key: cord-0718245-dlik8all authors: Mei, Jie; Cai, Yun; Xu, Rui; Yu, Xinqian; Chen, Lingyan; Ma, Tao; Gao, Tianshu; Gao, Fei; Zhu, Yichao; Zhang, Yan title: SARS-CoV-2 receptor ACE2 identifies immuno-hot tumors in breast cancer date: 2021-05-10 journal: bioRxiv DOI: 10.1101/2021.05.10.443377 sha: c732eb342aabe6f161e83ffeb7f59b581b22ece4 doc_id: 718245 cord_uid: dlik8all 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. Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection [1] . At [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] [9] [10] . Encouragingly, several analyses reveal that ACE2 correlates with the abundances of a number of tumorinfiltrating 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 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. 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] . 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. 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. 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] . 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. 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). 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] . 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 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 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 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. 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 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. 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. 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 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. Theoretically, BRCA patients with high ACE2 expression should have a higher response to immunotherapy because ACE2 identifies an inflamed TME. Immunerelated 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, 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. 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. 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. 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 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, PRnegative 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. 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 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 selfreplication. 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 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. 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. 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. CESC CHOL COAD DLBC ESCA GBM HNSC KICH KIRC KIRP LAML LGG LIHC LUAD LUSC MESO OV PAAD PCPG PRAD READ SARC SKCM STAD TGCT THCA THYM CD86 ENTPD1 HHLA2 ICOS ICOSLG IL2RA IL6 IL6R KLRC1 KLRK1 LTA MICB NT5E PVR RAET1E TMIGD2 TNFRSF14 TNFRSF17 TNFRSF18 TNFRSF25 TNFRSF4 TNFRSF8 TNFRSF9 TNFSF14 TNFSF15 TNFSF18 TNFSF4 TNFSF9 ULBP1 B2M OA PA1 TAP1 TAP2 TAPBP CCR1 CCR2 CCR4 CCR5 CCR6 CCR7 CCR8 CD86 ENTPD1 HHLA2 ICOS ICOSLG IL2RA IL6 IL6R KLRC1 KLRK1 LTA MICB NT5E PVR RAET1E TMIGD2 TNFRSF14 TNFRSF17 TNFRSF18 TNFRSF25 TNFRSF4 TNFRSF8 TNFRSF9 TNFSF14 TNFSF15 TNFSF18 TNFSF4 TNFSF9 ULBP1 B2M OA PA1 TAP1 TAP2 TAPBP CCR1 CCR2 CCR4 CCR5 CCR6 CCR7 CCR8 CCR9 CXCR1 CXCR2 CXCR4 CXCR5 CXCR6 Correlation Between ACE2 and PD-L1 TAP1 TAP2 TAPBP BTNL2 HHLA2 ICOS IL2RA IL6 IL6R KLRC1 KLRK1 LTA MICB PVR ULBP1 CCL1 CCL11 CCL13 CCL14 CCL15 CCL16 CCL17 CCL18 CCL19 CCL2 CCL20 CCL21 CCL22 CCL23 CCL24 CCL25 CCL26 CCL27 CCL28 CCL3 CCL4 CCL5 CCL7 CCL8 CX3CL1 CXCL1 CXCL10 CXCL11 CXCL12 CXCL13 CXCL14 CXCL16 CXCL17 CXCL2 CXCL3 CXCL5 CXCL6 CXCL9 XCL1 XCL2 A C C B L C A B R C A C E S C C H O L C O A D D L B C E S C A G B M H N S C K I C H K I R C K I R P L A M L L G G L I H C L U A D L U S C M E S O O V P A A D P C P G P R A D R E A D S A R C S K C M S T A D T G C T T H C A T H Y M U C E C U C S U VUCEC UCS UVM CCL1 CCL11 CCL14 CCL15 CCL16 CCL17 CCL18 CCL19 CCL2 CCL21 CCL22 CCL24 CCL25 CCL26 CCL27 CCL28 CCL4 CCL5 CCL7 CCL8 CXCL1 CXCL11 CXCL12 CXCL14 CXCL16 CXCL17 CXCL2 CXCL5 CXCL6 CXCL9 XCL1 XCL2 BTNL2 CD27 CD276 CD28 CD48* CCL1 CCL11 CCL14 CCL15 CCL16 CCL17 CCL18 CCL19 CCL2 CCL21 CCL22 CCL24 CCL25 CCL26 CCL27 CCL28 CCL4 CCL5 CCL7 CCL8 CXCL1 CXCL11 CXCL12 CXCL14 CXCL16 CXCL17 CXCL2 CXCL5 CXCL6 CXCL9 XCL1 XCL2 BTNL2 CD27 CD276 CD28 CD48A B C D OV CCR1 CCR10 CCR2 CCR3 CCR4 CCR5 CCR6 CCR7 CCR8 CCR9 CX3CR1 CXCR1 CXCR2 CXCR3 CXCR4 CXCR5 CXCR6 XCR1 B2M OA PA1 Group TAP1 TAP2 TAPBP BTNL2 HHLA2 ICOS IL2RA IL6 IL6R KLRC1 KLRK1 LTA MICB PVR ULBP1 