bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

TGFβ Suppresses Type 2 Immunity to Cancer

Ming Liu1, Fengshen Kuo2, Kristelle J. Capistrano1, Davina Kang1, Briana G. Nixon1,3, Wei Shi1,

Chun Chou1, Mytrang H. Do1,3, Shun Li1, Efstathios G. Stamatiades1, Yingbei Chen4, James J.

Hsieh3,5, A. Ari Hakimi3,6, Ichiro Taniuchi7, Timothy A. Chan2, Ming O. Li1,3*

1Immunology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New

York, NY, USA 10065

2Immunogenomics & Precision Oncology Platform (IPOP), Memorial Sloan Kettering Cancer

Center, New York, NY, USA 10065

3Immunology and Microbial Pathogenesis Program, Weill Cornell Graduate School of Medical

Sciences, Cornell University, New York, NY, USA 10065

4Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA 10065

5Molecular Oncology, Department of Medicine, Siteman Cancer Center, Washington University,

St. Louis, MO, USA, 63110

6Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center New York,

NY USA, 10065

7Laboratory for Transcriptional Regulation, RIKEN Center for Integrative Medical Sciences,

Yokohama, Japan 230-0045

* Correspondence: [email protected] bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Summary

The immune system employs two distinct defense strategies against infections: pathogen

elimination typified by type 1 immunity, and pathogen containment exemplified by type 2

immunity in association with tissue repair. Akin to infectious diseases, cancer progresses with

cancer cell acquisition of microorganism-like behavior propagating at the expense of the host.

While immunological mechanisms of cancer cell elimination are well defined, whether immune-

mediated cancer cell containment can be induced is poorly understood. Here we show that

ablation of transforming growth factor-β receptor II (TGFβRII) in CD4+ T cells promotes tumor

tissue healing and halts cancer progression. Notably, the restorative response is dependent on

the T helper 2 cytokine IL-4 fortifying vasculature organization that spares only proximal layers

of cancer cells from hypoxia, nutrient starvation and death. Thus, type 2 immunity represents

an effective cancer defense mechanism, and TGFβ signaling in helper T cells may be targeted

for novel cancer immunotherapy.

2 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Text

In response to invading pathogens, vertebrate immune system employs two discrete strategies

of host defense: pathogen elimination typified by type 1 immunity following innate immune

recognition of prokaryotic microorganisms1, and pathogen containment associated with tissue

repair manifested by type 2 immunity following the invasion of metazoan parasites that inflict

tissue damage2. Similar to infectious diseases, cancer imposes insults to the host with cancer

cell acquisition of microorganism-like behavior of clonal expansion. Indeed, cancer cell-targeted

type 1 immunity mediated by CD8+ cytotoxic T lymphocytes (CTLs), cytotoxic innate

lymphocytes and innate-like T cells represent host protective mechanisms of cancer cell

elimination3-5. Notwithstanding, the host impact of cancer cells is much more intimate than that

of microbes, and cancer pathogenicity, in particular that of solid tumors, is dependent on cancer

cell coercing a supportive environment that promotes tumor encroachments leading to the

impairment of tissue function6-9. Notably, tumor tissues resemble active wounds, and are

considered as “wounds that do not heal”10, implying that cancer microenvironment-targeted

tissue repair responses could restrain tumor progression.

Dissimilar from infectious diseases, cancer arises from malignant transformation of self-

cells. Consequently, immunological self-tolerance can be maladapted to repress cancer-elicited

immune responses. Indeed, multiple T cell self-tolerance regulators including Foxp3+ regulatory

T (Treg) cells, co-inhibitory receptors PD-1 and CTLA-4, and the regulatory cytokine

transforming growth factor-β (TGFβ) suppress immune control of tumor development11-15.

Depletion of tumor-infiltrating Treg cells via genetic approaches or administration of anti-PD-1

and anti-CTLA-4 result in CTL-dependent cancer cell elimination in animal models and

patients16,17. In transgenic mouse models of cancer, blockade of TGFβ signaling in T cells

suppresses tumor development18, which is correlated with expansion of tumor-reactive CTLs

that exhibit enhanced cancer cell killing in vitro. Nonetheless, as TGFβ exerts pleiotropic effects

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on multiple lineages of T cells19, its functional targets in vivo and the underlying mechanisms of

cancer regulation remain obscure.

TGFβ acts on CD4+ T cells to foster tumor growth

As CTLs are established effectors of cancer cell elimination, we set forth by examining whether

TGFβ directly suppressed CTL-mediated cancer surveillance in the MMTV-PyMT (PyMT) model

of breast cancer. Mice carrying a floxed allele of the Tgfbr2 gene (Tgfbr2fl/fl) encoding the TGFβ

receptor II (TGFβRII) were crossed with CD8Cre transgenic mice, which were further bred to the

PyMT background. Loss of TGFβRII was observed specifically in CD8+ T cells from

CD8CreTgfbr2fl/flPyMT mice (Fig. 1a), which led to enhanced effector/memory differentiation of

CD8+ T cell in the tumor-draining lymph nodes (Extended Data Fig. 1). Increased expression of

the cytolytic enzyme Granzyme B among PD-1-expressing CD8+ T cells was also detected in

the tumor (Fig. 1b). Surprisingly, mammary tumor growth was not suppressed in

CD8CreTgfbr2fl/flPyMT mice (Fig. 1c). These observations imply that additional

immunosuppressive mechanisms such as those mediated by PD-1 whose expression was

unaffected in tumor-infiltrating CD8+ T cells (Fig. 1b), may compensate for TGFβ signaling

blockade for CTL repression as suggested by recent studies20-23.

To investigate whether TGFβ acted on CD4+ T cells to indirectly suppress CTL-

dependent cancer surveillance, we crossed Tgfbr2fl/flPyMT mice with ThPOKCre mice that

predominantly targeted CD4+, but not CD8+, innate-like or TCRγδ T cells (Fig. 1d and Extended

Data Fig. 2a). Compared to control Tgfbr2fl/flPyMT mice, ThPOKCreTgfbr2fl/flPyMT mice exhibited

enhanced effector/memory differentiation of conventional CD4+ T cells and Treg cells, but not

CD8+ T cells, in the tumor-draining lymph nodes (Extended Data Fig. 2b). Yet, tumor-infiltrating

CD8+ T cells expressed higher levels of Granzyme B and lower levels of PD-1 (Fig. 1e). Such a

phenotypic change was observed in T cell-specific TGFβ1-deficeint mice that resist tumor

growth18. Indeed, profound inhibition of tumor progression was observed in

4 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

ThPOKCreTgfbr2fl/flPyMT mice (Fig. 1f). To determine whether the altered CTL response

accounted for the tumor growth phenotype, we crossed ThPOKCreTgfbr2fl/flPyMT mice to the

CD8-deficient background. Unexpectedly, tumor repression was unchanged in the absence of

CD8+ T cells (Fig. 1f), while depletion of CD4+ T cells restored tumor growth (Extended Data

Fig. 2c-2d). These findings suggest that CD4+ T cells are a prominent target of TGFβ in cancer

immune tolerance control, and the reduced tumor growth triggered by TGFβRII-deficient CD4+ T

cells is not mediated by CTLs.

Cancer cell death occurs with immune exclusion

To define the underlying mechanisms of cancer suppression in ThPOKCreTgfbr2fl/flPyMT mice,

we assessed proliferation and death of cancer cells by the expression of Ki67 and cleaved

Caspase 3 (CC3), respectively. Ki67 was expressed in about 20% and 40% mammary

epithelial cells in tumors from 8-week-old and 23-week-old Tgfbr2fl/flPyMT mice, which was

unaffected in ThPOKCreTgfbr2fl/flPyMT mice (Fig. 2a). In contrast, blockade of TGFβ signaling in

CD4+ T cells resulted in an approximate 13-fold increase of CC3-positive cells at 23-week of

age (Fig. 2a). Notably, dying cancer cells had a clustered distribution pattern (Fig. 2a), which

was not observed in CD8CreTgfbr2fl/flPyMT mice (Extended Data Fig. 3a), but was preserved in

ThPOKCreTgfbr2fl/flPyMT mice crossed onto the CD8-deficient background (Extended Data Fig.

3b). These observations imply that TGFβRII-deficient CD4+ T cells inhibit tumor progression

through the induction of cancer cell death.

The lack of a role for CTLs in tumor repression in ThPOKCreTgfbr2fl/flPyMT mice

prompted us to investigate whether CD4+ T cells might directly eradicate cancer cells as

suggested by recent studies24,25. To this end, we examined the localization of CD4+ T cells by

immunofluorescence staining. Tumor progression was associated with an approximate 5-fold

increase of intratumoral CD4+ T cells between 8-week-old and 23-week-old Tgfbr2fl/flPyMT mice,

while stromal CD4+ T cells were reduced (Fig. 2b). In contrast, blockade of TGFβ signaling in

5 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

CD4+ T cells led to approximate 6- and 16-fold increase of stromal CD4+ T cells in 8-week-old

and 23-week-old mice, respectively, while intratumoral CD4+ T cells were substantially reduced

in 23-week-old ThPOKCreTgfbr2fl/flPyMT mice (Fig. 2b). Furthermore, few intratumoral CD4+ T

cells were localized distant from the cancer cell death region (Fig. 2b), suggesting against direct

cancer cell killing by TGFβRII-deficient CD4+ T cells. To examine whether the preferential

tumor stromal localization of CD4+ T cells in ThPOKCreTgfbr2fl/flPyMT mice was applicable to

other hematopoietic lineage cells, we performed immunofluorescence staining with the pan-

leukocyte marker CD45. In contrast to the dominant tumor parenchyma localization of CD45+

cells in Tgfbr2fl/flPyMT mice, leukocytes were mostly localized in the tumor stroma of

ThPOKCreTgfbr2fl/flPyMT mice (Extended Data Fig. 4). The immune cell exclusion phenotype

suggests that TGFβRII-deficient CD4+ T cells unlikely induce cancer cell death directly or

indirectly via another leukocyte effector.

