Runx1 and Runx2 act in concert to suppress Wnt/β-catenin-driven mammary tumourigenesis

Runx1 and Runx2 act in concert to suppress Wnt/β-catenin-driven mammary tumourigenesis | 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 Runx1 and Runx2 act in concert to suppress Wnt/β-catenin -driven mammary tumourigenesis View ORCID Profile A.I. Riggio , K. Sweeney , View ORCID Profile R. Shaw , View ORCID Profile A. Khan , View ORCID Profile A. Lawlor , View ORCID Profile N. Ferrari , View ORCID Profile K. Gilroy , View ORCID Profile C. Bull , View ORCID Profile A.L. Young , View ORCID Profile D. Athineos , View ORCID Profile H. Hall , View ORCID Profile F. Ghaffar , View ORCID Profile C. Nixon , View ORCID Profile P.D Adams , E.W Roberts , View ORCID Profile C.J. Miller , View ORCID Profile P.D. Dunne , View ORCID Profile K.J. Campbell , View ORCID Profile E.R. Cameron , View ORCID Profile K Blyth doi: https://doi.org/10.1101/2025.03.16.643517 A.I. Riggio 1 Cancer Research UK Scotland Institute, Garscube Estate , Switchback Road, Glasgow, G61 1BD, UK 2 School of Cancer Sciences, University of Glasgow, Garscube Estate , Switchback Road, Glasgow, G61 1QH, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for A.I. Riggio K. Sweeney 1 Cancer Research UK Scotland Institute, Garscube Estate , Switchback Road, Glasgow, G61 1BD, UK 2 School of Cancer Sciences, University of Glasgow, Garscube Estate , Switchback Road, Glasgow, G61 1QH, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site R. Shaw 1 Cancer Research UK Scotland Institute, Garscube Estate , Switchback Road, Glasgow, G61 1BD, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for R. Shaw A. Khan 1 Cancer Research UK Scotland Institute, Garscube Estate , Switchback Road, Glasgow, G61 1BD, UK 2 School of Cancer Sciences, University of Glasgow, Garscube Estate , Switchback Road, Glasgow, G61 1QH, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for A. Khan A. Lawlor 1 Cancer Research UK Scotland Institute, Garscube Estate , Switchback Road, Glasgow, G61 1BD, UK 2 School of Cancer Sciences, University of Glasgow, Garscube Estate , Switchback Road, Glasgow, G61 1QH, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for A. Lawlor N. Ferrari 1 Cancer Research UK Scotland Institute, Garscube Estate , Switchback Road, Glasgow, G61 1BD, UK 2 School of Cancer Sciences, University of Glasgow, Garscube Estate , Switchback Road, Glasgow, G61 1QH, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for N. Ferrari K. Gilroy 1 Cancer Research UK Scotland Institute, Garscube Estate , Switchback Road, Glasgow, G61 1BD, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for K. Gilroy C. Bull 4 The Patrick G Johnston Centre for Cancer Research, Queen’s University Belfast , Belfast, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for C. Bull A.L. Young 1 Cancer Research UK Scotland Institute, Garscube Estate , Switchback Road, Glasgow, G61 1BD, UK 2 School of Cancer Sciences, University of Glasgow, Garscube Estate , Switchback Road, Glasgow, G61 1QH, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for A.L. Young D. Athineos 1 Cancer Research UK Scotland Institute, Garscube Estate , Switchback Road, Glasgow, G61 1BD, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for D. Athineos H. Hall 1 Cancer Research UK Scotland Institute, Garscube Estate , Switchback Road, Glasgow, G61 1BD, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for H. Hall F. Ghaffar 1 Cancer Research UK Scotland Institute, Garscube Estate , Switchback Road, Glasgow, G61 1BD, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for F. Ghaffar C. Nixon 1 Cancer Research UK Scotland Institute, Garscube Estate , Switchback Road, Glasgow, G61 1BD, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for C. Nixon P.D Adams 5 Sanford Burnham Prebys Medical Discovery Institute , North Torrey Pines Road, La Jolla, CA 92037, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for P.D Adams E.W Roberts 1 Cancer Research UK Scotland Institute, Garscube Estate , Switchback Road, Glasgow, G61 1BD, UK 2 School of Cancer Sciences, University of Glasgow, Garscube Estate , Switchback Road, Glasgow, G61 1QH, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site C.J. Miller 1 Cancer Research UK Scotland Institute, Garscube Estate , Switchback Road, Glasgow, G61 1BD, UK 2 School of Cancer Sciences, University of Glasgow, Garscube Estate , Switchback Road, Glasgow, G61 1QH, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for C.J. Miller P.D. Dunne 1 Cancer Research UK Scotland Institute, Garscube Estate , Switchback Road, Glasgow, G61 1BD, UK 4 The Patrick G Johnston Centre for Cancer Research, Queen’s University Belfast , Belfast, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for P.D. Dunne K.J. Campbell 1 Cancer Research UK Scotland Institute, Garscube Estate , Switchback Road, Glasgow, G61 1BD, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for K.J. Campbell E.R. Cameron 3 School of Biodiversity One Health and Veterinary Medicine, University of Glasgow , Glasgow, G61 1QH, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for E.R. Cameron For correspondence: Ewan.Cameron{at}glasgow.ac.uk Karen.Blyth{at}glasgow.ac.uk K Blyth 1 Cancer Research UK Scotland Institute, Garscube Estate , Switchback Road, Glasgow, G61 1BD, UK 2 School of Cancer Sciences, University of Glasgow, Garscube Estate , Switchback Road, Glasgow, G61 1QH, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for K Blyth For correspondence: Ewan.Cameron{at}glasgow.ac.uk Karen.Blyth{at}glasgow.ac.uk Abstract Full Text Info/History Metrics Supplementary material Preview PDF ABSTRACT The genes encoding transcription factor RUNX1 and its binding partner CBFB have been reported to be mutated in human breast cancer. Here, we provide evidence that Runx1 loss of function results in accelerated disease onset and tumour development in mouse models of breast cancer, in keeping with a tumour suppressor role for RUNX1 in this disease setting. Combined deletion of Runx1 and the related family member Runx2 resulted in mammary epithelial cells becoming exquisitely sensitive to WNT-driven transformation, with the emergence of multiple tumours early in life. Clonogenic assays indicated that Runx1 ablation induced a stem cell like phenotype in mammary epithelial cells, whilst transcriptome analysis demonstrated activation of multiple oncogenic pathways, especially when Runx2 was co- deleted. Interestingly, altered Runx expression in the mammary epithelium also drove profound alterations in the tumour microenvironment, impacting the immune landscape. These results highlight that Runx1 restricts some forms of breast cancer and inhibits the full oncogenic potential of aberrant WNT signalling. Loss of Runx2 itself did not result in tumour promotion, yet the dramatic effects of combined Runx1 and Runx2 loss suggest that Runx2 can substitute for Runx1 in dampening the oncogenic effects of WNT signalling. INTRODUCTION Breast cancer is the commonest malignant disease affecting women and, despite significant improvement in survival rates, remains a major cause of mortality ( 1 ). Rather than a single disease, breast cancer is a heterogeneous group of tumours defined by anatomical origin and classified by histopathological, clinical and molecular features ( 2 ). Based on receptor status, breast tumours are categorised into three subtypes: hormone receptor positive (oestrogen receptor, ER; progesterone receptor, PR), human epidermal growth factor receptor type 2 (HER2) positive and triple negative (TN, ER-/PR-/HER2-) ( 3 ). Molecular profiling has helped stratify these distinct diseases into separate intrinsic subtypes that largely, but not entirely, map to the well-established disease categories ( 4 – 6 ). While clinically useful, it is oversimplistic to assume each of the histological subtype is always defined by a common genetic landscape and indeed, molecular heterogenicity exists within well characterised types of breast cancer. The RUNX genes ( RUNX1, −2 and −3 ) encode transcription factors that bind to a common co-factor, CBFβ, and modulate expression of many target genes, either directly through binding to the RUNX DNA consensus sequence or indirectly through protein-protein interactions with other partners ( 7 ). The emergence of these genes as players in breast cancer is evident ( 8 ), yet their functionality in tumour development and progression is complex and contradictory ( 9 , 10 ). This is particularly true for RUNX1 where changes in gene expression have unpredictable outcomes on cell survival, differentiation and proliferation. Large-scale mutational and expression studies indicate that loss of RUNX1/CBFB is associated with a significant proportion of breast cancers. RUNX1 is a frequent mutational target, with genetic changes predicted to interfere with its DNA binding activity or binding to CBFβ, resulting in reduced function ( 11 – 13 ). CBFB is also frequently mutated, supporting the notion that RUNX1 acts as a tumour suppressor in breast cancer ( 11 , 12 ). These putative loss-of-function mutations in RUNX1 and CBFB were overrepresented in luminal breast cancers, suggesting that the RUNX1/CBFβ complex may restrict some subtypes ( 7 ). Mutational studies are supported by mRNA expression profiling and protein levels, where reduced RUNX1 is associated with breast cancer metastasis and high-grade tumours ( 14 , 15 ). We, and others, have previously demonstrated that Runx2 (but not Runx3 ) is expressed in the mammary epithelium, although at lower