Table 1.
Bilateral breast cancers used in this study.
Fig 1.
Clonality and copy number profiles.
(A) Schematic description of the clonality tool. Top: Each red circle represents one breast cancer sample, and following segmentation, the log2 ratios for each sample are compared all against all to generate a set of correlations from known unrelated tumors, with N(N-1)/2 patient comparisons for reference. Bottom: For samples from the same patient, a higher correlation between the two tumors (red arrows) compared to the correlation for unrelated samples (black arrows) is taken as evidence of clonality. (B) Clonal relationship of bilateral breast cancers. The distribution of logLR (LR = likelihood ratio) among comparisons of unrelated samples (gray) or tumors from the same patients (red) is shown along the x-axis, while y-axis show the case counts. The positions of three tumor pairs (15L/15R, 8L/8R, 10L/10R) that show strong evidence for clonality are highlighted (*). (C) Copy number profiles showing gains (red) and losses (blue) for all the bilateral breast cancers. 15L/15R, 8L/8R, and 10L/10R (arrows) show strong evidence of clonality based on comparison of their copy number segmentation ratio.
Fig 2.
SIVA knockdown and SIVA-D160N mutation increased migration and invasion capabilities on 231L cells.
(A) Schematic of SIVA protein showing the position (red line in exon 4) of point mutation identified in the “breast to breast” spread in patient 8. Domains: Amphipathic helix (SAH), death domain homology region (DDHR), cysteine rich domains (Cys-rich). (B) Stable knockdown of SIVA protein expression by shRNA (KO) and reintroduction of SIVA-WT protein (WT*), compared to endogenous (end) SIVA expression in empty vector control cells (EV) was demonstrated by Western blot analysis. (C) 231L cell migration (Boyden Chamber) and (D) 231L cell invasion (Matrigel matrix invasion) analyses were expressed as total cell counts in four fields of view (4 fov) normalized against EV control. Each bar represents the mean ± S.E.M (n = 3, total cells in 4 images/assay); One-way ANOVA (C: p = 0.0035 and D: p = 0.0.0002) followed by Tukey’s post-hoc test. (E) Overexpression of SIVA-WT (WT) and mutated SIVA (D160N) protein, compared to endogenous SIVA levels in EV control 231L cells was demonstrated by Western blot analysis. (F) Cell migration and (G) cell invasion analyses of 231L cells overexpressing wild type SIVA (WT) and SIVA mutation (D160N) were expressed as in (C, D). One-way ANOVA (F: p = 0.0002, G: p = 0.0005). (H) Proliferation XTT assays, each point represents the mean ± S.E.M (n = 2, 6 replicates/assay); simple linear regression (p = 0.8788). (I) SIVA-D160N expressing 231L cells increased spread of breast cancer cells in immuno-deficient NSG mice. 25K cells were injected into the lateral tail veins of female NSG mice (n = 5), and the proliferation and spread monitored by serial in vivo imaging (IVIS). Exposure times are 60s and 3s for 7 days and 21 days respectively. Luminescence counts is indicated in colored bar on the y-axis. (J) Absolute luminescent intensity of photons emitted from spreading tumor cells in each animal in the IVIS images (I) was quantified. Each line represents the mean ± SEM (n = 5). Mixed-effects analysis (p<0.0001) followed by Tukey’s post-hoc test. Post Test p values: *p<0.05, **p<0.01, ***p<0.001.
Fig 3.
SIVA-D160N on cell migration, invasion, and proliferation of 4T1 cells.
(A) Overexpression of SIVA wild type (WT) and SIVA-D160N mutant (D160N) in 4T1 cells as compared to empty vector (EV) by Western analysis. (B) 4T1 cell migration (Boyden Chamber) and (C) 4T1 cell invasion (Matrigel matrix invasion) analyses were expressed as total cell counts in four fields of view (4 fov) normalized against EV control. No differences were observed for cell migration, while SIVA-WT and SIVA-D160N overexpression were significantly more invasive than EV control. One-way ANOVA (p = 0.0682 migration, p<0.0001 invasion; n = 3, total cells in 4 images/assay). (D) Proliferation of 4T1 cells overexpressing SIVA-WT and SIVA-D160N was not significantly different from control (EV) 4T1 cells. Each line represents the mean ± SEM (n = 3, 6 replicates/assay); simple linear regression, p = 0.096. (E) Representative images for SIVA IHC staining of 4T1 cells (top images) and no primary control (small bottom images) showing the increased presence of SIVA-D160N expressing foci/aggregates. Scale bar 200 μm. (F) Quantification of aggregates in (E). B, C, F graphs represent mean ± SEM, One-way ANOVAs were followed by a Tukey’s post-hoc test: *p<0.05, ****p<0.0001.
Fig 4.
Expression of SIVA-D160N mutant in mouse 4T1 increases tumor growth and liver metastasis in vivo.
(A) Orthotopic injection of mammary fat pads with 4T1 cell expressing EV, WT or D160N, showed tumors expressing SIVA-D160N grew the fastest. Results are expressed as the mean ± SEM (n = 10). Mixed-effects analysis (p<0.0001, days post-injection, tumor size and interaction) was followed by a Tukey’s post-hoc test. (B) SIVA-D160N tumors were significantly larger than EV and WT tumors; one-way ANOVA (p = 0.0006; n = 10). (C) Percentage of Ki67 positive cells per field of view (1 mm2). One-way ANOVA (p<0.0001; n = 10; 3 images/assay). (D) Number of positive cells per area (1 mm2) following TUNEL staining. One-way ANOVA (p = 0.0004; n = 10; 3 images/assay). (E) Total SIVA-D160N METs/liver area (%) was significantly higher than in livers with SIVA-WT or control EV expressing 4T1 cells (n = 5, 5 images/animal). One-way ANOVA (p = 0.0352). B, C, D, E graphs represent mean ± SEM, One-way ANOVAs were followed by a Tukey’s post-hoc test: *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001.