Abstract
Invasive lobular carcinoma (ILC) is the second most common breast cancer subtype and accounts for 8–14% of all cases. Although the majority of human ILCs are characterized by the functional loss of E-cadherin (encoded by CDH1), inactivation of Cdh1 does not predispose mice to develop mammary tumors, implying that mutations in additional genes are required for ILC formation in mice. To identify these genes, we performed an insertional mutagenesis screen using the Sleeping Beauty transposon system in mice with mammary-specific inactivation of Cdh1. These mice developed multiple independent mammary tumors of which the majority resembled human ILC in terms of morphology and gene expression. Recurrent and mutually exclusive transposon insertions were identified in Myh9, Ppp1r12a, Ppp1r12b and Trp53bp2, whose products have been implicated in the regulation of the actin cytoskeleton. Notably, MYH9, PPP1R12B and TP53BP2 were also frequently aberrated in human ILC, highlighting these genes as drivers of a novel oncogenic pathway underlying ILC development.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Accession codes
Primary accessions
European Nucleotide Archive
Referenced accessions
European Nucleotide Archive
References
Martinez, V. & Azzopardi, J.G. Invasive lobular carcinoma of the breast: incidence and variants. Histopathology 3, 467–488 (1979).
Borst, M.J. & Ingold, J.A. Metastatic patterns of invasive lobular versus invasive ductal carcinoma of the breast. Surgery 114, 637–641 (1993).
Wong, H. et al. Lobular breast cancers lack the inverse relationship between ER/PR status and cell growth rate characteristic of ductal cancers in two independent patient cohorts: implications for tumor biology and adjuvant therapy. BMC Cancer 14, 826 (2014).
Niessen, C.M. & Gottardi, C.J. Molecular components of the adherens junction. Biochim. Biophys. Acta Biomembr. 1778, 562–571 (2008).
Moll, R., Mitze, M., Frixen, U.H. & Birchmeier, W. Differential loss of E-cadherin expression in infiltrating ductal and lobular breast carcinomas. Am. J. Pathol. 143, 1731–1742 (1993).
Vos, C.B. et al. E-cadherin inactivation in lobular carcinoma in situ of the breast: an early event in tumorigenesis. Br. J. Cancer 76, 1131–1133 (1997).
Ciriello, G. et al. Comprehensive molecular portraits of invasive lobular breast cancer. Cell 163, 506–519 (2015).
Rakha, E.A. et al. Clinical and biological significance of E-cadherin protein expression in invasive lobular carcinoma of the breast. Am. J. Surg. Pathol. 34, 1472–1479 (2010).
Boussadia, O., Kutsch, S., Hierholzer, A., Delmas, V. & Kemler, R. E-cadherin is a survival factor for the lactating mouse mammary gland. Mech. Dev. 115, 53–62 (2002).
Derksen, P.W.B. et al. Somatic inactivation of E-cadherin and p53 in mice leads to metastatic lobular mammary carcinoma through induction of anoikis resistance and angiogenesis. Cancer Cell 10, 437–449 (2006).
Derksen, P.W.B. et al. Mammary-specific inactivation of E-cadherin and p53 impairs functional gland development and leads to pleomorphic invasive lobular carcinoma in mice. Dis. Model. Mech. 4, 347–358 (2011).
Stange, D.E. et al. High-resolution genomic profiling reveals association of chromosomal aberrations on 1q and 16p with histologic and genetic subgroups of invasive breast cancer. Clin. Cancer Res. 12, 345–352 (2006).
Simpson, P.T. et al. Molecular profiling pleomorphic lobular carcinomas of the breast: evidence for a common molecular genetic pathway with classic lobular carcinomas. J. Pathol. 215, 231–244 (2008).
Buttitta, F. et al. PIK3CA mutation and histological type in breast carcinoma: high frequency of mutations in lobular carcinoma. J. Pathol. 208, 350–355 (2006).
Christgen, M. et al. Oncogenic PIK3CA mutations in lobular breast cancer progression. Genes Chromosom. Cancer 52, 69–80 (2013).
Ercan, C. et al. p53 mutations in classic and pleomorphic invasive lobular carcinoma of the breast. Cell. Oncol. (Dordr.) 35, 111–118 (2012).
Michaut, M. et al. Integration of genomic, transcriptomic and proteomic data identifies two biologically distinct subtypes of invasive lobular breast cancer. Sci. Rep. 6, 18517 (2016).
