Oncoprotein-specific molecular interaction maps (SigMaps) for cancer network analyses

Joshua Broyde; David R. Simpson; Diana Murray; Evan O. Paull; Brennan W. Chu; Somnath Tagore; Sunny J. Jones; Aaron T. Griffin; Federico M. Giorgi; Alexander Lachmann; Peter Jackson; E. Alejandro Sweet-Cordero; Barry Honig; Andrea Califano

doi:10.1038/s41587-020-0652-7

Oncoprotein-specific molecular interaction maps (SigMaps) for cancer network analyses

Joshua Broyde, David R. Simpson, Diana Murray, Evan O. Paull, Brennan W. Chu, Somnath Tagore, Sunny J. Jones, Aaron T. Griffin, Federico M. Giorgi, Alexander Lachmann, Peter Jackson, E. Alejandro Sweet-Cordero, Barry Honig, Andrea Califano

Research output: Contribution to journal › Article › peer-review

19 Scopus citations

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.

Original language	English
Pages (from-to)	215-224
Number of pages	10
Journal	Nature Biotechnology
Volume	39
Issue number	2
DOIs	https://doi.org/10.1038/s41587-020-0652-7
State	Published - Feb 2021
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2020, The Author(s), under exclusive licence to Springer Nature America, Inc.

Funding

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.

Funders	Funder number
National Institutes of Health	S10OD021764, T32CA09302
National Cancer Institute	U54CA209997, R01CA129562, R35CA197745, U01CA168426
National Institute of General Medical Sciences	T32GM082797, R01GM30518
Stand Up To Cancer	S10OD012351

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Broyde, J., Simpson, D. R., Murray, D., Paull, E. O., Chu, B. W., Tagore, S., Jones, S. J., Griffin, A. T., Giorgi, F. M., Lachmann, A., Jackson, P., Sweet-Cordero, E. A., Honig, B., & Califano, A. (2021). Oncoprotein-specific molecular interaction maps (SigMaps) for cancer network analyses. Nature Biotechnology, 39(2), 215-224. https://doi.org/10.1038/s41587-020-0652-7

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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{\textquoteright}s regulatory and signaling architecture is highly tissue specific.",

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doi = "10.1038/s41587-020-0652-7",

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AU - Sweet-Cordero, E. Alejandro

AU - Honig, Barry

AU - Califano, Andrea

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AB - 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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Oncoprotein-specific molecular interaction maps (SigMaps) for cancer network analyses

Abstract

Bibliographical note

Funding

UN SDGs

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