Experimental approaches to the study of cancer-stroma interactions: recent findings suggest a pivotal role for stroma in carcinogenesis.

Abstract

An increasing body of research indicates that stroma surrounding cancer cells plays an important role in the development and subsequent behavior of the tumor. Studies using a wide range of techniques, including stromal cell isolation, modification of stromal-specific gene expression, and recreation of specific microenvironment conditions in culture, have demonstrated that stroma can promote cancer and that the expression patterns within the stroma can influence clinical outcome. Major hurdles in the study of the cancer stroma revolve around the cellular complexity of the tumor microenvironment, both in modeling the microenvironment and discovering/isolating pure populations of stromal cell types.

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PATHOBIOLOGY IN FOCUS
Experimental approaches to the study of cancer–stroma
interactions: recent findings suggest a pivotal role for
stroma in carcinogenesis
Robert B West and Matt van de Rijn
An increasing body of research indicates that stroma surrounding cancer cells plays an important role in the development
and subsequent behavior of the tumor. Studies using a wide range of techniques, including stromal cell isolation, modi-
fication of stromal-specific gene expression, and recreation of specific microenvironment conditions in culture, have
demonstrated that stroma can promote cancer and that the expression patterns within the stroma can influence clinical
outcome. Major hurdles in the study of the cancer stroma revolve around the cellular complexity of the tumor micro-
environment, both in modeling the microenvironment and discovering/isolating pure populations of stromal cell types.
Laboratory Investigation advance online publication, 13 August 2007; doi:10.1038/labinvest.3700666
KEYWORDS: tumor microenvironment; stroma–cancer interaction; cancer stroma expression; fibroblasts; soft tissue tumors
ANALYSIS AND MANIPULATIONS OF TUMOR STROMA
The majority of cancer research has focused on the neoplastic
cells and as a result, the vast majority of therapies available to
oncologists are therapeutic agents that act on these cancer
cells. The cancers often develop resistance to these therapies,
in large part due to their inherent genomic instability.1An
alternative, emerging, avenue of therapy focuses on targeting
various non-neoplastic cells that are associated with the tu-
mor microenvironment, such as endothelial cells.2Since
stromal cells within the tumor are thought to be ‘normal’ and
less genetically labile than the neoplastic cells, development of
acquired resistance to therapy may be less likely. As such, the
tumor stroma may be an excellent target for directed therapy.
The rapidly growing field of stroma–cancer interaction has
been advanced by a number of elegant and useful studies.
A number of studies used isolated stromal cells from carcino-
mas to demonstrate that these cells have developed increased
ability to support cancerous growth compared to normal
stromal cells. Several examples stand out in this area. Olumi
et al3showed that fibroblasts isolated from prostatic tumor
stroma significantly increased growth of prostate epithelium.
This supporting effect of carcinoma-associated fibroblasts
(CAF) was not seen when these fibroblasts were isolated from
normal prostatic stroma. Similarly, a study by Orimo et al4
used CAF isolated from different patients in a model system
in which an MCF7-ras breast carcinoma tumor cell line was
co-injected with CAF into immunodeficient mice. The CAF
provided better support for MCF7 cell growth than fibro-
blasts derived from areas that were not intimately associated
with invasive carcinoma. Krtolica et al5showed that senes-
cent human stromal cells can stimulate pre-malignant and
malignant, but not normal, epithelial cells to proliferate in
culture and form tumors in mice. These findings suggest that
senescent fibroblasts could promote cancer in certain cir-
cumstances.
Another approach has been to selectively modify stromal
cell gene expression and observe the effect on oncogenesis. In
a study by Bhowmick et al,6fibroblasts had the TGF-b type II
receptor gene conditionally inactivated. This resulted in
prostatic intraepithelial neoplasia and invasive squamous cell
carcinoma of the forestomach. This result suggests that TGF-b
signaling involving fibroblasts can modulate the oncogenic
potential of adjacent epithelium.
Other groups have altered fibroblast culture conditions to
simulate specific aspects of stromal environments. Exposure
of serum-starved cultured fibroblasts to serum led to a
change in expression of a large number of genes, many of
which are known to be involved in wound healing.7Serum
exposure of fibroblasts occurs in wounds in vivo and activates
a wound-healing response and a similar reaction had been
Received 26 May 2007; revised 16 July 2007; accepted 17 July 2007; published online 13 August 2007
Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
Correspondence: Dr RB West, MD, PhD, Department of Pathology, Stanford University School of Medicine, 300 Pasteur Drive, Lane Building, Room 235, Stanford, CA
94305-5324, USA. E-mail: rbwest@stanford.edu
Laboratory Investigation (2007), 1–4
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hypothesized to occur in cancers.8Using the wound-healing
response gene set identified in fibroblast cultures, the authors
could identify a similar response in a subset of breast tumors
and this was associated with a poor clinical outcome. The
influence on progression of these wound-healing signatures
was not only restricted to breast carcinomas but was also seen
in a variety of other tumors including lung and gastric car-
cinoma. Similarly, a response to hypoxic conditions was ex-
