Literature DB >> 25734557

Breast Cancer: Radiogenomic Biomarker Reveals Associations among Dynamic Contrast-enhanced MR Imaging, Long Noncoding RNA, and Metastasis.

Shota Yamamoto1, Wonshik Han, Youngwoo Kim, Liutao Du, Neema Jamshidi, Danshan Huang, Jong Hyo Kim, Michael D Kuo.   

Abstract

PURPOSE: To perform a radiogenomic analysis of women with breast cancer to study the multiscale relationships among quantitative computer vision-extracted dynamic contrast material-enhanced (DCE) magnetic resonance (MR) imaging phenotypes, early metastasis, and long noncoding RNA (lncRNA) expression determined by means of high-resolution next-generation RNA sequencing.
MATERIALS AND METHODS: In this institutional review board-approved study, an automated image analysis platform extracted 47 computational quantitative features from DCE MR imaging data in a training set (n = 19) to screen for MR imaging biomarkers indicative of poor metastasis-free survival (MFS). The lncRNA molecular landscape of the candidate feature was defined by using an RNA sequencing-specific negative binomial distribution differential expression analysis. Then, this radiogenomic biomarker was applied prospectively to a validation set (n = 42) to allow prediction of MFS and lncRNA expression by using quantitative polymerase chain reaction analysis.
RESULTS: The quantitative MR imaging feature, enhancing rim fraction score, was predictive of MFS in the training set (P = .007). RNA sequencing analysis yielded an average of 55.7 × 10(6) reads per sample and identified 14 880 lncRNAs from a background of 189 883 transcripts per sample. Radiogenomic analysis allowed identification of three previously uncharacterized and five named lncRNAs significantly associated with high enhancing rim fraction, including Homeobox transcript antisense intergenic RNA (HOTAIR) (P < .05), a known predictor of poor MFS in patients with breast cancer. Independent validation confirmed the association of the enhancing rim fraction phenotype with both MFS (P = .002) and expression of four of the top five differentially expressed lncRNAs (P < .05), including HOTAIR.
CONCLUSION: The enhancing rim fraction score, a quantitative DCE MR imaging lncRNA radiogenomic biomarker, is associated with early metastasis and expression of the known predictor of metastatic progression, HOTAIR. (©) RSNA

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Year:  2015        PMID: 25734557     DOI: 10.1148/radiol.15142698

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  42 in total

1.  Image-Guided Biopsy in the Era of Personalized Cancer Care: Proceedings from the Society of Interventional Radiology Research Consensus Panel.

Authors:  Alda L Tam; Howard J Lim; Ignacio I Wistuba; Anobel Tamrazi; Michael D Kuo; Etay Ziv; Stephen Wong; Albert J Shih; Robert J Webster; Gregory S Fischer; Sunitha Nagrath; Suzanne E Davis; Sarah B White; Kamran Ahrar
Journal:  J Vasc Interv Radiol       Date:  2015-11-25       Impact factor: 3.464

Review 2.  Towards precision medicine: from quantitative imaging to radiomics.

Authors:  U Rajendra Acharya; Yuki Hagiwara; Vidya K Sudarshan; Wai Yee Chan; Kwan Hoong Ng
Journal:  J Zhejiang Univ Sci B       Date:  2018 Jan.       Impact factor: 3.066

3.  Heterogeneous Enhancement Patterns of Tumor-adjacent Parenchyma at MR Imaging Are Associated with Dysregulated Signaling Pathways and Poor Survival in Breast Cancer.

Authors:  Jia Wu; Bailiang Li; Xiaoli Sun; Guohong Cao; Daniel L Rubin; Sandy Napel; Debra M Ikeda; Allison W Kurian; Ruijiang Li
Journal:  Radiology       Date:  2017-07-14       Impact factor: 11.105

4.  Unsupervised Clustering of Quantitative Image Phenotypes Reveals Breast Cancer Subtypes with Distinct Prognoses and Molecular Pathways.

Authors:  Jia Wu; Yi Cui; Xiaoli Sun; Guohong Cao; Bailiang Li; Debra M Ikeda; Allison W Kurian; Ruijiang Li
Journal:  Clin Cancer Res       Date:  2017-01-10       Impact factor: 12.531

5.  A predictive model for distinguishing radiation necrosis from tumour progression after gamma knife radiosurgery based on radiomic features from MR images.

Authors:  Zijian Zhang; Jinzhong Yang; Angela Ho; Wen Jiang; Jennifer Logan; Xin Wang; Paul D Brown; Susan L McGovern; Nandita Guha-Thakurta; Sherise D Ferguson; Xenia Fave; Lifei Zhang; Dennis Mackin; Laurence E Court; Jing Li
Journal:  Eur Radiol       Date:  2017-11-24       Impact factor: 5.315

6.  Radiogenomics of lower-grade glioma: algorithmically-assessed tumor shape is associated with tumor genomic subtypes and patient outcomes in a multi-institutional study with The Cancer Genome Atlas data.

Authors:  Maciej A Mazurowski; Kal Clark; Nicholas M Czarnek; Parisa Shamsesfandabadi; Katherine B Peters; Ashirbani Saha
Journal:  J Neurooncol       Date:  2017-05-03       Impact factor: 4.130

Review 7.  Imaging genomics in cancer research: limitations and promises.

Authors:  Harrison X Bai; Ashley M Lee; Li Yang; Paul Zhang; Christos Davatzikos; John M Maris; Sharon J Diskin
Journal:  Br J Radiol       Date:  2016-02-11       Impact factor: 3.039

Review 8.  Background, current role, and potential applications of radiogenomics.

Authors:  Katja Pinker; Fuki Shitano; Evis Sala; Richard K Do; Robert J Young; Andreas G Wibmer; Hedvig Hricak; Elizabeth J Sutton; Elizabeth A Morris
Journal:  J Magn Reson Imaging       Date:  2017-11-02       Impact factor: 4.813

9.  Predicting Breast Cancer Molecular Subtype with MRI Dataset Utilizing Convolutional Neural Network Algorithm.

Authors:  Richard Ha; Simukayi Mutasa; Jenika Karcich; Nishant Gupta; Eduardo Pascual Van Sant; John Nemer; Mary Sun; Peter Chang; Michael Z Liu; Sachin Jambawalikar
Journal:  J Digit Imaging       Date:  2019-04       Impact factor: 4.056

10.  Using computer-extracted image phenotypes from tumors on breast magnetic resonance imaging to predict breast cancer pathologic stage.

Authors:  Elizabeth S Burnside; Karen Drukker; Hui Li; Ermelinda Bonaccio; Margarita Zuley; Marie Ganott; Jose M Net; Elizabeth J Sutton; Kathleen R Brandt; Gary J Whitman; Suzanne D Conzen; Li Lan; Yuan Ji; Yitan Zhu; Carl C Jaffe; Erich P Huang; John B Freymann; Justin S Kirby; Elizabeth A Morris; Maryellen L Giger
Journal:  Cancer       Date:  2015-11-30       Impact factor: 6.860

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