Title Running title: Author list...2021/05/10  · Yan Zhang: [email protected] # Corresponding...

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Title: SARS-CoV-2 receptor ACE2 identifies immuno-hot tumors in breast cancer Running title: ACE2 identifies immuno-hold tumors Author list: Jie Mei 1,2,* , Yun Cai 1,2,* , Rui Xu 2,* , Xinqian Yu 3,* , Lingyan Chen 1 , Tao Ma 4 , Tianshu Gao 2 , Fei Gao 2 , Yichao Zhu 3,5,# , Yan Zhang 1,# Note: Jie Mei, Yun Cai, Rui Xu and Xinqian Yu contributed equally to this work Authors’ affiliations: 1 Department of Oncology, Wuxi Maternal and Child Health Hospital Affiliated to Nanjing Medical University, Wuxi 214000, China. 2 Wuxi Clinical Medical College, Nanjing Medical University, Wuxi 214000, China. 3 Department of Physiology, Nanjing Medical University, Nanjing 211166, China. 4 Department of Breast Surgery, Wuxi Maternal and Child Health Hospital Affiliated to Nanjing Medical University, Wuxi 214000, China. 5 State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166, China. E-mails for Authors: Jie Mei: [email protected] Yun Cai: [email protected] Rui Xu: [email protected] Xinqian Yu: [email protected] Lingyan Chen: [email protected] Tao Ma: [email protected] Tianshu Gao: [email protected] Fei Gao: [email protected] Yichao Zhu: [email protected] . CC-BY-NC-ND 4.0 International license made available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprint this version posted May 10, 2021. ; https://doi.org/10.1101/2021.05.10.443377 doi: bioRxiv preprint

Transcript of Title Running title: Author list...2021/05/10  · Yan Zhang: [email protected] # Corresponding...

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Title: SARS-CoV-2 receptor ACE2 identifies immuno-hot tumors in breast cancer

Running title: ACE2 identifies immuno-hold tumors

Author list:

Jie Mei 1,2,*, Yun Cai 1,2,*, Rui Xu 2,*, Xinqian Yu 3,*, Lingyan Chen 1, Tao Ma 4, Tianshu

Gao 2, Fei Gao 2, Yichao Zhu 3,5,#, Yan Zhang 1,#

Note: Jie Mei, Yun Cai, Rui Xu and Xinqian Yu contributed equally to this work

Authors’ affiliations:

1 Department of Oncology, Wuxi Maternal and Child Health Hospital Affiliated to

Nanjing Medical University, Wuxi 214000, China.

2 Wuxi Clinical Medical College, Nanjing Medical University, Wuxi 214000, China.

3 Department of Physiology, Nanjing Medical University, Nanjing 211166, China.

4 Department of Breast Surgery, Wuxi Maternal and Child Health Hospital Affiliated

to Nanjing Medical University, Wuxi 214000, China.

5 State Key Laboratory of Reproductive Medicine, Nanjing Medical University,

Nanjing 211166, China.

E-mails for Authors:

Jie Mei: [email protected]

Yun Cai: [email protected]

Rui Xu: [email protected]

Xinqian Yu: [email protected]

Lingyan Chen: [email protected]

Tao Ma: [email protected]

Tianshu Gao: [email protected]

Fei Gao: [email protected]

Yichao Zhu: [email protected]

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Yan Zhang: [email protected]

# Corresponding author:

Yan Zhang, Ph. D

Department of Oncology

Wuxi Maternal and Child Health Hospital Affiliated to Nanjing Medical University

No. 48 Huaishu Rd., Wuxi 214000, China

E-mail: [email protected]

# Corresponding author:

Yichao Zhu, Ph. D

Department of Physiology

Nanjing Medical University

No. 101 Longmian Av., Nanjing 211166, China

E-mail: [email protected]

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Abstract

Angiotensin-converting enzyme 2 (ACE2) is known as a host cell receptor for Severe

Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which is identified to be

dysregulated in multiple tumors. Although the characterization of abnormal ACE2

expression in malignancies has been preliminarily explored, in-depth analysis of ACE2

in breast cancer (BRCA) has not been elucidated. A systematic pan-cancer analysis was

conducted to assess the expression pattern and immunological role of ACE2 based on

RNA-sequencing (RNA-seq) data downloaded from The Cancer Genome Atlas

(TCGA). Next, correlations between ACE2 expression immunological characteristics

in the BRCA tumor microenvironment (TME) were evaluated. Also, the role of ACE2

in predicting the clinical features and the response to therapeutic options in BRCA was

estimated. These findings were subsequently validated in another public transcriptomic

cohort as well as a recruited cohort. ACE2 was lowly expressed in most cancers

compared with adjacent tissues. ACE2 was positively correlated with

immunomodulators, tumor-infiltrating immune cells (TIICs), cancer immunity cycles,

immune checkpoints, and tumor mutation burden (TMB). Besides, high ACE2 levels

indicated the triple-negative breast cancer (TNBC) subtype of BRCA, lower response

to endocrine therapy and higher response to chemotherapy, anti-ERBB therapy,

antiangiogenic therapy and immunotherapy. To sum up, ACE2 correlates with an

inflamed TME and identifies immuno-hot tumors, which may be used as an auxiliary

biomarker for the identification of immunological characteristics in BRCA.

Key words: ACE2, breast cancer, tumor immunity, tumor microenvironment

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Introduction

Coronavirus disease 2019 (COVID-19) is infectious pneumonia caused by Severe

Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection [1]. At the end

of 2019, COVID-19 was originally reported in China in December 2019. Just over a

year, COVID-19 has transmitted fastly to almost all countries, which leads to a serious

global public health problem. Based on the latest statistics released by World Health

Organization, a total of 146,054,107 confirmed cases and 3,092,410 deaths have been

reported as of 25 April, 2021 (https://covid19.who.int/). Angiotensin-converting

enzyme 2 (ACE2) is recognized as a host cell receptor for SARS-CoV and SARS-CoV-

2 [2, 3]. Emerging research reported that the expression and distribution of ACE2 were

tissue-specific to some extent, which is enriched in the lung, esophagus, kidney, bladder,

testis, stomach and ileum using a single-cell RNA sequencing (RNA-seq) technique [4].

However, ACE2 is expressed in all organs, excepting for the prostate and brain,

although some organs exhibit low expression [5]. Thus, organs expressing high ACE2

appear to be more impressionable to SARS-CoV-2 infection in healthy individuals.

Breast cancer (BRCA) is a multifactorial disease, which has the highest incidence

in the world. In 2020, a total of 2,261,419 new cases and 684,996 deaths have been

reported according to the latest statistics [6]. Be a multifactorial disease, dysregulation

of the immune landscape acts as a significant role in the oncogenesis and development

of BRCA, which lays the molecular foundation for immunotherapy [7]. ACE2 is known

as a tumor suppressor and is lowly expressed in most cancers [8-10]. Encouragingly,

several analyses reveal that ACE2 correlates with the abundances of a number of tumor-

infiltrating immune cells (TIICs) in multiple cancers [11, 12]. However, indiscriminate

pan-cancer analysis neglects in-depth research on dominant tumor species, which may

lead to ignoring the great value of ACE2 in regulating tumor immunity and acting as a

indicator for the stratification of tumor immunogenicity.

Tumors are complex masses consisting of malignant as well as normal cells. The

multiple interplays between these cells via cytokines, chemokines and growth factors

constitute the tumor microenvironment (TME) [13]. TME could be crucial for the

response to several therapies and the prognosis. Tumors can be simply classified into

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cold or hot depending on their TME. Cold tumors are characterized as

immunosuppressive TME and insensitive to either chemotherapy or immunotherapy,

and hot tumors represent higher response rates to these therapies, which is featured by

T cell infiltration and immunosuppressive TME [14]. In principle, the hot tumors

exhibit a good response to immunotherapy, such as anti-PD-1/PD-L1 therapy [15].

Thus, distinguishing hot and cold tumors is a critical strategy to demarcate the response

to immunotherapy.

In this research, we first conducted a pan-cancer analysis of the expression and

immunological features of ACE2. We discovered that ACE2 exhibited the tightest

correlation with immunological factors in BRCA, which may be a dominant tumor

species for the in-depth analysis of the immunological role of ACE2. We also revealed

that ACE2 indicated an inflamed TME and identified immuno-hot tumors in BRCA,

and had the potential to estimate the molecular subtype of BRCA.

Methodology

Public datasets retrieval

The Cancer Genome Atlas (TCGA) data: The pan-cancer normalized RNA-seq datasets,

copy number variant (CNV) data processed by GISTIC algorithm, 450K methylation

data, mutation profiles, the activities of transcription factor (TF) calculated by RABIT,

and clinical information were obtained from UCSC Xena data portal

(https://xenabrowser.net/datapages/). The somatic mutation data were obtained from

TCGA (http://cancergenome.nih.gov/) and then used to calculate the tumor mutation

burden (TMB) by R package “maftools”. The abbreviations for TCGA cancer types are

shown in Table S1.

METABRIC data: The normalized RNA-seq dataset, CNV data processed by

GISTIC algorithm, mutation profiles and clinical data of BRCA patients in

METABRIC cohort were downloaded from cBioPortal data portal

(http://www.cbioportal.org/datasets) [16].

Prognostic analysis using PrognoScan

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PrognoScan database (http://dna00.bio.kyutech.ac.jp/PrognoScan/) was applied to

assess the prognostic value of ACE2 in BRCA across a large cohort of public

microarray datasets [17]. All the results were exhibited in the study.

Linked Omics database analysis

The Linked Omics database (http://www.linkedomics.org/login.php) is a web-based

tool to analyze multi-dimensional datasets [18]. The functional roles of ACE2 in BRCA

was predicted using the Linked Omics tool in term of Gene Ontology (GO) and Kyoto

Encyclopedia of Genes and Genomes (KEGG) pathways by the gene set enrichment

analysis (GSEA). Default options were used for all parameters.

Assessment of the immunological features in TME of BRCA

The immunological features of TME in BRCA contain immunomodulators, the

activities of the cancer immunity cycle, infiltration levels of TIICs, and the expression

of inhibitory immune checkpoints.

