Skip to content

Advertisement

Breast Cancer Research

  • Research article
  • Open Access

The footprint of the ageing stroma in older patients with breast cancer

  • †1, 2Email authorView ORCID ID profile,
  • †3,
  • 3,
  • 1, 2,
  • 4, 5,
  • 4, 5,
  • 4, 5,
  • 3,
  • 1, 2,
  • 6,
  • 6,
  • 7,
  • 3 and
  • 1, 2, 6
†Contributed equally
Breast Cancer Research201719:78

https://doi.org/10.1186/s13058-017-0871-0

©  The Author(s). 2017

Received: 20 December 2016

Accepted: 20 June 2017

Published: 3 July 2017

Abstract

Background

Tumours are not only composed of malignant cells but also consist of a stromal micro-environment, which has been shown to influence cancer cell behaviour. Because the ageing process induces accumulation of senescent cells in the body, this micro-environment is thought to be different in cancers occurring in old patients compared with younger patients. More specifically, senescence-related fibroblastic features, such as the senescence-associated secretory profile (SASP) and the induction of autophagy, are suspected to stimulate tumour growth and progression.

Methods

We compared gene expression profiles in stromal fields of breast carcinomas by performing laser capture microdissection of the cancer-associated stroma from eight old (aged ≥80 years at diagnosis) and nine young (aged <45 years at diagnosis) patients with triple-negative breast cancer. Gene expression data were obtained by microarray analysis (Affymetrix). Differential gene expression and gene set enrichment analysis (GSEA) were performed.

Results

Differential gene expression analysis showed changes reminiscent of increased growth, de-differentiation and migration in stromal samples of older versus younger patients. GSEA confirmed the presence of a SASP, as well as the presence of autophagy in the stroma of older patients.

Conclusions

We provide the first evidence in humans that older age at diagnosis is associated with a different stromal micro-environment in breast cancers. The SASP and the presence of autophagy appear to be important age-induced stromal features.

Keywords

  • Breast cancer
  • Stroma
  • Senescence
  • Senescence-associated secretory profile
  • Autophagy
  • Gene expression
  • Old patients
  • Ageing

Background

Oncological research over the past decades has been focussed primarily tumour cell characteristics. However, tumoural masses are not exclusively composed of malignant cells; they also comprise a stromal component containing endothelial cells, (myo)fibroblasts, smooth muscle cells, adipocytes and inflammatory cells. Research on the stromal component of tumour masses has shown that stromal characteristics are correlated with disease outcome and behaviour [1–10] in several malignancies. The stroma seems to play a very important role in tumour initiation, progression and metastatic spread [11, 12]. The fibroblasts contained in this stromal compartment show a specific phenotype and are called carcinoma-associated fibroblasts [13]. Because cellular senescence progressively occurs throughout a person’s lifetime in fibroblasts of various origins [14], it seems plausible that the characteristics of the stromal compartment of breast cancers would differ between young and older patients and that this could result in a pro-tumourigenic micro-environment with stimulation of proliferation, migration/invasion and de-differentiation.

The incidence of breast cancer, the most frequent tumour occurring in women, increases with age [15, 16]. Cancer in older patients is thought to arise from lifelong exposure to harmful stimuli, such as DNA-damaging agents, oxidative stress factors and telomeric loss. In addition, the micro-environmental changes caused by senescent cells might also be an important harmful trigger. Breast cancer in young patients usually reflects either a genetic defect or the impact of early life-transforming effects on an immature breast epithelium.

Senescence in general is a protective mechanism that shuts down damaged cells [17]. Nature has selected for this mechanism to protect young organisms from developing cancer. Senescent cells are forced into a state of irreversible growth arrest [18, 19] and exhibit a specific phenotype characterised by enlarged size, flattened morphology, senescence-associated β-galactosidase activity, reorganisation of chromatin into foci of heterochromatin and resistance to apoptosis [20]. They also acquire the so-called senescence-associated secretory profile (SASP) [21, 22], maintaining the growth arrest and recruiting immune cells towards the damaged cells in order to eradicate them. However, the SASP also seems to have a detrimental influence on nearby cells. Epithelial cells neighboured by senescent fibroblasts lose differentiated properties, become invasive and undergo full malignant transformation [20, 23–25]. In this process, a major role has been attributed to matrix metalloproteinase 3 [23] together with other components of the SASP [25, 26], such as inflammatory cytokines and chemokines. This concept of senescence as a useful cancer-protective mechanism in younger life but a detrimental cancer-promoting mechanism in later life has repeatedly been described as an example of ‘antagonistic pleiotropy’ [27, 28] in cellular or animal models [23, 24, 29–32]. Senescent cells have been reported in vivo in a variety of tissues of different organisms, including mice, primates and humans [14, 33–36]. Also, studies have provided evidence that increasing age does result in a higher frequency of senescent cells [14, 33, 34, 37], albeit mostly in the skin.

An additional mechanism that has been proposed to explain the tumour-promoting effects of a senescent micro-environment is the ‘the autophagic tumor stroma model of cancer’ [38–42]. This model states that fibroblasts, in transition to a senescent state, activate the autophagic process. During this so-called autophagy-to-senescence transition (AST), the cells shift towards an aerobic glycolysis metabolism, creating high-energy mitochondrial fuels that feed the nearby epithelial cancer cells. Autophagic fibroblasts were shown to have tumour- and metastasis-promoting activity [39]. The discovery of this concept was preceded by the finding that tumoural cells can induce AST in surrounding fibroblasts by secreting hydrogen peroxide that causes oxidative stress and activation of autophagy in the fibroblasts. This process was named the reverse Warburg effect (as opposed to the original idea, called the Warburg effect, by which aerobic glycolysis takes place in epithelial cancer cells). Fibroblasts displaying a constitutively activated autophagy programme turned out to show many morphological characteristics of senescence, including induction of P21 WAF1/CIP1 , which led to the hypothesis that AST is one of the mechanisms by which senescent stromal cells create a ‘fertile soil’ for the initiation and progression of cancer.

Despite this knowledge, stromal differences with increasing patient age have so far never been investigated in vivo, and little clinical evidence can be found for a more aggressive behaviour of tumour cells growing in a context of ‘older’ stroma. On the contrary, breast cancers in older patients have in general been shown to grow more slowly and to behave less aggressively, even when adjusting for different histological tumour characteristics [43, 44]. On these premises, we sought to compare gene expression profiles of tumour-adjacent stroma in older versus younger patients with breast cancer matched for other clinico-pathologic parameters.

Methods

Patient selection and clinical specimens

This study was approved by the ethics committee of the University Hospitals Leuven (Leuven, Belgium) in accordance with the International Conference on Harmonisation Harmonised Tripartite Guideline for Good Clinical Practice. Candidate patients were selected using the following criteria: (1) aged <45 years or ≥80 years, (2) no neo-adjuvant chemotherapy treatment or hormone treatment before surgery, (3) surgery for early triple-negative breast cancer (defined as oestrogen receptor [ER] and progesterone receptor [PR] <1% and human epidermal growth factor receptor 2 [HER2] <2+ by immunohistochemistry or fluorescent in situ hybridisation-negative) with fresh frozen resection specimens available (stored at −80 °C at the pathology department of the University Hospitals Leuven) and (4) no chronic inflammatory diseases to exclude confounding variables.

For each candidate patient, one section of the frozen tumour material was obtained for hematoxylin and eosin (H&E) staining. H&E-stained sections were evaluated for tumoural and stromal content and used to localise the best areas for stromal microdissection (see Fig. 1 as an example). Only tumour tissue blocks consisting of invasive tumour with representative carcinoma-associated stromal fields to allow laser capture microdissection (LSM) were selected. We selected tumours with a very low amount of or absent tumour-infiltrating lymphocytes to prevent bias in the gene expression analyses. On the basis of the above criteria, 17 female patients with breast cancer (9 young patients <45 years old at diagnosis and 8 old patients ≥80 years old at diagnosis) were included in the study.
Figure 1
Fig. 1

Haematoxylin and eosin-stain of selected tumour block for laser capture microdissection

Staining procedures and laser capture microdissection

Preparation of the tissue slides

For the selected patients, ten frozen sections of 10-μm thickness were mounted onto specific membrane slides (steel frames with polyethylene terephthalate membrane, catalogue number 11505151; Leica Microsystems, Wetzlar, Germany) and were kept at −80 °C until the staining and dissection procedure was started. All tissue slides underwent LCM within 7 days after preparation.

Staining

Prior to LCM, tumour slides were stained with cresyl violet following a procedure optimised for maximising RNA yield. Briefly, tumour slides were taken from −80 °C and were fixed into a 95% ethanol solution for 30 seconds. Next, they were transferred to ethanol solutions with progressively decreasing concentrations (75%, 50%) for 30 seconds each. Then, cresyl violet dye (cresyl violet acetate pure high-purity biological stain, catalogue number AC229630050; Acros Organics, Geel, Belgium) at a concentration of 0.2% was applied for 30–60 seconds, after which dehydration of the tissue was achieved by rinsing the slides with increasing concentrations of ethanol (50%, 75%, 95%, 100%, 100%) for 15 seconds each.