Vessel organization triggers cancer cell death

The preferential stromal localization of TGFβRII-deficient CD4+ T cells suggests that they may

regulate the host to endure the negative impact of a growing tumor with cancer cell death being

a secondary outcome. In line with previous studies10, the fast growing tumor in Tgfbr2fl/flPyMT

mice exhibited extensive extravascular deposition of fibrinogen (Fig. 3a), indicative of

vasculature damage. In contrast, fibrinogen was predominantly intravascular in

ThPOKCreTgfbr2fl/flPyMT mice (Fig. 3a). Perfusion with a biotinylation probe confirmed an overt

leaky vasculature in tumors from Tgfbr2fl/flPyMT, but not ThPOKCreTgfbr2fl/flPyMT mice

(Extended Data Fig. 5a). The vasculature damage phenotype in Tgfbr2fl/flPyMT mice co-

occurred with an irregularly shaped and bluntly ended microvasculature manifested by the

isolated staining of the endothelium marker CD31 (Fig. 3a). Strikingly, although the vessel

density was unaffected (Extended Data Fig. 5b), tumor vasculature was much more organized

in ThPOKCreTgfbr2fl/flPyMT mice with less isolated CD31+ endothelial cell staining (Fig. 3a).

6 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

These observations demonstrate that while tumors from Tgfbr2fl/flPyMT mice inflict chronic

tissue damage resembling “wounds that do not heal”10, tumors from ThPOKCreTgfbr2fl/flPyMT

mice are maintained in a healed state associated with an organized vasculature.

Pericytes are mesenchyme-derived cells that enwrap and stabilize capillaries and control

perfusion26. Notably, while approximate 12% endothelial cells with isolated CD31+ staining in

tumors from Tgfbr2fl/flPyMT mice were not bound by NG2+ pericytes (Fig. 3a-3b), the

endothelium was tightly ensheathed by pericytes in tumors from ThPOKCreTgfbr2fl/flPyMT mice

(Fig. 3b). Fibroblasts are heterogenous populations of ‘accessory’ cells that provide structural

support for ‘customer’ cell subsets including the endothelium27. Remarkably, approximate 63%

endothelial cells with isolated CD31+ staining in tumors from Tgfbr2fl/flPyMT mice were not

associated with GP38+ fibroblasts (Fig. 3a-3b), whereas extended layers of GP38+ cells

enclosed the vasculature in tumors from ThPOKCreTgfbr2fl/flPyMT mice (Fig. 3b). In addition to

the supportive cellular compartment, acellular components including extracellular matrix

proteins regulate vascular integrity28. Associated with the aberrant vessel patterning, the vessel

basement membrane proteins collagen IV and fibronectin were fragmented and disoriented in

tumors from Tgfbr2fl/flPyMT mice (Fig. 3c). In contrast, both collagen IV and fibronectin were

highly connected and colocalized with the endothelium in tumors from ThPOKCreTgfbr2fl/flPyMT

mice (Fig. 3c). Together, these findings reveal that blockade of TGFβ signaling in CD4+ T cells

promotes the generation of an organized and mature vasculature in the tumor.

Sprouting angiogenesis is induced in malignant tissues in response to hypoxia and

metabolic stresses, which resupplies oxygen and nutrients to a growing tumor29,30. Indeed,

excessive vessel branching in tumors from Tgfbr2fl/flPyMT mice was associated with few hypoxic

spots (Fig. 3d). In contrast, approximate 18-fold larger areas were positive for the hypoxic

probe in tumors from ThPOKCreTgfbr2fl/flPyMT mice (Fig. 3d). Notably, the hypoxic areas

exhibited a circular pattern, and was localized peripheral to the cancer cell death region with the

7 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

initiating hypoxia and cancer cell death area positioned about 60 μm and 101 μm inward to the

adjacent vasculature, respectively (Fig. 3d), which was in agreement with the diffusion range of

oxygen in tissue29,30. Together, these observations suggest that cancer cell death in

ThPOKCreTgfbr2fl/flPyMT mice is caused by severe hypoxia and/or depletion of nutrients, which

is enabled by an organized vasculature refractory to the damaging effect of a growing tumor.

TGFβ represses type 2 immunity to cancer

To define how TGFβRII-deficient CD4+ T cells reprogram the cancer microenvironment to fortify

vasculature organization, we analyzed tumor-infiltrating T cells from Tgfbr2fl/flPyMT and

ThPOKCreTgfbr2fl/flPyMT mice. Although the frequencies of tumor-associated CD4+Foxp3+ Treg

cells were not significantly altered, conventional CD4+Foxp3- T cells expanded at the expense of

CD8+ T cells in ThPOKCreTgfbr2fl/flPyMT mice (Extended Data Fig. 6). Importantly, Treg cell-

specific ablation of Tgfbr2 with Foxp3Cre transgenic mice did not affect tumor progression (Fig.

1f), supporting that TGFβ targets T helper (Th) cells to promote cancer immune evasion.

As a pleiotropic regulator of helper T cell responses, TGFβ potently suppresses Th1 and

Th2 cell differentiation characterized by expression of IFN-γ and IL-4, respectively19. Indeed,

increased frequencies of CD4+Foxp3- cells from tumor-draining lymph nodes and tumor tissues

of ThPOKCreTgfbr2fl/flPyMT mice produced IFN-γ and IL-4 (Fig. 4a and data not shown). In

transplantation models of murine cancer, recent studies have shown that Th1 cells and IFN-γ

promote pericyte coverage of the endothelium as well as vessel regression31,32. To interrogate

the function of IFN-γ in the transgenic breast cancer model, we crossed ThPOKCreTgfbr2fl/flPyMT

mice onto the IFN-γ-deficient background. Unexpectedly, the repressed tumor growth

phenotype was unaffected in the absence of IFN-γ (Fig. 4b). In addition, IFN-γ deficiency did not

affect the enhanced effector/memory phenotype of TGFβRII-deficient CD4+ T cells in the tumor-

draining lymph nodes, or their expansion in the tumor (Extended Data Fig. 7a-7b), while CD8+ T

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cell numbers were reduced (Extended Data Fig. 6 and 7b). Furthermore, extravascular

deposition of fibrinogen and the bluntly ended vasculature were observed in PyMT mice on the

IFN-γ-deficient background, but were suppressed in Ifng-/-ThPOKCreTgfbr2fl/flPyMT mice

(Extended Data Fig. 7c). Importantly, in the absence of IFN-γ, the severe hypoxia response

observed in ThPOKCreTgfbr2fl/flPyMT mice was preserved with clustered CC3-positive cells

distributed to the inner circle of the hypoxic region (Extended Data Fig. 7d). These observations

exclude IFN-γ as a mediator of cancer environment reprogramming by TGFβRII-deficient CD4+

T cells.

To investigate type 2 immune response in cancer regulation, we crossed

ThPOKCreTgfbr2fl/flPyMT mice to the IL-4-deficient background. IL-4 deficiency did not affect the

enhanced effector/memory differentiation phenotype of TGFβRII-deficient CD4+ T cells in the

tumor-draining lymph nodes, but their expansion in the tumor was attenuated (Extended Data

Fig. 6 and 8a-8b). In contrast to Ifng-/-ThPOKCreTgfbr2fl/flPyMT mice, Il4-/-

ThPOKCreTgfbr2fl/flPyMT mice had widespread extravascular deposition of fibrinogen associated

with a torturous and irregularly shaped vasculature (Extended Data Fig. 8c). Furthermore,

enhanced hypoxia and increased cancer cell death observed in ThPOKCreTgfbr2fl/flPyMT mice

were inhibited in the absence of IL-4 (Fig. 3d and Extended Data Fig. 8d), concomitant with

accelerated tumor growth (Fig. 4b). In adoptive T cell transfer experiments, conventional CD4+

T cells from ThPOKCreTgfbr2fl/fl, but not Tgfbr2fl/fl, Il4-/- or Il4-/-ThPOKCreTgfbr2fl/fl mice triggered

cancer cell death and halted tumor progression in PyMT recipients (Fig. 4c and Extended Data

Fig. 9a), revealing an essential function for IL-4 produced by TGFβRII-deficient CD4+ T cells in

cancer suppression. Depletion of tumor-associated macrophages that are prominently induced

in PyMT mice33 did not impair the repressed tumor growth phenotype in ThPOKCreTgfbr2fl/flPyMT

mice (Extended Data Fig. 9b-9c), suggesting that macrophages are unlikely effectors of

TGFβRII-deficient CD4+ T cells. Similar to the PyMT model, blockade of TGFβ signaling in

9 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

CD4+ T cells repressed tumor growth in a transplantation model of cancer, which was blunted

by neutralization of IL-4, but not IFN-γ (Extended Data Fig. 9d). These observations suggest a

critical role for type 2 immunity in mediating the anti-tumor response triggered by TGFβRII-

deficient CD4+ T cells.

A type 2 gene signature stratifies cancer risk

Aside from being the signature Th2 effector cytokine, IL-4 potently regulates T cell responses.

