Stromal subsets modulate T-cell infiltration in early breast cancer

Stromal subsets modulate T-cell infiltration in early breast cancer | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Stromal subsets modulate T-cell infiltration in early breast cancer View ORCID Profile Julia Chen , View ORCID Profile Hanyun Zhang , Travis Ruan , View ORCID Profile Sunny Wu , Iveta Slapetova , View ORCID Profile Ewan Millar , Peter Graham , View ORCID Profile Jodi Lynch , Lois Browne , View ORCID Profile Elgene Lim , View ORCID Profile Alexander Swarbrick doi: https://doi.org/10.1101/2025.10.20.683407 Julia Chen 1 St George Cancer Care Centre, St George Hospital , Sydney, Australia 2 Cancer Ecosystems Program, Garvan Institute of Medical Research, Sydney, Australia 3 School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales , Sydney, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Julia Chen Hanyun Zhang 2 Cancer Ecosystems Program, Garvan Institute of Medical Research, Sydney, Australia 3 School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales , Sydney, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Hanyun Zhang Travis Ruan 2 Cancer Ecosystems Program, Garvan Institute of Medical Research, Sydney, Australia 3 School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales , Sydney, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sunny Wu 4 Immunology Discovery & Oncology Bioinformatics , Genentech, CA USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sunny Wu Iveta Slapetova 5 Katharina Gaus Light Microscopy Facility, University of New South Wales , Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ewan Millar 6 Department of Anatomical Pathology, NSW Health Pathology, St George Hospital , Sydney, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ewan Millar Peter Graham 1 St George Cancer Care Centre, St George Hospital , Sydney, Australia 3 School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales , Sydney, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jodi Lynch 1 St George Cancer Care Centre, St George Hospital , Sydney, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jodi Lynch Lois Browne 1 St George Cancer Care Centre, St George Hospital , Sydney, Australia 3 School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales , Sydney, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Elgene Lim 2 Cancer Ecosystems Program, Garvan Institute of Medical Research, Sydney, Australia 3 School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales , Sydney, Australia 7 The Kinghorn Cancer Centre, St Vincent’s Hospital , Sydney, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Elgene Lim Alexander Swarbrick 2 Cancer Ecosystems Program, Garvan Institute of Medical Research, Sydney, Australia 3 School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales , Sydney, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alexander Swarbrick For correspondence: a.swarbrick{at}garvan.org.au Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Recent studies of the tumour microenvironment have elucidated the heterogeneity of stromal cells, with increasing evidence suggesting that stromal subsets play an important role in regulating anti-tumour immunity in breast cancer. However, the functional diversity of these cells within the tumour immune microenvironment and how they interact with immune cells in a spatial and clinical context remain poorly understood. We performed multiplex immunofluorescence on tumour microarrays from two cohorts consisting of 591 breast cancer patients to assess the abundance and spatial co-localisation of stromal and immune subsets and their correlation with clinicopathological features and patient outcomes. We found that stromal subsets are spatially distinct. We found that stromal cells were spatially distinct. A perivascular-like subset that was disseminated throughout the stroma rather than restricted to vessel-adjacent regions was enriched in an immune cold environment and associated with T-cell exclusion. Enrichment for PVLs was prognostic of poorer survival, independent of their role in T cell exclusion. An inflammatory-like cancer-associated fibroblast subset was associated with the stromal segregation of T cells and T cell exhaustion. Our findings highlight the differential impact of stromal subsets on immune infiltration and activation within the breast cancer TME with implications for patient outcomes. Introduction Breast cancer is the most commonly diagnosed cancer in women and despite advancements in treatment over the last few decades, it remains the leading cause of cancer-related death among females worldwide ( 1 ). Recent studies into the tumour microenvironment (TME) have revealed complex functional roles of stromal cells in tumorigenesis, progression and suppression ( 2 ). Although there is increasing evidence that stromal cells affect tumour immunity, the diversity of stromal cell subsets and their interactions with immune cells in a clinically relevant context remain poorly understood. Recent advances in technologies such as single-cell RNA-sequencing (scRNA-seq) have allowed the characterisation of tumours in tremendous resolution and results highlight the heterogeneity of stromal and immune cells within the TME. Different cancer-associated fibroblast (CAF) subpopulations or states with discrete phenotypic, transcriptional and functional properties have been described in various cancer types ( 3 – 7 ). In breast cancer, our previous scRNA-seq study has revealed four distinct populations of stromal cells: myofibroblast-like CAF (myCAF), inflammatory-like CAF (iCAF) and two perivascular-like cells (PVLs) in differentiated and immature states ( 8 , 9 ). Each of these subpopulations displayed distinct morphologies, spatial relationships and functional properties. Similarly, scRNA-seq of immune cells showed that a significant heterogeneity exists in the infiltrating T cell population ( 10 ). The effectiveness of CD8 + T cells is regulated by immune checkpoint molecules including PD-1 and its ligand PD-L1 and these are the targets of most currently available immunotherapies. One of the major limitations of single-cell technologies is the lack of information on the structural organisation of cells within a tumour, which has increasingly been recognised as a factor that contributes to the heterogeneity, variable tumour behaviours and differential response to treatments observed in most cancers ( 11 ). Spatial profiling has hence been explored to provide a more comprehensive view of the intricate cellular makeup of solid tumours, enabling examination of different cell types, their locations and interaction within different