Molecular Characterization and Clinical Relevance of Metabolic Expression Subtypes in Human Cancers

Authors

Xinxin Peng, University of Texas MD Anderson Cancer Center
Zhongyuan Chen, University of Texas MD Anderson Cancer Center
Farshad Farshidfar, University of Calgary
Xiaoyan Xu, University of Texas MD Anderson Cancer Center
Philip L. Lorenzi, University of Texas MD Anderson Cancer Center
Yumeng Wang, University of Texas MD Anderson Cancer Center
Feixiong Cheng, Dana-Farber Cancer Institute
Lin Tan, University of Texas MD Anderson Cancer Center
Kamalika Mojumdar, University of Texas MD Anderson Cancer Center
Di Du, University of Texas MD Anderson Cancer Center
Zhongqi Ge, University of Texas MD Anderson Cancer Center
Jun Li, University of Texas MD Anderson Cancer Center
George V. Thomas, Oregon Health & Science University
Kivanc Birsoy, Rockefeller University
Lingxiang Liu, Jiangsu Province Hospital
Huiwen Zhang, University of Texas Health Science Center at Houston
Zhongming Zhao, University of Texas Health Science Center at Houston
Calena Marchand, University of Waterloo
John N. Weinstein, University of Texas MD Anderson Cancer Center
Samantha J. Caesar-Johnson
John A. Demchok
Ina Felau
Melpomeni Kasapi
Martin L. Ferguson
Carolyn M. Hutter
Heidi J. Sofia
Roy Tarnuzzer
Zhining Wang
Liming Yang
Jean C. Zenklusen
Jiashan (Julia) Zhang
Sudha Chudamani
Jia Liu

Document Type

Journal Article

Publication Date

4-3-2018

Journal

Cell Reports

Volume

23

Issue

1

DOI

10.1016/j.celrep.2018.03.077

Keywords

carbohydrate metabolism; drug sensitivity; master regulator; prognostic markers; somatic drivers; The Cancer Genome Atlas; therapeutic targets; tumor subtypes

Abstract

© 2018 The Author(s) Metabolic reprogramming provides critical information for clinical oncology. Using molecular data of 9,125 patient samples from The Cancer Genome Atlas, we identified tumor subtypes in 33 cancer types based on mRNA expression patterns of seven major metabolic processes and assessed their clinical relevance. Our metabolic expression subtypes correlated extensively with clinical outcome: subtypes with upregulated carbohydrate, nucleotide, and vitamin/cofactor metabolism most consistently correlated with worse prognosis, whereas subtypes with upregulated lipid metabolism showed the opposite. Metabolic subtypes correlated with diverse somatic drivers but exhibited effects convergent on cancer hallmark pathways and were modulated by highly recurrent master regulators across cancer types. As a proof-of-concept example, we demonstrated that knockdown of SNAI1 or RUNX1—master regulators of carbohydrate metabolic subtypes—modulates metabolic activity and drug sensitivity. Our study provides a system-level view of metabolic heterogeneity within and across cancer types and identifies pathway cross-talk, suggesting related prognostic, therapeutic, and predictive utility. Peng et al. analyze a cohort of 9,125 TCGA samples across 33 cancer types to characterize tumor subtypes based on the expression of seven metabolic pathways. They find metabolic expression subtypes are associated with patient survivals and suggest the therapeutic and predictive relevance of subtype-related master regulators.

APA Citation

Peng, X., Chen, Z., Farshidfar, F., Xu, X., Lorenzi, P., Wang, Y., Cheng, F., Tan, L., Mojumdar, K., Du, D., Ge, Z., Li, J., Thomas, G., Birsoy, K., Liu, L., Zhang, H., Zhao, Z., Marchand, C., Weinstein, J., Caesar-Johnson, S., Demchok, J., Felau, I., Kasapi, M., Ferguson, M., Hutter, C., Sofia, H., Tarnuzzer, R., Wang, Z., Yang, L., Zenklusen, J., Zhang, J., Chudamani, S., & Liu, J. (2018). Molecular Characterization and Clinical Relevance of Metabolic Expression Subtypes in Human Cancers. Cell Reports, 23 (1). http://dx.doi.org/10.1016/j.celrep.2018.03.077