The information of 122 immunomodulators including major histocompatibility

complex (MHC), receptors, chemokines, and immunostimulators was collected from

the research of Charoentong et al. [19]. Considering the cancer immunity cycle which

contains seven stages reflects the anti-cancer immune response and the activities of

each step decide the fate of tumor cells, we subsequently calculated the activation

scores of each step by single sample gene set enrichment analysis (ssGSEA) according

to the expression level of specific signatures of each step [20]. Moreover, in order to

avoid calculation errors resulted from various algorithms which were developed to

explore the relative abundance of TIICs in TME, we comprehensively estimated the

infiltration levels of TIICs using the following independent algorithms: TIMER [21],

EPIC [22], MCP-counter [23], quanTIseq [24] and TISIDB [25]. ESTIMATE

algorithm was also performed to calculate Tumor Purity, ESTIMATE Score, Immune

Score and Stromal Score [26]. Furthermore, we also collected several well-known

effector genes of TIICs, and computed the T cell inflamed score according to the

expression levels and weighting coefficient of 18 genes reported by Ayer et al. [27].

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To verify the role of ACE2 in mediating cancer immunity in BRCA, we grouped

the patients into the high ACE2 and the low ACE2 group with the 50% cutoff based on

the expression levels of ACE2, and then analyzed the difference of the immunological

features of TME concerning the above aspects between the high and low ACE2 groups.

Immunophenoscore analysis

As previously reported, a patient’s immunophenoscore (IPS) can be calculated without

bias using machine learning by consideration of the 4 major categories of components

that measure immunogenicity: effector cells, immunosuppressive cells, MHC

molecules, and immunomodulators [19]. The IPS values of BRCA patients were

obtained from the Cancer Immunome Atlas (TCIA) (https://tcia.at/home).

Calculation of the enrichment scores of various gene signatures

According to previous research [28], we collected several gene-sets positively

associated with therapeutic response to immunotherapy, targeted therapy and

radiotherapy, and specific markers of biological process correlated with anti-tumor

immunity such as genes involved in DNA replication. The enrichment scores of these

signatures were obtained using the GSVA R package [29].

Prediction of therapeutic response

The role of ACE in predicting the response to chemotherapy was also evaluated. First,

BRCA-related drug-target genes were screened using the Drugbank database

(https://go.drugbank.com/). Besides, we predicted the response to anti-therapy for each

patient based on the Cancer Genome Project (CGP) database

(https://www.sciencedirect.com/topics/neuroscience/cancer-genome-project) as well.

Several common therapeutics, including vinblastine, cisplatin, doxorubicin, etoposide,

gefitinib, gemcitabine, paclitaxel, parthenolide, sunitinib and vinorelbine were selected.

The prediction process was performed by R package “pRRophetic” where the samples’

half-maximal inhibitory concentration (IC50) was calculated by ridge regression and

the prediction accuracy was assessed by 10-fold cross-validation according to the CGP

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training set. Default options were used for all parameters [30].

Clinical samples

Two tissue microarrays (TMAs, HBreD050Bc01 and HBreD090Bc03) were obtained

from Outdo Biotech (Shanghai, China). The HBreD050Bc01 microarray contained 40

BRCA and 10 adjacent samples. The HBreD090Bc03 microarray contained 85 BRCA

and 5 adjacent samples. Thus, a total of 125 BRCA samples and 15 adjacent samples

were involved in the current research.

Immunohistochemistry and semi-quantitative evaluation

Next, Immunohistochemistry (IHC) staining was conducted on these tissue slides. The

primary antibodies used in the research were as following: anti-ACE2 (1:3000 dilution,

Cat. ab15348, Abcam, Cambridge, UK), anti-CD8 (Ready-to-use, Cat. PA067, Abcarta,

Suzhou, China) and anti-PD-L1 (Ready-to-use, Cat. GT2280, GeneTech, Shanghai,

China). Antibody staining was visualized with DAB and hematoxylin counterstain, and

stained sections were scanned using Aperio Digital Pathology Slide Scanners. All

stained sections were independently evaluated by two independent pathologists. For

semi-quantitative evaluation of ACE2 and PD-L1 staining, the percentage of positively

stained cells was scored as 0-4: 0 (< 1%), 1 (1-5%), 2 (6-25%), 3 (26-50%) and 4 (>

50%). The staining intensity was scored as 0-3: 0 (negative), 1 (weak), 2 (moderate)

and 3 (strong). The immunoreactivity score (IRS) equals to the percentages of positive

cells multiplied with staining intensity. For CD8 staining, infiltration level was assessed

by estimating the percentage of cells with strong intensity of membrane staining in the

stroma cells.

Statistical analysis

Statistical analysis and figure exhibition performed using R language 4.0.0. The

statistical difference of continuous variables between the two groups was evaluated by

Wilcoxon rank sum test or Mann-Whitney test and chi-square test was used when the

categorical variables were assessed. Pearson’s correlation was used to evaluete the

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correlation between two variables. Receiver-operating characteristic (ROC) analysis

was plotted to assess the specificity and sensitivity of the candidate indicator, and the

area under the ROC curve (AUC) was generated for diagnostic biomarkers. Prognostic

values of categorical variables were assessed by log-rank test. For all analyses, P value

≤ 0.05 were deemed to be statistically significant.

Results

Expression and immunological roles of ACE2 across human cancers

After a systematic pan-cancer analysis of the expression of ACE2 in the TCGA

database, we discovered that ACE2 was lowly expressed in a fraction of cancers,

including BRCA, KICH, and LUAD. However, ACE2 also shown to be overexpressed

in CESE, KIRP, LIHC and UCEC (Figure 1A). Next, we conducted a pan-cancer

survival analysis about overall survival (OS), progression-free survival (PFS) using

Kaplan-Meier analysis. ACE2 emerged as a prognostic risk factor for both OS and PFS

in KIRC, LIHC and UCS (Figure S1A-B). However, these results need further

verification, especially based on recruited cohorts.

We next conducted a pan-cancer analysis aimed to examine the immunological

features of ACE2 in various tumors. The results uncovered that ACE2 was positively

correlated with most immunomodulators in BRCA (Figure 1B). We also calculated the

infiltrating levels of TIICs in the TME by the ssGSEA method. Similarly, ACE2 was

positively correlated with most TIICs in BRCA (Figure 1C). Moreover, we revealed

that the expression of ACE2 was positively related to the expression of several immune

checkpoints, including LAG3, TIGIT, CTLA4 and PD-L1 in BRCA (Figure 1D).

Although these positive correlations between ACE2 and immunological features were

found in other tumors, such as CESC, KIRC and PRAD, the highest correlation was

observed in BRCA. Moreover, according to previous research, ACE2 was not expressed

in immune cells, and the expression of ACE2 in bulk RNA-seq data was derived from

non-immune cells in all probability, such as tumor cells in the tissues.[31] Besides,

considering the positive correlation between ACE2 and PD-L1, an immune checkpoint

expressed on tumor cells, we conducted the TFs network analysis, and found that a

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mass of shared TFs that potentially regulated ACE2 and PD-L1 (Figure S2, Table S2).

Collectively, the expression pattern of ACE2 is TME- characteristic, which illustrates

the potential of ACE2 as an immune-related biomarker and therapeutic target in BRCA.

Potential regulatory factors, prognostic value and functions of ACE2 in BRCA

Mutations in ACE2 gene were rare (0.30%, Figure S3A), so the mutations seemed to

not be a dominating factor for ACE2 expression. The CNV pattern of ACE2 was shown

in Figure S3B. Remarkably, copy number amplification of the ACE2 upregulated the

expression of ACE2 mRNA (Figure S3B). Besides, methylation level was positively

correlated with ACE2 expression (Figure S3C). These findings suggest that epigenetic

modifications of the ACE2 gene might be essential for the regulation of expression.

Considering that ACE2 expression was not related to survival outcome in the

TCGA database, we next assess its prognostic role using PrognoScan tool. However,

the prognostic role of ACE2 in BRCA was inconsistent (Table S3). We speculated that

the prognostic value of ACE2 may be associated with subtypes and therapeutic

regimens in BRCA, which needed to be further studied.

Moreover, the functions of ACE2 in BRCA was analyzed using LinkedOmics tool.

GO enrichment analysis assessed the functions of ACE2 in term of three aspects,

including biological processes, cellular components and molecular functions. Plentiful

of statistically significant terms were found and the top 5 terms positively correlated

with ACE2 expression of each analysis were retained. As shown in Table S4, the most

critical terms were associated with immune-related processes. These results reveal that

ACE2 may act as a critical role in regulating anti-tumor immunity in BRCA.

ACE2 shapes an inflamed TME in BRCA

Considering that ACE2 was positively related to a great proportion of

immunomodulators in BRCA, we next explored in-depth immunological role of ACE2

in BRCA. Most MHC molecules were upregulated in the high ACE2 group, which

suggested that the ability of antigen presentation and processing was upregulated in the

high ACE2 group (Figure 2A). Besides, most chemokines and paired receptors were

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upregulated in the high ACE2 group (Figure 2A). These chemokines and receptors

facilitate the recruitment of effector TIICs, including CD8+T cells, TH17 cells and

antigen-presenting cells. Next, we calculated the infiltration level of TIICs using five

independent algorithms. Similar to the previous results, ACE2 was positively related to

the infiltration levels of the majority of immune cells using various algorithms (Figure

2B). The ESTIMATE method was next applied to estimate Tumor Purity, ESTIMATE

Score, Immune Score and Stromal Score. Compared with the low ACE2 group, the high

ACE2 group had enhanced ESTIMATE Score, Immune Score and Stromal Score but

decreased Tumor Purity (Figure 2C). In addition, ACE2 was negatively correlated with

the marker genes of immune cells, including CD8+T cell, dendritic cell, macrophage,

NK cell and Th1 cell (Figure 2D). Besides, the activities of the cancer immunity cycle

are a direct integrated manifestation of the functions of the chemokines and other

immunomodulators. In the high ACE2 group, activities of the most steps in the cycle

were revealed to be upregulated (Figure 2E). The expression of immune checkpoints

such as PD-1/PD-L1 was uncovered to be high in the inflamed TME [32]. In our

research, ACE2 was suggested to be positively related to most immune checkpoints,

including VTCN1, PD-L1, PD-1, CTLA4 and so on (Figure 2F). Totally, ACE2 is

tightly correlated with the development of an inflamed TME, which may act as a critical

role in identifying the immunogenicity of BRCA.