To explore the TGFβ-regulated IL-4-dependent gene expression program in helper T cells, we

isolated tumor-infiltrating CD4+CD25- T cells from Tgfbr2fl/flPyMT, ThPOKCreTgfbr2fl/flPyMT, Il4-/-

PyMT, and Il4-/-ThPOKCreTgfbr2fl/flPyMT mice, and performed RNAseq experiments. Compared

to wild-type T cells, TGFβRII-deficient T cells upregulated and downregulated 919 and 1507

genes, respectively, among which 238 and 879 were dependent on IL-4 (Supplementary Table

1). We were particularly interested in the 238 transcripts induced in TGFβRII-deficient T cells in

an IL-4-depenent manner, as it might define the functional effector program involved in cancer

suppression. Among the targets were those encoding stimulatory receptors and adhesion

molecules such as Icam1, Il1r2, Il3ra, Tnfrsf18 and Tnfrs4, signaling molecules such as Jak3,

Map2k3, Map3k10, Map3k11, Mapkapk2, Pik3c2b, Sh3bp2 and Zap70, calcium, glucose and

amino acid transporters including Orai1, Slc2a1, Slc2a8 and Slc7a5 as well as glycolytic

enzymes including Hk2, Ldha, Pgk1 and Pkm (Fig. 5a), which may collectively promote

expansion of TGFβRII-deficient CD4+ T cells in tumor (Extended Data Fig. 6 and 8b). In

addition, a number of transcripts encoding secreted molecules were upregulated in TGFβRII-

deficient T cells in an IL-4-depenent manner, which comprised of Th2 cytokines Il13, Il4 and Il5

as well as type 2 effector molecules including Areg, Metrnl, R3hdml and Retnlg (Fig. 5a), in

agreement with potent Th2 cell-inducing activities of IL-4. Indeed, transcripts including Batf,

Bhlhe40 and Pparg encoding transcription factors resided in major regulatory nodes of T cell

activation and Th2 cell differentiation34 were as well induced in an IL-4-dependent manner in

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TGFβRII-deficient T cells (Fig. 5a). Collectively, these observations suggest that IL-4 promotes

cancer immunity by engaging a positive feedback loop of T cell regulation, although IL-4 targets

other than T cells may also be involved.

To determine whether a similar type 2 immune response suppresses tumor development

in patients, we refined the IL-4-dependent upregulated gene list by excluding transcripts that did

not have human orthologs. We further utilized FPKM filters of non-hematopoietic cancer cell

line samples as well as tumor-infiltrating myeloid cells and type 1 lymphocytes, and derived a

putative human type 2 immune signature, consisting of 28 genes (Fig. 5b). Notably, when

applied to the bulk RNAseq datasets from the TCGA database, the signature stratified patients

for survival probability (Fig. 5c). In fact, clinically indolent cancers such as pheochromocytoma

and paraganglioma (PCPG) were among the cancer types with highest gene signature value,

while aggressive cancers including esophageal carcinoma (ESCA) had low score (Extended

Data Fig. 10a). Among breast invasive carcinoma (BRCA), estrogen receptor-, progesterone

receptor- or HER2-positive types, collectively grouped as non triple-negative (non-TN), had a

higher signature score than TN BRCA (Extended Data Fig. 10a), with its signature value further

differentiating patients for survival probability (Extended Data Fig. 10b). Likewise, clinically less

aggressive lower grade glioma (LGG) or kidney chromophobe (KICH) had higher signature

scores than ontologically related glioblastoma multiforme (GBM) or kidney renal clear cell

carcinoma (KIRC), respectively (Extended Data Fig. 10a), and their signature values further

stratified patients for better survival probability (Extended Data Fig. 10b). Histological analysis

revealed that compared to KIRC, KICH had a more organized vasculature with discernable

stromal demarcation and less isolated CD31+ endothelial cell staining (Extended Data Fig. 10c).

These findings imply a prominent role for type 2 immunity in restraining sprouting angiogenesis

and tumor progression in subsets of cancer patients.

11 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Discussion

As an ancestral cell growth factor family member, TGFβ exerts pleiotropic regulatory functions

on numerous pathophysiological processes including carcinogenesis and immune

responses14,15,35,36. In transplantation models of cancer, TGFβ suppression of tumor immunity

has been attributed to the inhibition of tumor-reactive CTLs20,21,37,38. Herein, in a sporadic breast

cancer model, we provided genetic evidence for Th2 cells, but not CTLs, as a direct target of

TGFβ-dependent tumor immune evasion. Using the same tumor model on a different genetic

background, Th2 cells were suggested to promote cancer metastasis39. How Th2 cells might be

involved in triggering different cancer outcomes remains unknown, but could be due to

contextual functions of IL-4 manifested with the blockade of TGFβ signaling in Th cells.

Nonetheless, the strong anti-tumor phenotype observed in our study challenges the view that

type 2 immunity is generally tumor-promoting, but is in agreement with the anti-tumor effects of

ectopically administered Th2 cells or overexpression of the Th2-promoting epithelial cell alarmin

TSLP40-42.

Enhanced activation and differentiation of TGFβRII-deficient CD4+ T cells in the tumor-

draining lymph nodes and their preferential stromal localization in the tumor tissue are

supported by our previous findings that TGFβ1 produced by CD4+ T cells, but not cancer cells,

suppresses the anti-tumor immune response18,43,44; yet, the nature of antigens and accessory

signals in promoting Th2 cell differentiation remain to be determined. The exact type 2 effector

mechanisms to preserve an ordered vasculature are also open for future investigation. In a rat

corneal neovascularization model, administration of IL-4 potently suppresses angiogenesis45.

Furthermore, “tumor-promoting inflammation”, considered as a cancer enabling characteristic46,

can be proangiogenic47-49; and the broad tumor immune cell exclusion phenotype observed in

ThPOKCreTgfbr2fl/flPyMT mice may as well contribute to vasculature organization consequent to

the inflammation-resolving activities of Th2 cells. Notably, a similar tumor immune cell

12 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

exclusion phenotype was observed in a subset of human breast cancer patients50, which is

associated with dominant CD4+ T cell responses and, importantly, predicts better patient

survival in line with our type 2 immune signature studies. Thus, the cancer environment-

targeted type 2 immune response could represent an evolutionarily conserved cancer defense

strategy. Manipulation of such a host-centric cancer immunity pathway may define a novel

cancer immunotherapy approach.

13 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

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Methods

Mice. CD8-/-, Ifng-/-, Il4-/- and YFP mice were purchased from the Jackson Laboratory. ThPOKCre

mice were provided by Dr. Ichiro Taniuchi1. CD8Cre, Foxp3Cre, Tgfrbr2fl/fl and MMTV-PyMT

(PyMT) mice were maintained in the laboratory as previously described2-4. All mice were

backcrossed to the C57BL/6 background, and maintained under specific pathogen-free

conditions. Animal experimentation was conducted in accordance with procedures approved by

the Institutional Animal Care and Use Committee of Memorial Sloan Kettering Cancer Center.

Tumor measurement. Starting from 13-week of age, mammary tumors in female PyMT mice

were measured weekly with a caliper. For macrophage or CD4+ T cell depletion, 100 μg anti-

Csf1r or 500 μg anti-CD4 (BioXcell) were intraperitoneally injected into tumor-bearing mice twice

a week. Tumor burden was calculated using the equation [(LxW2) x (π/6)], in which L and W

denote length and width. Total tumor burden was calculated by summing up individual tumor

volumes of each mouse with an end-point defined when total burden reached 3,000 mm3 or one

tumor reached 2,000 mm3, typically around 23-week of age. MC38 cells were a gift from Dr.

Jedd Wolchok5. 0.5 million MC38 cells were transplanted subcutaneously into Tgfbr2fl/fl and

ThPOKCreTgfbr2fl/fl mice followed or not by twice a week treatment with 100 μg anti-IFN-γ

(XMG1.2) or anti-IL-4 (11B11). Tumor growth was monitored. Researchers were blinded to

genotypes of mice during measurements.

Leukocyte isolation. Single-cell suspensions were prepared from lymph nodes by tissue

disruption with glass slides. The dissociated cells were passed through 70 μm filters and

pelleted. Tumor-infiltrating immune cells were isolated from mammary tumors as previously

described6. Briefly, tumor tissues were minced with a razor blade, and digested in 280 U/mL

Collagenase Type 3 (Worthington Biochemical) and 4 μg/mL DNase I (Sigma) in HBSS at 37°C

20 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

for 1 h and 15 min with periodic vortex every 20 min. Digested tissues were passed through 70

μm filters and pelleted. Cells were resuspended in 40% Percoll (Sigma) and layered above

60% Percoll. Sample was centrifuged at 1,900 g at 4°C for 30 min without brake. Cells at

interface were collected and used for flow cytometry analysis or sorting.

Flow cytometry. Fluorochrome-conjugated or biotinylated antibodies against mouse CD25

(PC61.5), CD4 (RM4-5), CD8 (53-6.7), CD44 (IM7), CD62L (MEL-14), Foxp3 (FJK-16s), IFN-γ

(XMG1.2), IL-4 (BVD6-24G2), NK1.1 (PK136), PD-1 (RMP1-130), TCRγδ (eBioGL3) and TCRβ

(H57-595) were purchased from eBioscience. Antibody against mouse CD45 (clone 30-F11)

was purchased from BD Biosciences. Antibody against granzyme B (GB11) was obtained from

Invitrogen. All antibodies were tested with their respective isotype controls. Cell-surface staining

was conducted by incubating cells with antibodies for 30 min on ice in the presence of 2.4G2

mAb to block FcγR binding. For Foxp3 and granzyme B staining, a transcription factor-staining

kit (Tonbo Biosciences) was used. To assess cytokine production, T cells were stimulated with

50 ng/mL phorbol 12-myristate 13-acetate (Sigma), 1 mM ionomycin (Sigma) in the presence of

Golgi-Stop (BD Biosciences) for 4 h at 37°C as previously described7. T cells were

subsequently stained for cell surface markers before intracellular cytokine staining. All data

were acquired using an LSRII flow cytometer (Becton Dickinson) and analyzed with FlowJo

software (Tree Star, Inc.).

