University of California San Francisco – 1

Chemical-genetic Interaction Mapping Strategy

The CTD² Center at University of California San Francisco (UCSF-1) developed a chemical-genetic interaction mapping strategy to uncover the impact of cancer gene expression on responses to a panel of emerging therapeutics.

To study the impact of aberrant gene activity in isolation, they developed an isogenic model of triple-negative breast cancer (TNBC) using the hormone receptor negative MCF10A non-tumorigenic cell line derived from healthy breast tissue which is diploid and largely devoid of somatic alterations.

They created 51 stable cell lines by ectopic expression of wild-type and mutant genes to model the impact of recurrent gene mutation, amplification, and overexpression common in breast and other cancers.

These cell lines were used in a drug screen to identify genes which could alter responses to a panel of 90 FDA-approved and emerging clinical compounds. The data represent a quantitative measure of the degree to which aberrant gene expression drives the sensitivity or resistance to compounds.

Experimental Approaches

To measure the impact of gene activation on cellular responses systematically, they screened their isogenic panel against a library of 90 anti-cancer therapeutics spanning multiple stages of clinical development, with 79% used in at least one clinical trial and targeting a broad variety of canonical cancer pathways and targets. They developed a robust screening method to quantitatively assess the impact of gene expression on drug responses. In this screen, isogenic cells expressing control vector or a gene of interest are plated separately and their relative proliferation after 72 hours of drug treatment is compared by high-content microscopy. Cell numbers from each line and treatment are compared to determine the effect size measured by the fold-change in cell number at the IC50 compared to control, and the significance of the effect over replicates. The p-value of significance was converted to a signed chemical-genetic interaction score (S) with positive values indicating that the expression of the gene drove drug resistance and negative values indicating that the gene caused drug sensitivity as compared to vector controls. The screen had a high correlation of scores across replicates (r=0.618) and an empirical false-discovery rate (FDR) of 1% and 10% at score cutoffs of approximately S=±4 and S=±2.5, respectively. The quantitative scores for 4,541 gene-drug interactions were determined, and 174 resistance interactions and 97 sensitivity interactions at S=±2 were identified, corresponding to a 10% FDR.

Construction of Directional Regulatory Networks Using Orthogonal CRISPR/Cas Screens

UCSF investigators developed an orthogonal CRISPR/Cas system which can be used to quantify gene regulation and construct directional regulatory networks. They combined two orthogonal Cas9 proteins from Streptococcus pyogenes and Staphylococcus aureus to carry out a dual screen in which one gene is activated while a second gene is deleted in the same cell.

Note: Sequencing data from CRISPRa screen and RNAseq are available at Sequence Read Archive accession number SRP127017 under BioProject ID PRJNA422995. Plasmids and their sequences are deposited at Addgene.

A Quantitative Chemical-genetic Interaction Map of Cancer Chemotherapy

CTD² researchers at UCSF-1 developed a quantitative map linking the influence of chemotherapeutic agents to tumor genetics. This chemical-genetic interaction map can aid in identifying new factors that dictate responses to chemotherapy and prioritize drug combinations.

Experimental Approaches

MCF10A cells were reverse transfected in 384-well plates (1,000 cells per well) using 5 ng of esiRNA (Sigma) with RNAiMax (0.05 mL per well) as a transfection reagent in quadruplicate. Cells were transfected for 24 hr, and then the entire plate was treated with one drug at a half maximal inhibitory concentration (IC50) or DMSO for 72 hr, after which cells were stained with Hoescht 33342 and counted using a Thermo CellInsight high-content microscope.

After drug or DMSO treatment, each plate was median centered to 2,000 cells per well to normalize relative proliferation rates. Plates had a minimum internal correlation across the 4 replicate wells of 0.7. Each well in the drug-treated plate was then compared to the same well in the DMSO-treated plate. We observed an overall linear relationship between drug and DMSO plates, indicating that most esiRNAs have no effect on drug sensitivity. Next, the set of 4 normalized replicate values in the DMSO plate was compared to the same in the drug plate, and both the fold change in cell number and the p-value of significance of this difference in medians were calculated using a modified t-test. The S score of genetic interaction is defined by the negative log10 of the t-test p-value and signed with either positive (gene loss drives resistance to drug) or negative (gene loss drives sensitivity to drug) values. False Discovery Rate (FDR) was calculated based on the percentage of negative-control knockdowns (GFP) whose score exceeded a given threshold. The described protocol is available in MATLAB, and code and raw data to recreate the dataset are available at https://github.com/BandyopadhyayLab/.

The gene expression changes in MCF10A cells with GPBP1 knockdown in the presence and absence of BMN673 are available from the Gene Expression Omnibus database (GSE101904).