Upload
others
View
5
Download
0
Embed Size (px)
Citation preview
The tumor microenvironment shapes lineage, transcriptional, and functional
diversity of infiltrating myeloid cells
Kutlu G. Elpek1,*, Viviana Cremasco1, Hua Shen2, Christopher J. Harvey1, Kai W
Wucherpfennig1, Daniel R. Goldstein2, Paul A. Monach3, Shannon J. Turley1,4.
1Department of Cancer Immunology and AIDS, Dana-Farber Cancer Institute, Boston,
Massachusetts, 02115; 2Departments of Internal Medicine and Immunobiology, Yale
School of Medicine, New Haven, Connecticut, 06510; 3Boston University School of
Medicine, Boston, Massachusetts 02118; 4Department of Microbiology and
Immunobiology, Harvard Medical School, Boston, Massachusetts 02115, USA.
*Present address: Jounce Therapeutics, Inc., Cambridge, Massachusetts 02138.
Corresponding author:
Shannon J Turley, Ph.D.
Tel: 617-632-4990
Fax: 617-582-7999
Email: [email protected]
Abbreviations and acronyms: TAM, tumor associated-macrophage; TAN, tumor
associated-neutrophil; MDSC, myeloid derived suppressive cell; DC, dendritic cell; HP,
haptoglobin.
on March 25, 2021. © 2014 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 31, 2014; DOI: 10.1158/2326-6066.CIR-13-0209
2
ABSTRACT
Myeloid cells play important regulatory roles within the tumor environment by directly
promoting tumor progression and modulating the function of tumor-infiltrating
lymphocytes, and as such, they represent a potential therapeutic target for the treatment
of cancer. Although distinct subsets of tumor-associated myeloid cells have been
identified, a broader analysis of the complete myeloid cell landscape within individual
tumors and also across different tumor types has been lacking. By establishing the
developmental and transcriptomic signatures of infiltrating myeloid cells from multiple
primary tumors we found that tumor-associated macrophages (TAMs) and tumor-
associated neutrophils (TANs), while present within all tumors analyzed, exhibited
strikingly different frequencies, gene expression profiles, and functions across cancer
types. We also evaluated the impact of anatomic location and circulating factors on the
myeloid cell composition of tumors. The makeup of the myeloid compartment was
determined by the tumor microenvironment rather than the anatomic location of tumor
development or tumor-derived circulating factors. Pro-tumorigenic and hypoxia-
associated genes were enriched in TAMs and TANs compared with splenic myeloid-
derived suppressor cells. While all TANs had an altered expression pattern of secretory
effector molecules, in each tumor type they exhibited a unique cytokine, chemokine and
associated receptor expression profile. One such molecule, haptoglobin, was uniquely
expressed by 4T1 TANs and identified as a possible diagnostic biomarker for tumors
characterized by the accumulation of myeloid cells. Thus, we have identified
considerable cancer-specific diversity in the lineage, gene expression, and function of
tumor-infiltrating myeloid cells.
on March 25, 2021. © 2014 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 31, 2014; DOI: 10.1158/2326-6066.CIR-13-0209
3
INTRODUCTION
The tumor microenvironment contains a multiplicity of stromal cells of
hematopoietic and non-hematopoietic developmental origin, such as T cells, B cells, NK
cells, myeloid cells, fibroblasts, pericytes, adipocytes, and endothelial cells, which
collectively shape the disease course [1-4]. Although specific roles have been identified
for discrete stromal subsets, factors controlling their recruitment, expansion, and function
in different tumors remain enigmatic. Therefore, a more complete characterization of
these subsets and a better understanding of how they are recruited to and expand within
growing tumors and metastases are of utmost importance to developing novel therapies
and improving existing ones against cancer.
Tumor growth is associated with the accumulation of a variety of myeloid cell
types [2]. Common myeloid cell progenitors in the bone marrow can give rise to myeloid
cells with immunosuppressive potential, oft referred to as myeloid-derived suppressor
cells (MDSCs). Monocyte-like CD11b+Gr1low and granulocyte/neutrophil-like
CD11b+Gr1hi subsets of MDSCs have been reported to accumulate in the spleen, liver,
blood, and bone marrow during tumor progression. Within tumors, myeloid cells with
similar phenotypes are referred to as tumor-associated macrophages (TAMs) or
neutrophils (TANs), possibly reflecting a more differentiated identity. In vitro studies have
shown that differentiation of bone marrow progenitors into MDSCs requires a
combination of cytokines, particularly IL-6 and G-CSF or GM-CSF, and the
transcriptional regulator CCAAT/enhancer-binding protein (C/EBP) [5]. Although splenic
MDSCs are considered a reservoir for tumor-infiltrating myeloid cells [6], the exact
relationship between these cells remains elusive. Accumulating evidence indicates that
MDSCs, whether in the spleen or in the tumor, have direct suppressive effects on
cytotoxic leukocytes. In addition to MDSCs, conventional and plasmacytoid dendritic
cells (DCs) may exert immunoregulatory effects in tumors [2] using a variety of
on March 25, 2021. © 2014 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 31, 2014; DOI: 10.1158/2326-6066.CIR-13-0209
4
mediators such as indoleamine 2,3-dioxygenase (IDO), inducible nitric oxide synthase
(iNOS), and arginase I to suppress T-cell proliferation, cytotoxicity and effector cytokine
production.
Given that myeloid cells with protumorigenic and immunomodulatory functions
have been observed in multiple animal tumor models and in cancer patients, they
represent important targets for immunotherapy. Efforts are underway to identify myeloid-
focused strategies. Approved chemotherapy agents, such as Gemcitabine [7], 5-
fluorouracil [8], and Sunitinib [9], can eliminate or prevent the accumulation of MDSCs,
especially in lymphoid organs, and retard tumor progression. Likewise, agents that block
myeloid recruitment to tumors, such as CSF1R inhibitors [10], hold clinical promise.
However, to improve current strategies and identify new universal targets for therapeutic
intervention, it is essential to understand how each myeloid cell subset from one tumor
relates to the same population in other tumor types.
In this study, we analyzed myeloid subsets in multiple murine tumors to study
how phenotype, frequency, and transcriptional profiles relate within different tumors,
using triple-negative 4T1 breast cancer, Her2+ breast cancer, and B16 melanoma as
models. Strikingly, each tumor type contained a distinct myeloid cell landscape, with
TAMs, TANs and DCs represented in all tumors but at markedly different ratios, while
systemic MDSC accumulation was exquisitely tumor-specific. Our data suggest that
tumor type, rather than anatomic location, dictates myeloid composition in the tumor
lesion. Furthermore, although each subset exhibits similar transcriptional signatures
associated with its identity in different tumors, our study demonstrates that functional
differences exist across myeloid subsets from different tumors. In particular, our data
suggest that haptoglobin may represent a biomarker for tumors characterized by
systemic accumulation of myeloid cells. Thus, our study provides important insights into
the identity and functional characteristics of tumor-associated myeloid subsets. Perhaps
on March 25, 2021. © 2014 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 31, 2014; DOI: 10.1158/2326-6066.CIR-13-0209
5
more importantly, our data support that in-depth transcriptomic analysis of tumor-
infiltrating myeloid cells may reveal novel therapeutically attractive targets for cancer.
on March 25, 2021. © 2014 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 31, 2014; DOI: 10.1158/2326-6066.CIR-13-0209
6
MATERIALS AND METHODS
Ethics statement
All animal work has been carried out in accordance with US National Institutes of Health
guidelines. This study is reviewed and approved by the Dana-Farber Cancer Institute
Animal Care and Use Committee (ACUC) (protocol IDs: 04-025 and 07-038).
Mice
Six-week old, sex-matched C57BL/6, Balb/c, or CD45.1+ (B6.SJL-PtprcaPepcb/BoyJ and
CBy.SJL(B6)-Ptprca/J) mice were purchased from Jackson Laboratories. F1
(Balb/cxC57BL/6) mice were bred in-house. RIP1-Tag2 mice were obtained from Mouse
Model of Human Cancer Consortium (NCI). Mice with spontaneous invasive pancreatic
ductal adenocarcinoma (PDA, Pdx1-Cre LSL-KrasG12D PtenL/+) were kindly provided by
Dr. DePinho (while at the Dana-Farber Cancer Institute; currently at the MD Anderson
Cancer Center) [11].
Cell lines and tumor models
B16, EL4, 4T1, A20, and CT26 cells obtained from ATCC. Pan02 cells were
obtained from the NCI DTP repository. The 4T07 cells were obtained from Dr. J.
Lieberman (Harvard University, MA); they were expanded, aliquoted, were banked in
liquid nitrogen. No additional authentication was performed on these cells. The Her2 cell
line was derived from a spontaneous breast tumor in a Balb/c Her-2/neu-transgenic
mouse that was found to lose Her-2/neu expression in vitro. Transgenic rat Her-2/neu
sequence was inserted into a pMFG retroviral vector to obtain a stably over-expressing
cell line. Cell lines were maintained in DMEM (B16 and EL4) or RPMI (4T1, Her2, A20,
Pan02, CT26, and 4T07) supplemented with 10% FBS and 1x penicillin/streptomycin.
on March 25, 2021. © 2014 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 31, 2014; DOI: 10.1158/2326-6066.CIR-13-0209
7
For subcutaneous (s.c.) tumors, 1-2.5x105 cells were injected into the flanks of
naive mice (4T1, Her2, CT26 and 4T07 into Balb/c; B16 and Pan02 into C57BL/6; A20
into Balb/cxCD45.1, EL4 into C57BL/6xCD45.1), and tumor growth was monitored using
calipers until tumor size reached 0.5-1 cm diameter. For transgenic models, tumors were
monitored and analyzed at different time points based on tumor growth (RIP-Tag2 at 11-
12 weeks, and PDA and Her2 transgenic at 11-24 weeks).
