Registered report: Coding-independent regulation of the tumor suppressor PTEN by competing endogenous mRNAs

The Reproducibility Project: Cancer Biology seeks to address growing concerns about reproducibility in scientific research by conducting replications of selected experiments from a number of high-profile papers in the field of cancer biology. The papers, which were published between 2010 and 2012, were selected on the basis of citations and Altmetric scores (Errington et al., 2014). This Registered Report describes the proposed replication plan of key experiments from “Coding-Independent Regulation of the Tumor Suppressor PTEN by Competing Endogenous 'mRNAs' by Tay and colleagues, published in Cell in 2011 (Tay et al., 2011). The experiments to be replicated are those reported in Figures 3C, 3D, 3G, 3H, 5A and 5B, and in Supplemental Figures 3A and B. Tay and colleagues proposed a new regulatory mechanism based on competing endogenous RNAs (ceRNAs), which regulate target genes by competitive binding of shared microRNAs. They test their model by identifying and confirming ceRNAs that target PTEN. In Figure 3A and B, they report that perturbing expression of putative PTEN ceRNAs affects expression of PTEN. This effect is dependent on functional microRNA machinery (Figure 3G and H), and affects the pathway downstream of PTEN itself (Figures 5A and B). The Reproducibility Project: Cancer Biology is a collaboration between the Center for Open Science and Science Exchange, and the results of the replications will be published by eLife. DOI: http://dx.doi.org/10.7554/eLife.12470.001

