The Breast 20 (2011) S3, S50–S55

Contents lists available at ScienceDirect

The Breast

journal homepage: www.elsevier .com/brst

Original Article

Tumor–stroma interactions a trademark for metastasis

Monica Moralesa,d, Evarist Planetb,d, Anna Arnal-Estapea,d, Milica Pavlovica,d, Maria Tarragonaa,d,Roger R. Gomisa,c, *

aOncology Programme, Institute for Research in Biomedicine (IRB-Barcelona), Barcelona, SpainbBiostatistics and Bioinformatics Unit, Institute for Research in Biomedicine (IRB-Barcelona), Barcelona, SpaincInstitucio Catalana de Recerca i Estudis Avancats (ICREA), Barcelona, SpaindThese authors contributed equally to this work.

a r t i c l e i n f o

Keywords:

Metastasis

Cancer

Stroma

TGFbBone remodelling

s u m m a r y

Aims: We aimed to unravel genes that are significantly associated with metastasis in order to

identify functions that support disseminated disease.

Methods and Results: We identify genes associated with metastasis and verify its clinical correlations

using publicly available primary tumor expression profile data sets. We used facilities in R and

Bioconductor (GSEA). Specific data structures and functions were imported. Our results show that

genes associated with metastasis in primary tumor enriched for pathways associated with immune

infiltration or cytokine–cytokine receptor interaction. As an example, we focus on the enrichment

of TGFBR2 and TGFb. A set of communication tools capital for tumor–stroma interactions that

define metastasis to the lung and support bone colonization.

Conclusions: We showed that tumor–stroma communication through cytokine–cytokine receptor

interaction pathway is selected in primary tumors with high risk of relapse. High levels of these

factors support systemic instigation of the far metastatic nest as well as local metastatic-specific

functions that provide solid ground for metastatic development.

© 2011 Elsevier Ltd. All rights reserved.

Introduction

Tumors are composed of several cell types including malignant

cells, which provide a complex scenario of interactions and define

what is known as tumor biology. Consistently, many experimental

studies have depicted the importance of non-transformed cell

types within tumors including endothelial cells, cellular entities of

the immune system as well as fibroblasts and other cell types.1

Interestingly, the degree of infiltration of these non-malignant

cells into a tumor sets the stage for its clinical classification and

its association with the most aggressive forms of the disease.

Growth at the primary site, spreading into the vasculature and

lymphatic system, and colonization of distant metastatic sites are

processes that need malignant cells and microenvironment, also

named stroma, interactions through both cancer cell and host-

derived factors.2,3 Indeed, cancers that never progress to invasive

forms lack vascularization, macrophage infiltration as well as large

areas with fibroblast and Extracellular Matrix depositions.4 This fact

can be attributed to the absence of recruiting factors produced

by tumor cells or, alternatively, due to host-derived factors that

* Corresponding author. Roger R. Gomis. Oncology Programme,

Institute for Research in Biomedicine (IRB Barcelona), PBB52 Parc

Cientıfic de Barcelona, C/Baldiri i Reixac 10–12, 08028 Barcelona,

Spain. Tel.: +34-93-403-9959; fax: +34-93-403-9960.

E-mail address: roger.gomis@irbbarcelona.org (R.R. Gomis).

precluded tumor progression to more aggressive forms.1,4 Similarly,

the metastatic stroma also encompasses non-malignant cells as

well as ECM matrix, embedded with a plethora of growth factors.

These non-malignant cells are shown to display unusual features

with characteristic behaviors and patterns of protein expression

at each distinct metastatic site.5,6 In summary, these observations

indicate that transformation does not suffice to produce aggressive

forms of cancer and that interaction with the tumor environment

accommodates tumor progression and metastasis.4

In breast cancer, radiographic appearance and histological

confirmation of presence of stromal factors within primary tumor

has been correlated with prognosis of disease (usually the degree

of fat or the presence of fibroblasts, macrophages or endothelial

cells). The stromal cells include carcinoma-associated fibroblasts

(CAFs), the blood and lymphatic vascular cells, and infiltrating

immune and inflammatory cells.7 The extent of infiltration has been

associated with tumor poor prognosis. Infiltrates of the immune

system, including macrophages, mast cells, and myeloid cells,

promote tumor progression by releasing factors and cytokines that

stimulate tissue remodeling and angiogenesis processes. Moreover,

the release of oxygen free radicals might contribute to exacerbate

genomic instability and suppress antitumor immunity.8,9 Only

natural killer cell infiltrates have been shown to have antitumor

activity in experimental models.9 Molecular profiling of the stromal

0960-9776/ $ – see front matter © 2011 Elsevier Ltd. All rights reserved.