Cell preparation and flow cytometry
Spleens and tumors were processed by gentle mechanical disruption with forceps
followed by enzymatic digestion using 0.2 mg/mL collagenase P (Roche), 0.8 mg/mL
dispase (Invitrogen), and 0.1 mg/ML DNase I (Sigma). Tissues were incubated in
digestion medium at 37ºC for 30 min; released cells were collected, filtered and the
remaining tissue was further processed. Blood was collected into tubes containing 2 mM
EDTA, and bone marrow cells were isolated by flushing media through tibias and
femurs. Red blood cells were lysed using ACK solution. Cells were resuspended in
FACS buffer containing 1% FBS and 2 mM EDTA.
Cells were stained in FACS buffer containing FcR-blocking antibody (2.4G2) with
combinations of fluorochrome-conjugated or biotinylated antibodies against CD11b
(M1/70), CD11c (N418), CD45 (30.F11), Gr1 (RB6-8C5), MHC class II (M5/114.15.2),
CD80 (16-10A1), CD86 (GL-1), CD40 (HM40-3), CD26 (H194-112), CD103 (2E7), cKit
(2B8), Flt3 (A2F10) or isotype controls purchased from BioLegend and BD Biosciences.
Cells were analyzed using BD FACS Aria II or BD FACSCalibur (BD Biosciences), and
FlowJo Software (Tree Star, Inc). For cell sorting from spleens, myeloid cells were
enriched by depletion of CD3+, CD19+ and CD49b+ cells using biotinylated antibodies
and anti-biotin beads (MACS, Miltenyi). In tumor samples, CD45+ cells were enriched by
positive selection using a biotinylated antibody and anti-biotin beads (EasySep,
on March 25, 2021. © 2014 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 31, 2014; DOI: 10.1158/2326-6066.CIR-13-0209
8
StemCell Technologies). After enrichment, cells were stained with fluorochrome-
conjugated antibodies and propidium iodide (Sigma) to exclude dead cells. Cells were
sorted using a BD FACS Aria II equipped with a 100 µm nozzle running at 20 psi. After
an initial sort to verify purity, a second sort was performed to collect myeloid cell
populations of 95-100% purity directly into TRIzol reagent (Invitrogen). Sample size for
each population was as follows: CD11b-/intGr1-CD11c+MHCII+ (N=4),
CD11b+Gr1lowCD11c+MHCII+ (N=3), CD11b+Gr1lowCD11c-MHCII- (N=3), and
CD11b+Gr1hiCD11c-MHCII- (N=2) from B16 tumors; CD11b+Gr1lowCD11c+MHCII+ (N=3),
CD11b+Gr1lowCD11c-MHCII- (N=3), and CD11b+Gr1hiCD11c-MHCII- (N=2) from 4T1
tumors; CD11b+Gr1lowCD11c+MHCII+ (N=3) and CD11b+Gr1hiCD11c-MHCII- (N=3) from
Her2 tumors; CD11b+Gr1lowCD11c-MHCII- (N=2) and CD11b+Gr1hiCD11c-MHCII- (N=3)
from spleens of 4T1 tumor bearing mice.
RNA isolation and microarray analysis
Total RNA was prepared from TRIzol using chloroform extraction according to the
manufacturer’s protocol, and 100 ng of RNA from each sample was used for
amplification, labeling, and hybridization by Expression Analysis, Inc. (Durham, NC).
Mouse Gene ST 1.0 chips (Affymetrix) were used for microarray analysis. All data are
MIAME-compliant and the raw data generated as part of the Immunological Genome
Project (ImmGen) have been deposited in a MIAME-compliant database [NCBI Gene
Expression Omnibus (GEO) data repository; record no: GSE15907 and GSE37448].
Various modules included in GenePattern platform (Broad Institute) were used for data
analysis. Additional myeloid cell populations from healthy tissues analyzed within the
same microchip batch of ImmGen were used as a comparison [F4/80 Macrophages from
peritoneal cavity (PC, n=2), Ly6C+ DCs from bone marrow (BM, n=2), CD24-Sirpa+ DC
from BM (n=3), CD24+Sirpa+ DC from BM (n=3), CD4 DC from spleen (n=1), CD11c-
on March 25, 2021. © 2014 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 31, 2014; DOI: 10.1158/2326-6066.CIR-13-0209
9
CD103+ DC from small intestine (SI, n=1), neutrophil from BM (C57BL/6, n=2), and
neutrophil from BM (Balb.c, n=2)]. Raw data were normalized with
ExpressionFileCreator module using the RMA method. The MultiPlot module was used
for dataset comparisons including fold-change analysis and statistical filtering. In each
analysis, we preprocessed the dataset for populations included in specific analyses. In
fold-change analyses, an arbitrary cutoff value of 2 was used, in combination with
coefficient of variation (CV)<0.5 for replicates. T-test P values <0.05 were considered
statistically significant for each probe. After filtering, we selected all probes with a mean
expression above 100 in at least one of the populations analyzed, which indicates actual
expression with 95% confidence. For hierarchical clustering, Pearson’s correlation was
used with datasets following log2 transformation, row centering, and row normalization in
HierarchicalClustering module. Heatmaps were constructed using HeatmapViewer
module. Pathway enrichments were analyzed by Ingenuity Pathway Analysis. DCs- and
macrophages-associated genes were determined previously [12,13], and a similar
analysis was performed to identify neutrophil-associated genes (unpublished data).
Principal component analyses were conducted using the PopulationDistances module
created by ImmGen (probes with mean expression value >120 selected, dataset log2
transformed, probes with top 15% variability selected).
Cytokine arrays. Cell culture supernatants from 4T1, Her2 and B16 cells were collected
in two separate batches when cells were ~80% confluent and analyzed with Mouse
Cytokine Antibody Array 3 (RayBiotech, Inc.) according to the manufacturer’s protocol.
Spot densities were measured using ImageJ (NIH), and relative amounts were
calculated based on positive and negative controls. Only cytokines and chemokines with
a positive signal are shown.
on March 25, 2021. © 2014 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 31, 2014; DOI: 10.1158/2326-6066.CIR-13-0209
10
ELISA for Haptoglobin (HP). Serum was obtained from naive or tumor-bearing mice.
Human sera were obtained from patients with metastatic breast carcinoma upon
enrollment into IRB approved vaccine trials at the Dana-Farber Cancer Institute.
Informed consent was obtained for each participant regarding usage of collected
samples for research purposes. Samples were collected by routine procedures prior to
beginning study treatment. HP levels were quantified using Mouse or Human
Haptoglobin ELISA (ICL, Inc.) according to the manufacturer’s protocol. Concentrations
were calculated using a four-parameter logistics curve.
Statistical analysis for non-microarray data
Data are expressed as mean±SD and were analyzed using one-tailed, unpaired
Student’s T-test. ELISAs were analyzed using ANOVA and Tukey’s test. P values <0.05
were considered statistically significant.
on March 25, 2021. © 2014 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 31, 2014; DOI: 10.1158/2326-6066.CIR-13-0209
11
RESULTS and DISCUSSION
Identification and enumeration of tumor-associated myeloid cells reveal tumor-
specific diversity
Thus far, it remains uncertain how tumor origin, localization, and history influence
the myeloid cell landscape of different solid malignancies. To address these questions,
we initially sought to compare the composition of myeloid cell infiltrates among 4T1 and
Her2, two distinct murine tumors sharing the same mammary tissue origin. Tumors were
established s.c. in the flanks of Balb/c mice and analyzed when they reached different
tumor sizes. We characterized multiple myeloid cell populations isolated from each
tumor type on the basis of myeloid-specific surface markers CD11b, Gr1, and CD11c by
cytofluorimetry. In both types of tumors and throughout tumor history, the CD45+
hematopoietic compartment was dominated by CD11b+ cells (>75%) (Fig. 1A,
Supplementary Fig. S1A). The intratumoral CD11b+ cells comprised three sub-
populations based on Gr1 and CD11c expression (Fig. 1A). In 4T1 tumors, the
hematopoietic compartment contained ~20% CD11b+Gr1hiCD11c- cells and >45%
CD11b+Gr1low; with >60% of the latter expressing CD11c. Compared to 4T1, Her2
tumors were infiltrated by a relatively small population of CD11b+Gr1hiCD11c- cells (3%);
while the majority (>95%) of myeloid cells were CD11b+Gr1lowCD11c+. We also found a
Gr1-CD11c+ population among CD11b- cells, which comprised <1% of all hematopoietic
cells in both 4T1 and Her2 tumors. Further analysis showed that CD11b+Gr1lowCD11c+
and CD11b-Gr1-CD11c+ cells in the tumor were largely MHCII+, whereas
CD11b+Gr1lowCD11c- and CD11b+Gr1hiCD11c- cells were MHCII- (Fig. 1B). A marked
accumulation of splenic CD11b+Gr1low and CD11b+Gr1hi cells (Fig. 1C) was observed in
4T1-bearing mice whereas the splenic numbers in mice with Her2 tumors were similar to
naive mice. These data suggest that each tumor type may contain a distinct myeloid cell
infiltrate.
