Introduction
There are thousands of mutations in cancer tissues (
1- Lawrence M.S.
- Stojanov P.
- Polak P.
- Kryukov G.V.
- Cibulskis K.
- Sivachenko A.
- Carter S.L.
- Stewart C.
- Mermel C.H.
- Roberts S.A.
- Kiezun A.
- Hammerman P.S.
- McKenna A.
- Drier Y.
- Zou L.
- et al.
Mutational heterogeneity in cancer and the search for new cancer-associated genes.
,
2- Zehir A.
- Benayed R.
- Shah R.H.
- Syed A.
- Middha S.
- Kim H.R.
- Srinivasan P.
- Gao J.
- Chakravarty D.
- Devlin S.M.
- Hellmann M.D.
- Barron D.A.
- Schram A.M.
- Hameed M.
- Dogan S.
- et al.
Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients.
), whereas the current list of clinically validated actionable variants only includes a handful of genetic markers (
3- Hyman D.M.
- Taylor B.S.
- Baselga J.
Implementing genome-driven oncology.
). Most of the somatic mutations in cancer are expected to be inconsequential passenger mutations that reflect the general genetic instability of the tumors (
4- Vogelstein B.
- Papadopoulos N.
- Velculescu V.E.
- Zhou S.
- Diaz Jr., L.A.
- Kinzler K.W.
Cancer genome landscapes.
). Discovery of the currently known driver mutations has been facilitated, in part, by their accumulation in mutational hot spots within their respective genes. Such examples include the somatic missense mutations at BRAF Val-600, KRAS Gly-12, and EGFR
7The abbreviations used are:
EGFR
epidermal growth factor receptor
RTK
receptor tyrosine kinase
TKI
tyrosine kinase inhibitor
IL
interleukin
SNV
single-nucleotide variant
RMSD
root mean square deviation
PDB
Protein Data Bank
iSCREAM
in vitro screen for activating mutations
HRP
horseradish peroxidase
MTT
3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide.
Leu-858 (
3- Hyman D.M.
- Taylor B.S.
- Baselga J.
Implementing genome-driven oncology.
). However, a great majority of mutations in cancer tissues are observed outside these hot spots (
5- Chang M.T.
- Asthana S.
- Gao S.P.
- Lee B.H.
- Chapman J.S.
- Kandoth C.
- Gao J.
- Socci N.D.
- Solit D.B.
- Olshen A.B.
- Schultz N.
- Taylor B.S.
Identifying recurrent mutations in cancer reveals widespread lineage diversity and mutational specificity.
), and very little direct information is currently available about their functional relevance. The functional characterization of the infrequent mutations, even at the biochemical level, has been technically challenging, given the large number of possible variants. There is a clear clinical need for better understanding of the functional significance of these mutations, both for the identification of markers to predict drug responses and for the discovery of novel actionable targets (
6- Dienstmann R.
- Jang I.S.
- Bot B.
- Friend S.
- Guinney J.
Database of genomic biomarkers for cancer drugs and clinical targetability in solid tumors.
). This need has only become more prominent in the era of large clinical cancer tissue-sequencing efforts and the expansion of the arsenal of available targeted drugs.
EGFR is a member of the ERBB subfamily of receptor tyrosine kinases (RTKs) and a well-characterized oncogene. There are currently 12 tyrosine kinase inhibitors (TKIs) or mAbs targeting EGFR (either as the sole target or among other RTKs) that have been approved by the Food and Drug Administration for clinical use. When used to treat non-small-cell lung cancer, the presence of activating mutations at exons 19 (deletions) or 21 (substitution L858R) in the tumor tissue has been shown to be critical for clinical response to TKIs, such as erlotinib or gefitinib (
7- Mok T.S.
- Wu Y.L.
- Thongprasert S.
- Yang C.-H.
- Chu D.-T.
- Saijo N.
- Sunpaweravong P.
- Han B.
- Margono B.
- Ichinose Y.
- Nishiwaki Y.
- Ohe Y.
- Yang J.J.
- Chewaskulyong B.
- Jiang H.
- Duffield E.L.
- et al.
Gefitinib or carboplatin–paclitaxel in pulmonary adenocarcinoma.
). However, over 1,000 different nonsynonymous variants have been reported along the 1,210 amino acid EGFR primary sequence (
Fig. 1), whereas functional validation has only been carried out for a fraction of these. Here, we set out to systematically address the activating potential of thousands of random
EGFR single-nucleotide variants using an
in vitro model of somatic evolution. We provide evidence indicating that few of the clinically rare variants promote significant growth advantage in an EGFR activity–dependent cell model.
