SUPPLEMENTAL FIGURE LEGENDS

Hematopoietic age at onset of triple-negative breast cancer dictates disease aggressiveness and progression

Timothy Marsh1,*,†, Irene Wong1,*, Jaclyn Sceneay1,2,*, Amey Barakat1, Yuanbo Qin1,2, Andreas Sjödin3, Elise Alspach4, Björn Nilsson5,6, Sheila A. Stewart4, and Sandra S. McAllister1,2,6,7

Figure S1; Related to Figure 2. (A) Time at which primary BPLER tumors were harvested in order to compare young and old mice with size-matched tumors

(shaded areas represent size groups). (B) Plasma levels of murine OPN

(mOPN) in young and old cancer-free and BPLER tumor bearing mice (n=13-15 per group). Data are represented as mean ± SEM, and analyzed using Student’s two-tailed t-test for significance; n.s. not significant.

Figure S2; Related to Figure 2. (A) Growth kinetics of distant HMLER-HR (red) tumors injected contralaterally to primary BPLER (black) tumors in old mice (n=5 per group) over 70-day time course. (B) Distant HMLER-HR tumor mass and incidence in aged BPLER-bearing and Matrigel control old mice (n=5 per group) from (A). Data represented as mean ± SEM, with tumor incidence indicated. (C)

Representative immunohistochemical analysis of HMLER-HR tumors from (A) stained for Ki67 and aSMA. Cell nuclei were counterstained with hematoxylin

(blue). Images were taken at 20x magnification. (D,E) HMLER-HR tumor cells were injected contralaterally to Matrigel in control young or old mice; n=7 per cohort. (D), or contralaterally to a dermal wound in young or old mice; n=8 per cohort (E). Graphs show final size of tumors that formed in each indicated cohort after 28 days. Data are represented as mean ± SEM, and analyzed using

Student’s t-test for significance; n.s. not significant. (F) Results from systemic instigation experiments in young (blue) and old (orange) mice; see Fig. 2A for experimental scheme. Data bars represent average tumor mass for distant tumors growing opposite TNBC or LBC relative to tumor mass opposite Matrigel control for each age group. Numbers above bars represent incidence of distant tumor formation. (G) Images of serial tissue sections of LBC MCF7Ras tumors

(left) and distant HMLER-HR tumors (right) stained for Ki67, MECA32 and

Granulin; cell nuclei counterstained with hematoxylin; scale bar=100m.

Figure S3; Related to Figure 4. (A) Staining of distant HMLER-HR tumors from young and old mice for LgT (green) and CD45 (red). Cell nuclei counterstained with DAPI (blue). Right image represents a BPLER tumor that had grown in a young mouse as stained for Sca1 (green) and GRN (red) to show co-staining

(yellow/orange); taken at 63x magnification. (B) CD45+/Sca+/cKit- cells as a percent of total bone marrow in McNeuA tumor-bearing FVB young mice, and cancer-free young and old FVB mice; n=3-5 per group; ***p<0.001, **p<0.01. (C)

GRN expression in CD45+/Sca1+/cKit- cells from indicated cohorts of FVB and nude mice relative to cancer-free young controls; n=3 per group analyzed in triplicate. (D, E) qPCR results for GRN (D) and CSF-1R (E) relative expression in

Sca1+/cKit- cells from indicated young and old cancer-free (CF) or tumor-bearing

(MCF7Ras) mice. Data are represented as mean ± SEM, and analyzed using one-way ANOVA and Student’s t-test for significance. Figure S4; Related to Figure 5. (A) Serial sections of tumors from indicated mice as stained by hematoxylin and eosin (H&E, top), the proliferation marker,

Ki67 (middle, brown), and Masson’s trichrome to visualize collagen deposition

(blue, bottom). Orange arrows indicate proliferating cells in the TME. Scale bar represents 100  m. (B) Bone marrow was harvested from 2 young mice bearing size-matched MDA-MB-435 tumors, pooled, and 106 BMCs mixed with 2.5 x105

HMLER-HR cells and injected subcutaneously into old recipient nude mice.

Kaplan-Meier curve represents incidence of HMLER-HR tumor formation in the old recipient cohort during a 90-day time course (n=10).

Figure S5; Related to Figure 5. (A) FACS density plots representing gating scheme for harvesting indicated BMC populations from young mice bearing

BPLER tumors. BMC populations were used for admixture with tumor cells; positively stained cell populations are indicated in red gates; remainder of cells represent the corresponding depleted populations. Table indicates the representation of each cell population as a percentage of the total bone marrow and the numbers of each population that were mixed with tumor cells and injected into mice. (B) Graph shows relative ability of indicated BMC populations to support tumor growth following admixture with HMLER-HR tumor cells (n=4 injections per cohort). Tumor Support Index was calculated based on the formula: a = Ave. mass of tumors at experimental end point of 97 d; b = no. BMCs admixed with tumor cells; c = incidence of tumor formation. 750,000 represents the number of BMCs used in the control cohort in which non-fractionated BMCs

(total bone marrow) was used and therefore represents the upper limit of admixed BMCs. These values were then normalized to the value for the control

“all BMC” cohort. (C)  SMA staining (red) in resulting HMLER-HR tumors following admixture with indicated BMC populations; nuclei counterstained with hematoxylin (blue).

Figure S6; Related to Figure 6. Gene expression profiles of Sca1+/cKit- BMCs harvested from young and old cancer-free mice. For the identification of differentially expressed genes (DEGs) the lmFit function using array weights were used to fit the gene-wise linear model to the microarray data. DEGs were obtained using the implemented top-table function and user-specific thresholds

(i.e., 2-fold change; p-value ≤ 0.1). The Benjamini and Hochberg method was used to correct the obtained p-values for multiple testing. Heatmaps were generated using the R package pheatmap.

Figure S7; Related to Figure 6. Gene expression profiles of Sca1+/cKit- BMCs harvested from young and old TNBC tumor-bearing mice. For the identification of differentially expressed genes (DEGs) the lmFit function using array weights were used to fit the gene-wise linear model to the microarray data. DEGs were obtained using the implemented top-table function and user-specific thresholds (i.e., 2-fold change; p-value ≤ 0.1). The Benjamini and Hochberg method was used to correct the obtained p-values for multiple testing. Heatmaps were generated using the R package pheatmap.