on March 25, 2021. © 2014 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 31, 2014; DOI: 10.1158/2326-6066.CIR-13-0209
12
Lineage determination of tumor-infiltrating myeloid cell subsets based on
transcriptional profiling
We next sought to determine the lineage of each tumor-infiltrating myeloid cell
population using transcriptomic analysis. To this end, myeloid subsets from 4T1 and
Her2 breast carcinomas, and B16 melanoma were sorted to high purity, according to the
ImmGen standard operating procedure. Transcriptional profiling data were generated on
Affymetrix ST1.0 microarrays adhering to profiling and quality control pipelines of
ImmGen [14]. We utilized hierarchical clustering of ~13.5K probes, after excluding those
with coefficient of variation values <0.5 and mean expression values <120 to compare
populations of tumor-infiltrating myeloid cells with representative myeloid cell subsets
(DCs, macrophages, and neutrophils) from lymphoid and non-lymphoid tissues of
healthy animals. Dendogram analysis based on gene clustering depicted similarities
between neutrophils and tumor-derived Gr1hi cells, macropahges and Gr1low cells
(regardless of CD11c expression), and conventional DCs and CD11b-Gr1-CD11c+ cells
(Fig. 1D). Hierarchical clustering of 139 probes corresponding to signature genes of
DCs, macrophages [12,13], and neutrophils (unpublished data) demonstrated that
tumor-infiltrating myeloid cell populations are indeed CD11c+ and CD11c TAMs, TANs,
and DCs (Fig. 1E and Supplementary Table S1).
Striking heterogeneity in myeloid cell infiltrates across different tumor types
Among the three tumor types analyzed, Her2 tumors were uniquely characterized
by the presence of a large CD11c+ TAM population whereas 4T1 tumors contained a
relatively large TAN population (Fig. 2A). In B16 tumors, myeloid cells comprised only
40% of tumor-infiltrating leukocytes compared to >75% in 4T1 and Her2 tumors (Fig.
2A). The aforementioned differences in myeloid abundance also reflected variation in the
abundance of other leukocytes infiltrating tumors including T, B, NK, and NKT cells, and
on March 25, 2021. © 2014 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 31, 2014; DOI: 10.1158/2326-6066.CIR-13-0209
13
pDCs. For example, B16 tumors were enriched in CD3+ T cells compared to 4T1 and
Her2 tumors, consistent with a more immunogenic environment in B16 melanoma (Fig.
2B). To further ascertain the degree of myeloid heterogeneity, we extended our analysis
to five additional transplanted tumors including 4T07 breast cancer, CT26 colon
carcinoma, EL4 T lymphoma, A20 B lymphoma, and Pan02 pancreatic ductal
adenocarcinoma (PDA), and three autochthonous mouse models including RIP-Tag2
(insulinoma), Her-2/neu (mammary carcinoma), and Pdx1-Cre LSL-KrasG12D PtenL/L
(PDA) mice. The same four major populations were present within each tumor model but
at significantly different frequencies across the tumors analyzed (Fig. 2A-C).
In all tumors, DCs were present at very low frequencies, with the highest levels in
B16, EL4 and RIP-Tag2 tumors (4-10%) (Fig. 2). Given that DCs account for only a
small percentage of hematopoietic cells within the tumors, and that the major subset
expressing CD11c in tumors is TAMs, our study also suggests that CD11c alone is an
unreliable marker for DCs in neoplastic lesions. Further analysis comparing DCs in B16
tumors to TAMs indicated that while TAMs expressed a variety of endocytic and
regulatory receptors including Pilrb, Sirpa, Lilrb4, and Clec5a (Supplementary Fig. S1B),
tumor DCs expressed signature genes such as Dpp4, Flt3, and Kit, as well as
CD8/CD103 DC-associated genes [13] including Itgae, Batf3, Tlr3, ifi205, and Xcr1
identifying them as CD11b-CD103+ tissue-resident DCs. Flow cytometric analysis
confirmed that DCs were the myeloid cells expressing high levels of CD26 (Dpp4), Flt3,
cKit, and CD103 (Itgae) protein in these tumors (Supplementary Fig. S1C, and data not
shown). Based on these distinctions, DCs may be considered as a reference population
in future studies to identify unique targets on TAMs and TANs to modulate their tumor-
promoting functions. For example, DCs lack the surface receptor Clec5a (MDL-1),
whereas expression in TAMs, TANs, and neutrophils is relatively high (>18-fold higher
on March 25, 2021. © 2014 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 31, 2014; DOI: 10.1158/2326-6066.CIR-13-0209
14
than in DCs), suggesting that it may represent an attractive target for antibody-mediated
depletion of CD11b+Gr1+ cells in tumors [15].
Differences in Gr1low and Gr1hi cells across several diverse tumors were not
restricted to the tumor lesion itself. For example, a substantial increase in MDSCs was
observed in the spleen and blood of 4T1 tumor-bearing mice throughout tumor
progression but not in mice bearing other tumor types (Supplementary Fig. S1D and
S1E).
Differences in myeloid cell composition arise from specificity of the tumor
microenvironment
The myeloid composition of different tumors is likely shaped by a combination of
tumor-specified growth factors. Indeed, 4T1, Her2, and B16 cell lines produce unique
combinations of cytokines and chemokines, suggesting that each tumor may create a
distinctive microenvironment (Supplementary Fig. S2A). These differences may also
explain why MDSCs preferentially accumulate in certain tumors. For example, high
levels of G-CSF and LIX (CXCL5) expression by 4T1 cells may contribute to systemic
accumulation of MDSCs in vivo [16,17]. To assess whether specific tumor
microenvironments directly influence the myeloid cell content, we analyzed myeloid cells
within the same tumor type growing at different anatomical locations. Comparison of
myeloid cell composition in subcutaneous Her2 tumors and the parental spontaneous
tumor in mammary glands revealed that both lesions contained high frequencies of
CD11c+ TAMs (50-72%) and relatively low frequencies of TANs (<5%) among
hematopoietic cells (Fig. 3A and Supplementary Fig. S2B). A strong similarity was
observed between the myeloid cell composition of subcutaneous 4T1 tumors and
spontaneous metastatic lesions in the peritoneal cavity, occurring within 2-3 weeks after
tumor challenge. Similar to primary 4T1 tumors, these metastases are enriched with
on March 25, 2021. © 2014 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 31, 2014; DOI: 10.1158/2326-6066.CIR-13-0209
15
TAMs (~50%, of which >60% are CD11c+) and TANs (15-21%) among hematopoietic
cells (Fig. 3A and Supplementary Fig. S2C). Furthermore, these results were
corroborated in a B16 metastatic model. When B16 cells are injected intravenously,
tumor nodules grow in lungs and kidneys within 2-3 weeks and contain a similar myeloid
landscape to subcutaneous tumors: ~30% TAMs (~45% expressing CD11c) and 4-9%
TANs among hematopoietic cells (Fig. 3A and Supplementary Fig. S2D). Altogether,
these data provide evidence that tumor type rather than its anatomic location shapes the
myeloid infiltrate.
Next, we tested the possible dominant effect of the microenvironment in dictating
myeloid cell composition by analyzing myeloid cells in mice with two tumors growing
simultaneously. For this purpose, we compared Her2 and 4T1 or Her2 and B16 tumors,
as each of these tumors has distinct myeloid infiltrates (Figs. 1 and 2). Balb/c mice were
inoculated with Her2 tumors under one flank and simultaneously with 4T1 tumors under
the contralateral flank (Fig. 3B and Supplementary Fig. 2E). Similarly, we established
Her2 tumors in the presence of B16 tumors on the opposite flank in F1 (Balb/c x
C57BL/6) mice (Figure 3C and Supplementary Fig. 2F). In both models, we did not
observe a difference in growth rate of either tumor when compared with tumors grown
alone (data not shown). Likewise, the myeloid content of each tumor, either alone or in
the presence of a second tumor in the same host was similar (Figs. 3B-C, left and
Supplementary Figs. S2E-F), even though there was pronounced systemic accumulation
of MDSCs associated with 4T1 tumor growth (Figs. 3B-C, right). Overall, these results
suggest that the signals attracting specific myeloid cells into tumors are driven by cancer
cells in the primary lesion regardless of anatomic location or presence of distant signals
from other microenvironments.
on March 25, 2021. © 2014 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 31, 2014; DOI: 10.1158/2326-6066.CIR-13-0209
16
Tumor-infiltrating myeloid cells exhibit a hypoxia-associated signature compared
to their splenic counterparts
As shown in Fig. 1, MDSCs accumulate in the spleens of 4T1 tumor-bearing
mice at high frequencies. As splenic MDSCs may serve as reservoirs for tumor-
infiltrating cells [6], it is important to understand how they relate to their counterparts
within tumors. Thus, we compared the gene expression profiles of CD11b+Gr1+ cells
from the spleens and tumors of 4T1 tumor-bearing mice. As shown in Fig. 4A, 263
probes were upregulated in TAMs (both CD11c- and CD11c+ subsets) within 4T1 tumors
compared to Gr1low MDSCs from spleens, whereas 152 probes were enriched in splenic
cells. In addition, 201 probes were enriched in TANs compared to splenic Gr1hi MDSCs,
while 647 probes were enriched in Gr1hi MDSCs (Fig. 4B). Next, we compared
expression of probes upregulated in 4T1 tumors to that of myeloid cells originated in
other tumor models. Hierarchical clustering indicated that TAMs, TANs, and splenic
MDSC populations clustered separately, confirming differences between tumor and
splenic myeloid cells. Notably, similar profiles were obtained from equivalent populations
originated from different tumor types (Fig. 4C), supporting that transcriptomic analysis
may illuminate universal features of tumor myeloid cells. Indeed, this analysis identified
several genes that highlight the pro-tumorigenic roles of tumor-infiltrating myeloid cells.
