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Updated: Aug 6, 2026
Studying Triple Negative Breast Cancer Using Orthotopic Breast Cancer Model
Published on: March 20, 2020
Metabolic signatures of triple-negative breast cancer
Gandhar Pusalkar1, Rakshitha Madamakki1, Parna Chakraborty1
1Cellular Nanomedicine and Chemical Biology, School of Biological Sciences, UM-DAE Centre for Excellence in Basic Sciences, University of Mumbai, Vidyanagari, 400098, Mumbai, India.
Abstract:
Triple-negative breast cancer (TNBC), being one of the most aggressive subtypes of breast malignancies, is characterized by poor prognosis and limited treatment options. As a leading cause of cancer-related mortality among women, TNBC poses unique clinical challenges due to the absence of effective targeted therapies. Conventional treatment strategies, including chemotherapy, often suffer from significant drawbacks such as drug resistance and intolerable side effects, underscoring an urgent need for innovative approaches to improve therapeutic outcomes. A defining hallmark of TNBC is its markedly altered cellular metabolism, which not only supports tumor growth and survival but also contributes to therapy resistance. Elucidating these metabolic alterations could provide critical insights into potential vulnerabilities that may be exploited for therapeutic intervention. This review provides a comprehensive overview of the major metabolic pathways that are dysregulated in TNBC and their relevance to disease progression and therapeutic intervention. Finally, we explore recent advances in metabolomics-driven precision medicine, highlighting the integration of artificial intelligence and machine learning approaches, covering aspects such as data processing, feature selection, and model construction that can accelerate the advancement of personalized treatment strategies for TNBC.
Insights
Triple-negative breast cancer (TNBC) has poor prognosis and limited treatments due to aggressive nature and altered metabolism. Understanding TNBC metabolic pathways and using AI in metabolomics can advance personalized therapies.
Area of Science:
- Oncology
- Metabolic pathways
- Cancer research
Background:
- Triple-negative breast cancer (TNBC) is an aggressive subtype with poor prognosis and few targeted therapies.
- Conventional treatments like chemotherapy face challenges including drug resistance and side effects.
- Altered cellular metabolism is a key feature supporting TNBC growth and therapy resistance.
Purpose of the Study:
- To provide a comprehensive overview of dysregulated metabolic pathways in TNBC.
- To discuss the relevance of these metabolic alterations to disease progression and treatment.
- To explore advances in metabolomics-driven precision medicine for TNBC.
Main Methods:
- Review of major metabolic pathways dysregulated in TNBC.
- Analysis of the role of metabolic alterations in TNBC progression and therapeutic resistance.
- Exploration of recent advances in metabolomics, artificial intelligence (AI), and machine learning (ML) for precision medicine.
Main Results:
- TNBC is characterized by significant metabolic alterations crucial for tumor growth and survival.
- Metabolic dysregulation contributes to therapeutic resistance in TNBC.
- Metabolomics combined with AI/ML offers potential for personalized TNBC treatment strategies.
Conclusions:
- Elucidating TNBC metabolic vulnerabilities is critical for developing novel therapeutic interventions.
- Metabolomics-driven precision medicine, integrating AI/ML, shows promise for advancing personalized treatment for TNBC.
- Further research into TNBC metabolism and AI applications can accelerate personalized therapeutic strategies.