Laser capture microdissection

After the staining procedure, LCM was accomplished within 30 minutes by using a laser microscope (LMD6500; Leica Microsystems). Dissected stromal pieces were immediately collected in an RNase/DNase-free capture vial containing 25 μl of stabilising RNA extraction buffer. During dissection, care was taken to avoid blood vessels, zones containing infiltrating immune cells, or fatty tissue. Dissection was restricted to fields contained within the perimeter of the invasive tumour or at the invasive front of the tumour, but in direct relationship with invasive epithelial nests. Pictures were taken before and after the dissection procedure (see Fig. 2 as an example). After finishing dissection for one tumour slide, 25 μl of RNA extraction buffer was added to the capture vial, and lysis was performed for 30 minutes at 42 °C. The obtained lysate was stored at −80 °C until further RNA extraction. For each patient, several tumour slides were laser-dissected using this procedure (seven to ten slides per patient according to size and amount of stromal fields within the tumour tissue).
Figure 2
Fig. 2

Laser capture microdissection of cancer associated stroma. a–c Microdissection procedure. d Yield of stromal pieces after repeated microdissection within the same tumour slide

RNA extraction and amplification

RNA isolation was performed using the Arcturus PicoPure RNA extraction kit (PicoPureTM Frozen RNA Isolation Kit, catalogue number KIT0202/KIT0204; Arcturus, Mountain View, CA, USA) according to the manufacturer’s protocol. Briefly, lysates from the same tumour were combined, and after addition of 50 μl of ethanol 70%, the pooled samples were passed onto pre-conditioned RNA extraction columns. After centrifugation and washing, DNase was applied onto the column to eliminate residual DNA (RNase-Free DNase Set, catalogue number 79254; QIAGEN, Hilden, Germany). After a washing step, the purified RNA was eluted from the column using 11 μl of elution buffer. Samples were subsequently tested for RNA quality (RNA Quality Indicator) on the Experion™ system (Bio-Rad Laboratories, Hercules, CA, USA) using high-sensitivity RNA chips, and concentrations were measured using the NanoDrop 2000 spectrophotometer (Thermo Scientific, Wilmington, DE, USA). The quality of the RNA varied between samples, which is a known limitation of the LSM procedure [45] (see Additional file 1). Prior to microarray analysis, RNA was pre-amplified using the Ovation PicoSL WTA System V2 (catalogue number 3312-24; NuGEN, Leek, The Netherlands). The Ribo-SPIA (single-primer isothermal amplification) technology implemented in this procedure is ideal for amplification of partially degraded and compromised RNA samples, contributes minimal coverage bias, and is highly reproducible [46]. The procedure is widely used in LCM projects and does not introduce significant bias into relative gene expression values [47, 48]. A clean-up step using the MinElute Reaction Cleanup Kit (catalogue number 28204; QIAGEN) was also incorporated into the amplification procedure. After NuGEN pre-amplification of the RNA samples, quantitative reverse transcription-polymerase chain reaction assessment of common housekeeping genes showed that the amplification procedure had resulted in highly concentrated complementary DNA fragments with sufficient size to be recognised by the primers (data not shown).

Gene expression analysis

Gene expression was analysed using Human Genome U133Plus2 microarray chips (Affymetrix, Santa Clara, CA, USA) at the J.C. Heuson Breast Cancer Translational Research Laboratory (Jules Bordet Institute, Brussels, Belgium) according to the manufacturer’s instructions. Standard quality assessments were conducted on the resulting files, and all samples passed quality assurance for further analysis. Expression values were computed using the frozen robust multi-array analysis (fRMA) normalisation method (‘frma’ package in Bioconductor) [49]. When multiple probe sets mapped to the same official gene symbol, we computed their average value. The expression data are available from the Gene Expression Omnibus (GEO) repository under accession number [GEO:GSE90521].

Statistical analysis

Differential expression analysis

To identify the genes that were differentially expressed in the two age categories (<45 years versus ≥80 years), we computed for each probe set the mean expression value in both age groups and calculated the fold change of these means (i.e., the ratio of the average expression of this particular gene in young and old patients). We used a Wilcoxon test. Genes with fold change greater than 1.5 or less than −1.5 were considered differentially expressed.

In silico validation

To validate the obtained differential gene expression, gene expression data sets from other projects in which researchers investigated laser-dissected stromal samples obtained from patients with breast cancer were retrieved. The data sets were available in the National Center for Biotechnology Information (NCBI) GEO database under accession numbers [GEO:GSE5847] [9], [GEO:GSE4823] [7] and [GEO:GSE14548] [8].

Gene set enrichment analysis

Gene set enrichment analysis (GSEA) was conducted using our local reimplementation of the GSEA algorithm developed at the Broad Institute [50]. Briefly, genes were ranked according to their fold change in young versus old patients, and an enrichment score (ES) ranging from −1 to 1 was computed. This score reflects to what extent the genes constituting a given reference class are enriched among the top up- or down-regulated genes of the differential expression analysis. Low (negative) ES values correspond to an enrichment of the reference class among genes that are up-regulated in old patients, whereas high (positive) ES values correspond to an enrichment of the reference class among genes that are up-regulated in young patients. The false discovery rate-adjusted p values associated with each ES value reflect the probability that an ES at least as high or as low could be obtained merely by chance. Adjusted p values <0.05 were considered significant.

Results

Patient demographics

For the purpose of the present study, 17 female patients (9 young patients <45 years old at diagnosis and 8 old patients aged ≥80 years at diagnosis) with available fresh frozen breast cancer resection specimens and with sufficient stroma to allow laser microdissection were selected. Extreme age categories were chosen to maximise the probability of detecting significant age-related differences. All patients underwent surgery for early breast cancer at the Multidisciplinary Breast Center (University Hospitals Leuven, Belgium) between 2000 and 2011. All patients had invasive ductal carcinomas >1.5 cm and were negative for ER, PR and HER2. Additional patient and tumour characteristics are summarised in Table 1. The choice of triple-negative breast cancers was made to exclude cancer-related confounding factors as much as possible.
Table 1

Patient and tumour characteristics

Patient

Age at diagnosis (years)

ER

PR

HER2

Tumour type

Tumour grade

Maximum tumour size (cm)

pT stage

pN stage

6

27

Neg

Neg

Neg

Ductal

3

2.3

2

0

5

30

Neg

Neg

Neg

Ductal

3

2.5

2

0

7

32

Neg

Neg

Neg

Ductal

3

2.2

2

0

1

33

Neg

Neg

Neg

Ductal

3

2.8

2

0

3

39

Neg

Neg

Neg

Ductal

3

3.0

2

0

2

43

Neg

Neg

Neg

Ductal

3

3.0

2

2a

4

44

Neg

Neg

Neg

Ductal

3

2.8

2

0

8

44

Neg

Neg

Neg

Ductal

3

3.5

2

0

9

44

Neg

Neg

Neg

Ductal

3

3.0

2

0

12

80

Neg

Neg

Neg

Ductal

3

4.0

2

0

16

82

Neg

Neg

Neg

Ductal

3

3.5

2

0

17

82

Neg

Neg

Neg

Ductal

3

1.5

1c

0

13

82

Neg

Neg

Neg

Ductal

2

3.0

2

3a

15

83

Neg

Neg

Neg

Ductal

3

3.8

2

1a

10

83

Neg

Neg

Neg

Ductal

3

3.2

2

0

11

86

Neg

Neg

Neg

Ductal

3

3.0

2

0

14

87

Neg

Neg

Neg

Ductal

3

2.0

1c

0

Abbreviations: pT pathological T stage, pN pathological N stage, ER Oestrogen receptor, PR Progesterone receptor, HER2 Human epidermal growth factor receptor 2

Differential gene expression analysis

A differential gene expression analysis using a 1.5-fold up- or down-regulation as the cut-off revealed 120 genes that were up-regulated in older subjects’ stromal samples and 107 genes that were down-regulated in older subjects’ stromal samples compared with younger subjects (Table 2). Heat maps constructed using the 25 top up- and down-regulated genes are shown in Fig. 3.
Table 2

Genes with greater than 1.5-fold or less than −1.5-fold expression and respective fold changes

Gene

Full name

Fold change

SPP1

Secreted phosphoprotein 1

−4.79

EPCAM

Epithelial cell adhesion molecule

−4.02

IL8

Interleukin 8

−2.74

NR4A2

Nuclear receptor subfamily 4, group A, member 2

−2.45

RGS2

Regulator of G-protein signaling 2, 24 kDa

−2.41

TREM1

Triggering receptor expressed on myeloid cells 1

−2.36

PROM1

Prominin 1

−2.27

SCG2

Secretogranin II

−2.22

LPL

Lipoprotein lipase

−2.20

SDC4

Syndecan 4

−2.19

SLC2A3

Solute carrier family 2 (facilitated glucose transporter), member 3

−2.13

PFKFB3

6-Phosphofructo-2-kinase/fructose-2,6-biphosphatase 3

−2.11

TNFRSF11B

Tumour necrosis factor receptor superfamily, member 11b

−2.11

WIF1

WNT inhibitory factor 1

−2.10

NAMPT

Nicotinamide phosphoribosyltransferase

−2.08

ENPEP

Glutamyl aminopeptidase (aminopeptidase A)

