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1 a Duct TEB b Id4 p63 DAPI Merge Id4 CK8 DAPI Merge c d e Supplementary Figure 1. Identification of Id4-positive MECs and characterization of the Comma-D model. (a) IHC analysis of ID4 expression in the duct of an adult mouse mammary gland and in a pubertal mouse terminal end bud (TEB) Scale bars= m. (b) Immunofluorescence analysis of ID4, p63 (upper panels) and CK8 (lower panels). Scale bars=2 m. (c) Whole mount analysis of transplants of Comma D cells into the cleared mammary fat pad of naïve BALB/C hosts. Scale bar = m. (d) Comma D transplants form bi-layered epithelial ducts with Id4 + myoepithelial cells. (e) Heterogenous Id4 (red) expression in Comma D cells in vitro determined by immunofluorescence. DAPI in blue marks nuclei. Scale bar = m. Representative images from at least mice or 3 independent experiments.

2 Supplementary Figure 2. Single cell RT-PCR heatmap. Heatmap of unsupervised hierarchical clustering of 63 genes in the cells from the Id4 upper and lower quartiles of expression in 7 individual cells captured across two independent experiments. The Id4 high quartile samples marked with a red circle and the Id4 low quartile samples marked with a green triangle.

3 h l h l h l h l Relative expression (log2) BoxPlot of Gene Expression By the Order of PCA Gene Scores Id4 Sox9 Cd24a FoxM1 Sfrp1 Foxc1 Itga6 Id3 Fzd6 Tcf4 Trpv6 Cryab Id2 Itgb3 Itgb1 Id1 Kcnn4 Kctd14 Egr2 Brca2 Rplp Gapdh Supplementary Figure 3. Boxplots of single cell gene expression data Single cell RT-PCR analysis of primitive stem cell markers in Id4 hi (top quartile; red) cells compared to Id4 lo (bottom quartile; green) cells. All genes with significantly altered expression are shown. P values for genes are the same as shown in Figure 1d.

4 Supplementary Figure 4. Expression of mammary lineage markers and ID gene mrna by Id4-/- MECs (a) Gating strategy to determine the linage negative (CD4 -, CD31 -, Ter119 -, BP1 - ), live, single cells (i). Gating strategy to examine CD24 + cells for CD29 and CD61 expression in Id4 +/- mice (ii) and Id4 -/- mice (iii) (b) Quantification of CD24 + mammary epithelial cell subpopulations CD29 high CD61 + stem myoepithelial cells, CD29 low CD61 + luminal progenitor cells, and CD29 low CD61 - mature luminal cells, data represents 6 independent experiments (c) Loss of Id4 leads to an increased mrna expression of Id1 and Id3 in the mouse mammary gland. *p<.2, **p<. unpaired t-test. n=4-. Graphs represent the mean ± SEM.

5 Supplementary Figure. Impact of Id4 overexpression of Comma D differentiation and proliferation (a) Proliferation was measured in Comma D epithelial cells expressing Id4 by cell counts. Graph represents the mean from 3 independent experiments ± SEM. (b) Western blot analysis of terminal luminal differentiation in response to cellular confluence and prolactin treatment of cells expressing Id4 or control, as measured by milk protein immunoblot. Specific milk proteins Lactotransferrin (LTF), -casein, -casein and whey acidic protein (WAP) are indicated with arrows. Representative western blot from 3 independent experiments. (c) Expression of -casein, cytokeratin-8 and basal cytokeratin-14 mrna following lactogenic stimuli in cells expressing Id4 or controls. *p<., **p<.1, ***p<.1 unpaired t-test. n=-7. Graphs represent the mean ± SEM. D=Day, D2=Day 2, D4=Day 4.

6 p-p38mapk (43kDa) Total p38mapk (43kDa) Comma-Db Comma-DS red Comma-Id4 Comma-siId4#1 Comma-siId4#2 Comma-siCont Comma-Mock b-actin(42kda ) Supplementary Figure 6. Impact of Id4 expression or knockdown on p38 MAPK. Expression levels of total and phosphorylated p38 MAPK in Comma D cells with overexpression of Id4 (left) or following expression of two independent ID4 shrna (right), determined by immunoblot.

7 Supplementary Figure 7. ID4 expression in clinical BLBC cohorts, and impact of ID4 knockdown on proliferation. (a) ID4 mrna expression in TCGA RNA-Seq data stratified by intrinsic subtype. (b) Id4 knockdown via shrna in MDA-MB-468 cell xenograft growth following transplantation into immunocompromised mice. *p<.1 (2-way ANOVA). (c) Id4 mrna expression in HCC-186 cells following doxycycline-induce shrna

8 expression (d) Inducible Id4 knockdown inhibits proliferation of HCC-186 cells. Doxycycline was added at time. Relative confluence was determined using image based analysis on an Incucyte device (e) Cellular morphology 96h following inducible Id4 shrna expression in HCC-186. (d&e) Results are representative of two independent experiments (f) Kaplan-Meier survival analysis of overall survival for 6 basal like breast cancer patients within the NKI-29 dataset 1 stratified by median ID4 mrna expression, p=.31, log-rank test. (g) Kaplan-Meier survival analysis of overall survival for 28 basal breast cancer patients stratified by median ID4 mrna expression using KM Plotter 2. p=.29, log-rank test.

9 Supplementary Figure 8. ID4 mrna expression in a panel of triple negative breast cancer cell lines. Gene expression derived by Taqman RT-PCR analysis and normalized to nontransformed HMEC-184 control. Cell lines are stratified into Basal A and Basal B using the classification of Neve et. al. 3 and further annotated with triple negative breast cancer subtypes identified by Lehmann et. al. 4 : Basal-like 1(BL-1), Basal-like 2 (BL-2), mesenchymal (M) and mesenchymal stem-like (MSL).

10 72hr 12 hr " Supplementary Figure 9. Full western blot of the cropped blot in Figure 2c. Western blot demonstrating ID4 knockdown by two independent shrna (shid4 #1 and shid4 #2) compared to a scrambled control (shcont) and GFP transduced Comma D cells. Scissors and red dash line indicate where the membrane was cut with the upper section blotted with an antibody to -actin and the lower section blotted with an antibody to Id4. shid4 #1 shid4 #2 shcont GFP shid4 #1 shid4 #2 shcont GFP b-actin(42kda ) Id4 (18kDa)

11 Supplementary Table 1. Limiting dilution analysis of Id4GFP-high versus Id4GFP-low cells. Number of cells injected per mammary fat pad Number of positive out growths Id4GFP-high Id4GFP-low 6/7 2/6 3/7 /7 Repopulating frequency 1/22 1/187 (9% confidence interval) (1/6-1/481) (1/42-1/6269) p value p=. Supplementary References 1 van de Vijver, M. J. et al. A gene-expression signature as a predictor of survival in breast cancer. The New England journal of medicine 347, (22). 2 Gyorffy, B. et al. An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,89 patients. Breast cancer research and treatment 123, , (2). 3 Neve, R. M. et al. A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes. Cancer Cell, 1-27 (26). 4 Lehmann, B. D. et al. Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. The Journal of clinical investigation 121, , (211).

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