Discussion
Here, we provide evidence based on an in vitro functional genetics screen that despite thousands of mutations identified in cancer patients, a relatively small number of EGFR variants have the potential to provide growth advantage under selection pressure. In total, 7,216 unique nonsynonymous EGFR SNVs were analyzed in a massively parallel manner based on their ability to promote IL-3–independent growth in the Ba/F3 cell model. Interestingly, only 21 of the 7,216 (0.38%), were significantly (q < 0.0001) enriched during 2 weeks of IL-3 deprivation.
The analysis covered 88.4% (854 of 966) of unique nonsynonymous SNVs reported in human cancer samples in the COSMIC database (
Fig. 1). Collectively, a search of four databases (COSMIC, cBioPortal, GENIE, and CCLE) indicated that 19 of the 21 identified putatively activating mutations were at residues that are mutated in clinical samples, either with an identical missense or nonsense mutation (10 of 21) or with a different amino acid substitution at the same residue (an additional 9 of 21) (
Table 1).
Table 1EGFR mutations in clinical databases
As the mutation strategy adopted for the in vitro analysis involved generation of, on average, 2.7 mutations per cDNA molecule, passenger mutations could evolve alongside the drivers, making the number of true identified driver mutations even smaller than 21. Indeed, cloning and independent expression of six selected mutation hits indicated that only three (50%) were capable of supporting growth. Moreover, none of the mutations, with the exception of the nonsense mutation L833* depicted to truncate the receptor kinase domain, was enriched to a greater extent than L858R, the best-characterized activating EGFR mutation.
In addition to recapitulating the clinical observation about the unique significance of L858R as a product of an
EGFR SNV, the distributions of the mutations along the structural domains of EGFR were roughly similar in our analysis and in the mutation maps of clinical sequencing efforts (compare
Figure 1,
Figure 3). Few hits were observed in the extracellular domain, whereas the mostly targeted region was the kinase domain.
In addition to the EGFR L858R mutation, the T790M mutation, previously shown to both promote growth and provide clinical resistance to first generation EGFR TKIs (
13- Regales L.
- Balak M.N.
- Gong Y.
- Politi K.
- Sawai A.
- Le C.
- Koutcher J.A.
- Solit D.B.
- Rosen N.
- Zakowski M.F.
- Pao W.
Development of new mouse lung tumor models expressing EGFR T790M mutants associated with clinical resistance to kinase inhibitors.
), was among the validated findings. As a novel finding, our analysis identified a previously uncharacterized activating EGFR mutation, A702V. Structural analyses indicated that the A702V mutation in the receiver kinase domain likely strengthens the hydrophobic interaction with the activator kinase stabilizing the active kinase dimer. Drug sensitivity assays with different EGFR-targeting therapeutics demonstrated selective sensitivity of Ba/F3 cells expressing EGFR A702V to the second-generation EGFR TKI afatinib, in addition to the EGFR antibody cetuximab. Interestingly, the somatic A702V mutation has also been reported in a clinical case of non-small-cell lung cancer, in a patient with partial response to a protocol including erlotinib (
20- Reckamp K.L.
- Krysan K.
- Morrow J.D.
- Milne G.L.
- Newman R.A.
- Tucker C.
- Elashoff R.M.
- Dubinett S.M.
- Figlin R.A.
A phase I trial to determine the optimal biological dose of celecoxib when combined with erlotinib in advanced non-small cell lung cancer.
). These observations suggest that the EGFR A702V mutation may have predictive value as a novel clinical target for EGFR inhibitors.
The reason that other EGFR variants, such as G719A, S768I, or L861Q, previously detected in cancer tissues and proposed to modulate EGFR activity or sensitivity to EGFR inhibitors (
21- Beau-Faller M.
- Prim N.
- Ruppert A.-M.
- Nanni-Metéllus I.
- Lacave R.
- Lacroix L.
- Escande F.
- Lizard S.
- Pretet J.-L.
- Rouquette I.
- de Crémoux P.
- Solassol J.
- de Fraipont F.
- Bièche I.
- Cayre A.
- et al.
Rare EGFR exon 18 and exon 20 mutations in non-small-cell lung cancer on 10 117 patients: a multicentre observational study by the French ERMETIC-IFCT network.
,
22- O'Kane G.M.
- Bradbury P.A.
- Feld R.
- Leighl N.B.
- Liu G.
- Pisters K.-M.
- Kamel-Reid S.
- Tsao M.S.
- Shepherd F.A.
Uncommon EGFR mutations in advanced non-small cell lung cancer.
23- Galli G.
- Corrao G.
- Imbimbo M.
- Proto C.