For example, genes involved in extracellular matrix remodeling (Mmp12, Mmp13,
Mmp14, Adam8, Tgm2), immunomodulation (Arg1, Nos2, Cd274, Ptgs2, Spp1) and
hypoxia regulation (Vegfa, Hif1a, Slc2a1) were markedly upregulated in TAMs and TANs
compared with splenic MDSCs. Interestingly, expression of these genes was similar
between CD11b-CD103+ tumor DCs, and DCs from tumor-free mice. This result
suggests that hypoxic responses are not uniform within the same microenvironment and
may be tightly regulated among tumor-infiltrating leukocytes. Immunosuppression and
tissue remodeling are critical functions for promoting tumor progression, and hypoxia,
on March 25, 2021. © 2014 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 31, 2014; DOI: 10.1158/2326-6066.CIR-13-0209
17
through hypoxia inducible factor 1 (Hif-1) [18], can promote transcriptional changes
that facilitate the selection of aggressive tumor cells. Expression analysis of Hif-1-
regulated hypoxia-associated genes [19] revealed that these genes are indeed enriched
in TAMs and TANs compared to splenic MDSCs and steady-state populations, indicating
reprogramming in the tumor environment (Fig. 4D and Supplementary Table S2).
Hypoxia and Hif-1 have been implicated in prolonged survival and reduced oxidative
burst in neutrophils [20,21], and as such may also contribute to the accumulation of
these short-lived cells.
Neutrophils in the tumor environment are pro-tumorigenic and exhibit an altered
functional profile
Our finding that the myeloid cell landscape of each tumor type is distinctive
raises the possibility that different tumor microenvironments impart unique functional
attributes in the myeloid cells therein. While TAMs in different tumors have been
extensively characterized by functional and transcriptional profiling approaches [22-26],
our understanding of TANs is limited. Recently, Fridlender et al. compared steady-state
neutrophils to splenic MDSCs and TANs in a mesothelioma model by transcriptional
profiling and identified striking differences [27]. However, this study focused only on a
single tumor and it is not known if such characteristics of TANs are common across
different tumors. By comparing TANs from 4T1, B16, and Her2 tumors to Gr1hi MDSCs
from 4T1 tumor-bearing mice and neutrophils from the bone marrow of naive mice (Fig.
4), we identified a conserved expression pattern of immunomodulatory and hypoxia-
related genes across these tumors. We also compared genes highly expressed in both
Gr1hi MDSCs and TANs from 4T1 tumors to their expression in steady-state neutrophils
(Fig. 5A, left) and found 214 probes selectively enriched in 4T1-associated neutrophils.
on March 25, 2021. © 2014 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 31, 2014; DOI: 10.1158/2326-6066.CIR-13-0209
18
Interestingly, many of these genes were similarly enriched in TANs from B16 and Her2
tumors compared to steady-state neutrophils (170 out of 214 passed CV<0.5 filter) (Fig.
5A, right). Heatmap analysis also indicated that these probes were highly expressed by
all TANs and Gr1hi MDSCs compared to steady-state neutrophils (Fig. 5B and
Supplementary Table S3). Importantly, these genes are involved in pathways critical for
tumor growth and immune responses, such as iNOS, IL-10, IL-6 and peroxisome
proliferator-activated receptor (PPAR) signaling (Fig. 5C). Collectively, our data support
that tumor-associated myeloid cells exhibit a pro-tumorigenic transcriptional program.
To further interrogate molecular programs in TANs across different tumors, a
side-by-side comparison of TANs from 4T1, Her2 and B16 tumors was performed. We
identified 43 probes enriched in 4T1 TANs, 61 in Her2 TANs, and 93 in B16 TANs (Fig.
6A). Genes involved in cell trafficking or differentiation, such as Cxcr1, Foxo3, Slc28a2,
were enriched 2-5 fold in 4T1 TANs and may regulate the marked accumulation of TANs
in this cancer type [28]. Given that our data point to a direct, tumor cell-specific
involvement in the recruitment and expansion of TANs, we analyzed the expression
levels of cytokines, cytokine receptors, chemokines, and chemokine receptors by TANs,
as these may account for the tumor-myeloid cell cross-talk regulating cell infiltration (Fig.
6B and Supplementary Table S4). Strikingly, we identified different expression profiles
for each TAN population, which may reflect the differences observed in the frequencies
of these populations in each tumor. In particular, TANs from 4T1 tumors expressed
significantly greater levels of Ltb4r1 (BLT1), a leukotriene B4 chemotactic factor for
neutrophils [29,30]. Interestingly, we also found enrichment of genes in the anti-
inflammatory PPAR signaling pathway (Fig. 5D), which is also a target for leukotriene
B4. We further analyzed the expression of PPAR target genes associated with
inflammation [31] in neutrophil subsets (Fig. 6C and Supplementary Table S5). While
on March 25, 2021. © 2014 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 31, 2014; DOI: 10.1158/2326-6066.CIR-13-0209
19
many of these genes are expressed at higher levels in TANs compared to Gr1hi MDSCs
and steady-state neutrophils, neutrophils from 4T1-bearing mice expressed higher levels
of Stat1, Stat2, and Stat3, among other genes. Stat3 is a mediator of CXCR2 expression
in neutrophils upon G-CSF stimulation [32]. In agreement with this, G-CSF is highly
expressed by 4T1 cells (Fig. S2A) and Cxcr2 is expressed at higher levels by 4T1 TANs
(Fig. 6B). Overall, these signaling pathways may incite the marked accumulation of
TANs and Gr1hi MDSCs in the 4T1 model. The relationship between neutrophil
populations from different tumors was also confirmed by the principal component
analysis to highlight functional differences. Neutrophils from naive mice clustered
separately and more closely to MDSCs on PC2 (Fig. 6D). On the other hand, while
TANs clustered separately from neutrophils and MDSCs, variability on PC1 reflected
tumor-to-tumor differences among TANs.
Haptoglobin levels are associated with myeloid cell accumulation
During acute inflammatory responses, neutrophils exocytose effector molecules
stored in specific granules [33]. Recently, Fridlender et al showed that some of these
molecules are actually down-regulated in TANs compared to those in steady-state
neutrophils and Gr1hi MDSCs [27]. To understand how this critical function is influenced
by different tumors, we compared the expression of these effector molecules in steady-
state neutrophils, in splenic Gr1hi MDSCs from 4T1 tumor-bearing mice, and in TANs
from 4T1, Her2, and B16 tumors. Heatmap analysis and hierarchical clustering indicated
that all TANs shared an altered expression profile compared to naive granulocytes (Fig.
7A and Supplementary Table S6). Interestingly, Gr1hi MDSCs expressed the majority of
the effector molecules. Among the molecules associated with neutrophil granules is the
acute phase responder Haptoglobin (HP) [34]. HP is known to be produced in the liver,
however recent studies indicated that it is also expressed by neutrophils and packaged
on March 25, 2021. © 2014 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 31, 2014; DOI: 10.1158/2326-6066.CIR-13-0209
20
into secretory granules [35]. Analysis of Hp expression by tumor-associated myeloid
cells revealed that Hp is not expressed by DCs or CD11c+ TAMs in tumors, and its level
is moderate in CD11c- TAMs and in TANs from B16 and Her2 tumors (Fig. 7B).
However, Hp expression is high in naive granulocytes, Gr1hi MDSCs, and TANs from
4T1 tumors. Accordingly, serum HP levels were elevated in mice bearing advanced 4T1
tumors but not in Her2 or B16 tumor-bearing hosts (Fig. 7C). Indeed, serum HP levels
correlated with the frequency of Gr1hi MDSCs in the blood of 4T1 tumor-bearing mice
(Fig. 7D). This select pattern suggests that HP expression may be associated with the
considerable myelopoiesis observed in the 4T1 setting (Supplementary Fig. S1E).
Although further studies are required to confirm a direct relationship between neutrophils
and HP levels, we detected elevated serum HP in breast cancer patients (Fig. 7E),
consistent with reports for glioblastoma and ovarian cancer patients [36,37]. Thus, HP
may serve as a diagnostic marker indicative of cancers with myeloid cell accumulation.
Tumors require a sustained influx of myeloid cells to support angiogenesis,
remodel peritumoral stroma, and suppress antitumor immunity. Therefore, tumor-
associated myeloid cells represent potential targets to improve immunotherapy as well
as chemotherapy against cancer. Although there are limited strategies available,
identifying novel targets for elimination or functional differentiation of these cells requires
a deeper understanding of their development, trafficking, and phenotypic and functional
characteristics. Moreover, to ascertain whether a broader therapeutic application is
feasible, it is critical to determine if key molecular attributes are conserved among
specific myeloid populations from tumors of different origins. A better understanding of
the functional differences among discrete myeloid subsets in tumor lesions as well as
secondary lymphoid organs may lay the groundwork for development of novel
therapeutics. In this regard, our study provides fundamental insights into functional
differences of tumor-infiltrating myeloid cells, and identifies potential molecular pathways
on March 25, 2021. © 2014 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 31, 2014; DOI: 10.1158/2326-6066.CIR-13-0209
21
that could be investigated for therapies aimed at reducing infiltration and/or
reprogramming function of tumor myeloid cells.
