Systems Biology of Breast Cancer: Molecular Profiling at the DNA, mRNA and miRNA Level; Relevance for Prognostication and Therapy Prediction

SciVee ◽  
2008 ◽  
Keyword(s):  
Breast Cancer ◽  
Systems Biology ◽  
Molecular Profiling ◽  
Mirna Level ◽  
Therapy Prediction

scholarly journals Integrating ensemble systems biology feature selection and bimodal deep neural network for breast cancer prognosis prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Li-Hsin Cheng ◽  
Te-Cheng Hsu ◽  
Che Lin
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Feature Selection ◽  
Systems Biology ◽  
Ensemble Learning ◽  
Microarray Data ◽  
Deep Neural Network ◽  
Prediction Models ◽  
Biological Knowledge ◽  
Prognosis Prediction

AbstractBreast cancer is a heterogeneous disease. To guide proper treatment decisions for each patient, robust prognostic biomarkers, which allow reliable prognosis prediction, are necessary. Gene feature selection based on microarray data is an approach to discover potential biomarkers systematically. However, standard pure-statistical feature selection approaches often fail to incorporate prior biological knowledge and select genes that lack biological insights. Besides, due to the high dimensionality and low sample size properties of microarray data, selecting robust gene features is an intrinsically challenging problem. We hence combined systems biology feature selection with ensemble learning in this study, aiming to select genes with biological insights and robust prognostic predictive power. Moreover, to capture breast cancer's complex molecular processes, we adopted a multi-gene approach to predict the prognosis status using deep learning classifiers. We found that all ensemble approaches could improve feature selection robustness, wherein the hybrid ensemble approach led to the most robust result. Among all prognosis prediction models, the bimodal deep neural network (DNN) achieved the highest test performance, further verified by survival analysis. In summary, this study demonstrated the potential of combining ensemble learning and bimodal DNN in guiding precision medicine.

Molecular profiling of triple negative breast cancer

2011 ◽  
Vol 32 (1-2) ◽  
pp. 73-84 ◽  
Author(s):  
Cynthia X. Ma ◽  
Jingqin Luo ◽  
Matthew J. Ellis
Keyword(s):  
Breast Cancer ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Molecular Profiling

scholarly journals Intra-tumor heterogeneity in breast cancer has limited impact on transcriptomic-based molecular profiling

BMC Cancer ◽  
2017 ◽  
Vol 17 (1) ◽  
Author(s):  
Govindasamy-Muralidharan Karthik ◽  
Mattias Rantalainen ◽  
Gustav Stålhammar ◽  
John Lövrot ◽  
Ikram Ullah ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Tumor Heterogeneity ◽  
Molecular Profiling ◽  
Limited Impact

scholarly journals The US–Latin America Cancer Research Network

2020 ◽  
Vol 6 (Supplement_1) ◽  
pp. 56-56
Author(s):  
Vidya Vedham ◽  
Marianne K. Henderson ◽  
Osvaldo Podhajcer ◽  
Andrea Llera ◽  
Marisa Dreyer Breitenbach ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Research ◽  
Latin America ◽  
Clinical Research ◽  
Latin American ◽  
Molecular Profiling ◽  
Research Network ◽  
Research Institutions ◽  
Cancer Research Network ◽  
The Us

PURPOSE The National Cancer Institute (NCI) Center for Global Health promotes global oncology research to reduce cancer burden worldwide. In 2009, NCI launched the Latin American Cancer Research Network (LACRN) to support a clinical cancer research network in Latin America. LACRN was started by a coalition of research institutions through bilateral collaborative agreements between the US Department of Health and Human Services and the governments of Argentina, Brazil, Chile, Mexico, and Uruguay. The LACRN is supported through a research contract to a study coordination center and subcontracts to 6 low- and middle-income country sites. The participating countries have a shared goal that meets the specific research needs of the regions. The overarching purpose of this endeavor is to implement high-quality standards for conducting clinical research studies and developing collaborative cancer research projects. METHODS NCI supported a clinical breast cancer project for LACRN, “Molecular profiling of breast cancer (MPBC) in Latin American women with stage II and III breast cancer receiving standard neo-adjuvant chemotherapy.” The molecular profiling of breast cancer study was conducted in 40 hospitals and research institutions across 5 countries with a study population of approximately 1,400 patients. RESULTS AND CONCLUSION Establishing a comprehensive network in Latin America and their research institutions yielded an incredible research resource that can be used in future studies, driven by the network. Throughout the process of developing and implementing studies, LACRN helped identify key elements of the functionality of research networks, such as the pivotal role of institutional and government commitment for sustainability; the importance of building multidisciplinary teams, transparent communications, and training; the ability to combine translational, epidemiology, and clinical research to close research gaps; and the application of new technologies to standard cancer clinical care.

Molecular profiling in breast cancer

2007 ◽  
Vol 8 (3) ◽  
pp. 185-198 ◽  
Author(s):  
Shannon R. Morris ◽  
Lisa A. Carey
Keyword(s):  
Breast Cancer ◽  
Molecular Profiling
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