layers of spatial organisation. Integration of this with clinical data from large cohorts enables the exploration of spatially defined TME components as predictors of treatment response and patient outcomes, with the potential to discover novel biomarkers and therapeutic targets, thereby improving the precision of cancer diagnosis and management ( 12 ). Here we performed multiplex immunofluorescence (mIF) on tumour microarrays (TMAs) from two primary breast cancer cohorts using a panel of stromal and immune markers defined using our scRNA-seq results. The abundance and spatial co-localisation of annotated stromal and immune subsets were linked with clinicopathological data to assess their clinical significance. Materials and Methods Clinical cohorts This study consists of TMA cores from 591 breast cancer patients with luminal and TNBC subtypes. All tumours were sampled pre-treatment. Luminal patients (n=369) were recruited as part of the Breast Boost trial where patients received wide local excision with whole breast irradiation with randomisation to cavity boost or no boost ( 13 ). The TNBC cases (n=) were identified by a review of the Oncology database at St George Hospital between 2004 and 2019 ( 14 ). Tumours were classified into molecular subtypes according to the following definitions: luminal A: ER + ≥1%, PR + >20%, HER2 - , Ki67 < 14%; Luminal B: ER + , PR + ≤ 20% and/or HER2 + and/or Ki67 ≥14%; TNBC: ER/PR - (<1%), HER2 - . TMA slides were constructed with 3×1mm cores sampled from the periphery of tumour blocks marked by a breast pathologist on a H&E slide. Paraffin sections were cut at 4μm onto Superfrost glass slides (ThermoFisher Scientific, Waltham, MA, USA) and stained using H&E using a Leica Bond Rx automated immunostainer machine (Leica Biosystems, Nussloch, Germany). Multiplexed immunofluorescence Opal 9 TM was used for mIF imaging (Akoya Biosicences, Marlborough, MA, USA), with primary antibody conditions optimised for DAB staining applied to both monoplex and multiplex optimisations. Each biomarker antibody was paired with a specific Opal fluorophore based on the co-expression patterns of biomarkers in the tissue and their anticipated protein expression levels ( Supplementary table 1 ). Image analysis Slides were scanned using the Vectra Polaris 3.0 (Akoya Biosciences) using 40 × magnification. Images were analysed using QuPath v0.2.3 ( https://qupath.github.io ). All TMA files and their corresponding TMA maps were imported using the TMA module. Tissue core identification numbers generated by the TMA de-arrayer were verified before analysis. Representative training slides were then selected to create cell detection using trainable machine learning algorithms in the pixel classifier. PanCK was used to segment tumour epithelium and stroma regions. DAPI was used to guide cell segmentation using the inbuilt cell detection algorithm. A separate cell classifier was created for each antibody. The combined tumour and cell classifiers were then applied to each TMA core. QuPath datasets were then linked with corresponding clinical data using patient ID. For patients with more than one core, the median values of these cores were used for analysis. The abundance of cell types was measured for each core as the percentage of cells per total number of detections. Cell proportions were computed within the stromal and tumour regions separately. To account for differences in locations, the proportion of immune cells were taken from the entire core whereas for stromal cells, proportions were taken from stromal regions only to reduce the impact of misclassified stromal cells in tumour regions. Spatial analysis To investigate the spatial proximity between cell types, we measured the proportion of cell type A with nearby cell type B within a radius of 30-100 µ m, with an interval of 10 µ m. Cell type A with at least one cell type B within 30 µ m were determined as B-adjacent cells. Type A cells without type B cells within 100 µ m were defined as B-distal cells. We also measured the minimal distance between a pair of cell types using the calculate_minimum_distances_between_celltypes function from the SPIAT package ( 15 ). The patient-level metrics were summarised as the median across TMA cores. Immune phenotypes To characterise patterns of immune distribution, we classified cores into three categories, immune cold, immune intermixing, and immune segregated. Immune cold was determined by a CD8 + T cell infiltration (sum of PD1 + CD8 + and PD1 - CD8 + ) lower than the lower quartile (about 0.3%). We then examined the co-localisation of CD8 + T and epithelial cells using the area under curve (AUC) of cross K function ( 15 ). A positive AUC score of cross K function indicates co-localisation of these cells while a negative AUC score indicates a separation of CD8 + T from epithelial cells. We classified cores with CD8 + T cell proportion over the median and a negative cross K AUC as immune segregated. These cores displayed immune aggregates forming outside the epithelial nest. An immune intermixing group was defined with CD8 + T proportion over the median and a positive cross K AUC. Association of spatial features with clinical variables We compared the enrichment of stromal subset-related features in groups of patients stratified by clinical characteristics. Features include fibroblast proportions in the stromal region, minimal distance between pairs of cells, proportion of cell type A with cell type B present within a 30μm neighbourhood, and the normalized mixing score defined by the interactions between cell type A and B divided by the sum of interactions between A-A and B-B ( 15 ) ( Supplementary table 2 ). Results Patient demographics A total of 1,356 TMA cores were processed, with a median of 3 cores per patient (range 1-6). 868 TMA cores were from luminal tumours and 488 cores from TNBC tumours ( Figure 1A ). In the luminal cohort, the majority of tumours were luminal A (n=275, 75%) and invasive ductal carcinoma (n = 317, 85.9%). Other histology subtypes include lobular carcinoma (n=34, 9.2%), micropapillary (n = 7, 1.9%), mucinous (n=10, 2.7%) and tubule-lobular (n = 1, 0.3%). 50% (n=184) of patients in this cohort received adjuvant endocrine therapy, 16% (n=59) received chemotherapy and 10% (n=37) received both. The median length of follow-up for this cohort is 16 years for survival. Analysis of clinicopathological features revealed that age, nodal status and molecular subtypes were independent predictors of patient survival when adjusted for other clinical factors, consistent with known results from literature ( Supplementary table 3 ). The interventional endpoint of this trial, boost vs no boost had no impact on patient outcome ( 13 ). For the TNBC cohort, the majority of patients had ductal histology (n=204, 91.9%) and node-negative disease (n=139, 62.6%). 