ACKNOWLEDGMENTS We thank the Immunological Genome Project team for technical help and discussions;
Drs. Dobrin Draganov and Glenn Dranoff for the Her2 cell line; and Dr. Ronald DePinho
for PDA mice. This study was supported by research funding from NIH T32 grant to
KGE, NIH T32 CA 070083-15 and Postdoctoral Fellowship from the Cancer Research
Institute to VC, NIH grants (AG028082, AG033049, AI098108, AI101918) and an
Established Investigator Award (0940006N) from American Heart Association to DRG,
NIH grants (ROI DK074500, PO1 AI045757, R21 CA182598), American Cancer Society
Research Scholar Grant, and Claudia Adams Barr Award for Innovative Cancer
Research to SJT.
on March 25, 2021. © 2014 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 31, 2014; DOI: 10.1158/2326-6066.CIR-13-0209
22
FIGURE LEGENDS
Figure 1. Multiple myeloid cell subsets are present within tumors of different
origin. A, Analysis of myeloid cells subsets within subcutaneous 4T1 and Her2 tumors
by flow cytometry. Hematopoietic cells were analyzed for the expression of CD11b and
Gr1, and gated populations in the dot-plots were further analyzed for CD11c expression.
B, MHCII expression by indicated subsets. C, Accumulation of CD11b+Gr1+ cells in the
spleens of 4T1 tumor-bearing mice (top) and Gr1 expression (bottom). Numbers indicate
percentage of cells for each gate or region. D, Dendrogram analysis based on
hierarchical clustering of tumor-associated and steady-state myeloid cells (SI, small
intestine, PC peritoneal cavity; Spl, spleen; BM, bone marrow; Tmr, tumor). Sample
sizes are indicated in Materials and Methods. E, Hierarchical clustering of the same
populations based on a list of 139 genes associated with DCs, macrophages or
neutrophils.
Figure 2. Abundance of myeloid cell subsets across different tumors. A, Whisker
box-plots summarizing the relative frequency of myeloid subsets among hematopoietic
cells within 4T1, Her2 and B16 tumors. B, Frequency of CD3+ T cells within
hematopoietic cells in 4T1, Her2 and B16 tumors. C, Frequency of myeloid cells in 4T07,
Pan02, EL4, CT26, PDA, RIP-Tag2 and A20 tumors (n=3-7). * P<0.05, ** P<001, ***
P<0.001.
on March 25, 2021. © 2014 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 31, 2014; DOI: 10.1158/2326-6066.CIR-13-0209
23
Figure 3. Tumor type dictates the composition of myeloid cell subsets within
tumors. A, Myeloid cells within s.c. and mammary gland of transgenic Her2 tumors
(n=4-7); within primary s.c. and spontaneous peritoneal metastatic 4T1 tumors (n=3);
and within s.c. B16 tumors or lung and kidney metastases (n=3-4). B, Left, Analysis of
myeloid subsets within tumors from Balb/c mice with single (Her2 or 4T1, n=5) or double
(Her2 and 4T1 on opposite flanks, n=8) tumors. Right, Frequency of myeloid cells in the
spleens of tumor-bearing mice. C, Left, Analysis of myeloid cell subsets within tumors
from F1 (Balb/c x C57BL/6) mice with single (Her2 or B16, n=3) or double (Her2 and B16
on opposite flanks, n=3) tumors. Right, Frequency of myeloid cells in the spleens of
tumor-bearing mice. Numbers indicate percentage of cells for each gate or region. N.S.,
not significant, * P<0.05, *** P<0.001.
Figure 4. Tumor-infiltrating myeloid cells express immunomodulatory genes. A,
Comparison of gene expression profiles of CD11c+ and CD11c- TAMs to Gr1low MDSCs
from 4T1 tumor-bearing mice based on 2-fold change (fold-change vs fold-change plot).
B, Comparison of gene expression profiles of TANs and Gr1hi MDSCs from 4T1 tumor-
bearing mice (fold-change vs t-test P value plot). C, Heatmap generated by hierarchical
clustering using probes identified in A and B for all tumor myeloid subsets. D, Heatmap
showing the expression of hypoxia-related genes by tumor-associated and steady-state
myeloid cells. Selected genes associated with the populations are indicated on the plots.
Figure 5. Expression of pro-tumorigenic genes by TANs. A, Comparison of gene
expression profiles of TANs and Gr1hi MDSCs from 4T1 tumor-bearing mice to naive
neutrophils (left, fold-change vs fold-change, 2-fold change). Expression of these probes
was further analyzed in fold-change vs fold-change plots comparing TANs from B16 and
Her2 tumors to naive neutrophils (right). 170 of the 214 probes passed the CV<0.5 filter.
on March 25, 2021. © 2014 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 31, 2014; DOI: 10.1158/2326-6066.CIR-13-0209
24
B, Heatmap showing the expression of the 170 probes identified in A across neutrophils.
C, Top 20 canonical pathways enriched for TANs and Gr1hi MDSCs compared to naive
neutrophils analyzed by Ingenuity Pathway Analysis.
Figure 6. Tumor type-specific transcriptional profiles of TANs. A, Fold-change vs
fold-change plot comparing 4T1 TANs to TANs in Her2 and B16, based on 2-fold
change. Selected genes associated with the populations are indicated on the plots. B,
Chemokine, chemokine receptor, cytokine, and cytokine receptor expression profiles of
TANs. C, PPAR-associated gene expression profiles of neutrophils. D, Principal
component analysis of neutrophil populations.
Figure 7. Altered expression profile of secretory granule molecules in TANs. A,
Expression of granular effector molecules by neutrophils. B, Mean expression value of
haptoglobin (Hp) across neutrophils and tumor-associated myeloid cells. C, Serum HP
levels in mice with small (S), medium (M), large (L) tumors and naive mice. D,
Correlation between the frequency of Gr1hi cells in blood and the serum HP
concentration. E, HP levels in serum from healthy donors (n=4) and breast cancer
patients (n=10). * P<0.05; ** P<0.01.
on March 25, 2021. © 2014 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 31, 2014; DOI: 10.1158/2326-6066.CIR-13-0209
25
SUPPLEMENTARY FIGURE LEGENDS
Figure S1. A, Tumor volume (top), frequency of CD11b+Gr1+ cells (middle) and
frequency of CD11c+ cells within CD11b+Gr1low cells in 4T1 and Her2 tumors at different
time points (Days 10, 18, 23. S, small; M, medium; L, large tumors). B, Comparison of
tumor DCs to CD11c+ and CD11c- TAMs from B16 tumors based on 2-fold change.
Selected genes associated with the populations are indicated on the plots. C,
Histograms showing the expression of CD26, Flt3, cKit, and CD103 by tumor myeloid
cells within B16 tumors analyzed by flow cytometry. Representative of 3 different
samples. Black lines, markers; gray-filled, isotype controls. Numbers indicate mean
fluorescence. D, Bar graph summarizing the frequencies of splenic CD11b+Gr1low (white)
and CD11b+Gr1hi (black) cells in each tumor model. E, Systemic accumulation of
MDSCs during 4T1 and Her2 tumor growth. Splenocyte numbers, frequency of
CD11b+Gr1+ cells in the spleen and blood (n=3 for each samples).
Figure S2. A, Relative amounts of cytokines and chemokines secreted by 4T1, Her2
and B16 cell lines detected by cytokine arrays (n=2 for each). Top, Relative signal >50 at
least in one tumor. Bottom, Relative signal <50 in all tumors. B-D, Bar graphs
summarize the percentage of TAMs (white) and TANs (black) in each Her2 (B), 4T1 (C)
and B16 (D) tumors in metastases and at different anatomical locations. E-F, Bar graphs
summarize the percentage of TAMs and TANs in mice with a single or double tumors;
Her2 and 4T1 (E), Her2 and B16 (F). N.S., not significant, * P<0.05.
on March 25, 2021. © 2014 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 31, 2014; DOI: 10.1158/2326-6066.CIR-13-0209
26
REFERENCES 1. Bussolati B, Grange C, Camussi G. Tumor exploits alternative strategies to achieve
vascularization. FASEB J. 2011;25:2874-82.
2. Gabrilovich DI, Ostrand-Rosenberg S, Bronte V. Coordinated regulation of myeloid
cells by tumours. Nat Rev Immunol. 2012;12: 253-68.
3. Rahir G, Moser M. Tumor microenvironment and lymphocyte infiltration. Cancer
Immunol Immunother. 2012;61:751-9.
4. Tlsty TD, Coussens LM. Tumor stroma and regulation of cancer development. Annu
Rev Pathol. 2006;1:119-50.
5. Marigo I, Bosio E, Solito S, Mesa C, Fernandez A, Dolcetti L, et al. Tumor-induced
tolerance and immune suppression depend on the C/EBPbeta transcription
factor. Immunity. 2010;32:790-802.