levels than Runx1 ( 16 , 17 ). Importantly, loss-of-function mutations of CBFB would ablate both RUNX1 and RUNX2 activity. Although the role of RUNX2 in breast cancer has not been fully explored, we showed that Runx2 supports mammary reconstitution and stem cell function ( 18 ). In contrast to its tumour suppressor role, transcriptome analyses showed RUNX1 upregulation in some subtypes ( 19 , 20 ) and enhanced protein expression is associated with poor outcome in TN tumours ( 21 ). Therefore, with an obvious, yet unresolved role in breast cancer, investigation of Runx genes in physiologically relevant models is warranted. As genetically engineered mouse models (GEMMs) have proved useful in dissecting the interaction of oncogenic pathways ( 22 ), we sought to assess the putative tumour suppressor functions of Runx1 using in vivo models. Loss of Runx1 accelerated disease in specific oncogenic backgrounds including a model with activated WNT signalling, an important alteration in breast cancer. Components of the WNT pathway, including β-catenin, are frequently overexpressed across breast cancer subtypes, associated with a stem-like phenotype and poorer outcomes ( 23 – 25 ). We show functional overlap between Runx1 and Runx2 in restricting Wnt/β-catenin -driven tumorigenesis, with Runx -deficient β-catenin-expressing mammary cells being exquisitely prone to tumour formation. Runx loss increases stemness of mammary cells and unleashes the oncogenic potential of deregulated WNT/β-catenin signalling. Furthermore, loss of Runx1 in the epithelium alters the tumour microenvironment. RESULTS Runx1 loss facilitates early tumour onset in mouse models of mammary cancer To investigate the possible tumour suppressor role of RUNX1 in breast cancer, we deleted Runx1 in preclinical mouse models. MMTV-PyMT is a well characterised breast cancer model ( 26 ), transcriptionally aligning with the luminal B subtype ( 27 ) and exhibiting early mammary hyperplasia progressing to highly aggressive tumours. To explore Runx1 loss on PyMT -driven tumourigenesis, we generated MMTV-PyMT;MMTV-Cre ; Runx1 fl/fl (hereafter PyMT+/R1KO ) mice and MMTV-PyMT ; MMTV-Cre;Runx1 wt/wt (hereafter PyMT+/WT ) controls. Given that PyMT -driven tumorigenesis and Cre -mediated recombination in this model could occur asynchronously and independently, a conditional tdRFP reporter ( 28 ) was introduced as a surrogate for MMTV-Cre mediated Runx1 allele excision (Supplement Fig. 1A). Loss of Runx1 significantly ( P = 0.0001) accelerated formation of mammary tumours in PyMT+/R1KO mice (average time to tumour onset of 50 days, ± 10.9) compared to PyMT+/WT mice (average time to tumour onset of 61 days, ± 11.5), indicating that RUNX1 normally restricts the oncogenic pathways activated by PyMT ( Fig. 1A ). No significant difference in tumour volume between cohorts was observed at tumour onset (Supplement Fig. 1A). To explore if Runx1 deletion was manifesting a selective advantage to the epithelial population primed for transformation, we assessed the proportion of tdRFP -positive cells in MMECs isolated from PyMT+/WT and PyMT+/R1KO mice at tumour notice, as well as from PyMT- negative counterparts at ∼60 days. Our results showed a significantly ( P < 0.01) increased percentage of tdRFP positivity in Runx1 -deleted MMECs, regardless of the status of the PyMT oncogene ( Fig. 1B ). Download figure Open in new tab Fig. 1. Loss of Runx1 facilitates early tumour onset in preclinical models of breast cancer. A. Time to tumour onset of PyMT+ mice wild type ( n = 44; WT ) or deleted for Runx1 ( n = 34; R1KO ). Average time to tumour onset with standard deviation shown: 61 days (± 11.5) for WT; 50 days (± 10.9) for R1KO mice. Each data point represents an individual mouse. NP: nulliparous. Statistical analysis performed with two-tailed unpaired t-test, *** P = 0.0001. B. Flow cytometric analysis of tdRFP expression in MMECs from PyMT+/WT ( n = 6) and PyMT+/R1KO ( n = 6) mice at tumour notice, and PyMT -negative controls ( PyMT-/WT, n = 7; PyMT-/R1KO, n = 6) at ∼60 days. Each data point represents percentage of tdRFP -positive MMECs, with smaller points representing individual glands (5-7 glands per mouse, see methods) and larger points representing the means from each mouse. Glands isolated from the same mouse are indicated by the same colour. Statistical analysis performed on the mean percentage of tdRFP -positive MMECs with ordinary one-way ANOVA with Sidak’s multiple comparisons test, ** P < 0.01. C. Time to tumour onset of BLG-Cre;Ctnnb1 wt/lox(ex3) ( B-cat+ ) mice wild type ( n = 12; WT ) or deleted for Runx1 ( n = 16; parous R1KO and n = 14; nulliparous R1KO ). Average time to tumour onset with standard deviation shown: 344 days (± 69.6) for parous WT ; 250 days (± 59.3) for parous R1KO ; 259 days (± 75.9) for nulliparous R1KO mice. Each data point represents an individual mouse. Parous (P) mice, filled circles; nulliparous (NP) mice, open circles. Statistical analysis performed with ordinary one-way ANOVA test with Dunnett’s multiple comparisons test, ** P < 0.01. D. Time to tumour onset in BLG-Cre;Trp53 fl/fl ( Trp53-/- ) parous (P) and nulliparous (NP) mice wild type ( n = 9 and n = 13, respectively; WT ) and deleted for Runx1 ( n = 8 and n = 15, respectively; R1KO ). Each data point represents an individual mouse. Average time to tumour onset with standard deviation shown. WT -P: 199 days (± 29.4); WT -NP: 195 days (± 26.7); R1KO- P: 195 days (± 15); R1KO -NP: 215 days (± 28.9). Statistical analysis performed with ordinary one-way ANOVA test with Sidak’s multiple comparisons test; ns, non-significant ( P > 0.05). We next tested the effects of Runx1 deletion in a novel model of Wnt/β-catenin -driven breast cancer by crossing mice carrying a stabilised form of β-catenin ( Ctnnb1 wt/lox(ex3) ) ( 29 ) onto the BLG-Cre line ( 30 ). BLG-Cre;Ctnnb1 wt/lox(ex3) mice (hereafter B-cat+ ), either Runx1 - proficient (hereafter B-cat+/WT ) or Runx1 -deficient (hereafter B-cat+/R1KO ), were generated (Supplement Fig. 1B). Loss of Runx1 resulted in a significant ( P < 0.01) acceleration of Wnt/β- catenin -driven tumorigenesis, with B-cat+/R1KO mice developing tumours at an average onset of 250 days (± 59.3) as opposed to the average 344 days (± 69.6) of B-cat+/WT animals ( Fig. 1C ). Of note, Runx1 deletion removed the need for parity required to maximise BLG-Cre expression, with 16 out of 20 (80%) nulliparous B-cat+/R1KO animals aged to 325 days presenting with palpable tumours compared to only 1 out of 12 (8%) nulliparous B-cat+/WT mice aged to the same time. Furthermore, tumour onset in nulliparous B-cat+/R1KO animals occurred at the same latency as the corresponding parous cohort (259 days, ± 75.9; Fig. 1C ). There was no difference in tumour volume between cohorts at tumour notice (Supplement Fig. 1B). Thus, BLG-Cre -mediated loss of Runx1 , in combination with constitutive activation of WNT/β-catenin, significantly accelerates mammary tumourigenesis. Interestingly, despite the tumour promoting effects of Runx1 deletion in both MMTV- PyMT and Wnt/β-catenin models, Runx1 loss did not accelerate tumour development in a BLG- Cre;Trp53 fl/fl GEMM ( Fig. 1D & Supplement Fig. 1C), pointing towards possible redundancy in the tumour suppressive actions of Trp53 and Runx1 . Genetic loss of Runx1 and Runx2 dramatically accelerates Wnt/β-catenin -driven mammary tumour development Given the clinical relevance of WNT/β-catenin in breast cancer ( 31 , 32 ) and the co-occurrence of WNT/RUNX gene alterations ( 33 ), we further investigated the tumour suppressor phenotype shown by Runx1 loss in the Wnt/β-catenin -driven model. It was previously reported that RUNX1 has a role in the differentiation of mature ER+ luminal mammary epithelial cells ( 34 ), whilst our group showed that RUNX2 is associated with mammary stemness ( 18 ). Given the auto and cross regulation between these proteins and that through a common binding site they regulate the same targets, we sought to understand the effects of combined Runx1 and Runx2 loss, hypothesising that Runx2 might substitute for Runx1 loss in restricting the oncogenic potential of WNT/β-catenin. Accordingly, crossing B-cat+ mice with Runx1 fl/fl ; Runx2 fl/fl animals (hereafter B-cat+/DKO ) resulted in a dramatic acceleration of disease onset ( P < 0.0001), with nulliparous B-cat+/DKO females developing lesions with a median tumour-free age of 56 days (± 14.9) compared to both parous B-cat+/R1KO (median tumour-free age of 266 days ± 59.3) and B-cat+/WT (median tumour-free age of 330 days ± 69.6) mice ( Fig. 2A and Supplement Fig. 1D). Indeed, tumour development was too rapid to permit even one round of pregnancy in B-cat+/DKO females. Loss of Runx2 alone ( B-cat+/R2KO ) did not affect the rate of tumour onset compared to B-cat+/WT mice ( Fig. 2A ), indicating that the extreme phenotype was only observed with loss of both genes. Download figure Open in new tab Fig. 2. Combined loss of Runx1 and Runx2 dramatically accelerates Wnt/ β -catenin - driven mammary tumour development. A. Time to tumour onset in B-cat+ mice wild-type for Runx ( n = 12; WT as in Fig. 1C ), Runx1 fl/fl ( n = 16; R1KO as in Fig. 1C ), Runx2 fl/fl ( n = 15; R2KO ) and Runx1 fl/fl ;Runx2 fl/fl ( n = 32; DKO ). Parous (P) mice, solid lines; nulliparous (NP) mice, dotted lines. Median survival of tumour-free mice with standard deviation shown: WT -P mice, 330 days (± 69.6); R1KO -P mice, 266 days (± 59.3); R2KO -P mice, 367 days (± 65.4); DKO -NP mice, 56 days (± 14.9). Statistical analysis performed