70.3% of patients received adjuvant chemotherapy, including both anthracycline-based and anthracycline-free regimens. The median length of follow-up was 4.5 years for survival. Older age, node positivity, no chemotherapy and large tumour size were associated with poorer survival whereas a high tumour-infiltrating lymphocyte (TIL) score was associated with better survival in the univariate analyses. Multivariate analysis showed a significant correlation between age and nodal status and overall survival (OS) after adjusting for other significant clinical characteristics as confounders ( Supplementary table 4 ). Download figure Open in new tab Figure 1. Cohort description and cell type annotations. A. Cohort overview (created with BioRender.com ). B. Cell types of interest defined by the expression of markers. C. mIF images were processed with tissue segmentation and cell segmentation to map the distribution of individual cells in stromal and tumour regions. D. mIF images showing marker staining and cell type annotation. E. Heatmap showing z-score-normalised median intensities of markers across cell types. Stars indicate the key markers of cell types used for classification. F. Median percentages of cell types within classified cells across the entire TMA core, stromal and tumour regions. G. Proportions of iCAFs, myCAFs, endothelial cells, and PVLs in the stromal region in luminal and TNBC cohorts. H. Proportions of PD1 - CD8 + T cells and PD1 + CD8 + T cells in the stromal and tumour regions in luminal and TNBC cohorts. I. Compositions of cell types in the entire TMA cores per patient. In the luminal cohort, the vertical dotted line separates patients with CD8 T% above and below the median CD8 T% of the TNBC cohort. Colour bars below the barplot denote the clinical variables per patient. Distinct stromal and immune landscapes in luminal and triple-negative breast cancer mIF data were processed using tissue and cell segmentation to locate individual cells in the tumour or stromal regions. Cells were classified into one of the 8 types based on the expression of 8 markers, including epithelial (PanCK + ), endothelial (CD31 + ), myCAF (CD140b + αSMA + ), iCAF (CD140b + α SMA - CD146 - ), differentiated-PVLs (dPVLs) (CD146 + THY1 - ), immature-PVL (imPVLs) (CD146 + THY1 + ), exhausted CD8 T cells (PD1 + CD8 + ), and other CD8 T cells (PD1 - CD8 + ) ( Figure 1B-D ). The marker intensity was consistent with the cell type definitions ( Figure 1E ). Cell proportions were quantified as the number of cells normalized by the total number of detected cells within the entire TMA core, as well as within tumour and stromal regions separately. Compositions of classified cells were showed in Figure 1F . Proportions per patient were summarised as the median across cores. TNBC tumours were enriched in endothelial cells compared to luminal tumours, while the proportion of iCAF, myCAF, and PVL were significantly elevated in luminal tumours (p<0.05, Figure 1G ). Within the stromal compartment, iCAF represented the most predominant cell type in both luminal and TNBC cases (median 20.59% in luminal and 7.89% in TNBC, Figure 1F, 1G ). The least abundant cell type is imPVL, accounting for 0.036% in Luminal and 0.041% in TNBC. Due to the sparsity of imPVL, we merged dPVL and imPVL into a general PVL group for downstream analyses. PVLs account for 7.66% (SD 6.45%) and 6.28% (SD 8.38%) of total detected cells in luminal and TNBC respectively, with a standard deviation comparable to that of iCAF and myCAF (iCAF, SD 10.75% in luminal, 7.37% in TNBC; myCAF, SD 8.59% in luminal, 7.36% in TNBC, Figure 1G ). In contrast, endothelial cells showed relatively stable abundance across patients (SD 1.73% in luminal, 3.12% in TNBC), despite their presumed spatial association with PVL cells ( Figure 1G ). In both stromal and tumour regions, TNBC tumours harboured a higher abundance of PD1 + CD8 + T cells than luminal cases (p<0.05, Figure 1H ), aligning with the notion that TNBC is a more immune-inflamed subtype and can benefit from immune checkpoint therapy ( 16 , 17 ). Surprisingly, Luminal cases showed a higher level of PD1 - CD8 + T in the tumour region than TNBC (p<0.001, Figure 1H ). Despite luminal tumours containing a lower proportion of CD8 + T in general than TNBC (1.58% v.s. 2.64%), 116 out of 369 (31.4%) luminal tumours showed a CD8 + T cell percentage higher than the median of TNBC tumours ( Figure 1I ), highlighting heterogeneity in immune infiltration within luminal tumours. Additionally, CD8 + T cell percentage in luminal tumours increased with younger age at diagnosis (p=0.025), no lymph node metastasis (p=0.013), and low grade compared to median grade (p=0.045, Supplementary Figure 1 ). We calculated the ratio of PD1 + CD8 + to total CD8 + T cells as an estimate of CD8 + T cell exhaustion. This ratio varied across patients, ranging from 0 to 0.55 in luminal (median = 0.05), and 0 to 1 (median = 0.26) in TNBC. Patients with higher overall CD8 + T cell proportions tend to have a higher PD1 + CD8 + ratio (luminal, Pearson’s R = 0.15, p = 0.005; TNBC, Pearson’s R = 0.22, p = 0.00086), suggesting that tumours more permissive of T cell infiltration also exhibit increased T cell exhaustion. Spatially distinct distribution of stromal subsets and correlation with T cell infiltration The three stromal subsets exhibited distinct spatial distributions. myCAFs were most closely associated with cancer cells, as indicated by their shortest median distance to tumour cells and their highest representation in cancer-adjacent compartments ( Figure 2A ). In contrast, iCAFs and PVL cells were predominantly located at greater distances from tumour regions. Download figure Open in new tab Figure 2. Spatial distribution of stromal cell types and their correlation with CD8+ T cell infiltration. A . Median distance from iCAFs, myCAFs, PVLs to their nearest epithelial cells, and the proportion of each stromal cell type with epithelial cells present within a 30 µ m radius. B. Correlation between the log-transformed proportions of CD8+ T cells and total stromal cells. C. PD1-CD8+ T cell percentages in TMA cores. Cores are stratified into low and high groups based on the median proportions of mCAFs, PVLs, and iCAFs within the total stromal population in the stromal region. We observed no significant association between overall CAF abundance within the stromal compartment and T-cell infiltration in luminal cases (Pearson’s R = 0.026, p = 0.62) ( Figure 2B ). In TNBC, however, there