Defining the landscape of ATP-competitive inhibitor resistance residues in protein kinases

Abstract

Kinases are involved in disease development and modulation of their activity can be therapeutically beneficial. Drug-resistant mutant kinases are valuable tools in drug discovery efforts, but the prediction of mutants across the kinome is challenging. Here, we generate deep mutational scanning data to identify mutant mammalian kinases that drive resistance to clinically relevant inhibitors. We aggregate these data with subsaturation mutagenesis data and use it to develop, test and validate a framework to prospectively identify residues that mediate kinase activity and drug resistance across the kinome. We validate predicted resistance mutations in CDK4, CDK6, ERK2, EGFR and HER2. Capitalizing on a highly predictable residue, we generate resistance mutations in TBK1, CSNK2A1 and BRAF. Unexpectedly, we uncover a potentially generalizable activation site that mediates drug resistance and confirm its impact in BRAF, EGFR, HER2 and MEK1. We anticipate that the identification of these residues will enable the broad interrogation of the kinome and its inhibitors.

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Fig. 1: Hotspot analysis of saturating and subsaturating mutagenesis screens identifies common sites that mediate ATP-competitive kinase inhibitor resistance.
Fig. 2: Key common drug-resistant positions mapped onto a ribbon structure of CDK6.
Fig. 3: Mutation of common drug-resistant positions in CDK6, CDK4 and ERK2 causes drug resistance.
Fig. 4: Mutation of common drug-resistant sites differentially impacts drug binding.
Fig. 5: Validation of common drug-resistant positions in EGFR, EML4-ALK, HER2 and BRAF that are associated with drug resistance in patient samples.
Fig. 6: Validation of drug-resistance phenotypes predicted for the Pocket Protector residue (position 32) in TBK1, CSNK2A1 and BRAF.
Fig. 7: Mutation of the Keymaster (position-61) residue leads to gain-of-function activity in protein kinases.

Data availability

All previously unpublished saturation mutagenesis data generated or analyzed during this study are included in this published article (and its Extended Data information files). All screening data described in this project can be found in the Supplementary Information or have been deposited in the National Center for Biotechnology Information Short Read Archive (BioProject accession number PRJNA559517). Source data for Fig. 3a,c,e, Fig. 4, Fig. 5a–d,g–j, Fig. 6a,b,d–f, Fig. 7b,d–f, Extended Data Fig. 4a, Extended Data Fig. 6d,e,g and Extended Data Fig. 7c,e are available online. Aggregated mutational scanning data, including previously published datasets, are available from the corresponding author on reasonable request.

Code availability

All code used to analyze mutant screening data (ORFCall) can be accessed at https://github.com/tedsharpe/ORFCall.

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Acknowledgements

We thank A. Burgin, C. Painter, B. Tomson and M. Rees for discussions and critical reading of the manuscript.

Author information

N.S.P. and C.M.J. designed the study and wrote the paper. D.H., M.D.C., L.B., O.C., S.K., U.N., A.W., S.P., Y.L., J.C., M.S., C.Z., T.K.H., P.R. and P.P. contributed data for this project. X.Y, C.Z. and N.S.P. analyzed the data, T.S.M., D.A.B., R.H., F.P., D.E.R and C.M.J. supervised the work.

Correspondence to C. M. Johannessen.

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Competing interests

C.M.J. is a full-time employee of Novartis Institutes of Biomedical Research, Inc. T.M. is a full-time employee of 10x Genomics.

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Peer review information Anke Sparmann was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Saturation mutagenesis of CDK4 and CDK6.

a Summary schematic of CDK6 and CDK4 saturating mutagenesis screens. b CDK6 and c CDK4 early time point mutant representation illustrated across the length of each protein. Each dot represents one of two replicate measurements (a and b). d and e Early time point reference correlations for CDK6 and CDK4 mutants. Each dot represents one of two replicate measurements. The pearson correlation score is reported on each graph. f Log(2)-fold-changes for each of four CDK6 replicate measurements was individually calculated with respect to the average of two replicate measurements of early time point mutant representation and shown as black dots. Replicated correlations of Log(2)-Fold-Changes Replicate A versus all three other replicates are shown with their corresponding Pearson correlations. g Log(2)-fold-changes for each of four CDK4 replicate measurements was individually calculated with respect to the average of two replicate measurements of early time point mutant representation and shown as black dots. Replicated correlations of Log(2)-Fold-Changes Replicate A versus all three other replicates are shown with their corresponding Pearson correlations.

Extended Data Fig. 2 Analysis of CDK4/6 saturation mutagenesis data.

a and b Scatter plots of the average Log(2)-Fold-Changes (LFCs, y-axis, n=4 replicate measurements) and native amino acid position (x-axis) for the complete mutagenesis data for CDK6 (a) and CDK4 (b) palbociclib resistance screens. Red lines indicate z-scores of 1.95, our significance threshold for all saturation data (see Methods). Normalized data can be found in Supplementary Tables 1-2. c and d Heatmap visualization of the data presented in (a) and (b) for CDK6 (c) and CDK4 (d). Colors correspond to the z-score of the LFCs. Darker blue indicates a z-score of -3 or lower and darker red indicates a z-score of +3 or higher (more red = more palbociclib resistant).

Extended Data Fig. 3 Spatial projection of CDK4/6 saturation mutagenesis phenotypic data onto the 3D structure of CDK6.

