MR Imaging Features of Retinoblastoma: Association with Gene Expression Profiles

作者：Jansen Robin W; de Jong Marcus C; Kooi Irsan E; Sirin Selma; Goericke Sophia; Brisse Herve J; Maeder Philippe; Galluzzi Paolo; van der Valk Paul; Cloos Jacqueline; Eekhout Iris; Castelijns Jonas A; Moll Annette C; Dorsman Josephine C; de Graaf Pim

来源：Radiology, 2018, 288(2): 506-515.

DOI：10.1148/radiol.2018172000

Purpose: To identify associations between magnetic resonance (MR) imaging features and gene expression in retinoblastoma.
Materials and Methods: A retinoblastoma MR imaging atlas was validated by using anonymized MR images from referral centers in Essen, Germany, and Paris, France. Images were from 39 patients with retinoblastoma (16 male and 18 female patients [the sex in five patients was unknown]; age range, 5-90 months; inclusion criterion: pretreatment MR imaging). This atlas was used to compare MR imaging features with genome-wide messenger RNA (mRNA) expression data from 60 consecutive patients obtained from 1995 to 2012 (35 male patients [58%]; age range, 2-69 months; inclusion criteria: pretreatment MR imaging, genome-wide mRNA expression data available). Imaging pathway associations were analyzed by means of gene enrichment. In addition, imaging features were compared with a predefined gene expression signature of photoreceptorness. Statistical analysis was performed with generalized linear modeling of radiology traits on normalized log2-transformed expression values. P values were corrected for multiple hypothesis testing.
Results: Radiogenomic analysis revealed 1336 differentially expressed genes for qualitative imaging features (threshold P = .05 after multiple hypothesis correction). Loss of photoreceptorness gene expression correlated with advanced stage imaging features, including multiple lesions (P = .03) and greater eye size (P<.001). The number of lesions on MR images was associated with expression of MYCN (P = .04). A newly defined radiophenotype of diffuse-growing, plaque-shaped, multifocal tumors displayed overexpression of SERTAD3 (P = .003, P = .049, and P = .06, respectively), a protein that stimulates cell growth by activating the E2F network.
Conclusion: Radiogenomic biomarkers can potentially help predict molecular features, such as photoreceptorness loss, that indicate tumor progression. Results imply a possible role for radiogenomics in future staging and treatment decision making in retinoblastoma.

• 出版日期2018-8