<i>In Silico</i> Approach for the Definition of radiomiRNomic Signatures for Breast Cancer Differential Diagnosis

• Main Authors: Francesca Gallivanone, Claudia Cava, Fabio Corsi, Gloria Bertoli, Isabella Castiglioni
• Format: Article
• Language: English
• Published: MDPI AG 2019-11-01
• Series: International Journal of Molecular Sciences
• Subjects: radiogenomics; radiomirnomics; breast cancer; magnetic resonance imaging; mri; micrornas/mirnas; pathways; network
• Online Access: https://www.mdpi.com/1422-0067/20/23/5825

Personalized medicine relies on the integration and consideration of specific characteristics of the patient, such as tumor phenotypic and genotypic profiling. Background: Radiogenomics aim to integrate phenotypes from tumor imaging data with genomic data to discover genetic mechanisms underlying tumor development and phenotype. Methods: 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. Results: We found six miRNAs correlated with imaging features in Luminal A (<i>miR-1537</i>, <i>-205</i>, <i>-335</i>, <i>-337</i>, <i>-452</i>, and <i>-99a</i>), seven miRNAs (<i>miR-142</i>, <i>-155</i>, <i>-190</i>, <i>-190b</i>, <i>-1910</i>, <i>-3617</i>, and <i>-429</i>) in HER2+, and two miRNAs (<i>miR-135b</i> and <i>-365-2</i>) 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. Conclusion: Our computational approach could be used to identify new radiomiRNomic profiles of multi-omics biomarkers for BC differential diagnosis and prognosis.