ACE2 predicts immune phenotype in BRCA

Theoretically, BRCA patients with high ACE2 expression should have a higher

response to immunotherapy because ACE2 identifies an inflamed TME. Immune-

related target expression levels commonly reflect the response to immunotherapy. As

expected, the expression levels of most immunotargets such as CD19, PD-1 and PD-

L1, were upregulated in the high ACE2 group (Figure 3A). T cell inflamed score is

developed using IFN-γ-related mRNA profiles to predict clinical response to PD-1

blockade [27], and BRCA patients in the high ACE2 group exhibited higher T cell

inflamed scores (Figure 3B). Besides, TMB level is another biomarker for the

prediction of the response to immunotherapy [33]. Foreseeably, in the low ACE2 group,

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the frequency of mutant genes and TMB were both lower compared with the high ACE2

group (Figure 3C-E). Given that the TMB levels were most enriched in the range of 0-

1200 (Figure S4), the comparison between the two groups was limited to the range of

1-1200 to avoid the effect of extremum. More importantly, TP53 exhibited incredibly

high mutation rates in the high ACE2 group (Figure 3C-D), which was a biomarker for

better response to immunotherapy [34, 35]. According to previous research, patients

with high levels of microsatellite instability (MSI-H) tend to be sensitive to

immunotherapy [33]. We next assess the status of mismatch repair (MMR) proteins and

ACE2 expression. The proportion of MSI-H in BRCA varied largely, from 0.2% to

18.6% [36], but in most the proportion of MSI-H in BRCA less than 5%. We set the

low 5% as the threshold of MMR protein deficiency. As expected, the proportion of

MLH1 and PMS2 deficiency in the high ACE2 group was higher than that in the low

ACE2 group, which indicated that BRCA patients with high ACE2 expression may

have a higher MSI-H proportion (Figure 3F). Using IPS as a surrogate of the response

to immunotherapy, we discovered that patients with high ACE2 expression had notably

higher IPS (Figure 3G). In summary, immunotherapy may be carried out in BRCA

patients with high ACE2 expression as they tend to be sensitive to immunotherapy.

ACE2 predicts molecular subtypes and therapeutic options in BRCA

We next evaluated the ACE2 expression and clinic-pathologic features of BRCA. As

Figure 4A exhibited, ACE2 expression was significantly associated with age,

histological type, molecular type, ER status and PR status, but not related to other

features. Specifically, ACE2 was upregulated in ER-negative, PR-negative and the

triple-negative breast cancer (TNBC) tissues (Figure S5A), and ROC analysis indicated

a notable diagnostic value in identifying these molecular subtypes (Figure S5B). TNBC

has been identified as a subtype with high aggressiveness and PD-L1 expression.

However, in the TCGA cohort, the prognosis of TNBC patients showed no notable

difference compared with non-TNBC patients (Figure S6), which may be due to various

therapies. This may account for why ACE2 was upregulated in TNBC subtype but not

related to prognosis.

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In addition, the enrichment scores, such as IFN-γ signature, APM signal, cell cycle,

DNA replication and et al., were higher in the high ACE2 group (Figure 4B). Thus,

targeted therapy suppressing these oncogenic pathways could be applied for the

treatment of BRCA with high ACE2 expression. Moreover, findings from the

Drugbank database revealed a remarkably higher response to chemotherapy, anti-

ERBB therapy (excluding Afatinib), antiangiogenic therapy and immunotherapy in the

high ACE2 group (Figure 4C). This shows that chemotherapy, anti-ERBB therapy,

antiangiogenic therapy and immunotherapy can be applied, either alone or in

combination, for the therapy of BRCA with high expression. However, BRCA with

lower ACE2 expression was possibly sensitive to endocrine therapy and Afatinib.

Moreover, IC50 of anti-cancer drugs in patients from the TCGA database according to

the pRRophetic algorithm was estimated. The results showed patients with high ACE2

expression were more sensitive to common anti-cancer drugs (Figure 4D). To sum up,

ACE2 is an indicator for the subtype of TNBC and resistance to endocrine therapy in

BRCA, but patients with high ACE2 expression tend to be sensitive to more therapeutic

opportunities, including chemotherapy, anti-ERBB therapy, antiangiogenic therapy and

immunotherapy.

ACE2 predicts immune phenotypes and clinical subtypes in independent cohorts

The above findings were next validated in the METABRIC database. Similar to the

evidence from the TCGA database, the infiltration levels of the majority of immune

cells using various algorithms were increased in the high ACE2 group (Figure 5A).

Besides, the enrichment scores of chemokine immunomodulator, MHC and receptor

were also higher in the high ACE2 group (Figure 5B). ACE2 was positively related to

the marker genes of immune cells (Figure 5C). As expected, immunotargets and T cell

inflamed scores were increased in the high ACE2 group as well (Figure 5D-E). We also

analyzed the association between TMB level and ACE2 expression. Although the TMB

levels were not remarkably different in the two groups (Figure S7), the notably various

mutant feature of TP53 was observed in the METABRIC database (Figure 5F-G). The

deficient frequency of MLH1 was higher, which implied the MSI-H may be common

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in the high ACE2 group (Figure S8). Besides, ROC analysis validated the notable

diagnostic value in identifying ER, PR status and TNBC subtype (Figure 5H). However,

similar to the result in the TCGA database, the prognosis of TNBC patients showed no

remarkable difference compared with non-TNBC patients (Figure S9). Furthermore,

we also performed IC50 prediction of anti-cancer drugs in patients from the

METABIRC database using the pRRophetic algorithm. As expected, patients with high

ACE2 expression were more sensitive to these anti-cancer drugs which were mentioned

in the previous analysis (Figure 5I).

To further validate above results, we also obtained a TMA cohort for verification,

which included 125 BRCA samples and 15 adjacent samples. As Figure 6A shown,

ACE2 was notably decreased in BRCA tissues in comparison to normal tissues (Figure

6A-B). In accord with the above results, ACE2 was overexpressed in ER-negative, PR-

negative and the TNBC tissues (Figure 6C). In addition, the current BRCA cohort was

divided into the low and high expression groups according to the median level of ACE2

expression (IRS ≤ 3 vs. IRS > 3). As Table 1 exhibited, ACE2 expression was

associated with age, ER status, PR status and molecular type (Table 1). Moreover, the

infiltrating level of CD8+T cell and PD-L1 expression were higher in the high ACE2

group (Figure 6D-F). In conclusion, ACE2 expression is related to clinical features and

immune phenotypes in BRCA. However, due to unavailable therapeutic information,

we are unable to assess the association between ACE2 expression and the response to

various therapies.

Discussion

COVID-19 is becoming a global concern and the major public threat in the last two

years. Cancer patients are more impressionable to COVID-19 infection due to the

underlying disease, which may be due to systemic reduced immunity or anticancer

therapy [37]. The situation could be more terrible in lung cancer patients as they already

have chronic pulmonary inflammation and the lung TME supports for SARS-CoV-2

and accelerates infection [38]. Besides, cancer patients have a worse prognosis after

SARS-CoV-2 infection. For example, lung cancer patients were reported to suffer more

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from severe events, including increased death rate and ICU admission rate [39].

Moreover, emerging studies suggest that SARS-CoV-2 infection could affect cancer

progression. Dormant cancer cells tend to survive after successful therapy of primary

tumors and localize in particular microanatomical sites of metastasis-prone organs [40].

Acute lung inflammation and neutrophil extracellular traps have been exhibited to

activate the exit from dormancy of breast dormant cancer cells respectively, leading to

distant metastasis [41].

However, an isolated case report has revealed that SARS-CoV-2 infection induced

complete spontaneous remission in a patient with lymphoma [42]. This may be because

of the SARS‐CoV‐2 infection activating an anti‐tumor immune response, as has been

previously reported, concurrent infections induced complete remission of diffuse large

B-cell lymphoma independent of any interventions [43]. Recently, oncolytic

virotherapy has developed as an encouraging anti-cancer therapy through virus self-

replication. On the other side, oncolytic virus enhances anti-tumor immunity responses

by increasing the immune infiltration and turn the tumor to be sensitive to

immunotherapy and chemotherapy [44]. Thus, we speculated SARS‐CoV‐2 infection

induced spontaneous remission of tumor has parallels with oncolytic virotherapy to

some extent. Whatever, the complicated interplay between the immune system and

cancer after SARS‐CoV‐2 infection needs to be further highlighted.

As crosstalk between COVID-19 and the tumor, ACE2 act as a significant role in

both SARS‐CoV‐2 infection and cancer. ACE2 has been found to be as a tumor

suppressor in various cancers. It was reported that ACE2 exerts anti-tumor roles by

suppressing tumor angiogenesis [8]. Dai et al. [11] reported that upregulated ACE2

expression was correlated with a favorable clinical outcome in hepatocellular

carcinoma. Besides, the associations between ACE2 with anti-tumor immunity and

immunotherapy were also explored. Yang et al. [12] reported that the overexpression

of ACE2 was significantly associated with enhanced immune infiltration in endometrial

cancer and renal papillary cell cancer. Bao et al. [31] uncovered tight correlations

between ACE2 expression and immune gene signatures in multiple cancers. However,

the crucial values of ACE2 in the identification of tumor immune status have not been

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evaluated.

In this research, we first conducted a pan-cancer analysis of the immunological

features of ACE2. We discovered that ACE2 exhibited the tightest correlation with

immunological factors in BRCA, and in-depth analysis of ACE2 in BRCA was

subsequently conducted. We found that ACE2 was positively related to the expression

of important immunomodulators, such as CCL5, CXCL10 and CXCR4. Besides, ACE2

was also positively related to increased TIICs and cancer immunity cycles. Namely, the

recruitment of effector TIICs was enhanced, thereby facilitating the formation of an

inflamed TME. Besides, we discovered that high ACE2 expression was correlated with

the enhanced response to immunotherapy by check the difference of immune

checkpoints expression, T cell inflamed score, TMB, MMR protein deficiency status

and IPS scores in the ACE2 high and low groups. Another important finding was that

high ACE2 levels indicated the negativity of hormone receptors, including ER, PR and

HER2 receptors. More importantly, ACE2 expression was increased in the TNBC

subtype of BRCA. TNBC is summarized by deadly aggressiveness and lacked

treatment [45]. However, PD-L1 was often overexpressed in TNBC [46], and its

response to immune checkpoint inhibition was encouraging.

Conclusions

In the current study, we revealed that ACE2 shaped an inflamed TME according to the

evidence that ACE2 positively related to the immunological patterns of TME in BRCA.

Besides, we uncovered that BRCA had the potential to estimate the response of

immunotherapy, the molecular subtypes and the response to several therapeutic

strategies. Overall, ACE2 may be used as a promising biomarker for the identification

of immunological features in BRCA.

Acknowledgments

This work was supported by the Natural Science Foundation of China (82073194), the

Major project of Wuxi Science and Technology Bureau (N20201006) and the Wuxi

Double-Hundred Talent Fund Project (BJ2020076).

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Availability of data and materials

All data supported the results in this study are showed in this published article and its

supplementary files. Besides, original data for bioinformatics analysis could be

downloaded from corresponding platforms.