with log-rank (Mantel-Cox) test, *** P < 0.001; **** P < 0.0001. B. Number of tumour-bearing mammary glands in B-cat+ cohorts from Fig. 2A harvested at clinical endpoint. Each data point represents an individual mouse. Parous (P) mice, filled circles; nulliparous (NP) mice, open circles. WT -P ( n = 9); R1KO -P ( n = 12); R2KO -P ( n = 12); DKO -NP ( n = 24). Statistical analysis performed with ordinary one- way ANOVA with Dunnett’s multiple comparisons test, * P < 0.05; **** P < 0.0001. C. Wholemounts and H&E images of abdominal mammary glands from 6- and 9-weeks old B-cat+ cohorts. One representative wholemount and H&E image is shown per genotype (of n = 5-7 mice per genotype). Scale bars of wholemounts = 2mm; H&E images = 1mm. Arrow illustrates lymph node. Examination of B-cat+/DKO mice showed a very distinct presentation, with multiple, sometimes coalescing tumours, as opposed to B-cat+/WT, B-cat+/R1KO and B-cat+/R2KO animals presenting with a smaller number of larger lesions (Supplement Fig. 2A). Accordingly, a significant increase in the number of tumour bearing glands was observed in B-cat+/DKO ( n = 9/10 on average; P < 0.0001) compared to B-cat+/WT ( n = 2/10 on average) mice ( Fig. 2B ). A modest, but significant, difference was also observed between B-cat+/R1KO ( n = 4/10 on average; P < 0.05) and B-cat+/WT controls ( Fig. 2B ). This pathological profile, combined with faster onset of disease, indicates that the threshold for Wnt/β-catenin- driven mammary tumour development is significantly lower with simultaneous loss of Runx1 and Runx2 activity. To track early neoplastic changes, we examined wholemounts in virgin females. Activation of Wnt/β-catenin signalling resulted in mild hyperplasia in control B-cat+/WT mice, as well as in B-cat+/R1KO and B-cat+/R2KO cohorts ( Fig. 2C ). However, B-cat+/DKO mice exhibited dramatic changes, with hyperplastic outgrowths and disrupted architecture as confirmed by wholemounts and H&E images ( Fig. 2C ). As this phenotype was not observed in the absence of activated β-catenin (Supplement Fig. 2B), loss of RUNX function alone does not overtly interfere with mammary gland development. Rather, RUNX proteins act in concert to suppress Wnt/β-catenin -driven expansion of a mammary epithelial cells. Loss of RUNX expands a population of mammary epithelial cells with stem-like properties in the presence of activated WNT/β-catenin signalling Given the early changes described above and the increased tumour-bearing glands at endpoint in B-cat+/DKO animals ( Fig. 2B ), we speculated that combined loss of Runx1 and Runx2 could alter stem/progenitor population(s) to favour Wnt/β-catenin -driven preneoplastic expansion and outgrowth of mammary tumours. Thus, we isolated MMECs from 7-9 weeks old B-cat+ mice and performed colony formation cell assay. Stingl et al. ( 35 ) demonstrated that fractions enriched for cells capable of mammary gland regeneration in vivo (putative stem cells referred to as mammary repopulating units, MRUs) can be distinguished from the more numerous cells capable of colony formation in vitro (putative progenitor cells referred to as mammary colony forming units, Ma-CFU) by the type of colonies formed. While Ma-CFU give rise to spherical acinar structures, MRUs generate solid, irregular-shaped colonies in Matrigel culture. Interestingly, B-cat+/DKO and B-cat+/R1KO cells showed significantly reduced ( P < 0.0001) acinar-like colonies in favour of a significant increase ( P < 0.0001) in solid-like colonies compared to B-cat+/WT MMECs ( Fig. 3A ). These results demonstrate that loss of RUNX function expands a pool of cells enriched for stem-like properties. Download figure Open in new tab Fig. 3. Loss of RUNX function alters mammary stem/progenitor populations. A. Graph (top) and representative pictures (bottom) of colony-forming cell assay showing relative proportions of acinar- and solid-like colonies from MMECs isolated from 7-9 weeks old B-cat + cohorts; Runx1 wild type ( n = 5; WT ); R1KO ( n = 4); DKO ( n = 3). Mean with standard error of n ≥ 3 independent experiments, each with 12 technical replicates per condition. One representative image shown per cohort ( n ≥ 3 biological replicates per cohort). Acinar and solid-like colonies depicted by white and black arrows, respectively. Statistical analysis performed with two-way ANOVA with Dunnett’s multiple comparisons test, **** P < 0.0001. Scale bar, 500µm. B. Analysis of mammary RNA-seq signatures derived from gene set enrichment analysis (GSEA) of FACS-sorted tdRFP- positive MMECs from 9-weeks old B-cat + females comparing three conditions: Runx1 knockout ( R1KO; n = 5) versus wildtype for Runx genes ( WT; n = 4), Runx1/Runx2 knockout ( DKO; n = 5) versus WT , and DKO versus R1KO . The gene sets analysed include LIM Mammary Stem Cell Up ( https://www.gsea/msigdb.org/gsea-msigdb/mouse/geneset/LIM_MAMMARY_STEM_CELL_UP.html ), LIM Mammary Mature Luminal Up ( https://www.gsea-msigdb.org/gsea/msigdb/mouse/geneset/LIM_MAMMARY_STEM_CELL_UP.html ), and LIM Mammary Luminal Progenitor Up ( https://www.gsea-msigdb.org/gsea/msigdb/mouse/geneset/LIM_MAMMARY_STEM_CELL_UP.html ). Each bar represents the normalised enrichment score (NES) for a specific gene set under each comparison, with higher NES values indicating stronger enrichment of the gene set in the corresponding condition. C. Normalised gene expression levels of selected genes in FACS-sorted tdRFP- positive MMECs as described for Fig. 3B . Individual data points represent expression levels per sample, with central line indicating the median. Gene expression normalised for sequencing depth, with significant differences between conditions assessed with Wald test and shown as p-values adjusted for multiple testing with Benjamini-Hochberg method. Both procedures performed with DESeq2; * P < 0.05; ** P < 0.01; *** P < 0.001; **** P < 0.0001. To help identify MMECs with simultaneous Wnt/β-catenin activation and Runx loss, B-cat+ mice ( WT , R1KO and DKO ) were crossed with the tdRFP line. RNA-seq analysis was then performed on flow sorted tdRFP- positive MMECs lacking Runx1 , or both Runx1 and Runx2 . Interestingly, an enrichment of gene sets aligning with mammary stem cells and negatively associated with mature luminal and progenitor populations, was observed in MMECs isolated from B-c at+/ R1KO mice compared to B-cat +/ WT animals ( Fig. 3B ). However, the same expression profile showed a stronger enrichment in MMECs from B- cat+/DKO mice ( Fig. 3B ), indicating that combined loss of Runx1 and Runx2 further shifts MMECs to a stem-like state. Accordingly, expression levels of luminal markers (i.e. Krt8 and Krt18 ) were reduced and basal markers (i.e. Krt5 and Krt14 ) increased in B-cat+/DKO cells compared to their WT counterpart ( Fig. 3C ), in line with a shift to more solid-like colonies and the gene expression signature of MRUs ( 35 ). In addition, genes important in cancer stem cells, epithelial-to-mesenchymal transition and aggressiveness, such as Aldh1a , Lgr4 , Lgr6 and Snai2 ( 36 – 39 ), were mildly and markedly increased in R1KO and DKO cells, respectively, compared to B-cat+/WT MMECs ( Fig. 3C ). Altogether, these data indicate a shift towards an earlier lineage stage when Wnt/β-catenin activation occurs with simultaneous Runx loss. Runx gene loss increases mammosphere formation in murine epithelial cells To study RUNX function in a more tractable system, we used CRISPR-Cas9 to target Runx1 and Runx2 separately and in combination, in the non-tumorigenic, mammary HC11 cell line ( 40 ) (Supplement Fig. 3A-C). There was no obvious change in phenotype or growth characteristics when cells with Runx1 and/or Runx2 loss were grown in 2D conditions (Supplement Fig. 3D), nor when Runx1 was overexpressed (Supplement Fig. 3B-D). However, Runx1 loss significantly increased mammosphere formation when HC11 cells were grown in suspension ( Fig. 4A ), whilst the reverse was observed upon Runx1 overexpression ( Fig. 4B ). By contrast, loss of Runx2 reduced mammosphere number ( Fig. 4C ), in line with previous results showing Runx2 driving a stem cell phenotype ( 18 ). Combined loss of Runx1 and Runx2 increased mammosphere formation similar to that with Runx1 loss ( Fig. 4C ), emphasising that RUNX1 is the predominant factor moving cells out of the stem cell compartment. Given the exquisite relationship between Runx and Wnt/β-catenin signalling revealed above, recombinant WNT3A ligand was added to mammosphere cultures. While WNT3A increased mammosphere formation in HC11 cells wild-type for Runx , as expected, it further potentiated stemness of R1KO and DKO clones ( Fig. 4D ). Altogether, these results suggest complementary effects of Wnt/β-catenin signalling and loss of Runx1 (and Runx2 ) on mammosphere formation. Download figure Open in new tab Fig. 4. Runx gene loss increases mammosphere formation in HC11 cells. A. Scatter dot-plot of primary (left) and secondary (right) mammospheres of HC11 cells with empty vector (Empty) or independent CRISPR/Cas9-deleted Runx1 clones (R1KO_a, R1KO_b, R1KO_c). Results show normalised mammosphere counts per well relative to the average mammosphere count in control (Empty) group. Smaller points represent technical replicates, larger points the mean of each experimental repeat with coloured points used to differentiate experimental replicates (also for B-D below). For primary mammospheres, there were 12-48 technical replicates per condition per experimental replicate ( n = 8). Error bars indicate normalised mean of all technical replicates ± standard deviation (Empty, 100 ± 11.5; R1KO_a , 158.1 ± 17.9; R1KO_b , 165.4 ± 20.7; R1KO_c , 166.3 ± 20.0). For secondary mammospheres, there were 12-24 technical replicates per condition for each experimental repeat ( n = 4). Error bars indicate normalised mean of all technical replicates ± standard deviation (Empty, 100.1 ± 9.6; R1KO_a , 179.1 ± 28.9; R1KO_b, 175.6 ± 31.1; R1KO_c , 174.5 ± 34.1). Statistical analysis performed with ordinary one-way ANOVA with Dunnett’s multiple comparisons test, **** P < 0.0001. B. Scatter dot-plot of primary mammospheres of HC11 cells overexpressing Runx1 ( Runx1P1 ) or empty vector control (Empty). Results show mammosphere counts relative to the average mammosphere count in control group (Empty). Error bars indicate normalised mean of all technical replicates ± standard deviation (Empty, 100 ± 18.6; Runx1P1 , 73.5 ±14.3) and data are representative of n = 4 experimental repeats. For each experiment, there were 8-12 technical replicates per condition. Statistical analysis performed with two-tailed unpaired t-test, **** P < 0.0001. C. Scatter dot-plot of primary mammospheres of HC11 cells first deleted for Runx2 and then transduced with lentiCRISPRv2-Neo empty vector (Empty) or containing a Runx1 -targeting gRNA plasmid to generate an independent Runx1 -deleted ( R1KO_d ), two Runx2 -deleted ( R2KO_a , R2KO_b ) and two Runx1/Runx2 -deleted ( DKO_a, DKO_b ) clones. Results show normalised mammosphere counts per well relative to the average mammosphere count in control group (empty). Error bars are normalised mean of all technical replicates with standard deviation (Empty, 100 ± 7.4 ; R1KO_d, 137.1 ± 9.1; R2KO_a , 80.1 ± 7.6; R2KO_b , 78.6 ± 9.6; DKO_a, 120.6 ± 7.9; DKO_b, 116.4 ± 7.7) and data are representative of n = 3 experimental repeats. For each experiment, there were 12 technical replicates per condition. Statistical analysis performed with ordinary one-way ANOVA with Dunnett’s multiple comparisons test, **** P < 0.0001. D. Scatter dot-plot of primary mammospheres of HC11 cells as for C in the presence or absence of recombinant WNT3A ligand. Error bars show mean of all technical replicates ± standard deviation for vehicle treated (Empty, 120.5 ± 6.2; R1KO_d , 145.4 ± 7.0; DKO_a , 136.8 ± 7.7; DKO_b , 137.7 ± 7.9) and WNT3A treated (Empty, 152.5 ± 8.1; R1KO_d , 191.3 ± 9.0; DKO_a , 193.4 ± 10.2; DKO_b, 193.1 ± 9.5) clones, of n = 2 experimental repeats. For each experiment, 12 technical replicates were tested per condition. Statistical analysis performed with ordinary one-way ANOVA with Sidak’s multiple comparisons test, **** P < 0.0001. Loss of Runx1 and Runx2 results in the activation of oncogenic pathways To understand the pathways driving accelerated Wnt/β-catenin -driven oncogenic development in the absence of both Runx genes, we explored RNA-seq data of the tdRFP- positive sorted MMECs. In line with the milder phenotype and modest reduction in tumour development observed between B-cat+/WT and B-cat+ / R1KO groups, only 88 differentially expressed genes reached significance upon Runx1 loss. By contrast, 1312 genes were significantly different between B-cat+/WT and B-cat+ / DKO cohorts, demonstrating a greatly altered gene expression in the absence of both Runx genes ( Fig. 5A ). Of note, 75/88 (85%) significantly altered genes within the B-cat+ / R1KO set were shared with the B-cat+/DKO cohort ( Fig. 5A ). When we interrogated gene set enrichment analysis and the Hallmark data sets using a significance cut- off of P < 0.01 and a false discovery rate of P < 0.05, 41 Hallmark gene sets were found to be enriched in the B-cat+/DKO dataset compared to B-cat+/WT, whilst only 28 were significantly enriched in the B-cat+/R1KO group relative to the B-cat+/WT . Notably, the majority of these gene sets (24/28) were also shared with the B-cat+/DKO cohort ( Fig. 5A ). Download figure Open in new tab Fig. 5. Combined loss of Runx1 and Runx2 results in the activation of key oncogenic pathways. RNA-seq analysis of MMECs isolated from 9-weeks old B-cat + /WT ( n = 4), B-cat + /R1KO ( n = 5) and B-cat + /DKO ( n = 5) females. Each sample comprised of pooled tdRFP -positive MMECs isolated from multiple mice of the same genotype (see Methods). A. Venn diagrams of the number of significantly altered (differentially expressed) genes (left) and enriched Hallmark Pathways identified by Gene Set Enrichment Analysis (GESA; right) comparing B-cat+/R1KO against B-cat+/WT, and B-cat+/DKO against B-cat+ / WT . The differential expression of a gene was considered significant if it had an absolute fold change greater than 1.5 and an adjusted P < 0.05, calculated using Wald test with DESeq2 package. Pathways’ significance determined based on a false discovery rate q-value threshold of 0.05. Circles represent sets of Differentially Expressed Genes (DEGs)/pathways identified in B-cat + / R1KO and B-cat + / DKO ; intersecting region shows DEGs/pathways common to both conditions; non- overlapping areas represent DEGs/pathways unique to each condition. B. Lollipop plot displaying normalised enrichment score (NES) from GSEA of the Hallmark Gene Sets comparing B-cat + / R1KO and B-cat + / DKO to B-cat + /WT . Each lollipop represents a gene set, with NES on the y-axis and the gene sets on the x-axis. Only gene sets that were significant (i.e. FDR q-value of 0.05 or less) in at least one of the conditions where used, with the position of the lollipop indicating the NES, and the size of the bubble corresponding to the -log10 of the FDR-adjusted q-value. Gene ranking for the analysis was based on the signal-to-noise ratio. These plots highlight the most significantly enriched gene sets in each condition, with larger bubbles indicating stronger statistical significance. Given that combined loss of Runx1 and Runx2 resulted in a dramatically accelerated oncogenic phenotype, we were interested in those gene sets that distinguished the B-cat+ / DKO group from the B-cat+ / R1KO one. These fell into two groups. The first refers to gene sets that showed an enriched score when the B-cat+ / DKO dataset was compared against the B- cat+ / R1KO one ( Fig. 5B ), including MYC, mTORC1, TGFb, EMT, IL6/JAK/STAT3, IL2/STAT5 and WNT/b-catenin signalling ( Fig. 5B ), possibly contributing to the more aggressive phenotype. The second group includes gene sets negatively enriched in B-cat+/R1KO MMECs compared to the B-cat+/WT ones yet positively enriched in B-cat+/DKO cells. Changes in these pathways (e.g. TNFA, hypoxia, PI3K/AKT/MTOR, ROS and apoptosis) might be key to explaining the dramatic differences observed upon simultaneous Runx1 and Runx2 loss ( Fig. 5B ). Deletion of Runx in mammary epithelial cells alters the immune microenvironment To investigate the wider effect of Runx deletion on the tumour microenvironment, RNA-seq was also carried out on whole mammary tissue from 9-weeks-old mice. PCA showed that samples within each genotype grouped together ( Fig. 6A ). In keeping with the B-cat+/R2KO cohort having a comparable tumour phenotype to the B-cat+/WT ( Fig. 2 ), these two groups were transcriptionally aligned, while the B-cat+/DKO one was the most divergent ( Fig. 6A ). Surprisingly, MetaCore pathway analysis revealed that 7/10 of the most significantly enriched pathways in the B-cat+/DKO cohort related to immune pathways ( Fig. 6B , lighter bars). These data suggest that loss of Runx1 and Runx2 in Wnt/β-catenin positive MMECs can profoundly change the immune landscape of surrounding tissues. Deconvolution analysis using two different approaches was applied. First, the Mouse Microenvironment Cell Population counter (mMCP-counter) ( 41 ) revealed 16 major immune and stromal cell types using gene expression profiles that identify and quantify different cellular populations. B-cat+ / DKO glands showed a markedly increased presence of immune cells within the mammary gland comprised of macrophages, granulocytes and T cells, including CD8 T cells ( Fig. 6C ). We then followed up with CIBERSORTx ( 42 ) and referenced our RNA-seq data against that of Bach et al. ( 43 ) who used single cell RNA-seq to fractionate different epithelial, stromal and immune cell types within the murine gland. Three different CD8+ T cell (CD8-1, CD8-2 and CD8-3) and two different CD4+ T cell (CD4-1 and CD4-2) subtypes were identified based on gene expression profiles ( 43 ). We noted clear alterations of these subsets in B-cat+ / DKO glands, with a decrease in CD8-1, CD8-2, CD4-2 and CTL populations and an increase in CD8-3 and T-reg cells ( Fig. 6D ). Our data also demonstrated that B-cat+ / DKO glands are enriched for a macrophage subtype termed Mo3, previously identified as tissue resident macrophages and associated with mammary ducts ( 44 ). Altogether, these results indicate that loss of Runx in Wnt/β-catenin -driven MMECs results in both profound tumour cell changes as well as in significant perturbations to non-tumour cells capable of altering the local environment. Download figure Open in new tab Fig. 6. Deletion of Runx genes in mammary epithelial cells alters the immune microenvironment. A. PCA plot of the top 500 most variable genes showing RNA-seq expression profiles for whole mouse mammary glands harvested from 9-weeks old B-cat + mice. Each point represents an individual mouse either: Runx wildtype (WT; black; n = 5), R1KO (blue; n = 5), R2KO (green; n = 5), and DKO (red; n = 4). PC1 and PC2, explaining 55% and 16% of the variance, are on the x- and y-axes, respectively. B. Top enriched pathways from MetaCore’s Canonical Pathway Maps representing