Cell sorting, RNA extraction and sequencing. T cells were FACS sorted to Trizol LS

(Invitrogen) and snap frozen in liquid nitrogen. RNA was prepared with a miRNeasy Mini Kit

according to the manufacturer’s instructions (Qiagen), and subject to quality control by Agilent

BioAnalyzer. 0.7-1 ng total RNA with an integrity index from 8.3 to 9.9 was amplified using the

SMART-Seq v4 Ultra Low Input RNA Kit (Clontech), with 12 cycles of amplification. 2.7 ng of

21 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

amplified cDNA was used to prepare libraries with the KAPA Hyper Prep Kit (Kapa Biosystems

KK8504). Samples were barcoded and used for 50bp/50bp paired end runs with the TruSeq

SBS Kit v4 (Illumina) on a HiSeq 2500 sequencer. An average of 45 million paired reads were

generated per sample. The percentage of mRNA bases per sample ranged from 75% to 81%.

Transcriptome analysis of differentially expressed genes. The raw sequencing FASTQ files

were aligned against the mm10 assembly by STAR8. Gene level count values were computed

by the summarizeOverlaps function from the R package "GenomicAlignments" with mm10

KnownGene as the base gene model for mouse samples9,10. The Union counting mode was

used and only mapped paired-reads were considered. FPKM (Fragments Per Kilobase Million)

values were then computed from gene level counts by using fpkm function from the R package

DESeq211. Differentially expressed gene analysis was performed through the R DESeq2

package. Given the raw count data and gene model used, DESeq2 normalized the expression

raw count data by sample specific size factor and took specified covariates into account while

testing for genes found with significantly different expression between 2 sample groups. The

comparisons included ThPOKCreTgfbr2fl/flPyMT (KO) group vs. Tgfbr2fl/flPyMT (WT) group, KO

group vs. Il4-/-ThPOKCreTgfbr2fl/flPyMT (Il4-/-KO) group, Il4-/-KO group vs. Il4-/-Tgfbr2fl/flPyMT (Il4-/-)

group, and Il4-/-KO group vs. WT group. The “log2FoldChange” column is the log2 value of the

ratio between target sample group and the reference sample group. Differentially expressed

genes were included if passed the following filters: baseMean > 50; Log2Foldchange > 0.9 or <

-0.9 (depending on the desired direction) and P value < 0.11. For heatmap generation, the

mean expression FPKM value in each mouse sample group was calculated. The ratio between

the sample group specific mean expression FPKM value and the average of 4 sample group

mean expression FPKM values was used and plotted in the heatmap through the R heatmap

package.

22 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Gene signature building. The differentially expressed genes in the same regulation direction

from comparing ThPOKCreTgfbr2fl/flPyMT (KO) group vs. Tgfbr2fl/flPyMT (WT) group and KO

group vs. Il4-/-ThPOKCreTgfbr2fl/flPyMT (Il4-/-KO) group were subjected for human homologous

gene search. The corresponding human homologous gene of mouse gene was identified

through referencing the Mouse Genome Informatics (MGI, http://www.informatics.jax.org/)

database as well as the NCBI Orthologs database

(ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/gene_orthologs.gz). Any mouse gene with missing human

homolog was excluded from building the human gene signature. Any human gene found with

expression greater than 25 FPKM in any non-hematopoietic Cancer Cell Line sample from

Broad Institute (https://portals.broadinstitute.org/ccle) or with expression greater than 25 FPKM

in any follow cytometry-sorted human RCC-infiltrated myeloid and type 1 lymphocyte samples

were excluded from the signature building.

Gene signature applied to TCGA Pan cancer cohort. Single-Sample Gene Set Enrichment

Analysis (ssGSEA) was done by applying the gene signature against the FPKM expression

values of TCGA Pan cancer samples through the R package “GSVA” which estimates the

signature enrichment score for each of RNASeq sample12. The TCGA Pan cancer cohort data

were downloaded from the GDC (https://gdc.cancer.gov/about data/publications/pancanatlas)

including RNA-seq expression matrix as well as the clinical data resource file.

Survival analysis K-M plot. Survival analyses and curves were performed and generated

according to the Kaplan–Meier method using SAS v.9.4 (SAS Institute Inc., Cary, North

Carolina). The log-rank test was used to determine the statistical significance of the overall

survival (OS) distributions between signature high and signature low groups defined by the

median signature ssGSEA score in TCGA Pan cancer cohort.

23 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Immunofluorescence staining. Antibodies against mouse CD31 (MEC13.3), CD3 (17A2) and

GP38 (8.1.1) were purchased from Biolegend. Antibody against mouse CD45 (30-F11) was

from BD Pharmingen. Antibodies against mouse Col IV (Cat. #2150-1470), Fibrinogen (Cat.

#4440-8004) and NG2 (Cat. #AB5320) were obtained from Bio-rad. Antibody against mouse

Fibronectin (Cat. #AB2033) was from EMD. Antibody against mouse Cleaved caspase 3 (Cat.

#9661S) was purchased from CST. Antibodies against mouse E-Cadherin (DECMA-1) and Ki67

(SolA15) were obtained from eBioscience. Antibodies against human E-Cadherin (Cat ab40772)

and CD31 (Cat #ab76533) were purchased from Abcam. Hypoxia detection kit was purchased

from Hypoxyprobe. Mouse tumor tissues with comparable size from control and experimental

groups were frozen in O.C.T. medium (Sakura Finetek USA) and sectioned at the thickness of

10 μm, before they were fixed and stained with antibodies or probes. Human clear cell and

chromophobe renal cell carcinoma paraffin sections with 5 μm thickness were dewaxed,

rehydrated, blocked and stained with antibodies. Slide sections were mounted with

VECTORSHIELD anti-fade mounting media (Vector Laboratories) and scanned by Pannoramic

Digital Slide Scanners (3DHISTECH LTD). Immunofluorescence images were analyzed with

CaseViewer and Fiji software, and further processed with Adobe Photoshop and Illustrator

software.

T cell activation and adoptive T cell transfer. CD4+CD25- T cells isolated from Tgfbr2fl/fl,

ThPOKCreTgfbr2fl/fl, Il4-/-Tgfbr2fl/fl and Il4-/-ThPOKCreTgfbr2fl/fl mice were activated with plate-bound

anti-CD3 (5 μg/mL) and soluble anti-CD28 (1 μg/mL) for 24 h. One million T cells were

intravenously transferred to tumor-bearing PyMT mice on a weekly basis.

Sulfo-NHS-biotin labeling. Deeply anesthetized mice were perfused with 10 mL Sulfo-NHS- LC-Biotin (Thermo Scientific; 21335) dissolved in PBS at 0.3 mg/mL, followed by 10 mL 2% PFA in PBS13.

24 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Statistical analysis. Related to Fig. 5 and Supplementary Table 1, differentially expressed

genes were compared between tumor-infiltrating CD4+CD25- T cells from Tgfbr2fl/flPyMT,

ThPOKCreTgfbr2fl/flPyMT, Il4-/-Tgfbr2fl/flPyMT and Il4-/-ThPOKCreTgfbr2fl/flPyMT mice. A gene list

was generated if passing the filters mentioned above. All statistical measurements are

displayed as mean ± SEM. For comparisons, unpaired student t test, two-tailed was conducted

using GraphPad Prism software; for paired distance comparisons, paired t-test was conducted

using GraphPad Prism software. For tumor growth, 2-way ANOVA was performed using

GraphPad Prism software.

25 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Acknowledgements

We thank members of the M.O.L. laboratory for helpful discussions. This work was supported

by a Howard Hughes Medical Institute Faculty Scholar Award (M.O.L.), an award from Mr.

William H. and Mrs. Alice Goodwin and the Commonwealth Foundation for Cancer Research

and the Center for Experimental Therapeutics at Memorial Sloan Kettering Cancer Center

(M.O.L.) and a Cancer Center Support Grant (P30 CA08748). B.G.N. and M.H.D. are recipients

of F31 CA210332 and F30 AI29273-03 awards from National Institutes of Health. C.C. and S.L.

are Cancer Research Institute Irvington Fellows supported by the Cancer Research Institute.

E.G.S. is a recipient of a Fellowship from the Alan and Sandra Gerry Metastasis and Tumor

Ecosystems Center of MSKCC. MSKCC has filed a patent application with the U.S. Patent and

Trademark Office directed toward methods and compositions of targeting CD4+ T helper cell

TGFβ signaling for caner immunotherapy.

Author Contributions

M.L. and M.O.L. were involved in all aspects of this study, including planning and performing

experiments, analysis and interpretation of data and writing the manuscript. F.K. and T.A.C.

processed and analyzed all sequencing data and wrote the manuscript. I.T. provided the key

mouse line. Y.C., J.J.H. and A.A.H. provided human RCC specimen. K.J.C., D.K., B.G.N.,

W.S., C.C., M.H.D., E.G.S. and S.L. assisted with mouse colony management and performed

experiments.

26 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Materials and Methods References

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Figure Legends

Figure 1 Blockade of TGFβ signaling in CD4+ T cells suppresses tumor development

independent of CD8+ T cells

a, Transforming growth factor-β receptor II (TGFβRII) expression on CD4+ T cells and CD8+ T

cells from the tumor-draining lymph nodes of Tgfbr2fl/flPyMT and CD8CreTgfbr2fl/flPyMT mice. b,

Representative flow cytometry plots and statistical analyses of programmed cell death

protein 1 (PD-1) and granzyme B (GzmB) expression in tumor-infiltrating CD8+ T cells from

Tgfbr2fl/flPyMT and CD8CreTgfbr2fl/flPyMT mice. c, Tumor measurements of Tgfbr2fl/flPyMT (n=8)

and CD8CreTgfbr2fl/flPyMT (n=7) mice. d, TGFβRII expression on CD4+ T cells and CD8+ T cells

from the tumor-draining lymph nodes of Tgfbr2fl/flPyMT and ThPOKCreTgfbr2fl/flPyMT mice. e,

Representative flow cytometry plots and statistical analyses of PD-1 and GzmB expression in

tumor-infiltrating CD8+ T cells from Tgfbr2fl/flPyMT and ThPOKCreTgfbr2fl/flPyMT mice. f,

Tumor measurements of Tgfbr2fl/flPyMT (n=7), ThPOKCreTgfbr2fl/flPyMT (n=5), CD8-/-

Tgfbr2fl/flPyMT (n=3), CD8-/-ThPOKCreTgfbr2fl/flPyMT (n=5) and Foxp3CreTgfbr2fl/flPyMT ( n = 3 )

mice. All data are shown as mean ± SEM. *: P<0.05; **: P<0.01; ***: P<0.001; ****: P<0.0001;

and ns: not significant.

