Ultrasonic Features and Molecular Subtype Predict Somatic Mutations in TP53 and PIK3CA Genes in Breast Cancer

Yunxia Huang; Yu Qiang; Le Jian; Zhou Jin; Qian Lang; Chen Sheng; Zhou Shichong; Chang Cai

doi:10.1016/j.acra.2022.02.021

Ultrasonic Features and Molecular Subtype Predict Somatic Mutations in TP53 and PIK3CA Genes in Breast Cancer

Acad Radiol. 2022 Dec;29(12):e261-e270. doi: 10.1016/j.acra.2022.02.021. Epub 2022 Apr 18.

Authors

Yunxia Huang¹, Yu Qiang², Le Jian¹, Zhou Jin¹, Qian Lang¹, Chen Sheng², Zhou Shichong³, Chang Cai¹

Affiliations

¹ Department of Ultrasonography, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, 270 Dongan Road, Xuhui, Sanghai, 200032, China.
² Department of Breast Surgery, Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China.
³ Department of Ultrasonography, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, 270 Dongan Road, Xuhui, Sanghai, 200032, China. Electronic address: sczhou@hotmail.com.

PMID: 35450798
DOI: 10.1016/j.acra.2022.02.021

Abstract

Rationale and objectives: To predict mutations in TP53 and PIK3CA genes in breast cancer using ultrasound (US) signatures and clinicopathology.

Materials and methods: In this study, we developed and trained a model in 386 breast cancer patients to predict TP53 and PIK3CA mutations. The clinicopathological and US characteristics (including two-dimensional and color Doppler US) were investigated. Statistically significant variables were used to build predictive models, then a combined model was developed using the multivariate logistic regression analysis.

Results: Univariate and multivariate analyses revealed that calcifications on US was an independent predictor of TP53 mutation (p < 0.05), whereas diameter on US and US type were independent predictors of PIK3CA mutation in breast cancer (all p < 0.05). Meanwhile, Luminal B/Human epidermal growth factor receptor two-positive (HER2+), HER2+/estrogen receptor-negative (ER-), and triple-negative breast cancer (TNBC) subtypes were strong predictors of TP53 mutation (odds ratio [OR] = 3.13, 3.18, 3.44, respectively, all p < 0.05). HER2+/ER- and TNBC subtypes were negative predictors of PIK3CA mutation (OR = 0.223, 0.241, respectively, all p < 0.05). The areas under curves (AUCs) for PIK3CA mutation in the training set increased from 0.553-0.610 to 0.741 in the multivariate model that combined US features and molecular subtype, with a sensitivity and specificity of 80.6% and 58.7%, respectively. The application of the multivariate model in the validation set achieved acceptable discrimination (AUC = 0.715). For TP53 mutation, the AUC was 0.653.

Conclusion: US is a non-invasive modality to recognize the presence of TP53 and PIK3CA mutation. The models combined with US features and molecular subtype have implications for the practical application of predicting gene mutation for individual decision-making regarding treatment planning.

Keywords: Breast cancer; Mutation; PIK3CA; TP53; Ultrasound.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Biomarkers, Tumor / genetics
Breast Neoplasms* / diagnostic imaging
Breast Neoplasms* / genetics
Breast Neoplasms* / pathology
Class I Phosphatidylinositol 3-Kinases / genetics
Female
Humans
Mutation / genetics
Triple Negative Breast Neoplasms* / diagnostic imaging
Triple Negative Breast Neoplasms* / genetics
Triple Negative Breast Neoplasms* / pathology
Tumor Suppressor Protein p53 / genetics
Ultrasonics

Substances

Tumor Suppressor Protein p53
Biomarkers, Tumor
Class I Phosphatidylinositol 3-Kinases
TP53 protein, human
PIK3CA protein, human