signalling and metabolic pathways are shown (for samples as in A). The -log10 of the FDR-corrected p-value indicates the probability of the observed intersection between the DEGs and the pathway gene set occurring by chance, as determined by a hypergeometric test. A higher -log10 p-value suggests a stronger association between the DEGs and the pathway “DKO vs WT” refers to DEGs in the Runx1 / Runx2 double knockout compared to wildtype. C. Heatmap displaying z-scored mMCP scores for immune and stromal cell populations in Runx wildtype (WT; n = 5) versus Runx1 / Runx2 double knockout (DKO; n = 4) 9- weeks old B-cat+ mammary glands (as in A). Each column represents an individual mouse. D. Heatmap of CIBERSORTx immune cell fractions in Runx wildtype (WT; n = 5) versus Runx1 / Runx2 double knockout (DKO; n = 4) 9-weeks old B-cat+ mammary glands (as in C and D). CIBERSORTx immune cell fractions calculated using a signature matrix created from single cell sequencing of BLG-cre;Brca1 f/f ;Trp53 +/- murine mammary glands from Bach et al . ( 43 ). Z-scores are representative of the gene expression relative to the mean expression, indicated by a diverging scale running from blue to red. Each column represents an individual mouse. DISCUSSION RUNX1 and CBFB have been reported to be genetically altered in breast cancer, with many of these changes believed to be loss-of-function mutations ( 11 , 12 , 34 ). In addition, loss of RUNX1 function is associated with disease progression ( 14 , 15 , 45 ). Despite this, it is unclear how the RUNX1/CBFβ complex protects against tumour development. In this study, we demonstrate that Runx1 loss accelerates mammary tumourigenesis in GEMMs driven by the PyMT oncogene or by sustained Wnt/β-catenin signalling, providing definitive in vivo proof that Runx1 fulfils a tumour suppressor role in breast cancer, at least in the context of these oncogenic pathways. However, this does not appear to be a universal phenomenon as no tumour acceleration was observed when Runx1 was deficient in mammary tumours arising from Trp53 loss. This was surprising given that Runx1 loss in MMECs had previously been linked with enrichment of genes associated with p53 activation ( 34 ). With the observation that arrested development of luminal epithelial cells lacking Runx1 could be rescued in the absence of p53 resulting in hyperproliferation of ER+ cells, this led to speculate that these events may pre-dispose to breast cancer ( 34 , 46 ). However, our results suggest that the tumour suppressive functions of Runx1 and Trp53 might overlap, consistent with TP53 and RUNX1 mutations not co-occurring in METABRIC or TCGA breast cancer datasets. Similarly, others reported that CBFB and TP53 mutations were also mutually exclusive in these datasets ( 47 ). Heightened WNT signalling is a common finding in many breast tumours ( 48 ), with aberrant β-catenin activation reported to be preferentially associated with poor clinical outcome in patients with invasive breast cancer, regardless of molecular subtypes ( 24 ). Moreover, deregulated WNT signalling is linked with aggressive tumour features, including metastasis and the maintenance of cancer stem cells ( 49 ). In the presence of activated β-catenin , Runx1 deficiency promotes tumourigenesis and enhances a stem-like phenotype in both MMECs in vivo, as well as HC11 cells in vitro . These results agree with others who have shown that Runx1 drives the differentiation of stem/progenitor mammary epithelial cells ( 34 , 50 ) and predicted that RUNX1 loss would result in the expansion of such cells ( 50 ). Although loss of Runx2 did not change the rate of tumour formation, accelerated tumour onset was observed when β-catenin expressing MMECs were deficient for both Runx1 and Runx2 . These results indicate that, in the absence of Runx1, Runx2 may curtail tumour development. Although different family members have unique roles during lineage programming and cell fate decisions, this largely arises from their distinct spatial and temporal expression patterns. Indeed, that the Runx genes have significant functional redundancy is not surprising, given they share a common binding site and regulate a common gene set, as established in a number of different systems ( 51 – 53 ). Our results show that loss of Runx2 can promote Wnt/β-catenin -driven mammary tumourigenesis, but only upon Runx1 loss. It is possible that, when both genes are functional, Runx2 represses the effects of Runx1 by keeping cells in a stem-like state. Accordingly, MMECs expressing activated β-catenin and lacking both Runx1 and Runx2 are more strongly enriched for gene sets representing mammary stem cells than those with only Runx1 loss, which themselves show a stronger stem cell programme than control β-catenin expressing MMECs. Our finding that combined loss of both genes further enhances this effect suggests a cryptic role for Runx2 in negatively regulating the stem cell phenotype, only revealed in the absence of Runx1 . Together, these suggest that both Runx1 and Runx2 act in concert to restrain the oncogenic potential and stem-like properties of aberrant Wnt/β-catenin . RNA-seq analysis revealed that upon β-catenin activation, many more genes are differentially expressed and many more oncogenic pathways activated when both Runx genes are lost. Particularly intriguing are those gene sets negatively enriched in cells that have lost Runx1 compared to activated β-catenin alone, but that are highly enriched when both Runx genes are lost (e.g. TNFA and hypoxia). Thus, the rapid tumour onset of multiple clones might be driven both by the expansion of a stem-like population of mammary cells and activation of different oncogenic pathways that are only unleashed in the absence of RUNX function. An additional finding was the marked enrichment of immune pathways observed in B- cat+ / DKO glands before the appearance of gross lesions. Intriguingly, T-regs were one of the cell types predicted to be enriched in glands with simultaneous epithelial loss of Runx1 and Runx2 , suggesting the establishment of an immune suppressive environment. Whilst the mMCP analysis revealed a general T cells increase, refined CIBERSORTx deconvolution indicated that certain T cells subtypes were reduced, whilst others expanded. Future work will understand whether this change in T cell subtypes restricts or promotes tumorigenesis. It is possible that the increase in Mo3 cells displayed by B-cat+ / DKO mice may be secondary to ductal proliferation as others have shown that hormone induced branching can drive expansion of this population ( 44 ). Intriguingly, ductal macrophages have been associated with an immunosuppressive function within the gland, characterized by an expression profile aligning with tumour associated macrophages ( 44 , 54 ). Thus, the altered immune populations might have the potential to influence the cellular milieu that nurtures emerging B-cat+ / DKO tumours, a concept worth further investigation considering that WNT-driven tumours are characteristically renowned to be immune-excluded ( 55 , 56 ). In summary, loss of RUNX function within Wnt/β-catenin -driven mammary epithelial cells alters the threshold for oncogenic transformation and changes the immune response towards this expanding population of abnormal cells. How these different effects interact and contribute to rapid tumour development observed will be the focus of future studies. MATERIALS AND METHODS Animal procedures Mouse experiments were performed with ethical approval from the University of Glasgow Animal Welfare and Ethical Review Board under the Animal (Scientific Procedures) Act. Mice were housed in a pathogen-free facility in individual ventilated cages on a 12h light/dark cycle with continual access to food, water and environmental enrichment. Genotyping was carried out by Transnetyx (Cordova, US). Mouse euthanasia was humanely performed. Transgenic lines and genetic models of mammary cancer Mouse Mammary Tumour Virus ( MMTV ) -Cre ( 57 ) and MMTV -Polyomavirus middle T antigen ( PyMT ) ( 26 ) lines were provided by WJ Muller. Tandem dimer red fluorescent ( Gt(ROSA)26Sortm1Hjf ) knock-in Cre -reporter line (hereafter tdRFP ) was obtained from the European Mouse Mutant Archive ( 28 ). Beta-lactoglobulin ( BLG ) -Cre ( 30 ); Ctnnb1 wt/lox(ex3) ( 29 ); Trp53 fl/fl ( 58 ); Runx1 fl/fl ( 59 ) and Runx2 fl/fl ( 18 ) alleles as previously published. MMTV- PyMT mice were crossed with MMTV-Cre;Runx1 fl/fl to generate PyMT+/R1KO and PyMT+/WT lines, backcrossed 10 generations to FVB/N [Charles River, UK]. BLG- Cre;Ctnnb1 wt/lox(ex3) mice, bred with Runx1 fl/fl and Runx2 fl/fl lines, were intercrossed to establish cohorts maintained on an outbred background (C57BL/6; FVB/NCrl) as confirmed by Transnetyx MiniMUGA ( 60 ). BLG-Cre;Trp53 fl/fl mice, bred with Runx1 fl/fl and Runx1 wt/wt colonies, were backcrossed 10 generations to FVB/N [Charles River, UK]. To maximize BLG- Cre expression ( 30 ), females were mated at 11-13 weeks for two rounds of pregnancy, except for three BLG-Cre;Ctnnb1 wt/lox(ex3) ;Runx2 fl/fl ( B-cat+/R2KO ) females mated at 7-9 weeks. Tumour onset, tumour volume and clinical endpoint analyses Mice were palpated for tumour formation twice weekly. Tumour onset was defined as lesions ≥2mm and ≤7mm. Mice with tumours >7mm at time of notice were excluded from analysis. Tumours were measured with calipers and volume (mm 3 ) calculated as follows: Volume = (width x width x length)/2. Mice were euthanised when tumours reached endpoint (largest diameter at 15mm or