Multi-omic single-cell snapshots reveal multiple independent trajectories to drug tolerance in a melanoma cell line

Yapeng Su, Melissa E. Ko, Hanjun Cheng, Ronghui Zhu, Min Xue, Jessica Wang, Jihoon W. Lee, Luke Frankiw, Alexander Xu, Stephanie Wong, Lidia Robert, Kaitlyn Takata, Dan Yuan, Yue Lu, Sui Huang, Antoni Ribas, Raphael Levine, Garry P. Nolan, Wei Wei, Sylvia K. Plevritis, Guideng Li (), David Baltimore () and James R. Heath ()
Additional contact information
Yapeng Su: California Institute of Technology
Melissa E. Ko: Stanford University School of Medicine
Hanjun Cheng: Institute for Systems Biology
Ronghui Zhu: California Institute of Technology
Min Xue: California Institute of Technology
Jessica Wang: California Institute of Technology
Jihoon W. Lee: California Institute of Technology
Luke Frankiw: California Institute of Technology
Alexander Xu: Institute for Systems Biology
Stephanie Wong: California Institute of Technology
Lidia Robert: University of California, Los Angeles
Kaitlyn Takata: California Institute of Technology
Dan Yuan: Institute for Systems Biology
Yue Lu: Institute for Systems Biology
Sui Huang: Institute for Systems Biology
Antoni Ribas: University of California, Los Angeles
Raphael Levine: UCLA
Garry P. Nolan: Stanford University
Wei Wei: Institute for Systems Biology
Sylvia K. Plevritis: Stanford University
Guideng Li: Chinese Academy of Medical Sciences and Peking Union Medical College
David Baltimore: California Institute of Technology
James R. Heath: California Institute of Technology

Nature Communications, 2020, vol. 11, issue 1, 1-12

Abstract: Abstract The determination of individual cell trajectories through a high-dimensional cell-state space is an outstanding challenge for understanding biological changes ranging from cellular differentiation to epigenetic responses of diseased cells upon drugging. We integrate experiments and theory to determine the trajectories that single BRAFV600E mutant melanoma cancer cells take between drug-naive and drug-tolerant states. Although single-cell omics tools can yield snapshots of the cell-state landscape, the determination of individual cell trajectories through that space can be confounded by stochastic cell-state switching. We assayed for a panel of signaling, phenotypic, and metabolic regulators at points across 5 days of drug treatment to uncover a cell-state landscape with two paths connecting drug-naive and drug-tolerant states. The trajectory a given cell takes depends upon the drug-naive level of a lineage-restricted transcription factor. Each trajectory exhibits unique druggable susceptibilities, thus updating the paradigm of adaptive resistance development in an isogenic cell population.

Date: 2020
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DOI: 10.1038/s41467-020-15956-9

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