Huang Yunxia, Guo Yi, Xiao Qin, Liang Shuyu, Yu Qiang, Qian Lang, Zhou Jin, Le Jian, Pei Yuchen, Wang Lei, Chang Cai, Chen Sheng, Zhou Shichong
Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China.
Department of Radiology, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, People's Republic of China.
Breast Cancer (Dove Med Press). 2023 Jul 11;15:461-472. doi: 10.2147/BCTT.S408997. eCollection 2023.
The emergence of genomic targeted therapy has improved the prospects of treatment for breast cancer (BC). However, genetic testing relies on invasive and sophisticated procedures.
Here, we performed ultrasound (US) and target sequencing to unravel the possible association between US radiomics features and somatic mutations in TNBC (n=83) and non-TNBC (n=83) patients. Least absolute shrinkage and selection operator (Lasso) were utilized to perform radiomic feature selection. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was utilized to identify the signaling pathways associated with radiomic features.
Thirteen differently represented radiomic features were identified in TNBC and non-TNBC, including tumor shape, textual, and intensity features. The US radiomic-gene pairs were differently exhibited between TNBC and non-TNBC. Further investigation with KEGG verified radiomic-pathway (ie, JAK-STAT, MAPK, Ras, Wnt, microRNAs in cancer, PI3K-Akt) associations in TNBC and non-TNBC.
The pivotal network provided the connections of US radiogenomic signature and target sequencing for non-invasive genetic assessment of precise BC treatment.
基因组靶向治疗的出现改善了乳腺癌（BC）的治疗前景。然而，基因检测依赖于侵入性且复杂的程序。
在此，我们对三阴性乳腺癌（TNBC，n = 83）和非三阴性乳腺癌（n = 83）患者进行了超声（US）检查和靶向测序，以揭示US影像组学特征与体细胞突变之间的可能关联。利用最小绝对收缩和选择算子（Lasso）进行影像组学特征选择。利用京都基因与基因组百科全书（KEGG）分析来识别与影像组学特征相关的信号通路。
在TNBC和非TNBC中鉴定出13种不同表现的影像组学特征，包括肿瘤形状、纹理和强度特征。TNBC和非TNBC之间的US影像组学-基因对表现不同。KEGG的进一步研究证实了TNBC和非TNBC中的影像组学-通路（即JAK-STAT、MAPK、Ras、Wnt、癌症中的微小RNA、PI3K-Akt）关联。
关键网络为BC精准治疗的非侵入性基因评估提供了US放射基因组特征与靶向测序之间的联系。
