100.0)	0 (0.0)	10.154 (0.006)	0.042	99 (58.2)	71 (41.8)	107.36 4.8 × 10⁻²⁴	<0.0001
2	756 (98.2)	14 (1.8)			495 (64.3)	275 (35.7)
3	918 (96.4)	34 (3.6)			799 (83.9)	153 (16.1)
Lymph node stage
1	1012 (97.8)	23 (2.2)	0.474 (0.789)	1.578	726 (70.1)	309 (29.9)	10.425 (0.005)	0.02
2	606 (97.4)	16 (2.6)			480 (77.2)	142 (22.8)
3	307 (97.2)	9 (2.8)			237 (75.0)	79 (25.0)
Nottingham Prognostic Index
Good	668 (98.2)	12 (1.8)	2.602 (0.272)	0.080	418 (61.5)	262 (38.5)	76.132 (2.9 × 10⁻¹⁷)	<0.0001
Moderate	1071 (97.3)	30 (2.7)			864 (78.5)	237 (21.5)
Poor	192 (96.5)	7 (3.5)			168 (84.4)	31 (15.6)
Histological type
Ductal	1500 (97.2)	44 (2.8)	6.880 (0.230)	1.150	1154 (74.7)	390 (25.3)	29.544 (0.00001)	0.0001
Lobular	145 (98.6)	2 (1.4)			88 (59.9)	59 (40.1)
Medullary	30 (93.8)	2 (6.3)			30 (93.8)	2 (6.2)
Miscellaneous	12 (100.0)	0 (0.0)			9 (75.0)	3 (25.0)
Special type	113 (100.0)	0 (0.0)			74 (66.8)	39 (33.2)
PAM50 subtype
Luminal A	710 (98.9)	8 (1.1)	40.515 (3.3 × 10⁻⁸)	<0.0001	423 (58.9)	295 (41.1)	248.3 (1.4 × 10⁻⁵²)	<0.0001
Luminal B	477 (97.7)	11 (2.3)			312 (63.9)	176 (36.1)
Basal	305 (92.7)	24 (7.3)			319 (97.0)	10 (3.0)
HER2+	235 (97.9)	5 (2.1)			219 (91.3)	21 (8.7)
Normal-like	198 (99.5)	1 (0.5)			172 (11.9)	27 (5.1)
MYC gain
No	1228 (98.9)	14 (1.1)	25.0 (5.5 × 10⁻⁷)	<0.0001	1446 (73.3)	528 (26.7)	0.132 (0.716)	1.432
Yes	703 (95.3)	35 (4.7)	25.0 (5.5 × 10⁻⁷)	<0.0001	4 (66.7)	2 (33.3)	0.132 (0.716)	1.432

PAM50 prediction analysis of microarray, HER2 human epidermal growth factor receptor 2

p value in bold in these tables means statistically significant associations

SLC7A5 expression in molecular BC subtypes

SLC7A5 protein expression was associated with negative hormone receptor status and HER2+ tumours (all p ≤ 0.002, Table 3) and it was highly expressed in triple negative (TN) and basal-like phenotype malignancies compared to non-TN and non-basal-like tumours (p < 0.001, Table 3). Similarly, high expression of SLC7A5 mRNA was significantly associated with hormone receptor negative (ER- and PgR-) and HER2+ tumours (all p < 0.001, Table 4). These results were in concordance with the findings of the Breast Cancer Gene-Expression Miner v4.0 (Additional file 4: Figure S1C-F) and TCGA data analysis (Additional file 2: Figure S4C-E).

Table 3

Association of SLC7A5 protein expression and the expression of other molecular biomarkers in the discovery and validation sets

SLC7A5 protein
Discovery set					Validation set
	Number (%)	Mean	p value	Adjusted p value	Number (%)	Mean	p value	Adjusted p value
ER
Negative	270 (25)	722.20	3.2 × 10⁻⁴⁸	<0.0001	300 (19)	1094.70	4.6 × 10⁻⁷⁶	<0.0001
Positive	827 (75)	492.45	3.2 × 10⁻⁴⁸	<0.0001	1240 (81)	692.06	4.6 × 10⁻⁷⁶	<0.0001
PgR
Negative	435 (41)	619.12	8.8 × 10⁻²⁷	<0.0001	612 (42)	855.53	1.3 × 10⁻³⁴	<0.0001
Positive	630 (59)	473.54	8.8 × 10⁻²⁷	<0.0001	853 (58)	645.09	1.3 × 10⁻³⁴	<0.0001
HER2
Negative	921 (87)	521.78	0.00004	0.0001	1337 (92)	718.53	0.001	0.002
Positive	143 (13)	601.54	0.00004	0.0001	116 (8)	824.67	0.001	0.002
Triple negative
No	896 (83)	503.50	4.5 × 10⁻³⁵	<0.0001	1286 (83)	696.76	1.5 × 10⁻⁶²	<0.0001
Yes	185 (17)	722.61	4.5 × 10⁻³⁵	<0.0001	225 (17)	1094.6	1.5 × 10⁻⁶²	<0.0001
Basal phenotype
No	794 (74)	510.96	6.8 × 10⁻¹³	<0.0001	N/A
Yes	285 (26)	620.90	6.8 × 10⁻¹³	<0.0001	N/A
P53 protein
Negative	760 (72)	499.14	4.1 × 10⁻¹³	<0.0001	N/A
Positive	298 (28)	606.92	4.1 × 10⁻¹³	<0.0001	N/A

ER oestrogen receptor, PgR progesterone receptor, HER2 human epidermal growth factor receptor, NA not applicable

p value in bold in these tables means statistically significant associations

Table 4

Association of SLC7A5 mRNA expression and the expression of other molecular biomarkers in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) series

