Supplementary Material and Methods s5

Supplementary Material

Fluorescence in situ hybridization. E/R-positive leukemia cell lines were regularly checked for the presence of the t(12;21) translocation as described previously.1

Assessment of protein phosphorylation. Fixed and permeabilized cells were incubated with fluorescence conjugated antibodies binding RPS6 (Ser235/236), AKT (Ser473), p38 (Thr180/Tyr182), NFκB (Ser529), STAT5 (Tyr694) and JNK (Thr183/Tyr185) (Cell Signaling Technology) for flow cytometry analyses (FACS Calibur BD-Pharmingen, Franklin Lakes, NJ). Protein phosphorylation was also determined by Western blot analysis and by PathScan Apoptosis Multi-Target Sandwich ELISA (Cell Signaling Technology) according to the manufacturer's recommendations.

Immunoblotting. The following primary antibodies were used: anti-ETV6 (SB89, kindly provided by P. Marynen, VIB, Leuven, Belgium), anti-RUNX1 (Active Motif, Carlsbad, CA), anti-AKT (pan) (#9272), anti-AKT (Ser473) (587F11), anti-RPS6 (pan) (54D2), anti-RPS6 (Ser235/236) (2F9) (all Cell Signaling Technology, Danvers, MA) and anti-GAPDH antibodies (6C5, Santa Cruz Biotechnology), used as a loading control. Secondary antibodies were infrared dye-labeled (LI-COR Biosciences, Lincoln, NE) and membranes were scanned with Odyssey Infrared Imaging System (LI-COR Biosciences).

RNA interference. Lentiviral particles that contain a U6 promoter based RNAi cassette for shRNA expression were generated using the Block iT Lentiviral RNAi Expression System (Invitrogen) and, following ultracentrifugation, titers were assessed by ELISA-based quantification of virus associated HIV-1 p24 (Cell Biolabs, San Diego, CA) according to the manufacturer’s recommendations. Leukemia cells were transduced by a multiplicity of infection of 10 in the presence of 6 µg/mL polybrene.

Gene expression analysis by microarray technology. RNA from E/R silenced REH and AT-2 cells was used for gene expression profiling on Affymetrix HG-U133-PLUS2 (Affymetrix, Santa Clara, CA). Analyses were performed in R using Bioconductor packages2 with mean values of biological replicates obtained from REH cells between day 20-30 (n = 3) and AT-2 cells between day 16-18 (n = 2) after transduction. Differentially expressed genes were determined by microarray analyses using three and two biological replicates from independent knock-down (KD) experiments of the REH and AT-2 cell lines, respectively, as well as appropriate control cells that were transduced with a non-targeting shRNA vector. Changes in gene expression profiles upon knockdown of E/R were followed on Affymetrix HG-U133-PLUS2 arrays (Affymetrix, Inc., Santa Clara, CA). cRNA target synthesis and GeneChip® processing were performed according to standard protocols (Affymetrix, Inc.). All further analyses were performed in R statistical environment using Bioconductor packages.2

Affymetrix CEL files for all experiments were normalized together using the gcrma logarithm.3 For further analysis probe sets (ps) were called present or absent using the Bioconductor package “panp”, and ps were excluded which showed very low expression across all arrays (P > 0.02). Subsequently ps were excluded that did not pass a non-stringent variability cut-off across all arrays (standard deviation > 0.05). Thereby ps were excluded that did not show any variation across all experiments. Finally, for each gene one ps was selected for further analysis by the criterion of maximizing the expression variation across arrays and thereby the most informative ps was chosen for each gene. This procedure yielded a final number of 9498 ps that were used for all further analysis.

Differentially expressed genes were determined using a moderated t-test in the R package “limma”.4 All P-values were corrected for multiple testing using the “Benjamini-Hochberg” correction method. Significantly differentially expressed genes in the E/R knockdown vs. control experiments were determined by calculating ratios for each gene between the two conditions for each experiment separately, thus yielding five biological replicates of relative expression for each gene (REH n = 3, AT-2 n = 2). Overall, relative gene expression was positively correlated with each other in all biological replicates. Then, for each gene, significance was determined using a weighted one-sample t-test against the null hypothesis of no expression change (μ = 0).

References

1. Konrad M, Metzler M, Panzer S, Östreicher I, Peham M, Repp R, et al. Late relapses evolve from slow-responding subclones in t(12;21)-positive acute lymphoblastic leukemia: evidence for the persistence of a preleukemic clone. Blood 2003; 101: 3635-3640.

2. Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 2004; 5: R80.

3. Wu Z, Irizarry R, Gentleman R, Martinez-Murillo F, Spencer F. A Model Based Background Adjustment for Oligonucleotide Expression Arrays. J American Statistical Association 2004; 99: 909-917.

4. Wettenhall JM, Smyth GK. limmaGUI: A graphical user interface for linear modeling of microarray data. Bioinformatics 2004; 20: 3705-3706.