Introduction microRNAs are one of the first identified classes of non-coding RNAs that can modulate the expression of mRNA-coding transcripts by binding to complementary regions in a target gene's sequence and repressing its expression. Thus, expression levels and availability of these microRNAs can influence gene expression, and there is growing evidence that misregulation of microRNAs is correlated with some forms of cancer (Sen et al., 2014). Naturally occurring microRNA 'sponges' have been shown to be effective in regulating gene expression by altering the levels of their cognate microRNAs (Choi et al., 2007;Karreth and Pandolfi 2013). Poliseno and colleagues proposed that pseudogenes, long non-coding RNAs with strong homology to coding sequences, could act as the modulators of gene expression as microRNA sponges (Poliseno et al., 2010). They demonstrated that the pseudogene PTENP1 could regulate the expression levels of PTEN via their cognate microRNAs miR-19b and miR-20a (Poliseno et al., 2010).
In this study, Tay and colleagues expanded upon the previous work to propose a unifying hypothesis of regulatory networks composed of competing endogenous RNAs (ceRNAs) Sen et al., 2014;Kartha and Subramanian, 2014). They suggest that protein-coding RNAs, not just non-coding RNAs, can cross-regulate each other based on competition for shared microRNA regulators; ceRNAs can titrate microRNAs from their target genes .
Continuing their focus on the regulation of PTEN, one of the most frequently mutated genes in cancer (Song et al., 2012), Tay and colleagues propose a computational model to identify ceRNAs de novo, termed MuTaME. Using MuTaME, they identified potential ceRNA regulators of PTEN, and validated if these candidate ceRNAs could modulate PTEN expression in a microRNA-dependent manner .
In Figure 3C, Tay and colleagues examine if silencing ceRNAs targeting PTEN would affect the expression levels of a luciferase construct carrying the 3'UTR of PTEN. They co-transfected DU145 cells with siRNAs against the candidate PTEN ceRNAs along with a luciferase-PTEN 3'UTR construct and measured luciferase activity. After confirming knockdown of each target ceRNA (Supplemental Figure 3A), they reported that the loss of three of their candidate ceRNAs -SERINC1, VAPA and CNOT6L, but not ZNF460 -reduced the luciferase activity of the PTEN 3'UTR construct. This experiment will be replicated in Protocol 1.
In Figure 3D, they demonstrated that only the 3'UTRs of the candidate ceRNAs were required to affect changes in the luciferase activity of the PTEN 3'UTR construct. Ectopic overexpression of the 3'UTRs of the three candidate ceRNAs relieved inhibition of the PTEN 3'UTR, as evidenced by increased luciferase activity as compared to controls. This experiment will be replicated in Protocol 2.
To test if this effect was dependent on microRNAs, Tay and colleagues repeated these experiments in DICER1 mutant HCT116 cells, in which the machinery required for microRNA function is abrogated. Transfection of wild type HCT116 cells with siRNAs targeting the candidate ceRNAs showed a marked reduction in PTEN protein levels, an effect that was not seen in the DICER1 mutant HCT116 cells (Figures 3G and H). Knockdown of the candidate ceRNAs was confirmed by RT-PCR (Supplemental Figure 3B). This experiment will be replicated in Protocol 3.
PTEN negatively regulates the PI3K/AKT pathway (Stambolic et al., 1998), so Tay and colleagues examined if ceRNA modulation affected the phosphorylation of AKT. Loss of CNOT6L and VAPA in DU145 cells elevated pAKT levels after serum stimulation, an effect that was also observed in wild-type HCT116 cells ( Figure 5A). However, this effect was abrogated in DICER1 mutant HCT116 cells ( Figure  5A). They also examined the effect of ceRNAs on the tumorigenic properties conferred by loss of PTEN. Silencing of the ceRNAs CNOT6L and VAPA increased cell proliferation of DU145 cells and wildtype HCT116 cells, similar to silencing of PTEN directly ( Figure 5B). This effect was less pronounced in DICER1 mutant HCT116 cells ( Figure 5B). These experiments will be replicated in Protocol 4 and 5.
Two papers published simultaneously provide support for the actions of ceRNA regulatory networks. Karreth and colleagues, from the same lab as Tay and colleagues, demonstrated in vivo evidence for the actions of ceRNA regulation using the sleeping beauty transposase system in a mouse model of melanoma to identify and confirm putative PTEN ceRNAs . Karreth and colleagues identified CNOT6L as a putative PTEN ceRNA through the sleeping beauty transposase system, providing further evidence that CNOT6L is indeed involved in PTEN regulation. Karreth and colleagues focused on ZEB2; using siRNA silencing, they reported that the loss of ZEB2 reduced PTEN protein levels, and affected downstream phosphorylation of AKT . As seen in Tay and colleagues, these effects were dependent on functional microRNA processing; ZEB2 depletion did not affect PTEN levels in DICER1 mutant HCT116 cells . Sumazin and colleagues used a bioinformatics approach to identify post-translational regulation and elucidated over 7,000 genes they proposed acted as miRNA sponges. By comparing the miRNA programs of genes, they could identify genes with common miR programs, indicating the potential for miRNA-mediated crosstalk between those two genes (Sumazin et al., 2011). They tested their findings by exploring the regulation of PTEN, demonstrating that silencing of putative miRNA program-mediated regulators (mPRs) of PTEN decreased PTEN expression, and, conversely, that the perturbation of PTEN levels could inversely affect the expression of its mPRs. These manipulations also affected tumor cell growth rates, indicating potential in vivo effects of changes to mPR regulatory networks (Sumazin et al., 2011). Since the publication of these three papers, numerous other examples of ceRNA regulation have been reported in muscle differentiation (Cesana et al., 2011), human embryonic stem cell renewal (Wang et al., 2013), regulation of sex determination by SRY (Granados-Riveron and Aquino-Jarquin 2014), breast cancer (Yang et al., 2014;Zheng et al., 2015a;2015b), lymphoma (Karreth et al., 2015) and the regulation of the tumor-related HMGA1 (Esposito et al., 2014).
The Pandolfi group followed up on their 2011 paper by generating a mathematical model to predict optimal conditions for ceRNA activity, based on a molecular titration mechanism whose effects were correlated to the relative levels of the ceRNA and its target (Ala et al., 2013). They then tested their in silico predictions by experimentally exploring the effect of VAPA on PTEN expression. While silencing of VAPA did decrease PTEN expression in all five cell lines tested, they noted that the amount of silencing was correlated with the initial VAPA:PTEN expression ratio (Ala et al., 2013). However, Denzler and colleagues challenge the notion that perturbations in ceRNA expression levels could affect target genes at all (Denzler et al., 2014). Denzler and colleagues and Ala and colleagues both state that ceRNA effects are dependent on the kinetics of binding, which in turn relies upon the ratio of microRNAs to target sites; increasing the number of target sites through expression of ceRNAs is postulated to affect target gene repression. By quantifying the absolute copy number of the well-studied highly abundant miR-122 and its target sites, Denzler and colleagues showed that large, physiologically unlikely changes in ceRNA expression levels would be required to alter the microRNA: target site ratio enough to perturb target gene expression, casting doubt on the ability of these putative ceRNAs to affect changes in target gene expression levels (Broderick and Zamore 2014;Denzler et al., 2014). This view was contradicted by Bosson and colleagues, who identified over 3,000 high affinity target sites they claimed could be affected by ceRNAs due to low endogenous microRNA: target site ratios (Bosson et al., 2014). The activity and impact of potential ceRNA networks is an area of active interest (for review, see de Giorgio et al., 2013).