amined in a variety of cultured cell types in a different set of
experiments.9The expression patterns of the hypoxia re-
sponse in culture were also represented in several carcinomas
and predicted a worse outcome.
A number of groups have successfully undertaken techni-
cally demanding studies to isolate and evaluate cancer stroma
expression. For example, Allinen et al10used flow cytometry
to separate different cell populations followed by SAGE to
generate gene expression profiles of different components of
the tumor microenvironment, including neoplastic and
stromal cell types. The authors could identify many genes
that were highly and specifically expressed at high levels in
each cell compartment. Owing to the complexity of this
method only a few breast cancers could be studied. More
importantly, the ‘sort by subtraction technique’ for isolating
stromal cells did not tease out possible different fibroblast
subtypes or activation states but instead treated all stromal
cells as a uniform population. In another study, a mouse
model of prostatic adenocarcinoma was used to generate
microdissected-purified stroma from both prostatic adeno-
carcinoma and prostatic intraepithelial neoplasia. The dif-
ferences in gene expression profiles of these two types of
stroma were found to predict outcome in both prostate and
breast cancer.11
The conclusions drawn from these and many other studies
indicate that tumor stroma does play a supporting and, ac-
cording to some studies, perhaps even an initiating role in
malignant epithelial tumor growth.12,13But some of these
studies also suggest that there is a variety of stromal responses
that can occur in the tumor microenvironment.
USING SOFT TISSUE TUMORS AS A MODEL SYSTEM TO
STUDY TUMOR STROMA
Given the intense interest in stroma, there is little knowledge
about normal stromal components and their expression. Yet
it has been shown that ‘normal’ fibroblasts can show mark-
edly different expression patterns. Therefore, it is possible
that there are as yet to be determined activation states or even
fibroblast subtypes. The study of normal connective tissue
and tumor stroma is hampered by a lack of markers that can
distinguish subtypes of stromal spindle cells (fibroblasts,
myofibroblasts, and possible unknown variants). These cells
are histologically quite similar and morphologic studies have
struggled to find correlation between form and function.
Experimental approaches to the study of connective tissue
components are thus complicated by the intimate coexistence
of many different cell types within the tissue. One approach
to this problem is to study the expression profiles of soft
tissue tumors (STTs) as surrogates for unrecognized cell types
in a variety of stromal responses, including cancer. STTs,
whether clinically benign or malignant, form a relatively pure
population of transformed normal connective tissue con-
stituents. We believe that STT-based studies can contribute in
an essential manner to this field by discovering new markers
that recognize distinct subtypes of these normal connective
tissue and tumor stroma spindle cells, allowing us to re-
cognize the diversity of cell types that hide under the terms
‘fibroblast’ or ‘myofibroblast’. A large number (over 100) of
distinct STTs exist and there are numerous examples of
markers specific for a particular tumor that can identify a
subpopulation of morphologically identical normal stromal
components.
Similar to lymphomas where each tumor is a clonal out-
growth of a particular lymphoid cell subtype, each STT type
may also be regarded as a clonal outgrowth of a particular
connective tissue cell type. In the past, lymphomas have been
used as ‘discovery tools’ for new monoclonal antibodies.14,15
Mice were immunized with lymphoma cells and the resulting
monoclonal antibodies often led to the discovery of distinct
lymphocyte subtypes. The lymphocyte differentiation anti-
gens would have been hard to identify if normal lymphoid
tissue, which includes a mixture of lymphocyte types, was
injected into mice as an immunogen. For example, T-helper
cells constitute a minority of the total lymphocyte population
and, in an immunization challenge with normal lymphoid
tissue, antibodies specific to T-helper cells would likely be lost
among antibodies to proteins from other lymphocyte types.
Therefore, to find markers that are expressed on a small
subset of cells, it is very useful to use tumors derived from
this subset of cells as immunogen. In our case, rather than
immunize mice with STT tissue, a more direct approach is to
perform gene expression profiling to find candidate markers.
This ‘in silico’ alternative to immunization generates many
more candidate markers, allows direct comparison of ex-
pression profiles for different STTs, is independent of the
complexities of the mouse immune system, and allows an
immediate assessment of expression in many situations, such
as the tumor microenvironment, by application to existing
gene expression profile data sets (Figure 1).
In our first investigation toward this goal, we used gene
expression profiles from two different STTs, desmoid-type
fibromatosis (DTF) and solitary fibrous tumor (SFT),
thought to be derived from ‘fibroblasts’, to identify their
counterparts, and possibly new subtypes, in normal con-
nective tissue.16The two similar ‘fibroblastic’ tumors, SFT
and DTF, are quite distinct tumors despite being composed
of cells with very similar morphology and yet have drama-
tically different clinical behavior. Comparing the SFT and
DTF gene expression profiles, we found a remarkably large
number of differentially expressed genes from different
functional categories. The distinguishing genes showed great
differences in function with genes involved in the fibrotic
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Study of cancer stroma interactions
RB West and M van de Rijn
PATHOBIOLOGY IN FOCUS