Authors’ contributions

Y Zhang and Y Zhu conceived the study and participated in the study design,

performance, coordination and project supervision. JM, YC, RX, XY, LC, TM, TG and

FG collected the public data and performed the bioinformatics analysis. JM performed

the IHC staining. Y Zhang and Y Zhu revised the manuscript. All authors reviewed and

approved the final manuscript.

Ethics approval and consent to participate

Ethical approval for the study of tissue microarray slide was granted by the Clinical

Research Ethics Committee, Outdo Biotech (Shanghai, China).

Competing interests

The authors have no competing interests.

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References 1. Li Q, Guan X, Wu P et al. Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-

Infected Pneumonia, N Engl J Med 2020;382:1199-1207.

2. Kuhn JH, Li W, Choe H et al. Angiotensin-converting enzyme 2: a functional receptor for SARS

coronavirus, Cell Mol Life Sci 2004;61:2738-2743.

3. Zhou P, Yang XL, Wang XG et al. A pneumonia outbreak associated with a new coronavirus

of probable bat origin, Nature 2020;579:270-273.

4. Zou X, Chen K, Zou J et al. Single-cell RNA-seq data analysis on the receptor ACE2 expression

reveals the potential risk of different human organs vulnerable to 2019-nCoV infection, Front Med

2020;14:185-192.

5. Mahalingam R, Dharmalingam P, Santhanam A et al. Single-cell RNA sequencing analysis of

SARS-CoV-2 entry receptors in human organoids, J Cell Physiol 2021;236:2950-2958.

6. Sung H, Ferlay J, Siegel RL et al. Global cancer statistics 2020: GLOBOCAN estimates of

incidence and mortality worldwide for 36 cancers in 185 countries, CA Cancer J Clin 2021.

7. Lu WC, Xie H, Yuan C et al. Genomic landscape of the immune microenvironments of brain

metastases in breast cancer, J Transl Med 2020;18:327.

8. Zhang Q, Lu S, Li T et al. ACE2 inhibits breast cancer angiogenesis via suppressing the

VEGFa/VEGFR2/ERK pathway, J Exp Clin Cancer Res 2019;38:173.

9. Chai P, Yu J, Ge S et al. Genetic alteration, RNA expression, and DNA methylation profiling of

coronavirus disease 2019 (COVID-19) receptor ACE2 in malignancies: a pan-cancer analysis, J

Hematol Oncol 2020;13:43.

10. de Carvalho Fraga CA, Farias LC, Jones KM et al. Angiotensin-Converting Enzymes (ACE and

ACE2) as Potential Targets for Malignant Epithelial Neoplasia: Review and Bioinformatics Analyses

Focused in Oral Squamous Cell Carcinoma, Protein Pept Lett 2017;24:784-792.

11. Dai YJ, Hu F, Li H et al. A profiling analysis on the receptor ACE2 expression reveals the

potential risk of different type of cancers vulnerable to SARS-CoV-2 infection, Ann Transl Med

2020;8:481.

12. Yang J, Li H, Hu S et al. ACE2 correlated with immune infiltration serves as a prognostic

biomarker in endometrial carcinoma and renal papillary cell carcinoma: implication for COVID-19,

Aging (Albany NY) 2020;12:6518-6535.

13. Duan Q, Zhang H, Zheng J et al. Turning Cold into Hot: Firing up the Tumor

Microenvironment, Trends Cancer 2020;6:605-618.

14. Gajewski TF. The Next Hurdle in Cancer Immunotherapy: Overcoming the Non-T-Cell-

Inflamed Tumor Microenvironment, Semin Oncol 2015;42:663-671.

15. Zemek RM, De Jong E, Chin WL et al. Sensitization to immune checkpoint blockade through

activation of a STAT1/NK axis in the tumor microenvironment, Sci Transl Med 2019;11.

16. Cerami E, Gao J, Dogrusoz U et al. The cBio cancer genomics portal: an open platform for

exploring multidimensional cancer genomics data, Cancer Discov 2012;2:401-404.

17. Mizuno H, Kitada K, Nakai K et al. PrognoScan: a new database for meta-analysis of the

prognostic value of genes, BMC Med Genomics 2009;2:18.

18. Vasaikar SV, Straub P, Wang J et al. LinkedOmics: analyzing multi-omics data within and

across 32 cancer types, Nucleic Acids Res 2018;46:D956-D963.

19. Charoentong P, Finotello F, Angelova M et al. Pan-cancer Immunogenomic Analyses Reveal

Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade,

.CC-BY-NC-ND 4.0 International licensemade available under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is

The copyright holder for this preprintthis version posted May 10, 2021. ; https://doi.org/10.1101/2021.05.10.443377doi: bioRxiv preprint

Page 19: Title Running title: Author list...2021/05/10  · Yan Zhang: fuyou2007@126.com # Corresponding author: Yan Zhang, Ph. D Department of Oncology Wuxi Maternal and Child Health Hospital

Cell Rep 2017;18:248-262.

20. Xu L, Deng C, Pang B et al. TIP: A Web Server for Resolving Tumor Immunophenotype

Profiling, Cancer Res 2018;78:6575-6580.

21. Li T, Fu J, Zeng Z et al. TIMER2.0 for analysis of tumor-infiltrating immune cells, Nucleic Acids

Res 2020;48:W509-W514.

22. Racle J, de Jonge K, Baumgaertner P et al. Simultaneous enumeration of cancer and immune

cell types from bulk tumor gene expression data, Elife 2017;6.

23. Becht E, Giraldo NA, Lacroix L et al. Estimating the population abundance of tissue-infiltrating

immune and stromal cell populations using gene expression, Genome Biol 2016;17:218.

24. Finotello F, Mayer C, Plattner C et al. Molecular and pharmacological modulators of the tumor

immune contexture revealed by deconvolution of RNA-seq data, Genome Med 2019;11:34.

25. Ru B, Wong CN, Tong Y et al. TISIDB: an integrated repository portal for tumor-immune

system interactions, Bioinformatics 2019;35:4200-4202.

26. Yoshihara K, Shahmoradgoli M, Martinez E et al. Inferring tumour purity and stromal and

immune cell admixture from expression data, Nat Commun 2013;4:2612.

27. Ayers M, Lunceford J, Nebozhyn M et al. IFN-gamma-related mRNA profile predicts clinical

response to PD-1 blockade, J Clin Invest 2017;127:2930-2940.

28. Hu J, Yu A, Othmane B et al. Siglec15 shapes a non-inflamed tumor microenvironment and

predicts the molecular subtype in bladder cancer, Theranostics 2021;11:3089-3108.

29. Hanzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and

RNA-seq data, BMC Bioinformatics 2013;14:7.

30. Geeleher P, Cox N, Huang RS. pRRophetic: an R package for prediction of clinical

chemotherapeutic response from tumor gene expression levels, PLoS One 2014;9:e107468.

31. Bao R, Hernandez K, Huang L et al. ACE2 and TMPRSS2 expression by clinical, HLA, immune,

and microbial correlates across 34 human cancers and matched normal tissues: implications for

SARS-CoV-2 COVID-19, J Immunother Cancer 2020;8.

32. Gajewski TF, Corrales L, Williams J et al. Cancer Immunotherapy Targets Based on

Understanding the T Cell-Inflamed Versus Non-T Cell-Inflamed Tumor Microenvironment, Adv

Exp Med Biol 2017;1036:19-31.

33. Ren D, Hua Y, Yu B et al. Predictive biomarkers and mechanisms underlying resistance to

PD1/PD-L1 blockade cancer immunotherapy, Mol Cancer 2020;19:19.

34. Sun H, Liu SY, Zhou JY et al. Specific TP53 subtype as biomarker for immune checkpoint

inhibitors in lung adenocarcinoma, EBioMedicine 2020;60:102990.

35. Cardona AF, Ruiz-Patino A, Arrieta O et al. Genotyping Squamous Cell Lung Carcinoma in

Colombia (Geno1.1-CLICaP), Front Oncol 2020;10:588932.

36. Ren XY, Song Y, Wang J et al. Mismatch Repair Deficiency and Microsatellite Instability in

Triple-Negative Breast Cancer: A Retrospective Study of 440 Patients, Front Oncol 2021;11:570623.

37. Gauci ML, Coutzac C, Houot R et al. SARS-CoV-2 vaccines for cancer patients treated with

immunotherapies: Recommendations from the French society for ImmunoTherapy of Cancer

(FITC), Eur J Cancer 2021;148:121-123.

38. Moujaess E, Kourie HR, Ghosn M. Cancer patients and research during COVID-19 pandemic:

A systematic review of current evidence, Crit Rev Oncol Hematol 2020;150:102972.

39. Malkani N, Rashid MU. SARS-COV-2 infection and lung tumor microenvironment, Mol Biol

Rep 2021;48:1925-1934.

.CC-BY-NC-ND 4.0 International licensemade available under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is

The copyright holder for this preprintthis version posted May 10, 2021. ; https://doi.org/10.1101/2021.05.10.443377doi: bioRxiv preprint

Page 20: Title Running title: Author list...2021/05/10  · Yan Zhang: fuyou2007@126.com # Corresponding author: Yan Zhang, Ph. D Department of Oncology Wuxi Maternal and Child Health Hospital

40. Francescangeli F, De Angelis ML, Zeuner A. COVID-19: a potential driver of immune-

mediated breast cancer recurrence?, Breast Cancer Res 2020;22:117.

41. Albrengues J, Shields MA, Ng D et al. Neutrophil extracellular traps produced during

inflammation awaken dormant cancer cells in mice, Science 2018;361.

42. Challenor S, Tucker D. SARS-CoV-2-induced remission of Hodgkin lymphoma, Br J Haematol

2021;192:415.

43. Buckner TW, Dunphy C, Fedoriw YD et al. Complete spontaneous remission of diffuse large

B-cell lymphoma of the maxillary sinus after concurrent infections, Clin Lymphoma Myeloma Leuk

2012;12:455-458.

44. Li L, Liu S, Han D et al. Delivery and Biosafety of Oncolytic Virotherapy, Front Oncol

2020;10:475.

45. Mei J, Hao L, Wang H et al. Systematic characterization of non-coding RNAs in triple-negative

breast cancer, Cell Prolif 2020;53:e12801.

46. Maeda T, Hiraki M, Jin C et al. MUC1-C Induces PD-L1 and Immune Evasion in Triple-

Negative Breast Cancer, Cancer Res 2018;78:205-215.