- Signorelli D.
- Ganzinelli M.
- Zilembo N.
- Vitali M.
- de Braud F.
- Garassino M.C.
- Lo Russo G.
Uncommon mutations in epidermal growth factor receptor and response to first and second generation tyrosine kinase inhibitors: a case series and literature review.
), were present but not enriched in the Ba/F3 cell–based screen is currently not known. However, given the pace and the timing of the emergence of IL-3–independent Ba/F3 clones expressing the different EGFR variants (
Fig. 4B), it is conceivable that the more active variants, such as L858R, outcompete weaker oncogenes during the selection. This hypothesis was supported by findings suggesting that the EGFR variants demonstrating greater autophosphorylation (
Fig. 5A) also emerged with a shorter refractory period upon IL-3 withdrawal (
Fig. 4B). Whereas this phenomenon may be considered a limitation for the analysis in poorly detecting some weaker oncogenes, the circumstances are not irrelevant for the evolution of mutations in living cancer tissues. Indeed, also in cancer, the strong oncogenes are expected to outcompete weaker ones (
24- Martincorena I.
- Raine K.M.
- Gerstung M.
- Dawson K.J.
- Haase K.
- Van Loo P.
- Davies H.
- Stratton M.R.
- Campbell P.J.
Universal patterns of selection in cancer and somatic tissues.
), and, for example, L858R and T790M are more frequently observed than G719A, S768I, and L861Q (
Fig. 1 and
Table 1). It is also noteworthy that the uncommon EGFR mutations have been shown to commonly co-occur with other EGFR mutations and that their clinical significance is variable and clearly less well-elucidated, as compared with L858R and T790M (
22- O'Kane G.M.
- Bradbury P.A.
- Feld R.
- Leighl N.B.
- Liu G.
- Pisters K.-M.
- Kamel-Reid S.
- Tsao M.S.
- Shepherd F.A.
Uncommon EGFR mutations in advanced non-small cell lung cancer.
,
23- Galli G.
- Corrao G.
- Imbimbo M.
- Proto C.
- Signorelli D.
- Ganzinelli M.
- Zilembo N.
- Vitali M.
- de Braud F.
- Garassino M.C.
- Lo Russo G.
Uncommon mutations in epidermal growth factor receptor and response to first and second generation tyrosine kinase inhibitors: a case series and literature review.
).
Using the IL-3–dependent lymphoid Ba/F3 cells for the functional selection step and drug sensitivity analyses introduces a cell background that cannot be considered representative of the molecular context of human cancer cells. However, the characteristic rapid pace of cell division, combined with the absolute dependence on exogenous IL-3 that can be compensated by endogenous expression of an active kinase, make the Ba/F3 cells ideal for the functional selection of activating variants. Moreover, the fact that the Ba/F3 cells have been widely used for addressing the functional activation of several receptor as well as nonreceptor tyrosine kinases (
8- Warmuth M.
- Kim S.
- Gu X.J.
- Xia G.
- Adrián F.
Ba/F3 cells and their use in kinase drug discovery.
) indicates that the same Ba/F3-based pipeline could be used for analyzing a number of kinases in addition to EGFR. Although effective in identifying activating SNVs, it is, however, important to keep in mind that our model was not designed to screen for short deletions, that, for example, in the case of EGFR have also been shown to have predictive value in the clinic (
11- Paez J.G.
- Jänne P.A.
- Lee J.C.
- Tracy S.
- Greulich H.
- Gabriel S.
- Herman P.
- Kaye F.J.
- Lindeman N.
- Boggon T.J.
- Naoki K.
- Sasaki H.
- Fujii Y.
- Eck M.J.
- Sellers W.R.
- et al.
EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy.
,
12- Lynch T.J.
- Bell D.W.
- Sordella R.
- Gurubhagavatula S.
- Okimoto R.A.
- Brannigan B.W.
- Harris P.L.
- Haserlat S.M.
- Supko J.G.
- Haluska F.G.
- Louis D.N.
- Christiani D.C.
- Settleman J.
- Haber D.A.
Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib.
). Given the findings that the different EGFR variants also differently promoted their own expression (or selection of a subpopulation of high expressors) (
Fig. 5), further development of the pipeline should also involve comprehensive expression analysis of each individual oncogene.
Taken together, our findings indicate that an in vitro clonal selection model with randomly mutated variants can be successfully used to identify gain-of-function mutations, given that a cell line addicted to the signaling of the gene of interest is available. In addition, we provide evidence supporting a conclusion that, despite thousands of somatic mutations reported in the EGFR gene, only a handful (less than 1%) are expected to function as active drivers.