was a modest negative correlation (Pearson’s R = –0.14, p = 0.039, Figure 2B When stratifying by stromal subset, divergent associations with CD8 + T cell infiltration emerged. Cores enriched in PVL cells exhibited reduced CD8 + T cell infiltration (p=0.002 for luminal; p=0.0026 for TNBC; Figure 2C ). Conversely, iCAF-enriched cores demonstrated increased CD8 + T cell infiltration (p<0.001 for luminal; p<0.001 for TNBC, Figure 2C ), indicating a possible role for iCAFs in supporting or permitting T cell entry into the tumour microenvironment. Immune compositions and spatial distribution reveal three immune infiltration phenotypes To further characterise immune infiltration patterns in breast cancer, we classified TMA cores into three immune phenotypes based on T cell enrichment and tumour–T cell colocalization ( Figures 3A, B ). The Immune cold phenotype was defined by total CD8 + T cell proportions below the median across all TMA cores (1.44% in luminal and 1.36% in TNBC). Immune segregated cores displayed spatial separation between tumour and T cell aggregates (cross-K AUC < 0, CD8 + T% ≥ 1.44% in Luminal, CD8 + T% ≥ 1.36% in TNBC). Immune intermixing cores exhibited co-localization of tumour cells and CD8 + T cells (cross-K AUC ≥ 0, CD8 + T% ≥ 1.44% in Luminal, CD8 + T% ≥ 1.36% in TNBC). Download figure Open in new tab Figure 3. Immune phenotypes defined by T cell proportions and the colocalization between T cells and epithelial cells. A. Cell type maps of represented cores from the luminal cohort with immune cold, immune segregated and immune intermixing phenotypes. B. mIF images of three immune phenotypes in luminal and TNBC cohorts. Image on the left shows the entire TMA core; image on the right shows a zoom-in region. The white box indicates the location of the zoom-in region in the TMA core. C. Percentages of immune phenotypes in luminal and TNBC cohorts. D. Kaplan– Meier (KM) curves for OS stratified by the presence of immune cold cores in the luminal cohort. E. Multivariable Cox proportional hazard regression analysis of OS for the presence of immune cold cores in the luminal cohort, considering age, lymph node metastasis, grade, tumour size, and molecular subtypes. F. KM curves for OS stratified by the presence of immune intermixing cores in the TNBC cohort. G. The ratio of PD1 + CD8 + T cells to all CD8 + T cells within immune cold, segregated, and intermixing cores. H. Percentages of iCAFs, myCAFs, and PVLs in total stromal cells within TMA cores of different immune phenotypes. The most prevalent phenotype was immune cold in both cohorts, with a slightly higher proportion in TNBC (48.62% in luminal, 50.20% in TNBC). This was followed by immune segregated (32.83% in luminal, 35.04% in TNBC) and immune intermixing (18.55% in luminal, 14.75% in TNBC; Figure 3C ). The presence of immune cold cores was associated with reduced survival in luminal tumours but not in TNBC (luminal: univariate p=0.0026; multivariate p=0.018, Fig. 3D, E ). While in TNBC, the presence of intermixing cores showed a trend to be associated with better survival (p=0.097, Figure 3F ). It is worth noting that an increased CD8+ T cell% was associated with better survival in the ER+ cohort (univariate p=0.027), but not significant when accounting for clinical variables (multivariate p=0.211). This suggests that the presence of an immune cold region better predicts the patient outcome than a summarised proportion of PD1+CD8+ or PD1-CD8+ T cells. PD1 + CD8 + T cell ratios display a similar level between segregated and intermixing cores in both subtypes, which were higher than that in the immune cold cores ( Fig. 3G ). The composition of CAF subsets varied by immune phenotype. In both cohorts, iCAFs were most abundant in immune segregated and intermixing phenotypes, and least abundant in immune cold cores ( Figure 3H ). In contrast, PVL cells were most abundant in immune cold cores ( Figure 3H ). myCAFs were more uniformly distributed, with slightly higher levels in intermixing phenotypes in luminal cases. These findings suggest that distinct stromal subsets influence T-cell infiltration in different manners. PVLs may play a role in excluding T cells from infiltrating the tumour whereas iCAFs may relate to separation of cancer and T cells. Endothelial-distal PVLs are associated with T-cell exclusion PVL is defined as a perivascular-like stromal cell population marked by CD146 positivity, these include pericytes, vascular smooth muscle cells and other mural cells. In previous studies, we observed that a subset of PVLs is spatially dissociated from endothelial cells, suggesting that these cells may have functions distinct from their conventional role in vascular contractility and blood flow regulation ( 9 ). In the luminal cases, we found that about 97.12% of PVLs were distant (>30 µ m) from endothelial cells. Even at a 100 µ m threshold, a large majority of PVLs remained distant from endothelial cells ( Figure 4A, B ). TNBC exhibited a lower proportion of disseminated PVLs (70.12% at 30 µ m, 22.11% at 100 µ m, Supplementary Figure 2A ), likely due to the higher endothelial abundance in TNBC compared to luminal subtypes ( Figure 1G ). There was no prominent disseminated PVL in normal breast samples, suggesting the dissociation of PVLs from endothelial cells is specific to tumours ( Figure 4B ). Download figure Open in new tab Figure 4. PVLs distal from endothelial cells are associated with T cell exclusion. PVLs were classified as adjacent if an endothelial cell was present within 30 µ m, and as distal if no endothelial cell was present within 100 µ m. A. Percentage of PVLs with endothelial cells present within a 10–100 µ m radius in the luminal cohort. B. mIF images of TMA cores exhibiting disseminated PVLs in luminal and TNBC tumours, and the colocalization of PVL and blood vessels in a normal breast sample. C. Percentages of CD8+ T cells in TMA cores with high and low disseminated PVL% stratified by the median. D. Mean percentages of PD1+CD8+ and PD1-CD8+ T cells in the neighbourhood of adjacent and disseminated PVLs. Neighbourhood is defined as the 10 nearest cells to each PVL. Error bar indicates the standard error. E, F. Percentages of disseminated and adjacent PVLs in the stromal region of cores classified as immune cold, segregated, or intermixing phenotypes. To investigate whether disseminated PVLs (PVL without endothelial cell present within 100 µ m) form at the expense of PVL-bounded vessels, we assessed the correlation between disseminated PVL and the proportion of endothelial cells with PVL present within 30 µ m. No significant association was observed (luminal, Pearson’s R=-0.037, p=0.51; TNBC, Pearson’s