Author of

• 11503

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  P2.03 - Poster Session 2 - Technology and Novel Development (ID 151)

  • Event: WCLC 2013
  • Type: Poster Session
  • Track: Biology
  • Presentations: 1
  • Moderators:
  • Coordinates: 10/29/2013, 09:30 - 16:30, Exhibit Hall, Ground Level

  • 11504

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    P2.03-001 - Gene Expression Profile of A549 Cells from Tissue of 4D Model Predicts Poor Prognosis in Lung Cancer Patients (ID 104)

    09:30 - 09:30  |  Author(s): Y. Zhang
    • Abstract

    Background
    Tumor microenvironment plays an important role in regulating cell growth and metastasis. Recently, we developed an ex vivo lung cancer model (4D) that forms perfusable tumor nodules on tissue that mimics human lung cancer histopathology and protease secretion patterns better than tumor cells grown on a petri dish (2D). In this study, we aim to determine if the gene expression profile of cells grown in the model (4D) is a better predictor of survival in lung cancer patients compared to the gene expression profile of cells grown on a matrigel (3D).

    Methods
    We compared the gene expression profile (Human OneArray v5 chip) of A549 cells, human lung cancer cell line, grown on petri dish (2D) against the same cells grown in the tissue of our ex vivo model (4D) and performed gene ontology (GO) analysis. We obtained gene expression data of A549 cells grown on petri dish (2D) and matrigel (3D) from GEO Accession No. GSE17347 and compared the gene expression profiles. We analyzed the differential gene signature for 4D and 3D in human lung cancer database for survival.

    Results
    Expression array analysis showed that there were 2954 gene probes differentially expressed between 2D and 4D. The analysis showed up-regulation of genes associated with mesenchymal cells (CDH2 and VIM). GO analysis showed up-regulation of several genes associated with extracellular matrix (MMP1, MMP9, MMP10, COL4A1, COL5A1), polarity (DLX2, GLI2, HOXD10, HOXD11), and cell fate and development (PPM1A, SALL1, SOX4, ZEB2, JAG1, SOX2, TP63). Moreover, expression array analysis of the 2D and 3D showed 1006 genes that were differentially expressed, with only 36 genes (4%) having the same expression pattern as differential expression between 2D and 4D. There was no difference in expression of genes associated with mesenchymal features (CDH2 and VIM) between 2D and 3D. Finally, the differential gene expression signature between 2D and 4D correlated with poor survival in patients with lung cancer (n = 1492, Log rank p = 1.5 x 10[-7]) while the differential gene expression signature between 2D and 3D correlated with good survival in patients with lung cancer (n = 1492, Log rank p = 1.7 x 10[-9]).

    Conclusion
    The genes necessary to form a perfusable nodule in the 4D model are the genes that are important in cell-matrix interaction, polarity and cell fate, which are lacking in a 2D model. The gene expression signature in the 4D model correlates with poor survival in lung cancer patients, which may be due to presence of more cells with mesenchymal features in the 4D model compared to 2D culture. On the other hand, the tumor cells grown on 3D model show the genes important in tumor cell interaction with matrix without difference in genes identify cells with mesenchymal features. Thus, there may be fewer cells with invasive properties, which may explain the good survival in lung cancer patients. The 4D ex vivo lung cancer model may be a better mimic of the natural progression of tumor growth in lung cancer patients compared to 3D model.