Experimental procedures
Cloning of constructs
WT human
EGFR cDNA was amplified from EGFR WT, a gift from Matthew Meyerson (Addgene plasmid 11011) (
25- Greulich H.
- Chen T.H.
- Feng W.
- Jänne P.A.
- Alvarez J.V.
- Zappaterra M.
- Bulmer S.E.
- Frank D.A.
- Hahn W.C.
- Sellers W.R.
- Meyerson M.
Oncogenic transformation by inhibitor-sensitive and -resistant EGFR mutants.
), using the primers 5′-GGGGACAAGTTTGTACAAAAAAGCAGGCTTCACCATGCGACCCTCCGGGACGG-3′ and 5′-GGGGACCACTTTGTACAAGAAAGCTGGGTTTCATGCTCCAATAAATTCACTGCTTTGTG-3′ to generate a 3,697-bp amplicon containing
EGFR cDNA flanked by
attB1 and
attB2 gateway recombination sites. The amplicon was cloned into the
pDONR221 vector (Tol2Kit) (
26- Kwan K.M.
- Fujimoto E.
- Grabher C.
- Mangum B.D.
- Hardy M.E.
- Campbell D.S.
- Parant J.M.
- Yost H.J.
- Kanki J.P.
- Chien C.-B.
The Tol2kit: a multisite gateway-based construction kit forTol2 transposon transgenesis constructs.
) using BP clonase II mix (Invitrogen) to generate
pDONR221-EGFR. A retroviral mammalian expression construct was made by a LR-Gateway recombination reaction between the
pDONR221-EGFR and
pBABEpuro-gateway, a gift from Matthew Meyerson (Addgene plasmid 51070) (
27- Greulich H.
- Kaplan B.
- Mertins P.
- Chen T.-H.T.
- Tanaka K.E.
- Yun C.-H.C.
- Zhang X.
- Lee S.-H.
- Cho J.
- Ambrogio L.
- Liao R.
- Imielinski M.
- Banerji S.
- Berger A.H.
- Lawrence M.S.
- et al.
Functional analysis of receptor tyrosine kinase mutations in lung cancer identifies oncogenic extracellular domain mutations of ERBB2.
), using LR clonase II mix (Invitrogen).
To create plasmids expressing individual
EGFR variants,
pDONR221-EGFR was mutated to generate the indicated point mutations using the primers listed in
Fig. S10. These mutations were subsequently cloned to
pBABEpuro-gateway by LR-Gateway recombination to create the expression constructs.
Generation of expression library of random EGFR mutants
The expression library for human EGFR mutants was generated by error-prone PCR using the GeneMorph II random mutagenesis kit (catalogue no. 200550, Agilent Technologies). Using pDONR221-EGFR as the template and the primer pair 5′-TTGATGCCTGGCAGTTCCCTA-3′ (which binds 78 bp upstream of the attL1 site at the 5′-end of the EGFR insert) and 5′-ATCTTGTGCAATGTAACATCAGAGATT-3′ (which binds 80 bp downstream of the attL2 site at the 3′-end of the EGFR insert), 4,043-bp amplicons were produced containing randomly mutated EGFR cDNAs flanked by the attL recombination sites. Following the instructions in the GeneMorph II random mutagenesis kit manual, 10 cycles of amplification were performed. The PCR product was gel-purified (NucleoSpin Gel and PCR Clean-up kit; Macherey Nagel) and cloned into a pBABEpuro-gateway vector with LR clonase II. ccdB-sensitive Escherichia coli (NovaBlue) were transformed with the LR reaction product to amplify the random mutation library plasmid.
Cell culture and generation of stable lines
To generate stable Ba/F3 lines expressing the EGFR library or single EGFR mutants, amphotropic Phoenix HEK293 cells were transfected with the respective pBABEpuro-gateway plasmid or the empty vector control using Fugene 6 transfection reagent (Promega) for production of retroviruses. Ba/F3 cells were transduced with the retroviral supernatants, and stable cell populations were selected in the presence of 2 μg/ml puromycin (Gibco) for 48 h. Ba/F3 cells were maintained in RPMI 1640 (Lonza), containing 1 mm l-glutamine (Lonza), 50 units/ml penicillin-streptomycin (Lonza), 10% FBS (Biowest), 5% conditioned medium from WEHI cells (as a source of IL-3), and 1 μg/ml puromycin. To select for EGFR mutations promoting growth in the absence of IL-3, 2.5 × 107 Ba/F3 cells stably transduced with the EGFR random mutant library were washed twice with 20 ml of PBS and maintained in a volume of 40 ml of RPMI 1640, containing 1 mm l-glutamine, 50 units/ml penicillin-streptomycin, 10% FBS, and 1 μg/ml puromycin.
