The tumor microenvironment shapes lineage, transcriptional ...€¦ · 29.03.2014 · blood, and...

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

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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.




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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




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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




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more importantly, our data support that in-depth transcriptomic analysis of tumor-

infiltrating myeloid cells may reveal novel therapeutically attractive targets for cancer.




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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.




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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,




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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-




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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.




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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.




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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.




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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




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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




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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




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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.




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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,




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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.




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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




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




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




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.




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.




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.




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.




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.




26

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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

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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

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3.7 1.7

100

101

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100

101

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Naive Her24T1

CD11c

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CD45+

0 102

103

104

105

0

20

40

60

80

100

0 102

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104

105

0

20

40

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80

0 102

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0 102

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100

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103

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15 85 20 80 25 75

Gr1

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CD11c-

D

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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

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100

101

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BtlaFlt3KmoP2ry10Hmgn3Bri3bpNapsaCcr7Klri1H2−Eb2Gpr68Traf1Gpr82KitGpr68Zbtb46Slamf7Rab30Dpp4Jak2Bcl11aHaaoAp1s3Spint2Tbc1d8Gpr114Pvrl1Gpr132Ass1Runx3AnpepH2−Q6CiitaH2−DMb2H2−AaH2−Ab1H2−EbCd164A930039A15RikTmem1951810011H11RikPecrComt1Pon3Mr1Fcgr1Camk1Myo7aMertkAbca1Cd14Pla2g15Plod1CtslTpp1Sepp1BlvrbGlulTspan14Tlr7Lamp2Tcn2Pld3Slc48a1Pla2g4aNlnPcyox1Tbxas1Akr1b10Amica1Cnn2Pstpip1Cbfa2t3Adam19Gm5068Fgl2Amica1Stfa2l1G0s2Fam122aGm5416Spatc1Prok2ArsgFert2Fgd4SqrdlPtplad2Tom1B430306N03RikCtsdFcgr3Clec5aTlr4Slc16a3Slfn1Csf3rSlfn4HdcRetnlgSgms2Cd300lfGrinaArg2Pdlim7Dok3Slc27a4TirapKlhl2Mboat7PxnPfkfb4Ppp1r3dPhospho1Sfxn5A530064D06Rik2010002M12RikDhrs9Gm99494732465J04RikFam123aKrt86RlfChi3l1MgamAsprv1Il1f9Mrgpra2aSlc2a3Abtb1Ankrd22Ceacam10Msl1Slc22a15Ccpg1Slc35a5A530023O14RikFoxd4E430024C06Rik

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FIGURE 2

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A%

Subsets

within

CD

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0

20

40

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80

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Other/

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TAMsCD11c-

TAMs

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4T07 Pan02 EL4 CT26 PDA RIP-Tag2 A20

*** *

*** ***

***

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4T1 Her2 B160

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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




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




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



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




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**




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,

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