MRI-based breast cancer radiogenomics using RNA profiling: association with subtypes in a single-center prospective study

  • Ah Young Park
  • , Mi Ryung Han
  • , Bo Kyoung Seo*
  • , Hye Yeon Ju
  • , Gil Soo Son
  • , Hye Yoon Lee
  • , Young Woo Chang
  • , Jungyoon Choi
  • , Kyu Ran Cho
  • , Sung Eun Song
  • , Ok Hee Woo
  • , Hyun Soo Park
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Background: There are few prospective studies on the correlations between MRI features and whole RNA-sequencing data in breast cancer according to molecular subtypes. The purpose of our study was to explore the association between genetic profiles and MRI phenotypes of breast cancer and to identify imaging markers that influences the prognosis and treatment according to subtypes. Methods: From June 2017 to August 2018, MRIs of 95 women with invasive breast cancer were prospectively analyzed, using the breast imaging-reporting and data system and texture analysis. Whole RNA obtained from surgical specimens was analyzed using next-generation sequencing. The association between MRI features and gene expression profiles was analyzed in the entire tumor and subtypes. Gene networks, enriched functions, and canonical pathways were analyzed using Ingenuity Pathway Analysis. The P value for differential expression was obtained using a parametric F test comparing nested linear models and adjusted for multiple testing by reporting Q value. Results: In 95 participants (mean age, 53 years ± 11 [standard deviation]), mass lesion type was associated with upregulation of CCL3L1 (sevenfold) and irregular mass shape was associated with downregulation of MIR421 (sixfold). In estrogen receptor-positive cancer with mass lesion type, CCL3L1 (21-fold), SNHG12 (11-fold), and MIR206 (sevenfold) were upregulated, and MIR597 (265-fold), MIR126 (12-fold), and SOX17 (fivefold) were downregulated. In triple-negative breast cancer with increased standard deviation of texture analysis on precontrast T1-weighted imaging, CLEC3A (23-fold), SRGN (13-fold), HSPG2 (sevenfold), KMT2D (fivefold), and VMP1 (fivefold) were upregulated, and IGLC2 (73-fold) and PRDX4 (sevenfold) were downregulated (all, P < 0.05 and Q < 0.1). Gene network and functional analysis showed that mass type estrogen receptor-positive cancers were associated with cell growth, anti-estrogen resistance, and poor survival. Conclusion: MRI characteristics are associated with the different expressions of genes related to metastasis, anti-drug resistance, and prognosis, depending on the molecular subtypes of breast cancer.

Original languageEnglish
Article number79
JournalBreast Cancer Research
Volume25
Issue number1
DOIs
Publication statusPublished - 2023 Dec

Bibliographical note

Publisher Copyright:
© 2023, The Author(s).

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Breast cancer
  • Magnetic resonance imaging
  • Molecular subtype
  • Radiogenomics
  • Texture analysis

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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