(190au) Exploring Tumor Metabolic Heterogeneity through Integration of Single Cell RNA-Seq Analysis and Genome-Scale Metabolic Models

Far from being a uniform population, cancer cells within a single tumor display a surprising amount of heterogeneity. This heterogeneity occurs in the genome (with multiple subclonal populations colonizing the tumor), in transcriptional regulation, and in protein allocation – within and across subclonal populations. Scientists and clinicians speculate that this multi-level heterogeneity is one of the primary causes leading to development of drug resistance and cancer recurrence. This issue affects the use of established drugs for treating cancers and the potential usefulness of drugs in development, including drugs targeting metabolism.

Though tumor heterogeneity at the single-cell scale is beginning to be understood at the genomic, transcriptomic, and proteomic levels, there is little understanding of how this molecular heterogeneity influences cancer cell metabolism. Understanding metabolic heterogeneity is crucial for predicting treatment efficacy of newly identified metabolic targets for cancer therapy^1,2. Therefore, we seek to understand how cancer metabolism is regulated within single cancer cells by integrating single cell RNA-seq analyses and genome-scale metabolic modeling.

We present an analytical pipeline to identify subpopulations of cells from within a tumor using RNA-seq data, to build subpopulation-specific genome-scale metabolic models using a probabilistic framework, and to simulate these models taking tumor heterogeneity into account. We apply this pipeline to tumor samples taken from breast cancer patients and predict metabolic targets that can inhibit tumor growth in all tumor cell subpopulations, inhibit growth in metastatic tumor populations, and potentially prevent cancer recurrence.

References

1. Agren, R., Mardinoglu, A., Asplund, A., Kampf, C., Uhlen, M., & Nielsen, J. (2014). Identification of anticancer drugs for hepatocellular carcinoma through personalized genome‐scale metabolic modeling. Molecular systems biology, 10(3), 721.

2. Ghaffari, P., Mardinoglu, A., Asplund, A., Shoaie, S., Kampf, C., Uhlen, M., & Nielsen, J. (2015). Identifying anti-growth factors for human cancer cell lines through genome-scale metabolic modeling. Scientific reports, 5, 8183.