Materials and methods
Unless otherwise noted, all protocol information was derived from the original paper, references from the original paper, or information obtained directly from the authors. An asterisk (*) indicates data or information provided by the Reproducibility Project: Cancer Biology core team. A hashtag (#) indicates information provided by the replicating lab. . All cells will be sent for mycoplasma testing and STR profiling. . DU145 cells are maintained in DMEM supplemented with 10% FBS, # 100 U/ml penicillin/100 mg/ml streptomycin, and # 2 mM glutamine at 37˚C in 5% CO 2 in a humidified atmosphere.  Note: At the time of analysis, we will perform the Shapiro-Wilk test and generate a quantilequantile plot to assess the normality of the data. We will also perform Levene's test to assess homoscedasticity. If the data appear skewed, we will perform the appropriate transformation to proceed with the proposed statistical analysis. If this is not possible, we will perform the equivalent non-parametric Wilcoxon-Mann-Whitney test.  This replication attempt will perform the statistical analysis listed above, compute the effects sizes, compare them against the reported effect size in the original paper, and use a meta-analytic approach to combine the original and replication effects, which will be presented as a forest plot.
Known differences from the original study All known differences are listed in the materials and reagents section above with the originally used item listed in the comments section. All differences have the same capabilities as the original and are not expected to alter the experimental design.

Provisions for quality control
Extracted RNA integrity will be reported with A 260/280 and A 260/230 absorbance ratios, and transfection efficiency will be checked using the siGLO control. qRT-PCR will be performed to confirm the silencing of ceRNA expression. The cells will be sent for mycoplasma testing confirming lack of contamination and STR profiling confirming cell line authenticity. Transfection efficiency will be recorded for each replicate and any transfection that does not contain >90% efficiency will be excluded and not continue through the rest of the procedure. Any modifications to the transfection protocol will be recorded, and the procedure will be maintained for the remaining replicates. All data obtained from the experiment -raw data, data analysis, control data and quality control datawill be made publicly available, either in the published manuscript or as an open access dataset available on the Open Science Framework (https://osf.io/oblj1/). . All cells will be sent for mycoplasma testing and STR profiling. . DU145 cells are maintained in DMEM supplemented with 10% FBS, # 100 U/ml penicillin/100 g/ml streptomycin, and # 2 mM glutamine at 37˚C in 5% CO 2 in a humidified atmosphere.
i. These will be biological replicates. b. Seed cells at 1.2 x 10 5 cells per well in 12 well dishes and incubate for 24 hr.
i. Seed 1 well per biological replicate: 7 transfections x 6 replicates. 1. 42 wells total seeded. c. Prepare the transfection mix by adding 100 ng of psiCHECK-2+PTEN3'UTR and 1 mg of 3'UTR plasmid to 100 ml of Opti-MEM. i. Transfect one well per replicate with each of the following: . empty vector control d. In a separate tube, mix 2 ml of Lipofectamine 2000 with 100 ml of Opti-MEM.
i. Scale the volume of reagents accordingly.
ii. Incubate for 10 min. e. Combine the plasmid and Lipofectamine mixes and incubate for an additional 20 min. f. Aliquot 200 ml of the plasmid and Lipofectamine transfection mix into each well. Mix gently and incubate at 37˚C in 5% CO 2 in a humidified atmosphere. i. Replace growth medium after 4 hr. g. Incubate for 72 hr. 2. Measure renilla and firefly luciferase activity as outlined in Protocol 1 Step 2.
Deliverables . Data to be collected: & Raw data of Renilla and firefly luciferase measures and a graph of luciferase activity for each cohort.

