Data from: Covalent disruptor of YAP-TEAD association suppresses defective Hippo signaling
Fan, Mengyang et al. (2022), Data from: Covalent disruptor of YAP-TEAD association suppresses defective Hippo signaling, Dryad, Dataset, https://doi.org/10.5061/dryad.rxwdbrvbn
The transcription factor TEAD, together with its coactivator YAP/TAZ, is a key transcriptional modulator of the Hippo pathway. Activation of TEAD transcription by YAP has been implicated in a number of malignancies, and this complex represents a promising target for drug discovery. Here, we employed covalent fragment screening approach followed by structure-based design to develop an irreversible TEAD inhibitor MYF-03-69. Using a range of in vitro and cell-based assays we demonstrated that through a covalent binding with TEAD palmitate pocket, MYF-03-69 disrupts YAP-TEAD association, suppresses TEAD transcriptional activity and inhibits cell growth of Hippo signaling defective malignant pleural mesothelioma (MPM). Further, a cell viability screening with a panel of 903 cancer cell lines indicated a high correlation between TEAD-YAP dependency and the sensitivity to MYF-03-69.
To validate MYF-03-69 as potent and selective pan-TEAD inhibitor, we interrogated the proteome-wide selectivity profile of MYF-03-69 on cysteine labeling using a streamlined cysteine activity-based protein profiling (SLC-ABPP) approach and generated the spreadsheet "Supplementary_Dataset_1._Proteome-wide_selectivity_profile_of_MYF-03-69_on_cysteines_labeling_using_SLC-ABPP_approach". We employed the cysteine reactive desthiobiotin iodoacetamide (DBIA) probe which was reported to map more than 8,000 cysteines and performed a competition study on NCI-H226 cells pretreated with 0.5, 2, 10 or 25 µM of MYF-03-69 for 3 hours in triplicate. The cysteines that were conjugated >50% (competition ratio CR>2) compared to DMSO control were analyzed and assigned to the protein targets. In the DMSO control group, although DBIA mapped 12,498 cysteines in total, the TEAD PBP cysteines were not detected. This might be due to low TEAD1-4 protein abundance and/or inability of the PBP cysteines to be labeled given that they are mostly modified by palmitate under physiological conditions. Among 12,498 mapped cysteines, only 7 cysteines were significantly labeled (i.e. exhibited >50% conjugation or CR>2) by 25 µM of MYF-03-69, and all of these sites exhibited dose-dependent engagement.
To study the whole transcriptome perturbation by TEAD inhibitor MYF-03-69, mRNA sequencing was performed in NCI-H226 cells that were treated with 0.1 μM, 0.5 μM, and 2 μM of MYF-03-69 and generated the spreadsheet "Supplementary_Dataset_2._List_of_differentially_expressed_genes_under_MYF-03-69_treatments". The genes that were differentially expressed with statistical significance (Fold change > 1.5 and adjusted p value < 0.05) are listed in this dataset.
To investigate whether TEAD inhibition by MYF-03-69 was selectively lethal to YAP/TEAD-dependent cancers, PRISM screening across a broad panel of cell lineages were performed and generated the spreadsheet "Supplementary_Dataset_3". 903 cancer cells were treated with TEAD inhibitor MYF-03-69 for 5 days. The viability values were measured at 8-point dose manner (3-fold dilution from 10 μM) and fitted a dose-response curve for each cell line. Area under the curve (AUC) was calculated as a measurement of compound effect on cell viability. CERES score of YAP1 or TEADs from CRISPR (Avana) Public 21Q1 dataset (DepMap) were listed in the spreadsheet and used to estimate gene-dependency. The CERES Score of most dependent TEAD isoform was used to represent TEAD dependency. With PRISM screen dataset of TEAD inhibitor MYF-03-69, we investigated whether TEAD inhibition recapulates genetically knockout outcome of YAP or TEADs and generated the spreadsheet "Supplementary_Dataset_4". Correlation analysis between compound PRISM sensitivity (log2.AUC of each cell line) and dependency of certain gene (CRISPR knockout score for each cell line, from DepMap Public 20Q4 Achilles_gene_effect.csv dataset) across the PRISM cell line panel. The Pearson correlation coefficients and associated p-values were computed. Positive correlations correspond to dependency correlating with increased sensitivity. The q-values (a corrected significance value accounting for false discovery rate) are computed from p-values using the Benjamini Hochberg algorithm. Associations with q-values above 0.1 are filtered out. This correlation analysis reveals that the dependency scores of TEAD1 and YAP1 according to genomic knockout dataset (DepMap portal) provided the highest correlation with the compound PRISM sensitivity profile. This is followed by TP53BP2, a gene that is also involved in Hippo pathway as activator of TAZ.