Figure 2 Blockade of TGFβ signaling in CD4+ T cells triggers cancer cell death and

stroma localization of CD4+ T cells

a, Representative immunofluorescence images and statistical analyses of E-Cadherin (green),

Ki67 (red) and cleaved Caspase 3 (CC3, cyan) expression in mammary tumor tissues from 8-

and 23-week-old Tgfbr2fl/flPyMT (wild-type, WT) and ThPOKCreTgfbr2fl/flPyMT (knockout, KO)

mice. The percentage of Ki67+E-Cadherin+ cells over total E-Cadherin+ epithelial cells was

calculated from multiple 0.02 mm2 regions (n=10 and 7 for WT and KO tumor tissues,

respectively). The percentage of CC3+ areas over total E-Cadherin+ areas was calculated from bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

multiple 0.02 mm2 regions (n=10 for WT and KO tumor tissues). b, Representative

immunofluorescence images and statistical analyses of E-Cadherin (green), CD4 (red), CD3

(white) and CC3 (cyan) expression in mammary tumor tissues from 8- and 23-week-old WT

and KO mice. Intratumoral (white arrows) and stromal (yellow arrows) CD4+ T cells were

counted from multiple 0.1 mm2 regions (n=8 for WT and KO tumor tissues). All data are shown

as mean ± SEM. **: P<0.01; ***: P<0.001; and ns: not significant.

Figure 3 Blockade of TGFβ signaling in CD4+ T cells promotes tumor tissue healing,

vessel organization, hypoxia and cancer cell death

a, Representative immunofluorescence images of fibrinogen (Fg, white), CD31 (red), cleaved

Caspase 3 (CC3, cyan) and E-Cadherin (green) in comparable individual tumors with sizes

around 8x8 mm in length and width from 23-week-old Tgfbr2fl/flPyMT (wild-type, WT) and

ThPOKCreTgfbr2fl/flPyMT (knockout, KO) mice. Extravascular (EV) Fg deposition events

(magenta arrows) were calculated from multiple 1 mm2 regions (n=9 for WT and KO tumor

tissues). Isolated CD31+ staining (yellow arrows) was counted from multiple 1 mm2 regions

(n=6 for WT and KO tumor tissues). b, Representative immunofluorescence images of NG2

(white), CD31 (red), GP38 (cyan) and E-Cadherin (green) in comparable individual tumors with

sizes around 8x8 mm in length and width from 23-week-old WT and KO mice. NG2-unbound

(magenta arrows) or GP38-unbound (yellow arrows) isolated CD31+ staining was counted from

multiple 1 mm2 regions (n=9 for WT and KO tumor tissues). c, Representative

immunofluorescence images of collagen IV (Col IV, white), CD31 (red), fibronectin (FN, cyan)

and E-Cadherin (green) in comparable individual tumors with sizes around 8x8 mm in length

and width from WT and KO mice. The average continuous lengths of Col IV and FN were

measured in multiple 1 mm2 regions (n=9 for WT and KO tumor tissues). d, Representative

immunofluorescence images of Hypoxic probe (HPP, white), CD31 (red), CC3 (cyan) and E-

30 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Cadherin (green) in comparable individual tumors with sizes around 8x8 mm in length and

width from WT and KO mice. The percentage of HPP+E-Cadherin+ regions over E-Cadherin+

epithelial regions was calculated from multiple 1 mm2 regions (n=9 for WT and KO tumor

tissues). The shortest distance of HPP+ regions (magenta dashed lines) or CC3+ regions

(yellow dashed lines) to CD31+ endothelial cells was measured in tumor tissues from KO mice

(n=9). The dashed boxes coupled with dashed lines show high magnification of selected tissue

regions. All data are shown as mean ± SEM. **: P<0.01; ****: P<0.0001.

Figure 4 Anti-tumor immunity triggered by TGFβ signaling blockade in CD4+ T cells is

dependent on IL-4, but not IFN-γ

a, Representative flow cytometry plots and statistical analyses of IL-4 and IFN-γ expression in

CD4+Foxp3- T cells from the tumor-draining lymph nodes of 23-week-old Tgfbr2fl/flPyMT (wild-

type, WT) and ThPOKCreTgfbr2fl/flPyMT (knockout, KO) mice. b, Tumor measurements of Ifng-/-

Tgfbr2fl/flPyMT (Ifng-/-, n=3), Ifng-/-ThPOKCreTgfbr2fl/flPyMT (Ifng-/-KO, n=6), Il4-/-Tgfbr2fl/flPyMT

(Il4-/-, n=4) and Il4-/-ThPOKCreTgfbr2fl/flPyMT (Il4-/-KO, n=4) mice. c, 16 to 17-week-old PyMT

mice bearing 5x5 mm tumors were transferred with CD4+CD25- T cells from Tgfbr2fl/fl (WT,

n=4), ThPOKCreTgfbr2fl/fl (KO, n=4), Il4-/-Tgfbr2fl/fl (Il4-/-, n=3) and Il4-/-ThPOKCreTgfbr2fl/fl (Il4-/-KO,

n=3) mice on a weekly basis for 6 weeks. Tumor burden was measured and plotted. All data

are shown as mean ± SEM. *: P<0.05; **: P<0.01; ***: P<0.001.

Figure 5 A TGFβ-regulated IL-4-dependent gene expression signature stratifies cancer

patients for survival probability

a, The transcriptome of tumor-infiltrating CD4+CD25- T cells from 23-week-old Tgfbr2fl/flPyMT

(wild-type, WT), ThPOKCreTgfbr2fl/flPyMT (knockout, KO), Il4-/-Tgfbr2fl/flPyMT (Il4-/-) and Il4-/-

ThPOKCreTgfbr2fl/flPyMT (Il4-/-KO) mice were assessed by RNA sequencing. The mean 31 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

expression FPKM value of differentially expressed genes (DEGs) that were upregulated in KO

compared to WT or Il4-/-KO samples was calculated and plotted. The ratio between mean

FPKM value of one group and average of mean FPKM values of all 4 groups was further

calculated. Representative DEGs were grouped based on the localization and function of their

encoded proteins. b, A Th2 gene expression signature was generated through comparing KO

vs. WT and KO vs. Il4-/-KO in the upregulated direction after additional filtering. c, The Th2

gene expression signature was used to perform survival analysis in TCGA Pan cancer cohort

data and survival curves were plotted for the signature high group (top 50%) and low group

(bottom 50%). The corresponding censored patient numbers are included in major time points.

32 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Extended Data Figure Legends

Extended Data Figure 1 Blockade of TGFβ signaling in CD8+ T cells promotes

effector/memory differentiation

Representative flow cytometry plots of CD62L and CD44 expression and statistical analyses of

the gated populations among CD4+Foxp3- T cells (top panel), CD4+Foxp3+ T cells (middle

panel) and CD8+ T cells (bottom panel) from the tumor-draining lymph nodes of Tgfbr2fl/flPyMT

and CD8CreTgfbr2fl/flPyMT mice. All statistical data are shown as mean ± SEM. **: P<0.01; ****:

P<0.0001; and ns: not significant.

Extended Data Figure 2 Blockade of TGFβ signaling in CD4+ T cells promotes

effector/memory differentiation and accounts for reduced tumor growth in

ThPOKCreTgfbr2fl/flPyMT mice

a, A breeding scheme of ThPOKCre mice with YFP mice and representative flow cytometry plots of YFP

expression in lymph node TCRβ+NK1.1-CD4+, TCRβ+NK1.1-CD8+, TCRβ-NK1.1+ and TCRγδ+ T cells

isolated from ThPOKCreYFP mice. b, Representative flow cytometry plots of CD62L and CD44

expression and statistical analyses of the gated populations among CD4+Foxp3- T cells (top

panel), CD4+Foxp3+ T cells (middle panel) and CD8+ T cells (bottom panel) from the tumor-

draining lymph nodes of Tgfbr2fl/flPyMT (wild-type, WT) and ThPOKCreTgfbr2fl/flPyMT (knockout,

KO) mice. c, Representative flow cytometry plots of CD4 and CD8 expression in T cells isolated from

control mice or mice treated with anti-CD4 (αCD4). d, Tumor measurements of WT (n=3) and KO

(n=3) mice treated with αCD4. All data are shown as mean ± SEM. ***: P<0.001; ****:

P<0.0001; and ns: not significant.

Extended Data Figure 3 CD8 deficiency does not affect cancer cell death triggered by

33 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

blockade of TGFβ signaling in CD4+ T cells

a, Representative immunofluorescence images and statistical analyses of E-Cadherin (green),

Ki67 (red) and cleaved Caspase 3 (CC3, cyan) e xp re s s i o n in mammary tumor tissues from

23-week-old Tgfbr2fl/flPyMT and CD8CreTgfbr2fl/flPyMT mice. The percentage of Ki67+E-Cadherin+

cells over total E-Cadherin+ epithelial cells was calculated from multiple 0.02 mm2 regions (n=9).