Confirmatory analysis plan
. Statistical Analysis of the Replication Data: Note: At the time of analysis, we will test for normality and homoscedasticity of the data. If the data appears skewed, we will perform the appropriate transformation to proceed with the proposed statistical analysis. If this is not possible, we will perform the equivalent non-parametric Wilcoxon-Mann-Whitney test.

Meta-analysis of original and replication attempt effect sizes:
& This replication attempt will perform the statistical analysis listed above, compute the effects sizes, compare them against the reported effect size in the original paper and use a meta-analytic approach to combine the original and replication effects, which will be presented as a forest plot.
Known differences from the original study All known differences are listed in the materials and reagents section above with the originally used item listed in the comments section. All differences have the same capabilities as the original and are not expected to alter the experimental design.

Provisions for quality control
The cells will be sent for mycoplasma testing confirming lack of contamination and STR profiling confirming cell line authenticity. All data obtained from the experiment -raw data, data analysis, control data and quality control data -will be made publicly available, either in the published manuscript or as an open access dataset available on the Open Science Framework (https://osf.io/oblj1/).

Sampling
. The experiment will be repeated four times (Western blot) and three times (qRT-PCR) for a minimum power of 80%.  i. These will be biological replicates. b. For each cell type (WT and DICER Ex5) seed cells at 1.3 x 10 5 cells per well in 12 well dishes i. Seed 11 wells per replicate: 5 transfections x 2 wells each (one for Step 2, one for

Materials and reagents
Step 3) and 1 transfection (siGlo) x 1 well. ii. Note: During the last replicate, only seed 6 wells per cell type (5 transfection conditions for Step 2) and 1 transfection condition for siGlo RISC free siRNA transfection control. c. Transfect cells with 100 nM siRNA (or siGLO controls) using Dharmafect 1 according to manufacturer's instructions. i. Note: make up a separate transfection mixture for each replicate.
ii. Transfect a pair of wells per replicate with each of the following: 1. siNC 2. siSER 3. siVAPA 4. siCNO 5. siPTEN iii. Transfect a single well per replicate with the following: 1. siGLO iv. After 24-48 hr, assess number of fluorescent cells transfected with siGLO to confirm >90% transfection efficiency. 1. If transfection is less than 90%, record efficiency, exclude replicate and omit it from the rest of the procedure. Repeat procedure until >90% efficiency is obtained. 2. If modification to transfection is needed, record and maintain modified steps for remaining replicates. d. Incubate for 72 hr at 37˚C in 5% CO 2 in a humidified atmosphere.
i. Replace growth medium after 4 hr. 2. Using one of each pair of wells (except during replicate 4), confirm siRNA knock down with qRT-PCR as in Protocol 1 Step 3. Measure mRNA expression for each siRNA transfection sample with its appropriate target and ß-ACTIN, and test each probe separately using RNA from the NC control transfection.  Note: At the time of analysis, we will test for normality and homoscedasticity of the data. If the data appears skewed, we will perform the appropriate transformation to proceed with the proposed statistical analysis. If this is not possible, we will perform the equivalent nonparametric This replication attempt will perform the statistical analysis listed above, compute the effects sizes, compare them against the reported effect size in the original paper and use a meta-analytic approach to combine the original and replication effects, which will be presented as a forest plot.
Known differences from the original study All known differences are listed in the materials and reagents section above with the originally used item listed in the comments section. All differences have the same capabilities as the original and are not expected to alter the experimental design.

