Professor of Biochemistry, Director, Vanderbilt Quantitative Systems Biology Center
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C-2310 Medical Center North
1161 21st Avenue South
Nashville, TN - 37232
Cancer Systems Biology
We combine experimental and theoretical approaches in efforts to produce a comprehensive understanding of cancer progression and resistance to treatment. In project on BRAF-mutant melanoma and EGFR-mutant non-small cell lung cancer, we conceived a novel drug sensitivity metric, the DIP rate, that makes it possible to incorporate drug-induced proliferation rates in predictive models of response. DIP rates can also be used to identify molecular and/or genetic determinants of drug sensitivity and resistance, in large-scale high-throughput screens. In small cell lung cancer, starting from bioinformatics analyses of large gene expression datasets, we clustered subsets of co-expressed gene modules, derived networks of transcription factors and simulated their dynamics using logic-based mathematical modeling. Resulting attractors point to distinct phenotypes whose relevance is underlined by their differential drug sensitivity. This workflow should be generalizable to predict single-cell state transitions in a cell population (normal or cancerous) subjected to perturbations. In breast cancer, we adopted information theory to quantify spontaneous loss of cell phenotypic identity, which may be relevant to progression and resistance.
1. Harris, L. A., Frick, P. L., Garbett, S. P., Hardeman, K.N., Paudel, B. B., Lopez, C. F., Quaranta, V. and Tyson, D. R. (2016). An unbiased metric of antiproliferative drug effect in vitro. Nat Methods 13, 497–500.
2. Udyavar A.R., Wooten,, D.J., Hoeksema M.D., Bansal, M., Califano, A., Estrada, L., Schnell, S., Irish, J.M., Massion, P.P., and Quaranta, V. (2016). Novel hybrid phenotype revealed in Small Cell Lung Cancer by a transcription factor network model that can explain tumor heterogeneity. Cancer Res. 77, 1063–1074. doi:10.1158/0008-5472.CAN-16-1467.
U01 CA215845-01 (Lopez, Quaranta) 07/01/17-06/30/22
Phenotype Transitions in Small Cell Lung Cancer
The goals of this proposal are: i) identify attractors of transcription factor networks that act as sources of phenotypic heterogeneity in SCLC; ii) reprogram SCLC cells to a drug-sensitive state via modulation of signaling pathways that impinge upon transcription factor network attractors.
Role: Contact co-Principal Investigator