.CC-BY-NC-ND 4.0 International licensemade available under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is

The copyright holder for this preprintthis version posted May 10, 2021. ; https://doi.org/10.1101/2021.05.10.443377doi: bioRxiv preprint

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Figure legends

Figure 1. The relationship between ACE2 and immunological features in pan-cancers

(A) Pan-cancer analysis of ACE2 expression in tumor and paracancerous tissues in the

TCGA database. Ns: no significant difference; *P < 0.05; **P < 0.01; ***P < 0.001;

****P < 0.0001. (B) Correlation between ACE2 and 122 immunomodulators

(chemokines, receptors, MHC and immunostimulators). The color reveals the

correlation coefficient. The asterisks reveal statistical difference assessed by Pearson

analysis. (C) Correlation between ACE2 and 28 TIICs calculated with the ssGSEA

algorithm. The color reveals the correlation coefficient. The asterisks reveal statistical

difference assessed by Pearson analysis. (D) Correlation between ACE2 and four

immune checkpoints, LAG3, TIGIT, CTLA4, PD-L1. The dots symbolize cancer types.

Figure 2. ACE2 shapes an inflamed TME in BRCA

(A) Expression levels of 122 immunomodulators between the high and low ACE2

groups in BRCA. (B) Differences in the levels of TIICs calculated using five algorithms

between the high and low ACE2 groups. (C) Differences in Tumor Purity, ESTIMATE

Score, Immune Score, and Stromal Score estimating by ESTIMATE method between

the high and low ACE2 groups. (D) Differences in the gene markers of the common

TIICs between the high and low ACE2 groups. (E) Correlation between ACE2 and

common inhibitory immune checkpoints. The color reveals the Pearson correlation

coefficient. (F) Differences in the various steps of the cancer immunity cycle between

the high and low ACE2 groups. Ns: no significant difference; *P < 0.05; **P < 0.01;

***P < 0.001; ****P < 0.0001.

Figure 3. Correlation between ACE2 and the immune phenotype in BRCA

(A) Differences in expression levels of immune-related targets the high and low ACE2

groups in BRCA. (B) Differences in T cell inflamed scores between the high and low

ACE2 groups. The T cell inflamed score is positively related to the response to cancer

immunotherapy. (C, D) Mutational landscape in the high and low ACE2 groups. (E)

Differences in TMB levels between the high and low ACE2 groups. (F) Differences in

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deficiency rates of MMR proteins between the high and low ACE2 groups. (G)

Differences in IPS scores between the high and low ACE2 groups.

Figure 4. ACE2 predicts the molecular subtype and the therapeutic options in BRCA

(A) Correlations between ACE2 and clinic-pathological features in BRCA. (B)

Correlations between ACE2 and the enrichment scores of several therapeutic signatures.

(C) Correlation between ACE2 and the drug-target genes extracted from the Drugbank

database. (D) Differences in IC50 of common anti-cancer drugs between the high and

low ACE2 groups. ****P < 0.0001.

Figure 5. Roles of ACE2 in predicting immune and clinical phenotypes in the

METABIRC cohort

(A) Differences in the levels of TIICs calculated using five algorithms between the high

and low ACE2 groups in BRCA. (B) Differences in enriched scores of chemokines,

receptors, MHC and immunostimulators between the high and low ACE2 groups. (C)

Differences in the gene markers of the common TIICs between the high and low ACE2

groups. (D) Differences in expression levels of immune-related targets between the high

and low ACE2 groups. (E) Differences in T cell inflamed scores between the high and

low ACE2 groups. (F, G) Mutational landscape in the high and low ACE2 groups. (H)

Diagnostic values of ACE2 expression in identifying ER, PR and triple-negative

subtypes. (I) Differences in IC50 of common anti-cancer drugs between the high and

low ACE2 groups. **P < 0.01; ****P < 0.0001.

Figure 6. Roles of ACE2 in predicting clinical and immune phenotypes in the recruited

TMA cohort

(A) Representative images revealing ACE2 expression in tumor and para-tumor tissues

using anti-ACE2 staining. Magnification, 200X; (B) Expression levels of ACE2 in

tumor and para-tumor tissues. (C) Expression levels of ACE2 in various molecular

subtypes. (D) Representative images revealing CD8+T cell infiltration and PD-L1

expression in the high and low ACE2 groups. Magnification, 200X; (E) Differences in

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CD8+T cell infiltration between the high and low ACE2 groups. (F) Differences in PD-

L1 expression between the high and low ACE2 groups.

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Table 1. Associations between ACE2 expression and clinico-pathological features in BRCA. Features Cases ACE2 expression χ2 value P value

low high Age

≤60 94 48 46 4.561 0.033 >60 31 9 22

T stage

≤2cm 47 18 29 1.979 0.159 >2cm 76 39 37

unknown 2a

N stage

N0 63 27 36 0.275 0.600 N1-N3 61 29 32

unknown 1b

M stage

N0 124 56 68 1.203 0.456c M1 1 1 0

Clinical stage

0-2 89 41 48 0.027 0.869 3-4 36 16 20

Grade

I-II 53 28 25 1.755 0.185 II-III 71 29 42

unknown 1

ER status

negative 63 18 45 14.847 <0.001 positive 62 39 23

PR status

negative 70 21 49 15.607 <0.001 positive 55 36 19

HER2 status

negative 78 35 43 0.102 0.750 positive 46 22 24

unknown 1

Molecular type

non-TNBC 88 47 41 6.758 0.009 TNBC 36 10 26

unknown 1

Note: a: T1-T3, not T4; b: The patient had distant metastasis; c: P value was calculated by Fisher test.

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CCL14CCL15CCL16CCL17CCL18CCL19CCL2

CCL21CCL22

CCL24CCL25CCL26CCL27CCL28

CCL4CCL5CCL7CCL8

CXCL1

CXCL11CXCL12

CXCL14CXCL16CXCL17CXCL2

CXCL5CXCL6CXCL9XCL1XCL2BTNL2CD27CD276CD28

CD48

CD86ENTPD1HHLA2ICOSICOSLGIL2RAIL6IL6RKLRC1KLRK1LTAMICBNT5EPVRRAET1ETMIGD2

TNFRSF14TNFRSF17TNFRSF18TNFRSF25TNFRSF4TNFRSF8TNFRSF9

TNFSF14TNFSF15TNFSF18TNFSF4TNFSF9ULBP1B2M

OA

PA1

TAP1TAP2TAPBPCCR1

CCR2

CCR4CCR5CCR6CCR7CCR8CCR9

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CCL1CCL11

CCL14CCL15CCL16CCL17CCL18CCL19CCL2

CCL21CCL22

CCL24CCL25CCL26CCL27CCL28

CCL4CCL5CCL7CCL8

CXCL1

CXCL11CXCL12

CXCL14CXCL16CXCL17CXCL2

CXCL5CXCL6CXCL9XCL1XCL2BTNL2CD27CD276CD28

CD48

CD86ENTPD1HHLA2ICOSICOSLGIL2RAIL6IL6RKLRC1KLRK1LTAMICBNT5EPVRRAET1ETMIGD2

TNFRSF14TNFRSF17TNFRSF18TNFRSF25TNFRSF4TNFRSF8TNFRSF9

TNFSF14TNFSF15TNFSF18TNFSF4TNFSF9ULBP1B2M

OA

PA1

TAP1TAP2TAPBPCCR1

CCR2

CCR4CCR5CCR6CCR7CCR8CCR9

CXCR1CXCR2

CXCR4CXCR5CXCR6XCR1

1

BRCA

5

15

Correlation

valu

e)

log Pa

aaa

5

15

Correlation

Correlation Between ACE2 and LA

BRCA

5

15

Correlation

valu

e)

Correlation

log Pa

aa

5

15

Correlation Between ACE2 and TIGIT

BRCA

Correlation

valu

e)

Correlation

log Pa

aaaa

5

15

25

Correlation Between ACE2 and CTLA4

BRCA

5

15

Correlation

valu

e)

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log Paaa

5

15

Correlation Between ACE2 and PD-L1

A

B C

D

OV

.CC-BY-NC-ND 4.0 International licensemade available under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is

The copyright holder for this preprintthis version posted May 10, 2021. ; https://doi.org/10.1101/2021.05.10.443377doi: bioRxiv preprint

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CCR1CCR10CCR2CCR3CCR4CCR5CCR6CCR7CCR8CCR9CX3CR1CXCR1CXCR2CXCR3CXCR4CXCR5CXCR6XCR1B2M

OA

PA1

TAP1TAP2TAPBPBTNL2

HHLA2ICOS

IL2RAIL6IL6RKLRC1KLRK1LTAMICB

PVR

ULBP1CCL1CCL11CCL13CCL14CCL15CCL16CCL17CCL18CCL19CCL2CCL20CCL21CCL22CCL23CCL24CCL25CCL26CCL27CCL28CCL3CCL4CCL5CCL7CCL8CX3CL1CXCL1CXCL10CXCL11CXCL12CXCL13CXCL14CXCL16CXCL17CXCL2CXCL3CXCL5CXCL6CXCL9XCL1XCL2

ACE2

Group

GroupHigh ALow A

ACE212

0

TypeChemokineImmunostimulatorMHCReceptor

0

2

4CCR1CCR10CCR2CCR3CCR4CCR5CCR6CCR7CCR8CCR9CX3CR1CXCR1CXCR2CXCR3CXCR4CXCR5CXCR6XCR1B2M

OA

PA1

TAP1TAP2TAPBPBTNL2

HHLA2ICOS

IL2RAIL6IL6RKLRC1KLRK1LTAMICB

PVR

ULBP1CCL1CCL11CCL13CCL14CCL15CCL16CCL17CCL18CCL19CCL2CCL20CCL21CCL22CCL23CCL24CCL25CCL26CCL27CCL28CCL3CCL4CCL5CCL7CCL8CX3CL1CXCL1CXCL10CXCL11CXCL12CXCL13CXCL14CXCL16CXCL17CXCL2CXCL3CXCL5CXCL6CXCL9XCL1XCL2

ACE212

0

TypeChemokineImmunostimulatorMHCReceptor

0

2

4 B cellCancer associated fibroblast

MacrophageNK cell

racterized cellT cell

Cytotoxic lymphocyteB lineageNK cellMonocytic lineageMyeloid dendritic cellNeutrophil

blastsB cellMacrophage M1Macrophage M2MonocyteNeutrophilNK cell

ry)