CCL1 CCL11 CCL13 CCL14 CCL15 CCL16 CCL17 CCL18 CCL19 CCL2 CCL20 CCL21 CCL22 CCL23 CCL24 CCL25 CCL26 CCL27 CCL28 CCL3 CCL4 CCL5 CCL7 CCL8 CX3CL1 CXCL1 CXCL10 CXCL11 CXCL12 CXCL13 CXCL14 CXCL16 CXCL17 CXCL2 CXCL3 CXCL5 CXCL6 CXCL9 XCL1 High A Low A A TumorPurity ns ns **** **** **** **** **** **** **** **** **** **** **** **** **** **** * **** **** **** **** **** **** + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia Angiotensin-converting enzyme 2: a functional receptor for SARS coronavirus A pneumonia outbreak associated with a new coronavirus of probable bat origin Single-cell RNA-seq data analysis on the receptor ACE2 expression reveals the potential risk of different human organs vulnerable to 2019-nCoV infection Single-cell RNA sequencing analysis of SARS-CoV-2 entry receptors in human organoids Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries Genomic landscape of the immune microenvironments of brain metastases in breast cancer ACE2 inhibits breast cancer angiogenesis via suppressing the Genetic alteration, RNA expression, and DNA methylation profiling of coronavirus disease 2019 (COVID-19) receptor ACE2 in malignancies: a pan-cancer analysis Angiotensin-Converting Enzymes (ACE and ACE2) as Potential Targets for Malignant Epithelial Neoplasia: Review and Bioinformatics Analyses Focused in Oral Squamous Cell Carcinoma A profiling analysis on the receptor ACE2 expression reveals the potential risk of different type of cancers vulnerable to SARS-CoV-2 infection ACE2 correlated with immune infiltration serves as a prognostic biomarker in endometrial carcinoma and renal papillary cell carcinoma: implication for COVID-19 Turning Cold into Hot: Firing up the Tumor Microenvironment The Next Hurdle in Cancer Immunotherapy: Overcoming the Non-T-Cell-Inflamed Tumor Microenvironment Sensitization to immune checkpoint blockade through activation of a STAT1/NK axis in the tumor microenvironment The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data PrognoScan: a new database for meta-analysis of the prognostic value of genes LinkedOmics: analyzing multi-omics data within and across 32 cancer types Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade TIP: A Web Server for Resolving Tumor Immunophenotype Profiling 0 for analysis of tumor-infiltrating immune cells Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data TISIDB: an integrated repository portal for tumor-immune system interactions Inferring tumour purity and stromal and immune cell admixture from expression data IFN-gamma-related mRNA profile predicts clinical response to PD-1 blockade Siglec15 shapes a non-inflamed tumor microenvironment and predicts the molecular subtype in bladder cancer GSVA: gene set variation analysis for microarray and RNA-seq data pRRophetic: an R package for prediction of clinical chemotherapeutic response from tumor gene expression levels 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 Cancer Immunotherapy Targets Based on Understanding the T Cell-Inflamed Versus Non-T Cell-Inflamed Tumor Microenvironment Predictive biomarkers and mechanisms underlying resistance to PD1/PD-L1 blockade cancer immunotherapy Specific TP53 subtype as biomarker for immune checkpoint inhibitors in lung adenocarcinoma Genotyping Squamous Cell Lung Carcinoma in Colombia (Geno1.1-CLICaP) Mismatch Repair Deficiency and Microsatellite Instability in Triple-Negative Breast Cancer: A Retrospective Study of 440 Patients SARS-CoV-2 vaccines for cancer patients treated with immunotherapies: Recommendations from the French society for ImmunoTherapy of Cancer (FITC) Cancer patients and research during COVID-19 pandemic: A systematic review of current evidence SARS-COV-2 infection and lung tumor microenvironment COVID-19: a potential driver of immunemediated breast cancer recurrence? Neutrophil extracellular traps produced during inflammation awaken dormant cancer cells in mice SARS-CoV-2-induced remission of Hodgkin lymphoma Complete spontaneous remission of diffuse large B-cell lymphoma of the maxillary sinus after concurrent infections Delivery and Biosafety of Oncolytic Virotherapy Systematic characterization of non-coding RNAs in triple-negative breast cancer MUC1-C Induces PD-L1 and Immune Evasion in Triple-Negative Breast Cancer The authors have no competing interests. -L1 positive MAFK, CCNT2, MAFF, BHLHE40, CREB1, TCF3, FOS, IRF1, TAF1, TBP, MEF2C, HDAC1, IKZF1, SPI1, STAT3, CEBPB, TFAP2C , EBF1, E2F6, E2F4, MBD4, POU2F2