The percentage of CC3+ areas over total E-Cadherin+ areas was calculated from multiple 0.02

mm2 regions (n=9). b, Representative immunofluorescence images and statistical analyses of

E-Cadherin (green), Ki67 (red) and cleaved Caspase 3 (CC3, cyan) e xp re s s i o n in

mammary tumor tissues from 23-week-old CD8-/-Tgfbr2fl/flPyMT (CD8-/-) and CD8-/-

ThPOKCreTgfbr2fl/flPyMT (CD8-/- knockout, CD8-/-KO) mice. The percentage of Ki67+E-Cadherin+

cells over total E-Cadherin+ epithelial cells was calculated from multiple 0.02 mm2 regions (n=9).

The percentage of CC3+ areas over total E-Cadherin+ areas was calculated from multiple 0.02

mm2 regions (n=9). All data are shown as mean ± SEM. ****: P<0.0001; and ns: not

significant.

Extended Data Figure 4 Blockade of TGFβ signaling in CD4+ T cells causes leukocyte

exclusion from the tumor parenchyma

Representative immunofluorescence images and statistical analyses of E-Cadherin (green),

CD45 (red) and cleaved Caspase 3 (CC3, cyan) expression in mammary tumor tissues from

23-week-old Tgfbr2fl/flPyMT (wild-type, WT) and ThPOKCreTgfbr2fl/flPyMT (knockout, KO) mice.

Intratumoral (white arrows) and stromal (yellow arrows) CD45+ leukocytes were counted from

multiple 0.1 mm2 regions (n=8 for WT and KO tumor tissues). All data are shown as mean ±

SEM. ***: P<0.001.

34 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Extended Data Figure 5 Blockade of TGFβ signaling in CD4+ T cells inhibits

vasculature leakage, but does not affect vessel density

a, Representative images of Sulfo-NHS-biotin (white), CD31 (red) and E-Cadherin (green)

staining and quantification of Sulfo-NHS-biotin density and cancer cell-associated Sulfo-NHS-

biotin+ events (magenta arrows) in mammary tumor tissues from 23-week-old Tgfbr2fl/flPyMT

(wild-type, WT) and ThPOKCreTgfbr2fl/flPyMT (knockout, KO) mice. The percentage of Sulfo-

NHS-biotin+ areas (n=15) and cancer-cell associated deposition events (n=9) were calculated

from multiple 0.8 mm2 regions. b, Quantification of CD31+ areas in multiple 1 mm2 regions of

mammary tumor tissues from 23-week-old WT and KO mice (n=6). All data are shown as mean

± SEM. ****: P<0.001; and ns: not significant.

Extended Data Figure 6 Blockade of TGFβ signaling in CD4+ T cells promotes expansion

of tumor-infiltrating CD4+Foxp3- T cells

Representative flow cytometry plots of TCRβ, NK1.1, CD4, CD8 and Foxp3 expression and

statistical analyses of the gated populations in tumor-infiltrating leukocytes from 23-week-old

Tgfbr2fl/flPyMT (WT) and ThPOKCreTgfbr2fl/flPyMT (KO) mice. All data are shown as mean ±

SEM. **: P<0.01; ***: P<0.001; and ns: not significant.

Extended Data Figure 7 IFN-γ deficiency does not impair cancer immunity triggered by

blockade of TGFβ signaling in CD4+ T cells

a, Representative flow cytometry plots of CD62L and CD44 expression and statistical

analyses of the gated populations among CD4+Foxp3- T cells (top panel) and CD4+Foxp3+

T cells (bottom panel) from Ifng-/-Tgfbr2fl/flPyMT (Ifng-/-) and Ifng-/-ThPOKCreTgfbr2fl/flPyMT (Ifng-/-

knockout, Ifng-/-KO) mice. b, Representative flow cytometry plots of TCRβ, NK1.1, CD4,

CD8 and Foxp3 expression and statistical analyses of the gated populations in tumor- 35 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

infiltrating leukocytes from 23-week-old Ifng-/- and Ifng-/-KO mice. c, Representative

immunofluorescence images of fibrinogen (Fg, white), CD31 (red), cleaved Caspase 3 (CC3,

cyan) and E-Cadherin (green) in comparable individual tumors with sizes around 8x8 mm in

length and width from 23-week-old Ifng-/- and Ifng-/-KO mice. Extravascular (EV) Fg deposition

events (magenta arrows) were calculated from multiple 1 mm2 regions (n=9 for Ifng-/- and Ifng-

/-KO tumor tissues). Isolated CD31+ staining (yellow arrows) was counted from multiple 1 mm2

regions (n=9 for Ifng-/- and Ifng-/-KO tumor tissues). d, Representative immunofluorescence

images of Hypoxic probe (HPP, white), CD31 (red), cleaved Caspase 3 (CC3, cyan) and E-

Cadherin (green) in comparable individual tumors with sizes around 8x8 mm in length and

width from Ifng-/- and Ifng-/-KO mice. The percentage of HPP+E-Cadherin+ regions over E-

Cadherin+ epithelial regions was calculated from multiple 1 mm2 regions (n=9 for Ifng-/- and

Ifng-/-KO tumor tissues). The shortest distance of HPP+ regions (magenta dashed lines) or

CC3+ regions (yellow dashed lines) to CD31+ endothelial cells was measured in tumor tissues

from Ifng-/-KO mice (n=9). All data are shown as mean ± SEM. *: P<0.05; **: P<0.01; ****:

P<0.0001; and ns: not significant.

Extended Data Figure 8 IL-4 deficiency impairs cancer immunity triggered by blockade of

TGFβ signaling in CD4+ T cells

a, Representative flow cytometry plots of CD62L and CD44 expression and statistical analyses

of the gated populations among CD4+Foxp3- T cells (top panel) and CD4+Foxp3+ T cells

(bottom panel) from Il4-/-Tgfbr2fl/flPyMT (Il4-/-) and Il4-/-ThPOKCreTgfbr2fl/flPyMT (Il4-/- knockout,

Il4-/-KO) mice. b, Representative flow cytometry plots of TCRβ, NK1.1, CD4, CD8 and Foxp3

expression and statistical analyses of the gated populations in tumor-infiltrating leukocytes

from 23-week-old Il4-/- and Il4-/-KO mice. c, Representative immunofluorescence images of

fibrinogen (Fg, white), CD31 (red), cleaved Caspase 3 (CC3, cyan) and E-Cadherin (green) in 36 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

comparable individual tumors with sizes around 8x8 mm in length and width from 23-week-old

Il4-/- and Il4-/-KO mice. Extravascular (EV) Fg deposition events (magenta arrows) were

calculated from multiple 1 mm2 regions (n=9 for Il4-/- and Il4-/-KO tumor tissues). Isolated CD31+

staining (yellow arrows) was counted from multiple 1 mm2 regions (n=9 for Il4-/- and Il4-/-KO

tumor tissues). d, Representative immunofluorescence images of Hypoxic probe (HPP, white),

CD31 (red), cleaved Caspase 3 (CC3, cyan) and E-Cadherin (green) in comparable individual

tumors with sizes around 8x8 mm in length and width from Il4-/- and Il4-/-KO mice. The

percentage of HPP+E-Cadherin+ regions over E-Cadherin+ epithelial regions was calculated

from multiple 1 mm2 regions (n=9 for Il4-/- and Il4-/-KO tumor tissues). All data are shown as

mean ± SEM. *: P<0.05; **: P<0.01; ***: P<0.001; ****: P<0.0001; and ns: not significant.

Extended Data Figure 9 Anti-tumor immunity triggered by TGFβ signaling blockade in

CD4+ T cells is dependent on IL-4, but not tumor-associated macrophages

a, 16-week-old PyMT mice bearing 5x5 mm tumors were transferred with the activated

CD4+CD25- T cells from Tgfbr2fl/fl (WT), ThPOKCreTgfbr2fl/fl (KO), Il4-/-Tgfbr2fl/fl (Il4-/-) and Il4-/-

ThPOKCreTgfbr2fl/fl (Il4-/-KO) mice on a weekly basis for 6 weeks. Representative

immunofluorescence images and statistical analyses of Ki67 (red) and cleaved Caspase 3 (CC3,

cyan) expression. The percentage of Ki67+E-Cadherin+ cells over total E-Cadherin+ epithelial

cells was calculated from multiple 0.02 mm2 regions (n=9). The percentage of CC3+ areas over

total E-Cadherin+ areas was calculated from multiple 0.02 mm2 regions (n=9). b, Representative

flow cytometry plots of F4/80+Ly6c- tumor-associated macrophage populations from control mice or mice

treated with anti-Csf1r (αCsf1r). c, Tumor measurements of WT PyMT (n=3) and KO PyMT (n=3)

mice treated with αCsf1r. d, Tumor measurements of WT (n=5), KO (n=4), αIL-4-treated WT

(n=5), αIL-4-treated KO (n=4), αIFN-γ-treated WT (n=3), and αIFN-γ-treated KO mice (n=3)

37 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

inoculated with MC38 tumor cells. All data are shown as mean ± SEM. *: P<0.05; **: P<0.01;

and ns: not significant.

Extended Data Figure 10: Distribution of a curated Th2 gene expression signature across

TCGA Pan cancer patients and its association with the survival probability of selected

patient groups and their vasculature status

a, The human Th2 gene signature enrichment score was estimated and plotted for each

RNASeq sample of TCGA Pan cancer patients. b, A Th2 gene signature was used to perform

survival analysis in TCGA non-triple negative (non-TN) and triple negative (TN) breast invasive

carcinoma (BRCA), brain lower grade glioma (LGG) and glioblastoma multiforme (GBM), kidney

chromophobe (KICH) and kidney renal clear cell carcinoma (KIRC) patients. The survival

curves were plotted for the signature high group (top 50%) and low group (bottom 50%). The

corresponding censored patient numbers are included in major time points. c, Representative

immunofluorescence images and statistical analyses of E-Cadherin (green) and CD31 (red)

expression in KICH (4 patients) and KIRC (8 patients) tumor tissues. Isolated CD31+ staining

was counted from multiple 0.2 mm2 regions (n=9). The stromal regions are marked by dotted

lines in KICH samples. All data are shown as mean ± SEM. ****: P<0.0001; and ns: not

significant.