R=0.077, p=0.26 , Supplementary Figure 2B, C ). This suggests the existence of a spatially distinct PVL population that is not vessel-dependent. We have observed a reduced PD1-CD8+ T cell abundance in PVL-enriched tumours ( Fig. 3C ). To explore the relationship between PVL spatial distribution and T cell infiltration, we examined the association between disseminated PVL abundance and CD8 + T cell proportions across cores. In luminal and especially in TNBC cohorts, CD8 + T cell infiltration was reduced in cores with a higher percentage of disseminated PVLs, suggesting that this PVL subpopulation may negatively regulate T cell entry (luminal: p=0.018; TNBC: p<0.001, Figure 4C ). To better understand this, we compared the cellular neighbourhoods surrounding PVLs distal versus adjacent to endothelial cells. In both luminal and TNBC, disseminated PVLs had lower proportions of PD1 + CD8 + T cells in their vicinity compared to adjacent PVLs (p<0.01, Figure 4D ). This supports the observation that disseminated PVLs are associated with T cell exclusion. As further evidence, the proportion of disseminated PVLs in the stromal region was highest in immune cold cores across both subtypes ( Figure 4E ). In contrast, adjacent PVLs were more abundant in segregated and intermixing subtypes ( Figure 4F ). Together, these findings suggest that the influence of PVLs on T cell exclusion was primarily driven by vessel independent PVLs. iCAFs constrain spatial contacts between T cells and cancer cells in luminal breast cancer In luminal tumours, iCAFs were enriched in immune segregated and intermixing cores, indicating that their presence is not directly associated with an immune cold phenotype ( Figure3H, 5A ). However, we observed that the minimal distance between CD8 + T cells and epithelial cells in luminal cancers was greater in the presence of iCAFs ( Figure 5B ), suggesting that iCAFs may spatially restrict T cells within the stromal compartment. Download figure Open in new tab Figure 5. iCAF are associated with the segregation of CD8 + T cells from tumour in the luminal cohort. A. Representative TMA core showing the spatial distribution of CD8⁺ T cells in iCAF-dense (blue) and epithelial-dense (red) regions. B. Minimal distance from CD8⁺ T cells to the nearest epithelial cell in TMA cores with low vs. high iCAF proportions. C. KM curves for OS stratified by the percentage of PD1⁻CD8⁺ T cells in iCAF-high vs. iCAF-low groups. D. Multivariable Cox proportional hazard regression analysis of OS for the proportion of CD8⁺ T cells% in iCAF-low group, considering age, lymph node metastasis, tumour grade, tumour size, and molecular subtype. Remarkably, CD8 + T cell infiltration was associated with improved patient survival for luminal tumours with low iCAF abundance in both univariate and multivariate analysis ( Figure 5C, D ), but this prognostic value was lost in iCAF-high tumours. Similar to iCAF, CD8 + T cell infiltration was associated with improved survival in myCAF-low tumours but not in myCAF-high tumours ( Supplementary Figure 3A ). However, the significance was lost after accounting for clinical variables, especially age at diagnosis ( Supplementary Figure 3B ). One possible explanation is that myCAFs may be associated with T cell dysfunction, although we were unable to explore this further due to limitations of our dataset. Clinical correlation of spatially resolved cell compositions To assess the correlation of spatial context of stromal subsets with clinical characteristics, we evaluated the enrichment of 7 features per stromal subset in patients stratified by prognostic variables from survival analysis: age at diagnosis, nodal metastasis, tumour grade, histological subtypes, molecular subtypes and TILs ( Supplementary table 3, 4 ). These features include the proportion of PVL, myCAF, and iCAF within the stromal compartment, together with their spatial proximity to endothelial, epithelial, and CD8 T cells measured by minimal distance, normalised mixing score, and proportion of adjacent cells ( Methods , Supplementary table 2 ). Strong association with clinical variables were defined by significant elevation of at least two spatial features within the same clinical group. In luminal tumours, increased proximity between PVL and cancer epithelial cells, as reflected by a higher proportion of cancer-adjacent PVLs and shorter PVL-epithelial minimal distance, was linked to lymph node metastasis ( Figure. 6A ). Enhanced iCAF-cancer proximity showed similar associations with lymph node metastasis ( Figure. 6A ), whereas myCAF-cancer adjacency did not show significant relationship with assessed clinical variables ( Supplementary Figure 4A ) On the other hand, closer proximity between iCAF and CD8 T cells was observed for tumours diagnosed at a younger age (Age<55, Figure 6A ). There was a similar trend for PVL-CD8 T cell proximity but were significant for the minimal distance only. Additionally, increased stromal iCAF proportions distinguished Luminal A subtype and low-grade tumours ( Figure 6A ). For all three subsets, their adjacency to endothelial cells increased in high-grade tumours ( Figure 6A , Supplementary Figure 3A ). This is due to the enrichment of endothelial cells in high-grade luminal cases ( Supplementary Figure 4B ). Download figure Open in new tab Figure 6. Association of iCAF- and PVL-related spatial features with clinical variables in the luminal and TNBC cohorts. Patients were stratified by clinical variables. Dot colours indicate patient groups in which the feature is increased. Dot outlines denote statistical significance of feature enrichment (red: significant; grey: not significant), assessed by the Wilcoxon rank-sum test with p-values corrected using the Benjamini-Hochberg method. NMS, normalised mixing score. C. Univariate analysis of the association between cell type proportions and OS in the luminal and TNBC cohorts. D. Multivariate analysis of the association between cell type proportions and OS in the luminal and TNBC cohorts. In TNBC, PVL-endothelial proximity correlated with nodal metastasis ( Figure. 