ulcerated) and number of tumour-bearing glands (of 10 in total) counted. Wholemounts and histology Virgin females were sacrificed at 6- and 9-weeks. For wholemount staining, right-side abdominal (#4R) mammary glands were excised, spread on glass slides, air-dried and placed overnight in Carnoy’s solution (6-parts EtOH/3-parts CHCl 3 /1-part glacial acetic acid). Slides were washed in EtOH/15 min (70%, 50%, 25%), rinsed in dH20/10 min and stained overnight in Carmine Alum. Glands were washed in EtOH/15 min (70%, 95%, 100%), cleared in Xylene, mounted with Pertex Mounting Media and coverslipped. Wholemounts were photographed with a Zeiss stereomicroscope. For H&E staining, left abdominal glands (#4L) were formalin- fixed, paraffin-embedded, sectioned at 4µm and mounted on glass slides by the CRUK Scotland Institute Histology Department. Mouse mammary epithelial cell (MMEC) extraction Mammary glands (with lymph nodes removed) were dissected, placed in serum-free working solution - 1X F-12 Nutrient Mixture (Ham), 1X Penicillin-Streptomycin-Glutamine (PSG) - chopped [Mcllwain] and tissue paste incubated (1.5 h/37°C) with serum-free digestion solution - 1X Ham, 1X PSG, 10% Collagenase (300U/ml)/Hyaluronidase (100U/ml) - in a shaking incubator (100rpm). Organoids were collected through centrifugation (350g/5 min), while red blood cells lysed in 0.8% NH 4 Cl (RT/5 min) and removed by aspiration. To remove stromal cells, pellet was re-suspended in 10% Foetal Bovine Serum (FBS)-supplemented working solution, plated and incubated (37°C/1 h). Floating epithelial organoids were isolated and centrifuged (350g/5 min). Pellet was washed with 1X Phosphate-Buffered Saline (PBS), re- suspended in 0.5-1ml dissociation solution - 1X PBS, 0.25% Trypsin, 1mM EGTA, 0.1mg/ml DNase I recombinant, RNase-free 10000U - and incubated (37°C/10 min) to obtain a single cell suspension. MMECs were purified by filtering (40µm) and centrifugation (350g/5 min). Flow cytometric analysis and fluorescence-activated cell sorting (FACS) MMECs were isolated from PyMT +/ WT and PyMT+/R1KO mice at tumour notice (average of 61 and 50 days, respectively). An equivalent time-point (∼60 days) was chosen for PyMT- negative cohorts. One abdominal gland (#4R) was retained for histology. Remaining glands were analysed as individual samples (except for thoracic glands combined due to their overlap, i.e. #2/3R and #2/3L). 7 samples were analysed from PyMT+ cohorts (#1R, #1L, #2/3R, #2/3L, #4L, #5R and #5L), 5 samples from PyMT- negative mice due to scarcity of MMECs in #1 and #5 glands, which were combined (i.e. #1RL, #2/3R, #2/3L, #4L, #5RL). Data were run on an Attune Nxt Flow Cytometer [Invitrogen] and analysed with FlowJo (version 9.9.6). For FACS of tdRFP -positive MMECs from B-cat+ cohorts, ten glands were pooled from each 9-week mouse, MMECs isolated, re-suspended in FACS buffer - 1X PBS, 2mM EDTA pH 8, 1% FBS - and stained with DAPI/5 min. For each experiment, tdRFP -negative MMECs were used to set gates. Cells were collected using a BD FACSAria™ III Cell Sorter. Colony forming cell assay MMECs from 7-9-weeks B-cat+ mice were plated onto 24-well plates (5×10 3 viable cells/well), embedded in 25µl of Matrigel and cultured with 500µl of medium - 1X EpiCult-B Basal Medium Mouse, 10% EpiCult-B Proliferation Supplements, 5% FBS, 1% PSG, 4µg/ml Heparin, 10ng/ml mouse epidermal growth factor (mEGF), 10ng/ml mouse fibroblast growth factor-2 (mFGF-2). MMECs were extracted from all ten glands per mouse and fed with 500µl of fresh medium at day 3. Representative pictures were taken at day 7-9 and number of acinar- and solid-like colonies counted using a Olympus CKK41 microscope (4x lens). HC11 cell assays HC11 and HEK293T cells [ATCC] were maintained in RPMI 1640 (+10% FBS, 1% PSG, 5µg/ml Insulin, 10ng/ml mEGF) or DMEM (+10% FBS, 1% PSG) media, respectively. For 2D growth assays, trypsinised cells were plated (5×10 3 viable cells/ml) and supplemented with fresh medium every 2-3 days. For each condition, four 24-wells were counted every 24h for 2-4 experimental repeats. For mammosphere assays, 5×10 3 viable HC11 cells/ml in mammosphere medium - RPMI 1640, 1% PSG, 20ng/ml mEGF, mFGF-2, 4µg/ml heparin, 1X B27 Supplement - were cultured in 24-well Ultra-Low Attachment plates at 5% CO 2 /37°C. Cells were supplemented with mEGF and mFGF-2 (20ng/ml) every 3 days. Recombinant mouse WNT3A protein [100ng/ml, Abcam] was added to mammosphere medium and supplemented every 3 days. Number of mammospheres per well was counted at day 7 using an Olympus CKX41 Inverted Phase Contrast Microscope (10x lens). Runx1 overexpression & CRISPR/CAS9 gene targeting For Runx1 overexpression, HEK293T cells were transfected with 10µg of pBABE-Puro (Empty or mRunx1P1 ), 7.5µg of psPAX2 [Addgene] and 4µg of pVSV-G [Addgene] plasmids using calcium phosphate with medium replacement 24h post-transfection. Virus-containing media, filtered through 0.45µM, were used to transduce HC11 cells with 10µg/ml polybrene. Cells were selected with 1.5µg/ml Puromycin. For CRISPR/CAS9-mediated targeting, guide RNAs (gRNAs) targeting Runx1 and Runx2 were designed using the Zhang Lab tool [MIT, Boston, MA]. pX-459-Puro (Empty or Runx1 -targeting gRNAs) constructs were used to transfect HC11 cells using Lipofectamine™ 2000 Transfection Reagent. Three different Runx1 -targeting gRNAs were tested ( mhRunx1 , mRunx1_A , mRunx1_B ). Three independent Runx1 -deleted (R1KO_a, R1KO_b, R1KO_c) HC11 clones were generated using the mRunx1_B gRNA sequence (5’-CGGTGCGCACTAGCTCGCCA). For Runx1/Runx2 deletion, HC11 cells were transfected with pX-459-Puro (Empty or Runx2 -targeting gRNA, i.e. mhRunx2 : 5’-GAGCGACGTGAGCCCGGTGG) constructs, then transduced with lentiCRISPRv2-Neo (Empty or Runx1 -targeting gRNA, i.e. mRunx1_B ) vectors as above. Upon antibiotic selection (800µg/ml G418 Sulphate/Geneticin) and single-cell cloning, an independent Runx1 -deleted (R1KO_d), two independent Runx2 -deleted (R2KO_a, R2KO_b) and two independent Runx1/Runx2 -deleted (DKO_a, DKO_b) HC11 clones were generated. Immunoblotting and antibodies HC11 cells were lysed in Pierce RIPA Buffer. Protein extracts, resolved on 4-12% NuPAGE Novex Bis-Tris 1.0mm Mini Protein Gels [Thermo Fisher Scientific], were transferred to Hybond-ECL nitrocellulose membranes [Amersham]. The following antibodies were used: RUNX1 [#8529, D4A6, Cell Signaling; 1:1000]; RUNX2 [#8486, D1H7, Cell Signaling; 1:1000]; GAPDH [#3683, 14C10, Cell Signaling; 1:1000] and horseradish peroxidase- conjugated anti-rabbit secondary antibody [#7074, Cell Signaling; 1:10 000]. Densitometry analysis performed using Image Lab software (Fiji ImageJ software version 1.53). RNA sequencing RNA extraction was performed using the RNAqueous™-Micro Total RNA Isolation Kit [Invitrogen] and residual genomic DNA removed using the DNA-free™ system . tdRFP- positive sorted cells were pooled from 3-6 individual mice and combined into a single 20µl RNA sample. NanoDrop 2000 Spectrophotometer was used to quantify and assess purity of extracted RNA. RNA quality was assessed on an Agilent 2200 TapeStation System using RNA ScreenTape Analysis. Samples with RNA integrity number (RIN) values ≥ 7 were used wherever possible (18/19 samples in Fig. 6 ) and RIN values ≥ 6 (8/14 samples) for tdRFP+ FACS cells ( Fig. 3 and 5 ). RNA-seq libraries were prepared using SMARTer Stranded Total RNA-Seq Kit v2 - Pico Input Mammalian [Takara Bio]. Library quality was assessed using Agilent 2200 TapeStation System with a High Sensitivity D1000 ScreenTape Analysis and quantity measured with a Qubit Fluorometer. Libraries were sequenced by W. Clarke at CRUK Scotland Institute Molecular Technologies Facility via the Illumina NextSeq 500 System with paired-end (2×36) sequencing using the MiniSeq High Output Reagent Kit (75-cycles). Illumina sequence data were demultiplexed using bcl2fastq (v2.20). Quality checks on raw RNA-Seq data files were done using FastQC ( https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ ) and FastQ Screen ( 61 ). RNA-Seq paired-end reads were aligned to the GRCm38.93 version of the mouse genome and annotation ( 62 ) using HiSat2 ( 63 ) and sorted using Samtools ( 64 ). Aligned genes were identified using HTSeq ( 65 ). Expression levels were determined and statistically analysed using the R environment ( https://www.r-project.org/ ) and utilising packages from the Bioconductor data analysis suite ( 66 ). Differential gene expression was analysed based on the negative binomial distribution using DESeq2 ( 67 ) and adaptive shrinkage using Apeglm ( 68 ). Identification of enriched biological functions was achieved using g:Profiler ( 69 ), GESA from the Broad Institute ( 70 ) and MetaCore from Clarivate Analytics ( https://portal.genego.com/ ). Computational analysis was documented at each stage using MultiQC ( 71 ), Jupyter Notebooks ( https://eprints.soton.ac.uk/403913/ ) and R Notebooks ( https://posit.co/ ). CIBERSORTx Analysis Signature matrix was generated from an annotated single-cell RNA-seq dataset of murine glands ( 43 ) to provide a reference to impute cell fractions from gene expression profiles. Signature matrix and normalised bulk RNA-seq datasets were run through CIBERSORTx to deconvolute the bulk RNA-seq datasets ( https://cibersortx.stanford.edu ). CIBERSORTx job was run under Impute Cell Fractions settings, relative run mode, >100 permutations for significance analysis, and with quantile normalisation disabled. CIBERSORTx results were exported as CSV files and imported into R Studio (v 4.3.2) for