Genetic dependencies associated with transcription factor activities in human cancer cell lines [SLAM-seq]

Transcription factors (TFs) are important mediators of aberrant transcriptional programs in cancer cells. In this study, we focused on TF activity (TFa) as a new biomarker for cell line-selective anti-proliferative effects, in that high TFa predicts sensitivity to loss-of-function of a given gene (i.e., genetic dependencies (GD)). Our linear regression-based framework identified 3,043 pan-cancer and 3,952 cancer-type-specific candidate TFa-GD associations from cell line data, which were then cross-examined for impact on survival in patient data. One of the most prominent biomarkers was TEAD1 activity, whose associations with its predicted GDs were validated through experimental evidence as proof of concept. Overall, these TFa-GD pairs represent an attractive resource for identifying innovative, biomarker-driven hypotheses for drug discovery programs in oncology. Overall design: To investigate the immediate transcriptional effects induced by the pharmacological modulation with the pan-TEAD VT107 inhibitor and the ITGAV-targeting antibody, Intetumumab, we performed thiol(SH)-linked alkylation for the metabolic sequencing of RNA (SLAM-seq) in the liver carcinoma cell lines: SNU-761. VT107 treatment was added at 0.1 uM and 2.15 uM concentrations and the respective amount of DMSO for samples to compare against. Samples were prepared in duplicates. Intetumumab Biosimilar – Anti-ITGAV_ITGB3 mAb (Prote Genix PX-TA1230) was added at 1.5 µg/ml and 5 µg/ml concentrations. We used Isotype IgG1, Kappa from human myeloma plasma (Sigma -Aldrich I5154) as a control. For the Intetumumab treatment group, samples were prepared in triplicates. Samples were collected at 4h after treatment. Samples were prepared for SLAM-seq using the protocol from Muhar et al., 2018. De novo transcript and data processing was performed with the SLAM DUNK pipeline (Neumann et al. 2019).