Abstract
Tumor-specific elucidation of physical and functional oncoprotein interactions could improve tumorigenic mechanism characterization and therapeutic response prediction. Current interaction models and pathways, however, lack context specificity and are not oncoprotein specific. We introduce SigMaps as context-specific networks, comprising modulators, effectors and cognate binding-partners of a specific oncoprotein. SigMaps are reconstructed de novo by integrating diverse evidence sources—including protein structure, gene expression and mutational profiles—via the OncoSig machine learning framework. We first generated a KRAS-specific SigMap for lung adenocarcinoma, which recapitulated published KRAS biology, identified novel synthetic lethal proteins that were experimentally validated in three-dimensional spheroid models and established uncharacterized crosstalk with RAB/RHO. To show that OncoSig is generalizable, we first inferred SigMaps for the ten most mutated human oncoproteins and then for the full repertoire of 715 proteins in the COSMIC Cancer Gene Census. Taken together, these SigMaps show that the cell’s regulatory and signaling architecture is highly tissue specific.
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Data availability
All data are provided in Supplementary Files. Source data are provided with this paper.
Code availability
R scripts and code for Naive Bayes and Random Forest classifiers and input data files to reproduce the results described are freely available at https://github.com/califano-lab/OncoSig.
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Acknowledgements
This work was supported by the National Cancer Institute (NCI) Outstanding Investigator Award R35CA197745 to A.C.; the NCI Cancer Target Discovery and Development Program U01CA168426 to A.C.; the NCI Research Centers for Cancer Systems Biology Consortium U54CA209997 to A.C. and B.H.; National Institute of General Medical Sciences grant R01GM30518 to B.H.; NCI grant R01CA129562 to E.A.S.C.; Innovative Research Grant from Stand Up to Cancer to E.A.S.C.; National Institutes of Health High-End Instrumentation Program grant S10OD012351 to A.C.; and NIH Shared Instrumentation Program grant S10OD021764 to A.C. J.B. was supported, in part, by the Ruth L. Kirschstein National Research Service Award Institutional Research Training Grant T32GM082797. D.R.S. was supported by the Ruth L. Kirschstein National Research Service Award Institutional Research Training Grant T32CA09302. Relevant ethical compliance was ensured by the Institutional Review Board of the Human Research Protection Program at the University of California, San Francisco.
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J.B., D.M., A.T.G., A.L. and F.M.G. performed computational analysis. J.B. performed machine learning. D.S. and P.K.J. performed, respectively, the knockdown and AP–MS experiments. E.O.P, B.W.C. and S.T. created oncosig.org. J.B., D.M. and S.J.J. compiled the codebook. J.B., D.S., E.A.S.C., A.C. and B.H. analyzed knockdown experiments. S.D.M., B.H., A.C. and E.A.S.C. designed research. D.M., B.H. and A.C. designed computational and experimental work and analyzed data. D.M., D.S., J.B., A.C. and B.H. assembled the data and wrote the paper.
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A.C. is the founder and an equity holder in DarwinHealth, a company that has licensed some of the algorithms used in this study from Columbia University. Columbia University is also an equity holder in DarwinHealth.
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Extended data
Extended Data Fig. 1 The top 40 predictions from the OncoSigNB algorithm for the KRAS LUAD SigMap were chosen for validation.
a, Performance of OncoSigNB at recovering the Ingenuity-derived positive gold standard set (PGSS) as a function of LRPost. At an LRPost = 53 (probability = 0.50) (vertical gray line), the OncoSigNB LUAD-specific KRAS SigMap contains 10% of the PGSS (horizontal gray line). The vertical red line corresponds to LRPost = 240, the cutoff used to obtain candidates for experimental validation. b, ROC curve analysis, evaluated as the recovery of the Ingenuity-derived PGSS (FPR ≤ 0.05), for 1) OncoSigNB (green curve, N = 1028)), 2) Pearson’s correlation between mRNA expression of KRAS and mRNA expression of other proteins in LUAD (blue curve), and 3) random performance (black curve). Recovery using 2-fold cross-validation (green) is essentially indistinguishable from recovery using 100-fold Monte-Carlo Cross-validation (not shown). 393 OncoSigNB LUAD-specific KRAS SigMap predictions are made for LRPost ≥ 53, which corresponds to probability ≥ 0.50 and FPR ≤ 0.019 (purple dot). 40 OncoSigNB LUAD-specific KRAS SigMap predictions are made for LRPost ≥ 240, which corresponds to probability ≥ 0.82 and FPR ≤ 0.0018 (yellow dot). The top 40 predictions are listed by gene name in (c). c, Orange and blue boxes contain, respectively, known upstream regulators and downstream effectors that are successfully recovered by OncoSigNB. Italicized text indicates proteins known to interact with KRAS via a physical protein-protein interaction. The box titled “validated predictions” shows the novel OncoSigNB predictions tested with the RNAi negative screen; those that were experimentally found to affect cell growth in a KRAS-dependent context are highlighted in bold text.