−2.07

ZNF331

Zinc finger protein 331

−2.07

ANXA3

Annexin A3

−2.06

HAPLN1

Hyaluronan and proteoglycan link protein 1

−2.05

CSN3

Casein kappa

−2.05

KRT23

Keratin 23 (histone deacetylase inducible)

−2.05

VEGFA

Vascular endothelial growth factor A

−2.03

STC1

Stanniocalcin 1

−2.01

EGLN3

Egl nine homolog 3 (C. elegans)

−1.97

ADM

Adrenomedullin

−1.96

G0S2

G0/G1 switch 2

−1.95

BAMBI

BMP and activin membrane-bound inhibitor homolog (Xenopus laevis)

−1.93

TDO2

Tryptophan 2,3-dioxygenase

−1.93

CD24

CD24 molecule

−1.92

DNER

Delta/Notch-like epidermal growth factor-related receptor

−1.92

IBSP

Integrin-binding sialoprotein

−1.91

HSPA2

Heat shock 70 kDa protein 2

−1.90

ERRFI1

ERBB receptor feedback inhibitor 1

−1.89

MUCL1

Mucin-like 1

−1.89

APOLD1

Apolipoprotein L domain containing 1

−1.89

SHISA2

Shisa homolog 2 (Xenopus laevis)

−1.88

GPX3

Glutathione peroxidase 3 (plasma)

−1.87

SERPINE1

Serpin peptidase inhibitor, clade E (nexin, plasminogen activator inhibitor type 1), member 1

−1.87

COL2A1

Collagen, type II, α 1

−1.86

CP

Ceruloplasmin (ferroxidase)

−1.85

COL9A3

Collagen, type IX, α 3

−1.85

ENO2

Enolase 2 (gamma, neuronal)

−1.84

FOSB

FBJ murine osteosarcoma viral oncogene homolog B

−1.84

TSPAN13

Tetraspanin 13

−1.82

CYP4X1

Cytochrome P450, family 4, subfamily X, polypeptide 1

−1.82

TFAP2C

Transcription factor AP-2γ (activating enhancer binding protein 2γ)

−1.81

EGR3

Early growth response 3

−1.81

SOX11

SRY (sex-determining region Y), box 11

−1.79

CLEC5A

C-type lectin domain family 5, member A

−1.78

CYP26B1

Cytochrome P450, family 26, subfamily B, polypeptide 1

−1.78

SLPI

Secretory leukocyte peptidase inhibitor

−1.78

PI15

Peptidase inhibitor 15

−1.78

RBP7

Retinol binding protein 7, cellular

−1.77

SERPINA3

Serpin peptidase inhibitor, clade A (α-1 antiproteinase, antitrypsin), member 3

−1.77

CCDC102B

Coiled-coil domain containing 102B

−1.75

MTHFD2

Methylenetetrahydrofolate dehydrogenase (NADP+ dependent) 2, methenyltetrahydrofolate cyclohydrolase

−1.74

CFI

Complement factor I

−1.74

FCGBP

Fc fragment of IgG binding protein

−1.73

GPNMB

Glycoprotein (transmembrane) NMB

−1.73

FCGR2A

Fc fragment of IgG, low affinity IIa, receptor (CD32)

−1.72

MAL2

Mal, T-cell differentiation protein 2

−1.72

UAP1

UDP-N-acteylglucosamine pyrophosphorylase 1

−1.71

IER3

Immediate early response 3

−1.70

COL4A1

Collagen, type IV, α 1

−1.69

EFNB2

Ephrin-B2

−1.69

FCGR2B

Fc fragment of IgG, low affinity IIb, receptor (CD32)

−1.69

BTBD3

BTB (POZ) domain containing 3

−1.68

FGF13

Fibroblast growth factor 13

−1.68

GALNT3

UDP-N-acetyl-α-d-galactosamine:polypeptide N-acetylgalactosaminyltransferase 3 (GalNAc-T3)

−1.67

INHBB

inhibin, β B

−1.66

MANSC1

MANSC domain containing 1

−1.65

DSP

Desmoplakin

−1.64

CLDN8

Claudin 8

−1.64

TUBB2B

Tubulin, β 2B

−1.64

PODXL

Podocalyxin-like

−1.63

EHF

ETS homologous factor

−1.63

TIPARP

TCDD-inducible poly(ADP-ribose) polymerase

−1.63

ANGPT2

Angiopoietin 2

−1.62

ADAMTS1

ADAM metallopeptidase with thrombospondin type 1 motif, 1

−1.62

GPR4

G protein-coupled receptor 4

−1.61

DBH

Dopamine β-hydroxylase (dopamine β-monooxygenase)

−1.61

GPR183

G protein-coupled receptor 183

−1.61

TFAP2A

Transcription factor AP-2 α (activating enhancer binding protein 2 α)

−1.60

SNORD89

Small nucleolar RNA, C/D box 89

−1.60

CXCL2

Chemokine (C-X-C motif) ligand 2

−1.60

CXADR

Coxsackie virus and adenovirus receptor

−1.60

TPRKB

TP53RK binding protein

−1.60

ETS2

v-ets erythroblastosis virus E26 oncogene homolog 2 (avian)

−1.60

RAPH1

Ras association (RalGDS/AF-6) and pleckstrin homology domains 1

−1.60

ADGRF5

Adhesion G protein-coupled receptor F

−1.60

CA2

Carbonic anhydrase II

−1.59

LIPA

Lipase A, lysosomal acid, cholesterol esterase

−1.59

PGM2

Phosphoglucomutase 2

−1.59

KRT19

Keratin 19

−1.58

MGAT5

Mannosyl (α-1,6-)-glycoprotein β-1,6-N-acetyl-glucosaminyltransferase

−1.58

NCF2

Neutrophil cytosolic factor 2

−1.57

RHOU

Ras homolog gene family, member U

−1.57

ALCAM

Activated leukocyte cell adhesion molecule

−1.57

LRRN1

Leucine-rich repeat neuronal 1

−1.57

OLR1

Oxidized low-density lipoprotein (lectin-like) receptor 1

−1.55

SLC19A2

Solute carrier family 19 (thiamine transporter), member 2

−1.55

PRPS2

Phosphoribosyl pyrophosphate synthetase 2

−1.55

MEGF10

Multiple EGF-like domains 10

−1.55

CYYR1

Cysteine/tyrosine-rich 1

−1.54

PLVAP

Plasmalemma vesicle-associated protein

−1.54

TM4SF1

Transmembrane 4 L6 family member 1

−1.54

PDGFA

Platelet-derived growth factor α polypeptide

−1.54

YBX2

Y box binding protein 2

−1.54

ATP2B1

ATPase, Ca2+-transporting, plasma membrane 1

−1.54

PCDHB2

Protocadherin β 2

−1.54

DNMT1

DNA (cytosine-5-)-methyltransferase 1

−1.54

S100A8

S100 calcium binding protein A8

−1.53

MAP2

Microtubule-associated protein 2

−1.53

ARRDC4

Arrestin domain containing 4

−1.52

FAM83D

Family with sequence similarity 83, member D

−1.52

LSR

Lipolysis stimulated lipoprotein receptor

−1.52

STK26

Serine/threonine protein kinase 26

−1.51

MIR181A2HG

MIR181A2 host gene (non-protein coding)

−1.51

VWA8

von Willebrand factor A domain containing 8

−1.51

MEST

Mesoderm-specific transcript homolog (mouse)

−1.51

ZNF835

Zinc finger protein 835

1.51

NAT1

N-acetyltransferase 1 (arylamine N-acetyltransferase)

1.51

EPSTI1

Epithelial stromal interaction 1 (breast)

1.51

LOC221946

Hypothetical LOC221946

1.51

OAS1

2′,5′-oligoadenylate synthetase 1, 40/46 kDa

1.52

SELL

Selectin L

1.52

COX6C

Cytochrome c oxidase subunit VIc

1.52

TRIM41

Tripartite motif-containing 41

1.52

IFI27

Interferon-α-inducible protein 27

1.52

IGF1

Insulin-like growth factor 1 (somatomedin C)

1.52

SCAMP1-AS1

SCAMP1 antisense RNA 1

1.52

CD207

CD207 molecule, langerin

1.52

IFI35

Interferon-induced protein 35

1.52

GGH

γ-Glutamyl hydrolase (conjugase, folylpolygammaglutamyl hydrolase)