A549 and NCI-H661 human lung cancer cells (ATCC) were maintained in Dulbecco's modified Eagle's medium (Lonza), containing 1 mm l-glutamine, 50 units/ml penicillin-streptomycin, and 10% FBS.
Next-generation sequencing
Genomic DNA (100 ng) extracted from the Ba/F3 cells (NucleoSpin Tissue, Macherey Nagel) as well as the original plasmid library (5 ng) used for transduction were PCR-amplified using primers 5′-GGGGACAAGTTTGTACAAAAAAGCAGGCTTCACCATGCGACCCTCCGGGACGG-3′ and 5′-GGGGACCACTTTGTACAAGAAAGCTGGGTTTCATGCTCCAATAAATTCACTGCTTTGTG-3′ and a 1:1 mixture of Phusion (Thermo) and Velocity (Bioline) high-fidelity DNA polymerases. The Nextera XT DNA Sample Preparation Kit (Illumina) was used to prepare sequencing libraries from these amplicons, which were sequenced on MiSeq with 150-bp paired-end sequencing, producing 6 million reads/sample. Filtering of low-quality reads and trimming of Nextera XT adapter sequences from the reads was performed with trimmomatic (version 0.36) (
28- Bolger A.M.
- Lohse M.
- Usadel B.
Trimmomatic: a flexible trimmer for Illumina sequence data.
) using the parameters recommended in the user guide for paired end sequencing (
http://www.usadellab.org/cms/?page=trimmomatic).
8Please note that the JBC is not responsible for the long-term archiving and maintenance of this site or any other third party hosted site.
The trimmed reads were aligned to “hg19” human reference genome using BWA-MEM (version 0.7.15) (
29Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM.
). The produced Sequence Alignment/Map (SAM) files were converted to Binary Alignment Map (BAM) files, sorted, and indexed using samtools (version 1.3.1) (
30- Li H.
- Handsaker B.
- Wysoker A.
- Fennell T.
- Ruan J.
- Homer N.
- Marth G.
- Abecasis G.
- Durbin R.
- 1000 Genome Project Data Processing Subgroup
The sequence alignment/map format and SAMtools.
). Variant calling was carried out using samtools by setting the maximum depth to 300,000 to accommodate all of the reads aligned to the reference genome. The NexteraXT libraries were not generated using a strand-specific protocol, and therefore, to identify potential sequencing artifacts, the result from bamreadcount (
https://github.com/genome/bam-readcount)
8 was used to calculate strand bias (ratio of the number of forward reads to the number of reverse reads aligning to a particular locus) for each detected variant in the samples. Variants with strand bias less than 0.1 and larger than 10 (
i.e. reads aligning at a locus with one orientation (5′–3′) being 10 times more abundant than the other orientation) were filtered out. Annovar (version 2017-07-17 01:17:05-0400) (
31- Wang K.
- Li M.
- Hakonarson H.
ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data.
) was used for functional annotation of the variants. Data analysis was carried out using R (version 3.4.1) (
), and plots were made using “ggplot2” (version 2.2.1) and “plotly” (version 4.7.1) R packages (
,
). The source code used to analyze the data produced in this study can be accessed at
https://gitlab.utu.fi/deecha/iSCREAM.EGFR.
8Statistical analyses
To identify the
EGFR mutations that were enriched during the clonal expansion of Ba/F3 cells under IL-3 depletion, -fold changes were calculated by comparing the variant allele frequencies of the mutations in the surviving cells with that in the original
EGFR mutant expression library. The approach was named
in vitro screen for
activating
mutations (iSCREAM). Using all of the observed -fold changes, a normal distribution was fitted (μ = 0.093 ± 0.011, σ = 0.967 ± 0.008) to log
2-transformed -fold change values using the “fitdistrplus” package in R (
35- Delignette-Muller M.L.
- Dutang C.
fitdistrplus: an R Package for fitting distributions.
). Then
p values were calculated for all of the mutations, and correction for multiple testing was performed using the Benjamini–Hochberg method (
10Controlling the false discovery rate: a practical and powerful approach to multiple testing.
). Mutations with
q < 0.0001 were considered significantly enriched, which corresponds to a false discovery rate of 0.01%.
Cell proliferation assay
Ba/F3 cells expressing WT
EGFR or selected
EGFR mutants were seeded at a density of 20,000 cells/well in 96-well plates. Cell viability was assessed using the MTT assay (Promega) in the presence or absence of 5% conditioned medium from WEHI cells as a source of IL-3. The growth curves indicating mean ± S.D. were plotted using the “ggplot2” R package (
). Sigmoidal curves were fitted using the “drc” (version 3.0-1) package in R (
36- Ritz C.