M. Morales et al. / The Breast 20 (2011) S50–S55 S51

compartment of human tumor specimens has provided information

of prognostic value.10

Fibroblasts function is strikingly impinged by the tumor context.

CAFs deliver many tumor promoting functions through the

production and remodeling of ECM, growth factors and its ability

to recruit angiogenic and immune cells.1,11 Unfortunately, the

mechanism by which tumor cells cancerize fibroblast and how

the latter, in turn, contribute to tumor progression remains largely

unknown. Dynamic communication between transformed cells and

stromal factors is necessary for cancer cells to exploit stromal func-

tionality towards the establishment of tumor microenvironment.

Collectively, these interactions provide important additional cues

to identify treatments that effectively tackle tumor progression.

The efforts to develop therapies that target tumor stroma can

be exemplified with the development of anti-angiogenic tumor

therapies. Three are the current clinically approved drugs and are

used in a wide range of tumor types. Yet, it has become clear that

the tumor stroma is dynamic, since evasion from anti-angiogenic

therapy is a fact12 and a matter of intense investigation.

Material and methods

Statistical analysis

All statistical analyses were performed using Bioconductor.13 The

patients’ information has been downloaded from GEO.14 Two sets of

data were used: Set 1: Union of GSE2603, GSE2034, GSE12276 and

Set 2: GSE5327. For each cohort individual gene’s expression has

been standardized to z-scores to make measurements comparable.

Using Set 1 a Cox proportional hazards model15 was tested for

each individual gene using the Bioconductor survival package. The

following deviations from the Cox proportional hazards model were

also tested: proportional hazards assumption and non-linear effect.

(1) we tested the proportional hazards assumption as implemented

in the cox.zph function of the survival package. A hazard being not

proportional in time means that the hazard has a bigger effect

in some moment of time and a weaker or opposite effect in

some other period of time. (2) We tested for a non-linear effect

of gene expression on survival via penalized splines of degree 4,

as implemented in the splines function of the survival package.

Therefore we obtained 3 P-values: regular proportional hazards,

non-proportional hazards and non-linearity. Each one reflects a

different kind of association between gene expression and overall

metastasis. All P-values were adjusted to correct for multiple

testing using the Benjamini Hochberg method.16 Based on the

three previous tests (on Set 1) the following gene signatures were

defined: (a) Up regulated genes: This signature contains genes that

have at least one of the three computed P-values < 0:05 and an

estimated positive hazard ratio in the Cox proportional hazards

model. (b) Down regulated genes signature: This signature contains

genes that have at least one of the three computed P-values <0:05

and an estimated negative hazard ratio in the Cox proportional

hazards model. (c) Together: This signature contains the genes from

the two previous signatures.

Gene set enrichment analysis

We used gene set enrichment analysis (GSEA)17 as implemented

in the Bioconductor library phenoTest18 to assess the degree of

association between gene signatures and metastasis in Set 2

(GSE5327).

GSEA requires ranking genes according to their association with

a given phenotype, and determining whether genes in a signature

tend to present either high (positively enriched) or low ranks

(negatively enriched). Association with metastasis was measured

with the following score: score = −log2(p) * sign(h) (1) where p

is the lowest P-value of the three previously described tests and h

is the hazard ratio (both computed on Set 2). Notice that a small

P-value in any of the previously described tests indicates a different

kind of association between gene expression and metastasis free

survival. The output of GSEA is an enrichment score (ES), a

normalized enrichment score (NES) which accounts for the size

of the gene set being tested, a P-value and an estimated FDR.

Computing NES, P-values and FDR requires a permutation-based

approach for which we computed 10,000 permutations.