6B ). Across all the three subsets, high proportions in the stroma and increased spatial separation from CD8 T cells and endothelial cells associated with reduced immune infiltration ( Figure 6B , Supplementary Figure 3C ). Additionally, enhanced iCAF and myCAF adjacency to cancer cells corresponded with reduced TILs ( Figure 6B , Supplementary Figure 3C ). These findings indicate that in TNBC, CD8 T cell and endothelial cell enrichment strongly correlate with clinical TIL assessment, whereas stromal cell abundance generally inversely correlates with immune infiltration. In terms of patient outcomes, we stratified cohorts into ‘high’ or ‘low’ groups based on median cell-type proportions across patients. In the luminal cohort, reduced PD1 - CD8 + T cells and iCAFs proportions, and high PVLs and endothelial cell proportions were associated with inferior overall survival (OS) ( Figure 6C ). In multivariate analysis adjusting for age, node metastasis, tumour grade, tumour size and molecular subtype, endothelial cell abundance remained significantly associated with worse survival ( Figure 6D ). In TNBC, similar trends were demonstrated for PD1 - CD8 + T cells, PVLs and endothelial cells, but none of the cell types achieved statistical significance in univariate or multivariate analysis ( Figure 6C ). Discussion In this study, we have characterised the spatially distinct features of stromal subsets in relation to immune cells ( Figure 7 ). This was done in two large breast cancer cohorts consisting of over 1,300 TMA cores from 591 patients. Both cohorts have well-annotated clinicopathological data as well as robust long-term outcome data with follow-up of up to 16 years. This gives our study sufficient power to identify clinically meaningful correlations, while addressing limitations of many prior spatial profiling studies that relied on only a small number of samples or patients and were therefore constrained by tissue selection biases and the challenges of both intra-tumoural and inter-tumoural heterogeneity. Download figure Open in new tab Figure 7. Diagrammatic summary of key findings. A. Disseminated PVLs are associated with T cell exclusion and are enriched in immune cold phenotype. B. myCAFs are located in close proximity to cancer cells, iCAFs and PVLs are disseminated throughout the stroma. C. iCAFs are enriched in immune segregated phenotype where T cells are present but restricted away from cancer cells. We make the novel observation that the large majority of PVLs are located distant from endothelial cells, despite their perivascular lineage ( Figure 7A ). In breast cancers, but not normal breast, the majority of PVLs were disseminated throughout the stroma rather than restricted to vessel-adjacent regions. Morphologically these cells closely resemble CAFs and share expression of canonical CAF markers such as ACTA2 and PDGFRβ and have most likely been mistakenly characterised as CAFs in prior studies. However, they are not CAFs but are of smooth muscle lineage and are characterised by expression of perivascular markers such as MCAM (CD146). Our previously published scRNA-seq data demonstrated distinct transcriptional differences between PVLs and CAFs, supporting their unique identity within the stromal compartment. This PVL subset resembles vascular smooth muscle cells characterised in normal breast tissues, transcriptionally and spatially distinct from pericytes ( 18 ). Our study is the first to link increased PVL proportion with CD8 T cell exclusion and a cold immunophenotype ( Figure 7A ), consistent across both luminal and TNBC cohorts. This discovery was uniquely enabled by the application of a marker panel targeting stromal and lymphocyte subsets to large cohorts of disease. In the luminal cohort, higher PVL proportion was prognostic of poorer OS. While this was not statistically significant in the TNBC cohort, potentially limited by its smaller sample size, a similar trend was observed. PVL-associated T cell exclusion was primarily driven by the subpopulation of disseminated PVLs, where increased endothelial-distal PVLs correlated with decreased CD8 T cell infiltration. Previous studies examining endothelial-pericyte interactions have observed that pericyte detach and migrate away from blood vessels as a response to aberrant PDGFβ signalling ( 19 , 20 ). These detached pericytes can then differentiate into myofibroblasts, contributing to fibrosis ( 21 , 22 ), and also lead to vessel instability and vulnerability ( 23 ). Our disseminated PVLs may represent these detached pericytes, however, our data suggests these cells do not differentiate into myCAF and maintain MCAM expression. More work is warranted to investigate signalling pathways driving these phenotypes, which may potentially be targeted to reverse associated effect on T cell infiltration. myCAFs and iCAFs have been extensively reported in the literature. Consistent with previous studies ( 8 , 24 , 25 ), we found that myCAFs were located in close proximity to epithelial cells whereas iCAFs were more broadly distributed throughout the stroma ( Figure 7B ). Our study further suggests that iCAFs may spatially restrict T cells within the stromal compartment, as evidenced by an association of iCAFs with a segregated phenotype ( Fig 2H ) and increased distance between CD8 + T cell and epithelial cells in the presence of iCAFs ( Figure 7C ). This is further supported by the diminished survival benefit conferred by T cell infiltration in iCAF-high tumours ( Figure 5C ). We propose that this may be mediated by chemokine directed migration of T cells towards iCAFs, which robustly express the T cell chemokines CXCL12 and CCL2 ( 8 ). Spatial immunophenotypes have been shown to correlate with survival and response to immune checkpoint inhibitors in breast cancer ( 26 – 28 ). By classifying our cores into immune cold, exclusion and intermixing phenotypes, we demonstrated a significantly poorer survival in patients with the presence of immune cold cores in luminal tumours, adding to existing evidence that spatial immune contexture is prognostic in breast cancer. Furthermore, we examined stromal phenotypes in these immunophenotypes and found an enrichment of PVLs in immune cold cores, consistent with our finding that PVLs correlate with T cell exclusion ( Figure 7A ). This was primarily driven by endothelial-distal PVLs whereas endothelial-adjacent PVLs were not enriched in immune cold cores. This also suggests that this effect was not attributable to blood vessel density. We found that iCAFs were enriched in immune segregated cores where T cells were present but restricted to the tumour periphery ( Figure 7C ). T-cell exclusion is one of the leading causes of resistance to immune checkpoint inhibition ( 29 ) and recent studies suggest a role of CAFs in mediating this ( 30 , 31 ). Our result provides further evidence to support this and suggests that PVLs and iCAFs contribute to this in different ways. While PVLs exclude T cells from migrating into the tumour, iCAFs were related to the segregation of cancer and T cells. For decades, conventional methods such as immunohistochemistry have been the primary tools for tumour characterisation. However, these methods are limited in their capacity to capture the complexity of