- Baty F.
- Streibig J.C.
- Gerhard D.
Dose-response analysis using R.
).
Western blot analysis
Cells were harvested and lysed in lysis buffer (10 mm Tris-HCl, pH 7.4, 1% Triton X-100, 150 mm NaCl, 1 mm EDTA, 10 mm NaF), supplemented with 2 mm phenylmethylsulfonyl fluoride, 10 mg/ml aprotinin, 10 mg/ml leupeptin, 10 mm Na4P2O7, and 1 mm Na3VO4. Lysates were analyzed for EGFR phosphorylation using the phospho-EGFR–specific antibody (catalogue no. 2220, Cell Signaling Technology) recognizing phosphorylated Tyr-1110. Anti-EGFR (sc-03 (Santa Cruz Biotechnology, Inc.) and catalogue no. 4267 (Cell Signaling Technology)), anti-ERBB3 (catalogue no. 4754, Cell Signaling Technology), and anti-MET (catalogue no. 8198, Cell Signaling Technology) were used to analyze total EGFR, ERBB3, and MET expression, respectively, and anti-β-actin (A5441; Sigma Aldrich) as a loading control. HRP-conjugated anti-rabbit (catalogue no. A16104, Invitrogen), HRP-conjugated anti-mouse (catalogue no. sc-2005, Santa Cruz Biotechnology), IRDye 800CW anti-rabbit (catalogue no. 925-32213, LI-COR), and IRDye 680RD anti-mouse (catalogue no. 925-68072, LI-COR) were used as secondary antibodies. The blots were imaged using chemiluminescence (WesternBright ECL HRP, Advansta) on the ImageQuant LAS-4000 imaging system (Fujifilm) or using near-IR fluorescence on the Odyssey CLx imaging system (LI-COR).
To address the effect of EGFR inhibitors on EGFR phosphorylation, cells were seeded at a density of 3 million cells/well in 12-well plates and treated with erlotinib (10–1000 nm; Santa Cruz Biotechnology) or afatinib (1–100 nm; Santa Cruz Biotechnology) for 3 h.
Flow cytometry analysis of EGFR expression
Ba/F3 clones were washed with azide-free PBS and stained with eBioscience Fixable Viability Dye eFluor 780 (catalogue no. 65-0865, Thermo Fisher Scientific) according to the manufacturer’s protocol. The cells were fixed with 4% paraformaldehyde and permeabilized with ice-cold methanol. Fixed cells were incubated with anti-EGFR (1:100; catalogue no. 4267, Cell Signaling Technologies) followed by incubation with Alexa Fluor 488–conjugated anti-rabbit (1:200; catalogue no. A-11034, Thermo Fisher Scientific). An LSR Fortessa flow cytometer was used with the BD FACSDiva software (version 8.0.1). The data were analyzed using the FlowJo software (version 10.5.3).
Real-time RT-PCR
RNA samples were extracted with TRIsure (Bioline) according to the manufacturer’s protocol. cDNA was synthesized with the SensiFast cDNA synthesis kit (Bioline). The analysis was carried out using TaqMan Universal Master Mix II (Applied Biosystems) with the following primers and probes: human EGFR forward, 5′-CCA CCT GTG CCA TCC AAA CT-3′ (Pharmacia); human EGFR reverse, 5′-GGC GAT GGA CGG GAT CTT-3′ (Pharmacia); human EGFR probe, 5′-FAM-CCA GGT CTT GAA GGC TGT CCA ACG AAT-TAMRA-3′ (Eurogentech); mouse GAPDH, Universal ProbeLibrary Mouse GAPDH Gene Assay 5046211001 (Sigma). Thermal cycling was performed using the QuantStudio 12K Flex Real-Time PCR System (Thermo Fisher Scientific) for 2 min at 50 °C and 10 min at 95 °C, followed by 40 cycles of 15 s at 95 °C and 1 min at 60 °C. Samples were analyzed in triplicates, and the S.D. of the CT values was <5% of the mean. EGFR mRNA expression was quantified using mouse GAPDH mRNA expression as a reference with the 2−ΔCT method.
Drug sensitivity assay
Ba/F3 cells were seeded at a density of 20,000 cells/well in 96-well plates. The cells were incubated in the presence of 0.0006–10 μ
m erlotinib (Santa Cruz Biotechnology) or afatinib (Santa Cruz Biotechnology) or 0.0006–10 μg/ml cetuximab (Erbitux, Merck) for 72 h before measuring the number of viable cells with the MTT assay. IL–3-independent clones were cultured in the absence of, and the control Ba/F3 cells were transduced with an empty vector in the presence of, 5% WEHI cell conditioned medium. Sigmoidal dose-response curves were fitted using 4-parameter logistic regression with the “drc” (version 3.0-1) package in R (
36- Ritz C.