Provisions for quality control
Extracted RNA integrity will be reported with A 260/280 and A 260/230 absorbance ratios, and transfection efficiency will be checked using the siGLO control. The cells will be sent for mycoplasma testing confirming lack of contamination and STR profiling confirming cell line authenticity. Transfection efficiency will be recorded for each replicate and any transfection that does not contain >90% efficiency will be excluded and not continue through the rest of the procedure. If the efficiency does not reach >90%, then any modifications to the transfection protocol will be recorded. qRT-PCR will be performed to confirm silencing of mRNA expression. Images of Ponceau staining to confirm protein transfer. All data obtained from the experiment -raw data, data analysis, control data, and quality control data -will be made publicly available, either in the published manuscript or as an open access dataset available on the Open Science Framework (https://osf.io/oblj1/).

Protocol 4: Effect of knock-down of ceRNA network genes on cell proliferation
This experiment tests the effects of siRNA-mediated depletion of PTEN, CNOT6L, and VAPA expression on cell proliferation in DU145, HCT116 WT, and HCT116 DICER Ex5 cells. It replicates Figure 5B.
. All cells will be sent for mycoplasma testing and STR profiling. ii. Incubate wells for measuring transfection efficiency for 24-48 hr, then assess number of fluorescent cells transfected with siGLO to confirm >90% transfection efficiency. 1. If transfection is less than 90%, record efficiency for attempt, exclude attempt and do not continue with the rest of the procedure. Repeat procedure until >90% efficiency is obtained. 2. If modification to transfection is needed during first attempt(s), record and maintain modified steps for remaining replicates. iii. Incubate wells for seeding the cell proliferation assay for 8 hr, then proceed to Step 2. iv. Incubate wells for qPCR for 72 hr, then proceed to Step 3. 2. Measure cell proliferation a. Eight hours after transfection, trypsinize and resuspend cells. Split each well into 1 well each of four 12-well plates, seeding 20,000 cells/well. Incubate overnight. i. Two 12-well plates (a set) will provide sufficient wells to accommodate all replicates for one day of the time course per cell line. ii. 8 plates will be needed per cell line for a full 4 day time course. b. Starting on the following day (d0), fix one set of plates per cell line per day. i

Confirmatory analysis plan
. Statistical Analysis of the Replication Data: Note: At the time of analysis, we will test for normality and homoscedasticity of the data. If the data appears skewed, we will perform the appropriate transformation in order to proceed with the proposed statistical analysis. If this is not possible we will perform the equivalent nonparametric Wilcoxon-Mann-Whitney test. . siNC vs. each siRNA, for each cell line (6 comparisons total). . Meta-analysis of original and replication attempt effect sizes: & This replication attempt will perform the statistical analysis listed above, compute the effects sizes, compare them against the reported effect size in the original paper and use a meta-analytic approach to combine the original and replication effects, which will be presented as a forest plot.

Known differences from the original study
All known differences are listed in the materials and reagents section above, with the originally used item listed in the comments section. All differences have the same capabilities as the original and are not expected to alter the experimental design.