visualization. mMCPCounter Analysis mMCPCounter (mMCPcounter.estimate) is a function within the mMCPcounter (v 1.1.0) R package in R studio ( 41 ). mMCPcounter.estimate was used to generate scores for 16 cell populations. Heatmaps of cell population Z-scores were plotted using the ComplexHeatmap (v 2.14.0) R package. To compare statistics of the boxplots for the different cell populations, the stats (v 4.2.2) R package was used to perform a t test between each of the conditions. Statistical analysis Statistical analyses performed using GraphPad Prism (version 10). Specific statistical tests used are indicated in figure legends. All error bars represent mean ± SD, unless otherwise indicated. DATA AVAILABILITY All data generated and/or analysed during the current study are available from the corresponding authors. Full RNA-seq datasets will be publicly available on the Sequence Read Archive database when accepted for publication. AUTHOR CONTRIBUTIONS Conceptualization : AIR, KJC, ERC, KB Methodology : AIR, KS, NF, KG, CB, HH, CN, KB Investigation : AIR, KS, NF, DA, KJC Formal Analysis : AIR, KS, RS, AL, KG, CB, ALY, HH, PDA, EWR, CJM, PDD, ERC, KB Data Curation : RS, AL Visualization : AIR, KS, RS, AK, AL, ERC Validation : AK, AL, FG, KJC Resources : ALY, PDD Supervision : PDA, EWR, CJM, PDD, KJC, ERC, KB Funding Acquisition : KB, EWR, CJM, PDD Writing – original draft : AIR, ERC, KB Writing – reviewing/editing : AIR, RS, AK, AL, KJC, ERC, KB All authors have approved the final version of the manuscript. FUNDING This work was directly funded by Cancer Research UK core funding awarded to KB (A29799) and the CRUK Scotland Institute (A17196/A31287) and from Breast Cancer Now (2016NovPhD859). AIR, KS, AK, AL, NF, DA, KJC were supported by Cancer Research UK core funding awarded to KB (grant number A29799). KS was supported by Breast Cancer Now (2016NovPhD859) awarded to KB. CRUK core funding to EWR (A1920) and CJM (A29801). ALY funded by the Beatson Cancer Charity (22-23-063). The labs of CJM, KB and PD have funding from the MRC National Mouse Genetics Network (grants MC_PC_21039, MC_PC_20142, MC_PC_21048), which supported HH. COMPETING INTERESTS The authors declare no competing interests. ACKNOWLEDGMENTS Authors wish to acknowledge all funding sources listed below. We thank the Advanced Technologies and Core Services at the CRUK Scotland Institute, especially the Biological Services Unit, the Histology Facility, William Clarke and Molecular Technologies, the Flow Cytometry Facility, and the Beatson Advanced Imaging Resource (BAIR). We are grateful to all past and present members of the Blyth lab for useful discussion and continued support during the project. Thank you to Barry Gusterson, Joanna Morris (University of Glasgow) and Matthew Smalley (Cardiff University) for advice and discussion, and to Catherine Winchester (CRUK Scotland Institute) for critical review of the manuscript and advice on research integrity. Schematics were prepared using BioRender with licence to K Blyth. REFERENCES 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. ↵ Nolan E , Lindeman GJ , Visvader JE . Deciphering breast cancer: from biology to the clinic . Cell . 2023 ; 186 ( 8 ): 1708 – 28 . OpenUrl CrossRef PubMed 3. ↵ Dawson SJ , Rueda OM , Aparicio S , Caldas C . A new genome-driven integrated classification of breast cancer and its implications . Embo j . 2013 ; 32 ( 5 ): 617 – 28 . OpenUrl Abstract / FREE Full Text 4. ↵ Perou CM , Sørlie T , Eisen MB , van de Rijn M , Jeffrey SS , Rees CA , et al. Molecular portraits of human breast tumours . Nature . 2000 ; 406 ( 6797 ): 747 – 52 . OpenUrl CrossRef PubMed Web of Science 5. Sørlie T , Perou CM , Tibshirani R , Aas T , Geisler S , Johnsen H , et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications . Proc Natl Acad Sci U S A . 2001 ; 98 ( 19 ): 10869 – 74 . OpenUrl Abstract / FREE Full Text 6. ↵ Curtis C , Shah SP , Chin SF , Turashvili G , Rueda OM , Dunning MJ , et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups . Nature . 2012 ; 486 ( 7403 ): 346 – 52 . OpenUrl CrossRef PubMed Web of Science 7. ↵ Khan AS , Campbell KJ , Cameron ER , Blyth K . The RUNX/CBFβ Complex in Breast Cancer: A Conundrum of Context . Cells . 2023 ; 12 ( 4 ). 8. ↵ Nik-Zainal S , Davies H , Staaf J , Ramakrishna M , Glodzik D , Zou X , et al. Landscape of somatic mutations in 560 breast cancer whole-genome sequences . Nature . 2016 ; 534 ( 7605 ): 47 – 54 . OpenUrl CrossRef PubMed 9. ↵ Blyth K , Cameron ER , Neil JC . The RUNX genes: gain or loss of function in cancer . Nat Rev Cancer . 2005 ; 5 ( 5 ): 376 – 87 . OpenUrl CrossRef PubMed Web of Science 10. ↵ Riggio AI , Blyth K . The enigmatic role of RUNX1 in female-related cancers - current knowledge & future perspectives . Febs j . 2017 ; 284 ( 15 ): 2345 – 62 . OpenUrl CrossRef PubMed 11. ↵ Banerji S , Cibulskis K , Rangel-Escareno C , Brown KK , Carter SL , Frederick AM , et al. Sequence analysis of mutations and translocations across breast cancer subtypes . Nature . 2012 ; 486 ( 7403 ): 405 – 9 . OpenUrl CrossRef PubMed Web of Science 12. ↵ Ellis MJ , Ding L , Shen D , Luo J , Suman VJ , Wallis JW , et al. Whole-genome analysis informs breast cancer response to aromatase inhibition . Nature . 2012 ; 486 ( 7403 ): 353 – 60 . OpenUrl CrossRef PubMed Web of Science 13. ↵ Cornen S , Guille A , Adélaïde J , Addou-Klouche L , Finetti P , Saade MR , et al. Candidate luminal B breast cancer genes identified by genome, gene expression and DNA methylation profiling . PLoS One . 2014 ; 9 ( 1 ): e81843 . OpenUrl CrossRef PubMed 14. ↵ Ramaswamy S , Ross KN , Lander ES , Golub TR . A molecular signature of metastasis in primary solid tumors . Nat Genet . 2003 ; 33 ( 1 ): 49 – 54 . OpenUrl CrossRef PubMed Web of Science 15. ↵ Kadota M , Yang HH , Gomez B , Sato M , Clifford RJ , Meerzaman D , et al. Delineating genetic alterations for tumor progression in the MCF10A series of breast cancer cell lines . PLoS One . 2010 ; 5 ( 2 ): e9201 . OpenUrl CrossRef PubMed 16. ↵ McDonald L , Ferrari N , Terry A , Bell M , Mohammed ZM , Orange C , et al. RUNX2 correlates with subtype-specific breast cancer in a human tissue microarray, and ectopic expression of Runx2 perturbs differentiation in the mouse mammary gland . Dis Model Mech . 2014 ; 7 ( 5 ): 525 – 34 . OpenUrl Abstract / FREE Full Text 17. ↵ Owens TW , Rogers RL , Best S , Ledger A , Mooney AM , Ferguson A , et al. Runx2 is a novel regulator of mammary epithelial cell fate in development and breast cancer . Cancer Res . 2014 ; 74 ( 18 ): 5277 – 86 . OpenUrl Abstract / FREE Full Text 18. ↵ Ferrari N , Riggio AI , Mason S , McDonald L , King A , Higgins T , et al. Runx2 contributes to the regenerative potential of the mammary epithelium . Sci Rep . 2015 ; 5 : 15658 . OpenUrl CrossRef PubMed 19. ↵ Karn T , Pusztai L , Holtrich U , Iwamoto T , Shiang CY , Schmidt M , et al. Homogeneous datasets of triple negative breast cancers enable the identification of novel prognostic and predictive signatures . PLoS One . 2011 ; 6 ( 12 ): e28403 . OpenUrl CrossRef PubMed 20. ↵ Rody A , Karn T , Liedtke C , Pusztai L , Ruckhaeberle E , Hanker L , et al. A clinically relevant gene signature in triple negative and basal-like breast cancer . Breast Cancer Res . 2011 ; 13 ( 5 ): R97 . OpenUrl CrossRef PubMed 21. ↵ Ferrari N , Mohammed ZM , Nixon C , Mason SM , Mallon E , McMillan DC , et al. Expression of RUNX1 correlates with poor patient prognosis in triple negative breast cancer . PLoS One . 2014 ; 9 ( 6 ): e100759 . OpenUrl CrossRef PubMed 22. ↵ Kersten K , de Visser KE , van Miltenburg MH , Jonkers J . Genetically engineered mouse models in oncology research and cancer medicine . EMBO Mol Med . 2017 ; 9 ( 2 ): 137 – 53 . OpenUrl Abstract / FREE Full Text 23. ↵ Monteiro J , Gaspar C , Richer W , Franken PF , Sacchetti A , Joosten R , et al. Cancer stemness in Wnt-driven mammary tumorigenesis . Carcinogenesis . 2014 ; 35 ( 1 ): 2 – 13 . OpenUrl CrossRef PubMed 24. ↵ Li S , Li S , Sun Y , Li L . The expression of β-catenin in different subtypes of breast cancer and its clinical significance . Tumour Biol . 2014 ; 35 ( 8 ): 7693 – 8 . OpenUrl CrossRef PubMed 25. ↵ Patel DK , Kesharwani R , Verma A , Al-Abbasi FA , Anwar F , Kumar V . Scope of Wnt signaling in the precise diagnosis and treatment of breast cancer . Drug Discov Today . 2023 ; 28 ( 7 ): 103597 . OpenUrl CrossRef PubMed 26. ↵ Guy CT , Cardiff RD , Muller WJ . Induction of mammary tumors by expression of polyomavirus middle T oncogene: a transgenic mouse model for metastatic disease . Mol Cell Biol . 1992 ; 12 ( 3 ): 954 – 61 . OpenUrl Abstract / FREE Full Text 27. ↵ Pfefferle AD , Herschkowitz JI , Usary J , Harrell JC , Spike BT , Adams JR , et al. Transcriptomic classification of genetically engineered mouse models of breast cancer identifies human subtype counterparts . Genome Biol . 2013 ; 14 ( 11 ): R125 . OpenUrl CrossRef PubMed 28. ↵ Luche H , Weber O , Nageswara Rao T , Blum C , Fehling HJ . Faithful activation of an extra-bright red fluorescent protein in “knock-in” Cre-reporter mice ideally suited for lineage tracing studies . Eur J Immunol . 2007 ; 37 ( 1 ): 43 – 53 . OpenUrl CrossRef PubMed Web of Science 29. ↵ Harada N , Tamai Y , Ishikawa T , Sauer B , Takaku K , Oshima M , et al. Intestinal polyposis in mice with a dominant stable mutation of the beta-catenin gene . Embo j . 1999 ; 18 ( 21 ): 5931 – 42 . OpenUrl Abstract / FREE Full Text 30. ↵ Selbert S , Bentley DJ , Melton DW , Rannie D , Lourenço P , Watson CJ , et al. Efficient BLG-Cre mediated gene deletion in the mammary gland . Transgenic Res . 