Desmedt, C. et al. Genomic characterization of primary invasive lobular breast cancer. J. Clin. Oncol. 34, 1872–1881 (2016).
Collier, L.S., Carlson, C.M., Ravimohan, S., Dupuy, A.J. & Largaespada, D.A. Cancer gene discovery in solid tumors using transposon-based somatic mutagenesis in the mouse. Nature 436, 272–276 (2005).
March, H.N. et al. Insertional mutagenesis identifies multiple networks of cooperating genes driving intestinal tumorigenesis. Nat. Genet. 43, 1202–1209 (2011).
Parker, J.S. et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J. Clin. Oncol. 27, 1160–1167 (2009).
Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumors. Nature 490, 61–70 (2012).
Boelens, M.C. et al. PTEN loss in E-cadherin-deficient mouse mammary epithelial cells rescues apoptosis and results in development of classical invasive lobular carcinoma. Cell Rep. 16, 2087–2101 (2016).
Liu, X. et al. Somatic loss of BRCA1 and p53 in mice induces mammary tumors with features of human BRCA1-mutated basal-like breast cancer. Proc. Natl. Acad. Sci. USA 104, 12111–12116 (2007).
Carroll, J.S. et al. Chromosome-wide mapping of estrogen receptor binding reveals long-range regulation requiring the forkhead protein FoxA1. Cell 122, 33–43 (2005).
Hurtado, A., Holmes, K.A., Ross-Innes, C.S., Schmidt, D. & Carroll, J.S. FOXA1 is a key determinant of estrogen receptor function and endocrine response. Nat. Genet. 43, 27–33 (2011).
Goldhirsch, A. et al. Strategies for subtypes—dealing with the diversity of breast cancer: highlights of the St. Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011. Ann. Oncol. 22, 1736–1747 (2011).
Koudijs, M.J. et al. High-throughput semiquantitative analysis of insertional mutations in heterogeneous tumors. Genome Res. 21, 2181–2189 (2011).
de Ridder, J., Uren, A., Kool, J., Reinders, M. & Wessels, L. Detecting statistically significant common insertion sites in retroviral insertional mutagenesis screens. PLOS Comput. Biol. 2, e166 (2006).
de Jong, J. et al. Computational identification of insertional mutagenesis targets for cancer gene discovery. Nucleic Acids Res. 39, e105 (2011).
Canisius, S., Martens, J.W.M. & Wessels, L.F.A. A novel independence test for somatic alterations in cancer shows that biology drives mutual exclusivity but chance explains most co-occurrence. Genome Biol. 17, 261 (2016).
Grassie, M.E., Moffat, L.D., Walsh, M.P. & MacDonald, J.A. The myosin phosphatase targeting protein (MYPT) family: a regulated mechanism for achieving substrate specificity of the catalytic subunit of protein phosphatase type 1δ. Arch. Biochem. Biophys. 510, 147–159 (2011).
Zhang, P. et al. ASPP1/2–PP1 complexes are required for chromosome segregation and kinetochore–microtubule attachments. Oncotarget 6, 41550–41565 (2015).
Zhang, Y. et al. The tumor suppressor proteins ASPP1 and ASPP2 interact with C-Nap1 and regulate centrosome linker reassembly. Biochem. Biophys. Res. Commun. 458, 494–500 (2015).
Muzumdar, M.D., Tasic, B., Miyamichi, K., Li, L. & Luo, L. A global double-fluorescent Cre reporter mouse. Genesis 45, 593–605 (2007).
Schramek, D. et al. Direct in vivo RNAi screen unveils myosin IIa as a tumor suppressor of squamous cell carcinomas. Science 343, 309–313 (2014).
Conti, M.A. et al. Conditional deletion of nonmuscle myosin II-A in mouse tongue epithelium results in squamous cell carcinoma. Sci. Rep. 5, 14068 (2015).
Huijbers, I.J. et al. Using the GEMM–ESC strategy to study gene function in mouse models. Nat. Protoc. 10, 1755–1785 (2015).
Reis-Filho, J.S. et al. FGFR1 emerges as a potential therapeutic target for lobular breast carcinomas. Clin. Cancer Res. 12, 6652–6662 (2006).
Xian, W. et al. Fibroblast growth factor receptor 1–transformed mammary epithelial cells are dependent on RSK activity for growth and survival. Cancer Res. 69, 2244–2251 (2009).
Turner, N. & Grose, R. Fibroblast growth factor signaling: from development to cancer. Nat. Rev. Cancer 10, 116–129 (2010).