SLC7A5 mRNA expression
	Number (%)	Mean	t test	p value	Adjusted p value
Estrogen receptor
Negative	474 (24)	9.543	26.90	5.6 × 10^{− 113}	<0.0001
Positive	1506 (76)	7.943	26.90	5.6 × 10^{− 113}	<0.0001
Progesterone receptor
Negative	940 (47)	8.862	18.73	1.07 × 10⁻⁷¹	<0.0001
Positive	1040 (53)	7.841	18.73	1.07 × 10⁻⁷¹	<0.0001
HER2
Negative	1733 (88)	8.216	−12.35	1.1 × 10⁻²⁹	<0.0001
Positive	247 (12)	9.095	−12.35	1.1 × 10⁻²⁹	<0.0001
Triple negative (ER-, PR-, HER2-)
No	1660 (84)	8.065	−22.12	1.9 × 10⁻⁷³	<0.0001
Yes	320 (16)	9.676	−22.12	1.9 × 10⁻⁷³	<0.0001
Basal phenotype
No	1645 (83)	8.036	−25.70	1.5 × 10⁻⁹¹	<0.0001
Yes	329 (17)	9.788	−25.70	1.5 × 10⁻⁹¹	<0.0001
TP53 mutation
Wild-type	721 (88)	8.132	−7.47	1.2 × 10⁻¹¹	<0.0001
Mutation	99 (12)	9.148	−7.47	1.2 × 10⁻¹¹	<0.0001

ER oestrogen receptor, PgR progesterone receptor, HER2 human epidermal growth factor receptor 2

p value in bold in these tables means statistically significant associations

When comparing the levels of SLC7A5 mRNA expression in the intrinsic (prediction analysis of microarray 50 (PAM50)) subtypes [36], high expression was observed in basal-like, HER2+ and lLuminal B tumours (Fig. 3f, p < 0.001). Similarly, within the METABRIC integrative clusters, high SLC7A5 mRNA expression was associated with clusters 5 (ERBB2 amplified), 9 (luminal B subgroup) and 10 (TN/basal-like) (p < 0.001, Fig. 3e). In the SCMGENE subtypes there was higher expression of SLC7A5 mRNA in the ER+/HER2- high proliferation class (luminal B) compared with the ER+/HER2- low proliferation class (luminal A) (p < 0.001, Fig. 3g). Association between SLC7A5 mRNA and PAM50 subtypes was confirmed using the Breast Cancer Gene-Expression Miner v4.0 (Additional file 4: Figure S1G). There was lower expression of SLC7A5 protein in the low-proliferation tumours than in the other defined molecular subtypes subtypes (p < 0.001, Table 1).

At the gene level, there was a greater copy number gain of SLC7A5 (p < 0.001, Table 2 in the basal-like subtype, while SCL7A5 copy number loss was mainly observed in the luminal A subtype (p < 0.001, Table 2).

SLC7A5 expression and other associated markers

Correlation between SLC7A5 mRNA and associated genes was investigated in the METABRIC dataset (Table 5). The genes were selected based on previous publications, and were either regulatory genes or others that share or support the SLC7A5 biological function, which focused mainly on glutamine transport and glutamine metabolism [2, 5, 15, 37–41]. There was positive correlation between SLC7A5 mRNA expression and the expression of regulatory genes, several amino acid transporters and genes involved in the glutamine-proline regulatory axis. There was a positive relationship between SLC7A5 and MYC, mTOR and ATF4 (p < 0.001) and the positive relationship between MYC, HIF2A and SLC7A5 was only observed in luminal B tumours (p = 0.01 and p < 0.001, respectively).

Table 5

Correlation of SLC7A5 expression with the expression of other related genes in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) data

	SLC7A5 mRNA expression
	All cases (n = 1980)		Luminal A (n = 368)		Luminal B (n = 367)		HER2+ (n = 110)		Triple negative (n = 150)
	Correlation Coefficient (p value)			Adjusted p value
Regulatory and other associated genes
MYC	0.133 (2.4 × 10⁻⁹)	<0.0001	0.012 (0.752)	4.145	0.155 (0.001)	0.019	0.066 (0.310)	4.650	0.103 (0.062)	0.434
mTOR	0.085 (0.0001)	0.001	−0.005 (0.904)	1.824	0.088 (0.052)	0.728	−0.023 (0.723)	5.784	0.067 (0.226)	1.130
VEGFA	0.352 (6.4 × 10⁻⁵⁹)	<0.0001	0.166 (0.000008)	0.0002	0.260 (5.3 × 10⁻⁹)	<0.0001	0.269 (0.00002)	0.0005	0.244 (0.000008)	0.0002
HIF2A	−0.050 (0.028)	0.168	−0.023 (0.536)	4.896	0.215 (0.000002)	<0.0001	0.112 (0.083)	1.328	−0.282 (1.8 × 10⁻⁷)	<0.0001
ATF4	0.159 (1.0 × 10⁻¹²)	<0.0001	− 0.029 (0.433)	5.100	0.057 (0.208)	2.080	0.143 (0.026)	0.468	0.108 (0.050)	0.450
Glutamine-proline regulatory axis
GLS	0.048 (0.032)	0.192	0.008 (0.829)	3.428	0.055 (0.222)	1.998	−0.006 (0.927)	4.635	−0.115 (0.038)	0.456
ALDH4A1	−0.053 (0.019)	0.133	0.018 (0.638)	4.512	0.063 (0.163)	1.793	−0.028 (0.663)	5.967	−0.134 (0.015)	0.225
PRODH	0.004 (0.858)	1.716	−0.034 (0.369)	4.763	−0.032 (0.483)	2.415	0.037 (0.573)	5.73	0.030 (0.582)	1.746
PYCR1	0.32 (1.5 × 10⁻⁵⁰)	<0.0001	0.143 (0.0001)	0.001	0.253 (1.3 × 10⁻⁸)	<0.0001	0.210 (0.001)	0.024	0.303 (1.9 × 10⁻⁸)	<0.0001
ALDH18A1	0.222 (1.6 × 10⁻²³)	<0.0001	0.151 (0.00004)	0.0008	0.144 (0.001)	0.018	0.168 (0.009)	0.180	0.356 (2.9 × 10⁻¹¹)	<0.0001
GLUL	−0.18 (3.3 × 10⁻¹⁶)	<0.0001	0.134 (0.0003)	0.005	0.008 (0.863)	1.726	−0.001 (0.991)	1.982	−0.122 (0.028)	0.392
GLUD1	−0.38 (4.3 × 10⁻⁶⁹)	<0.0001	− 0.161 (0.00001)	0.0002	− 0.237 (1.1 × 10⁻⁷)	<0.0001	−0.148 (0.022)	0.418	−0.112 (0.042)	0.420
Glutamine/glutamate transporters
SLC1A5	0.29 (4.5 × 10⁻⁴¹)	<0.0001	0.170 (0.000005)	0.0001	0.150 (0.001)	0.017	0.208 (0.001)	0.023	0.25 (0.000002)	<0.0001
SLC3A2	0.17 (1.1 × 10⁻¹⁴)	<0.0001	0.067 (0.072)	3.780	0.193 (0.00001)	0.0002	0.184 (0.004)	0.084	0.158 (0.004)	0.064
SLC6A19	0.004 (0.869)	1.738	0.041 (0.273)	4.428	−0.008 (0.859)	2.577	0.047 (0.473)	5.676	−0.103 (0.061)	0.488
SLC7A6	0.362 (2.7 × 10⁻⁶²)	<0.0001	0.254 (5.3 × 10⁻¹²)	<0.0001	0.33 (2.0 × 10^{− 14})	<0.0001	0.284 (0.000008)	0.0002	0.071 (0.201)	1.206
SLC7A7	0.19 (4.6 × 10⁻¹⁹)	<0.0001	0.007 (0.857)	2.712	0.085 (0.061)	0.793	0.041 (0.530)	5.830	−0.22 (0.00005)	0.0001
SLC7A8	−0.42 (1.1 × 10⁻⁸⁸)	<0.0001	−0.115 (0.002)	0.034	−0.103 (0.022)	0.352	−0.203 (0.002)	0.044	−0.40 (3.9 × 10^{− 14})	<0.0001
SLC7A9	− 0.068 (0.002)	0.01	0.025 (0.510)	4.82	0.044 (0.333)	2.331	−0.123 (0.056)	0.952	0.283 (1.8 × 10⁻⁷)	<0.0001
SLC38A1	−0.10 (0.000003)	<0.0001	−0.041 (0.270)	3.549	0.039 (0.391)	2.346	0.053 (0.413)	5.369	0.113 (0.041)	0.451
SLC38A2	−0.055 (0.015)	0.120	−0.074 (0.048)	1.05	−0.103 (0.022)	0.330	0.007 (0.917)	5.502	−0.119 (0.032)	0.416
SLC38A3	0.18 (8.3 × 10⁻¹⁷)	<0.0001	0.140 (0.0001)	0.002	0.046 (0.311)	2.488	0.003 (0.958)	2.874	0.196 (0.0003)	0.005
SLC38A5	0.011 (0.627)	2.574	−0.069 (0.066)	1.08	−0.077 (0.090)	1.080	−0.017 (0.793)	5.551	−0.017 (0.757)	1.514
SLC38A7	0.306 (3.8 × 10⁻⁴⁴)	<0.0001	0.270 (2.0 × 10⁻¹³)	<0.0001	0.32 (1.2 × 10⁻¹³)	<0.0001	0.064 (0.324)	4.536	0.177 (0.001)	0.017
SLC38A8	0.023 (0.312)	1.560	−0.019 (0.612)	4.466	0.011 (0.801)	3.204	−0.006 (0.930)	3.720	−0.039 (0.482)	1.928

