Abstract P6-10-12: Texture heterogeneity of breast tumour in magnetic resonance imaging can be explained by differentially regulated genes
Authors: Jianan Chen, Yutaka Amemiya, Gregory Kuling, Homa Fashandi, Yulia Yerofeyeva, Heba Hussein, Elzbieta Slodkowska, Fiona Ginty, Arun Seth, Martin Yaffe, Anne L. Martel
Journal: Cancer Research (2020)

Abstract Background: Magnetic resonance imaging (MRI) and molecular profiling of tumour tissues have become standard techniques to study breast cancer in recent years. However, despite the myriad imaging and genetic subtypes that have been identified, the underlying biological mechanisms of MRI features are seldom explained, and differentially regulated genes are rarely linked to the phenotypic appearance of tumours. In this study, we propose to fill this gap in knowledge by investigating the unbiased correlations between MRI phenotypes and differential gene expressions in breast cancer. Methods: Patients diagnosed during 2002-15 with invasive breast cancer who went through surgery were retrospectively reviewed for magnetic resonance imaging (MRI) and genomics analysis. In total, we collected dynamic contrast-enhanced subtraction MRI and RNA sequencing results of surgical specimens from a cohort of 56 patients. Of these, 31 patients (aged 33 to 72 years) met our inclusion criteria. Tumour lesion segmentation was performed by a radiologist who has 10 years of experience. We extracted features that quantitatively describe tumour appearance from the segmented lesions using pyradiomics (v2.0.0). We then grouped the tumours into two imaging subtypes using an unsupervised clustering approach (SIMLR, v1.10.0). To probe the underlying biological mechanisms behind the difference in tumour appearance, we performed differential expression analysis (edgeR, v3.26.5) and pathway enrichment analysis (g:profiler) between the two imaging subtypes. Multiple testing correction was conducted with Benjamini-Hochberg correction using a false discovery rate of 0.05. Results: We classified the breast tumours from our cohort into two imaging subtypes that have distinct levels of heterogeneity in texture (p=0.004). We found a list of genes that were significantly differentially expressed between the heterogenous (n=20) and homogenous (n=11) subtypes (Table 1), and their associated biological pathways. We found that the pathways controlling cell growth (p=0.022), cell migration and invasion (p=0.023), estrogen regulation (p=0.022) and DNA damage repair (p=0.015) mechanisms may have contributed to increased heterogeneity in tumour presentation when imaged with MRI. Conclusion: The underlying biological mechanisms affecting breast MRI texture can be investigated by linking tumour appearance to gene expression profiling. Our results suggest that texture heterogeneity in breast MRI could be linked to a number of differentially expressed genes that may be further investigated as a biomarker of cancer risk assessment or recurrence. Further studies with a larger cohort will be conducted to validate and extend these results. Table 1. Differentially expressed genes between heterogenous and homogenous imaging subtypes.Genes upregulated in heterogeneous imaging subtypeLog fold changeAdjusted p valueABCC138.760.0022PROL17.760.0331TDRD127.100.0204SLC12A22.030.0204IGFBP3-1.500.0331UBASH3B-1.740.0331LYZ-2.120.0291CCL5-2.120.0204PDCD1LG2-2.180.0359SLC7A11-2.480.0363GNLY-3.090.0022GZMB-3.100.0204PTHLH-3.270.0246GSTM5-3.370.0325GNAO1-3.430.0363MYOZ1-3.580.0269CLEC4C-3.640.0246AJAP1-3.760.0325IL28B-3.930.0415CCL25-4.770.0325PI15-5.491.48e-7 Citation Format: Jianan Chen, Yutaka Amemiya, Gregory Kuling, Homa Fashandi, Yulia Yerofeyeva, Heba Hussein, Elzbieta Slodkowska, Fiona Ginty, Arun Seth, Martin Yaffe, Anne L. Martel. Texture heterogeneity of breast tumour in magnetic resonance imaging can be explained by differentially regulated genes [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P6-10-12.