1998 ; 7 ( 5 ): 387 – 96 . OpenUrl CrossRef PubMed Web of Science 31. ↵ Xu X , Zhang M , Xu F , Jiang S . Wnt signaling in breast cancer: biological mechanisms, challenges and opportunities . Mol Cancer . 2020 ; 19 ( 1 ): 165 . OpenUrl CrossRef PubMed 32. ↵ Abreu de Oliveira WA , El Laithy Y , Bruna A , Annibali D , Lluis F. Wnt Signaling in the Breast: From Development to Disease . Front Cell Dev Biol . 2022 ; 10 : 884467 . OpenUrl CrossRef PubMed 33. ↵ Sweeney K , Cameron ER , Blyth K . Complex Interplay between the RUNX Transcription Factors and Wnt/β-Catenin Pathway in Cancer: A Tango in the Night . Mol Cells . 2020 ; 43 ( 2 ): 188 – 97 . OpenUrl PubMed 34. ↵ van Bragt MP , Hu X , Xie Y , Li Z . RUNX1, a transcription factor mutated in breast cancer, controls the fate of ER-positive mammary luminal cells . Elife . 2014 ; 3 : e03881 . OpenUrl CrossRef PubMed 35. ↵ Stingl J , Eirew P , Ricketson I , Shackleton M , Vaillant F , Choi D , et al. Purification and unique properties of mammary epithelial stem cells . Nature . 2006 ; 439 ( 7079 ): 993 – 7 . OpenUrl CrossRef PubMed Web of Science 36. ↵ Guo W , Keckesova Z , Donaher JL , Shibue T , Tischler V , Reinhardt F , et al. Slug and Sox9 cooperatively determine the mammary stem cell state . Cell . 2012 ; 148 ( 5 ): 1015 – 28 . OpenUrl CrossRef PubMed Web of Science 37. Blaas L , Pucci F , Messal HA , Andersson AB , Josue Ruiz E , Gerling M , et al. Lgr6 labels a rare population of mammary gland progenitor cells that are able to originate luminal mammary tumours . Nat Cell Biol . 2016 ; 18 ( 12 ): 1346 – 56 . OpenUrl CrossRef PubMed 38. Wei Y , Li Y , Chen Y , Liu P , Huang S , Zhang Y , et al. ALDH1: A potential therapeutic target for cancer stem cells in solid tumors . Front Oncol . 2022 ; 12 : 1026278 . OpenUrl CrossRef PubMed 39. ↵ Yue Z , Yuan Z , Zeng L , Wang Y , Lai L , Li J , et al. LGR4 modulates breast cancer initiation, metastasis, and cancer stem cells . Faseb j . 2018 ; 32 ( 5 ): 2422 – 37 . OpenUrl CrossRef PubMed 40. ↵ Ball RK , Friis RR , Schoenenberger CA , Doppler W , Groner B . Prolactin regulation of beta-casein gene expression and of a cytosolic 120-kd protein in a cloned mouse mammary epithelial cell line . Embo j . 1988 ; 7 ( 7 ): 2089 – 95 . OpenUrl CrossRef PubMed Web of Science 41. ↵ Petitprez F , Levy S , Sun CM , Meylan M , Linhard C , Becht E , et al. The murine Microenvironment Cell Population counter method to estimate abundance of tissue-infiltrating immune and stromal cell populations in murine samples using gene expression . Genome Med . 2020 ; 12 ( 1 ): 86 . OpenUrl CrossRef PubMed 42. ↵ Newman AM , Steen CB , Liu CL , Gentles AJ , Chaudhuri AA , Scherer F , et al. Determining cell type abundance and expression from bulk tissues with digital cytometry . Nat Biotechnol . 2019 ; 37 ( 7 ): 773 – 82 . OpenUrl CrossRef PubMed 43. ↵ Bach K , Pensa S , Zarocsinceva M , Kania K , Stockis J , Pinaud S , et al. Time-resolved single-cell analysis of Brca1 associated mammary tumourigenesis reveals aberrant differentiation of luminal progenitors . Nat Commun . 2021 ; 12 ( 1 ): 1502 . OpenUrl CrossRef PubMed 44. ↵ Dawson CA , Pal B , Vaillant F , Gandolfo LC , Liu Z , Bleriot C , et al. Tissue-resident ductal macrophages survey the mammary epithelium and facilitate tissue remodelling . Nat Cell Biol . 2020 ; 22 ( 5 ): 546 – 58 . OpenUrl CrossRef PubMed 45. ↵ Hong D , Fritz AJ , Finstad KH , Fitzgerald MP , Weinheimer A , Viens AL , et al. Suppression of Breast Cancer Stem Cells and Tumor Growth by the RUNX1 Transcription Factor . Mol Cancer Res . 2018 ; 16 ( 12 ): 1952 – 64 . OpenUrl Abstract / FREE Full Text 46. ↵ Matsuo J , Mon NN , Douchi D , Yamamura A , Kulkarni M , Heng DL , et al. A Runx1- enhancer Element eR1 Identified Lineage Restricted Mammary Luminal Stem Cells . Stem Cells . 2022 ; 40 ( 1 ): 112 – 22 . OpenUrl CrossRef PubMed 47. ↵ Malik N , Yan H , Yang HH , Ayaz G , DuBois W , Tseng YC , et al. CBFB cooperates with p53 to maintain TAp73 expression and suppress breast cancer . PLoS Genet . 2021 ; 17 ( 5 ): e1009553 . OpenUrl CrossRef PubMed 48. ↵ Koval A , Katanaev VL . Dramatic dysbalancing of the Wnt pathway in breast cancers . Sci Rep . 2018 ; 8 ( 1 ): 7329 . OpenUrl CrossRef PubMed 49. ↵ Ordaz-Ramos A , Tellez-Jimenez O , Vazquez-Santillan K . Signaling pathways governing the maintenance of breast cancer stem cells and their therapeutic implications . Front Cell Dev Biol . 2023 ; 11 : 1221175 . OpenUrl CrossRef PubMed 50. ↵ Sokol ES , Sanduja S , Jin DX , Miller DH , Mathis RA , Gupta PB . Perturbation- expression analysis identifies RUNX1 as a regulator of human mammary stem cell differentiation . PLoS Comput Biol . 2015 ; 11 ( 4 ): e1004161 . OpenUrl CrossRef PubMed 51. ↵ Fukushima-Nakase Y , Naoe Y , Taniuchi I , Hosoi H , Sugimoto T , Okuda T . Shared and distinct roles mediated through C-terminal subdomains of acute myeloid leukemia/Runt- related transcription factor molecules in murine development . Blood . 2005 ; 105 ( 11 ): 4298 – 307 . OpenUrl Abstract / FREE Full Text 52. Morita K , Suzuki K , Maeda S , Matsuo A , Mitsuda Y , Tokushige C , et al. Genetic regulation of the RUNX transcription factor family has antitumor effects . J Clin Invest . 2017 ; 127 ( 7 ): 2815 – 28 . OpenUrl CrossRef PubMed 53. ↵ Shin B , Hosokawa H , Romero-Wolf M , Zhou W , Masuhara K , Tobin VR , et al. Runx1 and Runx3 drive progenitor to T-lineage transcriptome conversion in mouse T cell commitment via dynamic genomic site switching . Proc Natl Acad Sci U S A . 2021 ; 118 ( 4 ). 54. ↵ Franklin RA , Liao W , Sarkar A , Kim MV , Bivona MR , Liu K , et al. The cellular and molecular origin of tumor-associated macrophages . Science . 2014 ; 344 ( 6186 ): 921 – 5 . OpenUrl Abstract / FREE Full Text 55. ↵ Dangaj D , Barras D , Coukos G . Tumor Landscapes: β-Catenin Drives Immune Desertification . Clin Cancer Res . 2019 ; 25 ( 10 ): 2943 – 5 . OpenUrl Abstract / FREE Full Text 56. ↵ Luke JJ , Bao R , Sweis RF , Spranger S , Gajewski TF . WNT/β-catenin Pathway Activation Correlates with Immune Exclusion across Human Cancers . Clin Cancer Res . 2019 ; 25 ( 10 ): 3074 – 83 . OpenUrl Abstract / FREE Full Text 57. ↵ Andrechek ER , Hardy WR , Siegel PM , Rudnicki MA , Cardiff RD , Muller WJ . Amplification of the neu/erbB-2 oncogene in a mouse model of mammary tumorigenesis . Proc Natl Acad Sci U S A . 2000 ; 97 ( 7 ): 3444 – 9 . OpenUrl Abstract / FREE Full Text 58. ↵ Jonkers J , Meuwissen R , van der Gulden H , Peterse H , van der Valk M , Berns A . Synergistic tumor suppressor activity of BRCA2 and p53 in a conditional mouse model for breast cancer . Nat Genet . 2001 ; 29 ( 4 ): 418 – 25 . OpenUrl CrossRef PubMed Web of Science 59. ↵ Growney JD , Shigematsu H , Li Z , Lee BH , Adelsperger J , Rowan R , et al. Loss of Runx1 perturbs adult hematopoiesis and is associated with a myeloproliferative phenotype . Blood . 2005 ; 106 ( 2 ): 494 – 504 . OpenUrl Abstract / FREE Full Text 60. ↵ Sigmon JS , Blanchard MW , Baric RS , Bell TA , Brennan J , Brockmann GA , et al. Content and Performance of the MiniMUGA Genotyping Array: A New Tool To Improve Rigor and Reproducibility in Mouse Research . Genetics . 2020 ; 216 ( 4 ): 905 – 30 . OpenUrl Abstract / FREE Full Text 61. ↵ Wingett SW , Andrews S . FastQ Screen: A tool for multi-genome mapping and quality control . F1000Res . 2018 ; 7 : 1338 . OpenUrl 62. ↵ Cunningham F , Achuthan P , Akanni W , Allen J , Amode MR , Armean IM , et al. Ensembl 2019 . Nucleic Acids Res . 2019 ; 47 ( D1 ): D745 – d51 . OpenUrl CrossRef PubMed 63. ↵ Kim D , Paggi JM , Park C , Bennett C , Salzberg SL . Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype . Nat Biotechnol . 2019 ; 37 ( 8 ): 907 – 15 . OpenUrl CrossRef PubMed 64. ↵ Li H , Handsaker B , Wysoker A , Fennell T , Ruan J , Homer N , et al. The Sequence Alignment/Map format and SAMtools . Bioinformatics . 2009 ; 25 ( 16 ): 2078 – 9 . OpenUrl CrossRef PubMed Web of Science 65. ↵ Anders S , Pyl PT , Huber W . HTSeq--a Python framework to work with high- throughput sequencing data . Bioinformatics . 2015 ; 31 ( 2 ): 166 – 9 . OpenUrl CrossRef PubMed Web of Science 66. ↵ Huber W , Carey VJ , Gentleman R , Anders S , Carlson M , Carvalho BS , et al. Orchestrating high-throughput genomic analysis with Bioconductor . Nat Methods . 2015 ; 12 ( 2 ): 115 – 21 . OpenUrl CrossRef PubMed 67. ↵ Love MI , Huber W , Anders S . Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 . Genome Biol . 2014 ; 15 ( 12 ): 550 . OpenUrl CrossRef PubMed 68. ↵ Zhu A , Ibrahim JG , Love MI . Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences . Bioinformatics . 2019 ; 35 ( 12 ): 2084 – 92 . OpenUrl CrossRef PubMed 69. ↵ Raudvere U , Kolberg L , Kuzmin I , Arak T , Adler P , Peterson H , et al. g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update) . Nucleic Acids Res . 2019 ; 47 ( W1 ): W191 – w8 . OpenUrl CrossRef PubMed 70. ↵ Subramanian A , Tamayo P , Mootha VK , Mukherjee S , Ebert BL , Gillette MA , et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles . Proc Natl Acad Sci U S A . 2005 ; 102 ( 43 ): 15545 – 50 . OpenUrl Abstract / FREE Full Text 71. ↵ Ewels P , Magnusson M , Lundin S , Käller M . MultiQC: summarize analysis results for multiple tools and samples in a single report . Bioinformatics . 2016 ; 32 ( 19 ): 3047 – 8 . OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted March 17, 2025. Download PDF Supplementary Material Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. 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