A mammalian functional-genetic approach to characterizing cancer therapeutics

Abstract

Identifying mechanisms of drug action remains a fundamental impediment to the development and effective use of chemotherapeutics. Here we describe an RNA interference (RNAi)-based strategy to characterize small-molecule function in mammalian cells. By examining the response of cells expressing short hairpin RNAs (shRNAs) to a diverse selection of chemotherapeutics, we could generate a functional shRNA signature that was able to accurately group drugs into established biochemical modes of action. This, in turn, provided a diversely sampled reference set for high-resolution prediction of mechanisms of action for poorly characterized small molecules. We could further reduce the predictive shRNA target set to as few as eight genes and, by using a newly derived probability-based nearest-neighbors approach, could extend the predictive power of this shRNA set to characterize additional drug categories. Thus, a focused shRNA phenotypic signature can provide a highly sensitive and tractable approach for characterizing new anticancer drugs.

Figure 1. Functional characterization of chemotherapeutic drugs according to patterns of shRNA-conferred drug resistance or sensitivity

(a) A diagram showing the principle of GFP-based competition assays. Suppression of genes that alter drug sensitivity leads to changes in the percentage of GFP-positive cells after treatment, which can be used to calculate the RI (see Methods). (b) Unsupervised clustering of RI values of 15 reference compounds. Agglomerative hierarchical clustering was performed on log-transformed RI values for the initial 15 reference drugs, using a correlation metric and centroid linkage. Bootstrapping data is shown to indicate clustering robustness. ‘Approximately unbiased’ (AU) values from the PVclust function are indicated next to the relevant branches in the clustergram. (c) The branching pattern for SAHA, DAC and Rosco and the 15 reference chemodrugs. Numbers below the dendogram demarcate drug categories. (d) A heat map showing the response of cells expressing shRNAs targeting the Bim transcriptional regulator Chop and Foxo3a to SAHA and DAC. Log-transformed RI values are shown.

Figure 2. RNAi-based characterization of a compound derivative of bendamustine

(a) The chemical structures of bendamustine and a chemical derivative, CY190602. (b) Dose response curves comparing the viability of the multiple myeloma cell lines RPMI-8226 (top) and MM1S (bottom) following treatment with bendamustine or CY190602. (c) RI patterns for bendamustine, CY190602 and a related compound, chlorambucil (CBL). Bendamustine and CY190602 were used at LD80–90 of 110 μ|M and 1.4 μM, respectively. (d) The branching pattern for the 18 reference drugs plus bendamustine and CY190602.

Figure 3. Identification and functional characterization of ill-defined genotoxic drugs

(a) A heat map showing the response of cells expressing shATM, shChk2 or shp53 to 16 genotoxic (upper panel) and 15 nongenotoxic (lower panel) chemotherapeutics (see Supplementary Table 2 for drug abbreviations). (b) The shATM-Chk2-p53 response signature for apigenin (APG) and NSC3852 (NSC). (c) The branching pattern for the 18 reference compounds plus APG and NSC. APG clusters with the TopoII poisons Dox and VP-16, whereas NSC clusters with the TopoI poison CPT. (d) a comparison of the shTopoI and shTopoII response signatures for APG and NSC3852 with response signatures derived from established TopoI (CPT and CPT11) and TopoII poisons (Dox, Mito and VP-16). Although NSC3852 and APG show response patterns characteristic of TopoI and TopoII poisons, respectively, none of the other genotoxic drugs showed either of these resistance and sensitivity patterns. (e) A graph showing the number of surviving shTopoII, shTopoI or vector control-expressing cells 12 days after drug treatment with APG or NSC3852. In each case, one million cells were plated before treatment. Data shown are mean ± s.e.m. from three independent experiments.

Figure 4. A feature reduction identifies a reduced eight-shRNA set

(a) Analysis of the dataset used for Figure 1c, using a randomized search strategy. The graph shows the relative efficacy of drug prediction as a function of increasing shRNA subset size. The maximum predictability for 2,000 iterations at each shRNA subset size is shown. (b) a graph showing the correlation between enrichment or depletion of cells expressing shRNAs from the original eight-shRNA set and cells expressing shRNAs from the additional eight-shRNA set after drug treatment. Each square represents the log₂RI values following single-drug treatment of cells expressing an original shRNA (x axis) or the second shRNA targeting the same gene (y axis). The slope of the best-fit line is 0.64, indicating that the absolute RI values are consistently lower in cells expressing hairpins from the second eight-shRNA set. (c) A heat map showing the relative enrichment (red) or depletion (blue) of a second set of shRNAs (labeled with asterisks) targeting each of the genes in the eight-shRNA signature. The associated dendograms show clustering between shRNA pairs, as well as clustering of small molecules into the same seven categories predicted from the 29-shRNA signature.

Figure 5. A reduced shRNA signature can accurately predict drug mechanism of action

(a) A diagram of the possible outcomes for a test compound when it is compared to the training set. A test compound could be interpolated within the definition of a drug category that is provided by the training set (left). Alternatively, a test compound could be outside of the drug category (right). Our probabilistic nearest-neighbors algorithm attempts to define an ‘acceptable’ category extension. (b) A schematic depicting the methodology behind probabilistic nearest-neighbors predictions. An initial training set with empirically validated drug categories is used to calculate the drug category-specific cluster sizes. This same methodology is used for compounds whose known mechanism of action is distinct from a particular drug category. (c) The increase in the drug category definition that is observed by forcing these empirically derived negative controls to cluster in an erroneous category is used to build a null distribution and an empirical cumulative distribution function.

Figure 6. Adaptation of the eight-shRNA signature to a distinct cell line

A heat map comparing the eight-shRNA response signatures of Myc p19^Arf−/− lymphoma cells and p185+ BCR-Abl leukemia cells following treatment with alkylating agents in a model of acute lymphoblastic leukemia. The eight-shRNA signature from p185+ BCR-Abl p19^Arf−/− leukemia cells can identify CDDP as an alkylating agent when CBL and MMC are used as a reference set.