Correction of copy number induced false positives in CRISPR screens

<div><p>Cell autonomous cancer dependencies are now routinely identified using CRISPR loss-of-function viability screens. However, a bias exists that makes it difficult to assess the true essentiality of genes located in amplicons, since the entire amplified region can exhibit lethal scores. These false-positive hits can either be discarded from further analysis, which in cancer models can represent a significant number of hits, or methods can be developed to rescue the true-positives within amplified regions. We propose two methods to rescue true positive hits in amplified regions by correcting for this copy number artefact. The Local Drop Out (LDO) method uses the relative lethality scores within genomic regions to assess true essentiality and does not require additional orthogonal data (e.g. copy number value). LDO is meant to be used in screens covering a dense region of the genome (e.g. a whole chromosome or the whole genome). The General Additive Model (GAM) method models the screening data as a function of the known copy number values and removes the systematic effect from the measured lethality. GAM does not require the same density as LDO, but does require prior knowledge of the copy number values. Both methods have been developed with single sample experiments in mind so that the correction can be applied even in smaller screens. Here we demonstrate the efficacy of both methods at removing the copy number effect and rescuing hits from some of the amplified regions. We estimate a 70–80% decrease of false positive hits with either method in regions of high copy number compared to no correction.</p></div

MET Specific LDO correction in MKN45 in two different screens.

<p><b>A)</b> Sensitivity conferred by each guide (black dots) within the MET amplicon in MKN45 summarized by a boxplot for each gene in the amplicon. The red line displays the inverted copy number value scaled to the data. <b>B)</b> Sensitivity conferred by each guide (black dots) within the MET amplicon in MKN45 summarized by a boxplot for each gene in the amplicon. The red line displays the inverted copy number value scaled to the data.</p

LDO removes the copy number effect across samples and maintains sensitivity of essential genes.

<p><b>A)</b> Boxplot of dependency scores across copy number for uncorrected and LDO corrected data. <b>B)</b> The recall curve for essential, nonessential and amplified genes is shown before and after LDO copy number correction in the cell line DAN-G. <b>C)</b> The area under the recall curve is shown across samples for the essential, nonessential and amplified genes.</p

Overview of data analysis on COPD cohort.

<p>Overview of data analysis on COPD cohort.</p

Univariate regression analysis of protein analytes versus lung function parameters in COPD subjects with and without metabolic syndrome.

<p>Significance (<i>p</i> values) and effect sizes (spearman correlation) are listed for biomarker associations with lung function parameters. Interaction <i>p</i> values indicate significance of differences in biomarker associations with lung function parameters, between metabolic syndrome and non- metabolic syndrome groups.</p

Multivariate analysis of protein analyte data for COPD subjects.

<p>Spearman correlation and adjusted R squared values were computed using test set samples, in a 5-fold nested cross-validation scheme, averaged over 10 random seeds. R squared values were adjusted for the number of predictor terms in the model.</p

Correlation network illustrating functional co-clustering of analytes associated with FEV₁, FEV₁/FVC and DLCO.

<p>Analytes are plotted in a network using Cytoscape <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038629#pone.0038629-Shannon1" target="_blank">[83]</a> where nodes represent analytes and edges represent significant correlations (<i>r</i> >0.4, <i>p</i><0.05, corrected for multiple testing). Analytes are colored according to whether they were associated with FEV₁ related parameters (green), DLCO (red) or both DLCO and FEV₁ related parameters (orange) in univariate regression. Node size is proportional to the number of lung function parameters that showed significant association with a given analyte. Clusters of co-expressed analytes with similar function are highlighted by dotted regions in the graph as neutrophil function (orange), systemic inflammation (blue) and growth factor pathways (grey).</p

Protein analyte differences between COPD and control disease severity groups.

<p>Data are expressed as median (interquartile range) in ng/ml for individual analytes, except for Fibrinogen which is in mg/dl.</p><p>All analyte data shown are from profiling on the RBM Luminex platform, except for Fibrinogen which was tested at Hospital Grosshansdorf. COPD subjects were grouped as GOLD I/II (mild/moderate) and GOLD III/IV (severe/very severe). ANOVA was used for group-wise comparisons, except for analytes noted with *, which did not follow a normal distribution and a non-parametric Kruskal Wallis test was used.</p

Association of MPO with FEV₁/FVC and Fibrinogen with DLCO in COPD patients with and without metabolic syndrome.

<p>Log2-transformed levels of MPO (A, C) and Fibrinogen (B, D) (ng/ml for MPO and mg/dl for Fibrinogen) are plotted against covariate adjusted values for FEV₁/FVC and DLCO, respectively in COPD patients with (A, B) and without (C, D) metabolic syndrome (<i>r</i> values indicate spearman correlation, covariates include age, sex, BMI, pack years and smoking status).</p

Potential miR-223 targets are repressed in a miR-223 −/− system.

<p>A) miR-223 expression across the profiled cell types (bars) is plotted against the relative expression profile (lines) of 82 genes identified as potential miR-223 targets (TargetScan, significant negative correlation). Red line represent mean expression profile of target genes, dotted line represents mean expression across cell types. B) 82 genes were identified in our study as being significant miR-223 targets. We used the data from a previously published miR-223 −/− system <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0029979#pone.0029979-Baek1" target="_blank">[25]</a> to see if those targets would correspondingly be de-repressed when miR-223 is knocked-out. 62 of these 82 genes had matching mouse homologs (in red). The change in expression of these genes was compared against all TargetScan predicted miRNA target genes, which included predicted targets not negatively correlated with miRNA expression in our dataset (234 genes, in blue). Fold-change for all probe sets is also plotted in this figure as a null distribution (black).</p