T cell regulator Tregs)Myeloid dendritic celluncharacterized cellB cell

NeutrophilMacrophage

ritic cellActiv T cellCentral memor T cell

fector memeor T cellActiv T cellCentral memor T cell

fector memeor T cellT follicular helper cell

T cellType 1 T helper cellType 17 T helper cellType 2 T helper cellRegulatory T cellActivated B cellImmature B cellMemory B cellNK cell

right NK cellcell

Myeloid derived suppressor cellNK T cellActivated dendritic cellPlasmacytoid dendritic cellImmature dendritic cellMacrophage

Mast cellMonocyteNeutrophil

GroupHigh ALow A

ACE212

0

Algorithm

MCPquanTIseq

0

1

2

3

0

1000

2000

Low A

High A

StromalScore

0

2000

4000

Low A

High A

ImmuneScore

0

2500

5000

Low A

High A

A

Low A

High A

TumorPurity

ns ns **** **** **** **** **** **** **** **** **** **** **** **** **** **** * **** **** **** **** **** ****

0

3

6

T ce

ll re

crui

ting

T ce

ll re

crui

ting

T ce

ll re

crui

ting

Th1

cell

recr

uitin

g

ritic

cel

l rec

ruiti

ng

Th22

cel

l rec

ruiti

ng

Mac

roph

age

recr

uitin

g

Mon

ocyt

e re

crui

ting

Neu

troph

il re

crui

ting

NK

cell

recr

uitin

g

ruiti

ng

Baso

phil

recr

uitin

g

Th17

cel

l rec

ruiti

ng

B ce

ll re

crui

ting

Th2

cell

recr

uitin

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Treg

cel

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ruiti

ng

ruiti

ng

variable

valu

e

High A Low A

Rel

ease

of c

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r cel

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s

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cer a

ntig

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Prim

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and

activ

atio

n

Infil

tratio

n of

imm

une

cells

into

tum

or

Rec

ogni

tion

of c

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r cel

ls b

y T

cells

Killin

g of

can

cer c

ells

CXCL10CXCR3CCL5

L

TBX21

CRTAMIL7RNCR1PRR5LSPON2

CYBBLILRA2MARCOMMP8MS4A6ACCL4CTLA4

IL21R

SLC15A3

GroupHigh ALow A

ACE212

0

Type

ritic cellMacrophageNK cellTh1 cell

0

2

4

Type

0

1

A VTC

N1

CTL

A4H

AVC

R2

LAIR

1PV

R ACAM

1BT

LA

KLR

C1

LA

AVTCN1

CTLA4HAVCR2

LAIR1PVR

ACAM1BTLA

KLRC1

LA

ACE2

Group

ACE2

Group

A B C

D F

E

.CC-BY-NC-ND 4.0 International licensemade available under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is

The copyright holder for this preprintthis version posted May 10, 2021. ; https://doi.org/10.1101/2021.05.10.443377doi: bioRxiv preprint

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P = 0.170

6

8

10

Low ACE2

High ACE2

IPS

CTL

A4-n

eg-P

D1-

neg

IPS CTLA4-neg-PD1-neg

P < 0.0001

3

5

7

9

11

Low ACE2

High ACE2

IPS

CTL

A4-n

eg-P

D1-

pos

IPS CTLA4-neg-PD1-pos

P = 0.0001

6

8

10

Low ACE2

High ACE2

IPS

CTL

A4-p

os-P

D1-

neg

IPS CTLA4-pos-PD1-neg

P < 0.0001

3

6

9

Low ACE2

High ACE2

IPS

CTL

A4-p

os-P

D1-

pos

IPS CTLA4-pos-PD1-pos

P < 0.0001

7.5

10.0

12.5

High ACE2

Low ACE2

Panc

ance

r T c

ell i

nfla

med

sco

re

P < 0.0001 P < 0.0001 P < 0.0001 P < 0.0001 P < 0.0001 P < 0.0001 P < 0.0001

0

5

10

15

CD19 PDCD1 CD274 CTLA4 IDO1 TNFRSF17 PVR

Rel

ative

exp

ress

ion

leve

l

Group High ACE2 Low ACE2

High ACE2 (92.81%)

0

5366

SPTA1RYR3

RUNX1FAT3DMD

SYNE2MUC4PTEN

USH2AFLG

MAP2K4NEB

SYNE1RYR2

ZFHX4NCOR1HMCN1MUC16KMT2C

MAP3K1CDH1

TTNGATA3

TP53PIK3CA

4%4%4%4%4%4%4%5%5%5%5%5%5%5%6%6%6%10%12%12%13%16%17%19%38%

0 194

Frame_Shift_InsNonsense_MutationMissense_MutationFrame_Shift_Del

In_Frame_DelSplice_SiteMulti_Hit

Low ACE2 (88.67%)

P < 0.0001

0

500

1000

High ACE2

Low ACE2

TMB

leve

l

Deficiency Normal

0

4161

LYSTCSMD1

DNAH17PKHD1L1

NEBMUC17

MAP3K1DMD

OBSCNLRP2

CSMD3HMCN1SPTA1SYNE1

PTENRYR2

USH2AKMT2CGATA3

FLGMUC16

CDH1TTN

PIK3CATP53

5%5%5%5%5%6%6%6%6%6%6%7%7%7%7%7%8%8%8%9%12%13%22%31%50%

0

Missense_MutationSplice_SiteNonsense_MutationFrame_Shift_Ins

Frame_Shift_DelIn_Frame_DelIn_Frame_InsMulti_Hit

251

Deficiency Normal

Deficiency Normal

Deficiency Normal

High ACE2

Low ACE2

A B

F

DC E

G

95.23%

4.77%

94.68%

5.32%

0

25

50

75

100

perc

enta

ge

MSH2 P = 0.782

95.05%

4.95%

94.86%

5.14%

0

25

50

75

100

perc

enta

ge

MSH6 P = 1.000

92.48%

7.52%

97.43%

2.57%

0

25

50

75

100

perc

enta

ge

MLH1 P < 0.001

92.84%

7.16%

97.06%

2.94%

0

25

50

75

100

perc

enta

ge

PMS2 P = 0.002

High ACE2

Low ACE2

High ACE2

Low ACE2

High ACE2

Low ACE2

.CC-BY-NC-ND 4.0 International licensemade available under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is

The copyright holder for this preprintthis version posted May 10, 2021. ; https://doi.org/10.1101/2021.05.10.443377doi: bioRxiv preprint

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Vinblastine$TUBBMethotrexate$TYMSGemcitabine$TYMSCisplatin$TFGemcitabine$CMPK1Docetaxel$MAP2Vinblastine$TUBE1

Group

ACE2

12

0

Type

Chemotherapy

0

1

2

Afatinib$ERBB4Cetuximab$C1QACetuximab$C1QBCetuximab$C1QCCetuximab$FCGR2BCetuximab$FCGR2CTrastuzumab$EGFRCetuximab$EGFRCetuximab$C1RCetuximab$C1S

ERBB therapy

Bevacizumab$VEGFASorafenib$KITPaclitaxel$MAP2Sunitinib$CSF1RPazSorafenib$FLT1Sunitinib$FLT1Sunitinib$FLT4Sorafenib$FLT4

Antiangiogenic therapyAtezolizumab$CD274Durvalumab$CD274Avelumab$CD274

Immunotherapy

age

subdivision

ER status

PR status

HER2 status

molecular type

histological type

T stage

N stage

M stage

clinical stage

recurrence status

vital status

vital status ns

living

deceased

recurrence status ns

tumor free

with tumor

unknown

clinical stage ns

Stage I

Stage II

Stage III

Stage IVunknown

M stage ns

M0

M1

unknown

N stage ns

N0

N1

N2

unknown

T stage ns

T1

T2

T4

unknown

histological type ***Infiltrating Carcinoma NOS

Infiltrating Ductal Carcinoma

Infiltrating Lobular Carcinoma

Medullary Carcinoma

Metaplastic Carcinoma

Mixed Histology (please specify)

Mucinous Carcinoma

Other, specify

unknown

molecular type ****TNBC

unknown

HER2 status ns

negative

positive

unknown

PR status ****negative

positive

unknown

ER status ****negative

positive

unknown

subdivision ns

left

right

age ****90

ACE2

Group

Low ACE2

High ACE2

Group

ACE2

12

0

Low ACE2

High ACE2

ACE2

Group

e

APM signal

Cell cycle

DNA replication

Progesterone-mediated oocyte maturation

Proteasome

Spliceosome

EGFR ligands

WNT work

Hypoxia

0

1

2

0

1

2ACE2

Group

Group

ACE2

12

0

Low ACE2

High ACE2

Endocrinotherapy

A

B C

D

**** **** **** **** **** **** **** **** **** **** ****

0

4

Vinblastine Cisplatin Docetaxel Doxorubicin Etoposide Gefitinib Gemcitabine Paclitaxel Parthenolide Sunitinib Vinorelbine

pred

icte

dPty

pe

Group High ACE2 Low ACE2

ER antagonist$ESR1PR antagonist$PGR

way

MicroRNAs in cancer

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CD8ACD8BCXCL10CXCR3CCL5GZMAGZMBIFNGPRF1TBX21C1QBCRTAMIL7RNCR1PRR5LSPON2C1QACLEC5ACYBBLILRA2MARCOMS4A6ACCL4CTLA4SLAMF1FGL2IL21RSIGLEC1SLC15A3

ACE2

Group Group

Low ACE2

High ACE2

ACE2

12

5

CD8+T cell

DC

Macrophage

NK cell

Th1 cell

0

2

4

CD8ACD8BCXCL10CXCR3CCL5GZMAGZMBIFNGPRF1TBX21C1QBCRTAMIL7RNCR1PRR5LSPON2C1QACLEC5ACYBBLILRA2MARCOMS4A6ACCL4CTLA4SLAMF1FGL2IL21RSIGLEC1SLC15A3

ACE2

Group

12

5

Type

0

2

4P < 0.0001

0.0

0.4

0.8

Low ACE2

High ACE2

Expr

essi

on

ChemokineP < 0.0001

0.0

0.3

0.6

0.9

Low ACE2

High ACE2

Expr

essi

on

Immunostimulator

P < 0.0001

0.0

0.4

0.8

Low ACE2

High ACE2

Expr

essi

on

ReceptorP < 0.0001

0.0

0.4

0.8

Low ACE2

High ACE2

MHC

Expr

essi

on

P < 0.0001 P < 0.0001 P = 0.059 P < 0.0001 P < 0.0001 P < 0.0001 P < 0.0001

6

8

10

CD19 PDCD1 CD274 CTLA4 IDO1 TNFRSF17 PVR

Rel

ativ

e ex

pres

sion

Group High ACE2 Low ACE2

0

81

NCOR2LAMA2AKAP9

CBFBTG

BIRC6HERC2ARID1A

TBX3USH2A

RYR2DNAH5

MLL2DNAH2

DNAH11CDH1

AHNAKSYNE1KMT2C

MAP3K1AHNAK2

MUC16GATA3

TP53PIK3CA

5%6%6%6%6%6%6%6%6%7%7%7%7%7%8%9%9%10%13%13%15%16%16%24%46%

0 426

Missense_MutationFrame_Shift_DelFrame_Shift_InsSplice_SiteNonsense_Mutation

In_Frame_DelIn_Frame_InsNonstop_MutationMulti_Hit

Low ACE2 (94.17%)