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response being highly expressed in DTF, whereas many genes
associated with basement membrane synthesis being highly
expressed in SFT. Tissue staining show that tumor-specific
markers are also differentially expressed in normal connective
tissue cells, including inflammatory responses that lead to
scar formation, such as a fistula tract, and specialized stroma
Figure 1 The effect of clustering breast carcinomas with stroma-specific genes. Generally, gene microarray data on breast carcinomas are clustered with
genes that pass a number of quality criteria. In this process (a), genes that are expressed in carcinoma cells tend to play a predominant role in clustering
breast cancer cases based on similarities between tumor cells from different patients. For example, patient samples with ‘red’ tumor cells will co-cluster
independently of the type of fibroblasts (blue vs orange) that surround them. It is caused by the fact that the number of genes that are expressed by the
different fibroblast types and stroma is relatively low. A list of genes specific for different types of stromal fibroblasts can be discovered by performing gene
expression profiles on two types of fibroblast-like soft tissue tumors resulting in a list of approximately 400 genes that are expressed in a mutually exclusive
manner between the soft tissue tumors (b). When breast carcinomas are next clustered using the information for these 400 genes only, as only shown in (c),
breast carcinomas will tend to cluster there based on the type of fibroblasts that surround them. For example, the breast carcinomas in the left most branch
of the dendrogram, in orange, are surrounded by the ‘orange’-type fibroblasts driven by the high expression levels of genes specific for the ‘orange’ soft
tissue tumor-type fibroblast; see bar on the right-hand side of (c). In contrast, carcinoma specimens where the ‘blue’ type of fibroblast surrounds the tumor
cells will be co-clustered based on the high level of expression found in the specimens of genes specific for the ‘blue’ type of soft tissue tumor.
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RB West and M van de Rijn

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around dermal sweat glands. Based on these differences in
expression we hypothesized that the cells of origin for each
lesion may perform different functions in normal connective
tissue.
We found that these markers are also differentially ex-
pressed in the tumor microenvironment. These markers were
often mutually exclusively expressed in the stroma of differ-
ent breast carcinoma cases. We analyzed the expression of
these genes in a large gene expression profile data set of breast
cancer. When the breast carcinomas were clustered based on
the SFT and DTF sets of positively expressed genes only, a
subset of DTF-associated genes defined a distinct group of
breast carcinomas. Interestingly, a previous study used serial
analysis of gene expression on sorted components of the
breast cancer microenvironment.10Some of the genes found
to be highly expressed in their ‘myoepithelial/myofibroblast’
cell population are also present in the DTF gene list. A
subsequent study examining the expression differences be-
tween prostatic intraepithelial neoplasia and invasive pro-
static adenocarcinoma also found genes present in the DFT
gene list.11In addition, two groups of genes associated with
SFT defined a second group of breast carcinomas. Not all
DTF genes contributed equally to the separation of breast
carcinomas with a DTF-like stroma for the rest of the tumors.
Thus, the breast carcinoma gene expression data set can be
seen as a ‘filter’ to highlight genes that are most important in
describing the stromal subtypes in carcinoma. The two
groups of breast carcinoma, distinguished by expression of
genes in the stroma but not in the tumor cells, showed a
remarkable difference in clinical outcome. Because the genes
are discovered in STTs, we expect that the majority of these
genes will be expressed in the stromal cells and not expressed
in the neoplastic epithelial cells of the carcinoma. This as-
sumption has so far been supported by histological analysis
of expression of five markers and by GO-term analysis.
Almost all ‘discovery-type’ gene expression studies use
unsupervised expression data to cluster the tumors, which
results in the tumors being clustered based mainly on the
gene expression patterns of the neoplastic cells. More subtle
variations in gene expression patterns of the tumor stroma
can be lost in the analysis, overwhelmed by the neoplastic cell
expression patterns. By analyzing these carcinoma data sets
with a gene set restricted to connective tissue-related genes,
such as those derived from STT gene signatures, we can
uncover these latent tumor stroma expression patterns.
In conclusion, there is ample evidence that the stroma
within the tumor microenvironment plays an important role
in cancer biology. The specific interactions remain to be
elucidated. In the future, this field may uncover novel thera-
peutic approaches that target the cancer–stroma interaction.
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4 Laboratory Investigation | Volume 00 00 2007 | www.laboratoryinvestigation.org
Study of cancer stroma interactions
RB West and M van de Rijn
PATHOBIOLOGY IN FOCUS