- Baty F.
- Streibig J.C.
- Gerhard D.
Dose-response analysis using R.
). Dose-response curves indicating mean ± S.D. were plotted using “ggplot2” (
).
Structural analyses of EGFR kinase domains
To evaluate the likely consequences of the A702V mutation in human EGFR, the A702V mutation was modeled into the X-ray structures (PDB codes 2GS2 (
17- Zhang X.
- Gureasko J.
- Shen K.
- Cole P.A.
- Kuriyan J.
An allosteric mechanism for activation of the kinase domain of epidermal growth factor receptor.
) (2.8 Å resolution) and 2GS6 with bound ATP analogue (
17- Zhang X.
- Gureasko J.
- Shen K.
- Cole P.A.
- Kuriyan J.
An allosteric mechanism for activation of the kinase domain of epidermal growth factor receptor.
) (2.6 Å resolution)) of EGFR using the mutation function of the modeling and visualization program Bodil (
37- Lehtonen J.V.
- Still D.-J.
- Rantanen V.V.
- Ekholm J.
- Björklund D.
- Iftikhar Z.
- Huhtala M.
- Repo S.
- Jussila A.
- Jaakkola J.
- Pentikäinen O.
- Nyrönen T.
- Salminen T.
- Gyllenberg M.
- Johnson M.S.
BODIL: a molecular modeling environment for structure-function analysis and drug design.
). Symmetry operations were used to generate the asymmetric dimer structures consisting of a receiver and activator kinase domain. The rotamer function in Bodil was used to explore alternative conformations of the valine side chain. Ala-702 is located on the extended juxtamembrane segment B, and in the receiver kinase, this segment extends across the dimer interface with the activator kinase. Visualization with Bodil indicates that the A702V mutation would very likely enhance hydrophobic contacts with Ile-941 of the activator kinase, strengthening the interface of the active asymmetric dimer.
Fig. 6 was prepared using Bodil.
Human EGFR domains (2GS2, active, no bound ligand (
17- Zhang X.
- Gureasko J.
- Shen K.
- Cole P.A.
- Kuriyan J.
An allosteric mechanism for activation of the kinase domain of epidermal growth factor receptor.
); erlotinib-bound, active 1M17 (
38- Stamos J.
- Sliwkowski M.X.
- Eigenbrot C.
Structure of the epidermal growth factor receptor kinase domain alone and in complex with a 4-anilinoquinazoline inhibitor.
) and inactive 4HJO (
39- Park J.H.H.
- Liu Y.
- Lemmon M.A.A.
- Radhakrishnan R.
Erlotinib binds both inactive and active conformations of the EGFR tyrosine kinase domain.
); and 4G5J, afatinib-bound (
19- Solca F.
- Dahl G.
- Zoephel A.
- Bader G.
- Sanderson M.
- Klein C.
- Kraemer O.
- Himmelsbach F.
- Haaksma E.
- Adolf G.R.
Target binding properties and cellular activity of afatinib (BIBW 2992), an irreversible ErbB family blocker.
)) were superposed with the active domain bound to an ATP analogue (2GS6) using Vertaa in Bodil. RMSD values are reported over equivalent Cα atom pairs from the two structures within 3.5 Å of each other after superpositioning. The respective 2.6, 2.75, and 2.8 Å resolution structures of human EGFR domains with bound erlotinib, active (1M17) and inactive (4HJO), and afatinib (4G5J) were obtained from the PDB.
Molecular dynamic simulations and free energy of binding calculations
To understand the dynamic states and differences between the WT (PDB code 2GS2) and A702V mutant EGFR kinase asymmetric dimer structures, molecular dynamics simulations were carried out using the AMBER package (version 18) (
40- Case D.A.
- Betz R.M.
- Cerutti D.S.
- Cheatham I.I.I.
- Darden T.E.T.A.
- Duke R.E.
- Giese T.J.
- Gohlke H.
- Goetz A.W.
- Homeyer N.
- Izadi S.
- Janowski P.
- Kaus J.
- Kovalenko A.
- Lee T.S.
- et al.
) and the ff14SB force field (
41- Maier J.A.
- Martinez C.
- Kasavajhala K.
- Wickstrom L.
- Hauser K.E.
- Simmerling C.
ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB.