1.52

NOX4

NADPH oxidase 4

1.53

CNTN3

Contactin 3 (plasmacytoma associated)

1.53

CCL5

Chemokine (C-C motif) ligand 5

1.54

GALNT1

UDP-N-acetyl-α-d-galactosamine:polypeptide N-acetylgalactosaminyltransferase 1 (GalNAc-T1)

1.54

SPON1

Spondin 1, extracellular matrix protein

1.54

SEMA3C

Sema domain, immunoglobulin domain (Ig), short basic domain, secreted, (semaphorin) 3C

1.54

DDX60L

DEAD (Asp-Glu-Ala-Asp) box polypeptide 60-like

1.55

TNFSF10

Tumor necrosis factor (ligand) superfamily, member 10

1.55

CXCL14

Chemokine (C-X-C motif) ligand 14

1.55

WISP2

WNT1 inducible signaling pathway protein 2

1.55

STAT1

Signal transducer and activator of transcription 1, 91 kDa

1.55

COMP

Cartilage oligomeric matrix protein

1.56

IGLJ3

Immunoglobulin lambda joining 3

1.56

LRRC17

Leucine-rich repeat containing 17

1.56

IFI44

Interferon-induced protein 44

1.56

ISG15

ISG15 ubiquitin-like modifier

1.56

FBLN2

Fibulin 2

1.57

SLC6A6

Solute carrier family 6 (neurotransmitter transporter, taurine), member 6

1.57

MX2

Myxovirus (influenza virus) resistance 2 (mouse)

1.57

SH3D19

SH3 domain containing 19

1.57

TRBC1

T-cell receptor β constant 1

1.58

SGCE

Sarcoglycan, epsilon

1.58

IGHM

Immunoglobulin heavy constant mu

1.58

DCBLD1

Discoidin, CUB and LCCL domain containing 1

1.59

PPAPDC1A

Phosphatidic acid phosphatase type 2 domain containing 1A

1.59

BST2

Bone marrow stromal cell antigen 2

1.59

MFAP2

Microfibrillar-associated protein 2

1.60

PDGFD

Platelet-derived growth factor D

1.60

IGKC

Immunoglobulin kappa constant

1.60

CST1

Cystatin SN

1.61

CCL8

Chemokine (C-C motif) ligand 8

1.61

RASGRF2

Ras protein-specific guanine nucleotide-releasing factor 2

1.61

MX1

Myxovirus (influenza virus) resistance 1, interferon-inducible protein p78 (mouse)

1.63

PDGFRL

Platelet-derived growth factor receptor-like

1.63

ALDH1L2

Aldehyde dehydrogenase 1 family, member L2

1.63

FAM198B

Family with sequence similarity 198, member B

1.63

MIR100HG

Mir-100-let-7a-2 cluster host gene

1.64

GAPT

GRB2-binding adaptor protein, transmembrane

1.65

SELM

Selenoprotein M

1.65

DSCAM-AS1

DSCAM antisense RNA 1

1.66

STMN2

Stathmin-like 2

1.69

FBLN5

Fibulin 5

1.70

IFIT3

Interferon-induced protein with tetratricopeptide repeats 3

1.70

SFRP4

Secreted frizzled-related protein 4

1.71

ACKR4

Atypical chemokine receptor 4

1.71

CPNE2

Copine II

1.71

PSMB9

Proteasome (prosome, macropain) subunit, β type, 9 (large multifunctional peptidase 2)

1.72

ST6GAL2

ST6 β-galactosamide α-2,6-sialyltranferase 2

1.72

NEXN

Nexilin (F actin binding protein)

1.72

CD52

CD52 molecule

1.72

MFAP5

Microfibrillar associated protein 5

1.73

RARRES3

Retinoic acid receptor responder (tazarotene induced) 3

1.75

GXYLT2

Glucoside xylosyltransferase 2

1.75

HMCN1

Hemicentin 1

1.76

EFEMP1

EGF-containing fibulin-like extracellular matrix protein 1

1.78

IL21R

Interleukin 21 receptor

1.78

C8orf4

Chromosome 8 open reading frame 4

1.78

LINC01503

Long intergenic non-protein coding RNA 1503

1.78

OLFML3

Olfactomedin-like 3

1.79

CILP

Cartilage intermediate layer protein, nucleotide pyrophosphohydrolase

1.81

MVB12A

Multivesicular body subunit 12A

1.82

SCUBE2

Signal peptide, CUB domain, EGF-like 2

1.83

WNT2

Wingless-type MMTV integration site family member 2

1.85

APOL3

Apolipoprotein L3

1.87

ADRA2A

Alpha-2A adrenergic receptor

1.89

HIST1H3I

Histone cluster 1, H3i

1.92

SLC46A3

Solute carrier family 46, member 3

1.92

ARHGAP28

Rho GTPase activating protein 28

1.93

KANK4

KN motif and ankyrin repeat domains 4

1.93

SDC1

Syndecan 1

1.95

CMPK2

Cytidine monophosphate (UMP-CMP) kinase 2, mitochondrial

1.96

IFI44L

Interferon-induced protein 44-like

1.97

FMO1

Flavin containing monooxygenase 1

1.98

TMEM119

Transmembrane protein 119

1.99

FNDC1

Fibronectin type III domain containing 1

2.00

ADAMDEC1

ADAM-like, decysin 1

2.00

TPSAB1

Tryptase α/β1

2.02

CPA3

Carboxypeptidase A3 (mast cell)

2.02

MMP3

Matrix metallopeptidase 3 (stromelysin 1, progelatinase)

2.05

IFI6

Interferon, α-inducible protein 6

2.06

IFIT1

Interferon-induced protein with tetratricopeptide repeats 1

2.06

SFRP2

Secreted frizzled-related protein 2

2.09

TRIM6

Tripartite motif-containing 6

2.10

TPSB2

Tryptase β2 (gene/pseudogene)

2.19

RSAD2

Radical S-adenosyl methionine domain containing 2

2.28

LOXL1

Lysyl oxidase-like 1

2.30

OMD

Osteomodulin

2.35

IGJ

Immunoglobulin J polypeptide, linker protein for immunoglobulin α and mu polypeptides

2.44

FCGR1A

Fc fragment of IgG, high affinity Ia, receptor (CD64)

2.47

MATN3

Matrilin 3

2.55

IGLV@

Immunoglobulin lambda variable cluster

2.65

OGN

Osteoglycin

2.99

EPYC

Epiphycan

3.04

Negative values for fold change indicate up-regulation in older patient samples; positive fold change values indicate up-regulation in younger patient samples

Figure 3
Fig. 3

Heat maps constructed using the 25 top up-regulated and the 25 top down-regulated genes. Blue represents young patients, and green represents old patients

Data validation

We used publicly available data sets ([GEO:GSE5847] [9], 34 samples; [GEO:GSE4823] [7], 33 samples; [GEO:GSE14548] [8], 9 samples) to validate our findings because of the limited size of our study group. We found a significant overlap for ten genes, of which five showed higher expression in older patients (p < 0.01) and five showed lower expression in older patients (p < 0.01). Venn diagrams depicting the overlapping genes are shown in Fig. 4; gene details are listed in Table 3.
Figure 4
Fig. 4

Venn diagrams showing the intersection of our own expression data (‘study data’) and the publicly available data (‘metadata’). a The up-regulated genes in young stromal samples. b The down-regulated genes in young stromal samples

Table 3

Significant up- or down-regulated genes after validation in the external validation data set (see Fig. 4)

Gene

Full name

Fold change

RARRES3

Retinoic acid receptor responder (tazarotene induced) 3

1.75

SFRP4

Secreted frizzled-related protein 4

1.71

SCUBE2

Signal peptide, CUB domain, EGF-like 2

1.83

NAT1

N-acetyltransferase 1 (arylamine N-acetyltransferase)

1.51

COMP

Cartilage oligomeric matrix protein

1.56

ANXA3

Annexin A3

−2.06

PROM1

Prominin 1

−2.27

FGF13

Fibroblast growth factor 13

−1.68

TUBB2B

Tubulin, beta 2B

−1.64

WIF1

WNT inhibitory factor 1

−2.10

Negative values for fold change indicate up-regulation in old patient samples, and positive values indicate up-regulation in young patient samples

Gene set enrichment analysis

Next, we performed GSEA to measure the expression of pre-defined gene sets related to specific biological processes. The resulting ES, which ranges from −1 to 1, reflects the enrichment in genes of a given reference class among the top up- or down-regulated genes from the individual gene ranking. Plots are shown in Figs. 5 and 6. The genes that were included in each GSEA, with respective literature references, are listed in Table 4.
Figure 5
Fig. 5

Gene set enrichment analysis plots. a Senescence genes. b The DNA damage response process Low values correspond to enrichment of the genes in older patients, and high values correspond to enrichment of the genes in younger patients. ES Enrichment score