Extended Data Fig. 2 The OncoSigRF and OncoSigNB algorithms produce highly similar KRAS LUAD SigMaps.
a, Comparison of ROC curves (FPR ≤ 0.05) for LUAD-specific KRAS SigMaps predicted by OncoSigNB (green and blue curves) and OncoSigRF (orange and red curve) trained on the Ingenuity PGSS and the MSigDB PGSS, respectively. b, Gene Set Enrichment Analysis (GSEA) of the top 100 OncoSigNB LUAD-specific KRAS SigMap predictions at the top of the OncoSigRF LUAD-specific KRAS SigMap predictions. Ranking is based on OncoSigRF score (SRF). Both the OncoSigNB predictions tested in the knockdown experiments (red lines) and the remaining top 100 OncoSigNB predictions (blue lines) are highly enriched at the top of the OncoSigRF predictions (p = 5.6 ×10−8 and p = 1.7 ×10-19, respectively).
Extended Data Fig. 3 The log2FC of shRNA abundance is plotted against the novel proteins tested in the KRAS negative selection screen.
The 3–5 points plotted for a given protein are shRNAs that target the mRNA for that protein (N = 100). The X-axis is sorted by mean log2FC for all shRNAs targeting each gene. Colors change from red to green with mean log2FC.
Extended Data Fig. 4 OncoSigRF predictions are highly enriched in oncogenic KRASMut dependencies.
a, GSEA of KRASMut synthetic lethal partners25 (blue lines, N = 216) and the top 500 OncoSigRF LUAD-specific KRAS SigMap predictions obtained by training on a modified PGSS for which the intersection with the synthetic lethal set was removed. Inset is the GSEA using all OncoSigRF predictions obtained in this way, where the ranking is OncoSigRF score. Enrichment analysis was performed with the aREA (analytic Rank-based Enrichment Analysis) algorithm10. b, Enrichment of the protein resistance-signature to ERK inhibitor SCH77298428 (blue lines, N = 24) within OncoSigRF LUAD-specific KRAS SigMap predictions10. c, Enrichment of proteins involved in response to Reactive Oxygen Species (GO:0000302)8,29 (blue lines, N = 276) within OncoSigRF LUAD-specific KRAS SigMap predictions10.
Extended Data Fig. 5 OncoSigRF KRAS SigMaps exhibit tissue context specificity.
a, ROC curves for the OncoSigRF KRAS SigMaps in LUAD (red), LUSC (gray) COAD (brown), and PAAD (orange) for FPR ≤ 0.05. Performance is evaluated as the recovery of established KRAS pathway proteins. b, Gene set enrichment analysis (GSEA) of KRASMut synthetic lethal partners, as determined by Corcoran et al27 (N = 48, blue lines). To avoid training and testing on the same proteins, OncoSigRF predictions for COAD-specific KRAS SigMap proteins were obtained by training on a modified PGSS from which any established KRASMut synthetic lethal protein had been previously removed. Enrichment analysis was performed with the aREA (analytic Rank-based Enrichment Analysis) algorithm10. c, Scatterplot of OncoSigRF scores for KRAS SigMap proteins in PAAD-vs-LUAD (N = 19,789). Each dot represents the scores for one protein. Darker colored points have high scores (SRF ≥ 0.5) in at least one context, and lighter colored points score poorly in both contexts (SRF ≤ 0.5). R2PAAD/LUAD = 0.037. d, OncoSigRF COAD-specific KRAS SigMap in the form depicted conceptually in Fig. 1a. To prevent visual cluttering, only the top 33 OncoSigRF predictions (FPR ≤ 0.01) that are also VIPER-inferred KRAS interactors (p ≤ 0.01), PrePPI-predicted KRAS physical interactors, or both, are depicted. Bold and regular text node labels represent established and novel predictions, respectively; orange and blue node colors represent upstream regulators and downstream effectors, respectively; red, blue, and black node borders represent predictions that are druggable (Drug Repurposing Hub22), KRASMut synthetic lethal from the literature and validated here (see text), and both, respectively; orange and blue solid lines and gray nodes represent PrePPI-predicted physical interactors of KRAS.
Extended Data Fig. 6 OncoSigRF SigMaps for hypermutated oncoproteins are retrospectively validated.
a, Pairwise overlap of established pathway proteins (left) and the OncoSigRF LUAD-specific SigMaps (FPR ≤ 0.01, right) for the ten hyper-mutated oncoproteins (names of columns and rows). Percent overlap is color-coded according to the scale at top. b, SigMap predictions are highly enriched in 600 EGFR-centric network proteins52 (p = 2.3 ×10−43). Enrichment analysis was performed with the aREA (analytic Rank-based Enrichment Analysis) algorithm10. c, Box plots of the OncoSigRF LUAD-specific EGFR SigMap scores for two subsets of the curated EGFR pathway proteins from Astsaturov et al:52 those identified as EGFR synthetic lethal partners (red, N = 58) and those not identified as synthetic lethal (grey, N = 542). The p-value (2 ×10−4) was calculated using Welch’s two sample t-test.
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Broyde, J., Simpson, D.R., Murray, D. et al. Oncoprotein-specific molecular interaction maps (SigMaps) for cancer network analyses. Nat Biotechnol 39, 215–224 (2021). https://doi.org/10.1038/s41587-020-0652-7
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DOI: https://doi.org/10.1038/s41587-020-0652-7
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