@Article{or.2026.078924,
AUTHOR = {Yen-Dun Tony Tzeng, Chen-Yueh Wen, Su-Boon Yong, Zhi-Hong Wen, An-Jen Chiang, Chia-Jung Li},
TITLE = {Navigating the Metabolic-Genomic Paradigm: Mitochondrial Reprogramming as a Driver of Cancer Plasticity},
JOURNAL = {Oncology Research},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/or/online/detail/26786},
ISSN = {1555-3906},
ABSTRACT = {Breast cancer (BC) management has transitioned from histological classification to molecular subtyping, yet therapeutic resistance and intratumor heterogeneity remain critical clinical challenges. This review examines the emerging paradigm shift toward integrating mitochondrial metabolism into the precision medicine framework. We detail the complex mitonuclear crosstalk where nuclear genetic alterations, such as Breast Cancer 1 (<i>BRCA1</i>) deficiency and <i>TP53</i> mutations, fundamentally reprogram mitochondrial bioenergetics. Specifically, the loss of BRCA1 function triggers a systemic NAD<sup>+</sup> depletion trap through <i>PARP1</i> hyperactivation, while oncogenic drivers like <i>MYC</i> coordinate with <i>PGC1α</i> to enhance mitochondrial biogenesis for metastatic survival. We evaluate the diagnostic potential of mitochondrial DNA heteroplasmy and machine learning derived metabolic gene signatures as high performance biomarkers for patient stratification and the detection of minimal residual disease via liquid biopsy. Furthermore, we analyze current clinical efforts to target mitochondrial vulnerabilities, including respiratory chain inhibitors like metformin and BH3 mimetics, while highlighting the significant challenges posed by metabolic plasticity and nutrient competition in the tumor microenvironment. The analysis of clinical trial data, such as the MA.32 study, suggests that metabolic interventions require precise patient selection based on specific metabolic phenotypes rather than broad application. Looking forward, the integration of genome scale metabolic models and artificial intelligence (AI) offers a transformative pathway to simulate patient specific metabolic fluxes and identify novel synthetic lethal targets. By bridging the gap between nuclear genomic drivers and dynamic mitochondrial adaptations, this review aims to provide a preliminary framework for the exploration of metabolic-genomic precision oncology in BC.},
DOI = {10.32604/or.2026.078924}
}