Dataset Information

Deep Phospho- and Phosphotyrosine Proteomics Identified Active Kinases and Phosphorylation Networks in Colorectal Cancer Cell Lines Resistant to Cetuximab.

ABSTRACT: Abnormality in cellular phosphorylation is closely related to oncogenesis. Thus, kinase inhibitors, especially tyrosine kinase inhibitors (TKIs), have been developed as anti-cancer drugs. Genomic analyses have been used in research on TKI sensitivity, but some types of TKI resistance have been unclassifiable by genomic data. Therefore, global proteomic analysis, especially phosphotyrosine (pY) proteomic analysis, could contribute to predict TKI sensitivity and overcome TKI-resistant cancer. In this study, we conducted deep phosphoproteomic analysis to select active kinase candidates in colorectal cancer intrinsically resistant to Cetuximab. The deep phosphoproteomic data were obtained by performing immobilized metal-ion affinity chromatography-based phosphoproteomic and highly sensitive pY proteomic analyses. Comparison between sensitive (LIM1215 and DLD1) and resistant cell lines (HCT116 and HT29) revealed active kinase candidates in the latter, most of which were identified by pY proteomic analysis. Remarkably, genomic mutations were not assigned in most of these kinases. Phosphorylation-based signaling network analysis of the active kinase candidates indicated that SRC-PRKCD cascade was constitutively activated in HCT116 cells. Treatment with an SRC inhibitor significantly inhibited proliferation of HCT116 cells. In summary, our results based on deep phosphoproteomic data led us to propose novel therapeutic targets against cetuximab resistance and showed the potential for anti-cancer therapy.

SUBMITTER: Abe Y

PROVIDER: S-EPMC5585238 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Publications

Deep Phospho- and Phosphotyrosine Proteomics Identified Active Kinases and Phosphorylation Networks in Colorectal Cancer Cell Lines Resistant to Cetuximab.

Abe Yuichi Y Nagano Maiko M Kuga Takahisa T Tada Asa A Isoyama Junko J Adachi Jun J Tomonaga Takeshi T

Scientific reports 20170905 1

Abnormality in cellular phosphorylation is closely related to oncogenesis. Thus, kinase inhibitors, especially tyrosine kinase inhibitors (TKIs), have been developed as anti-cancer drugs. Genomic analyses have been used in research on TKI sensitivity, but some types of TKI resistance have been unclassifiable by genomic data. Therefore, global proteomic analysis, especially phosphotyrosine (pY) proteomic analysis, could contribute to predict TKI sensitivity and overcome TKI-resistant cancer. In t ...[more]

PMID: 28874695

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Project description:Although short tandem repeat (STR) analysis is available as a reliable method for the determination of the genetic origin of cell lines, the occurrence of misauthenticated cell lines remains an important issue. Reasons include the cost, effort and time associated with STR analysis. Moreover, there are currently no methods for the discrimination between isogenic cell lines (cell lines of the same genetic origin, e.g. different cell lines derived from the same organism, clonal sublines, sublines adapted to grow under certain conditions). Hence, additional complementary, ideally low-cost and low-effort methods are required that enable (1) the monitoring of cell line identity as part of the daily laboratory routine and 2) the authentication of isogenic cell lines. In this research, we automate the process of cell line identification by image-based analysis using deep convolutional neural networks. Two different convolutional neural networks models (MobileNet and InceptionResNet V2) were trained to automatically identify four parental cancer cell line (COLO 704, EFO-21, EFO-27 and UKF-NB-3) and their sublines adapted to the anti-cancer drugs cisplatin (COLO-704rCDDP1000, EFO-21rCDDP2000, EFO-27rCDDP2000) or oxaliplatin (UKF-NB-3rOXALI2000), hence resulting in an eight-class problem. Our best performing model, InceptionResNet V2, achieved an average of 0.91 F1-score on tenfold cross validation with an average area under the curve (AUC) of 0.95, for the 8-class problem. Our best model also achieved an average F1-score of 0.94 and 0.96 on the authentication through a classification process of the four parental cell lines and the respective drug-adapted cells, respectively, on a four-class problem separately. These findings provide the basis for further development of the application of deep learning for the automation of cell line authentication into a readily available easy-to-use methodology that enables routine monitoring of the identity of cell lines including isogenic cell lines. It should be noted that, this is just a proof of principal that, images can also be used as a method for authentication of cancer cell lines and not a replacement for the STR method.

Dataset Information

Deep Phospho- and Phosphotyrosine Proteomics Identified Active Kinases and Phosphorylation Networks in Colorectal Cancer Cell Lines Resistant to Cetuximab.

Publications

Deep Phospho- and Phosphotyrosine Proteomics Identified Active Kinases and Phosphorylation Networks in Colorectal Cancer Cell Lines Resistant to Cetuximab.

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