Provisions for quality control
Extracted RNA integrity will be reported with A 260/280 and A 260/230 absorbance ratios, and transfection efficiency will be checked using the siGLO control. Cells will be sent for mycoplasma testing confirming lack of contamination and STR profiling confirming cell line authenticity. Transfection efficiency will be recorded for each replicate and any transfection that does not contain >90% efficiency will be excluded and not continue through the rest of the procedure. Any modifications to the transfection protocol will be recorded and the procedure will be maintained for the remaining replicates. All data obtained from the experiment -raw data, data analysis, control data and quality control data -will be made publicly available, either in the published manuscript or as an open access dataset available on the Open Science Framework (https://osf.io/oblj1/).

Protocol 5: Knock-down of ceRNA network genes results in AKT activation
This experiment tests the effects of siRNA-mediated depletion of PTEN, CNOT6L, and VAPA expression on AKT activation in DU145, HCT116 WT, and HCT116 Dicer Ex5 cells. It replicates Figure 5A.

Sampling
. This experiment will be repeated at least 7 times for a minimum power of 80%. . HCT116 cells (wild-type and mutant) are maintained in DMEM with 10% FBS, # 100 U/ml penicillin/100 mg/ml streptomycin, and # 2 mM glutamine at 37˚C/5% CO 2 in a humidified atmosphere.
. All cells will be sent for mycoplasma testing and STR profiling.
1. Transfect DU145, HCT116 WT, and HCT116 DICER Ex5 cells with siRNAs a. Seed cells for subsequent experiments with 1.3 x 10 5 cells per well in a 12-well plate: i. For measuring transfection efficiency (Step 1c ii): ii. Incubate wells for measuring transfection efficiency for 24-48 hr, then assess number of fluorescent cells transfected with siGLO to confirm >90% transfection efficiency. 1. If transfection is less than 90%, record efficiency, exclude attempt and do not continue with the rest of the procedure. Repeat procedure until >90% efficiency is obtained. 2. If modification to transfection is needed, record and maintain modified steps for remaining replicates. 2. Stimulate activation of AKT then measure levels of phosphorylated AKT by Western blot.
a. After 72 hr, serum-starve cells overnight: replace media with serum-free media and incubate overnight (approximately 16 hr). b. The following morning, harvest one well at 0 min (pre-stimulation), re-stimulate the remaining cells by adding the appropriate volume of warmed 100% FBS to existing media in each trio of matched wells for a 10% final concentration. Incubate wells for 5 or 15 min. i. Harvest one well at 5 min and one well at 15 min post FBS addition. c. Harvest cells and perform Western blot as specified in Protocol 3 step 3.
i. Note: load 10 mg of protein per well. ii. Probe membranes specific primary antibodies 1. pAKT (Ser473); 1:1000 2. total AKT; 1:1000 a. Loading control iii. Note from original author: Phosphorylated proteins are less stable in lysis buffer than non-phosphorylated proteins. Try to use fresh lysates for subsequent western blotting as far as possible. Transfer samples to the protein loading buffer as fast as possible and keep freeze thaw cycles to an absolute minimum. d. Normalize pAKT to total AKT for each sample. 3. Repeat at least 6 additional times.

Confirmatory analysis plan
. Statistical Analysis of the Replication Data: Note: At the time of analysis, we will test for normality and homoscedasticity of the data. If the data appears skewed, we will perform the appropriate transformation to proceed with the proposed statistical analysis. If this is not possible, we will perform the equivalent non-parametric Wilcoxon-Mann-Whitney test.
& Two-way ANOVA of normalized pAKT levels of DU145 cells transfected with siRNA for VAPA, CNOT6L, PTEN, or siNC measured at 0 min, 5 min, and 15 min followed by Bonferroni-corrected planned contrasts: . siNC vs each siRNA, collapsed across all times (3 contrasts total).
Three-way ANOVA (3x4x2) of normalized pAKT levels of HCT116 WT or HCT116 DICER Ex5 cells transfected with siRNA for VAPA, CNOT6L, PTEN, or siNC measured at 0 min, 5 min, and 15 min: . HCT116 WT cells with the following Bonferroni-corrected planned contrasts: . siNC vs. each siRNA, collapsed across all times (3 contrasts total). . HCT116 DICER Ex5 cells with the following Bonferroni-corrected planned contrasts: . siNC vs. each siRNA, collapsed across all times (3 contrasts total). . Meta-analysis of original and replication attempt effect sizes: & The replication data (mean and 95% confidence interval) will be plotted with the original reported data value plotted as a single point on the same plot for comparison.
Known differences from the original study All known differences are listed in the materials and reagents section above, with the originally used item listed in the comments section. All differences have the same capabilities as the original and are not expected to alter the experimental design.