p value in bold in these tables means statistically significant associations

High SLC7A5 mRNA expression was specifically associated with the enzymes involved with conversion of glutamine (Gln) to proline, where PYCR1 and ALDH18A1 showed a positive relationship with SLC7A5 in almost all subtypes (p < 0.02).

The majority of glutamine transporters were significantly associated with SLC7A5 expression primarily in triple negative tumours and to a lesser extent luminal and HER2+ subtypes. SLC7A5 was significantly correlated with SLC1A5 in all subtypes (p < 0.02).

TP53 mutations were also highly prevalent in breast tumours where there was high SLC7A5 mRNA expression (p < 0.001, Tables 3 and 4). Moreover, high SLC7A5 protein was positively associated with high p53 protein (p < 0.001).

SLC7A5 protein expression was significantly expressed in breast tumours with high Ki67, and the upstream effector MYC (p < 0.001, Table 6). SLC1A5, GLS, PYCR1 and PIK3CA were significantly expressed in breast tumours with high expression of SLC7A5 (p < 0.001), while the low expression of SLC7A5 was associated with high levels of p-mTORC1 (p < 0.001) (Table 6).

Table 6

Correlation between SLC7A5 protein expression and other biomarkers in the discovery set

SLC7A5 protein
Biomarker	Correlation coefficient	p value	Adjusted p value
c-MYC	0.164	8.2 × 10⁻⁷	<0.0001
Ki67	0.311	1.1 × 10⁻²¹	<0.0001
P-mTORC1	−0.150	0.00001	<0.0001
PIK3CA	0.190	3.4 × 10⁻⁷	<0.0001
SLC1A5	0.331	1.1 × 10⁻²⁵	<0.0001
GLUD1	0.053	0.09	0.180
GLS	0.371	2.2 × 10⁻³⁰	<0.0001
PYCR1	0.283	1.07 × 10⁻¹⁶	<0.0001

p value in bold in these tables means statistically significant associations

SLC7A5 expression and patient outcome

Both high SLC7A5 mRNA (p < 0.001) (Fig. 4a) and protein (p < 0.001) expression were associated with poor BCSS in the discovery and validation sets (Fig. 5a, b). This association was anticipated as the cutoff was based on the prediction of BCSS.

Fig. 4
SLC7A5 mRNA and breast cancer patient outcome. a SLC7A5 vs breast-cancer-specific survival (BCSS) in all cases. b SLC7A5 vs BCSS in luminal A tumours. c SLC7A5 vs BCSS in luminal B tumours. d SLC7A5 vs BCSS in triple negative tumours. e SLC7A5 vs BCSS in human epidermal growth factor receptor 2 (HER2+) tumours

Fig. 5
SLC7A5 and breast cancer patient outcome. a SLC7A5 vs breast-cancer-specific survival (BCSS) in all discovery set cases. b SLC7A5 vs BCSS in all validation set cases. c SLC7A5 vs BCSS in oestrogen receptor (ER) + low proliferation tumours in the combined discovery and validation cases. d SLC7A5 vs BCSS in ER + high proliferation tumours in the combined discovery and validation cases. e SLC7A5 vs BCSS of triple negative tumours in the combined discovery and validation cases. f SLC7A5 vs BCSS human epidermal growth factor receptor 2 (HER2)+ tumours in the combined discovery and validation cases

While SLC7A5 mRNA expression was not predictive of BCSS in any specific molecular class (Fig. 4b-e), high expression of SLC7A5 protein was only predictive of shorter BCSS in ER+ high proliferation (p = 0.007, Fig. 5d) and HER2+ tumours (p = 0.03, Fig. 5f). There was no association between SLC7A5 protein and outcome in ER+ low proliferation (Fig. 5c) or in TNBC (Fig. 5e). In multivariate Cox regression analysis, SLC7A5 mRNA was a predictor of shorter BCSS independent of tumour size, grade or lymph node stage (p = 0.006, Additional file 5: Table S3) but not in any specific subtype. However, SLC7A5 protein was significant only in the ER+ high-proliferation tumours (p = 0.02, Table 7) and not in any other subtypes (data not shown).