KEGG pathway enrichment analysis

KEGG pathway enrichment analysis was performed using a

Wilcoxon test based GSEA, as implemented in the Bioconductor

library phenoTest.

Results

Identification of genes associated with metastasis

We identified genes whose expression is associated with aggressive

metastatic behavior in breast cancer primary tumors. We performed

analysis to test the association between each gene expression

level and its probability of recurrence in two independent sets

of breast cancer primary tumor expression profiles. These tumors

are representative of all BC subtypes and metastatic sites. Both

databases and their clinical annotations are publicly available (gene

expression omnibus: GSE 2603, 2034, 12276 for MSKCC/EMC and

NKI-29819). The MSKCC/EMC data set is composed of the gene

expression profiles of 560 breast cancer samples, determined using

the U133A affymetrix, and its clinical annotation for recurrence and

was obtained in two independent institutions.20 The second data set

was elaborated at the NKI, using a custommade chip, containing the

expression profile of 298 breast cancers and its clinical annotation.

Collectively, our analysis identified a group of genes significantly

selected for and associated with metastasis colonization to distant

sites in breast cancer using the MSKCC/EMC data set. This

group of genes that showed a significant trend of regulation

(increased/decreased) might be of tumor or stroma origin since

no separation between tumor cell types was performed prior to

profiling. Next, we performed gene set enrichment analysis (GSEA)17

in an independent cohort of breast cancer primary tumor gene

expression profiles, the NKI-298, using the previously identified

group of genes. This approach tests the phenotypic association with

risk of metastasis of each of the genes identified in the MSKCC/EMC

cohort in the NKI-298 data set. This association resulted to be strong

(normalized enrichment score of 2.3 and −0.9 for genes upregulated

or downregulated using adjusted p-values) (Fig. 1). Since false

discovery rates were minimal, this result is best interpreted as how

well experimentally selected genes in the MSKCC/EMC metacohort

associated with short time to metastasis in the NKI-298 cohort.

Interestingly, the same procedures applied to randomly generated

sets of 500 genes did not perform significantly.

Identification of tumor–stroma interacting program

To investigate what functions are enriched or attenuated in

primary breast cancers, KEGG pathway enrichment analysis was

performed using a Wilcoxon test based GSEA to measure the

association of the previously identified metastasis associated genes

with pathway-specific gene expression signatures (using KEGG

pathway functions). Interestingly, 10 pathways are significantly

enriched among the 197 tested (Table 1 and Fig. 2). These include

pathways related to immune response processes and immune

cells (Intestinal immune network of IgA production, Allograft

rejection, Graft-vs-host disease, viral mycarditis, hematopoietic cell

S52 M. Morales et al. / The Breast 20 (2011) S50–S55

NES (fdr<0.001)

NE

S

20

2

Gene list rank1 2000 4000 6000 8000 10000

RA

NK

MET No MET

NES (fdr=0.506)

1 2000 4000 6000 8000 10000

Gene list rank

RA

NK

NE

S

10

1

NE

S

20

2

NE

S

20

2

NKI-298 Cohort (n=298)

MET No MET

Genes Positively Associated with Metastasis Genes Negatively Associated with Metastasis

10

1

RA

NK

METASTASIS

Genes Associated (+/-) with Metastasis

MSKCC/EMC Cohort (n=560)

Fig. 1. Gene set enrichment analysis of the phenotypic association of genes previously associated with high risk of metastasis in the MSKCC/EMC primary tumor cohort,

with the metastasis hazard ratio (HR) in the NKI-298 primary tumor cohort. Genes are separated accordingly to whether they were positively or inversely associated with

metastasis in the MSKCC/EMC cohort. RANK: hazard ratio; NES: normalized enriched score; FDR: false discovery rate.

Table 1

Pathway gene set enrichment analysis using 197 KEGG pathway functions. Genes significantly associated with risk of metastasis

in the MSKCC/EMC primary tumor expression profile data set (n = 560) were tested for their association with pathway-specific

gene expression signatures in an independent primary tumor expression profile data set, NKI-298 (n =298). The list depicts

the 10 pathways significantly enriched among the 197 tested.

