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

Arturo López Pineda, Henry Ato Ogoe, Jeya Balaji Balasubramanian, Claudia Rangel Escareño, Shyam Visweswaran, James Gordon Herman, Vanathi Gopalakrishnan

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Article number184
JournalBMC Cancer
Volume16
Issue number1
DOIs
StatePublished - Mar 4 2016
Externally publishedYes

Keywords

  • Adenocarcinoma of Lung
  • Bayes Theorem
  • DNA Methylation
  • Squamous Cell Carcinoma

ASJC Scopus subject areas

  • Oncology
  • Cancer Research
  • Genetics

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    Pineda, A. L., Ogoe, H. A., Balasubramanian, J. B., Rangel Escareño, C., Visweswaran, S., Herman, J. G., & Gopalakrishnan, V. (2016). On Predicting lung cancer subtypes using 'omic' data from tumor and tumor-adjacent histologically-normal tissue. BMC Cancer, 16(1), [184]. https://doi.org/10.1186/s12885-016-2223-3