Figure 6
Fig. 6

Gene set enrichment analysis plots. a The senescence-associated secretory profile process. b The autophagy-to-senescence transition or ‘reverse Warburg effect’. Low values correspond to enrichment of the genes in older patients, and high values correspond to enrichment of the genes in younger patients. ES Enrichment score

Table 4

Groups of candidate genes related to a specific pathophysiological process, built to perform gene set enrichment analysis, and their respective references

Gene group

Involved genes

References

Cellular senescence

CDKN1A, CDKN2A, TP53, RB1, GLB1

[17–19, 69–71]

DNA damage response

ATM, NBN, CHEK2

[21, 73]

Senescence-associated secretory profile

IL1A, IL6, IL6R, IL6ST, IL8, CXCL1, CXCL2, CXCL3, CSF2, IL7, ICAM1, TNFRSF11B, HGF, IGFBP4, CCL8, PLAUR, IGFBP2, CCL26, IL13, CCL20, ICAM3, PGF, TNFRSF1A, TNFRSF1B, CCL13, CCL16, TNFRSF10C, CCL2, FAS, ANG, IGFBP6, IL1B, (CCL3), TIMP2, IL11, OSM, LEP, AXL, KITLG, FGF7, IL15, FGF2, IGFBP1, MIF

[17, 21, 22, 26]

Autophagy-to-senescence transition

CAV1, CTSB, BNIP3, PRKAA1, PRKAA2, LAMP2, MAP1LC3B, ATG16L1, HIF1A, NFKB1, DRAM1, TP73, MAPK8, E2F1, STK11

[33–37]

Senescence genes

In the individual gene expression analysis, no significant difference was found for genes known to be associated with senescence, such as CDKN1A, CDKN2A, TP53, GLB1 or the retinoblastoma (RB) genes. Nevertheless, the enrichment analysis for this gene set resulted in an ES of −0.53, suggesting enrichment of senescence genes in the stroma of older patients, although statistical significance was not reached (p = 0.09) (Fig. 5a). The lack of significance might be due to the small sample size of the reference classes.

DNA damage response

None of the three most important components of the DNA damage response, namely ATM, NBN (NBS1) and CHK2, were differentially expressed between young and old stromal tissues. Similar results were found when we applied GSEA to the DNA damage response gene set. The estimated gene score was 0.57, which did not reach statistical significance (p = 0.10) (Fig. 5b).

Senescence-associated secretory profile

Several genes involved in the SASP showed a deregulated gene expression profile, suggesting an enrichment of SASP in the stroma of older patients, including CXCL2 (over-expressed in older stromal tissues; fold change 1.59), TNFRSF11B (over-expressed in older stromal tissues; fold change 2.11) and CCL8 (down-regulated in older stromal tissues; fold change −1.61). Of interest, GSEA confirmed a significant enrichment in SASP-related genes within the stroma of older patient samples with an ES of −0.21 (p = 0.04) (Fig. 6a).

The reverse Warburg effect: autophagy genes

None of the genes described to be involved in the AST showed a relevant difference in gene expression between young and old stroma at the individual gene level. However, when compiling them together in the GSEA, we found a highly significant enrichment of autophagy genes in the stroma of older patient samples (ES −0.42; p < 0.01) (Fig. 6b).

Discussion

The reason for the age-related increase in cancers has been debated for decades. Besides cumulative DNA damage throughout life, the accumulation of senescent cells is assumed to create a tumour-promoting micro-environment through phenomena such as the SASP and the AST. So far, studies investigating the impact of stromal senescence on tumour development have been based on in vitro fibroblast cultures where senescence was artificially induced [23, 29]. As a consequence of this approach, an overload of senescent or pre-senescent fibroblasts was present in these experiments. We do not know if this accurately reflects the situation in spontaneous cancers. Accumulation of senescent cells with age has been studied mostly in fibroblasts localised in the skin [14], but data on the frequency of senescent fibroblasts in the older breast are lacking. Moreover, controversy exists regarding whether findings on in vitro senescence can be extrapolated to the situation in vivo. We therefore aimed in this study to investigate the molecular footprint of the older breast cancer micro-environment in order to support in vivo confirmation of key concepts such as ageing/senescence, DNA damage response, SASP and AST. Senescence in the surrounding stroma is expected to result in a pro-tumourigenic micro-environment with stimulation of proliferation, migration/invasion and de-differentiation. However, this had never been shown in spontaneously occurring breast cancers. For this purpose, we selected two groups of patients with triple-negative breast cancer belonging to extreme age categories, isolated cancer-associated stromal fields via LCM and investigated their gene expression profiles.

Differential gene expression analysis using a cut-off of a 1.5-fold change in expression revealed 120 up-regulated and 107 down-regulated genes in the stromal parts of older patients compared with the younger patients. Validation of these findings using publicly available stromal data revealed a set of ten differentially expressed genes between young and old stromal samples. The young stromal samples showed mainly up-regulation in genes such as RARRES3, SCUBE2, SFRP4, COMP and NANT1 that preclude migration and invasion by stabilising the cells in the extracellular matrix and stimulate differentiation [41, 51–60]. Significant up-regulation in the older stromal micro-environment was shown instead for genes that are involved in proliferation, de-differentiation and angiogenesis. Four genes, namely ANXA3, PROM1, FGF13 and TUBB2B, seem to restrain differentiation and promote cell proliferation, invasiveness and metastasis [61–71]. The fifth up-regulated gene in the older stromal samples, WIF1, is a negative inhibitor of the Wnt (Wingless-type)/β-catenin signalling pathway. It is thought to inhibit proliferation and to induce differentiation and cellular senescence by up-regulation of tumour suppressor genes such as TP53 or P21 [72, 73]. Although the proliferation-inhibiting and differentiation-inducing effects of this gene seem to be in contradiction with the proliferation- and metastasis-promoting activity of the other four up-regulated genes in the older stromal samples, its senescence-inducing function may be an obvious explanation for the age-related stromal expression of WIF1 in our study. Taken together, on the basis of our data, we found evidence of a more tumour-favourable micro-environment in the stromal samples from older patients than in those from younger patients.

As an additional analysis, we applied a candidate gene approach by assembling sets of genes on the basis of the literature. We specifically looked at the individual gene expression results for these genes, but we also compiled them using a gene set enrichment strategy that reflects the representation of these genes among the top up- or down-regulated genes in old and young stromal samples.

The molecular process of senescence is characterised by up-regulation of several senescence genes. The most documented ones are TP53, CDKN2A (P16), CDKN1A (P21) and RB [74–76]. These major senescence-inducing genes did not show significantly different expression values between young and old stroma in the individual gene analysis. Nevertheless, we observed in older patients an up-regulation of PAI-1 (SERPINE1), a matrix-remodelling enzyme, which has also been described as a crucial regulator of ageing and senescence by acting downstream of TP53 and upstream of insulin-like growth factor binding protein 3 [77], and of WIF1, described above as an inhibitor of the Wnt/β-catenin signalling pathway and an inducer of senescence, and it was also significantly increased in samples from old compared with young patients (Table 2).

These findings could be indicative of more widespread cellular senescence in our older stromal samples compared with the young ones. Gene enrichment analysis based on five key senescence genes, including the ones mentioned above, showed a tendency towards a more prominent senescence trait in older stroma, but no significant ES was reached for this process in the older patient samples. Therefore, we cannot decisively conclude that samples from the older patient group show increased senescence.

The DNA damage response is a biological process that, upon severe DNA damage, triggers the switch towards a permanent growth arrest [78]. It was found that the molecular senescence programme can be induced only when this DNA damage response has been activated for a sufficiently long time period [21]. We could not demonstrate any clear difference in the individual expression of three key players involved in the DNA damage response (ATM, NBN or CHEK2) [78], nor did we find significant enrichment for this set of genes in the older patient samples.

In our stromal gene expression study, only a few of the SASP components described by Coppé et al. as overproduced by senescent cell cultures [26] showed significant age-related differential expression: CXCL2 and osteoprotegerin (TNFRSF11B), a member of the tumour necrosis factor receptor superfamily, which both showed overexpression in the old patient samples, as well as CCL8, which showed down-regulation in the older versus the younger stromal samples. Of interest, when compiling all the components of the SASP together in the gene enrichment analysis, we indeed confirmed a significant enrichment in SASP genes among genes up-regulated in the older patient samples, confirming for the first time the presence of the SASP phenotype in human breast cancers in vivo.

Autophagy is assumed to precede or parallel the process of senescence [38], as described by the term autophagy-to-senescence transition (AST). Typical markers for AST are loss of caveolin 1 (CAV1) and up-regulation of BNIP3, BNIP3L, Beclin-1, Cathepsin B and ATG16L1. Our individual gene expression results did not show relevant up-regulation of single autophagy-related genes in the older stromal samples, but compilation of these genes into a GSEA showed highly significant enrichment for autophagy genes in the older stromal samples. Thus, in addition to the presence of SASP, we also confirmed the presence of AST in the older stromal cancer milieu.

