ATARiS: computational quantification of gene suppression phenotypes from multisample RNAi screens

Abstract

Genome-scale RNAi libraries enable the systematic interrogation of gene function. However, the interpretation of RNAi screens is complicated by the observation that RNAi reagents designed to suppress the mRNA transcripts of the same gene often produce a spectrum of phenotypic outcomes due to differential on-target gene suppression or perturbation of off-target transcripts. Here we present a computational method, Analytic Technique for Assessment of RNAi by Similarity (ATARiS), that takes advantage of patterns in RNAi data across multiple samples in order to enrich for RNAi reagents whose phenotypic effects relate to suppression of their intended targets. By summarizing only such reagent effects for each gene, ATARiS produces quantitative, gene-level phenotype values, which provide an intuitive measure of the effect of gene suppression in each sample. This method is robust for data sets that contain as few as 10 samples and can be used to analyze screens of any number of targeted genes. We used this analytic approach to interrogate RNAi data derived from screening more than 100 human cancer cell lines and identified HNF1B as a transforming oncogene required for the survival of cancer cells that harbor HNF1B amplifications. ATARiS is publicly available at http://broadinstitute.org/ataris.

Figure 1.

ATARiS accounts for patterns in RNAi reagent data in order to quantify the phenotypic effect of gene suppression in each sample. (A–D) Hypothetical phenotypic data from four RNAi reagents, all designed to target the same gene, in five independent samples from two classes, A and B. (A) Samples 1, 2, and 3 each have at least two reagents that score below a desired threshold (purple dotted line); thus, according to “frequency approach” methods, this gene may be a “hit” in those samples. (B) A line connecting each reagent's effects across the samples reveals additional information. Specifically, we note that it is possible (as in this scenario) that different shRNAs drive the determination of hits in each sample when samples are each analyzed separately as in A. (C) For each reagent, the difference between its mean values in class A and class B is shown, reducing much of the noise from individual samples. Reagents 3 and 4 both show differential effects between the classes and would suggest that two-class-based analytic methods select this gene as a hit. (D) ATARiS phenotype values for each of the screened samples. Phenotype values represent relative gene-level effects in each individual sample by incorporating information from trends across all samples, favoring reagents that produce correlated effects (i.e., reagents 1 and 2 from B). If the user chooses to assess whether differential effects exist between classes A and B, this example would show no significant difference by avoiding uncorrelated reagents 3 and 4. (E) Real data from the Project Achilles data set for shRNAs targeting BRAF. Median-normalized screening data across 102 samples are displayed as barplots in sample order of ascending BRAF phenotype value. Boxed numbers display −log₁₀ P-values of the Spearman correlation coefficient for the two shRNAs labeled in the corresponding row and column. (Red) shRNAs with correlated effects that are incorporated into the BRAF gene solution.

Figure 2.

Influence of data set size and context on ATARiS results. (A,B) Robustness of ATARiS for data sets of smaller sample size. ATARiS results from 100 sets of randomly selected samples for each indicated sample size were compared with ATARiS results from the full 102-sample Achilles data set. We determined the percentage of genes with a solution in the Achilles data set results that are also represented in results from fewer samples (A). For 100 randomly selected genes, we also compared phenotype values in each sample to the values generated by ATARiS for the corresponding sample when using the full 102-sample data set (B). Standard deviations are based on phenotype values across all 102 samples for each gene independently. For each gene, differences between phenotype values from smaller data sets compared with the full data set are depicted in standard deviation units. (C) The robustness of shRNA selection by ATARiS is demonstrated by simulating independent screening data sets. One hundred pairs of disjoint sets of samples were randomly generated for the sample sizes indicated. Each set was independently analyzed by ATARiS. For each pair of sets, the overlap in shRNA used to generate solutions was determined. Boxplot displays the size of the overlap for each pair as a fraction of the average number of shRNAs used in the analysis of each set. P-value < 2.2 × 10⁻¹⁶ for all results, χ² test of independence. (D) ATARiS was used to analyze sets of samples that harbor either wild-type BRAF only, or an equal number of samples that harbor wild-type and mutant BRAF. One hundred randomly generated sets of samples were analyzed for each sampling size. The percentage of sets for which a BRAF solution was found is shown.

Figure 3.

ATARiS consistency scores are associated with on-target gene suppression. Consistency scores computed by ATARiS and corresponding protein suppression levels by immunoblotting are shown for shRNAs targeting (A) BRAF and (B) PIK3CA. A higher consistency score represents greater confidence that the effects produced by the shRNA are due to suppression of the target gene. Immunoblotting for the effect of each shRNA compared with control shRNA was performed in cell line A549 and percent suppression compared with control shRNA was calculated based on quantification by ImageJ software. Shading of the axis labels corresponds to data bars of the same type. (*) Reagents used in the gene's ATARiS gene solution.

