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QSurface: fast identification of surface expression markers in cancers.

ABSTRACT: BACKGROUND:Cell surface proteins have provided useful targets and biomarkers for advanced cancer therapies. The recent clinical success of antibody-drug conjugates (ADCs) highlights the importance of finding selective surface antigens for given cancer subtypes. We thus attempted to develop stand-alone software for the analysis of the cell surface transcriptome of patient cancer samples and to prioritize lineage- and/or mutation-specific over-expression markers in cancer cells. RESULTS:A total of 519 genes were selected as surface proteins, and their expression was profiled in 14 cancer subtypes using patient sample transcriptome data. Lineage/mutation-oriented analysis was used to identify subtype-specific surface markers with statistical confidence. Experimental validation confirmed the unique over-expression of predicted surface markers (MUC4, MSLN, and SLC7A11) in lung cancer cells at the protein level. The differential cell surface gene expression of cell lines may differ from that of tissue samples due to the absence of the tumor microenvironment. CONCLUSIONS:In the present study, advanced 3D models of lung cell lines successfully reproduced the predicted patterns, demonstrating the physiological relevance of cell line-based 3D models in validating surface markers from patient tumor data. Also QSurface software is freely available at http://compbio.sookmyung.ac.kr/~qsurface .

SUBMITTER: Hong Y

PROVIDER: S-EPMC5861488 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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QSurface: fast identification of surface expression markers in cancers.

Hong Yourae Y Park Choa C Kim Nayoung N Cho Juyeon J Moon Sung Ung SU Kim Jongmin J Jeong Euna E Yoon Sukjoon S

BMC systems biology 20180319 Suppl 2

<h4>Background</h4>Cell surface proteins have provided useful targets and biomarkers for advanced cancer therapies. The recent clinical success of antibody-drug conjugates (ADCs) highlights the importance of finding selective surface antigens for given cancer subtypes. We thus attempted to develop stand-alone software for the analysis of the cell surface transcriptome of patient cancer samples and to prioritize lineage- and/or mutation-specific over-expression markers in cancer cells.<h4>Results ...[more]

PMID: 29560830

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Project description:(This section will be revisited in good time prior to publication, for final approval) Research within the field of functional genomics provides a more profound understanding of living organisms, by elucidating the interaction between genes and gene products. This is of increasing importance in many fields, and especially within cancer research. With the use of microarray technology, the gene expression of a vast number of genes can be measured simultaneously. This allows us insight into tumor biology, and an opportunity to obtain knowledge of diagnostic and prognostic value, as well as hints towards the causes of cancer. In this study the gene expression in a selection of human neuroendocrine (NE) and non-neuroendocrine tumor cell lines were compared. Both qualitative and quantitative methods were applied to get an insight into the NE tumor biology. NE cells are found in practically every organ and tissue in the body and are assigned to synthesize peptide hormones and take up amino acids and transform these into biogenic amines by carboxylation. NE tumors arise after malignant transformation of neuroendocrine cells and are usually highly differentiated and have a low growth rate, which makes this a high prevalence tumor type. These rather rare tumors characteristically express NE markers, such as chromogranin A, and contains characteristic dense-secretory core granules. By using cDNA microarray analysis, the gene expression profiles of various NE tumor cell lines (e. g. carcinoid, neuroblastoma, medullary thyroid carcinoma and endocrine pancreatic tumor) were compared with those of non-NE tumor cells. As a reference the commercially available Universal human RNA reference (Stratagene) was used. Verification of the results were done by real time RT-PCR analysis after the NE nature of the cell lines was stated by immunohistochemical, electron microscopic and light microscopic examinations. A large number of genes were differentially expressed in NE cell lines vs. the non-NE cell lines, and were found to be of interest in NE tumor biology. Some of these genes were previously known in NE tumor biology, whereas the vast majority of the specified genes are novel in the context of NE tumor biology. Genes with little previous information were also identified and thought to be an asset in the search of new NE markers. A feasible prospective of the study would be expansion with additional samples cell lines and a more thorough verification with real time RT-PCR analysis. This would enhance the credibility and thus the utility of the findings. Future studies on the biological functions of the candidate genes and the mechanisms of their regulation, will hopefully enable suggestions of new NE markers of prognostic or diagnostic value, and eventually new drug targets. Keywords: neuroendocrine, neuroendocrine markers, gene expression profiling, cDNA microarray The hybridisation was performed by an experienced laboratory technician Eli Helge, (Dept. of Laboratory Medicine, Children`s and Women`s health, NTNU) at the microarray core facility. Three separate runs with 10, 11 and 3 slides was needed to obtain the 20 hybridisations (dyeswap x 10 cell lines) planned for this study. Of the 10 cell lines 6 were neuroendocrine and 4 were non-neuroendocrine. For comparison between these two groups the universal human reference RNA (Stratagene) was used as the "control" on each slide. An adapted version of the 3DNA Array 350 protocol (Genisphere) was used. The slides were scanned with a ScanArrayTMExpressHT (Packard BioScience) twice immediately after the hybridisation was completed to avoid photo bleaching of the fluorescent dyes.

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QSurface: fast identification of surface expression markers in cancers.

Publications

QSurface: fast identification of surface expression markers in cancers.

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