In this study, regarding gene expression levels, we report, for the first time to our knowledge, the presence of SASP and AST in older stromal samples, supporting the previously published in vitro and xenograft findings. This does not, however, solve the paradox between the stimulatory effect that these processes are supposed to have on proximate malignant cells and the clinical finding that breast cancer in older patients behaves in a rather more indolent instead of a more aggressive way [79]. Also, it remains puzzling that we found evidence for SASP and AST in older stromal samples, which are senescence-related phenomena, whereas we did not find convincing evidence for increased senescence in these samples. The small sample size, together with the low number of genes defining the ‘senescence’ programme, could partly explain the lack of significance for major senescence-related genes such as TP53, CDKN2A and pRB, both at the individual level and in the GSEAs.

Besides the small sample size of the study, the broad age interval between the patient groups and the difference in menopausal status between the groups introduce other potential biases, because not only the stroma but also infiltrating immune cells are believed to be altered by hormonal changes. Ideally, our findings would be further investigated in larger patient cohorts including other age categories, and validated by proteomic analysis of the stromal tissue. LCM is a demanding technique, however, limiting the number of patient samples that can be processed. We attempted to compensate for some of these limitations by including a validation strategy on publicly available gene expression data.

Conclusions

We report, for the first time to our knowledge, the involvement of key pathophysiological concepts of cancer and ageing, such as the SASP and the AST, in vivo in human cancer patients. These remarkable findings justify further research to fully elucidate the role of the ageing stroma in (breast) tumour development and progression. In the first place, this research should be extended in other subtypes of breast cancer and more age categories.

Notes

Abbreviations

AST: 

Autophagy-to-senescence transition

CAV1: 

Caveolin 1

ER: 

Oestrogen receptor

ES: 

Enrichment score

fRMA: 

Frozen robust multi-array analysis

GEO: 

Gene Expression Omnibus

GSEA: 

Gene set enrichment analysis

H&E: 

Haematoxylin and eosin

HER2: 

Human epidermal growth factor receptor 2

LCM: 

Laser capture microdissection

MMP3: 

Matrix metalloproteinase 3

NCBI: 

National Center for Biotechnology Information

PR: 

Progesterone receptor

RB: 

Retinoblastoma

SASP: 

Senescence-associated secretory profile

SPIA: 

Single-primer isothermal amplification

Declarations

Acknowledgements

Not applicable.

Funding

HW is a recipient of the ‘Fonds voor Wetenschappelijk Onderzoek – Vlaanderen (FWO).’ The study was supported by a grant from the Belgian ‘Stichting tegen Kanker’. BB is a recipient of a grant from the ‘Vlaamse Liga tegen Kanker’ (VLK).

Availability of data and materials

The data sets generated during the present study are available from the NCBI GEO database under accession number [GEO:GSE90521]. Validation data sets that were used are available from the NCBI GEO database under the accession numbers [GEO:GSE5847; GEO:GSE4823; GEO:GSE14548].

Authors’ contributions

BB and DF contributed to the design of the study, performed the LCM and gene expression analysis procedures, and were the major authors of the manuscript. BB, DF, SH, CS and HW contributed to the design of the study, the interpretation of the study results and the writing of the manuscript. AS, PS, PN and DL contributed to the collection of the patient samples and to the writing of the manuscript. OG, KvdE and GF provided their technical expertise in LCM. GF contributed to the pathological interpretation of the patient samples and contributed to manuscript writing. SB and YB contributed to the statistical analysis and validation procedures as well as to manuscript writing. All authors read and approved the final manuscript.

Ethics approval and consent to participate

This study was approved by the ethics committee of the University Hospitals Leuven (Leuven, Belgium) in accordance with the International Conference on Harmonisation Harmonised Tripartite Guideline for Good Clinical Practice. Only patient tissue samples from our research biobank were used. All patients whose biological material is included in this biobank signed an informed consent form at the time of surgery, allowing the use of their tissue and medical data for scientific research.

Consent for publication

All patients signed an informed consent form at the time of surgery, allowing use of their tissue and medical data for scientific research, including scientific publication.

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.

Authors’ Affiliations

(1)
Laboratory of Experimental Oncology (LEO), Department of Oncology, KU Leuven, Leuven, Belgium
(2)
Department of General Medical Oncology, Leuven Cancer Institute, University Hospitals Leuven, Leuven, Belgium
(3)
Breast Cancer Translational Research Laboratory, Institut Jules Bordet, Universite Libre de Bruxelles, Brussels, Belgium
(4)
Department of Imaging and Pathology, Laboratory of Translational Cell & Tissue Research, KU Leuven, Leuven, Belgium
(5)
Department of Pathology, University Hospitals Leuven, Leuven, Belgium
(6)
Multidisciplinary Breast Center, University Hospitals Leuven, Leuven, Belgium
(7)
Department of Oncology, Laboratory for Translational Genetics, Vesalius Research Center (VRC), Vlaams Instituut voor Biotechnologie (VIB) and KU Leuven, Leuven, Belgium