Ross, J.S. et al. Relapsed classic E-cadherin (CDH1)-mutated invasive lobular breast cancer shows a high frequency of HER2 (ERBB2) gene mutations. Clin. Cancer Res. 19, 2668–2676 (2013).
Vicente-Manzanares, M., Ma, X., Adelstein, R.S. & Horwitz, A.R. Nonmuscle myosin II takes center stage in cell adhesion and migration. Nat. Rev. Mol. Cell Biol. 10, 778–790 (2009).
Pecci, A. et al. Pathogenetic mechanisms of hematological abnormalities of patients with MYH9 mutations. Hum. Mol. Genet. 14, 3169–3178 (2005).
Flagiello, D. et al. Highly recurrent der(1;16)(q10;p10) and other 16q arm alterations in lobular breast cancer. Genes Chromosom. Cancer 23, 300–306 (1998).
Van Hook, K. et al. ΔN-ASPP2, a novel isoform of the ASPP2 tumor suppressor, promotes cellular survival. Biochem. Biophys. Res. Commun. 482, 1271–1277 (2017).
Rotem, S. et al. The structure and interactions of the proline-rich domain of ASPP2. J. Biol. Chem. 283, 18990–18999 (2008).
Brinkman, E.K., Chen, T., Amendola, M. & van Steensel, B. Easy quantitative assessment of genome editing by sequence-trace decomposition. Nucleic Acids Res. 42, e168 (2014).
Annunziato, S. et al. Modeling invasive lobular breast carcinoma by CRISPR–Cas9-mediated somatic genome editing of the mammary gland. Genes Dev. 30, 1470–1480 (2016).
Dupuy, A.J., Akagi, K., Largaespada, D.A., Copeland, N.G. & Jenkins, N.A. Mammalian mutagenesis using a highly mobile somatic Sleeping Beauty transposon system. Nature 436, 221–226 (2005).
Huijbers, I.J. et al. Rapid target gene validation in complex cancer mouse models using re-derived embryonic stem cells. EMBO Mol. Med. 6, 212–225 (2014).
Henneman, L. et al. Selective resistance to the PARP inhibitor olaparib in a mouse model for BRCA1-deficient metaplastic breast cancer. Proc. Natl. Acad. Sci. USA 112, 8409–8414 (2015).
Krause, S., Brock, A. & Ingber, D.E. Intraductal injection for localized drug delivery to the mouse mammary gland. J. Vis. Exp. 80, e50692 (2013).
Doornebal, C.W. et al. A preclinical mouse model of invasive lobular breast cancer metastasis. Cancer Res. 73, 353–363 (2013).
Cardiff, R.D. et al. The mammary pathology of genetically engineered mice: the consensus report and recommendations from the Annapolis meeting. Oncogene 19, 968–988 (2000).
Ewald, A.J., Brenot, A., Duong, M., Chan, B.S. & Werb, Z. Collective epithelial migration and cell rearrangements drive mammary branching morphogenesis. Dev. Cell 14, 570–581 (2008).
Ball, R.K., Friis, R.R., Schoenenberger, C.A., Doppler, W. & Groner, B. Prolactin regulation of β-casein gene expression and of a cytosolic 120-kDa protein in a cloned mouse mammary epithelial cell line. EMBO J. 7, 2089–2095 (1988).
Love, M.I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA–seq data with DESeq2. Genome Biol. 15, 550 (2014).
Johnson, W.E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).
Gaujoux, R. & Seoighe, C. A flexible R package for non-negative matrix factorization. BMC Bioinformatics 11, 367 (2010).
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10 (2011).
Langmead, B. & Salzberg, S.L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
de Ruiter, J.R. et al. Identifying transposon insertions and their effects from RNA-sequencing data. Nucleic Acids Res. https://doi.org/10.1093/nar/gkx461 (2017).
Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal. 6, pl1 (2013).
