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BMC Cancer. 2016 Mar 4;16:184. doi: 10.1186/s12885-016-2223-3.

On Predicting lung cancer subtypes using 'omic' data from tumor and tumor-adjacent histologically-normal tissue.

Author information

1
Department of Biomedical Informatics, University of Pittsburgh School of Medicine, 5607 Baum Boulevard, 15206, Pittsburgh, PA, USA. arl68@pitt.edu.
2
Department of Biomedical Informatics, University of Pittsburgh School of Medicine, 5607 Baum Boulevard, 15206, Pittsburgh, PA, USA. hao9@pitt.edu.
3
Department of Biomedical Informatics, University of Pittsburgh School of Medicine, 5607 Baum Boulevard, 15206, Pittsburgh, PA, USA. jeya@pitt.edu.
4
Department of Computational Genomics, National Institute of Genomic Medicine, Periferico Sur No. 4809, Col. Arenal Tepepan, Tlalpan, 14610, Mexico City, Mexico. crangel@inmegen.gob.mx.
5
Department of Biomedical Informatics, University of Pittsburgh School of Medicine, 5607 Baum Boulevard, 15206, Pittsburgh, PA, USA. shv3@pitt.edu.
6
Division of Hematology/Oncology, Department of Medicine, University of Pittsburgh School of Medicine, UPMC Cancer Pavilion, 5150 Centre Avenue, 15232, Pittsburgh, PA, USA. hermanj3@upmc.edu.
7
Department of Biomedical Informatics, University of Pittsburgh School of Medicine, 5607 Baum Boulevard, 15206, Pittsburgh, PA, USA. vanathi@pitt.edu.

Abstract

BACKGROUND:

Adenocarcinoma (ADC) and squamous cell carcinoma (SCC) are the most prevalent histological types among lung cancers. Distinguishing between these subtypes is critically important because they have different implications for prognosis and treatment. Normally, histopathological analyses are used to distinguish between the two, where the tissue samples are collected based on small endoscopic samples or needle aspirations. However, the lack of cell architecture in these small tissue samples hampers the process of distinguishing between the two subtypes. Molecular profiling can also be used to discriminate between the two lung cancer subtypes, on condition that the biopsy is composed of at least 50 % of tumor cells. However, for some cases, the tissue composition of a biopsy might be a mix of tumor and tumor-adjacent histologically normal tissue (TAHN). When this happens, a new biopsy is required, with associated cost, risks and discomfort to the patient. To avoid this problem, we hypothesize that a computational method can distinguish between lung cancer subtypes given tumor and TAHN tissue.

METHODS:

Using publicly available datasets for gene expression and DNA methylation, we applied four classification tasks, depending on the possible combinations of tumor and TAHN tissue. First, we used a feature selector (ReliefF/Limma) to select relevant variables, which were then used to build a simple naïve Bayes classification model. Then, we evaluated the classification performance of our models by measuring the area under the receiver operating characteristic curve (AUC). Finally, we analyzed the relevance of the selected genes using hierarchical clustering and IPA® software for gene functional analysis.

RESULTS:

All Bayesian models achieved high classification performance (AUC > 0.94), which were confirmed by hierarchical cluster analysis. From the genes selected, 25 (93 %) were found to be related to cancer (19 were associated with ADC or SCC), confirming the biological relevance of our method.

CONCLUSIONS:

The results from this study confirm that computational methods using tumor and TAHN tissue can serve as a prognostic tool for lung cancer subtype classification. Our study complements results from other studies where TAHN tissue has been used as prognostic tool for prostate cancer. The clinical implications of this finding could greatly benefit lung cancer patients.

PMID:
26944944
PMCID:
PMC4778315
DOI:
10.1186/s12885-016-2223-3
[Indexed for MEDLINE]
Free PMC Article

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