For "Supplementary_Dataset_1._Proteome-wide_selectivity_profile_of_MYF-03-69_on_cysteines_labeling_using_SLC-ABPP_approach", the date was collected on NCI-H226 cells using the same methods reported in reference paper Reimagining high-throughput profiling of reactive cysteines for cell-based screening of large electrophile libraries | Nature Biotechnology. (Kuljanin, M.; Mitchell, D. C.; Schweppe, D. K.; Gikandi, A. S.; Nusinow, D. P.; Bulloch, N. J.; Vinogradova, E. V.; Wilson, D. L.; Kool, E. T.; Mancias, J. D.; Cravatt, B. F.; Gygi, S. P., Reimagining high-throughput profiling of reactive cysteines for cell-based screening of large electrophile libraries. Nature Biotechnology 2021, 39, 630-641) The competition ratio CR was calculated as descibed in the above reference paper.
For "Supplementary_Dataset_2._List_of_differentially_expressed_genes_under_MYF-03-69_treatments", the date was collected on NCI-H226 cells treated with MYF-03-69 at indicated concentrations for 6 hours (n=3). The RNA was extracted using RNeasy plus mini kit (Qiagen, cat no.74134) according to the manufacturer instructions. Then libraries were prepared using Roche Kapa mRNA HyperPrep strand specific sample preparation kits from 200 ng of purified total RNA according to the manufacturer’s protocol on a Beckman Coulter Biomek i7. The finished dsDNA libraries were quantified by Qubit fluorometer and Agilent TapeStation 4200. Uniquely dual indexed libraries were pooled in an equimolar ratio and shallowly sequenced on an Illumina MiSeq to further evaluate library quality and pool balance. The final pool was sequenced on an Illumina NovaSeq 6000 targeting 40 million 100bp read pairs per library at the Dana-Farber Cancer Institute Molecular Biology Core Facilities. Sequenced reads were aligned to the UCSC hg19 reference genome assembly and gene counts were quantified using STAR (v2.7.3a). Differential gene expression testing was performed by DESeq2 (v1.22.1). RNAseq analysis was performed using the VIPER snakemake pipeline. KEGG pathway enrichment analysis was performed through metascape webportal.
For "Supplementary_Dataset_3", the date was collected using the methods reported in reference paper Discovering the anticancer potential of non-oncology drugs by systematic viability profiling | Nature Cancer. Briefly, up to 931 barcoded cell lines in pools of 20-25 were thawed and plated into 384-well plates (1250 cells/well for adherent cell pools, 2000 cells/well for suspension or mixed suspension/adherent cell pools) containing compound (top concentration: 10 µM, 8-point, threefold dilution). All conditions were tested in triplicate. Cells were lysed after 5 days of treatment and mRNA based Luminex detection of barcode abundance from lysates was carried out as in the reference paper above. Luminex median fluorescence intensity (MFI) data was input to a standardized R pipeline (https://github.com/broadinstitute/prism_data_processing) to generate viability estimates relative to vehicle treatment for each cell line and treatment condition, and to fit dose-response curves from viability data. CERES score of YAP1 or TEADs from CRISPR (Avana) Public 21Q1 dataset (DepMap) were downloaded from DepMap portal (DepMap Data Downloads) and listed with the viability data.
For "Supplementary_Dataset_4", the data was correlation analysis results of "Supplementary_Dataset_3", which was performed in the R pipeline mentioned above (https://github.com/broadinstitute/prism_data_processing).
Dana-Farber Cancer Institute