References

  1. Bergamaschi A, Tagliabue E, Sørlie T, Naume B, Triulzi T, Orlandi R, et al. Extracellular matrix signature identifies breast cancer subgroups with different clinical outcome. J Pathol. 2008;214:357–67.View ArticlePubMedGoogle Scholar
  2. Finak G, Bertos N, Pepin F, Sadekova S, Souleimanova M, Zhao H, et al. Stromal gene expression predicts clinical outcome in breast cancer. Nat Med. 2008;14:518–27.View ArticlePubMedGoogle Scholar
  3. Beck AH, Sangoi AR, Leung S, Marinelli RJ, Nielsen TO, van de Vijver MJ, et al. Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Sci Transl Med. 2011;3:108ra113.View ArticlePubMedGoogle Scholar
  4. Bianchini G, Qi Y, Alvarez RH, Iwamoto T, Coutant C, Ibrahim NK, et al. Molecular anatomy of breast cancer stroma and its prognostic value in estrogen receptor-positive and -negative cancers. J Clin Oncol. 2010;28:4316–23. Published errata appear in J Clin Oncol. 2010;28:4868; and J Clin Oncol. 2012;30:679Google Scholar
  5. Calon A, Lonardo E, Berenguer-Llergo A, Espinet E, Hernando-Momblona X, Iglesias M, et al. Stromal gene expression defines poor-prognosis subtypes in colorectal cancer. Nat Genet. 2015;47:320–9.View ArticlePubMedGoogle Scholar
  6. Casey T, Bond J, Tighe S, Hunter T, Lintault L, Patel O, et al. Molecular signatures suggest a major role for stromal cells in development of invasive breast cancer. Breast Cancer Res Treat. 2009;114:47–62.View ArticlePubMedGoogle Scholar
  7. Finak G, Sadekova S, Pepin F, Hallett M, Meterissian S, Halwani F, et al. Gene expression signatures of morphologically normal breast tissue identify basal-like tumors. Breast Cancer Res. 2006;8:R58.View ArticlePubMedPubMed CentralGoogle Scholar
  8. Ma XJ, Dahiya S, Richardson E, Erlander M, Sgroi DC. Gene expression profiling of the tumor microenvironment during breast cancer progression. Breast Cancer Res. 2009;11:R7.View ArticlePubMedPubMed CentralGoogle Scholar
  9. Boersma BJ, Reimers M, Yi M, Ludwig JA, Luke BT, Stephens RM, et al. A stromal gene signature associated with inflammatory breast cancer. Int J Cancer. 2008;122:1324–32.View ArticlePubMedGoogle Scholar
  10. Vargas AC, McCart Reed AE, Waddell N, Lane A, Reid LE, Smart CE, et al. Gene expression profiling of tumour epithelial and stromal compartments during breast cancer progression. Breast Cancer Res Treat. 2012;135:153–65.View ArticlePubMedGoogle Scholar
  11. Kalluri R, Zeisberg M. Fibroblasts in cancer. Nat Rev Cancer. 2006;6:392–401.View ArticlePubMedGoogle Scholar
  12. Joyce JA, Pollard JW. Microenvironmental regulation of metastasis. Nat Rev Cancer. 2009;9:239–52.View ArticlePubMedGoogle Scholar
  13. Haviv I, Polyak K, Qiu W, Hu M, Campbell I. Origin of carcinoma associated fibroblasts. Cell Cycle. 2009;8:589–95.View ArticlePubMedGoogle Scholar
  14. Jeyapalan JC, Ferreira M, Sedivy JM, Herbig U. Accumulation of senescent cells in mitotic tissue of aging primates. Mech Ageing Dev. 2007;128:36–44.View ArticlePubMedGoogle Scholar
  15. Wildiers H, Heeren P, Puts M, Topinkova E, Janssen-Heijnen ML, Extermann M, et al. International Society of Geriatric Oncology consensus on geriatric assessment in older patients with cancer. J Clin Oncol. 2014;32:2595–603.View ArticlePubMedPubMed CentralGoogle Scholar
  16. Decoster L, Van Puyvelde K, Mohile S, Wedding U, Basso U, Colloca G, et al. Screening tools for multidimensional health problems warranting a geriatric assessment in older cancer patients: an update on SIOG recommendations. Ann Oncol. 2015;26:288–300.View ArticlePubMedGoogle Scholar
  17. Campisi J. d’Adda di Fagagna F. Cellular senescence: when bad things happen to good cells. Nat Rev Mol Cell Biol. 2007;8:729–40.View ArticlePubMedGoogle Scholar
  18. Ben-Porath I, Weinberg RA. When cells get stressed: an integrative view of cellular senescence. J Clin Invest. 2004;113:8–13.View ArticlePubMedPubMed CentralGoogle Scholar
  19. Ben-Porath I, Weinberg RA. The signals and pathways activating cellular senescence. Int J Biochem Cell Biol. 2005;37:961–76.View ArticlePubMedGoogle Scholar
  20. Krtolica A, Campisi J. Cancer and aging: a model for the cancer promoting effects of the aging stroma. Int J Biochem Cell Biol. 2002;34:1401–14.View ArticlePubMedGoogle Scholar
  21. Rodier F, Coppé JP, Patil CK, Hoeijmakers WA, Muñoz DP, Raza SR, et al. Persistent DNA damage signalling triggers senescence-associated inflammatory cytokine secretion. Nat Cell Biol. 2009;11:973–9. A published erratum appears in Nat Cell Biol. 2009;11:1272.Google Scholar
  22. Orjalo AV, Bhaumik D, Gengler BK, Scott GK, Campisi J. Cell surface-bound IL-1α is an upstream regulator of the senescence-associated IL-6/IL-8 cytokine network. Proc Natl Acad Sci U S A. 2009;106:17031–6.View ArticlePubMedPubMed CentralGoogle Scholar
  23. Parrinello S, Coppe JP, Krtolica A, Campisi J. Stromal-epithelial interactions in aging and cancer: senescent fibroblasts alter epithelial cell differentiation. J Cell Sci. 2005;118:485–96.View ArticlePubMedPubMed CentralGoogle Scholar
  24. Krtolica A, Parrinello S, Lockett S, Desprez PY, Campisi J. Senescent fibroblasts promote epithelial cell growth and tumorigenesis: a link between cancer and aging. Proc Natl Acad Sci U S A. 2001;98:12072–7.View ArticlePubMedPubMed CentralGoogle Scholar
  25. Coppé JP, Desprez PY, Krtolica A, Campisi J. The senescence-associated secretory phenotype: the dark side of tumor suppression. Annu Rev Pathol. 2010;5:99–118.View ArticlePubMedPubMed CentralGoogle Scholar
  26. Coppé JP, Patil CK, Rodier F, Sun Y, Muñoz DP, Goldstein J, et al. Senescence-associated secretory phenotypes reveal cell-nonautonomous functions of oncogenic RAS and the p53 tumor suppressor. PLoS Biol. 2008;6:2853–68.View ArticlePubMedGoogle Scholar
  27. Williams PD, Day T. Antagonistic pleiotropy, mortality source interactions, and the evolutionary theory of senescence. Evolution. 2003;57:1478–88.View ArticlePubMedGoogle Scholar
  28. Rose M, Charlesworth B. A test of evolutionary theories of senescence. Nature. 1980;287:141–2.View ArticlePubMedGoogle Scholar
  29. Liu D, Hornsby PJ. Senescent human fibroblasts increase the early growth of xenograft tumors via matrix metalloproteinase secretion. Cancer Res. 2007;67:3117–26.View ArticlePubMedGoogle Scholar
  30. Begley LA, Kasina S, MacDonald J, Macoska JA. The inflammatory microenvironment of the aging prostate facilitates cellular proliferation and hypertrophy. Cytokine. 2008;43:194–9.View ArticlePubMedPubMed CentralGoogle Scholar
  31. Wang YC, Yu SQ, Wang XH, Han BM, Zhao FJ, Zhu GH, et al. Differences in phenotype and gene expression of prostate stromal cells from patients of varying ages and their influence on tumour formation by prostate epithelial cells. Asian J Androl. 2011;13:732–41.View ArticlePubMedPubMed CentralGoogle Scholar
  32. Bianchi-Frias D, Vakar-Lopez F, Coleman IM, Plymate SR, Reed MJ, Nelson PS. The effects of aging on the molecular and cellular composition of the prostate microenvironment. PLoS One. 2010;5:e12501.View ArticlePubMedPubMed CentralGoogle Scholar
  33. Herbig U, Ferreira M, Condel L, Carey D, Sedivy JM. Cellular senescence in aging primates. Science. 2006;311:1257.View ArticlePubMedGoogle Scholar
  34. Dimri GP, Lee X, Basile G, Acosta M, Scott G, Roskelley C, et al. A biomarker that identifies senescent human cells in culture and in aging skin in vivo. Proc Natl Acad Sci U S A. 1995;92:9363–7.View ArticlePubMedPubMed CentralGoogle Scholar
  35. Satyanarayana A, Wiemann SU, Buer J, Lauber J, Dittmar KE, Wüstefeld T, et al. Telomere shortening impairs organ regeneration by inhibiting cell cycle re-entry of a subpopulation of cells. EMBO J. 2003;22:4003–13.View ArticlePubMedPubMed CentralGoogle Scholar
  36. Molofsky AV, Slutsky SG, Joseph NM, He S, Pardal R, Krishnamurthy J, et al. Increasing p16 INK4a expression decreases forebrain progenitors and neurogenesis during ageing. Nature. 2006;443:448–52.View ArticlePubMedPubMed CentralGoogle Scholar
  37. Ressler S, Bartkova J, Niederegger H, Bartek J, Scharffetter-Kochanek K, Jansen-Dürr P, et al. p16 INK4A is a robust in vivo biomarker of cellular aging in human skin. Aging Cell. 2006;5:379–89.View ArticlePubMedGoogle Scholar
  38. Capparelli C, Guido C, Whitaker-Menezes D, Bonuccelli G, Balliet R, Pestell TG, et al. Autophagy and senescence in cancer-associated fibroblasts metabolically supports tumor growth and metastasis via glycolysis and ketone production. Cell Cycle. 2012;11:2285–302.View ArticlePubMedPubMed CentralGoogle Scholar
  39. Salem AF, Whitaker-Menezes D, Lin Z, Martinez-Outschoorn UE, Tanowitz HB, Al-Zoubi MS, et al. Two-compartment tumor metabolism: autophagy in the tumor microenvironment and oxidative mitochondrial metabolism (OXPHOS) in cancer cells. Cell Cycle. 2012;11:2545–56.View ArticlePubMedPubMed CentralGoogle Scholar