Table 7

SLC7A5 protein expression and patient outcome in the combined discovery and validation sets in all cases and in ER-positive high proliferation tumours

SLC7A5 protein
	All cases			ER+ high proliferation
Parameter	Hazard ratio (95% CI)	p value	Adjusted p value	Hazard ratio (95% CI)	p value	Adjusted p value
SLC7A5	1.001 (1.000–1.003)	0.063	0.126	1.004 (1.001–1.006)	0.006	0.024
Lymph node stage	2.060 (1.813–2.341)	1.7 × 10⁻²⁸	<0.0001	1.756 (1.427–2.161)	1.04 × 10⁻⁷	<0.0001
Size	1.365 (1.111–1.678)	0.003	0.009	1.169 (0.838–1.632)	0.358	0.716
Grade	2.454 (2.023–2.977)	1.8 × 10⁻²⁰	<0.0001	1.756 (1.154–2.672)	0.009	0.027

p value in bold in these tables means statistically significant associations

Likewise, high SLC7A5 protein expression was associated with shorter distant metastases-free survival (DMFS) (p < 0.001; Additional file 6: Figure S2A, B) within the ER+ high-proliferation class (p = 0.03, Additional file 6: Figure S2D) but not in other subtypes (Additional file 6: Figure S2C, E, F). This association was identified in the discovery set and validated in the validation set. The relationship between high SLC7A5 mRNA expression and poor patient outcome in ER+ disease, but not ER- disease, was confirmed using Breast Cancer Gene-Expression Miner (Additional file 7: Figure S3A, B, C).

Discussion

Breast cancer is a heterogeneous disease with various subtypes [42] differing in terms of morphology, molecular and biological profiles, response to therapy and clinical behaviour. In addition, different subtypes exhibit disparity in their metabolic pathways and their nutritional needs. The most common form of BC (~ 55–80%) is the ER+/luminal tumour [43, 44], and tumours that belong to this class are also variable in terms of recurrence, mortality rates and disease prognosis [43]. Therefore, understanding the biology of BC and exploring the metabolic pathways could help to identify potential novel therapeutic targets.

Cancer cells must alter their metabolism in order to satisfy the demands of necessary energy and cellular building blocks. It is widely known that amino acid transport systems play a principal role in sustaining the proliferation of cancer cells by supplying the required amino acids for protein synthesis and by activation of nutrient signalling through the mTORC1 complex. This study has revealed for the first time that SLC7A5 is a key amino acid transporter in the more aggressive and highly proliferative ER+ tumours.

SLC7A5 is located in 16q24.2. This study showed that SLC7A5 deletion, but not amplification, was significantly correlated with all the annotated genes located in the same chromosomal region, indicating that the deletion was not locus-specific. Interestingly, E-cadherin (CDH1), which was located in 16q22.1, was also implicated. It is widely known that most lobular tumours harbour loss of heterozygosity (LOH) at chromosome 16 and are missing the wild type CDH1 allele [45]. In this study, approximately 40% of METABRIC cases with SLC7A5 loss were associated with invasive lobular histology. In addition, SLC7A5 protein expression in lobular carcinoma has a relatively lower mean rank value compared to the other histological subtypes, confirming that deletions involve large segments of q16, which can reflect the BC phenotype.

SLC7A5 is widely expressed in many human cancers and various cancer cell lines [46]. The current study included two large discovery and validation cohorts to confirm the significant association between the high SLC7A5 protein expression and the poor prognostic clinico-pathological parameters, including larger tumour size, higher grade and poor NPI. Furthermore, high SLC7A5 expression was significantly associated with higher expression of the proliferative marker (Ki67). This supports the results of previous studies which demonstrated that these two biomarkers are significantly correlated in tongue cancer [47], neuroendocrine carcinoma of the lung [48], thymic carcinoma [49] and breast cancer [50], indicating that SLC7A5 is critical for proliferation in cancer cells.

With respect to the breast cancer ER+ subtypes, SLC7A5 expression was lower in ER+ tumours that have low proliferation (luminal A subtype) compared with the highly proliferative ER+ (luminal B) malignancies, and it was primarily associated with poor patient outcome and shorter DMFS in the latter class only. This is most likely due to their heavier energy and nutrient requirements for cell survival, proliferation and metastasis. This was anticipated, as it has been shown that over expression of SLC7A5 is actively involved in the proliferation of vascular smooth muscle cells [51] and it is co-expressed with vascular endothelial growth factor (VEGF) in the primary and metastatic sites of many cancers [37], which may be implicated for the metastatic process. In this study the most significant positive correlation between mRNA expression of SLC7A5 and VEGFA was identified in the luminal B subtype. In this regard, Bartlett et al. included SLC7A5 as a part of the five-gene Mammostrat® immunohistochemistry panel, where the higher expression is used to predict recurrence-free survival (RFS), DMFS and overall survival (OS) in ER+ breast cancer during endocrine therapy [52]. However, they did not consider the different molecular subtypes of BC.

SLC7A5 mRNA and protein was also highly expressed in TNBC and HER2+ BC, in concordance with Furuya et al. [50]. However, in these subtypes the significant association between SLC7A5 protein expression and patient outcome was only observed in the HER2+ tumours. Among all BC subtypes, SLC7A5 protein expression was an independent predictor of short BCSS in ER+ high-proliferation tumours only. In this regard, the larger sample of ER+ high-proliferation cases might reflect the stronger significance compared with the smaller sample of HER2+ and TNBC tumours. We therefore suggest that further confirmation in larger cohorts of HER2+ and TN tumours is warranted.