Pathway Id Pathway Name Enrichment

Score

p-value

4672 Intestinal immune network for IgA production 15.08 0.00069

5330 Allograft rejection 10.82 0.00094

4060 Cytokine-cytokine receptor interaction 12.42 0.00110

5332 Graft-versus-host disease 10.70 0.00178

5416 Viral myocarditis 9.91 0.00255

4640 Hematopoietic cell lineage 14.26 0.01228

5140 Leishmaniasis 12.68 0.01228

5320 Autoimmune thyroid disease 10.35 0.02199

4610 Complement and coagulation cascades 6.51 0.02645

4940 Type I diabetes mellitus 10.60 0.04883

lineage, leishmaniasis, autoimmune thyroid disease, complement

and coagulation cascades and type I diabetes mellitus), which are

known to be a major constituent of tumor stroma and to be strongly

associated with tumor poor prognosis and recurrence. Interestingly,

the third main pathway differentially represented in tumors

associated with high risk of recurrence was the “cytokine–cytokine

receptor interaction pathway”, which highlights the importance

of communication signals within cell types present in the tumor.

Among the 276 genes that composed this pathway, almost one

third (76 genes) are differentially expressed in tumors of the most

aggressive nature (Table 2), including a wide range of signals and

functions that could potentially foster tumor and stroma specific

tumor progression functions.

Tumor–stroma specific functions

Example of biological significance of this analysis is strong

upregulation of Transforming Growth Factor beta (TGFb) receptor

M. Morales et al. / The Breast 20 (2011) S50–S55 S53

KEGG:4267

Intestinal immune network for IgA production (pval<0.001) *

20 15 10 5 0 5

0.0

00.0

50.1

00.1

5

N = 43 Bandwidth = 0.8368

Den

sit

y(lo

g)

20

020

(*) pvalue adjustment method: BHGene list rank

1 2000 4000 6000 8000 10000 12000

Average

Tumors

Metastatic

Tumors

KEGG:4060

Cytokyne-Cytokine receptor interaction (pval=0.001) *

20 10 0 10 20

0.0

00.0

40.0

80.1

2

N = 240 Bandwidth = 1.047

Den

sit

y(lo

g)

20

020

(*) pvalue adjustment method: BHGene list rank

1 2000 4000 6000 8000 10000 12000

KEGG:04010

MAPK signaling pathway (pval=0.997) *

20 10 0 10 20

0.0

00.0

40.0

8

N = 249 Bandwidth = 1.319

Den

sit

y(lo

g)

20

020

(*) pvalue adjustment method: BHGene list rank

1 2000 4000 6000 8000 10000 12000

Average

Tumors

Metastatic

TumorsAverage

Tumors

Metastatic

Tumors

(A) (B) (C)

Fig. 2. KEGG pathway enrichment analysis was performed using a Wilcoxon test based GSEA, as implemented in the Bioconductor library phenoTest. The three pathways

depicted in this figure represent: (A) the most enriched pathway (KEGG:4267), (B) Cytokine-cytokine receptor interaction pathway (KEGG:4060) and (C) one of the pathway

non enriched (KEGG:04010). A plot defining the number of tumors according to the average expression of the genes in the pathway of interest is depicted in the upper part

of each figure. In green, highlighted tumors with annotations of metastasis. In black, the line that sets the expression level for the average of all tumors.

Table 2

Among the 276 genes that composed the cytokine–cytokine receptor interaction pathway, this table highlights those 76 genes that

are significantly differentially expressed in primary tumors that relapse (patients follow up time: 120 months).

