eVIP2: Expression-based variant impact phenotyping to predict the function of gene variants.

While advancements in genome sequencing have identified millions of somatic mutations in cancer, their functional impact is poorly understood. We previously developed the expression-based variant impact phenotyping (eVIP) method to use gene expression data to characterize the function of gene varian...

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Autores principales:	Alexis M Thornton, Lishan Fang, April Lo, Maria McSharry, David Haan, Casey O'Brien, Alice H Berger, Marios Giannakis, Angela N Brooks
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/dd3122e631b94c459cfeb06f2dc62a6e
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spelling	oai:doaj.org-article:dd3122e631b94c459cfeb06f2dc62a6e2021-12-02T19:57:34ZeVIP2: Expression-based variant impact phenotyping to predict the function of gene variants.1553-734X1553-735810.1371/journal.pcbi.1009132https://doaj.org/article/dd3122e631b94c459cfeb06f2dc62a6e2021-07-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009132https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358While advancements in genome sequencing have identified millions of somatic mutations in cancer, their functional impact is poorly understood. We previously developed the expression-based variant impact phenotyping (eVIP) method to use gene expression data to characterize the function of gene variants. The eVIP method uses a decision tree-based algorithm to predict the functional impact of somatic variants by comparing gene expression signatures induced by introduction of wild-type (WT) versus mutant cDNAs in cell lines. The method distinguishes between variants that are gain-of-function, loss-of-function, change-of-function, or neutral. We present eVIP2, software that allows for pathway analysis (eVIP Pathways) and usage with RNA-seq data. To demonstrate the eVIP2 software and approach, we characterized two recurrent frameshift variants in RNF43, a negative regulator of Wnt signaling, frequently mutated in colorectal, gastric, and endometrial cancer. RNF43 WT, RNF43 R117fs, RNF43 G659fs, or GFP control cDNA were overexpressed in HEK293T cells. Analysis with eVIP2 predicted that the frameshift at position 117 was a loss-of-function mutation, as expected. The second frameshift at position 659 has been previously described as a passenger mutation that maintains the RNF43 WT function as a negative regulator of Wnt. Surprisingly, eVIP2 predicted G659fs to be a change-of-function mutation. Additional eVIP Pathways analysis of RNF43 G659fs predicted 10 pathways to be significantly altered, including TNF-α via NFκB signaling, KRAS signaling, and hypoxia, highlighting the benefit of a more comprehensive approach when determining the impact of gene variant function. To validate these predictions, we performed reporter assays and found that each pathway activated by expression of RNF43 G659fs, but not expression of RNF43 WT, was identified as impacted by eVIP2, supporting that RNF43 G659fs is a change-of-function mutation and its effect on the identified pathways. Pathway activation was further validated by Western blot analysis. Lastly, we show primary colon adenocarcinoma patient samples with R117fs and G659fs variants have transcriptional profiles similar to BRAF missense mutations with activated RAS/MAPK signaling, consistent with KRAS signaling pathways being GOF in both variants. The eVIP2 method is an important step towards overcoming the current challenge of variant interpretation in the implementation of precision medicine. eVIP2 is available at https://github.com/BrooksLabUCSC/eVIP2.Alexis M ThorntonLishan FangApril LoMaria McSharryDavid HaanCasey O'BrienAlice H BergerMarios GiannakisAngela N BrooksPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 7, p e1009132 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Alexis M Thornton Lishan Fang April Lo Maria McSharry David Haan Casey O'Brien Alice H Berger Marios Giannakis Angela N Brooks eVIP2: Expression-based variant impact phenotyping to predict the function of gene variants.
description	While advancements in genome sequencing have identified millions of somatic mutations in cancer, their functional impact is poorly understood. We previously developed the expression-based variant impact phenotyping (eVIP) method to use gene expression data to characterize the function of gene variants. The eVIP method uses a decision tree-based algorithm to predict the functional impact of somatic variants by comparing gene expression signatures induced by introduction of wild-type (WT) versus mutant cDNAs in cell lines. The method distinguishes between variants that are gain-of-function, loss-of-function, change-of-function, or neutral. We present eVIP2, software that allows for pathway analysis (eVIP Pathways) and usage with RNA-seq data. To demonstrate the eVIP2 software and approach, we characterized two recurrent frameshift variants in RNF43, a negative regulator of Wnt signaling, frequently mutated in colorectal, gastric, and endometrial cancer. RNF43 WT, RNF43 R117fs, RNF43 G659fs, or GFP control cDNA were overexpressed in HEK293T cells. Analysis with eVIP2 predicted that the frameshift at position 117 was a loss-of-function mutation, as expected. The second frameshift at position 659 has been previously described as a passenger mutation that maintains the RNF43 WT function as a negative regulator of Wnt. Surprisingly, eVIP2 predicted G659fs to be a change-of-function mutation. Additional eVIP Pathways analysis of RNF43 G659fs predicted 10 pathways to be significantly altered, including TNF-α via NFκB signaling, KRAS signaling, and hypoxia, highlighting the benefit of a more comprehensive approach when determining the impact of gene variant function. To validate these predictions, we performed reporter assays and found that each pathway activated by expression of RNF43 G659fs, but not expression of RNF43 WT, was identified as impacted by eVIP2, supporting that RNF43 G659fs is a change-of-function mutation and its effect on the identified pathways. Pathway activation was further validated by Western blot analysis. Lastly, we show primary colon adenocarcinoma patient samples with R117fs and G659fs variants have transcriptional profiles similar to BRAF missense mutations with activated RAS/MAPK signaling, consistent with KRAS signaling pathways being GOF in both variants. The eVIP2 method is an important step towards overcoming the current challenge of variant interpretation in the implementation of precision medicine. eVIP2 is available at https://github.com/BrooksLabUCSC/eVIP2.
format	article
author	Alexis M Thornton Lishan Fang April Lo Maria McSharry David Haan Casey O'Brien Alice H Berger Marios Giannakis Angela N Brooks
author_facet	Alexis M Thornton Lishan Fang April Lo Maria McSharry David Haan Casey O'Brien Alice H Berger Marios Giannakis Angela N Brooks
author_sort	Alexis M Thornton
title	eVIP2: Expression-based variant impact phenotyping to predict the function of gene variants.
title_short	eVIP2: Expression-based variant impact phenotyping to predict the function of gene variants.
title_full	eVIP2: Expression-based variant impact phenotyping to predict the function of gene variants.
title_fullStr	eVIP2: Expression-based variant impact phenotyping to predict the function of gene variants.
title_full_unstemmed	eVIP2: Expression-based variant impact phenotyping to predict the function of gene variants.
title_sort	evip2: expression-based variant impact phenotyping to predict the function of gene variants.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/dd3122e631b94c459cfeb06f2dc62a6e
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eVIP2: Expression-based variant impact phenotyping to predict the function of gene variants.

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