6. Cortez-Retamozo V, Etzrodt M, Newton A, Rauch PJ, Chudnovskiy A, Berger C, et al.
Origins of tumor-associated macrophages and neutrophils. Proc Natl Acad Sci U
S A. 2012;109:2491-6.
7. Le HK, Graham L, Cha E, Morales JK, Manjili MH, Bear HD. Gemcitabine directly
inhibits myeloid derived suppressor cells in BALB/c mice bearing 4T1 mammary
carcinoma and augments expansion of T cells from tumor-bearing mice. Int
Immunopharmacol. 2009;9:900-9.
8. Vincent J, Mignot G, Chalmin F, Ladoire S, Bruchard M, Chevriaux A, et al. 5-
Fluorouracil selectively kills tumor-associated myeloid-derived suppressor cells
on March 25, 2021. © 2014 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 31, 2014; DOI: 10.1158/2326-6066.CIR-13-0209
27
resulting in enhanced T cell-dependent antitumor immunity. Cancer Res.
2010;70:3052-61.
9. Ko JS, Zea AH, Rini BI, Ireland JL, Elson P, Cohen P, et al. Sunitinib mediates
reversal of myeloid-derived suppressor cell accumulation in renal cell carcinoma
patients. Clin Cancer Res. 2009;15:2148-57.
10. DeNardo DG, Brennan DJ, Rexhepaj E, Ruffell B, Shiao SL, Madden SF, et al.
Leukocyte complexity predicts breast cancer survival and functionally regulates
response to chemotherapy. Cancer Discov. 2011;1:54-67.
11. Ying H, Elpek KG, Vinjamoori A, Zimmerman SM, Chu GC, Yan H, et al. PTEN is a
major tumor suppressor in pancreatic ductal adenocarcinoma and regulates an
NF-kappaB-cytokine network. Cancer Discov. 2011;1:158-69.
12. Gautier EL, Shay T, Miller J, Greter M, Jakubzick C, Ivanov S, et al. Gene-
expression profiles and transcriptional regulatory pathways that underlie the
identity and diversity of mouse tissue macrophages. Nat Immunol. 2010;13:1118-
28.
13. Miller JC, Brown BD, Shay T, Gautier EL, Jojic V, Cohain A, et al. Deciphering the
transcriptional network of the dendritic cell lineage. Nat Immunol. 2012;13:888-
99.
14. Heng TS, Painter MW. The Immunological Genome Project: networks of gene
expression in immune cells. Nat Immunol. 2008;9:1091-4.
15. Cheung R, Shen F, Phillips JH, McGeachy MJ, Cua DJ, Heyworth PG, Pierce RH.
Activation of MDL-1 (CLEC5A) on immature myeloid cells triggers lethal shock in
mice. J Clin Invest. 2011;121:4446-61.
16. Toh B, Wang X, Keeble J, Sim WJ, Khoo K, Wong WC, et al. Mesenchymal
transition and dissemination of cancer cells is driven by myeloid-derived
suppressor cells infiltrating the primary tumor. PLoS Biol. 2011;9:e1001162.
on March 25, 2021. © 2014 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 31, 2014; DOI: 10.1158/2326-6066.CIR-13-0209
28
17. Waight JD, Hu Q, Miller A, Liu S, Abrams SI. Tumor-derived G-CSF facilitates
neoplastic growth through a granulocytic myeloid-derived suppressor cell-
dependent mechanism. PLoS One. 2011;6:e27690.
18. Shay JE, Celeste Simon M. Hypoxia-inducible factors: crosstalk between
inflammation and metabolism. Semin Cell Dev Biol. 2012;23:389-94.
19. Favaro E, Lord S, Harris AL, Buffa FM. Gene expression and hypoxia in breast
cancer. Genome Med. 2011;3:55.
20. McGovern NN, Cowburn AS, Porter L, Walmsley SR, Summers C, Thompson AA, et
al. Hypoxia selectively inhibits respiratory burst activity and killing of
Staphylococcus aureus in human neutrophils. J Immunol. 2011;186:453-63.
21. Walmsley SR, Print C, Farahi N, Peyssonnaux C, Johnson RS, Cramer T, et al.
Hypoxia-induced neutrophil survival is mediated by HIF-1alpha-dependent NF-
kappaB activity. J Exp Med. 2005;201:105-15.
22. Duff MD, Mestre J, Maddali S, Yan ZP, Stapleton P, Daly JM. Analysis of gene
expression in the tumor-associated macrophage. J Surg Res. 2007;142:119-28.
23. Ojalvo LS, King W, Cox D, Pollard JW. High-density gene expression analysis of
tumor-associated macrophages from mouse mammary tumors. Am J Pathol.
2009;174:1048-64.
24. Ojalvo LS, Whittaker CA, Condeelis JS, Pollard JW. Gene expression analysis of
macrophages that facilitate tumor invasion supports a role for Wnt-signaling in
mediating their activity in primary mammary tumors. J Immunol. 2010;184:702-
12.
25. Pucci F, Venneri MA, Biziato D, Nonis A, Moi D, Sica A, et al. A distinguishing gene
signature shared by tumor-infiltrating Tie2-expressing monocytes, blood
"resident" monocytes, and embryonic macrophages suggests common functions
and developmental relationships. Blood. 2009;114:901-14.
on March 25, 2021. © 2014 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 31, 2014; DOI: 10.1158/2326-6066.CIR-13-0209
29
26. Stearman RS, Dwyer-Nield L, Grady MC, Malkinson AM, Geraci MW. A macrophage
gene expression signature defines a field effect in the lung tumor
microenvironment. Cancer Res. 2008;68:34-43.
27. Fridlender ZG, Sun J, Mishalian I, Singhal S, Cheng G, Kapoor V, et al.
Transcriptomic analysis comparing tumor-associated neutrophils with
granulocytic myeloid-derived suppressor cells and normal neutrophils. PLoS
One. 2012;7:e31524.
28. Turrel-Davin F, Tournadre A, Pachot A, Arnaud B, Cazalis MA, Mougin B, Miossec
P. FoxO3a involved in neutrophil and T cell survival is overexpressed in
rheumatoid blood and synovial tissue. Ann Rheum Dis. 2010;69:755-60.
29. Kim ND, Chou RC, Seung E, Tager AM, Luster AD. A unique requirement for the
leukotriene B4 receptor BLT1 for neutrophil recruitment in inflammatory arthritis.
J Exp Med. 2006;203:829-35.
30. Saiwai H, Ohkawa Y, Yamada H, Kumamaru H, Harada A, Okano H, et al. The
LTB4-BLT1 axis mediates neutrophil infiltration and secondary injury in
experimental spinal cord injury. Am J Pathol. 2010;176:2352-66.
31. Rakhshandehroo M, Knoch B, Muller M, Kersten S. Peroxisome proliferator-
activated receptor alpha target genes. PPAR Res. 2010;2010 pii:612089.
32. Nguyen-Jackson H, Panopoulos AD, Zhang H, Li HS, Watowich SS. STAT3 controls
the neutrophil migratory response to CXCR2 ligands by direct activation of G-
CSF-induced CXCR2 expression and via modulation of CXCR2 signal
transduction. Blood. 2010;115:3354-63.
33. Borregaard N. Neutrophils, from marrow to microbes. Immunity. 2010;33:657-70.
34. Wang Y, Kinzie E, Berger FG, Lim SK, Baumann H. Haptoglobin, an inflammation-
inducible plasma protein. Redox Rep. 2001;6:379-85.
on March 25, 2021. © 2014 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 31, 2014; DOI: 10.1158/2326-6066.CIR-13-0209
30
35. Theilgaard-Monch K, Jacobsen LC, Nielsen MJ, Rasmussen T, Udby L, Gharib M, et
al. Haptoglobin is synthesized during granulocyte differentiation, stored in
specific granules, and released by neutrophils in response to activation. Blood.
2006;108:353-61.
36. Kumar DM, Thota B, Shinde SV, Prasanna KV, Hegde AS, Arivazhagan A, et al.
Proteomic identification of haptoglobin alpha2 as a glioblastoma serum
biomarker: implications in cancer cell migration and tumor growth. J Proteome
Res. 2010;9:5557-67.
