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

doi:10.1016/j.isci.2023.108059

Metabolic stratification of human breast tumors reveal subtypes of clinical and therapeutic relevance

iScience. 2023 Sep 26;26(10):108059. doi: 10.1016/j.isci.2023.108059. eCollection 2023 Oct 20.

Authors

Mohammad A Iqbal^{1

2}, Shumaila Siddiqui³, Kirk Smith⁴, Prithvi Singh⁵, Bhupender Kumar⁶, Salem Chouaib^{1

2

7}, Sriram Chandrasekaran^{4

8

9}

Affiliations

¹ Thumbay Research Institute for Precision Medicine, Gulf Medical University, Ajman, United Arab Emirates.
² College of Medicine, Gulf Medical University, Ajman, United Arab Emirates.
³ CSIR-Central Drug Research Institute, Lucknow, Uttar Pradesh, India.
⁴ Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.
⁵ Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia (A Central University), New Delhi, India.
⁶ Department of Microbiology, Swami Shraddhanand College, University of Delhi, New Delhi, Delhi, India.
⁷ INSERM UMR 1186, Gustave Roussy, EPHE, Faculty of Medicine, University of Paris-Saclay, Villejuif, France.
⁸ Program in Chemical Biology, University of Michigan, Ann Arbor, MI, USA.
⁹ Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI, USA.

Abstract

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.

Keywords: Medical informatics; cancer; computational bioinformatics.