Cancer

Intratumoral Heterogeneity in Brain Tumors

Our current research focuses on studying intratumoral heterogeneity in brain tumors. Intratumoral heterogeneity is a major issue as it contributes to drug resistance and tumor recurrence. Heterogeneity occurs at the genomic, epigenomic, transcriptomic, metabolomic, and cellular scale and complicates our understanding of brain tumors. However, the continual and rapid developments in high-throughput omics technologies and computational analytical techniques enable the research community to tease apart this heterogeneity at the single-cell level.

Our research aims to analyze brain tumor genomic and transcriptional heterogeneity at the single-cell level. Building off statistically driven genome-scale models (Plaisier et al. 2016) of the underlying causal, mechanistic interactions in brain tumors, We hope to develop quantitative models of systems-level regulatory networks driving the molecular states of populations of single cells within a tumor. Systems-level network models provide a broad perspective of the numerous interactions occurring and the non-intuitive emergent cellular behavior arising from such interactions. Understanding how these behaviors arise would enable us to predict how the molecular states of sub-populations within tumors may change under a given treatment. Consequently, this would help determine what treatment strategies could force a tumor (and its heterogeneous subpopulations) into a “susceptible”, i.e. treatable state. Conceivably, network models may be used to anticipate what potential recurrent subpopulations may arise and how they may be best treated with current drug molecules and the increasing number of promising CAR T-cell therapies available. This work is possible only through multiple, essential collaborations with local clinicians and researchers deeply involved in treating patients with brain tumors and CAR T-cell immunotherapy development.

Team

Publications

Plaisier, Christopher L., Sofie O’Brien, Brady Bernard, Sheila Reynolds, Zac Simon, Chad M. Toledo, Yu Ding, David J. Reiss, Patrick J. Paddison, and Nitin S. Baliga. “Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis.” Cell Systems 3, no. 2 (August 2016): 172–86. https://doi.org/10.1016/j.cels.2016.06.006. Cite

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