Acknowledgements
We are grateful to E. Wientjens, J. Houthuijzen, M. van Miltenburg, O. Bleijerveld, B. van Gerwen, B. Siteur, R. de Korte-Grimmerink, N. Proost, U. Boon, C. Brambillasca, R. Mezzadra, S. Cornelissen and L. Braaf for providing technical suggestions and/or help with the experiments, S. Canisius for critical reading of the manuscript and N. Hynes (Friederich Miescher Institute, Basel) for the HC11 cells. We thank the NKI animal facility, the animal pathology facility, the mouse clinic transgenic and intervention unit, the core facility molecular pathology and biobanking (CFMPB), and the genomics core facility for their expert technical support. Financial support was provided by the Netherlands Organization for Scientific Research (NWO: Cancer Genomics Netherlands (CGCNL), Cancer Systems Biology Center (CSBC), the Netherlands Genomics Initiative (NGI) grants Zenith 93512009 (J.J.), VENI 016156012 (M.N.) and VICI 91814643 (J.J.), the EU Seventh Framework Program (EurocanPlatform project 260791 (J.J.) and Infrafrontier-I3 project 312325 (J.J.)), the European Research Council (ERC Synergy project CombatCancer) (J.J.), and a National Roadmap grant for Large-Scale Research Facilities from NWO (J.J.).
Author information
Authors and Affiliations
Contributions
S.M.K., K.S., S.A., E.S., A.P.D. and E.v.d.B. performed laboratory experiments; J.R.d.R. identified the insertion sites and CISs, analyzed the RNA sequencing data sets and performed the other bioinformatic analyses; C.K. and J.J.t.H. assisted in the initial bioinformatic analysis for the identification of insertion sites and CISs; S.K. and J.W. assessed the histology of mouse tumors and quantified the immunohistochemically stained images; D.J.A. was responsible for sequencing the transposon insertions; M.J.K. initiated the breeding of the mouse lines; M.N., L.F.A.W. and J.J. supervised the experiments; and J.R.d.R., S.M.K., L.F.A.W. and J.J. wrote the manuscript.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing financial interests.
Integrated supplementary information
Supplementary Figure 1 Survival curves for the individual T2/Onc transgenic mouse lines, distribution and immunohistochemical stainings of the different tumor morphologies.
(a) Survival curves Wap-Cre;Cdh1F/F;SB mice carrying the T2/Onc transposon donor loci on chromosomes 1 and 15 (n = 142 and n = 126 mice, respectively). ns, no significant difference (Mantel–Cox test). (b) Tumor morphologies of Wap-Cre;Cdh1F/F (n = 16) and Wap-Cre;Cdh1F/+;SB (n = 4) female mice. (c) Immunohistochemical detection of E-cadherin, cytokeratin 1, cytokeratin 8 and vimentin in the different tumor morphologies. Scale bar, 50 μm.
Supplementary Figure 2 Additional plots accompanying the PAM50 gene expression analyses.
(a) Unsupervised clustering (Euclidean distance, average linkage) of the SB-induced tumors with reference mouse models and human breast cancer samples from TCGA, showing the expression of all 46 orthologous mouse genes from the PAM50 gene signature. (b,c) Expression of known marker genes associated with squamous and spindle cell tumors, showing high expression of the corresponding markers in the associated molecular subtype. Boxes extend from the third (Q3) to the first (Q1) quartile (IQR), with the line at the median; whiskers extend to Q3 + 1.5 IQR and Q1 − 1.5 IQR. Points beyond the ends of the whiskers are outliers. SC, spindle cell-like; SQ, squamous-like. (d) Unsupervised clustering (Euclidean distance, average linkage) of the SB-induced molecular subtypes with the reference luminal and basal-like mouse models.
Supplementary Figure 3 Distribution of morphology and expression subtypes across the T2/Onc transgenic mouse lines.
(a) Distribution of morphology across the two T2/Onc transgenic lines, showing that there is no significant bias between the two lines. Lack of any significant bias was confirmed using pairwise Fisher’s exact tests with Benjamini–Hochberg correction. (b) Distribution of subtypes across the two T2/Onc transgenic lines, also showing no significant bias between the two lines.
Supplementary Figure 4 Histograms detailing various statistics of the identified insertions, both for all insertions and for insertions within CISs.
(a) Distribution of the insertion depths (as indicated by ShearSplink’s ULP score) over all insertions. (b) Distribution of insertion clonality scores. (c) Distribution of the maximum insertion depth (maximum ULP score) per sample. (d) Distribution of the number of insertions per sample, with a median of 29 insertions per sample. (e–h) Same statistics as in a–d, but calculated for the CIS insertions, with a median of 5 CIS insertions per sample.
Supplementary Figure 5 Additional details on the clonality of the candidate genes.
(a) Ranking of genes by their overall frequency and the median clonality of their insertions. For samples with multiple insertions in the same gene, we used the clonality of the strongest insertion to avoid underestimating the median clonality due to local hopping. For clarity, only the main candidates (occurring in six or more samples) are labeled. (b) Clonality distribution of insertions in all candidate genes, ranked (from left to right) by the frequency of each gene.