Entrez ID Gene Symbol Score(HR) p-value Entrez ID Gene Symbol Score(HR) p-value

83729 INHBE −21.708248 2.92×10−7 50616 IL22 6.59472647 0.01034641

4982 TNFRSF11B −20.588914 6.34×10−7 1956 EGFR 6.56945906 0.01052921

7048 TGFBR2 19.5467269 1.31×10−6 4352 MPL 6.51882317 0.01090533

268 AMH −18.065165 3.65×10−6 2829 XCR1 −6.3553726 0.01221356

608 TNFRSF17 17.9808451 3.87×10−6 8795 TNFRSF10B 6.30589746 0.01263967

2324 FLT4 17.9268502 4.01×10−6 5159 PDGFRB 6.29128275 0.01276836

3556 IL1RAP 13.1392613 0.00011084 3445 IFNA8 6.28368885 0.01283575

3563 IL3RA 13.028401 0.00011969 7173 TPO −6.2663073 0.01299133

11009 IL24 −12.828113 0.00013752 4254 KITLG −6.1526191 0.0140565

6846 XCL2 12.5317298 0.00016888 6347 CCL2 5.94766833 0.01620218

3605 IL17A −12.521353 0.0001701 3082 HGF 5.92222551 0.01649045

6364 CCL20 −12.258245 0.00020413 5155 PDGFB −5.8380134 0.01748167

1437 CSF2 12.0770635 0.00023144 29949 IL19 5.81574125 0.01775364

1270 ZFP91 12.0226356 0.00024034 2321 FLT1 5.65024634 0.01991161

6361 CCL17 11.801464 0.00028016 3554 IL1R1 5.54066881 0.02148288

7423 VEGFB −11.520199 0.00034047 3568 IL5RA 5.53016573 0.02163985

3953 LEPR −11.410602 0.00036734 7043 TGFB3 −5.403455 0.02362642

1438 CSF2RA 10.5542234 0.00066506 1237 CCR8 −5.3145362 0.02512842

53832 IL20RA 10.4967512 0.00069209 27190 IL17B −5.2949196 0.02547243

3579 CXCR2 10.316842 0.00078401 958 CD40 5.29271559 0.02551137

2920 CXCL2 9.93710942 0.00102007 8792 TNFRSF11A 5.29069681 0.0255471

3452 IFNA21 −9.7595997 0.00115363 8742 TNFSF12 5.2382249 0.02649337

59067 IL21 −9.440235 0.00143948 3594 IL12RB1 −5.1766419 0.02764875

7293 TNFRSF4 9.03980792 0.00189997 3581 IL9R −5.0142976 0.03094183

8794 TNFRSF10C 9.02275354 0.00192256 3626 INHBC 5.00852718 0.03106584

944 TNFSF8 8.98842098 0.00196886 6375 XCL1 4.99046559 0.03145721

57007 CXCR7 8.68101814 0.00243643 2056 EPO 4.98733435 0.03152556

3627 CXCL10 8.5928068 0.00259005 4055 LTBR −4.9791495 0.03170492

4049 LTA 8.5215358 0.00272121 3593 IL12B −4.9605186 0.03211701

3559 IL2RA −8.1221656 0.00358909 3586 IL10 4.86044492 0.03442392

655 BMP7 −8.0418943 0.00379445 27242 TNFRSF21 −4.8298567 0.03516157

1439 CSF2RB 7.81675876 0.00443528 1950 EGF 4.76184242 0.03685892

3952 LEP 7.712337 0.00476821 5156 PDGFRA −4.6124917 0.04087913

6374 CXCL5 7.67184963 0.00490392 3442 IFNA5 4.59366923 0.04141596

3577 CXCR1 7.54456524 0.00535623 1896 EDA 4.45568187 0.04557284

2689 GH2 7.262358 0.00651347 6373 CXCL11 4.31021924 0.05040745

8200 GDF5 −7.2222756 0.00669697 7040 TGFB1 4.30568667 0.05056607

S54 M. Morales et al. / The Breast 20 (2011) S50–S55

type II. This gene is included in the cytokine–cytokine receptor

interaction pathway (Table 2). Perturbations of TGFb signaling

are central to tumorigenesis and tumor progression in a cancer

cell dependent and independent manner.21 TGFbR2-inactivatingmutations are often found in colon cancers and are associated