4370

46

COL12A1BIRC6

NOTCH1COL6A3

UTRNTG

AKAP9DNAH5HERC2

PDE4DIPRYR2MLL2

MAP3K1USH2AGATA3

DNAH2CDH1

AHNAKDNAH11

KMT2CSYNE1

AHNAK2MUC16PIK3CA

TP53

5%5%5%6%6%6%7%7%7%7%8%8%8%8%9%9%9%10%11%12%15%18%19%40%47%

0

Missense_MutationFrame_Shift_InsSplice_SiteFrame_Shift_DelNonsense_Mutation

In_Frame_DelIn_Frame_InsNonstop_MutationTranslation_Start_SiteMulti_Hit

High ACE2 (96.77%)

P < 0.0001

7

8

9

10

11

High ACE2

Low ACE2

Panc

ance

r T c

ell i

nfla

med

sco

re

B cellCancer associated fibroblastCD4+T cell CD8+T cell Endothelial cellMacrophageNK celluncharacterized cellT cellCD8+T cellCytotoxic lymphocytesB lineageNK cellMonocytic lineageMyeloid dendritic cellNeutrophilEndothelial cellFibroblastB cellMacrophage M1Macrophage M2MonocyteNeutrophilNK cellCD4+T cell (non.regulatory)CD8+T cell T cell regulatory (Tregs)Myeloid dendritic celluncharacterized cell

Activated CD8+T cellCentral memory CD8+T cellEffector memeory CD8+T cellActivated CD4+T cellCentral memory CD4+T cellEffector memeory CD4+T cellT follicular helper cellGamma delta T cellType 1 T helper cellType 17 T helper cellType 2 T helper cellRegulatory T cellActivated B cellImmature B cellMemory B cellNK cellCD56bright NK cellCD56dim NK cellMyeloid derived suppressor cellNK T cellActivated dendritic cellPlasmacytoid dendritic cellImmature dendritic cellMacrophageEosinophilMast cellMonocyteNeutrophil

ACE2

Group

GroupLow ACE2

High ACE2

ACE2

12

5

AlgorithmEPICMCPquanTIseq

TISIDB

0

1

2

3A B C

D

E F G

Sens

itivi

ty

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

ER status : 0.816PR status: 0.666TNBC : 0.779

**** **** ** ** **** **** ** **** **** **** ****

0

4

Vinblastine Cisplatin Docetaxel Doxorubicin Etoposide Gefitinib Gemcitabine Paclitaxel Parthenolide Sunitinib Vinorelbine

pred

icte

dPty

pe

Group High ACE2 Low ACE2H I

TIMER

B cellCD4+T cell CD8+T cell NeutrophilMacrophageDendritic cell

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A B C

D E F

Para

-tum

orTu

mor

Low

Hig

h

anti-ACE2 anti-CD8 anti-PD-L1

anti-ACE2

Low ACE2

High ACE2

Low ACE2

High ACE2

The

pro

porti

on o

f CD

8+T

cell

PD-L

1 IR

S

ACE2

IRS

Para-tu

mor

Tumor ER-

ER+PR-

PR+HER2-

HER2+TNBC

non-T

NBC

P = 0.031 P < 0.001

P = 0.007 P = 0.042

P < 0.001 P = 0.282 P = 0.035

ACE2

IRS

0

2

4

6

8

0

5

10

15

20

25

0

2

4

6

8

0

2

4

6

8

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Supplemental Figure legends

Figure S1. Forest plots of prognostic value of ACE2 in predicting (A) OS and (B) PFS

in pan-cancer analysis.

Figure S2. TFs that correlated with (A) ACE2 and (B) PD-L1 calculated by RABIT

algorithm in BRCA.

Figure S3. Potential regulatory factors of ACE2 in BRCA.

(A) Mutations in ACE2 gene. (B) The associations between CNV pattern and ACE2

expression in BRCA. (C) The correlation between methylation level and ACE2

expression.

Figure S4. Mutational density curve in BRCA. The TMB levels were most enriched in

the range of 0-1200.

Figure S5. ACE2 predicts the molecular subtype in BRCA

(A) Expression level of ACE2 in different molecular subtypes in BRCA. (B) Diagnostic

values of ACE2 expression in identifying ER, PR and triple-negative subtypes.

Figure S6. Survival analysis of patients with TNBC compared with that with non-

TNBC (TCGA)

Figure S7. Differences in TMB levels between the high and low ACE2 groups in BRCA

(METABIRC).

Figure S8. Differences in deficiency rates of MMR proteins between the two groups

(METABIRC).

Figure S9. Survival analysis of patients with TNBC compared with that with non-

TNBC (METABIRC).

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KIRC

LIHC

UVM

MESO

UCS

LGG

KIRP

ACC

GBM

HNSC

LUSC

COAD

ESCA

OV

KICH

LAML

PRAD

READ

CESC

SARC

SKCM

LUAD

THYM

STAD

PAAD

BRCA

PCPG

DLBC

THCA

BLCA

CHOL

UCEC

TGCT

P value

<0.0001

0.013

0.021

0.045

0.048

0.071

0.082

0.087

0.097

0.106

0.126

0.239

0.239

0.287

0.293

0.385

0.386

0.396

0.451

0.455

0.464

0.466

0.471

0.513

0.538

0.556

0.598

0.608

0.653

0.814

0.823

0.921

0.991

HR

1.70

1.56

3.00

1.62

2.04

0.73

1.73

1.95

0.75

1.24

1.24

1.34

0.76

1.17

2.10

1.19

1.76

1.52

1.20

1.16

0.90

0.90

0.60

1.11

0.88

1.10

1.48

0.69

0.80

1.04

1.11

1.04

1.01

95%CI

0 1 2 3 4 5 6 7 8

KIRC

LIHC

UCS

KICH

MESO

UVM

LUSC

HNSC

COAD

GBM

ACC

OV

BLCA

CESC

TGCT

SARC

LUAD

READ

LGG

CHOL

KIRP

STAD

DLBC

UCEC

BRCA

PCPG

THCA

PRAD

PAAD

SKCM

ESCA

THYM

P value

<0.0001

0.003

0.005

0.036

0.050

0.063

0.086

0.119

0.155

0.167

0.171

0.185

0.254

0.420

0.431

0.466

0.486

0.528

0.580

0.595

0.620

0.631

0.673

0.678

0.854

0.896

0.912

0.958

0.964

0.965

0.967

0.998

HR

1.98

1.56

2.65

5.15

1.70

2.10

1.33

1.25

1.37

0.78

1.54

1.20

1.19

1.21

0.78

1.13

0.91

1.34

0.93

0.79

1.14

0.92

0.77

0.88

1.03

0.95

1.03

0.99

1.01

1.00

0.99

1.00

95%CI

0 2.5 5 7.5 10 12.5 15 17.5 20 22.5

A B

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HSF1

MAFK

CCNT2MAFF

ZNF143BHLHE40

SIX5

BCLAF1

MYC

SP1

SP2

SP4

CREB1

NR2C2

GATA2

GTF2F1

RFX5MAZTCF7L2

ZKSCAN1

RUNX3

ELK4

ZNF274

KDM5A

CTCF

FOXP2NFATC1

HMGN3

RBBP5ESRRA

EGR1GTF2B

ZBTB33SUPT20H

TCF3

FOS

IRF3

IRF1

TAF1

ESR1

TBP

HNF4G

MEF2C

MEF2A

PRDM1

PHF8

HDAC1

YY1

HDAC6

FOXM1

IKZF1

SPI1

STAT3

STAT2

STAT1

FOSL1

FOSL2

RXRA

EP300

IRF4

SREBF1

NRF1

WRNIP1

EZH2

ELF1 TAL1

MYBL2

CEBPB

TFAP2C

UBTF

ETS1

SUZ12

MTA3

ZBTB7A RAD21REST

EBF1

E2F6

E2F4

E2F1MBD4

POU2F2

ATF2

0

100

200

300

Correaltion

2v

HSF1

MAFK

CCNT2

MAFFZNF143 BHLHE40SIX5

MYC

SP1

SP2

SP4

CREB1

GATA3

GTF2F1

MAZ

ZNF274CTCF

HMGN3

FOXA1

SAP30CBX3

GTF2B

TCF3

FOS

ZEB1

IRF3

IRF1

TAF1

ESR1

TBP

HNF4G

MEF2C

PHF8

HDAC1HDAC2

YY1

FOXM1IKZF1

SPI1

STAT3

SRF

RXRA

MAX

SREBF1

NRF1

EZH2

ELF1

TAL1

MYBL2

TEAD4

CEBPB

TFAP2C

MTA3

NR3C1

REST

EBF1

E2F6

E2F4

E2F1

MBD4

POU2F2

ATF2

0

50

100

150

Correaltion

2v

A B

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0.5

1.0

1.5

2.0

0 5 10ACE2 expression

ACE2

met

hyla

tion

CNV -2 CNV 0 CNV 1 CNV 2

02

46

810

1214

ACE2

exp

ress

ion

P = 0.048 R = 0.410, P <0.0001BA

L162F

Peptidase_M2Peptidase_M2

0 805aa200 400 600

0

5

AC

E2

Mut

atio

ns

A

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0 1000 2000 3000 4000 5000

0.00

00.

005

0.01

00.