cellular diversity within tumours. The application of mIF and digital pathology enables the use of expanded protein panels, providing a more comprehensive, spatially resolved view of tumours and their TME. However, these technologies require greater standardisation and automation of staining and imaging protocols to enhance reproducibility and clinical applicability ( 12 ). One of the limitations of our findings is the potential overcalling of iCAFs due to the lack of a positive marker for iCAF in our mIF panel. iCAFs were annotated as PDGFRβ + cells negative for other stromal markers (αSMA, CD146, THY1) and hence may include non-iCAF phenotypes belonging to subsets not annotated by these markers, such as antigen-presenting CAFs and matrix CAFs ( 2 ). This study is based on TMA cores rather than whole slide sections, and selected regions may not be representative of the whole tumour and its heterogeneity. Several studies investigated the number of TMA cores required for accurate representation of whole slide TIL assessment in breast cancer, with one study finding that 4 × 0.6mm cores were adequate for accurate TIL assessment while another study suggested that a 2mm core from a tumour had 98.9% accuracy in representing the TIL population of the tumour ( 32 , 33 ). Both of these compared favourably with our use of three 1mm cores per case on average. There are limited studies examining the scoring of fibroblasts in TMA cores compared to whole tissue sections. While the QuPath algorithm worked well to allow objective quantitative cell annotation suitable for application to large clinical cohorts with efficient high throughput of data, the training of slides was semi-quantitative, subject to interobserver variability. We addressed this by manually cross-checking cores across different slides, supervised by a Pathologist. To assess long-term outcomes of OS, we have selected older cohorts with long-term follow-up data. The downside of this is that treatment paradigms, particularly systemic treatments, have significantly evolved since these samples were collected. Hence our clinical outcome assessment is more relevant to examining the natural history of the disease, rather than reflecting outcome with contemporary treatment paradigms. Conclusion Using mIF to map the stromal-immune dynamics in breast cancer in two large clinical cohorts, our study highlights that stromal subsets exhibit distinct spatial distribution, interaction with immune cells and clinical correlations. The clinical correlations identified in our study warrant further investigation as potential biomarkers, ideally through incorporation into prospective clinical trials. Specific stromal-immune interactions may also be explored as novel therapeutic targets to modulate the tumour immune microenvironment and enhance responses to immunotherapy. Author contributions J.C.: project design, methodology, image and data analysis, manuscript writing and review H.Z.: data analysis, manuscript writing and review T.R.: supervision, manuscript review S.W.: project design, methodology I.S.: methodology, image acquisition and analysis E.M. : sample acquisition, methodology, supervision, manuscript review P.G./J.L./L.B.: sample acquisition, clinical data collection, manuscript review E.L.: project design, resources, supervision, funding provision, manuscript review A.S.: project design, resources, supervision, manuscript review. Conflict of interest A.S. has a consultant role with Phenomic AI and receives research support from 10X Genomic Inc and Nanostring Technologies. Acknowledgements J.C. received scholarships from the UNSW Scientia program and SPHERE Cancer Clinical Academic Group. A.S. is a BCRF investigator, the Peter Chair in Breast Cancer Research, and is supported by the generosity of John McMurtrie, AM and Deborah McMurtrie. E.L. is an endowed chair of National Breast Cancer Foundation Australia. This work was supported by a grant from the National Breast Cancer Foundation Australia. Funder Information Declared UNSW Sydney National Breast Cancer Foundation, https://ror.org/0120ky124 Footnotes ↵ * Co-first authors REFERENCE 1. ↵ Bray F , Laversanne M , Sung H , Ferlay J , Siegel RL , Soerjomataram I , et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries . CA Cancer J Clin . 2024 ; 74 ( 3 ): 229 – 63 . OpenUrl CrossRef PubMed 2. ↵ Chhabra Y , Weeraratna AT . Fibroblasts in cancer: Unity in heterogeneity . Cell . 2023 ; 186 ( 8 ): 1580 – 609 . OpenUrl CrossRef PubMed 3. ↵ Bartoschek M , Oskolkov N , Bocci M , Lovrot J , Larsson C , Sommarin M , et al. Spatially and functionally distinct subclasses of breast cancer-associated fibroblasts revealed by single cell RNA sequencing . Nat Commun . 2018 ; 9 ( 1 ): 5150 . OpenUrl CrossRef PubMed 4. Elyada E , Bolisetty M , Laise P , Flynn WF , Courtois ET , Burkhart RA , et al. Cross-Species Single-Cell Analysis of Pancreatic Ductal Adenocarcinoma Reveals Antigen-Presenting Cancer-Associated Fibroblasts . Cancer Discov . 2019 ; 9 ( 8 ): 1102 – 23 . OpenUrl Abstract / FREE Full Text 5. Lambrechts D , Wauters E , Boeckx B , Aibar S , Nittner D , Burton O , et al. Phenotype molding of stromal cells in the lung tumor microenvironment . Nat Med . 2018 ; 24 ( 8 ): 1277 – 89 . OpenUrl CrossRef PubMed 6. Puram SV , Tirosh I , Parikh AS , Patel AP , Yizhak K , Gillespie S , et al. Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer . Cell . 2017 ; 171 ( 7 ): 1611 – 24 e24 . OpenUrl CrossRef PubMed 7. ↵ Sebastian A , Hum NR , Martin KA , Gilmore SF , Peran I , Byers SW , et al. Single-Cell Transcriptomic Analysis of Tumor-Derived Fibroblasts and Normal Tissue-Resident Fibroblasts Reveals Fibroblast Heterogeneity in Breast Cancer . Cancers (Basel) . 2020 ; 12 ( 5 ). 8. ↵ Wu SZ , Al-Eryani G , Roden DL , Junankar S , Harvey K , Andersson A , et al. A single-cell and spatially resolved atlas of human breast cancers . Nat Genet . 2021 ; 53 ( 9 ): 1334 – 47 . OpenUrl CrossRef PubMed 9. ↵ Wu SZ , Roden DL , Wang C , Holliday H , Harvey K , Cazet AS , et al. Stromal cell diversity associated with immune evasion in human triple-negative breast cancer . EMBO J . 2020 ; 39 ( 19 ): e104063 . OpenUrl CrossRef PubMed 10. ↵ Savas P , Virassamy B , Ye C , Salim A , Mintoff CP , Caramia F , et al. Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis . Nat Med . 2018 ; 24 ( 7 ): 986 – 93 . OpenUrl CrossRef PubMed 11. ↵ Elhanani O , Ben-Uri R , Keren L . Spatial profiling technologies illuminate the tumor microenvironment . Cancer Cell . 2023 ; 41 ( 3 ): 404 – 20 . OpenUrl CrossRef PubMed 12. ↵ Chen J , Larsson L , Swarbrick A , Lundeberg J . Spatial landscapes of cancers: insights and opportunities . Nat Rev Clin Oncol . 