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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/24386

Title: Gene Expression Signature Differentiates Histology But Not Progression Status of Early-Stage NSCLC
Authors: Charkiewicz, Radoslaw
Niklinski, Jacek
Claesen, Jurgen
Sulewska, Anetta
Kozlowski, Miroslaw
Michalska-Falkowska, Anna
Reszec, Joanna
Moniuszko, Marcin
Naumnik, Wojciech
Niklinska, Wieslawa
Issue Date: 2017
Publisher: ELSEVIER SCIENCE INC
Citation: TRANSLATIONAL ONCOLOGY, 10(3), p. 450-458
Abstract: Advances in molecular analyses based on high-throughput technologies can contribute to a more accurate classification of non-small cell lung cancer (NSCLC), as well as a better prediction of both the disease course and the efficacy of targeted therapies. Here we set out to analyze whether global gene expression profiling performed in a group of early-stage NSCLC patients can contribute to classifying tumor subtypes and predicting the disease prognosis. Gene expression profiling was performed with the use of the microarray technology in a training set of 108 NSCLC samples. Subsequently, the recorded findings were validated further in an independent cohort of 44 samples. We demonstrated that the specific gene patterns differed significantly between lung adenocarcinoma (AC) and squamous cell lung carcinoma (SCC) samples. Furthermore, we developed and validated a novel 53-gene signature distinguishing SCC from AC with 93% accuracy. Evaluation of the classifier performance in the validation set showed that our predictor classified the AC patients with 100% sensitivity and 88% specificity. We revealed that gene expression patterns observed in the early stages of NSCLC may help elucidate the histological distinctions of tumors through identification of different gene-mediated biological processes involved in the pathogenesis of histologically distinct tumors. However, we showed here that the gene expression profiles did not provide additional value in predicting the progression status of the early-stage NSCLC. Nevertheless, the gene expression signature analysis enabled us to perform a reliable subclassification of NSCLC tumors, and it can therefore become a useful diagnostic tool for a more accurate selection of patients for targeted therapies.
Notes: [Charkiewicz, Radoslaw; Niklinski, Jacek; Sulewska, Anetta; Michalska-Falkowska, Anna; Naumnik, Wojciech] Med Univ Bialystok, Dept Clin Mol Biol, Waszyngtona 13, PL-15269 Bialystok, Poland. [Claesen, Jurgen] Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat, B-3590 Diepenbeek, Belgium. [Kozlowski, Miroslaw] Med Univ Bialystok, Dept Thorac Surg, Marii Sklodowskiej Curie 24a, PL-15276 Bialystok, Poland. [Reszec, Joanna] Med Univ Bialystok, Dept Med Pathomorphol, Waszyngtona 13, PL-15269 Bialystok, Poland. [Moniuszko, Marcin] Med Univ Bialystok, Dept Regenerat Med & Immune Regulat, Waszyngtona 13, PL-15269 Bialystok, Poland. [Naumnik, Wojciech] Med Univ Bialystok, Dept Lung Dis 1, Zurawia 14, PL-15540 Bialystok, Poland. [Niklinska, Wieslawa] Med Univ Bialystok, Dept Histol & Embryol, Waszyngtona 13, PL-15269 Bialystok, Poland.
URI: http://hdl.handle.net/1942/24386
DOI: 10.1016/j.tranon.2017.01.015
ISI #: 000407707600018
ISSN: 1944-7124
Category: A1
Type: Journal Contribution
Appears in Collections: Research publications

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