015

Den

sity

TMB level

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P < 0.0001 P < 0.0001 P < 0.0001 P = 0.120

0

5

10

non-TNBC

ER negat

ive

PR negat

ive

HER2 nega

tive

Relat

ive A

CE2

expr

essio

n

TNBC

ER positi

ve

PR positi

ve

HER2 posi

tive

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

ER status: 0.856PR status: 0.766

r

A

B

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++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

++++++++++ ++++++ + +

++ ++

+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

+ ++

++ +

P = 0.120

0.00

0.25

0.50

0.75

1.00

0 100 200 300Time

Surv

ival

pro

babi

lity

Strata + +Tr Tr

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P = 0.230

0

20

40

60

80

High ACE2 Low ACE2

TMB

leve

l

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Deficiency Normal

Deficiency Normal

Deficiency Normal

Deficiency Normal

High ACE2

Low ACE2

High ACE2

Low ACE2

High ACE2

Low ACE2

High ACE2

Low ACE2

94.75%

5.25%

95.17%

4.83%

0

25

50

75

100

perc

enta

ge

MSH2 P = 0.753

95.80%

4.20%

94.12%

5.88%

0

25

50

75

100

perc

enta

ge

MSH6 P = 0.116

93.38%

6.62%

96.53%

3.47%

0

25

50

75

100

perc

enta

ge

MLH1 P = 0.002

0

94.22%

5.78%

95.69%

4.31%

25

50

75

100

perc

enta

ge

PMS2 P = 0.173

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+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

+++++++

+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

+++++++

+

P = 0.630

0.00

0.25

0.50

0.75

1.00

0 100 200 300Time

Surv

ival

pro

babi

lity

Strata + +Tr Tr

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Table S1. Table of abbreviations. Abbreviation Full name ACC Adrenocortical carcinoma BLCA Bladder urothelial carcinoma BRCA Breast invasive carcinoma CESC Cervical squamous cell carcinoma and endocervical adenocarcinoma CHOL Cholangio carcinoma COAD Colon adenocarcinoma DLBC Lymphoid neoplasm diffuse large B-cell lymphoma ESCA Esophageal carcinoma GBM Glioblastoma multiforme HNSC Head and neck squamous cell carcinoma KICH Kidney chromophobe carcinoma KIRC Kidney renal clear cell carcinoma KIRP Kidney renal papillary cell carcinoma LAML Acute myeloid leukemia LGG Brain lower grade glioma LIHC Liver hepatocellular carcinoma LUAD Lung adenocarcinoma LUSC Lung squamous cell carcinoma MESO Mesothelioma OV Ovarian serous cystadenocarcinoma PAAD Pancreatic adenocarcinoma PCPG Pheochromocytoma and paraganglioma PRAD Prostate adenocarcinoma READ Rectum adenocarcinoma SARC Sarcoma SKCM Skin cutaneous melanoma STAD Stomach adenocarcinoma TGCT Testicular germ cell tumors THCA Thyroid carcinoma THYM Thymoma UCEC Uterine corpus endometrial carcinoma UCS Uterine carcinosarcoma UVM Uveal melanoma

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Table S2. TFs that correlate with ACE2 and PD-L1. Group Intersect TFs ACE2 negative & PD-L1 negative HSF1, ZNF143, SIX5, MYC, SP1, SP2, SP4,

GTF2F1, MAZ, ZNF274, HMGN3, GTF2B, IRF3, ESR1, HNF4G, PHF8, YY1, RXRA, SREBF1, NRF1, EZH2, ELF1, MYBL2, REST

ACE2 negative & PD-L1 positive CTCF, FOXM1, TAL1, MTA3, E2F1, ATF2 ACE2 positive & PD-L1 negative N.A. ACE2 positive & PD-L1 positive MAFK, CCNT2, MAFF, BHLHE40, CREB1,

TCF3, FOS, IRF1, TAF1, TBP, MEF2C, HDAC1, IKZF1, SPI1, STAT3, CEBPB, TFAP2C, EBF1, E2F6, E2F4, MBD4, POU2F2

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Table S3. Prognostic values of ACE2 in BRCA across public microarray datasets. Dataset Probe ID Cases Endpoint HR 95% CI P value GSE7378 219962_at 54 DFS 0.26 0.01 - 8.86 0.175 GSE7378 222257_s_at 54 DFS 0.41 0.02 - 7.59 0.154 GSE4922-GPL96 222257_s_at 249 DFS 0.71 0.49 - 1.04 0.043 GSE4922-GPL96 219962_at 249 DFS 1.03 0.85 - 1.24 0.145 GSE1456-GPL96 222257_s_at 159 DSS 1.41 0.89 - 2.26 0.007 GSE1456-GPL96 219962_at 159 DSS 1.04 0.74 - 1.46 0.186 E-TABM-158 222257_s_at 117 DSS 1.22 0.66 - 2.26 0.124 E-TABM-158 219962_at 117 DSS 1.48 0.80 - 2.74 0.118 GSE3494-GPL96 219962_at 236 DSS 1.11 0.87 - 1.43 0.096 GSE3494-GPL96 222257_s_at 236 DSS 0.77 0.47 - 1.27 0.062 GSE19615 219962_at 115 DMFS 1.32 0.72 - 2.41 0.038 GSE19615 222257_s_at 115 DMFS 1.47 0.92 - 2.35 0.024 GSE6532-GPL570 219962_at 87 DMFS 3.76 0.91 - 15.59 0.065 GSE6532-GPL570 222257_s_at 87 DMFS 3.36 1.21 - 9.30 0.168 GSE9195 219962_at 77 DMFS 0.30 0.01 - 12.36 0.109 GSE9195 222257_s_at 77 DMFS 0.44 0.05 - 4.21 0.192 GSE12093 219962_at 136 DMFS 0.93 0.62 - 1.39 0.197 GSE12093 222257_s_at 136 DMFS 1.08 0.71 - 1.66 0.306 GSE11121 219962_at 200 DMFS 0.81 0.62 - 1.04 0.059 GSE11121 222257_s_at 200 DMFS 0.73 0.53 - 1.02 0.013 GSE2034 219962_at 286 DMFS 0.93 0.79 - 1.10 0.080 GSE2034 222257_s_at 286 DMFS 0.92 0.72 - 1.16 0.068 E-TABM-158 219962_at 117 DMFS 1.36 0.73 - 2.54 0.133 E-TABM-158 222257_s_at 117 DMFS 1.41 0.87 - 2.29 0.060 GSE2990 219962_at 125 DMFS 1.55 1.02 - 2.34 0.070 GSE2990 222257_s_at 125 DMFS 1.54 0.86 - 2.76 0.082 GSE2990 219962_at 54 DMFS 0.81 0.25 - 2.61 0.100 GSE2990 222257_s_at 54 DMFS 0.79 0.41 - 1.49 0.102 GSE7390 222257_s_at 198 DMFS 1.19 1.04 - 1.36 0.005 GSE7390 219962_at 198 DMFS 1.16 1.03 - 1.30 0.003 GSE9893 10330 155 OS 0.83 0.61 - 1.11 0.027 GSE1456-GPL96 219962_at 159 OS 1.05 0.78 - 1.40 0.247 GSE1456-GPL96 222257_s_at 159 OS 1.28 0.84 - 1.93 0.029 E-TABM-158 219962_at 117 OS 1.39 0.78 - 2.47 0.232 E-TABM-158 222257_s_at 117 OS 1.19 0.69 - 2.07 0.204 GSE7390 222257_s_at 198 OS 1.23 1.08 - 1.41 0.001 GSE7390 219962_at 198 OS 1.18 1.05 - 1.33 0.002 GSE12276 222257_s_at 204 RFS 1.11 1.00 - 1.22 0.005 GSE12276 219962_at 204 RFS 1.12 1.02 - 1.22 0.003 GSE6532-GPL570 219962_at 87 RFS 3.76 0.91 - 15.59 0.065 GSE6532-GPL570 222257_s_at 87 RFS 3.36 1.21 - 9.30 0.168 GSE9195 219962_at 77 RFS 0.49 0.05 - 4.52 0.107

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GSE9195 222257_s_at 77 RFS 0.60 0.13 - 2.70 0.130 GSE1378 21336 60 RFS 1.38 1.06 - 1.79 0.001 GSE1379 21336 60 RFS 1.36 1.08 - 1.73 0.000 GSE1456-GPL96 219962_at 159 RFS 1.04 0.77 - 1.39 0.239 GSE1456-GPL96 222257_s_at 159 RFS 1.19 0.77 - 1.83 0.075 E-TABM-158 222257_s_at 117 RFS 1.19 0.69 - 2.07 0.204 E-TABM-158 219962_at 117 RFS 1.39 0.78 - 2.47 0.232 GSE2990 222257_s_at 62 RFS 0.79 0.47 - 1.31 0.031 GSE2990 219962_at 125 RFS 1.35 0.88 - 2.06 0.113 GSE2990 222257_s_at 125 RFS 1.32 0.75 - 2.30 0.078 GSE2990 219962_at 62 RFS 0.71 0.27 - 1.86 0.145 GSE7390 219962_at 198 RFS 1.06 0.95 - 1.17 0.034 GSE7390 222257_s_at 198 RFS 1.09 0.96 - 1.24 0.033

DFS: Disease Free Survival; DSS: Disease Specific Survival; DMFS: Distant Metastasis Free Survival; OS: Overall Survival; RFS: Relapse Free Survival.

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Table S4. GO and KEGG pathway enrichment analyses of ACE2 in BRCA. Gene Set Description ES NES P value Biological process GO:0002250 adaptive immune response 0.592 1.974 <0.001 GO:0002237 response to molecule of bacterial origin 0.576 1.900 <0.001 GO:1990868 response to chemokine 0.655 1.895 <0.001 GO:0060326 cell chemotaxis 0.568 1.867 <0.001 GO:0031349 positive regulation of defense response 0.559 1.865 <0.001 Cell component GO:0005790 smooth endoplasmic reticulum 0.717 1.749 <0.001 GO:0001533 cornified envelope 0.689 1.898 <0.001 GO:0097038 perinuclear endoplasmic reticulum 0.682 1.544 0.020 GO:0001891 phagocytic cup 0.671 1.533 0.019 GO:0042611 MHC protein complex 0.666 1.452 0.023 Molecular function GO:0016701 oxidoreductase activity, acting on single

donors with incorporation of molecular oxygen

0.774 1.779 <0.001

GO:0016917 GABA receptor activity 0.755 1.670 <0.001 GO:0042287 MHC protein binding 0.725 1.707 <0.001 GO:0001530 lipopolysaccharide binding 0.720 1.689 0.003 GO:0070003 threonine-type peptidase activity 0.688 1.563 0.024 KEGG hsa04650 Natural killer cell mediated cytotoxicity 0.654 1.987 <0.001 hsa04060 Cytokine-cytokine receptor interaction 0.599 1.969 <0.001 hsa05340 Primary immunodeficiency 0.741 1.889 <0.001 hsa00601 Glycosphingolipid biosynthesis 0.803 1.875 <0.001 hsa00380 Tryptophan metabolism 0.726 1.843 <0.001

ES: Enrichment score; NES: Normalized enrichment score.

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