SUPPLEMENTARY FIGURE LEGENDS

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1 SUPPLEMENTARY FIGURE LEGENDS Supplementary Figure 1 Negative correlation between mir-375 and its predicted target genes, as demonstrated by gene set enrichment analysis (GSEA). 1 The correlation between expression levels of mir-375 and 19,389 genes was calculated using matched mirna and mrna data from 98 prostate tumors in the Taylor cohort. 2 Genes were subsequently ranked according to Pearson correlation coefficient (r) value (shown by a heat map), and GSEA (Preranked analysis) was implemented using the Broad Institute s public GenePattern server, using default parameters. Lists of predicted mir-375 target genes were downloaded from TargetRank 3 and TargetScan 4 websites. Running enrichment scores are plotted (top graph) and normalized enrichment scores (NES) and P values are indicated. Supplementary Figure 2 Correlation between mir-375 and genes altered in prostate cancer, as demonstrated by GSEA. See Supplementary Figure Legend 1 for more detail on the methodology. The gene sets 5-7 were obtained from MSigDB. Plots on the left are gene sets down-regulated in prostate cancer, which are negatively with mir-375. Plots on the right are gene sets upregulated in prostate cancer, which are positively with mir-375. Supplementary Figure 3 Negative correlation between mir-375 and EMT gene sets, as demonstrated by GSEA. See Supplementary Figure Legend 1 for more detail on the methodology. The gene sets 8-1 were obtained from MSigDB. Supplementary Figure 4 (A) MiR-375 inhibits migration of DU145 and C4-2B cells. (B) MiR-375 inhibits growth of DU145 and C4-2B cells. Live and dead cells were counted using Trypan blue assays. P values were determined using unpaired t tests (NS, not significant; **, P <.1). Supplementary Figure 5 (A) Knockdown of YAP1 inhibits migration of DU145 and C4-2B cells. (B) Knockdown of YAP1 inhibits growth of DU145 and C4-2B cells. Live and dead cells were counted using Trypan blue assays. P values were determined using unpaired t tests (**, P <.1; ***, P <.1; ****, ). Supplementary Figure 6 Dose-dependent over-expression of FLAG-tagged YAP1 (FLAG-YAP1) in C4-2B cells. Cells were transfected with the indicated amount of expression vector and two days later total protein was assessed by Western blotting using an antibody (sc-1547; Santa Cruz) that detects both the FLAG-tagged form and the endogenous YAP1. Supplementary Figure 7 Deletion of E-box-like motifs 3 (Δ3), 4 (Δ4) or both (Δ3,4) does not affect ZEB1 regulation of the mir-375 promoter. DU145 cells were transfected with either control (sictrl) or ZEB1 sirna (sizeb1) and the indicated pgl4 constructs and luciferase activity measured. Bars represent the average of 6 wells per treatment, and error bars are ± SEM. P values were determined using unpaired t tests (*, P <.5; **, P <.1).

2 Supplementary Figure 8 ZEB1 and YAP1 are negatively with mir-375 and positively with each other. Graphs show correlations in 177 tumors from the TCGA dataset; data was obtained from The Cancer Genome Atlas (TCGA) data portal. P and r values were calculated using Pearson s tests. Supplementary Figure 9 mir-375 is positively with an androgen signaling signature 11 in the Taylor (left) and TCGA (right) cohorts. See Supplementary Figure Legend 1 for more detail on the methodology.

3 Input gene lists: mir-375 predicted targets TargetRank TargetScan NES = NES = Positively Negatively Supplementary Figure 1

4 Input gene lists: Genes down-regulated in prostate cancer Input gene lists: Genes up-regulated in prostate cancer NES = NES = 3.64 NES = NES = 1.98 NES = NES = 2.32 Positively Negatively Supplementary Figure 2

5 Input gene lists: Genes upregulated during EMT NES = NES = NES = -3.4 NES = Positively Negatively Supplementary Figure 3

6 A Relative wound density (%) DU NC 2 mir Time (hours) C4-2B NC 2 mir Time (hours) B Cell count x DU145 ** ** Cell count x C4-2B NC mir-375 NC mir-375 ** Supplementary Figure 4