37. Zhao C, Annamalai L, Guo C, Kothandaraman N, Koh SC, Zhang H, et al.
Circulating haptoglobin is an independent prognostic factor in the sera of patients
with epithelial ovarian cancer. Neoplasia. 2007;9:1-7.
on March 25, 2021. © 2014 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 31, 2014; DOI: 10.1158/2326-6066.CIR-13-0209
A
FIGURE 1
0 102
103
104
105
0
103
104
105
62
21
15
Gr1
CD
11
bCD45+ CD11b+Gr1low CD11b+Gr1hi CD11b-Gr1-
0 102
103
104
105
0
103
104
105
4T1
Her2
82
3
13
0 102
103
104
105
0
20
40
60
80
100
0 102
103
104
105
0
20
40
60
80
100
0 102
103
104
105
0
20
40
60
80
0 102
103
104
105
0
20
40
60
80
100
30838618
C
100
CD11b+Gr1low
CD11c+
CD11b+Gr1low
CD11c-
CD11b+Gr1hi
CD11c-
CD11b-Gr1-
CD11c+
100
101
102
103
104
100
101
102
103
104
2.91.6
100
101
102
103
104
100
101
102
103
104
3.7 1.7
100
101
102
103
104
100
101
102
103
104
451.2
CD
11
b
Naive Her24T1
CD11c
Spleen
CD45+
0 102
103
104
105
0
20
40
60
80
100
0 102
103
104
105
0
20
40
60
80
0 102
103
104
105
0
20
40
60
80
100100
92 4 96
4T1
Her2
MHCII
0 102
103
104
105
0
20
40
60
80
100
96
100
101
102
103
104
100
101
102
103
104
100
101
102
103
104
100
101
102
103
104
100
101
102
103
104
100
101
102
103
104
15 85 20 80 25 75
Gr1
CD11b+
CD11c-
D
CD11b-CD103+ DC SI
Ly6C+PDCA1+ DC BM
CD4+ DC SplCD24+Sirpa- DC BMCD24-Sirpa+ DC BMCD11b-CD11c+ B16 Tmr
F4/80hi Mac PCGr1lowCD11c- 4T1 SplGr1lowCD11c- 4T1 TmrGr1lowCD11c- B16 TmrGr1lowCD11c+ B16 TmrGr1lowCD11c+ 4T1 TmrGr1lowCD11c+ Her2 Tmr
Neutro B6 BMNeutro Balbc BMCD11b+Gr1hi 4T1 TmrCD11b+Gr1hi 4T1 SplCD11b+Gr1hi Her2 TmrCD11b+Gr1hi B16 Tmr
E
Expression (Log2)
3.27 13.50
Dendritic cell
Macrophage
Neutrophil
Signature gene
0
10
20
30
40
100
101
102
103
104
94 6
100
101
102
103
104
0
1
2
3
4
5
928
CD11c
0
20
40
60
100
101
102
103
104
595
100
101
102
103
104
0
20
40
60
34 66
0
3
6
9
12
100
101
102
103
104
8218
100
101
102
103
104
0
5
10
15 96 4
BtlaFlt3KmoP2ry10Hmgn3Bri3bpNapsaCcr7Klri1H2−Eb2Gpr68Traf1Gpr82KitGpr68Zbtb46Slamf7Rab30Dpp4Jak2Bcl11aHaaoAp1s3Spint2Tbc1d8Gpr114Pvrl1Gpr132Ass1Runx3AnpepH2−Q6CiitaH2−DMb2H2−AaH2−Ab1H2−EbCd164A930039A15RikTmem1951810011H11RikPecrComt1Pon3Mr1Fcgr1Camk1Myo7aMertkAbca1Cd14Pla2g15Plod1CtslTpp1Sepp1BlvrbGlulTspan14Tlr7Lamp2Tcn2Pld3Slc48a1Pla2g4aNlnPcyox1Tbxas1Akr1b10Amica1Cnn2Pstpip1Cbfa2t3Adam19Gm5068Fgl2Amica1Stfa2l1G0s2Fam122aGm5416Spatc1Prok2ArsgFert2Fgd4SqrdlPtplad2Tom1B430306N03RikCtsdFcgr3Clec5aTlr4Slc16a3Slfn1Csf3rSlfn4HdcRetnlgSgms2Cd300lfGrinaArg2Pdlim7Dok3Slc27a4TirapKlhl2Mboat7PxnPfkfb4Ppp1r3dPhospho1Sfxn5A530064D06Rik2010002M12RikDhrs9Gm99494732465J04RikFam123aKrt86RlfChi3l1MgamAsprv1Il1f9Mrgpra2aSlc2a3Abtb1Ankrd22Ceacam10Msl1Slc22a15Ccpg1Slc35a5A530023O14RikFoxd4E430024C06Rik
CD
11
b- C
D1
03
+ D
C S
IC
D24
+S
irpa
- DC
BM
CD
24
- Sirp
a+ D
C B
MC
D4
+ D
C S
pl
CD
11
b- C
D1
1c
+ B
16
Tm
rL
y6
C+P
DC
A1
+ D
C B
M
Ne
utr
o B
6 B
MN
eu
tro
Ba
lbc B
M
CD
11
b+G
r1h
i 4T
1 T
mr
CD
11
b+G
r1h
i 4T
1 S
pl
CD
11
b+G
r1h
i He
r2 T
mr
CD
11
b+G
r1h
i B1
6 T
mr
F4
/80
hi M
ac P
C
Gr1
lowC
D1
1c
- 4
T1
Sp
l
Gr1
lowC
D1
1c
- B
16
Tm
rG
r1lo
wC
D1
1c
+ B
16
Tm
rG
r1lo
wC
D1
1c
- 4
T1
Tm
rG
r1lo
wC
D1
1c
+ 4
T1
Tm
rG
r1lo
wC
D1
1c
+ H
er2
Tm
r
CD
11
b
B
on March 25, 2021. © 2014 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 31, 2014; DOI: 10.1158/2326-6066.CIR-13-0209
FIGURE 2
C
0
20
40
60
80
100
%S
ubsets
within
CD
45
+ in tum
or
A%
Subsets
within
CD
45
+ in tum
or
0
20
40
60
80
100
Other/
non-myeloidDCsTANsCD11c+
TAMsCD11c-
TAMs
4T1
Her2
B16
Other/
non-myeloidDCsTANsCD11c+
TAMsCD11c-
TAMs
4T07 Pan02 EL4 CT26 PDA RIP-Tag2 A20
*** *
*** ***
***
***
* **
*
***
**
4T1 Her2 B160
10
30
20
**
**
%C
D3
+ w
ithin
CD
45
+ in tum
or
B
on March 25, 2021. © 2014 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 31, 2014; DOI: 10.1158/2326-6066.CIR-13-0209
FIGURE 3
Gr1
CD11c
CD
11
bPrimary
(s.c.)
100
101
102
103
104
100
101
102
103
104
59
7
100
101
102
103
104
100
101
102
103
104
696
4 96
Mammary gland
(transgenic)
CD45+
TAMs
100
101
102
103
104
100
101
102
103
104
44 12
100
101
102
103
104
100
101
102
103
104
60 21
Peritoneal met
(spont.)
100
101
102
103
104
100
101
102
103
104
333.8
100
101
102
103
104
100
101
102
103
104
314
100
101
102
103
104
100
101
102
103
104
283
Lung met
(i.v.)
Kidney met
(i.v.)
Gr1
CD11c
A Her2 4T1 B16
B
100
101
102
103
104
100
101
102
103
104
72
2
100
101
102
103
104
100
101
102
103
104
322.3
100
101
102
103
104
100
101
102
103
104
831.3
100
101
102
103
104
100
101
102
103
104
3310
100
101
102
103
104
100
101
102
103
104
713.7
100
101
102
103
104
100
101
102
103
104
5817.3
100
101
102
103
104
100
101
102
103
104
77.411.4
100
101
102
103
104
100
101
102
103
104
5821
C
CD
11
b
Her2 4T1
Gr1
CD11c
Her2 4T1
Single tumor Double tumor
CD45+
TAMs
100
101
102
103
104
0
20
40
60
100
101
102
103
104
0
30
60
90
120
298
100
101
102
103
104
0
10
20
30
40
50
25
75
100
101
102
103
104
0
10
20
30
40
50
29
71
100
101
102
103
104
0
5
10
1556 44
100
101
102
103
104
0
5
10
15
20
25 61 39
100
101
102
103
104
0
5
10
15
20
25
62 38
4
0
20
40
60
80
100
101
102
103
10 100
101
102
103
104
0
20
40
60
80
100
100
101
102
103
104
0
10
20
30
40
793
2674
0
20
40
60
100
101
102
103
104 10
010
110
210
310
4
0
10
20
30
40
51 49
100
101
102
103
104
0
20
40
60
36 64694
100
101
102
103
104
0
50
100
150
5 95
100
101
102
103
104
0
20
40
60
80
4 96 54 46
Primary
(s.c.)
CD11c
Gr1
Primary
(s.c.)
CD
11
b
Gr1
CD11c
CD45+
TAMs
Her2 B16 Her2 B16
Single tumor Double tumor
%C
ells
in
CD
45
+ N.S.
N.S.
Her2 4T1 Double0
20
30
40
10
Gr1low MDSC Gr1hi MDSC
***
*
Spleen
%C
ells
in
CD
45
+ N.S.
N.S.