Supplementary Figure 6 Distribution of the candidate genes over the identified molecular subtypes.
Bar plots indicating the distribution of all candidate genes over the molecular subtypes. Red bars indicate significant associations between candidate genes and the corresponding molecular subtype (FDR < 0.1, one-sided Fisher’s exact test with Benjamini–Hochberg correction). (a,b) This analysis was performed for both the set of 99 tumors with an ILC morphology (a) and the full set of 123 tumor samples (to increase statistical power) (b). The latter analysis identified an additional enrichment for Trps1 in the combined mILC-1 and mILC-2 subtypes, suggesting that Trps1 may play a role in the ILC morphology of these tumors. Additionally, the mILC-2 subtype was further enriched for insertions in Arid1a and Rasgrf1, indicating that expression differences between mILC-1 and mILC-2 may in part be driven by insertions in these genes.
Supplementary Figure 7 Additional details on the insertion patterns and differential expression of the candidate genes.
(a) Insertion pattern in Gab1, showing bias toward activating insertions. (b) Boxplots for the main candidate genes showing the difference in expression after the insertion sites in the corresponding gene between samples with and without an insertion. The boxplots and P values were calculated using IM-Fusion’s differential expression test, which essentially compares the expression of exons after the insertion sites in each gene between samples with an insertion and samples without an insertion, after normalizing for differences in overall expression between samples (see the Online Methods for more details). Boxes extend from the third (Q3) to the first (Q1) quartile (IQR), with the line at the median; whiskers extend to Q3 + 1.5 IQR and Q1 − 1.5 IQR. P values were calculated using the non-parametric Mann–Whitney U test. Green/purple boxplots indicate significant increases and decreases in expression (P < 0.05), respectively. (c) Insertion pattern in Trps1, showing bias toward truncating/inactivating insertions.
Supplementary Figure 8 Representative images and quantification of immunohistochemical staining for phosphorylated ERK1/2, a downstream protein of the RAS/MAPK signaling pathway.
(a) Representative images of different percentages of phosphorylated ERK1/2(Thr202/Tyr204) staining in SB-induced tumors. Scale bar, 50 μm. (b) Quantification of phosphorylated ERK1/2(Thr202/Tyr204) staining in the different molecular subtypes. Mean ± s.d.
Supplementary Figure 9 Overview of the mutations and copy-number events in the TGCA breast cancer data set (816 samples) for each of the main candidate genes and CDH1.
Percentages indicate the fraction of tumors with alterations in the respective genes.
Supplementary Figure 10 Myh9 haploinsufficiency in ILC formation in SB-induced tumor-derived cell lines with or without Myh9 insertion.
(a) Distribution of the number of insertions per sample in Myh9, showing that the majority of the tumors show single insertions in the gene. (b) PCR amplification of the transposon–Myh9 junction fragments in polyclonal SB-induced tumor-derived cell lines. (c) Heterozygous Myh9 insertions by PCR in clones derived from two SB-induced tumor cell lines. Green color indicates a correct clone; red color indicates an incorrect clone.
Supplementary Figure 11 Expression of truncated Ppp1r12a/b and Trp53bp2 and PPP1R12A protein expression in SB-induced tumors.
(a–c) Truncated Ppp1r12a/b and Trp53bp2 in SB-induced tumors, as visualized by northern blot analysis. (d) Expression of PPP1R12A in SB-induced tumors with and without insertions in Ppp1r12a, as visualized by immunoblotting using an anti-PPP1R12A antibody. β-actin is shown as a loading control. (e) Coimmunoprecipitation of PP1 in HC11 cells expressing GFP, truncated PPP1R12A or TRP53BP2, as visualized by immunoblotting using anti-FLAG and anti-PP1 antibodies.
Supplementary Figure 12 Characterization of cell death, cell survival markers and activation of a p53 response in E-cadherin-deficient cells expressing truncated PPP1R12A or TRP53BP2 or showing reduced levels of MYH9.
