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Deep learning framework for comprehensive molecular and prognostic stratifications of triple-negative breast cancer.
Zhao, Shen; Yan, Chao-Yang; Lv, Hong; Yang, Jing-Cheng; You, Chao; Li, Zi-Ang; Ma, Ding; Xiao, Yi; Hu, Jia; Yang, Wen-Tao; Jiang, Yi-Zhou; Xu, Jun; Shao, Zhi-Ming.
Afiliação
  • Zhao S; Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, China.
  • Yan CY; Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China.
  • Lv H; Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai 200032, China.
  • Yang JC; Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, China.
  • You C; Greater Bay Area Institute of Precision Medicine, Guangzhou 511466, China.
  • Li ZA; Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai 200032, China.
  • Ma D; Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China.
  • Xiao Y; Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, China.
  • Hu J; Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, China.
  • Yang WT; Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, China.
  • Jiang YZ; Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai 200032, China.
  • Xu J; Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, China.
  • Shao ZM; Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China.
Fundam Res ; 4(3): 678-689, 2024 May.
Article em En | MEDLINE | ID: mdl-38933195
ABSTRACT
Triple-negative breast cancer (TNBC) is the most challenging breast cancer subtype. Molecular stratification and target therapy bring clinical benefit for TNBC patients, but it is difficult to implement comprehensive molecular testing in clinical practice. Here, using our multi-omics TNBC cohort (N = 425), a deep learning-based framework was devised and validated for comprehensive predictions of molecular features, subtypes and prognosis from pathological whole slide images. The framework first incorporated a neural network to decompose the tissue on WSIs, followed by a second one which was trained based on certain tissue types for predicting different targets. Multi-omics molecular features were analyzed including somatic mutations, copy number alterations, germline mutations, biological pathway activities, metabolomics features and immunotherapy biomarkers. It was shown that the molecular features with therapeutic implications can be predicted including the somatic PIK3CA mutation, germline BRCA2 mutation and PD-L1 protein expression (area under the curve [AUC] 0.78, 0.79 and 0.74 respectively). The molecular subtypes of TNBC can be identified (AUC 0.84, 0.85, 0.93 and 0.73 for the basal-like immune-suppressed, immunomodulatory, luminal androgen receptor, and mesenchymal-like subtypes respectively) and their distinctive morphological patterns were revealed, which provided novel insights into the heterogeneity of TNBC. A neural network integrating image features and clinical covariates stratified patients into groups with different survival outcomes (log-rank P < 0.001). Our prediction framework and neural network models were externally validated on the TNBC cases from TCGA (N = 143) and appeared robust to the changes in patient population. For potential clinical translation, we built a novel online platform, where we modularized and deployed our framework along with the validated models. It can realize real-time one-stop prediction for new cases. In summary, using only pathological WSIs, our proposed framework can enable comprehensive stratifications of TNBC patients and provide valuable information for therapeutic decision-making. It had the potential to be clinically implemented and promote the personalized management of TNBC.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article