Her2 B16 Double0
4
6
8
2
Gr1low MDSC Gr1hi MDSC
Spleen
on March 25, 2021. © 2014 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 31, 2014; DOI: 10.1158/2326-6066.CIR-13-0209
FIGURE 4
1 100.1
Fold change
(4T1 CD11c+ TAM vs Gr1low MDSC)
263
152
1
0.2
10
Fo
ld c
ha
ng
e
(4T
1 C
D11
c- T
AM
vs G
r1lo
w M
DS
C)A
1 100.2
Fold change (4T1 TAN vs Gr1hi MDSC)
100
10-1
10-2
10-3
10-4
10-5
647 20110-6
10-7
KdrCar9Pdk1Cdh1Ccnd1PdgfbIgf2Igfbp2TgfaSerpine1Flt1Cxcl12Mmp2MetCdkn1aPfkfb3Egln3Nos2Slc2a1Arg1VegfaHif1aHk2Hk1Gys1LdhaPgk1Pkm2Eno1AldoaMmp9Slc2a3Kdm5bCcng2Cxcr4PlaurCrebbpCrebbp
B
C
Gr1
low M
DS
C 4
T1
Gr1
hi M
DS
C 4
T1
CD
11
c- T
AM
4T
1C
D1
1c
- T
AM
B1
6C
D1
1c
+ T
AM
B1
6C
D1
1c
+ T
AM
4T
1C
D1
1c
+ T
AM
He
r2T
AN
4T
1T
AN
B1
6T
AN
He
r2
CD
11
b- C
D1
03
+ D
C S
I
Ly6
C+P
DC
A1
+ D
C B
M
CD
4+ D
C S
pl
CD
24
+S
irpa
- DC
BM
CD
24
- Sirp
a+ D
C B
M
DC
B1
6 T
mr
F4
/80
hi M
ac P
C
Ne
utr
o B
6N
eu
tro
Ba
lbc
Gr1
hi M
DS
C 4
T1
Gr1
low M
DS
C 4
T1
TA
N 4
T1
TA
N B
16
TA
N H
er2
CD
11
c- T
AM
4T
1C
D1
1c
- T
AM
B1
6C
D1
1c
+ T
AM
B1
6C
D1
1c
+ T
AM
4T
1C
D1
1c
+ T
AM
He
r2
D
Expression (Log2)
3.87 13.68
Expression (Log2)
4.74 13.54
Mmp14
Mmp12Spp1
Cd274
Ptps2
Arg1
Tgm2
VegfaNos2
Adam8Slc2a1
Hif1a
Arg1
Cd274
Tgm2
VegfaSlc2a1
t-te
st P
va
lue
(lo
g1
0)
Abca1
Cxcl16
Serpinb9
Mgl2
Havcr2
Cxcl10
Il1a
Niacr1
Arg1Egln3
Cstb Tnfrsf26
Hypoxia-associated genes
on March 25, 2021. © 2014 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 31, 2014; DOI: 10.1158/2326-6066.CIR-13-0209
FIGURE 5
A
1 100.1
Fold change (4T1 TAN vs Naive Neutrophil)
214
1
0.1
10
Fo
ld c
ha
ng
e
(4T
1 G
r1h
i MD
SC
vs N
aiv
e N
eu
tro
ph
il)
1 100.1
Fold change (Her2 TAN vs Naive Neutrophil)
170
1
0.1
10
Fo
ld c
ha
ng
e
(B1
6 T
AN
vs N
aiv
e N
eu
tro
ph
il)
B
0 1 2 3 4 5 6 7 8 9
IL-10 Signaling
Hepatic Fibrosis/Hepatic Stellate
Cell Activation
LXR/RXR Activation
Hepatic Cholestasis
iNOS Signaling
VDR/RXR Activation
IL-6 Signaling
PPAR Signaling
AHR Signaling
Role of Macrophages/Fibroblasts/
Endothelial Cells in RA
Glucocorticoid Receptor Signaling
MIF Regulation of Innate Immunity
Eumelanin Biosynthesis
LPS/IL-1 Mediated Inhibition
of RXR Function
Acute Phase Response Signaling
Molecular Mechanisms of Cancer
Production of NO and ROS
in MacrophagesIL-12 Signaling and Production
in Macrophages
HGF Signaling
PPARa/RXRa activation
Ingeniuty Canonical Pathway
Top 20 -Log10
(p-value)
Gr1
hi M
DS
C 4
T1
TA
N 4
T1
TA
N B
16
TA
N H
er2
Ne
utr
o B
6N
eu
tro
Ba
lb
C
Expression (Log2)
4.23 13.73 on March 25, 2021. © 2014 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 31, 2014; DOI: 10.1158/2326-6066.CIR-13-0209
FIGURE 6
BA
Ifna1Ifna9IfnzFlt3lIfna7Il1bIfna5Il17aIl31Il13Il25Il16LtbIfna2MifIl12aIl15Il18Il1f9Il17cCsf3Ifna14Il23aTn fIl12bIl1aTgfb1
Il2raIl2rbIl7rIl2rgIl10rbIl12rb1Il9rIl17rbIl11ra1Il6raIl17raIl13ra1Il28raIl15raIl27ra
Ccr2Ccr5
Ccr1Ccr10Ccr7Xcr1Cxcr3Cxcr4Cxcr1Cxcr2Ltb4r1
Fold change (4T1 vs B16 TANs)
Fo
ld c
ha
ng
e (
4T
1 v
s H
er2
TA
Ns)
TA
N 4
T1
TA
N B
16
TA
N H
er2
TA
N 4
T1
TA
N B
16
TA
N H
er2
TA
N 4
T1
TA
N B
16
TA
N H
er2
Expression (Log2)
5.73 13.37
Expression (Log2)
5.53 11.34
Expression (Log2)
5.67 13.68
Expression (Log2)
5.91 12.47
B6
Balb/cMDSC
4T1
TAN
B16
TAN
Her2
TAN
C
Ccl12Ccl2Ccl5Ccl4Ccl7Ccl9Ccl22Ccl8Ccl3Ccl6Ccl27aCxcl2Cxcl3Ccl1Cx3cl1Ccl17Cxcl1Cxcl14Cxcl16Xcl1Cxcl10Cxcl9Ccl19Ccl11Cxcl17Ccl25Cxcl11Ccrl2
TA
N B
16
TA
N 4
T1
TA
N H
er2
1 100.1
1
0.1
10
Ltb4r1
Slc28a2Foxo3Cxcr1
6143
93
Ccgn2Gbp1
Irgm2 Irf47
Irf1
Plxdc2
ArsbItgb5
Axl
Ccr2
Ccl8Trim12a
Cd36
Ly6a
Thbs1
Ccl7
LifrEmr1Vcam1Il18Pla1aCcl2Il1rnMt2Mt1Ccl3Cxcl10NfkbiaIcam1Ifi47 7CebpbIl1b bBirc3Irgm2Stat2Stat3Il1rapSteap4Stat1Il6raOrm2Lcn2Traf2Nfkb1
Cx3cr1
Gr1
hi M
DS
C 4
T1
TA
N 4
T1
TA
N B
16
TA
N H
er2
Ne
utr
o B
6N
eu
tro
Ba
lb
D
Expression (Log2)
5.77 13.87
−0.2
0
0.2
0.4
−0.20
0.20.4
−0.4
−0.2
0
0.2
0.4
PC2 (14.6%)
PC1
(66.4%)
PC3
(5.9%)
Cytokine ChemokineCytokine
receptor
Chemokine
receptor
on March 25, 2021. © 2014 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 31, 2014; DOI: 10.1158/2326-6066.CIR-13-0209
FIGURE 7
B
A
C
Mp
oE
lan
eC
tsg
Prtn
3B
pi
Slc
11
a1
Fcg
r3M
mp
2C
d1
4L
yz2
Lyz1
Cyb
aL
tfC
am
pM
mp
25
Itgb
2Itg
am
Mm
p8
Mm
p9
Fp
r1L
cn
2H
p
Me
an
exp
ressio
n v
alu
e
0
200
400
600
800
S M L S M L
4T1 Her2Naive
L
B16
Se
rum
HP
co
nce
ntr
atio
n (μ
g/m
l)
Gr1hi MDSC 4T1
TAN 4T1TAN B16
TAN Her2
Neutro B6Neutro Balb
Ne
utr
o B
6
Ne
utr
o B
alb
0
2000
4000
6000
8000
Gr1
hi M
DS
C 4
T1
TA
N 4
T1
TA
N H
er2
TA
N B
16
Gr1
low M
DS
C 4
T1
CD
11
c- T
AM
4T
1
CD
11
c- T
AM
B1
6
CD
11
c+ T
AM
4T
1
CD
11
c+ T
AM
B1
6
CD
11
c+ T
AM
He
r2
DC
B1
6
*
**
Exp
ressio
n (L
og
2 )
5,4
31
3.9
0
D
%G
r1h
i in
blo
od
0
50
100
100 200 300 400 5000
Serum HP concentration (μg/ml)
R2=0.71
0
1000
2000
3000
4000
5000
6000
7000
Se
rum
HP
co
nce
ntr
atio
n (μ
g/m
l)
Healthy
donor
Breast cancer
patient
E**
on March 25, 2021. © 2014 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 31, 2014; DOI: 10.1158/2326-6066.CIR-13-0209
Published OnlineFirst March 31, 2014.Cancer Immunol Res Kutlu G Elpek, Viviana Cremasco, Hua Shen, et al. and functional diversity of infiltrating myeloid cellsThe tumor microenvironment shapes lineage, transcriptional,
Updated version
10.1158/2326-6066.CIR-13-0209doi:
Access the most recent version of this article at:
Material
Supplementary
http://cancerimmunolres.aacrjournals.org/content/suppl/2014/04/03/2326-6066.CIR-13-0209.DC1
Access the most recent supplemental material at:
Manuscript
Authoredited. Author manuscripts have been peer reviewed and accepted for publication but have not yet been
E-mail alerts related to this article or journal.Sign up to receive free email-alerts
Subscriptions
Reprints and
To order reprints of this article or to subscribe to the journal, contact the AACR Publications
Permissions
Rightslink site. Click on "Request Permissions" which will take you to the Copyright Clearance Center's (CCC)
.http://cancerimmunolres.aacrjournals.org/content/early/2014/03/29/2326-6066.CIR-13-0209To request permission to re-use all or part of this article, use this link
on March 25, 2021. © 2014 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 31, 2014; DOI: 10.1158/2326-6066.CIR-13-0209