Supplementary Table 1: Differentially expressed genes in tumor-infiltrating CD4+CD25- T

cells from Tgfbr2fl/flPyMT (wild-type, WT), ThPOKCreTgfbr2fl/flPyMT (knockout, KO), Il4-/-

Tgfbr2fl/flPyMT (Il4-/-) and Il4-/-ThPOKCreTgfbr2fl/flPyMT (Il4-/-KO) mice.

38

bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. a d CD4+ T cells CD8+ T cells CD4+ T cells CD8+ T cells % of Max % of Max

TGF RII TGF RII

T g f b r 2 f l/f lPyMT Isotype control T g f b r 2 f l/f lPyMT Isotype control CD8CreT g f b r 2 f l/f lPyMT ThPOKCreT g f b r 2 f l/f lPyMT b T g f b r 2 f l/f lPyMT CD8CreT g f b r 2 f l/f lPyMT e T g f b r 2 f l/f lPyMT ThPOKCreT g f b r 2 f l/f lPyMT 55.3 6.8 38.9 24.1 62.1 3.3 9.4 10.2 PD-1 PD-1

22.4 15.5 29.1 7.9 21.8 12.7 47.6 32.9 Gzm B Gzm B

f l/f l 80 T g f b r 2 f l/f lPyMT 100 T g f b r 2 PyMT * * Cre f l/f l CD8CreT g f b r 2 f l/f lPyMT ThPOK T g f b r 2 PyMT **** 80 60 *** ** cells ns cells T T + + 60 40 ns 40 ns 20 20 % of CD8 % of CD8

0 0 - + + - - + + -

- - Gz mB - Gz mB + Gz mB + Gz mB Gz mB - Gz mB + Gz mB + Gz mB

PD-1 PD-1 PD-1 PD-1 PD-1 PD-1 PD-1 PD-1 c f 3000 T g f b r 2 f l/f lPyMT 3000 T g f b r 2 f l/f lPyMT ) ) 3 3 2500 CD8CreT g f b r 2 f l/f lPyMT 2500 ThPOKCreT g f b r 2 f l/f lPyMT C D 8 -/-T g f b r 2 f l/f lPyMT 2000 2000 C D 8 -/-ThPOKCreT g f b r 2 f l/f lPyMT den (mm 1500 den (mm 1500 F ox p3CreT g f b r 2 f l/f lPyMT bu r bu r r 1000 r 1000 **

500 500 ** Tumo Tumo ** 0 0 13 14 15 16 17 18 19 20 21 22 23 13 14 15 16 17 18 19 20 21 22 23 Age (w eek) Age (w eek)

Fi g . 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. a WT KO ns 60 ns WT

40 100 μm (%) + 8-w eek old Ki67 20 KO 0 8-w eek 23-w eek 100 μm

WT KO 100 ***

WT 80 60 40 100 μm 20 area (%)

+ 5 4 23-w eek old ns

CC3 3 2 KO 1 0 8-w eek 23-w eek 100 μm E-Cadherin Ki67 CC3 DAPI b WT KO 20 *** WT ell s c

+ 15

50 μm CD4 10 al

8-w eek old ns 5 KO Intrat umor 0 8-w eek 23-w eek 50 μm

WT KO 160 ** WT ell s

c 140 + 80 *** 50 μm

CD4 50

23-w eek old 20 9 6 St romal

KO 3 0 8-w eek 23-w eek 50 μm E-Cadherin CD4 CD3 CC3 DAPI

Fi g . 2 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

a 250 c 400 **** **** m) 200 300 150 event s

200 μm + 200 μm 200 g WT 100 WT connectivity ( μ +

EV F 100 50

0 Col IV 0

20 μm WT KO 20 μm WT KO 150 400 ** **** m) 300

staining 100 +

200 μm 200 μm 200 KO KO 50

connectivity ( μ 100 + FN Isolated CD31 0 0

20 μm WT KO 20 μm WT KO F g CD31 CC3 E-Cadherin DAPI Col IV CD31 FN E-Cadherin DAPI b 25 d 60 **** **** 20

staining 40 + 15 200 μm 100200 μ mμm area (% ) + WT WT 10 20 NG2-unbound 5 HPP

isolated CD31 0 0

20 μm WT KO 50 μm WT KO 100 140

**** m) μ

( **** 80 +

staining 110 + 60 200 μm 200 μm KO KO 40 80

GP38-unbound 20 Distance to CD31 isolated CD31 50 0 HPP CC3 20 μm WT KO 50 μm NG2 CD31 GP38 E-Cadherin DAPI HPP CD31 CC3 E-Cadherin DAPI Fi g . 3 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. a 1.9 0.3 20 WT KO

*** - 15

*** F ox p3 96.3 1.5 + 10 12.9 1.7 % of CD4 5

0

IF N- 79.7 5.7 IF N- IL-4 IL-4 b 3000 If n g -/-

-/- 2500 If n g KO ) 3 Il4-/- 2000 Il4-/-KO

1500

1000 ** umor burden (mm T 500 * * ** 0 14 15 16 17 18 19 20 21 22 23 Age (w eek) c 3000 WT T cells )

3 2500 KO T cells Il4-/- T cells 2000 Il4-/-KO T cells

1500

1000

umor burden (mm *** T ** 500 * 0 0 1 2 3 4 5 6 Treatment time (w eek)

Fi g . 4 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. a Cell surface proteins Signaling molecules Nuclear factors Metabolic enzymes Secreted molecules Adora2a Adcy1 AA467197 Abhd17c 4932438H23Rik Ctla4 Ccdc184 Batf Acod1 Areg D17H6S56E−5 Ddit4 Bcl3 Adam8 Ccl1 Ffar4 Dnajc12 Bhlhe40 Agpat4 Ccl3 Fpr1 Dusp14 Cited1 Ass1 Ccl4 Gpr18 Dusp28 Crabp1 Bace2 Crispld2 Icam1 Fam110a Ctdnep1 Bcat1 Csf1 Icosl Fam25c Dnlz Ckb Csf2 Il1r2 Fkbp5 Emd Dgat2 Ebi3 Il3ra Gadd45b Epas1 Dpagt1 Enho Kcnh1 Gadd45g Epop Espl1 Fgb Lag3 Gcn1 Esrra Fasn Fgg Lpar3 Gipc2 Foxa1 Furin Hilpda Ltb4r1 Gpc1 H2afx Gpld1 Hp Ly6a Ica1l Hexim2 Gsr Il10 Ly6c1 Itpk1 Hist3h2ba Hdc Il13 Ly6d Jak3 Irf8 Hk2 Il4 Ly6g6c Krt19 Med11 Ldha Il5 Mfsd2a Krt7 Mif4gd Mboat7 Lgals7 Orai1 Map2k3 Mxd1 Mvk Metrnl Pdcd1lg2 Map3k10 Nab2 Nos2 Ngf Pqlc1 Map3k11 Nfil3 Odc1 R3hdml Pqlc2 Mapkapk2 Pparg Pgk1 Retnlg Ramp3 Nfkbia Zbtb46 Pgm2 S100a8 Sdc1 Nfkbid Zc3h12a Pkm S100a9 Sema7a Nfkbie Zfp219 Plscr1 Saa1 Slc16a3 Nfkbiz Zfp628 Prss22 Saa2 Slc26a10 Nrgn Zscan10 Qsox1 Saa3 Slc2a1 Pik3c2b Zswim4 Rpusd3 Serpina3f WT KO Il4 Slc2a8 Prr7 Il4 St3gal3 Serpinb6b

Slc37a4 Reep6 -/- -/- Tnfaip8l1 Serpinb9b Slc39a1 Rrad KO Tpi1 Stc2 Slc7a5 Sdf4 Upp1 Wfdc2 Sorcs2 Sgtb Vars Wnt11 WT KO Il4 Il4 WT KO Il4 Syngr3 Sh3bp2 Il4 -/- -/- Tmem158 Socs3 -/- -/- KO Tnfrsf18 Spata2 KO Tnfrsf4 Stk32c Tnfsf9 Sv2c Tpbg Tacstd2 Tspan5 Tpm2 Upk3a Zap70 0 1 2 3 4 WT KO Il4 Il4 WT KO Il4 Il4 -/- -/- -/- -/- KO KO

b c 1.0 ACOD1 DUSP28 IL13 SPATA2 + Censored Logrank P<0.0001 ADORA2A EPOP IL3RA SV2C 0.8 AGPAT4 FFAR4 IL4 SYNGR3 0.6 Top 50% C21orf62 GCN1 IL5 ZNF219 0.4

CCDC184 GPLD1 KCNH1 ZNF628 0.2 Bottom 50%

CCL3L3 ICA1L R3HDML ZSCAN10 Survival probability 0.0 4996 313 34 3 0 CITED1 ICOSL SLC26A10 ZSWIM4 5006 233 19 3 0 0 100 200 300 400 Time (month)

Fig. 5 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

T g f b r 2 f l/f lPyMT CD8CreT g f b r 2 f l/f lPyMT - ns 100 f l/f l 78.7 9.4 82.5 7.7 p3 90 T g f b r 2 PyMT CD8CreT g f b r 2 f l/f lPyMT F ox 80 + 70 20 ns