Previous studies have shown regulation of SLC7A5 by other proteins including the tumour oncogene Myc, which induces SLC7A5 [4, 5]. In the current study, the relationship between SLC7A5 and other regulatory proteins in both mRNA and protein expression was investigated. A positive relationship was observed between SLC7A5 and Myc in both protein and mRNA levels, and this correlation remained significant only in luminal B subtype, when different subtypes were investigated. ATF4-dependent transcripts also encode for SLC7A5 upon amino acid deprivation [39] and in this study there was positive correlation between ATF4 and SLC7A5 gene expression, in line with expectations. A previous study showed that activation of the HIF2α pathway increases mTORC1 activity by upregulating expression of the amino acid carrier SLC7A5 [38] and the current study confirmed the positive correlation between HIF2α and SLC7A5, which was only observed in luminal B tumours. SLC7A5 functions by importing essential amino acids to cancer cells and research has detailed the role of amino acids, particularly leucine, in activating mTORC1, which in turn controls protein translation and cell proliferation, and prevents apoptosis in malignant cells [2, 3]. This study showed positive correlation between SLC7A5 and mTOR at the mRNA level. However, there were conflicting results in the analysis of protein levels of SLC7A5, whereby high SLC7A5 expression was associated with lower expression of the mTORC1 phosphorylated at ser (2448), which was included in this study. This was unsurprising as Cheng et al. confirmed that phosphorylation of mTORC1 at ser (2448), which is stimulated by growth factors, was mutually exclusive with mTORC1 phosphorylated at thr (2446), which is regulated by amino acids [53]. These observations may explain why SLC7A5 over expression is primarily associated with poor outcome only in the high proliferation ER+ tumours.

This study further investigated the association of SLC7A5 expression with other glutamine transporters, in which some variability in the expression of amino acid transporters across molecular subtypes was observed. For example, the TN subtype was the main class associated with the transporters required for glutamine influx, perhaps because it depends on delivery of glutamine instead of synthesis. In contrast, positive correlation between SLC7A5 and the glutamine synthase enzyme GLUL was observed in luminal A tumours, suggesting that this subtype might rely on glutamine neosynthesis rather than uptake. SLC1A5 functionally couples with SLC7A5 to allow the cellular influx and efflux of glutamine, as SLC1A5 mediates uptake of glutamine, while SLC7A5 uses intracellular glutamine concentrations to adjust the essential amino acid cytoplasmic pool for metabolic demands and signalling to mTORC1 [15]. Here we observed that SLC7A5 and SLC1A5 are positively correlated in all the BC subtypes.

Previous studies have raised awareness and revealed the importance of the proline-glutamine (Pro-Gln) regulatory axis in BC. SLC7A5 appears to have a pivotal role in this regulatory axis, as its expression was highly associated with the enzymes that mediate glutamate degradation to form the amino acid proline, which has been shown to play a role in assisting tumour growth by different mechanisms [54].

Blocking of SLC7A5 using its inhibitor, 2-aminobicyclo-(2,2,1)-heptane-2-carboxylic acid (BCH), efficiently decreased colony formation of MDA-MB-231 TNBC cells [55]. Even though the consequences of blocking SLC7A5 in the highly proliferative ER+ tumours remain undetermined, this study suggests that SLC7A5 can potentially be used as a therapeutic target for luminal B BC.

Conclusion

This study revealed and confirmed that the glutamine transporter SLC7A5 is associated with poor prognostic characteristics and poor survival outcome. Over expression of SLC7A5 appears to play a role in the proliferation and progression of the aggressive ER+ subtype of breast cancer, thus it could act as a potential therapeutic target. Functional assessment is necessary to reveal the specific role played by this amino acid transporter in the highly proliferative subclass and HER2+ BC.

Abbreviations

BC:: Breast cancer

BCH:: 2-Aminobicyclo-(2,2,1)-heptane-2-carboxylic acid

BCSS:: Breast cancer specific survival

CDH1:: E-cadherin

CISH:: Chromogenic in situ hybridisation

CK:: Cytokeratin

CNA:: Copy number aberration

CNV:: Copy number variation

DM:: Distant metastasis

DMFS:: Distant metastasis-free survival

ECL:: Enhanced chemiluminescence

ER:: Oestrogen receptor

Gln:: Glutamine

HER2:: Human epidermal growth factor receptor 2

kDa:: KiloDalton

LN:: Lymph node

LOH:: Loss of heterozygosity

METABRIC:: Molecular Taxonomy of Breast Cancer International Consortium

mTORC1:: Mammalian target of rapamycin complex1

NPI:: Nottingham Prognostic Index

PAM50:: Prediction analysis of microarray 50

PBS:: Phosphate-buffered saline

PgR:: Progesterone receptor

Pro:: Proline

SNP:: Single nucleotide polymorphism

TBS:: Tris-buffered saline

TCGA:: The Cancer Genome Atlas

TMA:: Tissue microarray

TN:: Triple negative

TNBC:: Triple negative breast cancer

VEGF:: Vascular endothelial growth factor

Declarations

Acknowledgements

We thank the Nottingham Health Science Biobank and Breast Cancer Now Tissue Bank for the provision of tissue samples.

Funding

Not applicable.

Availability of data and materials

The datasets generated during the current study are available from the corresponding author on reasonable request.

Authors’ contributions

RE contributed to writing, IHC staining, scoring, data analysis and interpretation; MLC contributed to writing and reviewing the manuscript; IM helped in scoring and double scoring; MDR and CCN helped in the laboratory work; IOE and EAR contributed to writing and reviewing the manuscript; ARG contributed to study design, data analysis and interpretation and writing and reviewing the manuscript. All authors read and approved the final manuscript.