2024 ; 21 ( 9 ): 660 – 74 . OpenUrl CrossRef PubMed 13. ↵ Millar EK , Graham PH , O’Toole SA , McNeil CM , Browne L , Morey AL , et al. Prediction of local recurrence, distant metastases, and death after breast-conserving therapy in early-stage invasive breast cancer using a five-biomarker panel . J Clin Oncol . 2009 ; 27 ( 28 ): 4701 – 8 . OpenUrl Abstract / FREE Full Text 14. ↵ Wang J , Browne L , Slapetova I , Shang F , Lee K , Lynch J , et al. Multiplexed immunofluorescence identifies high stromal CD68(+)PD-L1(+) macrophages as a predictor of improved survival in triple negative breast cancer . Sci Rep . 2021 ; 11 ( 1 ): 21608 . OpenUrl CrossRef PubMed 15. ↵ Feng Y , Yang T , Zhu J , Li M , Doyle M , Ozcoban V , et al. Spatial analysis with SPIAT and spaSim to characterize and simulate tissue microenvironments . Nat Commun . 2023 ; 14 ( 1 ): 2697 . OpenUrl CrossRef PubMed 16. ↵ Cortes J , Rugo HS , Cescon DW , Im SA , Yusof MM , Gallardo C , et al. Pembrolizumab plus Chemotherapy in Advanced Triple-Negative Breast Cancer . N Engl J Med . 2022 ; 387 ( 3 ): 217 – 26 . OpenUrl CrossRef PubMed 17. ↵ Schmid P , Cortes J , Dent R , Pusztai L , McArthur H , Kummel S , et al. Event-free Survival with Pembrolizumab in Early Triple-Negative Breast Cancer . N Engl J Med . 2022 ; 386 ( 6 ): 556 – 67 . OpenUrl CrossRef PubMed 18. ↵ Kumar T , Nee K , Wei R , He S , Nguyen QH , Bai S , et al. A spatially resolved single-cell genomic atlas of the adult human breast . Nature . 2023 ; 620 ( 7972 ): 181 – 91 . OpenUrl CrossRef PubMed 19. ↵ Schrimpf C , Teebken OE , Wilhelmi M , Duffield JS . The role of pericyte detachment in vascular rarefaction . J Vasc Res . 2014 ; 51 ( 4 ): 247 – 58 . OpenUrl CrossRef PubMed 20. ↵ Stratman AN , Schwindt AE , Malotte KM , Davis GE . Endothelial-derived PDGF-BB and HB-EGF coordinately regulate pericyte recruitment during vasculogenic tube assembly and stabilization . Blood . 2010 ; 116 ( 22 ): 4720 – 30 . OpenUrl Abstract / FREE Full Text 21. ↵ Humphreys BD , Lin SL , Kobayashi A , Hudson TE , Nowlin BT , Bonventre JV , et al. Fate tracing reveals the pericyte and not epithelial origin of myofibroblasts in kidney fibrosis . Am J Pathol . 2010 ; 176 ( 1 ): 85 – 97 . OpenUrl CrossRef PubMed Web of Science 22. ↵ Lin SL , Kisseleva T , Brenner DA , Duffield JS . Pericytes and perivascular fibroblasts are the primary source of collagen-producing cells in obstructive fibrosis of the kidney . Am J Pathol . 2008 ; 173 ( 6 ): 1617 – 27 . OpenUrl CrossRef PubMed Web of Science 23. ↵ Caruso RA , Fedele F , Finocchiaro G , Pizzi G , Nunnari M , Gitto G , et al. Ultrastructural descriptions of pericyte/endothelium peg-socket interdigitations in the microvasculature of human gastric carcinomas . Anticancer Res . 2009 ; 29 ( 1 ): 449 – 53 . OpenUrl Abstract / FREE Full Text 24. ↵ Andersson A , Larsson L , Stenbeck L , Salmen F , Ehinger A , Wu SZ , et al. Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions . Nat Commun . 2021 ; 12 ( 1 ): 6012 . OpenUrl CrossRef PubMed 25. ↵ Ohlund D , Handly-Santana A , Biffi G , Elyada E , Almeida AS , Ponz-Sarvise M , et al. Distinct populations of inflammatory fibroblasts and myofibroblasts in pancreatic cancer . J Exp Med . 2017 ; 214 ( 3 ): 579 – 96 . OpenUrl Abstract / FREE Full Text 26. ↵ Hammerl D , Martens JWM , Timmermans M , Smid M , Trapman-Jansen AM , Foekens R , et al. Spatial immunophenotypes predict response to anti-PD1 treatment and capture distinct paths of T cell evasion in triple negative breast cancer . Nat Commun . 2021 ; 12 ( 1 ): 5668 . OpenUrl CrossRef PubMed 27. Shiao SL , Gouin KH , 3rd . , Ing N , Ho A , Basho R , Shah A , et al. Single-cell and spatial profiling identify three response trajectories to pembrolizumab and radiation therapy in triple negative breast cancer . Cancer Cell . 2024 ; 42 ( 1 ): 70 – 84 e8 . OpenUrl CrossRef PubMed 28. ↵ Wang XQ , Danenberg E , Huang CS , Egle D , Callari M , Bermejo B , et al. Spatial predictors of immunotherapy response in triple-negative breast cancer . Nature . 2023 ; 621 ( 7980 ): 868 – 76 . OpenUrl CrossRef PubMed 29. ↵ Sharma P , Hu-Lieskovan S , Wargo JA , Ribas A. Primary, Adaptive, and Acquired Resistance to Cancer Immunotherapy . Cell . 2017 ; 168 ( 4 ): 707 – 23 . OpenUrl CrossRef PubMed 30. ↵ Feig C , Jones JO , Kraman M , Wells RJ , Deonarine A , Chan DS , et al. Targeting CXCL12 from FAP-expressing carcinoma-associated fibroblasts synergizes with anti-PD-L1 immunotherapy in pancreatic cancer . Proc Natl Acad Sci U S A . 2013 ; 110 ( 50 ): 20212 – 7 . OpenUrl Abstract / FREE Full Text 31. ↵ Mariathasan S , Turley SJ , Nickles D , Castiglioni A , Yuen K , Wang Y , et al. TGFbeta attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells . Nature . 2018 ; 554 ( 7693 ): 544 – 8 . OpenUrl CrossRef PubMed 32. ↵ Khan AM , Yuan Y . Biopsy variability of lymphocytic infiltration in breast cancer subtypes and the ImmunoSkew score . Sci Rep . 2016 ; 6 : 36231 . OpenUrl PubMed 33. ↵ Mani NL , Schalper KA , Hatzis C , Saglam O , Tavassoli F , Butler M , et al. Quantitative assessment of the spatial heterogeneity of tumor-infiltrating lymphocytes in breast cancer . Breast Cancer Res . 2016 ; 18 ( 1 ): 78 . OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted October 21, 2025. 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Share Stromal subsets modulate T-cell infiltration in early breast cancer Julia Chen , Hanyun Zhang , Travis Ruan , Sunny Wu , Iveta Slapetova , Ewan Millar , Peter Graham , Jodi Lynch , Lois Browne , Elgene Lim , Alexander Swarbrick bioRxiv 2025.10.20.683407; doi: https://doi.org/10.1101/2025.10.20.683407 Share This Article: Copy Citation Tools Stromal subsets modulate T-cell infiltration in early breast cancer Julia Chen , Hanyun Zhang , Travis Ruan , Sunny Wu , Iveta Slapetova , Ewan Millar , Peter Graham , Jodi Lynch , Lois Browne , Elgene Lim , Alexander Swarbrick bioRxiv 2025.10.20.683407; doi: https://doi.org/10.1101/2025.10.20.683407 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Cancer Biology Subject Areas All Articles Animal Behavior and Cognition (7642) Biochemistry (17715) Bioengineering (13907) Bioinformatics (42005) Biophysics (21472) Cancer Biology (18624) Cell Biology (25534) Clinical Trials (138) Developmental Biology (13391) Ecology (19935) Epidemiology (2067) Evolutionary Biology (24356) Genetics (15617) Genomics (22529) Immunology (17753) Microbiology (40437) Molecular Biology (17200) Neuroscience (88697) Paleontology (667) Pathology (2840) Pharmacology and Toxicology (4829) Physiology (7653) Plant Biology (15171) Scientific Communication and Education (2046) Synthetic Biology (4304) Systems Biology (9827) Zoology (2272)