Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Via F. Cervi 93, 20090 Segrate-Milan, Milan, Italy.
Laboratory of Nanomedicine and Molecular Imaging, Istituti Clinici Scientifici Maugeri IRCCS, via Maugeri 4, 27100 Pavia, Italy.
Int J Mol Sci. 2019 Nov 20;20(23):5825. doi: 10.3390/ijms20235825.
Personalized medicine relies on the integration and consideration of specific characteristics of the patient, such as tumor phenotypic and genotypic profiling.
Radiogenomics aim to integrate phenotypes from tumor imaging data with genomic data to discover genetic mechanisms underlying tumor development and phenotype.
We describe a computational approach that correlates phenotype from magnetic resonance imaging (MRI) of breast cancer (BC) lesions with microRNAs (miRNAs), mRNAs, and regulatory networks, developing a radiomiRNomic map. We validated our approach to the relationships between MRI and miRNA expression data derived from BC patients. We obtained 16 radiomic features quantifying the tumor phenotype. We integrated the features with miRNAs regulating a network of pathways specific for a distinct BC subtype.
We found six miRNAs correlated with imaging features in Luminal A (, , , , , and ), seven miRNAs (, , , , , , and ) in HER2+, and two miRNAs ( and ) in Basal subtype. We demonstrate that the combination of correlated miRNAs and imaging features have better classification power of Luminal A versus the different BC subtypes than using miRNAs or imaging alone.
Our computational approach could be used to identify new radiomiRNomic profiles of multi-omics biomarkers for BC differential diagnosis and prognosis.
个性化医学依赖于整合和考虑患者的特定特征，如肿瘤表型和基因型分析。
放射组学旨在整合肿瘤影像学数据与基因组数据中的表型，以发现肿瘤发生和表型的遗传机制。
我们描述了一种计算方法，该方法将乳腺癌（BC）病变的磁共振成像（MRI）中的表型与 microRNAs（miRNAs）、mRNAs 和调控网络相关联，从而构建一个 radiomiRNomic 图谱。我们验证了我们的方法与从 BC 患者获得的 MRI 和 miRNA 表达数据之间的关系。我们获得了 16 个定量肿瘤表型的放射组学特征。我们将这些特征与调控特定 BC 亚型特定途径网络的 miRNA 进行了整合。
我们发现了六个与 Luminal A（、、、、、和）成像特征相关的 miRNA，七个与 HER2+相关的 miRNA（、、、、、、和），以及两个与 Basal 亚型相关的 miRNA（和）。我们证明，与单独使用 miRNA 或成像特征相比，相关 miRNA 和成像特征的组合对 Luminal A 与不同 BC 亚型的分类能力更好。
我们的计算方法可用于识别 BC 多组学生物标志物的新 radiomiRNomic 图谱，用于鉴别诊断和预后。