(a) Representative dot plots depicting the proportions of Annexin V (AN)- and/or propidium iodide (PI)-positive cells 72 h after seeding of AdCre-transduced Cdh1F/F MMECs with simultaneous transduction of Lenti-GFP, Lenti-Ppp1r12aex1-9 or Lenti-Trp53bp2ex13-18. (b) Quantification of Annexin V– and PI-positive cells from AdCre-transduced Cdh1F/F MMECs with simultaneous transduction of Lenti-GFP, Lenti-Ppp1r12aex1-9 or Lenti-Trp53bp2ex13-18. Asterisks indicate P < 0.05 (Welch’s t-test). Mean ± s.d. of three independent experiments. (c) Expression of total and phosphorylated AKT, ERK1/2 and S6 72 h after seeding of AdCre-transduced Cdh1F/F MMECs with simultaneous transduction of Lenti-GFP, Lenti-Ppp1r12aex1-9 or Lenti-Trp53bp2ex13-18, as visualized by immunoblotting. β-actin is shown as a loading control. (d) Cell survival of AdCre-transduced Cdh1F/F;mT/mG MMECs with simultaneous shRNA-mediated knockdown of Myh9 with different shRNAs, as quantified by using real-time IncuCyte imaging for 200 h. Mean ± s.d. of three independent experiments. (e) PCR amplification of the transposon–Trp53bp2 and transposon–Myh9 junction fragments in polyclonal SB-induced tumor-derived cell lines. (f) Expression of p53 and p21 in SB-induced tumor-derived cell lines with insertions in Trp53bp2 or Myh9 as compared to SB-induced tumor-derived cell line controls, as visualized by immunoblotting. Cells were treated for 6 h with water (control) or doxorubicin (Dox; 1 μM). β-actin is shown as a loading control. (g) Expression of p53 and p21 in SB-induced tumor-derived clones with insertions in Myh9 as compared to SB-induced tumor-derived control clones, as visualized by immunoblotting. Cells were treated for 6 h with water (control) or doxorubicin (Dox; 1 μM). β-actin is shown as a loading control. Exp, exposure.
Supplementary Figure 13 Overview of Cre-conditional alleles and distribution of the tumor morphology in the different genetically engineered mouse models.
(a) Depiction of Cre-conditional invCAG-Ppp1r12aex1-9-IRES-Luc and invCAG-Trp53bp2ex13-18-IRES-Luc alleles in the Col1a1 locus. Cre-mediated recombination allows inversion of the CAG promoter, resulting in expression of PPP1R12A1-418aa or TRP53BP2766-1134aa accompanied by luciferase expression. (b) Recombination status of the Cre-conditional alleles in tumors from Wap-Cre;Cdh1F/F;Ppp1r12 aex1-9 and Wap-Cre;Cdh1F/F;Trp53bp2ex13-18 mice, as visualized by PCR. EcadF and EcadD are PCRs to detect the Cdh1F and Cdh1Δ alleles, respectively. ShuttleR detects the recombined (p1 and p3; 1,054 bp) and non-recombined (p1 and p2; 897 bp) Cre-conditional alleles (primer positions are shown in a). (c) Histological classification of tumors from Wap-Cre;Cdh1F/F;Ppp1r12aex1-9 (n = 27) and Wap-Cre;Cdh1F/F;Trp53bp2ex13-18 (n = 30) mice. (d) Histological classification of tumors (n = 11) from Wap-Cre;Cdh1F/F;Cas9 mice injected with Lenti-CRISPR-sgMyh9.
Supplementary Figure 14 Clustering statistics for different numbers of clusters in the NMF subtype analysis.
(a) Overview of various clustering statistics for different numbers of clusters (2–5) in the NMF analysis of the SB-induced tumors. (b) Consensus maps for the same cluster sizes, showing the consistency of cluster assignments for the different numbers of clusters.
Supplementary information
Supplementary Text and Figures
Supplementary Figures 1–14 and Supplementary Note (PDF 5345 kb)
Supplementary Table 1
Overview of sample information including sample IDs, mouse IDs, T2/Onc lines, histopathology types, RNA-seq IDs, metastasis sites and subtypes. (XLSX 15 kb)
Supplementary Table 2
Overview of the identified candidate genes and various statistics for each gene. (XLSX 11 kb)
Supplementary Table 3
Overview of all primers used in this study for genotyping, cloning and northern blotting. (XLSX 12 kb)
Supplementary Table 4
Detailed information about antibodies and antigen retrieval methods used in immunohistochemical experiments. (XLSX 10 kb)
Rights and permissions
About this article
Cite this article
Kas, S., de Ruiter, J., Schipper, K. et al. Insertional mutagenesis identifies drivers of a novel oncogenic pathway in invasive lobular breast carcinoma. Nat Genet 49, 1219–1230 (2017). https://doi.org/10.1038/ng.3905
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/ng.3905