Dataset Information

Oncogenic mutations within the β3-αC loop of EGFR/ERBB2/BRAF/MAP2K1 predict response to therapies.

ABSTRACT:

Background

β3-αC loop is a highly conserved structural domain across oncogene families, which is a switch for kinase activity. There have been numerous researches on mutations within β3-αC loop in EGFR, but relatively less in ERBB2, BRAF, and MAP2K1. In addition, previous studies mainly focus on β3-αC deletion in EGFR, which is the most common type affecting kinase activity and driving lung cancer. Other mutation types are not well studied.

Methods

Here we analyzed the profile of β3-αC loop mutations in a total of 10,000 tumor biopsy and/or ctDNA patient samples using hybridization capture-based next-generation sequencing.

Results

We identified 1616 mutations within β3-αC loop in this cohort. Most mutations were located in EGFR, with less percentage in ERBB2, BRAF, and MAP2K1. EGFR β3-αC deletions occurred at a high percentage of 96.7% and were all drug-relevant. We also detected rare EGFR β3-αC insertions and point mutations, most of which were related to EGFR TKIs resistance. ERBB2 β3-αC deletions were only found in breast cancers and sensitive to EGFR/ERBB2 inhibitor. Moreover, BRAF and MAP2K1 mutations within β3-αC loop also demonstrated drugs relevance.

Conclusion

Our study showed that oncogenic mutations within the β3-αC loop of ERBB2, MAP2K1, and BRAF are analogous to that of EGFR, which have profound effect on drug response. Understanding the mutation profile within the β3-αC loop is critical for targeted therapies.

SUBMITTER: Zhang B

PROVIDER: S-EPMC7549570 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Publications

Oncogenic mutations within the β3-αC loop of EGFR/ERBB2/BRAF/MAP2K1 predict response to therapies.

Zhang Biao B Chen Yongsheng Y Dai Pingping P Yu Haoda H Ma Jianhui J Chen Chen C Zhang Yan Y Guan Yanfang Y Chen Rongrong R Liu Tao T Wang Jiayin J Yang Ling L Yi Xin X Xia Xuefeng X Ma Haitao H

Molecular genetics & genomic medicine 20200805 10

<h4>Background</h4>β3-αC loop is a highly conserved structural domain across oncogene families, which is a switch for kinase activity. There have been numerous researches on mutations within β3-αC loop in EGFR, but relatively less in ERBB2, BRAF, and MAP2K1. In addition, previous studies mainly focus on β3-αC deletion in EGFR, which is the most common type affecting kinase activity and driving lung cancer. Other mutation types are not well studied.<h4>Methods</h4>Here we analyzed the profile of ...[more]

PMID: 32757330

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Project description:In this study we selected three breast cancer cell lines (SKBR3, SUM149 and SUM190) with different oncogene expression levels involved in ERBB2 and EGFR signaling pathways as a model system for the evaluation of selective integration of subsets of transcriptomic and proteomic data. We assessed the oncogene status with reads per kilobase per million mapped reads (RPKM) values for ERBB2 (14.4, 400, and 300 for SUM149, SUM190, and SKBR3, respectively) and for EGFR (60.1, not detected, and 1.4 for the same 3 cell lines). We then used RNA-Seq data to identify those oncogenes with significant transcript levels in these cell lines (total 31) and interrogated the corresponding proteomics data sets for proteins with significant interaction values with these oncogenes. The number of observed interactors for each oncogene showed a significant range, e.g., 4.2% (JAK1) to 27.3% (MYC). The percentage is measured as a fraction of the total protein interactions in a given data set vs total interactors for that oncogene in STRING (Search Tool for the Retrieval of Interacting Genes/Proteins, version 9.0) and I2D (Interologous Interaction Database, version 1.95). This approach allowed us to focus on 4 main oncogenes, ERBB2, EGFR, MYC, and GRB2, for pathway analysis. We used bioinformatics sites GeneGo, PathwayCommons and NCI receptor signaling networks to identify pathways that contained the four main oncogenes and had good coverage in the transcriptomic and proteomic data sets as well as a significant number of oncogene interactors. The four pathways identified were ERBB signaling, EGFR1 signaling, integrin outside-in signaling, and validated targets of C-MYC transcriptional activation. The greater dynamic range of the RNA-Seq values allowed the use of transcript ratios to correlate observed protein values with the relative levels of the ERBB2 and EGFR transcripts in each of the four pathways. This provided us with potential proteomic signatures for the SUM149 and 190 cell lines, growth factor receptor-bound protein 7 (GRB7), Crk-like protein (CRKL) and Catenin delta-1 (CTNND1) for ERBB signaling; caveolin 1 (CAV1), plectin (PLEC) for EGFR signaling; filamin A (FLNA) and actinin alpha1 (ACTN1) (associated with high levels of EGFR transcript) for integrin signalings; branched chain amino-acid transaminase 1 (BCAT1), carbamoyl-phosphate synthetase (CAD), nucleolin (NCL) (high levels of EGFR transcript); transferrin receptor (TFRC), metadherin (MTDH) (high levels of ERBB2 transcript) for MYC signaling; S100-A2 protein (S100A2), caveolin 1 (CAV1), Serpin B5 (SERPINB5), stratifin (SFN), PYD and CARD domain containing (PYCARD), and EPH receptor A2 (EPHA2) for PI3K signaling, p53 subpathway. Future studies of inflammatory breast cancer (IBC), from which the cell lines were derived, will be used to explore the significance of these observations.

Dataset Information

Oncogenic mutations within the β3-αC loop of EGFR/ERBB2/BRAF/MAP2K1 predict response to therapies.

Background

Methods

Results

Conclusion

Publications

Oncogenic mutations within the β3-αC loop of EGFR/ERBB2/BRAF/MAP2K1 predict response to therapies.

Similar Datasets

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