Abstract Immune checkpoint inhibitor (ICI) therapy has drastically improved the treatment strategies for mucosal head and neck squamous cell cancer (HNSCC). To increase treatment response rates for highly targeted ICI therapies, predictive and prognostic biomarkers are being actively explored. Specifically, the cytokine expression patterns in the tumor microenvironment (TME) are now recognized as key to understanding immune responsive and resistant phenotypes. Cytokines play an essential role in the regulation of the TME, specifically in modulating the proliferation and differentiation of immune cells. Here, we have studied spatial signatures of various cytokines within the TME of metastatic/recurrent HNSCC tumors treated with Pembrolizumab/Nivolumab. We utilized the PhenoCycler-Fusion to perform whole-slide, single cell resolution spatial phenotyping of the TME of HNSCC tumors from a cohort of n=40 patients. The discovery cohort consisted of patients who had complete vs. partial vs. stable vs. progressive responses to ICI therapy. Transcriptomic profiling of more than 60 RNA targets for various subfamily of chemokines, interleukins, and immune cell lineages was achieved using Akoya’s novel high plex RNA detection technology. Our study identified distinct spatial signatures that implicate certain cytokines in either tumor progression or regression. Specifically, we have identified areas of high and low CXCL9 and CXCL10 expression in several tumor regions that reflect immune-cell landscapes associated with resistance and sensitivity to immunotherapy. Our study demonstrates the power of unbiased spatial phenotyping with whole-slide imaging to identify biomarkers associated with response to ICI therapy in HNSCC.
Immune checkpoint blockade therapies (ICB) have led to durable benefits in a subset of patients in non-small cell lung cancer (NSCLC). Developing predictive signatures of response and resistance to ICB requires a thorough understanding of the tumor biology and the tumor microenvironment. In this study, we spatially profiled the tumor-immune contexture and metabolic activity of first-line ICB treated patients to gain further understanding of the functional cell types, states, and interactions to predict treatment sensitivity and resistance.
Methods
We profiled the tumor microenvironment using an ultra-deep multiplexed immunofluorescence (mIF) panel of 45 proteins two independent retrospective cohorts of NSCLC patients treated with ICB (n=117, 72 with clinical outcome). Utilizing Nucleai's deep learning multiplex imaging analysis pipeline, we identified distinct tumor phenotypes based on metabolic and immune activity. More than 1000 spatial features were calculated combining cell types, phenotypic state, spatial context, and cell-cell interactions. We built a multivariate model of clinical benefit from spatial features in a training set (n=53) and validated the model on unseen samples (n=19). Model performance was evaluated using ROC analysis, as well as association with clinical endpoints to therapy (progression free survival PFS, overall survival OS).
Results
271,193 cells were segmented from 117 tissue cores of NSCLC, and assigned to 13 cell types by known marker expression profiles. We identified cell subtypes, by clustering positive prediction probabilities of metabolically and immunologically relevant markers. Five distinct tumor clusters and phenotypes were identified by the differential expression of CD44, G6PD, Hexokinase-1, HLA-A, PD-L1, and oxidative phosphorylation proteins. ICB-related PFS was significantly associated with the predominant tumor clusters in each patient. Patients in the CD44-high tumor cluster had a prolonged PFS vs the G6PD and Hexokinase-1 high tumor clusters (median PFS of 23.2, 3.0 and 3.3 months respectively, p=0.02). The multivariate ICB outcome prediction model incorporated spatial features associated with response under univariate analysis within each tumor cluster. Performance evaluation of the model demonstrated high AUC (0.84, p=0.007) and compared to the predicted resistance group, the predicted responders group had higher clinical benefit rate (81.8% vs. 25, p=0.02), as well as higher median PFS (13.7 vs 2.3 months, p=0.0007) and median OS (Not reached vs. 9.3 months, p=0.001).
Conclusions
Taken together, our study provides an mIF-based predictive model for ICB response in NSCLC, based on immuno-metabolic profiles of the tumor. This work underscores the importance of immuno-metabolic profiling of the tissue for building accurate predictive models for treatment outcomes.
Ethics Approval
Informed Written Consent was obtained for this study from study participants. The study has University of Queensland Human Research Ethics approval.
Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer that has few effective treatment options due to its lack of targetable hormone receptors. Whilst the degree of tumour infiltrating lymphocytes (TILs) has been shown to associate with therapy response and prognosis, deeper characterization of the molecular diversity that may mediate chemotherapeutic response is lacking. Here we applied targeted proteomic analysis of both chemotherapy sensitive and resistant TNBC tissue samples by the Nanostring GeoMx Digital Spatial Platform (DSP). By quantifying 68 targets in the tumour and tumour microenvironment (TME) compartments and performing differential expression analysis between responsive and non-responsive tumours, we show that increased ER-alpha expression and decreased 4-1BB and MART1 within the stromal compartments is associated with adjuvant chemotherapy response. Similarly, higher expression of GZMA, STING and fibronectin and lower levels of CD80 were associated with response within tumour compartments. Univariate overall-survival (OS) analysis of stromal proteins supported these findings, with ER-alpha expression (HR=0.19, p=0.0012) associated with better OS while MART1 expression (HR=2.3, p=0.035) was indicative of poorer OS. Proteins within tumour compartments consistent with longer OS included PD-L1 (HR=0.53, p=0.023), FOXP3 (HR=0.5, p=0.026), GITR (HR=0.51, p=0.036), SMA (HR=0.59, p=0.043), while EPCAM (HR=1.7, p=0.045), and CD95 (HR=4.9, p=0.046) expression were associated with shorter OS. Our data provides early insights into the levels of these markers in the TNBC tumour microenvironment, and their association with chemotherapeutic response and patient survival.
Abstract Introduction Immunotherapies, such as immune checkpoint inhibitors (ICI) have shown durable benefit in a subset of non-small cell lung cancer (NSCLC) patients. The mechanisms for this are not fully understood, however the composition and activation status of the cellular milieu contained within the tumour microenvironment (TME) is becomingly increasingly recognised as a driving factor in treatment-refractory disease. Methods Here, we employed multiplex IHC (mIHC), and digital spatial profiling (DSP) to capture the targeted immune proteome and transcriptome of tumour and TME compartments of pre-treatment samples from a 2 nd line NSCLC ICI-treated cohort (n=41 patients; n=25 responders, n=16 non-responders). Results We demonstrate by mIHC that the interaction of CD68 + macrophages with PD1 + , FoxP3 + cells is significantly enriched in ICI refractory tumours (p=0.012). Our study revealed that patients sensitive to ICI therapy expressed higher levels of IL2 receptor alpha (CD25, p=0.028) within the tumour compartments, which corresponded with the increased expression of IL2 mRNA (p=0.001) within their stroma, indicative of key conditions for ICI efficacy prior to treatment. IL2 mRNA levels within the stroma positively correlated with the expression of pro-apoptotic markers cleaved caspase 9 (p=2e-5) and BAD (p=5.5e-4) and negatively correlated with levels of memory T cells (CD45RO) (p=7e-4). Immuno-inhibitory markers CTLA-4 (p=0.021) and IDO-1 (p=0.023) were also supressed in ICI-responsive patients. Of note, tumour CD44 (p=0.02) was depleted in the response group and corresponded inversely with significantly higher stromal expression of its ligand SPP1 (osteopontin, p=0.008). Analysis of differentially expressed transcripts indicated the potential inhibition of stromal interferon-gamma (IFNγ) activity, as well as estrogen-receptor and Wnt-1 signalling activity within the tumour cells of ICI responsive patients. Cox survival analysis indicated tumour CD44 expression was associated with poorer prognosis (HR=1.61, p=0.01), consistent with its depletion in ICI sensitive patients. Similarly, stromal CTLA-4 (HR=1.78, p=0.003) and MDSC/M2 macrophage marker ARG1 (HR=2.37, p=0.01) were associated with poorer outcome while levels of apoptotic marker BAD (HR=0.5, p=0.01) appeared protective. Interestingly, stromal mRNA for E-selectin (HR=652, p=0.001), CCL17 (HR=70, p=0.006) and MTOR (HR=1065, p=0.008) were highly associated with poorer outcome, indicating pro-tumourigenic features in the tumour microenvironment that may facilitate ICI resistance. Conclusions Through multi-modal approaches, we have dissected the characteristics of NSCLC and provide evidence for the role of IL2 and stromal activation by osteopontin in the efficacy of current generations of ICI therapy. The enrichment of SPP1 in the stroma of ICI sensitive patients in our data is a novel finding, indicative of stromal activation that may aid immune cell survival and activity despite no clear association with increased levels of immune infiltrate.