Ethics approval and consent to participate

This study was approved by the Nottingham Research Ethics Committee 2 under the title “Development of a molecular genetic classification of breast cancer”.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Additional files

Additional file 1: Table S1. Clinicopathological parameters of the METABRIC and Nottingham discovery and validation series. (DOCX 16 kb)

Additional file 2: Figure S4. SLC7A5 mRNA expression, in the TCGA data, and its association with copy number alteration (A), staging system (B), ER status (C), PR status (D) and HER2 (E) status. (PPTX 176 kb)

Additional file 3: Table S2. List of genes with copy number loss that was significantly associated with SLC7A5 deletion in the METABRIC and TCGA data. (DOCX 16 kb)

Additional file 4: Figure S1. SLC7A5 gene expression and its association, using Breast Cancer Gene-Expression Miner v4.0, with: tumour grade (A), NPI (B), ER status (C), PR status (D), HER2 status (E), Triple Negative status (F)and PAM50 subtypes (G). (PPTX 117 kb)

Additional file 5: Table S3. SLC7A5 mRNA and patient outcome. (DOCX 14 kb)

Additional file 6: Figure S2. SLC7A5 vs DMFS in all cases in the discovery set (A), all cases in the validation set (B), ER + low proliferation tumours in the combined discovery and validation set cases (C), ER + high proliferation tumours in the combined discovery and validation set cases (D), triple negative tumours in the combined discovery and validation set cases (E) and HER2+ tumours in the combined discovery and validation set (F). (PPTX 407 kb)

Additional file 7: Figure S3. SLC7A5 mRNA and breast cancer patient outcome using Breast Cancer Gene-Expression Miner in unselected cases (K), ER+ disease (L) and ER- disease (M). (PPTX 80 kb)

Authors’ Affiliations

(1)

Academic Pathology, Division of Cancer and Stem Cells, School of Medicine, University of Nottingham, Nottingham City Hospital, Nottingham, UK

(2)