All data for the CDK6 palbociclib resistance screen was reduced to a single metric representing the maximum z-score observed at each position (average of n=4 independent replicates for each of n=19 substitutions). For positions that showed maximum z-scores over 1.95, the z-score was mapped onto the crystal structure of CDK6 bound to palbociclib (PDB 2EUF), either as a ribbon (a) or space-filling diagram (b). c View of the palbociclib binding pocket in CDK6 (PDB 2EUF). For a-c, light orange represents a z-score greater than 1.95 and red represents a z-score of 6 or higher.

Extended Data Fig. 4 Validation of CDK4/6 saturation mutagenesis data.

a Common drug resistant positions found in the CDK6 screen for palbociclib resistance are tested here for resistance to two other CDK4/CDK6 inhibitors: abemaciclib and ribociclib. Average cumulative population doublings after 14 days of Meljuso cells exogenously expressing wild-type (WT) or mutant CDK6 in the absence of drug are shown in turquoise. Treatment with 0.9 µM abemaciclib is shown in light green, and 8 µM ribociclib is shown in dark green, (n=2 independent experiments), error bars represent standard deviation. The correlation of resistance phenotypes in the CDK6 (b) or CDK4 (c) primary screens compared to population doublings in validation experiments, to phosphorylated Rb (compared to no drug) validation experiments, as well as the correlation of validation experiments to each other. Pearson correlations for each set are reported on the graph. Each dot represents a single measurement of the representative western blot shown in main figures. Source data

Extended Data Fig. 5 Assessment of drug binding to CDK6 mutants using CETSA and DSF.

a Coomasie stained acrylamide gel of purified FLAG-CDK6 mutants used for DSF assays. b DSF assay for investigating the interaction of the drug palbociclib with wild-type and mutant CDK6. The normalized first derivative melt profiles of purified wild-type CDK6, F98E, I19W, V45M, and H100F mutants in the absence (gray trace) or presence (blue trace) of the drug palbociclib. The black arrow indicates the melting profile of CDK6 alone in the absence of the drug. The temperature at which the derivative is the most negative (minimum) is used to calculate the melting temperature (Tm, DTm), average of n = 5 replicate measurements. c CETSA assays of kinase dead CDK6 (K43M) in the presence or absence of palbociclib, average of n=2 replicate measurements.

Extended Data Fig. 6 Confirmation of generalizable kinase mutants beyond CDK4/6.

a WT and mutant EML4-ALK drug sensitivity were measured by Western Blotting of downstream phosphorylation of AKT, representative image of n=2 individual experiments. b Levels of WT and mutant HER2 exogenously expressed in T47D cells were measured by western blotting, representative image of n=2 individual experiments. c Activity of BRAF position 61 (L485S) was measured by western blotting for downstream phosphorylation of MEK and ERK, representative image of n=2 individual experiments. d Bar graphs showing the percentage of all kinases harboring each amino acid at the wild-type residue of position 32, 61 117 and 119. All data is from the kinome alignment described in Kumar, R. D. & Bose, R. (2017). e Bar graphs illustrating the average resistance phenotypes (z-score) caused by all possible amino acid substitutions at the Pocket Protector, Keymaster, Gatekeeper and Gatekeeper +2 residues of CDK4 (n=4 replicate measurements), CDK6 (n=4 replicate measurements) and ERK2 (n=6 replicate measurements), based on CDK4/6 primary screening data and data from Brenan et al. (2016), n= f) Activity of CSNK2A1 mutants as assessed by western blot of downstream signaling, including phosphorylated AKT (pAKT1), representative image of n=2 individual experiments. g Proliferation assay assessing the sensitivity of SKMEL5 cells transduced with WT or mutant CSNK2A1 and treated with CX4945, plotted as the mean of 2 replicate measurements. h Activity of CSNK2A1 mutants as assessed by western blot of downstream signaling, including phosphorylated AKT (pAKT1) corresponding to samples in (g), representative image of n=2 individual experiments. Source data

Extended Data Fig. 7 Phenotypes of mutant CDK4/6 in standard growth conditions.

Scatter plots of the z-scores of the Log(2)-Fold-Changes and native amino acid position for DMSO (vehicle only, n=2 replicate measurements) calculated relative to the early time point reference for CDK6 (a) or CDK4 (b), n=2 replicate measurements. c Proliferation assay assessing the sensitivity of A375 cells transduced with WT or mutant MEK1 treated with the MEK-inhibitor Trametinib (n=4 replicate measurements for each mutant at each concentration). d Activity of WT or mutant MEK1 as assessed via western blot analysis of downstream signaling, representative blot of n=2 individual experiments. e Dabrafenib complementation assays to measure activity of A375 cells transduced with WT or I99M mutant MEK1 (n=4 replicate measurements for each mutant at each concentration). f Illustration of active and auto-inhibited forms of kinases as illustrated by CDK2 structures (PDB: 2JGZ (active) and PDB: 1HCK (auto-inhibited). Activation loops are colored purple while keymaster positions are colored magenta. Source data

Supplementary information

Reporting Summary

Supplementary Table 1

CDK6 saturation mutagenesis data

Supplementary Table 2

CDK6 saturation mutagenesis data

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Source Data Extended Data Fig. 7

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Persky, N.S., Hernandez, D., Do Carmo, M. et al. Defining the landscape of ATP-competitive inhibitor resistance residues in protein kinases. Nat Struct Mol Biol 27, 92–104 (2020) doi:10.1038/s41594-019-0358-z

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