Thrombotic and microvascular complications are frequently seen in deceased COVID-19 patients. However, whether this is caused by direct viral infection of the endothelium or inflammation-induced endothelial activation remains highly contentious.Here, we use patient autopsy samples, primary human endothelial cells and an in vitro model of the pulmonary epithelial-endothelial cell barrier.We show that primary human endothelial cells express very low levels of the SARS-CoV-2 receptor ACE2 and the protease TMPRSS2, which blocks their capacity for productive viral infection, and limits their capacity to produce infectious virus. Accordingly, endothelial cells can only be infected when they overexpress ACE2, or are exposed to very high concentrations of SARS-CoV-2. We also show that SARS-CoV-2 does not infect endothelial cells in 3D vessels under flow conditions. We further demonstrate that in a co-culture model endothelial cells are not infected with SARS-CoV-2. Endothelial cells do however sense and respond to infection in the adjacent epithelial cells, increasing ICAM-1 expression and releasing pro-inflammatory cytokines.Taken together, these data suggest that in vivo, endothelial cells are unlikely to be infected with SARS-CoV-2 and that infection may only occur if the adjacent pulmonary epithelium is denuded (basolateral infection) or a high viral load is present in the blood (apical infection). In such a scenario, whilst SARS-CoV-2 infection of the endothelium can occur, it does not contribute to viral amplification. However, endothelial cells may still play a key role in SARS-CoV-2 pathogenesis by sensing adjacent infection and mounting a pro-inflammatory response to SARS-CoV-2.
Abstract Non-small cell lung cancer (NSCLC), including adenocarcinoma and squamous cell carcinoma subtypes, is a leading cause of cancer deaths worldwide. Treatment of NSCLC has advanced from chemotherapy modalities to the use of immunotherapy, namely immune checkpoint inhibitors (ICIs) which enhance the adaptive immune response against tumour cells. Thus, while the immune cell composition of tumours and its influence on treatment outcomes is poorly understood, it likely holds the key to effective, personalized treatment regimens. Here we profiled an adjuvant chemotherapy (n=61) as well as a second line immunotherapy (n=42) NSCLC cohort by the Phenocycler CODEX technology (highplex spatial proteomics) to investigate the association between immune composition and patient outcome. We applied a panel of 38 markers to delineate naïve, memory, cytotoxic and hyperactivated T cell states, as well as B cells, Tregs and myeloid lineage innate immune cell types. Our study sought to understand the heterogeneity of tumour-immune composition across patients and investigate the spatial neighbourhoods and clusters that these cells inhabit within TMA cores. We used Phenoplex™ software for tissue segmentation (into classes for tumor, stroma, artifacts, blood vessels, etc.), cellular segmentation, and cellular phenotyping based on thresholds for each marker, and then performed spatial analyses (distances, interactions, neighborhoods) using SpatialMap1 to identify cellular motifs associated with clinical phenotypes. Our study has identified spatial features associated with immune contexture linked to therapy outcome in both chemotherapy and immunotherapy modalities. Taken together, our study demonstrates the utility of spatial proteomics to identify cellular features associated with outcome to therapy in lung cancer. 1) Trevino A, Ivison G, Hamel S, Chiou A (2022). SpatialMap: Analysis of Spatial Biology Data. R package version 0.4.57. Citation Format: James Monkman, Afshin Moradi, Connor O'Leary, Zhenqin Wu, Steven Hamel, David Mason, Fabian Schneider, James Robert Mansfield, Aaron Meyer, Ken O'Byrne, Arutha Kulasinghe. Immune profiling of immunotherapy and adjuvant chemotherapy pretreatment NSCLC tissues by CODEX. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4627.