  40. Martinez-Outschoorn UE, Pavlides S, Howell A, Pestell RG, Tanowitz HB, Sotgia F, et al. Stromal-epithelial metabolic coupling in cancer: integrating autophagy and metabolism in the tumor microenvironment. Int J Biochem Cell Biol. 2011;43:1045–51.View ArticlePubMedPubMed CentralGoogle Scholar
  41. Chiavarina B, Whitaker-Menezes D, Migneco G, Martinez-Outschoorn UE, Pavlides S, Howell A, et al. HIF1-α functions as a tumor promoter in cancer associated fibroblasts, and as a tumor suppressor in breast cancer cells: Autophagy drives compartment-specific oncogenesis. Cell Cycle. 2010;9:3534–51.View ArticlePubMedPubMed CentralGoogle Scholar
  42. Martinez-Outschoorn UE, Whitaker-Menezes D, Pavlides S, Chiavarina B, Bonuccelli G, Casey T, et al. The autophagic tumor stroma model of cancer or “battery-operated tumor growth”: a simple solution to the autophagy paradox. Cell Cycle. 2010;9:4297–306.View ArticlePubMedPubMed CentralGoogle Scholar
  43. Benz CC. Impact of aging on the biology of breast cancer. Crit Rev Oncol Hematol. 2008;66:65–74.View ArticlePubMedGoogle Scholar
  44. Wildiers H, Kunkler I, Biganzoli L, Fracheboud J, Vlastos G, Bernard-Marty C, et al. Management of breast cancer in elderly individuals: recommendations of the International Society of Geriatric Oncology. Lancet Oncol. 2007;8:1101–15.View ArticlePubMedGoogle Scholar
  45. Fend F, Raffeld M. Laser capture microdissection in pathology. J Clin Pathol. 2000;53:666–72.View ArticlePubMedPubMed CentralGoogle Scholar
  46. Morse AM, Carballo V, Baldwin DA, Taylor CG, McIntyre LM. Comparison between NuGEN’s WT-Ovation Pico and one-direct amplification systems. J Biomol Tech. 2010;21:141–7.PubMedPubMed CentralGoogle Scholar
  47. Singh R, Maganti RJ, Jabba SV, Wang M, Deng G, Heath JD, et al. Microarray-based comparison of three amplification methods for nanogram amounts of total RNA. Am J Physiol Cell Physiol. 2005;288:C1179–89.View ArticlePubMedGoogle Scholar
  48. Vermeulen J, Derveaux S, Lefever S, De Smet E, De Preter K, Yigit N, et al. RNA pre-amplification enables large-scale RT-qPCR gene-expression studies on limiting sample amounts. BMC Res Notes. 2009;2:235.View ArticlePubMedPubMed CentralGoogle Scholar
  49. McCall MN, Bolstad BM, Irizarry RA. Frozen robust multiarray analysis (fRMA). Biostatistics. 2010;11:242–53.View ArticlePubMedPubMed CentralGoogle Scholar
  50. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102:15545–50.View ArticlePubMedPubMed CentralGoogle Scholar
  51. Scharadin TM, Eckert RL. TIG3: an important regulator of keratinocyte proliferation and survival. J Invest Dermatol. 2014;134:1811–6.View ArticlePubMedPubMed CentralGoogle Scholar
  52. Morales M, Arenas EJ, Urosevic J, Guiu M, Fernández E, Planet E, et al. RARRES3 suppresses breast cancer lung metastasis by regulating adhesion and differentiation. EMBO Mol Med. 2014;6:865–81.View ArticlePubMedPubMed CentralGoogle Scholar
  53. Lin YC, Lee YC, Li LH, Cheng CJ, Yang RB. Tumor suppressor SCUBE2 inhibits breast-cancer cell migration and invasion through the reversal of epithelial-mesenchymal transition. J Cell Sci. 2014;127:85–100.View ArticlePubMedGoogle Scholar
  54. Lin YC, Chen CC, Cheng CJ, et al. Domain and functional analysis of a novel breast tumor suppressor protein, SCUBE2. J Biol Chem. 2011;286:27039–47.View ArticlePubMedPubMed CentralGoogle Scholar
  55. Johansson I, Nilsson C, Berglund P, Lauss M, Ringnér M, Olsson H, et al. Gene expression profiling of primary male breast cancers reveals two unique subgroups and identifies N-acetyltransferase-1 (NAT1) as a novel prognostic biomarker. Breast Cancer Res. 2012;14:R31.View ArticlePubMedPubMed CentralGoogle Scholar
  56. Hsu TH, Chu CC, Jiang SY, Hung MW, Ni WC, Lin HE, et al. Expression of the class II tumor suppressor gene RIG1 is directly regulated by p53 tumor suppressor in cancer cell lines. FEBS Lett. 2012;586:1287–93.View ArticlePubMedGoogle Scholar
  57. Guo P, Shi ZL, Liu A, Lin T, Bi FG, Shi MM, et al. Cartilage oligomeric matrix protein gene multilayers inhibit osteogenic differentiation and promote chondrogenic differentiation of mesenchymal stem cells. Int J Mol Sci. 2014;15:20117–33.View ArticlePubMedPubMed CentralGoogle Scholar
  58. Guo P, Shi ZL, Liu A, Lin T, Bi F, Shi M, et al. Effects of cartilage oligomeric matrix protein on bone morphogenetic protein-2-induced differentiation of mesenchymal stem cells. Orthop Surg. 2014;6:280–7.View ArticlePubMedGoogle Scholar
  59. Ford CE, Jary E, Ma SS, Nixdorf S, Heinzelmann-Schwarz VA, Ward RL. The Wnt gatekeeper SFRP4 modulates EMT, cell migration and downstream Wnt signalling in serous ovarian cancer cells. PLoS One. 2013;8:e54362.View ArticlePubMedPubMed CentralGoogle Scholar
  60. Endo Y, Yamashita H, Takahashi S, Sato S, Yoshimoto N, Asano T, et al. Immunohistochemical determination of the miR-1290 target arylamine N-acetyltransferase 1 (NAT1) as a prognostic biomarker in breast cancer. BMC Cancer. 2014;14:990.View ArticlePubMedPubMed CentralGoogle Scholar
  61. Zhang L, Li H, Ge C, Li M, Zhao FY, Hou HL, et al. Inhibitory effects of transcription factor Ikaros on the expression of liver cancer stem cell marker CD133 in hepatocellular carcinoma. Oncotarget. 2014;5:10621–35.View ArticlePubMedPubMed CentralGoogle Scholar
  62. Zhai JM, Sun SJ, Wang W, Zeng C. Expression of annexin A3 in gastric cancer and its correlation with proliferation and apoptosis. Asian Pac J Cancer Prev. 2014;15:3001–4.View ArticlePubMedGoogle Scholar
  63. Zeng C, Ke Z, Song Y, Yao Y, Hu X, Zhang M, et al. Annexin A3 is associated with a poor prognosis in breast cancer and participates in the modulation of apoptosis in vitro by affecting the Bcl-2/Bax balance. Exp Mol Pathol. 2013;95:23–31.View ArticlePubMedGoogle Scholar
  64. Yu SY, Li Y, Fan LQ, Zhao Q, Tan BB, Liu Y. Impact of Annexin A3 expression in gastric cancer cells. Neoplasma. 2014;61:257–64.View ArticlePubMedGoogle Scholar
  65. Xia P, Song CL, Liu JF, et al. Prognostic value of circulating CD133+ cells in patients with gastric cancer. Cell Prolif. 2015;48:311–7.View ArticlePubMedGoogle Scholar
  66. Okada-Ban M, Thiery JP, Jouanneau J. Fibroblast growth factor-2. Int J Biochem Cell Biol. 2000;32:263–7.View ArticlePubMedGoogle Scholar
  67. Jiang S, Pei L, Yang ZL, Liu G. Prognostic value of the stem cell markers EpCAM and CD133 expression of gallbladder adenocarcinoma. Hepatogastroenterology. 2014;61:574–9.PubMedGoogle Scholar
  68. Facchiano A, Russo K, Facchiano AM, De Marchis F, Facchiano F, Ribatti D, et al. Identification of a novel domain of fibroblast growth factor 2 controlling its angiogenic properties. J Biol Chem. 2003;278:8751–60.View ArticlePubMedGoogle Scholar
  69. Cucchiarelli V, Hiser L, Smith H, Frankfurter A, Spano A, Correia JJ, et al. β-Tubulin isotype classes II and V expression patterns in nonsmall cell lung carcinomas. Cell Motil Cytoskeleton. 2008;65:675–85.View ArticlePubMedGoogle Scholar
  70. Bernard-Marty C, Treilleux I, Dumontet C, Cardoso F, Fellous A, Gancberg D, et al. Microtubule-associated parameters as predictive markers of docetaxel activity in advanced breast cancer patients: results of a pilot study. Clin Breast Cancer. 2002;3:341–5.View ArticlePubMedGoogle Scholar
  71. Sabet MN, Rakhshan A, Erfani E, Madjd Z. Co-expression of putative cancer stem cell markers, CD133 and Nestin, in skin tumors. Asian Pac J Cancer Prev. 2014;15:8161–9.View ArticlePubMedGoogle Scholar
  72. Ramachandran I, Ganapathy V, Gillies E, Fonseca I, Sureban SM, Houchen CW, et al. Wnt inhibitory factor 1 suppresses cancer stemness and induces cellular senescence. Cell Death Dis. 2014;5:e1246.View ArticlePubMedPubMed CentralGoogle Scholar
  73. Schlüter H, Stark HJ, Sinha D, Boukamp P, Kaur P. WIF1 is expressed by stem cells of the human interfollicular epidermis and acts to suppress keratinocyte proliferation. J Invest Dermatol. 2013;133:1669–73.View ArticlePubMedGoogle Scholar
  74. Meek DW. Regulation of the p53 response and its relationship to cancer. Biochem J. 2015;469:325–46.View ArticlePubMedGoogle Scholar
  75. Sharpless NE, Sherr CJ. Forging a signature of in vivo senescence. Nat Rev Cancer. 2015;15:397–408.View ArticlePubMedGoogle Scholar
  76. Pallis AG, Hatse S, Brouwers B, Pawelec G, Falandry C, Wedding U, et al. Evaluating the physiological reserves of older patients with cancer: the value of potential biomarkers of aging? J Geriatr Oncol. 2014;5:204–18.View ArticlePubMedGoogle Scholar
  77. Eren M, Boe AE, Klyachko EA, Vaughan DE. Role of plasminogen activator inhibitor-1 in senescence and aging. Semin Thromb Hemost. 2014;40:645–51.View ArticlePubMedGoogle Scholar
  78. Fumagalli M. d’Adda di Fagagna F. SASPense and DDRama in cancer and ageing. Nat Cell Biol. 2009;11:921–3.View ArticlePubMedGoogle Scholar
  79. Dreyer G, Vandorpe T, Smeets A, Forceville K, Brouwers B, Neven P, et al. Triple negative breast cancer: clinical characteristics in the different histological subtypes. Breast. 2013;22:761–6.View ArticlePubMedGoogle Scholar

Copyright

© The Author(s). 2017

Advertisement

Breast Cancer Research

ISSN: 1465-542X

Contact us