supplementary_data

SUPPLEMENTARY DATASupplementary Data 1. Splicing event prior knowledge: known cancer driver events (from the hand-curated list and AS Cancer Atlas), gene pan-essentiality, and known cancer driver genes from COSMIC’s Cancer Gene Census.Supplementary Data 2. Genes in ReactomeDB pathways annotated on the basis of being a known cancer driver gene listed in COSMIC-CGC, a gene that contains a known cancer driver exon, a pan-essential gene, or a gene that contains a potential cancer driver exon.Supplementary Data 3. Demeter2 gene dependencies.Supplementary Data 4. CCLE gene expression as TPM.Supplementary Data 5. CCLE exon inclusion as PSI.Supplementary Data 6. CCLE cell line metadata.Supplementary Data 7. Summaries of fitted models of splicing dependency.Supplementary Data 8. ROC evaluation of LR p-value predictive power to re-discover known cancer driver exons, known mutational cancer driver genes and pan-essential genes. Supplementary Data 9. Mapping of shRNAs to Gencode v44 human transcriptome and to VastDB exons.Supplementary Data 10. Simulation of PSI uncertainty.Supplementary Data 11. Evaluation of uncertainty in predicted splicing dependencies.Supplementary Data 12. Summary statistics of Demeter2 gene dependencies. Supplementary Data 13. Splicing dependency model evaluation of p-value thresholds.Supplementary Data 14. Splicing dependency model evaluation of Pearson correlation thresholds.Supplementary Data 15. Gene set overlap analysis of genes bearing at least one potential cancer driver exonSupplementary Data 16. Predicted splicing dependencies in CCLE cell lines.Supplementary Data 17. Summary statistics of predicted splicing dependencies in CCLE cell lines.Supplementary Data 18. Gene mutation frequency in CCLE cell lines.Supplementary Data 19. Differential analysis of maximum harm score profiles between recurrently mutated splicing factors and their wild-type state.Supplementary Data 20. Processed exon-level CRISPR-screen from Thomas et al.18.Supplementary Data 21. Predicted changes in cell proliferation with splicing dependency models in Thomas et al.1.Supplementary Data 22. Processed exon-level CRISPR-screen from Gonatopoulos-Pournatzis et al.2.Supplementary Data 23. Predicted changes in cell proliferation with splicing dependency models from Gonatopoulos-Pournatzis et al.2.Supplementary Data 24. ENCORE exon inclusion as PSI.Supplementary Data 25. ENCORE gene expression as TPM.Supplementary Data 26. ENCORE sample metadata.Supplementary Data 27. Predicted changes in cell proliferation with splicing dependency models in the ENCORE dataset.Supplementary Data 28. Evaluation of splicing dependency models at predicting the effects of perturbing multiple exons simultaneously on cell proliferation.Supplementary Data 29. Differential splicing analysis for protein-impacting exons (VastDB annotation) in primary tumors compared with solid tissue normal samples across 13 different types of cancer from the TCGA.Supplementary Data 30. Differential splicing analysis for protein-impacting exons (VastDB annotation) in primary tumors compared with solid tissue normal samples across 7 different subtypes of cancer from the TCGA.Supplementary Data 31. Predicted changes in cell proliferation with splicing dependency models in the cancer cell lines used for experimental validation of our selection of cancer driver exons with therapeutic potential.Supplementary Data 32. Measured cancer cell clonogenic density upon treatment with SSOs.Supplementary Data 33. Measured fluorescence of the 3 cancer cell lines cultured for 96h.Supplementary Data 34. Preprocessed IC50 for drugs in GDSC screens.Supplementary Data 35. Known drug–gene targets.Supplementary Data 36. Summaries of fitted drug–exon interaction models.Supplementary Data 37. Evaluation of indirect drug-exon associations occurring in the pathway of the drug target according to ReactomeDB.Supplementary Data 38. Shortest path lengths from drug–exon association to closest drug target using STRINGDB protein-protein interaction network.Supplementary Data 39. Predicted drug sensitivity (log-IC50) across the CCLE.Supplementary Data 40. Splicing dependency analysis for Pietilä et al.4.Supplementary Data 41. Predicted drug sensitivity (log-IC50) in Pietilä et al.4.Supplementary Data 42. ROC analysis for Pietilä et al.4.<br>REFERENCES1. Thomas, J. D. et al. RNA isoform screens uncover the essentiality and tumor-suppressor activity of ultraconserved poison exons. Nat. Genet. 52, 84–94 (2020).2. Gonatopoulos-Pournatzis, T. et al. Genetic interaction mapping and exon-resolution functional genomics with a hybrid Cas9–Cas12a platform. Nat. Biotechnol. 38, 638–648 (2020).3. Van Nostrand, E. L. et al. A large-scale binding and functional map of human RNA-binding proteins. Nature 583, 711–719 (2020).4. Pietilä, E. A. et al. Co-evolution of matrisome and adaptive adhesion dynamics drives ovarian cancer chemoresistance. Nat. Commun. 12, 3904 (2021).<br>