CD4 ns

f ns 10

% o % 0 2.7 9.2 3.5 6.3 + ns

p3 60 ns 49.2 16.1 54.3 11.7 F ox + 40 ns

20 ns

of CD4 0 %

100 2.9 31.8 6.0 28.0 ****

+ 80 Lorem ipsum dolor sit amet, 73.7 19.3 55.1 29.3 consectetuer adipiscing elit, sed 60 diam nonummy nibh euismod **** 40 tincidunt ut laoreet dolore magna ** % of CD8 aliq uam erat volutpat. Ut w isi 20 ns enim ad minim veniam, q uis 0 nostrud ex erci tation ullamcorper lo hi hi lo

CD62L 2.4 4.7 3.3 12.3

hi CD44 hi CD44 lo CD44 lo CD44 CD44

CD62L CD62L CD62L CD62L

SF1 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. a ThPOKCreYF P STOP YF P Rosa26 ThPOKCre

82.0% TCRNK1.1-CD4+ Loxp 4.0% TCRNK1.1-CD8+ ThPOKCreYF P % of Max 1.0% TCRNK1.1+ 0.8% TCR+ YF P b WT KO 100 ****

85.0 6.5 58.9 20.7 - WT KO 80 F ox p3

+ 60 **** **** 40 ns 20 1.1 7.4 2.1 18.3 % of CD4 0 46.3 18.4 31.7 16.6 + 80 *** 60 ns F ox p3 + ns 40 **** 20

2.9 32.4 22.0 29.7 % of CD4 0 72.5 22.3 69.8 25.5 100 ns

+ 80 60 ns 40

% of CD8 20 *** ns 0 lo hi hi lo CD62L 1.7 3.5 2.3 2.4

CD44 hi CD44 hi CD44 lo CD44 lo CD44

CD62L CD62L CD62L CD62L

c Control CD4 d ) 3 3000 W T + CD4 19.6 0.1 94.0 0.0 2500 KO + CD4 2000 1500 CD8 1000 500 umor burden (mm 10.7 69.6 6.0 0.0 T 0 14 15 16 17 18 19 20 21 22 23 CD4 Age (w eek)

SF2 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. a T g f b r 2 f l/f lPyMT CD8CreT g f b r 2 f l/f lPyMT 80 PyMT

f l/f l ns 60 T g f b r 2

100 μm

% % 40

20 Cre PyMT ns f l/f l

CD8 0 + + T g f b r 2

100 μm Ki67 CC3 E-Cadherin Ki67 CC3 DAPI b C D 8 -/- C D 8 -/-KO 60 ns -/-

C D 8 **** 40 100 μm % %

20 KO -/-

C D 8 0 + +

100 μm Ki67 CC3 E-Cadherin Ki67 CC3 DAPI

SF3 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

WT KO

300 *** 230 WT 160 *** ell s 90 c + 20 100 μm 10 CD45 5

KO 0

Stromal 100 μm Intratumoral E-Cadherin CD45 CC3 DAPI

SF4 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. a WT KO

Sulf o-NHS-biotin CD31 E-Cadherin DAPI

60 40 **** ns events 30 + f 40 in o -bio tin

ty 20 -bio t

o-NH S 20 Den si

ul f 10 S % % o-NH S ul f Cancer cell-associated

0 S 0

WT KO WT KO b 25 ns

20

15 area (% ) + 10

CD31 5

0

W T KO

SF5 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

WT KO

8.9 8.6 13.3 13.9 NK 1.1

TCR

65.9 1.2 42.5 1.7 CD8

7.7 25.2 5.6 50.2 CD4

4.6 2.5 F ox p3

TCR

ns 20 WT KO

15 ***

cell s ** +

10

5 % of CD45 ns

0 - + - +

+ NK 1.1 CD8 + F ox p3 + F ox p3

TCR CD4 CD4

SF6 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. a If n g -/- If n g -/- KO b If n g -/- If n g -/- KO 83.9 6.9 56.5 22.4 NK 1.1

7.4 34.0 1.3 7.9 3.4 17.7

49.5 23.7 27.4 18.5 TCR 46.7 1.6 2.6 0.4 CD8

CD62L 2.3 24.5 21.7 32.4 6.4 45.3 6.3 90.7 CD44 CD4 100 ** If n g -/- - 80 If n g -/- KO

F ox p3 7.7 2.2

+ 60 * F ox p3 40 ** 91.3 97.8

% of CD4 20 ns CD4 0 + ** -/- 60 50 * If n g * + If n g -/- KO **

F ox40 p3 ns + * 25 * 20 ns

% of CD45 0 - + - +

% of CD4 0 lo hi hi lo CD8 + + NK 1.1 F ox p3 + F ox p3 hi CD44 hi CD44 lo CD44 lo CD44 CD4 CD4 TCR CD62L CD62L CD62L CD62L c 200 **** 150 ****

-/- 150 staining

+ 100 If n g even ts

+ 100 g 100 μm F 50 50 EV KO Isolated CD31 -/- 0 0 -/- -/-

If n g KO KO If n g -/- If n g -/- 100 μm If n g If n g F g CD31 CC3 E-Cadherin DAPI d 80 140

**** m) μ

-/- **** (

60 +

If n g 110 40 area (% )

50 μm + 20 80 HPP KO

-/- 0 -/- Distance to CD31 50 If n g KO If n g -/- 50 μm If n g HPP CC3 HPP CD31 CC3 E-Cadherin DAPI SF7 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. a Il4-/- Il4-/-KO b Il4-/- Il4-/-KO 90.2 4.2 61.1 25.8

8.9 8.6 NK 1.1

0.8 4.8 1.0 12.1

56.4 19.9 36.6 25.3 TCR

62.0 1.2 52.3 2.6 CD8

CD62L 2.5 21.2 15.9 22.2

CD44 12.6 24.3 6.3 38.8 100 **** Il4-/- CD4 - 80 Il4-/-KO

4.3 4.9 F ox p3

+ 60 ****

40 F ox p3 ***

% of CD4 20 ns 0 80 CD4 + * 30 -/- 60 Il4 + ns Il4-/-KO F ox p3

+ ** ns ns 20 40 ns

20 10 ns % of CD45

% of CD4 ns 0 0 lo hi hi lo - + - +

CD8 hi CD44 hi CD44 lo CD44 lo CD44 + NK 1.1 + F ox p3 + F ox p3

CD4 CD4 CD62L CD62L CD62L CD62L TCR c 200 150 ns ns -/- 150 staining IL 4 + 100 even ts

+ 100

100 μm g

F 50 50 EV KO Isolated CD31

-/- 0 0 -/- -/- Il4 Il4 -/- KO Il4 -/- KO Il4 100 μm Il4 F g CD31 CC3 E-Cadherin DAPI 9 d ns -/-

Il4 6 area (% )

50 μm + 3 HPP KO -/-

Il4 0 -/-

Il4 -/- KO 50 μm SF8 Il4 HPP CD31 CC3 E-Cadherin DAPI bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. a ns ns 70 ns 60 W cells T T 50 (% ) + 40 Ki67 30

20

KO T cells KO T -/- WT KO Il4 -/- KO Il4

* 80 ** * T cells T

-/- 60 Il4

40 area (% ) +

CC3 20

0

KO T cells KO T -/- -/- WT KO Il4 -/- KO Il4 Il4

Ki67 CC3 DAPI Control Csf 1r b c ) 3 3000 WT + Csf 1r 2500 KO + Csf 1r 2000 1500

Ly6c 33.3 0.5 1000 * 500 *

umor burden (mm * T 0 14 15 16 17 18 19 20 21 22 F 4/ 80 Age (w eek) d 3500

) WT 3 3000 KO 2500 W T + IL-4 2000 KO + IL-4 1500 W T + IF N- ** 1000 KO + IF N- ** umor burden (mm 500 T * 0 0 4 8 12 16 20 24 Time post transplantation (day)

SF9 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.04.977629; this version posted March 5, 2020. The copyright holder for this preprint a (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

0

-2000

-4000 Th2 gene signature score (ssGSEA) ESCA OV BRCA TN BLCA CESC CHOL KIRP KICH MESO SARC COAD THCA SKCM READ THYM UCS UCEC DLBC UVM TGCT GBM LGG PCPG STAD LUAD PRAD LUSC PAAD KIRC BRCA non-TN HNSC LIHC ACC

b BRCA non-TN BRCA TN 1.0 + Censored 1.0 + Censored 0.8 Logrank P=0.0851 0.8 Logrank P=0.8719 0.6 0.6 Top 50% Top 50% Bottom 50% 0.4 Bottom 50% 0.4 0.2 0.2 470 127 41 10 7 3 0 78 15 3 0 0.0 0.0 Survival probability 456 143 34 7 3 2 0 90 32 9 1 1 0 0 50100 150 300250200 0 50 100 150 200 250 LGG GBM 1.0 + Censored 1.0 + Censored 0.8 Logrank P=0.0020 0.8 Logrank P=0.7162 Top 50% 0.6 0.6

0.4 Bottom 50% Top 50% 0.4 0.2 0.2 Bottom 50% 257 40 12 4 1 80 15 4 0 0.0 0.0 257 43 9 2 0 80 15 3 2 1 Survival probability 0 50 100 150 200 0 20 40 60 80 KICH KIRC 1.0 1.0 + Censored 0.8 Top 50% 0.8 Logrank P=0.2032 0.6 0.6 Bottom 50% + Censored Bottom 50% Top 50% 0.4 Logrank P=0.0115 0.4 0.2 0.2 33 44 10 2 267 175 93 36 13 4 0 0.0 0.0 32 43 8 0 266 177 107 50 26 6 0 Survival probability 0 50 100 150 0 2550 75 150125100 Time (month) Time (month) c

60 **** KIRC

stainin g 40 +

50 μm 20 Isolated CD31 0 KICH

KIRC KICH 50 μm SF10 E-Cadherin CD31 DAPI