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. 2018 Jun 29;9:2546. doi: 10.1038/s41467-018-04647-1

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© The Author(s) 2018

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 3 — New experiments to test ISLE-based predictions on growth inhibition and drug combinations. a, b cSL-based prediction of growth inhibition in a knockdown screen in oral cancer. a Growth rate prediction: The number of downregulated cSL partners of a gene (X-axis; cSL-score) is associated with the percentage of growth inhibition observed after its knockdown (Y-axis; quantified as percent-growth inhibition compared to control, mean and s.e.m). Each bin of cSL-score shows significant differences (FDR-corrected t-test P < 0.2*, see Supplementary Note 1). b cSL-based context-specific prediction of growth inhibition in hypoxic vs normoxic conditions. The fold change of cSL-score (X-axis in logscale) in normoxia vs. hypoxia shows a positive correlation with the corresponding growth inhibition fold change (Y-axis in logscale), correctly predicting the differentially observed growth inhibition in more than 71% of the 38 cases observed experimentally (marked as red dots in the 1st and 3rd quadrants). c, d The figures depict the representative dose response curves (see Supplementary Fig. 7B, C for other cell lines) of the predicted synergistic drug combinations of c ABT263 (BCL2L1 inhibitor) and the OSI906 (IGF1R inhibitor) and d GDC0941 (PIK3CA inhibitor) and the MK2206 (AKT1 inhibitor). The percentage of cell line survival (Y-axis) was measured at varying doses of OSI906 (respectively MK2206), with and without ABT263 (respectively GDC0941) treatment at 5uM (X-axis). The dashed lines denote the percentage of cell line survival at varying levels of OSI906 (MK2206) without ABT263 (GDC0941) treatments, and the solid lines denote the percentage of cell line survival at varying levels of OSI906 (MK2206) in the presence of 5 μM of ABT263 (GDC0941). The combined drug treatments are significantly more effective than the single treatments based on the analysis of variance (P < 3.17E-11 (c) and P < 2.71E-8 (d)). The Fa–CI curve for all drug combinations are in Supplementary Fig. 7D, and the full experimental measurements are presented in Supplementary Data 11, 12