Radiogenomic Analysis of Breast Cancer by Using B-Mode and Vascular US and RNA Sequencing.
Ah Young ParkMi-Ryung HanKyong Hwa ParkJung Sun KimGil Soo SonHye Yoon LeeYoung Woo ChangEun Kyung ParkSang Hoon ChaYunjung ChoHyosun HongKyu Ran ChoSung Eun SongOk Hee WooJu-Han LeeJaehyung ChaBo-Kyoung SeoPublished in: Radiology (2020)
Background Radiogenomic investigations for breast cancer provide an understanding of tumor heterogeneity and discover image phenotypes of genetic variation. However, there is little research on the correlations between US features of breast cancer and whole-transcriptome profiling. Purpose To explore US phenotypes reflecting genetic alteration relevant to breast cancer treatment and prognosis by comparing US images of tumor with their RNA sequencing results. Materials and Methods From January to October 2016, B-mode and vascular US images in 31 women (mean age, 49 years ± 9 [standard deviation]) with breast cancer were prospectively analyzed. B-mode features included size, shape, echo pattern, orientation, margin, and calcifications. Vascular features were evaluated by using microvascular US and contrast agent-enhanced US: vascular index, vessel morphologic features, distribution, penetrating vessels, enhancement degree, order, margin, internal homogeneity, and perfusion defect. RNA sequencing was conducted with total RNA obtained from a surgical specimen by using next-generation sequencing. US features were compared with gene expression profiles, and ingenuity pathway analysis was used to analyze gene networks, enriched functions, and canonical pathways associated with breast cancer. The P value for differential expression was extracted by using a parametric F test comparing nested linear models. Results Thirteen US features were associated with various patterns of 340 genes (P < .05). Nonparallel orientation at B-mode US was associated with upregulation of TFF1 (log twofold change [log2FC] = 4.0; P < .001), TFF3 (log2FC = 2.5; P < .001), AREG (log2FC = 2.6; P = .005), and AGR3 (log2FC = 2.6; P = .003). Complex vessel morphologic structure was associated with upregulation of FZD8 (log2FC = 2.0; P = .01) and downregulation of IGF1R (log2FC = -2.0; P = .006) and CRIPAK (log2FC = -2.4; P = .01). The top networks with regard to orientation or vessel morphologic structure were associated with cell cycle, death, and proliferation. Conclusion Compared with RNA sequencing, B-mode and vascular US features reflected genomic alterations associated with hormone receptor status, angiogenesis, or prognosis in breast cancer. © RSNA, 2020 Online supplemental material is available for this article.
Keyphrases
- single cell
- cell cycle
- genome wide
- copy number
- rna seq
- cell proliferation
- deep learning
- signaling pathway
- magnetic resonance
- gene expression
- breast cancer risk
- poor prognosis
- type diabetes
- dna methylation
- machine learning
- optical coherence tomography
- contrast enhanced
- pi k akt
- metabolic syndrome
- insulin resistance
- convolutional neural network
- soft tissue
- polycystic ovary syndrome
- adipose tissue
- genome wide identification
- functional connectivity
- resting state
- circulating tumor cells
- diffusion weighted