Distinct patterns of DNA copy number alteration are associated with different clinicopathological features and gene-expression subtypes of breast cancer

2006 ◽  
Vol 45 (11) ◽  
pp. 1033-1040 ◽  
Author(s):  
Anna Bergamaschi ◽  
Young H. Kim ◽  
Pei Wang ◽  
Therese Sørlie ◽  
Tina Hernandez-Boussard ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Copy Number ◽  
Copy Number Alteration ◽  
Clinicopathological Features ◽  
Dna Copy Number

scholarly journals A Multimodal Affinity Fusion Network for Predicting the Survival of Breast Cancer Patients

2021 ◽  
Vol 12 ◽  
Author(s):  
Weizhou Guo ◽  
Wenbin Liang ◽  
Qingchun Deng ◽  
Xianchun Zou
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Clinical Data ◽  
Copy Number ◽  
Copy Number Alteration ◽  
Survival Prediction ◽  
Term Survival ◽  
Multimodal Data ◽  
Structured Information ◽  
Fusion Feature

Accurate survival prediction of breast cancer holds significant meaning for improving patient care. Approaches using multiple heterogeneous modalities such as gene expression, copy number alteration, and clinical data have showed significant advantages over those with only one modality for patient survival prediction. However, existing survival prediction methods tend to ignore the structured information between patients and multimodal data. We propose a multimodal data fusion model based on a novel multimodal affinity fusion network (MAFN) for survival prediction of breast cancer by integrating gene expression, copy number alteration, and clinical data. First, a stack-based shallow self-attention network is utilized to guide the amplification of tiny lesion regions on the original data, which locates and enhances the survival-related features. Then, an affinity fusion module is proposed to map the structured information between patients and multimodal data. The module endows the network with a stronger fusion feature representation and discrimination capability. Finally, the fusion feature embedding and a specific feature embedding from a triple modal network are fused to make the classification of long-term survival or short-term survival for each patient. As expected, the evaluation results on comprehensive performance indicate that MAFN achieves better predictive performance than existing methods. Additionally, our method can be extended to the survival prediction of other cancer diseases, providing a new strategy for other diseases prognosis.

scholarly journals Patient-Specific Network for Personalized Breast Cancer Therapy with Multi-Omics Data

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 225
Author(s):  
Claudia Cava ◽  
Soudabeh Sabetian ◽  
Isabella Castiglioni
Keyword(s):  
Breast Cancer ◽  
Cancer Therapy ◽  
Protein Interaction Network ◽  
Copy Number ◽  
Tumor Heterogeneity ◽  
Copy Number Alteration ◽  
Interaction Network ◽  
Computational Approach ◽  
Patient Specific ◽  
Basal Breast Cancer

The development of new computational approaches that are able to design the correct personalized drugs is the crucial therapeutic issue in cancer research. However, tumor heterogeneity is the main obstacle to developing patient-specific single drugs or combinations of drugs that already exist in clinics. In this study, we developed a computational approach that integrates copy number alteration, gene expression, and a protein interaction network of 73 basal breast cancer samples. 2509 prognostic genes harboring a copy number alteration were identified using survival analysis, and a protein–protein interaction network considering the direct interactions was created. Each patient was described by a specific combination of seven altered hub proteins that fully characterize the 73 basal breast cancer patients. We suggested the optimal combination therapy for each patient considering drug–protein interactions. Our approach is able to confirm well-known cancer related genes and suggest novel potential drug target genes. In conclusion, we presented a new computational approach in breast cancer to deal with the intra-tumor heterogeneity towards personalized cancer therapy.

Detection of copy number alteration of MTA1 in human breast cancer

2009 ◽  
Vol 6 (4) ◽  
pp. 245-249
Author(s):  
Mengquan Li ◽  
Jingruo Li ◽  
Mingxun Chen ◽  
Juntao Bao
Keyword(s):  
Breast Cancer ◽  
Copy Number ◽  
Human Breast Cancer ◽  
Copy Number Alteration ◽  
Human Breast
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