Abstract The composition and activation status of the cellular milieu contained within the tumour microenvironment (TME) is becoming increasingly recognized as a driving factor for immunotherapy response. Here, we employed multiplex immunohistochemistry (mIHC), and digital spatial profiling (DSP) to capture the targeted immune proteome and transcriptome of tumour and TME compartments from an immune checkpoint inhibitor (ICI)‐treated ( n = 41) non‐small cell lung cancer (NSCLC) patient cohort. We demonstrate by mIHC that the interaction of CD68 + macrophages with PD1 + , FoxP3 + cells is enriched in ICI refractory tumours ( p = 0.012). Patients responsive to ICI therapy expressed higher levels of IL2 receptor alpha (CD25, p = 0.028) within their tumour compartments, which corresponded with increased IL2 mRNA ( p = 0.001) within their stroma. In addition, stromal IL2 mRNA levels positively correlated with the expression of pro‐apoptotic markers cleaved caspase 9 ( p = 2e −5 ) and BAD ( p = 5.5e −4 ) and negatively with levels of memory marker, CD45RO ( p = 7e −4 ). Immuno‐inhibitory markers CTLA‐4 ( p = 0.021) and IDO‐1 ( p = 0.023) were suppressed in ICI‐responsive patients. Tumour expression of CD44 was depleted in the responsive patients ( p = 0.02), while higher stromal expression of one of its ligands, SPP1 ( p = 0.008), was observed. Cox survival analysis also indicated tumour CD44 expression was associated with poorer prognosis (hazard ratio [HR] = 1.61, p = 0.01), consistent with its depletion in ICI‐responsive patients. Through multi‐modal approaches, we have dissected the characteristics of NSCLC immunotherapy treatment groups and provide evidence for the role of several markers including IL2, CD25, CD44 and SPP1 in the efficacy of current generations of ICI therapy.
Abstract Background: Cutaneous squamous cell carcinoma (cSCC) is the second most common skin cancer and represents a major global health burden. Approximately 50% of cSCC patients develop primary resistance and 20% will develop secondary resistance to immune checkpoint inhibitors (ICI). There are limited biomarkers that reflect the tumor dynamics predictive of response to ICI therapy, and therefore we need to explore new biomarkers that can better understand the cSCC tumor microenvironment (TME). Methods: Our retrospective study profiled pre-treatment cutaneous skin cancer tissues from patients with immunotherapy sensitive and resistant diseases. In this exploratory study, we designed a high-dimensional > 50-plex antibody panel identifying cell lineages, activation states, and immune checkpoints. In addition, we deployed a combination of antibodies for a novel multiplexed metabolic readout, to enhance our in situ profiling beyond well-known TME targets. Whole-slide spatial phenotyping was conducted on the PhenoCycler-Fusion spatial biology platform. Highplex image analysis was performed using Phenoplex (Visiopharm), including tissue segmentation and cellular phenotyping. Here, we used a deep learning approach to discriminate tumor regions and margins based on their morphology. Spatial analyses including phenotypic composition, cell-to-cell localization, and cellular neighborhoods were performed and compared to clinicopathological findings and responses to ICI therapy. Results: We have profiled the TME of cSCC via ultrahigh-plex, single-cell spatial analyses of whole tissues. Our data have revealed unique cell phenotype compositions and metabolic states within the TME of different patient cohorts. Areas of metabolic activity were visualized and compartmentalized to determine spatial distributions of inherent sensitivity and resistance to ICI therapy. Additionally, a comparison of the phenotypic expression patterns between immunotherapy sensitive and resistant cohorts in the tumor, stromal and margin regions showed relevant differences in the immune content. Taken together, this high-dimensional imaging approach of the cSCC TME has revealed new tissue insights. Conclusion: There is a need to understand the contexture of the cSCC immune microenvironment. Here we identify distinct cellular phenotypic compositions and metabolic states in tissues from immune-competent and immunocompromised cSCC. We thereby confirm biological differences in tissue composition and cellular interactions between these tissues and provide putative new biomarkers for cSCC patient stratification. Citation Format: Niyati Jhaveri, Dmytro Klymyshyn, Bassem B. Cheikh, James Monkman, Dan Winkowski, James Mansfield, Subham Basu, Michael Prater, Nadine Nelson, Gabrielle Belz, Oliver Braubach, Arutha Kulasinghe. The development of a spatial metabolic map of immunotherapy sensitive and resistant cutaneous skin carcinoma. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6774.