Provisions for quality control
The cells will be sent for mycoplasma testing confirming lack of contamination and STR profiling confirming cell line authenticity. Transfection efficiency will be recorded for each replicate and any transfection that does not contain >90% efficiency will be excluded and not continue through the rest of the procedure. Any modifications to the transfection protocol will be recorded, and the procedure will be maintained for the remaining replicates. Images of Ponceau staining to confirm protein transfer. All data obtained from the experiment -raw data, data analysis, control data, and quality control data -will be made publicly available, either in the published manuscript or as an open access dataset available on the Open Science Framework (https://osf.io/oblj1/).

Power calculations
For additional details on power calculations, please see analysis scripts and associated files on the Open Science Framework: https://osf.io/c8hb5 Protocol 1 Summary of original luciferase activity data: . Note: data provided by original authors for Figure 3C siRNA Luciferase activity SD N

Test family
. Two-tailed t test, difference between two independent means, Bonferroni correction: alpha error = 0.01

Power calculations
. Performed with G*Power software, version 3.1.7 (Faul et al., 2007). Summary of original qPCR gene expression data: . Note: data provided by original authors for Figure S3A . We estimated SD to be 0.001, when it was reported as zero. Test family . Due to the large variance, the following parametric tests are only used for comparison purposes. The sample size is based on the non-parametric tests listed above.
. Two-tailed t test, difference from a constant, Bonferroni correction: alpha error = 0.01

Power calculations
. Performed with G*Power software, version 3.1.7 (Faul et al., 2007). Protocol 2 Summary of original Luciferase data: . Note: data provided by original authors for Figure 3D.

Protocol 3
Summary of original Western blot data: . Note: data provided by original authors for Figure 3H. Summary of original mRNA expression data: . Note: data provided by original authors for Figure S3B.

Protocol 4
Summary of original cell proliferation data: . Note: data of mean values provided by original authors for Figure 5B. Test family . Due to the large variance, the following parametric tests are only used for comparison purposes. The sample size is based on the non-parametric tests listed above.
. Two way ANOVA: Fixed effects, special, main effects and interactions, alpha error = 0.05

Power calculations
. Performed with G*Power software, version 3.1.7 (Faul et al., 2007). . ANOVA F test statistic and partial h 2 performed with R software, version 3. Protocol 5 Summary of original AKT Activation data . Note: data provided by original authors for Figure 5A.

&
We used the average band intensity for siNC since they were measured twice.

Test family
. 3-Way ANOVA: Fixed effects, special, main effects and interactions, alpha error = 0.025 for HCT116 WT and HCT116 DicerEx5 cells comparing AKT activation over time.

Power calculations
. Performed with G*Power software, version 3.1.7 (Faul et al., 2007). . ANOVA F test statistic and partial h 2 performed with R software, version 3.1.2 (R Core Team 2015).
& For a given relative variance, 10,000 simulations were run and the F statistic and partial h 2 was calculated for each simulated data set.
In order to produce quantitative replication data, we will run the experiment seven times. Each time we will quantify band intensity. We will determine the standard deviation of band intensity across the biological replicates and combine this with the reported value from the original study to simulate the original effect size. We will use this simulated effect size to determine the number of replicates necessary to reach a power of at least 80%. We will then perform additional replicates, if required, to ensure that the experiment has more than 80% power to detect the original effect.