to microsatellite instability.22 These results provide evidence of

the pathway tumor suppressor function. However, TGFb signaling

is also known to function as a tumor promoter.23 TGFb staining

correlates with metastasis in breast, prostate and colon cancers.24,25

Particularly, TGFb transcriptional program is enriched and drives

ER-negative breast cancer metastasis to the lung.26 These findings

indicate that excessive TGFb stimulation is an indispensible

prerequisite for tumor progression. Who produces and how is

TGFb sensed is a matter of great interest. During tumorigenesis

and tumor progression, the stroma cells affect the characteristcs

of tumor cells through diverse mechanisms. TGFb can suppress

inflammation, recognition and destruction of tumor cells through

the regulation of immune cell function. Moreover, it also modulates

the interaction between fibroblasts and tumor cells in a process that

might resemble active wound healing. Lately, TGFb has also been

linked with the stroma capacity to maintain cancer stem cells and

their niche.27,28

Discussion

Collectively, the above-described results highlight the strong

contribution that tumor–stroma interactions have to define the

metastatic outcome of breast primary tumors. Particularly, in our

analysis the cytokine–cytokine receptor interaction pathway is

among those mostly changed between poorly and highly aggressive

tumors. A recent study has shed some light on how tumor–stroma

interactions that ocurr in the primary site have the capacity to

endow tumors cells with metastatic capacities in a tissue specific

manner. For instance, TGFb signaling in ER-negative breast cancer

microenvironment primes cancer cells for pulmonary metastasis.26

Inhibition of this pathway in ER-negative breast cancer cells

attenuates its ability to generate lung metastasis when implanted

orthotopically in a mouse model. This process relays on the vascular

remodeling gene, Angiopoietin-like 4 (ANGPTL4), identified as a

TGFb target in ER-negative breast cancer and whose upregulation

was associated with lung metastasis. Particularly, tumor cell-

derived ANGPTL4 was shown to disassemble endothelial cell

junctions supporting vascular permeability at the lung capillaries

where tumor cells released by the primary tumor lodge prior

to extravasate and colonize the lungs. Interestingly, the TGFbresponsive program in cancer cells was associated in breast primary

tumors with relapse to the lung but not other tissues. TGFb activityhas also been proved fundamental in ER-negative bone metastasis.

However, in this case, the signaling activity is selectively acquired

at the metastatitc site.29 This far-site metastasis selection process

defines a whole new set of tumor–host interactions.

Recent findings highlight how growth of metastatic cells, at

distinct sites from where the primary tumor was originated, is

dependent on aberrant hormone or cytokine production. This,

indicates that tumors might perturb host organs from the distance

and this process might act as a driving force metastatic seeding. For

example, evidences point towards a process of metastasis nesting

at the lungs.30 This process includes recruitment of bone marrow

cells (BMC) possibly mediated by accumulation of lysyl oxidase at

the lungs, creating a trap for the recruitment and settlement of

circulating tumor cells in this particular metastatic site. This process

of distant systemic instigation of metastatic nesting has several

implications, including influence on how and when to apply anti-

metastatic therapies to tackle the dissemination process.

Finally, local tumor–host interactions are well established

to occur at the metastatic sites where the instigating breast

carcinomas are able to perturb targeted tissue homeostasis. Bone

metastatic breast cancer cells select for the expression of OPN, IL6,

IL11, PTHLH and other factors that support osteoclast differentiation,

which perturbs the normal equilibrium between bone synthesis and

degradation and supports metastatic growth by constant release of

growth factors within the niche.29,31,32 This tumor-metastatic host

interaction might open the avenues for new therapies, as it is the

use of zoledronic acid to prevent survival and bone remodeling by

disseminated breast cancer cells that metastasis to the bone.33

In summary, we showed that tumor–stroma communication

through cytokine–cytokine receptor interaction pathway is selected

in primary tumors with high risk of relapse. Cross talk between

tumor and stroma might support local metastatic-specific functions

and provide solid ground for metastatic development. A process,

otherwise, defined in breast cancer by slow kinetics and

inefficiency.34 These results also point the need for further

development of targeted tumor–host containing chemotherapy

regimes in order to reach inhibition of metastatic dissemination

and growth.

Acknowledgements

A.A.E. is a recipient of a doctoral fellowship from the Ministerio

de Educacion of Spain. M.P. is a recipient of a doctoral fellowship

from “La Caixa”. R.R.G. is a Researcher of the Institucio Catalana de

Recerca i Estudis Avancats. Financial support was provided by the

BBVA foundation, AECC and the Ministerio de Ciencia e Innovacion

of Spain (SAF2010–21171) to R.R.G.

Conflict of interest statement

The authors have no conflict of interest to declare.

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