The utilization of single-cell resolved spatial transcriptomics to delineate immune responses during SARS-CoV-2 infection was able to identify M1 macrophages to have elevated expression of IFI27 in areas of infection.
'/%3e%3c/defs%3e%3cpath fill='%23012169' d='M0 0h10080v5040H0z'/%3e%3cpath stroke='%23fff' stroke-width='.6' d='m0 0 6 3m0-3L0 3' clip-path='url(%23b)' transform='scale(840)'/%3e%3cpath stroke='%23e4002b' stroke-width='.4' d='m0 0 6 3m0-3L0 3' clip-path='url(%23c)' transform='scale(840)'/%3e%3cpath stroke='%23fff' stroke-width='840' d='M2520 0v2520M0 1260h5040'/%3e%3cpath stroke='%23e4002b' stroke-width='504' d='M2520 0v2520M0 1260h5040'/%3e%3cg fill='%23fff'%3e%3cuse xlink:href='%23d' x='2520' y='3780'/%3e%3cuse xlink:href='%23a' x='7560' y='4200'/%3e%3cuse xlink:href='%23a' x='6300' y='2205'/%3e%3cuse xlink:href='%23a' x='7560' y='840'/%3e%3cuse xlink:href='%23a' x='8680' y='1869'/%3e%3cuse xlink:href='%23e' x='8064' y='2730'/%3e%3c/g%3e%3c/svg%3e)
'/%3e%3cpath id='l' d='M-26.25 0h52.5v-12h-40.5v-16h33v-12h-33v-11H25v-12h-51.25z'/%3e%3cpath id='k' d='M-31.5 0h12v-48l14 48h11l14-48V0h12v-70H14L0-22l-14-48h-17.5z'/%3e%3cpath id='b' fill-rule='evenodd' d='M0 0a31.5 35 0 0 0 0-70A31.5 35 0 0 0 0 0m0-13a18.5 22 0 0 0 0-44 18.5 22 0 0 0 0 44'/%3e%3cpath id='d' fill-rule='evenodd' d='M-31.5 0h13v-26h28a22 22 0 0 0 0-44h-40zm13-39h27a9 9 0 0 0 0-18h-27z'/%3e%3cpath id='n' d='M-15.75-22C-15.75-15-9-11.5 1-11.5s14.74-3.25 14.75-7.75c0-14.25-46.75-5.25-46.5-30.25C-30.5-71-6-70 3-70s26 4 25.75 21.25H13.5c0-7.5-7-10.25-15-10.25-7.75 0-13.25 1.25-13.25 8.5-.25 11.75 46.25 4 46.25 28.75C31.5-3.5 13.5 0 0 0c-11.5 0-31.55-4.5-31.5-22z'/%3e%3cuse xlink:href='%23a' id='o' transform='scale(31.5)'/%3e%3cuse xlink:href='%23a' id='p' transform='scale(26.25)'/%3e%3cuse xlink:href='%23a' id='r' transform='scale(21)'/%3e%3cuse xlink:href='%23a' id='q' transform='scale(15)'/%3e%3cuse xlink:href='%23a' id='s' transform='scale(10.5)'/%3e%3cg id='m'%3e%3cclipPath id='c'%3e%3cpath d='M-31.5 0v-70h63V0zM0-47v12h31.5v-12z'/%3e%3c/clipPath%3e%3cuse xlink:href='%23b' clip-path='url(%23c)'/%3e%3cpath d='M5-35h26.5v10H5z'/%3e%3cpath d='M21.5-35h10V0h-10z'/%3e%3c/g%3e%3cg id='h'%3e%3cuse xlink:href='%23d'/%3e%3cpath d='M28 0c0-10 0-32-15-32H-6c22 0 22 22 22 32'/%3e%3c/g%3e%3cg id='a' fill='%23fff'%3e%3cg id='f'%3e%3cpath id='e' d='M0-1v1h.5' transform='rotate(18 0 -1)'/%3e%3cuse xlink:href='%23e' transform='scale(-1 1)'/%3e%3c/g%3e%3cuse xlink:href='%23f' transform='rotate(72)'/%3e%3cuse xlink:href='%23f' transform='rotate(-72)'/%3e%3cuse xlink:href='%23f' transform='rotate(144)'/%3e%3cuse