). The A702V mutant structure was generated using chimera (
42- Pettersen E.F.
- Goddard T.D.
- Huang C.C.
- Couch G.S.
- Greenblatt D.M.
- Meng E.C.
- Ferrin T.E.
UCSF Chimera—a visualization system for exploratory research and analysis.
). To carry out molecular dynamics simulations, both the WT and mutant EGFR structures were solvated with explicit TIP3P water molecules (
43- Jorgensen W.L.
- Chandrasekhar J.
- Madura J.D.
- Impey R.W.
- Klein M.L.
Comparison of simple potential functions for simulating liquid water.
) in an octahedral box. The distance between the dimer surface atoms and the edge of the box was set to 10 Å. Following this, 16 sodium ions were added to neutralize both dimer systems. Additionally, 150 m
m Na
+/Cl
− ions were incorporated into the system. Periodic boundary conditions were ensured, and the particle mesh Ewald algorithm (
44- Essmann U.
- Perera L.
- Berkowitz M.L.
- Darden T.
- Lee H.
- Pedersen L.G.
A smooth particle mesh Ewald method.
) was used to treat electrostatic interactions with a distance cutoff of 9 Å. Before conducting the production simulation, 3,000 cycles of energy minimization were carried out while gradually tapering off from the 25 kcal mol
−1 Å
−2 restraint applied to the solute atoms. The systems were then heated to 300 K during 100 ps with a 10 kcal mol
−1 Å
−2 restraint on solute atoms. Afterward, a 900-ps equilibration at constant pressure (1 bar) was employed while reducing the restraint gradually to 0.1 kcal mol
−1 Å
−2. The equilibration was finalized with restraint-free 10-ns simulation. Finally, the production simulation was performed for 100 ns at constant temperature (300 K) and pressure (1 bar) that were maintained using the Berendsen algorithm (
45- Berendsen H.J.C.
- Postma J.P.M.
- van Gunsteren W.F.
- DiNola A.
- Haak J.R.
Molecular dynamics with coupling to an external bath.
). Trajectories were saved every 10 ps, and the resulting snapshots were further analyzed using the programs CPPTRAJ (
46- Roe D.R.
- Cheatham 3rd, T.E.
PTRAJ and CPPTRAJ: software for processing and analysis of molecular dynamics trajectory data.
) and VMD (
47- Humphrey W.
- Dalke A.
- Schulten K.
VMD: visual molecular dynamics.
). These trajectories were also utilized to compute free energy of binding calculations for both the WT and mutant EGFR dimers using the molecular mechanics-generalized Born surface area (MMGBSA) method, implemented in the AMBER program (
40- Case D.A.
- Betz R.M.
- Cerutti D.S.
- Cheatham I.I.I.
- Darden T.E.T.A.
- Duke R.E.
- Giese T.J.
- Gohlke H.
- Goetz A.W.
- Homeyer N.
- Izadi S.
- Janowski P.
- Kaus J.
- Kovalenko A.
- Lee T.S.
- et al.
).
Author contributions
D. C., K. J. K., I. P., and K. E. conceptualization; D. C., I. P., V. K. O., M. K., M. Z. T., J. P. K., P. A. J., M. S. J., and K. E. formal analysis; D. C. validation; D. C., M. Z. T., M. S. J., and K. E. visualization; D. C., K. J. K., I. P., M. S. J., L. L. E., and K. E. methodology; D. C., K. J. K., I. P., M. S. J., and K. E. writing-original draft; D. C., K. J. K., I. P., J. P. K., P. A. J., M. S. J., L. L. E., and K. E. writing-review and editing; V. K. O., M. K., M. Z. T., and M. S. J. data curation; J. P. K. resources; K. E. supervision; K. E. funding acquisition; K. E. investigation; K. E. project administration.
Article info
Publication history
Published online: April 05, 2019
Received in revised form:
March 23,
2019
Received:
October 18,
2018
Edited by Eric R. Fearon
Footnotes
This article contains Figs. S1–S10.
P. A. Jänne has received consulting fees from AstraZeneca, Boehringer Ingelheim, Pfizer, Merrimack Pharmaceuticals, Roche/Genentech, Chugai Pharmaceuticals, ACEA Biosciences, and Ariad Pharmaceuticals and sponsored research funding from Astellas Pharmaceuticals, AstraZeneca, Daiichi Sankyo, and PUMA and receives post-marketing royalties on DFCI-owned intellectual property on EGFR mutations licensed to Lab Corp. K. Elenius has a research agreement with Boehringer Ingelheim and ownership interest in Abomics, Orion, and Roche.
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Copyright
© 2019 Chakroborty et al.