Drug mechanism-of-action discovery through the integration of pharmacological and CRISPR screens

Abstract

36Low success rates during drug development are due in part to the difficulty of 37 defining drug mechanism-of-action and molecular markers of therapeutic activity. Here, 38 we integrated 199,219 drug sensitivity measurements for 397 unique anti-cancer drugs 39 and genome-wide CRISPR loss-of-function screens in 484 cell lines to systematically 40 investigate in cellular drug mechanism-of-action. We observed an enrichment for 41 positive associations between drug sensitivity and knockout of their nominal targets, 42 and by leveraging protein-protein networks we identified pathways that mediate drug 43 response. This revealed an unappreciated role of mitochondrial E3 ubiquitin-protein 44 ligase MARCH5 in sensitivity to MCL1 inhibitors. We also estimated drug on-target and 45 off-target activity, informing on specificity, potency and toxicity. Linking drug and gene 46 dependency together with genomic datasets uncovered contexts in which molecular 47 networks when perturbed mediate cancer cell loss-of-fitness, and thereby provide 48 independent and orthogonal evidence of biomarkers for drug development. This study 49 illustrates how integrating cell line drug sensitivity with CRISPR loss-of-function 50 screens can elucidate mechanism-of-action to advance drug development. 51 inhibitors ( Supplementary Figure 1d and1e). 126 127 Cell fitness effects for 16,643 gene knockouts have been measured using genome-128 wide CRISPR-Cas9 screens at the Sanger and Broad Institutes (Meyers et al, 2017; Behan et 129 al, 2019; DepMap, 2019) (Supplementary Table 4). The first PC across the cell lines (6.8% 130 variance explained) separated the two institutes of origin ( Supplementary Figure 2a), 131 consistent with a comparative analysis performed on an overlapping set of cell lines (Dempster 132 et al, 2019). Growth rate was less significantly associated with CRISPR knockout response 133 (Supplementary Figure 2b and 2c). 134 157 are MCL1 and BCL2 selective inhibitors, respectively. Gene fitness log2 fold-changes (FC) are scaled by using 158 Supplementary Figure 2. Overview of the CRISPR-Cas9 datasets. a, PCA analysis of the samples in the 616 CRISPR-Cas9 screens, samples institute of origin is highlighted. b, correlation coefficients between all top 10 PCs 617 and growth rate. c, correlation between cell lines growth rate and PC3 (Pearson correlation coefficient reported in 618 the top left). 619 23 620 Supplementary Figure 3. Drug response and gene fitness associations. a, total number of drugs utilised in the 621 study and the different levels of information available: 'All' represents all the drugs including replicates screened 622 with different technologies (GDSC1 and GDSC2); 'Unique' counts the number of unique drug names; 'Annotated' 623 shows the number of unique drugs with manual annotation of nominal targets; and 'Target tested' represents the 624 number of unique drugs, with target information, for which the target has been knocked-out in the CRISPR-Cas9 625 screens. b, histogram of the drug-gene associations effect sizes ...

CEN‐tools: an integrative platform to identify the contexts of essential genes

“…Future versions of CEN‐tools, and as more data becomes available, will integrate an analysis for confounding factors more directly. An option for this could be identifying associations between gene essentialities and a given context using a mixed effect linear model while considering defined set of contexts as covariates, an approach that has been used very recently for the identification of drug‐gene associations from essentiality screens (Gonçalves et al , 2020). Currently, to aid the users in interpreting the statistical associations we have included a number of confidence annotations.…”

CEN-tools: An integrative platform to identify the ‘contexts’ of essential genes

“…Future versions of CEN-tools, and as more data becomes available, will integrate an analysis for confounding factors more directly. An option for this could be identifying associations between gene essentialities and a given context using a mixed effect linear model while considering defined set of contexts as covariates, an approach that has been used very recently for the identification of drug-gene associations from essentiality screens (Gonçalves et al, 2020). Currently, to aid the users in interpreting the statistical associations we have included a number of confidence annotations.…”

Integrated cross-study datasets of genetic dependencies in cancer

“…Pharmacological and CRISPR screens are being increasingly integrated as a means to elucidate drug mechanism-of-action 28 . Here we used drug sensitivity analysis to compare the Recall of significant associations between a drug sensitivity profile and its nominal targets' CRISPR dependency profile, across cell lines.…”

“…Thus, we systematically calculated Spearman's correlation between the dependency profile of a drug target across different pre-processing methods with the profile of cell line sensitivity to that drug. As indicators of drug sensitivity, we used viability reduction indicators (IC50 values) from two studies 29,30 for 307 unique compounds mapped to their targets 28 , across 486 cell lines included in our integrated datasets. Next we compared the resulting drug-response/target-dependency correlation patterns across pre-processing methods.…”