An unbiased in vitro screen for activating epidermal growth factor receptor mutations

A1 Journal article (refereed)

Internal Authors/Editors

Publication Details

List of Authors: Deepankar Chakroborty, Kari J. Kurppa, Ilkka Paatero, Veera K. Ojala, Marika Koivu, Mahlet Z. Tamirat, Jussi P. Koivunen, Pasi A. Jänne, Mark S. Johnson, Laura L. Elo, Klaus Elenius
Publisher: American Society for Biochemistry and Molecular Biology, Inc
Publication year: 2019
Journal: Journal of Biological Chemistry
Journal acronym: JBC
Volume number: 294
Issue number: 24
Start page: 9377
End page: 9389


Cancer tissues harbor thousands of mutations, and a given oncogene may
be mutated at hundreds of sites, yet only a few of
these mutations have been functionally tested.
Here, we describe an unbiased platform for the functional
of thousands of variants of a single receptor
tyrosine kinase (RTK) gene in a single assay. Our in vitro screen for activating mutations
(iSCREAM) platform enabled rapid analysis of mutations conferring
gain-of-function RTK activity promoting clonal
growth. The screening strategy included a somatic
model of cancer evolution and utilized a library of 7,216 randomly
epidermal growth factor receptor (EGFR)
single-nucleotide variants that were tested in murine lymphoid Ba/F3
cells. These cells depend on exogenous interleukin-3
(IL-3) for growth, but this dependence can be
compensated by ectopic EGFR overexpression, enabling selection for
EGFR mutants. Analysis of the enriched
mutants revealed EGFR A702V, a novel activating variant that
structurally stabilized the
EGFR kinase dimer interface and conferred
sensitivity to kinase inhibition by afatinib. As proof of concept for
our approach,
we recapitulated clinical observations and
identified the EGFR L858R as the major enriched EGFR variant.
Altogether, iSCREAM
enabled robust enrichment of 21 variants from a
total of 7,216 EGFR mutations. These findings indicate the power of this screening platform for unbiased identification of activating RTK variants
that are enriched under selection pressure in a model of cancer heterogeneity and evolution.


Last updated on 2020-02-04 at 05:29