In Silico Approach for the Definition of radiomiRNomic Signatures for Breast Cancer Differential Diagnosis

Francesca Gallivanone, Claudia Cava, Fabio Corsi, Gloria Bertoli, Isabella Castiglioni, Francesca Gallivanone, Claudia Cava, Fabio Corsi, Gloria Bertoli, Isabella Castiglioni

Abstract

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 (miR-1537, -205, -335, -337, -452, and -99a), seven miRNAs (miR-142, -155, -190, -190b, -1910, -3617, and -429) in HER2+, and two miRNAs (miR-135b and -365-2) 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.

Keywords: MRI; RadiomiRNomics; breast cancer; magnetic resonance imaging; microRNAs/miRNAs; network; pathways; radiogenomics.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Breast cancer (BC) Luminal A. (A) The pathways able to classify Luminal A vs. normal samples (p-value < 0.01, |logFoldChange (FC)| > 1). For each pathway (in different colours), miRNAs (indicated in red) able to regulate differentially expressed genes (DEGs) are indicated. (B) Relationships between pathways and miRNAs (green nodes, pathways; purple nodes, miRNAs). (C) Heatmap of the correlation between imaging features and miRNAs. The color intensity in the figure shows the corresponding p-value; yellow cells indicate greater statistical significance.
Figure 2
Figure 2
BC Luminal B. (A) Pathways able to classify Luminal B vs. normal samples (p-value < 0.01, |logFC| > 1). For each pathway (in different colours), miRNAs (in red) able to regulate DEGs are indicated. (B) Relationships between pathways and miRNAs (green nodes are the pathways and purple nodes are the miRNAs). (C) Heatmap of the correlation between imaging features and miRNAs. The color intensity in the figure shows the corresponding p-value; yellow cells indicate greater statistical significance.
Figure 3
Figure 3
BC HER2+. (A) The pathways able to classify HER2+ BC vs. normal samples (p-value < 0.01, |logFC| > 1). For each pathway (indicated in different colours), miRNAs (in red) able to regulate DEGs are indicated. (B) Relationships between pathways and miRNAs (the green nodes are the pathways and purple nodes the miRNAs). (C) Heatmap of the correlation between imaging features and miRNAs. The color intensity in the figure shows the corresponding p-value; yellow cells indicate greater statistical significance.
Figure 4
Figure 4
BC Basal. (A) The pathways able to classify basal BC vs. normal samples (p-value < 0.01, |logFC| > 1). For each pathway (in different colours), miRNAs (in red) able to regulate DEGs are indicated. (B) Relationships between pathways and miRNAs (the green nodes are the pathways and purple nodes are the miRNAs). (C) Heatmap of the correlation between imaging features and miRNAs. The color intensity in the figure shows the corresponding p-value; yellow cells indicate greater statistical significance.
Figure 5
Figure 5
Relative expression of miR-99a (A), miR-135b (B) and miR-155 (C) in Luminal A vs. all the other BC subtypes (Luminal B, Basal, HER2+) (n = 9) (*** t-test, p-value < 0.001).
Figure 6
Figure 6
Workflow of the proposed approach.

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