Breast Institute, Nottingham University Hospitals NHS Trust, Nottingham, UK

References

Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646–74.View ArticlePubMedGoogle Scholar
Bond P. Regulation of mTORC1 by growth factors, energy status, amino acids and mechanical stimuli at a glance. J Int Soc Sports Nutr. 2016;13:8.View ArticlePubMedPubMed CentralGoogle Scholar
Bar-Peled L, Sabatini DM. Regulation of mTORC1 by amino acids. Trends Cell Biol. 2014;24(7):400–6.View ArticlePubMedPubMed CentralGoogle Scholar
Gao P, Tchernyshyov I, Chang TC, Lee YS, Kita K, Ochi T, Zeller KI, De Marzo AM, Van Eyk JE, Mendell JT, et al. c-Myc suppression of miR-23a/b enhances mitochondrial glutaminase expression and glutamine metabolism. Nature. 2009;458(7239):762–5.View ArticlePubMedPubMed CentralGoogle Scholar
Hayashi K, Jutabha P, Endou H, Anzai N. c-Myc is crucial for the expression of LAT1 in MIA Paca-2 human pancreatic cancer cells. Oncol Rep. 2012;28(3):862–6.View ArticlePubMedGoogle Scholar
Reynolds MR, Lane AN, Robertson B, Kemp S, Liu Y, Hill BG, Dean DC, Clem BF. Control of glutamine metabolism by the tumor suppressor Rb. Oncogene. 2014;33(5):556–66.View ArticlePubMedGoogle Scholar
Kanai Y, Segawa H, Miyamoto K, Uchino H, Takeda E, Endou H. Expression cloning and characterization of a transporter for large neutral amino acids activated by the heavy chain of 4F2 antigen (CD98). J Biol Chem. 1998;273(37):23629–32.View ArticlePubMedGoogle Scholar
Jewell JL, Kim YC, Russell RC, Yu FX, Park HW, Plouffe SW, Tagliabracci VS, Guan KL. Metabolism. Differential regulation of mTORC1 by leucine and glutamine. Science (New York, NY). 2015;347(6218):194–8.View ArticleGoogle Scholar
Kimball SR. Regulation of translation initiation by amino acids in eukaryotic cells. Prog Mol Subcell Biol. 2001;26:155–84.View ArticlePubMedGoogle Scholar
Bonfils G, Jaquenoud M, Bontron S, Ostrowicz C, Ungermann C, De Virgilio C. Leucyl-tRNA synthetase controls TORC1 via the EGO complex. Mol Cell. 2012;46(1):105–10.View ArticlePubMedGoogle Scholar
Yanagida O, Kanai Y, Chairoungdua A, Kim DK, Segawa H, Nii T, Cha SH, Matsuo H, Fukushima J, Fukasawa Y, et al. Human L-type amino acid transporter 1 (LAT1): characterization of function and expression in tumor cell lines. Biochim Biophys Acta. 2001;1514(2):291–302.View ArticlePubMedGoogle Scholar
Kobayashi H, Ishii Y, Takayama T. Expression of L-type amino acid transporter 1 (LAT1) in esophageal carcinoma. J Surg Oncol. 2005;90(4):233–8.View ArticlePubMedGoogle Scholar
Yoon JH, Kim IJ, Kim H, Kim HJ, Jeong MJ, Ahn SG, Kim SA, Lee CH, Choi BK, Kim JK, et al. Amino acid transport system L is differently expressed in human normal oral keratinocytes and human oral cancer cells. Cancer Lett. 2005;222(2):237–45.View ArticlePubMedGoogle Scholar
Nakanishi K, Matsuo H, Kanai Y, Endou H, Hiroi S, Tominaga S, Mukai M, Ikeda E, Ozeki Y, Aida S, et al. LAT1 expression in normal lung and in atypical adenomatous hyperplasia and adenocarcinoma of the lung. Virchows Arch. 2006;448(2):142–50.View ArticlePubMedGoogle Scholar
Fuchs BC, Bode BP. Amino acid transporters ASCT2 and LAT1 in cancer: partners in crime? Semin Cancer Biol. 2005;15(4):254–66.View ArticlePubMedGoogle Scholar
Curtis C, Shah SP, Chin SF, Turashvili G, Rueda OM, Dunning MJ, Speed D, Lynch AG, Samarajiwa S, Yuan Y, et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature. 2012;486(7403):346–52.PubMedPubMed CentralGoogle Scholar
Šidák Z. Rectangular confidence regions for the means of multivariate normal distributions. J Am Stat Assoc. 1967;62(318):626–33.Google Scholar
Ciriello G, Gatza ML, Beck AH, Wilkerson MD, Rhie SK, Pastore A, Zhang H, McLellan M, Yau C, Kandoth C, et al. Comprehensive molecular portraits of invasive lobular breast cancer. Cell. 2015;163(2):506–19.View ArticlePubMedPubMed CentralGoogle Scholar
Kao J, Salari K, Bocanegra M, Choi YL, Girard L, Gandhi J, Kwei KA, Hernandez-Boussard T, Wang P, Gazdar AF, et al. Molecular profiling of breast cancer cell lines defines relevant tumor models and provides a resource for cancer gene discovery. PLoS One. 2009;4(7):e6146.View ArticlePubMedPubMed CentralGoogle Scholar
Abd El-Rehim DM, Ball G, Pinder SE, Rakha E, Paish C, Robertson JF, Macmillan D, Blamey RW, Ellis IO. High-throughput protein expression analysis using tissue microarray technology of a large well-characterised series identifies biologically distinct classes of breast cancer confirming recent cDNA expression analyses. Int J Cancer. 2005;116(3):340–50.View ArticlePubMedGoogle Scholar
McCarty KS Jr, McCarty KS Sr. Histochemical approaches to steroid receptor analyses. Semin Diagn Pathol. 1984;1(4):297–308.PubMedGoogle Scholar
Green AR, Aleskandarany MA, Agarwal D, Elsheikh S, Nolan CC, Diez-Rodriguez M, Macmillan RD, Ball GR, Caldas C, Madhusudan S, et al. MYC functions are specific in biological subtypes of breast cancer and confers resistance to endocrine therapy in luminal tumours. Br J Cancer. 2016;114(8):917–28.View ArticlePubMedPubMed CentralGoogle Scholar
Aleskandarany MA, Rakha EA, Ahmed MA, Powe DG, Ellis IO, Green AR. Clinicopathologic and molecular significance of phospho-Akt expression in early invasive breast cancer. Breast Cancer Res Treat. 2011;127(2):407–16.View ArticlePubMedGoogle Scholar
Abdel-Fatah TM, Powe DG, Ball G, Lopez-Garcia MA, Habashy HO, Green AR, Reis-Filho JS, Ellis IO. Proposal for a modified grading system based on mitotic index and Bcl2 provides objective determination of clinical outcome for patients with breast cancer. J Pathol. 2010;222(4):388–99.View ArticlePubMedGoogle Scholar
Elsheikh S, Green AR, Aleskandarany MA, Grainge M, Paish CE, Lambros MB, Reis-Filho JS, Ellis IO. CCND1 amplification and cyclin D1 expression in breast cancer and their relation with proteomic subgroups and patient outcome. Breast Cancer Res Treat. 2008;109(2):325–35.View ArticlePubMedGoogle Scholar
Aleskandarany MA, Rakha EA, Macmillan RD, Powe DG, Ellis IO, Green AR. MIB1/Ki-67 labelling index can classify grade 2 breast cancer into two clinically distinct subgroups. Breast Cancer Res Treat. 2011;127(3):591–9.View ArticlePubMedGoogle Scholar
Jerjees DA, Negm OH, Alabdullah ML, Mirza S, Alkaabi M, Hameed MR, Abduljabbar R, Muftah A, Nolan CC, Green AR, et al. The mammalian target of rapamycin complex 1 (mTORC1) in breast cancer: the impact of oestrogen receptor and HER2 pathways. Breast Cancer Res Treat. 2015;150(1):91–103.View ArticlePubMedGoogle Scholar
Aleskandarany MA, Rakha EA, Ahmed MA, Powe DG, Paish EC, Macmillan RD, Ellis IO, Green AR. PIK3CA expression in invasive breast cancer: a biomarker of poor prognosis. Breast Cancer Res Treat. 2010;122(1):45–53.View ArticlePubMedGoogle Scholar
Lancashire LJ, Powe DG, Reis-Filho JS, Rakha E, Lemetre C, Weigelt B, Abdel-Fatah TM, Green AR, Mukta R, Blamey R, et al. A validated gene expression profile for detecting clinical outcome in breast cancer using artificial neural networks. Breast Cancer Res Treat. 2010;120(1):83–93.View ArticlePubMedGoogle Scholar
Green AR, Caracappa D, Benhasouna AA, Alshareeda A, Nolan CC, Macmillan RD, Madhusudan S, Ellis IO, Rakha EA. Biological and clinical significance of PARP1 protein expression in breast cancer. Breast Cancer Res Treat. 2015;149(2):353–62.View ArticlePubMedGoogle Scholar