xlink:href='%23f' transform='rotate(216)'/%3e%3c/g%3e%3c/defs%3e%3crect width='100%25' height='100%25' x='-50%25' y='-50%25' fill='%23009b3a'/%3e%3cpath fill='%23fedf00' d='M-1743 0 0 1113 1743 0 0-1113z'/%3e%3ccircle r='735' fill='%23002776'/%3e%3cclipPath id='g'%3e%3ccircle r='735'/%3e%3c/clipPath%3e%3cpath fill='%23fff' d='M-2205 1470a1785 1785 0 0 1 3570 0h-105a1680 1680 0 1 0-3360 0z' clip-path='url(%23g)'/%3e%3cg fill='%23009b3a' transform='translate(-420 1470)'%3e%3cuse xlink:href='%23b' y='-1697.5' transform='rotate(-7)'/%3e%3cuse xlink:href='%23h' y='-1697.5' transform='rotate(-4)'/%3e%3cuse xlink:href='%23i' y='-1697.5' transform='rotate(-1)'/%3e%3cuse xlink:href='%23j' y='-1697.5' transform='rotate(2)'/%3e%3cuse xlink:href='%23k' y='-1697.5' transform='rotate(5)'/%3e%3cuse xlink:href='%23l' y='-1697.5' transform='rotate(9.75)'/%3e%3cuse xlink:href='%23d' y='-1697.5' transform='rotate(14.5)'/%3e%3cuse xlink:href='%23h' y='-1697.5' transform='rotate(17.5)'/%3e%3cuse xlink:href='%23b' y='-1697.5' transform='rotate(20.5)'/%3e%3cuse xlink:href='%23m' y='-1697.5' transform='rotate(23.5)'/%3e%3cuse xlink:href='%23h' y='-1697.5' transform='rotate(26.5)'/%3e%3cuse xlink:href='%23j' y='-1697.5' transform='rotate(29.5)'/%3e%3cuse xlink:href='%23n' y='-1697.5' transform='rotate(32.5)'/%3e%3cuse xlink:href='%23n' y='-1697.5' transform='rotate(35.5)'/%3e%3cuse xlink:href='%23b' y='-1697.5' transform='rotate(38.5)'/%3e%3c/g%3e%3cuse xlink:href='%23o' x='-600' y='-132'/%3e%3cuse xlink:href='%23o' x='-535' y='177'/%3e%3cuse xlink:href='%23p' x='-625' y='243'/%3e%3cuse xlink:href='%23q' x='-463' y='132'/%3e%3cuse xlink:href='%23p' x='-382' y='250'/%3e%3cuse xlink:href='%23r' x='-404' y='323'/%3e%3cuse xlink:href='%23o' x='228' y='-228'/%3e%3cuse xlink:href='%23o' x='515' y='258'/%3e%3cuse xlink:href='%23r' x='617' y='265'/%3e%3cuse xlink:href='%23p' x='545' y='323'/%3e%3cuse xlink:href='%23p' x='368' y='477'/%3e%3cuse xlink:href='%23r' x='367' y='551'/%3e%3cuse xlink:href='%23r' x='441' y='419'/%3e%3cuse xlink:href='%23p' x='500' y='382'/%3e%3cuse xlink:href='%23r' x='365' y='405'/%3e%3cuse xlink:href='%23p' x='-280' y='30'/%3e%3cuse xlink:href='%23r' x='200' y='-37'/%3e%3cuse xlink:href='%23o' y='330'/%3e%3cuse xlink:href='%23p' x='85' y='184'/%3e%3cuse xlink:href='%23p' y='118'/%3e%3cuse xlink:href='%23r' x='-74' y='184'/%3e%3cuse xlink:href='%23q' x='-37' y='235'/%3e%3cuse xlink:href='%23p' x='220' y='495'/%3e%3cuse xlink:href='%23r' x='283' y='430'/%3e%3cuse xlink:href='%23r' x='162' y='412'/%3e%3cuse xlink:href='%23o' x='-295' y='390'/%3e%3cuse xlink:href='%23s' y='575'/%3e%3c/svg%3e)