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Deciphering Genomic Underpinnings of Quantitative MRI-Based Radiomic Phenotypes of Invasive Breast Carcinoma

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Y Zhu

Y Zhu1*, H Li2 , W Guo3 , K Drukker2 , S Yang1 , L Lan2 , M Giger2& , Y Ji1,2& for the TCIA Breast Cancer Group, (1) NorthShore University HealthSystem, Evanston, IL, (2) University of Chicago, Chicago, IL, (3) Fudan University, Shanghai, China


TU-CD-BRB-6 (Tuesday, July 14, 2015) 10:15 AM - 12:15 PM Room: Ballroom B

Purpose: Magnetic Resonance Imaging(MRI) has been routinely used for diagnosis and assessment of breast cancer. Despite its wide applications in clinical practice, the relationship between the observed tumor MRI phenotypes and the genomic mechanism of tumorigenesis remains under-explored, largely due to lack of data on both imaging and genomics for the same tumors.

Methods: We combined data from The Cancer Genome Atlas(TCGA) and The Cancer Image Archive(TCIA), that included quantitatively extracted MRI phenotypes of 91 breast invasive carcinomas and their multi-layer genomic data. Gene set enrichment analysis and regression analysis were performed to identify associations between tumor MRI radiomic phenotypes and various genomic and molecular subtypes of tumors. Patient groups defined by radiomic phenotypes and genomic platforms were also associated with tumor pathological stages and molecular receptor status using Fisher's exact test.

Results: Significant associations (adjusted p-values ≤ 0.1) were identified between radiomic phenotypes (characterizing tumor size, shape, margin, enhancement texture, and blood flow kinetics) and genomic features involved in multiple molecular regulation layers (including pathway gene expressions, pathway copy number variations, gene somatic mutations, miRNA expressions, and protein expressions). Transcriptional activities of various genetic pathways were dominantly positively associated with tumor size and blurred tumor margin. miRNA activity significantly associated with tumor size and enhancement textures, but not with phenotypes describing tumor shape, margin, and blood flow kinetics. Patient groups defined by radiomic phenotypes were associated with tumor T stage and overall stage (p-values ≤ 0.072). Genomic platforms defined patient groups associated with the status of progesterone and estrogen receptors (p-values ≤ 0.0000427) and pathological stages (p-values ≤ 0.056).

Conclusion: We present these findings as a resource shedding insight on the connection between underlying genetic mechanisms and observed tumor radiomic phenotypes, which forms a basis for future studies using non-invasive MRI techniques for accurate cancer diagnosis and prognosis.

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