Elston CW, Ellis IO. Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up. C. W. Elston & I. O. Ellis. Histopathology 1991; 19; 403-410. Histopathology. 2002;41(3a):151–2. discussion 152–153View ArticlePubMedGoogle Scholar
Senkus E, Kyriakides S, Ohno S, Penault-Llorca F, Poortmans P, Rutgers E, Zackrisson S, Cardoso F. Primary breast cancer: ESMO clinical practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2015;26(Suppl 5):v8–30.View ArticlePubMedGoogle Scholar
Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, Jacobsen A, Byrne CJ, Heuer ML, Larsson E, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2(5):401–4.View ArticlePubMedGoogle Scholar
Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, Sun Y, Jacobsen A, Sinha R, Larsson E, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 2013;6(269):pl1.View ArticlePubMedPubMed CentralGoogle Scholar
Huret JL, Ahmad M, Arsaban M, Bernheim A, Cigna J, Desangles F, Guignard JC, Jacquemot-Perbal MC, Labarussias M, Leberre V, et al. Atlas of genetics and cytogenetics in oncology and haematology in 2013. Nucleic Acids Res. 2013;41(Database issue):D920–4.PubMedGoogle Scholar
Parker JS, Mullins M, Cheang MC, Leung S, Voduc D, Vickery T, Davies S, Fauron C, He X, Hu Z, et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol. 2009;27(8):1160–7.View ArticlePubMedPubMed CentralGoogle Scholar
Kaira K, Oriuchi N, Imai H, Shimizu K, Yanagitani N, Sunaga N, Hisada T, Tanaka S, Ishizuka T, Kanai Y, et al. l-type amino acid transporter 1 and CD98 expression in primary and metastatic sites of human neoplasms. Cancer Sci. 2008;99(12):2380–6.View ArticlePubMedGoogle Scholar
Elorza A, Soro-Arnaiz I, Melendez-Rodriguez F, Rodriguez-Vaello V, Marsboom G, de Carcer G, Acosta-Iborra B, Albacete-Albacete L, Ordonez A, Serrano-Oviedo L, et al. HIF2alpha acts as an mTORC1 activator through the amino acid carrier SLC7A5. Mol Cell. 2012;48(5):681–91.View ArticlePubMedGoogle Scholar
Adams CM. Role of the transcription factor ATF4 in the anabolic actions of insulin and the anti-anabolic actions of glucocorticoids. J Biol Chem. 2007;282(23):16744–53.View ArticlePubMedGoogle Scholar
Craze ML, Cheung H, Jewa N, Coimbra ND, Soria D, El-Ansari R, Aleskandarany MA, Cheng KW, Diez-Rodriguez M, Nolan CC. MYC regulation of glutamine-proline regulatory axis is key in luminal B breast cancer. Br J Cancer. 2018;118(2):258-265.Google Scholar
El Ansari R, McIntyre A, Craze ML, Ellis IO, Rakha EA, Green AR. Altered glutamine metabolism in breast cancer; subtype dependencies and alternative adaptations. Histopathology. 2018;72(2):183–90.View ArticlePubMedGoogle Scholar
Perou CM, Sorlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, Pollack JR, Ross DT, Johnsen H, Akslen LA, et al. Molecular portraits of human breast tumours. Nature. 2000;406(6797):747–52.View ArticlePubMedGoogle Scholar
Dawson SJ, Rueda OM, Aparicio S, Caldas C. A new genome-driven integrated classification of breast cancer and its implications. EMBO J. 2013;32(5):617–28.View ArticlePubMedPubMed CentralGoogle Scholar
Rakha EA, El-Sayed ME, Green AR, Paish EC, Powe DG, Gee J, Nicholson RI, Lee AH, Robertson JF, Ellis IO. Biologic and clinical characteristics of breast cancer with single hormone receptor positive phenotype. J Clin Oncol. 2007;25(30):4772–8.View ArticlePubMedGoogle Scholar
Berx G, Cleton-Jansen AM, Strumane K, de Leeuw WJ, Nollet F, van Roy F, Cornelisse C. E-cadherin is inactivated in a majority of invasive human lobular breast cancers by truncation mutations throughout its extracellular domain. Oncogene. 1996;13(9):1919–25.PubMedGoogle Scholar
Wang Q, Holst J. L-type amino acid transport and cancer: targeting the mTORC1 pathway to inhibit neoplasia. Am J Cancer Res. 2015;5(4):1281–94.PubMedPubMed CentralGoogle Scholar
Toyoda M, Kaira K, Ohshima Y, Ishioka NS, Shino M, Sakakura K, Takayasu Y, Takahashi K, Tominaga H, Oriuchi N, et al. Prognostic significance of amino-acid transporter expression (LAT1, ASCT2, and xCT) in surgically resected tongue cancer. Br J Cancer. 2014;110(10):2506–13.View ArticlePubMedPubMed CentralGoogle Scholar
Kaira K, Oriuchi N, Imai H, Shimizu K, Yanagitani N, Sunaga N, Hisada T, Kawashima O, Iijima H, Ishizuka T, et al. Expression of L-type amino acid transporter 1 (LAT1) in neuroendocrine tumors of the lung. Pathol Res Pract. 2008;204(8):553–61.View ArticlePubMedGoogle Scholar
Kaira K, Oriuchi N, Imai H, Shimizu K, Yanagitani N, Sunaga N, Hisada T, Ishizuka T, Kanai Y, Endou H, et al. L-type amino acid transporter 1 (LAT1) is frequently expressed in thymic carcinomas but is absent in thymomas. J Surg Oncol. 2009;99(7):433–8.View ArticlePubMedGoogle Scholar
Furuya M, Horiguchi J, Nakajima H, Kanai Y, Oyama T. Correlation of L-type amino acid transporter 1 and CD98 expression with triple negative breast cancer prognosis. Cancer Sci. 2012;103(2):382–9.View ArticlePubMedGoogle Scholar
Liu XM, Reyna SV, Ensenat D, Peyton KJ, Wang H, Schafer AI, Durante W. Platelet-derived growth factor stimulates LAT1 gene expression in vascular smooth muscle: role in cell growth. FASEB J. 2004;18(6):768–70.View ArticlePubMedGoogle Scholar
Bartlett JM, Thomas J, Ross DT, Seitz RS, Ring BZ, Beck RA, Pedersen HC, Munro A, Kunkler IH, Campbell FM, et al. Mammostrat as a tool to stratify breast cancer patients at risk of recurrence during endocrine therapy. Breast Cancer Res. 2010;12(4):R47.View ArticlePubMedPubMed CentralGoogle Scholar
Cheng SW, Fryer LG, Carling D, Shepherd PR. Thr2446 is a novel mammalian target of rapamycin (mTOR) phosphorylation site regulated by nutrient status. J Biol Chem. 2004;279(16):15719–22.View ArticlePubMedGoogle Scholar
Phang JM, Liu W, Hancock CN, Fischer JW. Proline metabolism and cancer: emerging links to glutamine and collagen. Curr Opin Clin Nutr Metab Care. 2015;18(1):71–7.View ArticlePubMedGoogle Scholar
Liang Z, Cho HT, Williams L, Zhu A, Liang K, Huang K, Wu H, Jiang C, Hong S, Crowe R, et al. Potential biomarker of L-type amino acid transporter 1 in breast cancer progression. Nucl Med Mol Imaging. 2011;45(2):93–102.View ArticlePubMedGoogle Scholar

Breast Cancer Research

The amino acid transporter SLC7A5 confers a poor prognosis in the highly proliferative breast cancer subtypes and is a key therapeutic target in luminal B tumours

Abstract

Background

Methods

Results

Conclusions

Keywords

Background

Methods

SLC7A5 copy number and gene expression

Patients and tumours

Western blotting

Tissue arrays and immunohistochemical analysis

Statistical analysis

Results

SLC7A5 genomic profiling

SLC7A5 expression in breast cancer

SLC7A5 and clinicopathological parameters

SLC7A5 expression in molecular BC subtypes

SLC7A5 expression and other associated markers

SLC7A5 expression and patient outcome

Discussion

Conclusion

Abbreviations

Declarations

Acknowledgements

Funding

Availability of data and materials

Authors’ contributions

Ethics approval and consent to participate

Consent for publication

Competing interests

Publisher’s Note

Authors’ Affiliations

References

Copyright

Breast Cancer Research

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