Figure 4.

ATARiS gene phenotype values reflect biological dependencies. (A) Correspondence between gene mutation status and ATARiS phenotype values for BRAF, PIK3CA, and KRAS. Each vertical bar represents a single screened sample, colored by mutation status. In each plot, samples are ordered by increasing phenotype values. (AUC) Area under receiver operating characteristic curve. P-value, assessed by Mann-Whitney test. (B) Low-throughput validation of the relationship between gene phenotype scores and gene dependency. Six cell lines infected with shKRAS were counted 4 d post-selection to determine cell number relative to infection with control shRNA. Immunoblots were performed using lysates from each sample collected at 2 d post-selection and stained using primary antibodies from Santa Cruz Biotechnology KRAS (sc-30) or actin (sc-1615). Immunoblot lanes correspond to bars in the graph directly above. Horizontal bar orders all cell lines with known KRAS mutation status in increasing order by ATARiS phenotype value, with validated samples marked by corresponding triangles. (Gray) KRAS wild-type; (black) KRAS mutant; (error bars) ±1 SD (n = 3); (n.s.) non-specific band. (C) Genes differentially required in sample classes defined by recurrent amplification or deletion peaks. Recurrent genomic peaks were identified by GISTIC analysis across genomic data for samples from the Cancer Cell Line Encyclopedia. For each peak existing in at least six samples screened in Project Achilles (n = 101), two classes of samples were defined based on presence or absence of the peak. Genes that are differentially required in samples harboring the peak as compared with samples that do not (FDR <0.25) were determined. The distribution of the number of significantly differential genes is shown. For comparison, the same analysis was performed using classes defined by random permutation of peak assignments.

Figure 5.

ATARiS phenotype values enable phenotype-based analyses for biological discovery. (A) Identifying genomic predictors of dependency on E2F3. Genomic features are shown ranked by their correspondence to E2F3 phenotype values as measured by the area under receiver operating characteristic curve (AUC). (Amp) Amplification and (Del) deletion peaks, as determined by GISTIC. Columns correspond to individual cell lines. (Red) Genomic alterations pertinent to E2F3. (B) Correlations between gene phenotype value profiles to CCND1 gene solution. Gene solutions are ranked by their similarity to the CCND1 gene solution using the Pearson correlation coefficient. P-values were generated by permutation of sample labels. (C) Identifying significant cancer genes by integrating expression data and phenotype values. Gene solutions are ranked by increasing the Pearson correlation coefficient between the solution and expression data for the corresponding gene. Thus, genes that are essential in samples with high expression and less essential in samples with low expression are more negatively correlated, and receive higher ranks. P-values were calculated from a null distribution derived by permutation of sample labels. (Red) Previously reported gene dependencies in cancer. Numbers following gene names in B and C indicate gene solution number (see the Supplemental Data). (FDR) False discovery rate.

Figure 6.

Characterizing the role of HNF1B in cancer. (A) Immunoblot of HNF1B after expression of five independent shRNAs designed to target HNF1B. (*) The two shRNAs incorporated into the ATARiS solution, which also have the highest consistency scores. (B) Cell viability upon exogenous expression of HNF1B or GFP in an HNF1B-sensitive cell line OE33 with stable integration of doxycycline-inducible expression of shHNF1B-1. Each bar in the graph corresponds to the immunoblot lane directly below. (C) Relative viability of a panel of cell lines upon suppression of control or two HNF1B-specific shRNAs. Cell lines with high levels of HNF1B are shown in bold text. Each bar in the graph corresponds to the immunoblot lane directly below. Each boxed image derives from a separately exposed gel, as the HNF1B-amplified samples express much higher endogenous levels of HNF1B (Supplemental Fig. 10). Data for HT29 are shown in panel A. (D) HNF1B-sensitive cell line HT29 expressing shHNF1B-1 or shControl was implanted subcutaneously into immunocompromised mice. ShHNF1B-1 was used for all experiments since it has potent effects and is specific for HNF1B, as shown in panels A and B. Tumor volume was monitored biweekly, and lysates were collected pre-implantation and from tumors at 4 wk. (*) P-value <0.05; (**) P-value <0.01 (one-tailed Student's t-test). (E) HNF1B or LacZ was expressed in HA1EM cells and anchorage-independent growth was determined. Representative photos shown after 6 wk. (Error bars) ±1 SD (n = 3).