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Single-cell analysis resolves the cell state transition and signaling dynamics associated with melanoma drug-induced resistance

Su, Yapeng and Wei, Wei and Robert, Lidia and Xue, Min and Tsoi, Jennifer and Garcia-Diaz, Angel and Homet Moreno, Blanca and Kim, Jungwoo and Ng, Rachel H. and Lee, Jihoon W. and Koya, Richard C. and Comin-Anduix, Begonya and Graeber, Thomas G. and Ribas, Antoni and Heath, James R. (2017) Single-cell analysis resolves the cell state transition and signaling dynamics associated with melanoma drug-induced resistance. Proceedings of the National Academy of Sciences of the United States of America, 114 (52). pp. 13679-13684. ISSN 0027-8424. http://resolver.caltech.edu/CaltechAUTHORS:20171211-152922263

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Abstract

Continuous BRAF inhibition of BRAF mutant melanomas triggers a series of cell state changes that lead to therapy resistance and escape from immune control before establishing acquired resistance genetically. We used genome-wide transcriptomics and single-cell phenotyping to explore the response kinetics to BRAF inhibition for a panel of patient-derived BRAF^(V600)-mutant melanoma cell lines. A subset of plastic cell lines, which followed a trajectory covering multiple known cell state transitions, provided models for more detailed biophysical investigations. Markov modeling revealed that the cell state transitions were reversible and mediated by both Lamarckian induction and nongenetic Darwinian selection of drug-tolerant states. Single-cell functional proteomics revealed activation of certain signaling networks shortly after BRAF inhibition, and before the appearance of drug-resistant phenotypes. Drug targeting those networks, in combination with BRAF inhibition, halted the adaptive transition and led to prolonged growth inhibition in multiple patient-derived cell lines.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1073/pnas.1712064115DOIArticle
http://www.pnas.org/content/114/52/13679PublisherArticle
http://www.pnas.org/content/suppl/2017/12/06/1712064115.DCSupplementalPublisherSupporting Information
ORCID:
AuthorORCID
Kim, Jungwoo0000-0002-5215-2044
Heath, James R.0000-0001-5356-4385
Additional Information:© 2017 National Academy of Sciences. Published under the PNAS license. Edited by Herbert Levine, Rice University, Houston, TX, and approved November 14, 2017 (received for review July 6, 2017). Published online before print December 11, 2017. We acknowledge the following agencies and foundations for support: NIH Grants U54 CA199090 (to J.R.H., W.W., and A.R.), P01 CA168585 (to T.G.G. and A.R.), and R35 CA197633 (to A.R.); the Dr. Robert Vigen Memorial Fund, the Garcia-Corsini Family Fund, the Ressler Family Fund, and the Grimaldi Family Fund (A.R.); the Jean Perkins Foundation (J.R.H.); University of California, Los Angeles, Broad Stem Cell Research Center Seed Fund for Small Cell Cancer Pilot Studies, and the Phelps Family Foundation (W.W.); and ACS Research Scholar Award (RSG-12-257-01-TBE), MRA Established Investigator Award (20120279), and University of California, Los Angeles, Clinical and Translational Science Institute Grant UL1TR000124 (to T.G.G.). We acknowledge University of California, Los Angeles, Jonsson Comprehensive Cancer Center (JCCC) membership (NIH/NCI P30CA016042) for using the JCCC Flow Cytometry Core and Genomics Shared Resource. L.R. was supported by the V Foundation-Gil Nickel Family Endowed Fellowship and a scholarship from SEOM. J.T. was supported by NIH T32-CA009120. Author contributions: W.W., A.R., and J.R.H. designed research; Y.S., W.W., L.R., M.X., J.T., A.G.-D., B.H.M., J.K., R.H.N., J.W.L., R.C.K., and B.C.-A. performed research; Y.S. and W.W. developed the computational model; Y.S., W.W., L.R., M.X., J.T., A.G.-D., B.H.M., T.G.G., A.R., and J.R.H. analyzed data; W.W., A.R., and J.R.H. supervised the study; and Y.S., W.W., L.R., M.X., A.R., and J.R.H. wrote the paper. Conflict of interest statement: J.R.H. and A.R. are affiliated with Isoplexis, which is seeking to commercialize the single-cell barcode chip technology. This article is a PNAS Direct Submission. Data deposition: The RNA-seq data reported in this paper have been deposited in the ArrayExpress database (accession no. E-MTAB-5493). This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1712064115/-/DCSupplemental.
Funders:
Funding AgencyGrant Number
NIHU54 CA199090
NIHP01 CA168585
NIHR35 CA197633
Dr. Robert Vigen Memorial FundUNSPECIFIED
Garcia-Corsini Family FundUNSPECIFIED
Ressler Family FundUNSPECIFIED
Grimaldi Family FundUNSPECIFIED
Jean Perkins FoundationUNSPECIFIED
University of California Los Angeles (UCLA)UNSPECIFIED
Broad Stem Cell Research CenterUNSPECIFIED
Phelps Family FoundationUNSPECIFIED
American Chemical SocietyRSG-12-257-01-TBE
Melanoma Research Alliance20120279
NIHUL1TR000124
NIHP30CA016042
V Foundation-Gil Nickel Family Endowed FellowshipUNSPECIFIED
NIH Predoctoral FellowshipT32-CA009120
Subject Keywords:single-cell analysis; cell state transition; adaptive resistance; Markov chain model; melanoma
Record Number:CaltechAUTHORS:20171211-152922263
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20171211-152922263
Official Citation:Yapeng Su, Wei Wei, Lidia Robert, Min Xue, Jennifer Tsoi, Angel Garcia-Diaz, Blanca Homet Moreno, Jungwoo Kim, Rachel H. Ng, Jihoon W. Lee, Richard C. Koya, Begonya Comin-Anduix, Thomas G. Graeber, Antoni Ribas, and James R. Heath Single-cell analysis resolves the cell state transition and signaling dynamics associated with melanoma drug-induced resistance PNAS 2017 114 (52) 13679-13684; published ahead of print December 11, 2017, doi:10.1073/pnas.1712064115
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:83813
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:12 Dec 2017 21:53
Last Modified:11 Jan 2018 18:23

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