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    Tumour heterogeneity revealed by unsupervised decomposition of dynamic contrast-enhanced magnetic resonance imaging is associated with underlying gene expression patterns and poor survival in breast cancer patients

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    Type
    Dataset
    Authors
    Fan, Ming
    Xia, Pingping
    Liu, Bin
    Zhang, Lin
    Wang, Yue
    Gao, Xin cc
    Li, Lihua
    KAUST Department
    Computational Bioscience Research Center (CBRC)
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Structural and Functional Bioinformatics Group
    Date
    2019
    Permanent link to this record
    http://hdl.handle.net/10754/665109
    
    Metadata
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    Abstract
    Abstract Background Heterogeneity is a common finding within tumours. We evaluated the imaging features of tumours based on the decomposition of tumoural dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data to identify their prognostic value for breast cancer survival and to explore their biological importance. Methods Imaging features (n = 14), such as texture, histogram distribution and morphological features, were extracted to determine their associations with recurrence-free survival (RFS) in patients in the training cohort (n = 61) from The Cancer Imaging Archive (TCIA). The prognostic value of the features was evaluated in an independent dataset of 173 patients (i.e. the reproducibility cohort) from the TCIA I-SPY 1 TRIAL dataset. Radiogenomic analysis was performed in an additional cohort, the radiogenomic cohort (n = 87), using DCE-MRI from TCGA-BRCA and corresponding gene expression data from The Cancer Genome Atlas (TCGA). The MRI tumour area was decomposed by convex analysis of mixtures (CAM), resulting in 3 components that represent plasma input, fast-flow kinetics and slow-flow kinetics. The prognostic MRI features were associated with the gene expression module in which the pathway was analysed. Furthermore, a multigene signature for each prognostic imaging feature was built, and the prognostic value for RFS and overall survival (OS) was confirmed in an additional cohort from TCGA. Results Three image features (i.e. the maximum probability from the precontrast MR series, the median value from the second postcontrast series and the overall tumour volume) were independently correlated with RFS (p values of 0.0018, 0.0036 and 0.0032, respectively). The maximum probability feature from the fast-flow kinetics subregion was also significantly associated with RFS and OS in the reproducibility cohort. Additionally, this feature had a high correlation with the gene expression module (r = 0.59), and the pathway analysis showed that Ras signalling, a breast cancer-related pathway, was significantly enriched (corrected p value = 0.0044). Gene signatures (n = 43) associated with the maximum probability feature were assessed for associations with RFS (p = 0.035) and OS (p = 0.027) in an independent dataset containing 1010 gene expression samples. Among the 43 gene signatures, Ras signalling was also significantly enriched. Conclusions Dynamic pattern deconvolution revealed that tumour heterogeneity was associated with poor survival and cancer-related pathways in breast cancer.
    Citation
    Fan, M., Pingping Xia, Liu, B., Zhang, L., Wang, Y., Gao, X., & Lihua Li. (2019). Tumour heterogeneity revealed by unsupervised decomposition of dynamic contrast-enhanced magnetic resonance imaging is associated with underlying gene expression patterns and poor survival in breast cancer patients. figshare. https://doi.org/10.6084/M9.FIGSHARE.C.4703822
    Publisher
    figshare
    DOI
    10.6084/m9.figshare.c.4703822
    Relations
    Is Supplement To:
    • [Article]
      Fan, M., Xia, P., Liu, B., Zhang, L., Wang, Y., Gao, X., & Li, L. (2019). Tumour heterogeneity revealed by unsupervised decomposition of dynamic contrast-enhanced magnetic resonance imaging is associated with underlying gene expression patterns and poor survival in breast cancer patients. Breast Cancer Research, 21(1). doi:10.1186/s13058-019-1199-8. DOI: 10.1186/s13058-019-1199-8 HANDLE: 10754/659544
    ae974a485f413a2113503eed53cd6c53
    10.6084/m9.figshare.c.4703822
    Scopus Count
    Collections
    Structural and Functional Bioinformatics Group; Computer Science Program; Computational Bioscience Research Center (CBRC); Datasets; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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