Metabolic stratification of human breast tumors reveal subtypes of clinical and therapeutic relevance
Mohammad A. Iqbal,
Shumaila Siddiqui,
Kirk Smith,
Prithvi Singh,
Bhupender Kumar,
Salem Chouaib,
Sriram Chandrasekaran
Affiliations
Mohammad A. Iqbal
Thumbay Research Institute for Precision Medicine, Gulf Medical University, Ajman, United Arab Emirates; College of Medicine, Gulf Medical University, Ajman, United Arab Emirates; Corresponding author
Shumaila Siddiqui
CSIR-Central Drug Research Institute, Lucknow, Uttar Pradesh, India
Kirk Smith
Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
Prithvi Singh
Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia (A Central University), New Delhi, India
Bhupender Kumar
Department of Microbiology, Swami Shraddhanand College, University of Delhi, New Delhi, Delhi, India
Salem Chouaib
Thumbay Research Institute for Precision Medicine, Gulf Medical University, Ajman, United Arab Emirates; College of Medicine, Gulf Medical University, Ajman, United Arab Emirates; INSERM UMR 1186, Gustave Roussy, EPHE, Faculty of Medicine, University of Paris-Saclay, Villejuif, France
Sriram Chandrasekaran
Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA; Program in Chemical Biology, University of Michigan, Ann Arbor, MI, USA; Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI, USA; Corresponding author
Summary: Extensive metabolic heterogeneity in breast cancers has limited the deployment of metabolic therapies. To enable patient stratification, we studied the metabolic landscape in breast cancers (∼3000 patients combined) and identified three subtypes with increasing degrees of metabolic deregulation. Subtype M1 was found to be dependent on bile-acid biosynthesis, whereas M2 showed reliance on methionine pathway, and M3 engaged fatty-acid, nucleotide, and glucose metabolism. The extent of metabolic alterations correlated strongly with tumor aggressiveness and patient outcome. This pattern was reproducible in independent datasets and using in vivo tumor metabolite data. Using machine-learning, we identified robust and generalizable signatures of metabolic subtypes in tumors and cell lines. Experimental inhibition of metabolic pathways in cell lines representing metabolic subtypes revealed subtype-specific sensitivity, therapeutically relevant drugs, and promising combination therapies. Taken together, metabolic stratification of breast cancers can thus aid in predicting patient outcome and designing precision therapies.