7 A Relative wound density (%) DU145 4 sictrl 2 siyap Time (hours) C4-2B sictrl 2 siyap Time (hours) B Cell count x DU145 *** **** Cell count x C4-2B sictrl siyap1 sictrl siyap1 ** Supplementary Figure 5

8 Mock 5 YAP1 expression vector (ng) FLAG-YAP1 YAP1 Ponceau (total protein) Supplementary Figure 6

9 4 sictrl * Luciferase activity sizeb1 ** ** Empty 3 4 3,4 pgl4 reporter constructs Supplementary Figure 7

10 Normalized YAP1 expression p <.1 r = Normalized mir-375 expression 2 Normalized ZEB1 expression p <.1 7 r = Normalized mir-375 expression 2 Normalized YAP1 expression p <.1 9 r = Normalized ZEB1 expression Supplementary Figure 8

11 Input gene lists: Genes upregulated by androgen treatment in prostate cancer cells Taylor cohort TCGA cohort NES = 1.91 P =.34 NES = 1.69 P =.6 Positively Negatively Supplementary Figure 9

12 Supplementary Table 1. Circulating tumor cell (CTC) counts in a cohort of men with metastatic castration-resistant prostate cancer. Sample identification CTC count 1_ _2 46 1_4 84 2_1 1 2_2 2 3_1 3_1 3_2 3_3 3_4 1 3_5 3_6 3_8 3_9 1 4_1 4_1 4_2 4_3 4_4 1 4_5 4_6 4_7 4_8 4_9 5_1 9 5_1 2 5_11 9 5_2 4 5_3 5_4 5_6 5_7 5_8 6_1 1 6_1 6_11 3 6_2 6_3 6_5 6_6 14 6_7 5 6_9 3 7_1 7_1 7_2 7_3 7_4 7_5 7_6 7_7 7_8 7_9

13 REFERENCES FOR SUPPLEMENTARY DATA 1 Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genomewide expression profiles. Proc Natl Acad Sci U S A 25; 12: Taylor BS, Schultz N, Hieronymus H, Gopalan A, Xiao Y, Carver BS et al. Integrative genomic profiling of human prostate cancer. Cancer Cell 21; 18: Nielsen CB, Shomron N, Sandberg R, Hornstein E, Kitzman J, Burge CB. Determinants of targeting by endogenous and exogenous micrornas and sirnas. RNA 27; 13: Lewis BP, Burge CB, Bartel DP. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microrna targets. Cell 25; 12: Liu P, Ramachandran S, Ali Seyed M, Scharer CD, Laycock N, Dalton WB et al. Sex-determining region Y box 4 is a transforming oncogene in human prostate cancer cells. Cancer Res 26; 66: Tomlins SA, Mehra R, Rhodes DR, Cao X, Wang L, Dhanasekaran SM et al. Integrative molecular concept modeling of prostate cancer progression. Nature genetics 27; 39: Wallace TA, Prueitt RL, Yi M, Howe TM, Gillespie JW, Yfantis HG et al. Tumor immunobiological differences in prostate cancer between African-American and European-American men. Cancer Res 28; 68: Taube JH, Herschkowitz JI, Komurov K, Zhou AY, Gupta S, Yang J et al. Core epithelial-to-mesenchymal transition interactome gene-expression signature is associated with claudin-low and metaplastic breast cancer subtypes. Proc Natl Acad Sci U S A 21; 17: Gotzmann J, Fischer AN, Zojer M, Mikula M, Proell V, Huber H et al. A crucial function of PDGF in TGF-beta-mediated cancer progression of hepatocytes. Oncogene 26; 25: Jechlinger M, Grunert S, Tamir IH, Janda E, Ludemann S, Waerner T et al. Expression profiling of epithelial plasticity in tumor progression. Oncogene 23; 22: Wang G, Jones SJ, Marra MA, Sadar MD. Identification of genes targeted by the androgen and PKA signaling pathways in prostate cancer cells. Oncogene 26; 25: