The protein tyrosine phosphatase SHP2 is crucial for oncogenic transformation of acute myeloid leukemia (AML) cells expressing mutated receptor tyrosine kinases. SHP2 is required for full RAS-ERK activation to promote cell proliferation and survival programs. Allosteric SHP2 inhibitors act by stabilizing SHP2 in its autoinhibited conformation and are currently being tested in clinical trials for tumors with overactivation of the RAS/ERK pathway, alone and in various drug combinations. In this study, we established cells with acquired resistance to the allosteric SHP2 inhibitor SHP099 from two FLT3-ITD (internal tandem duplication)-positive AML cell lines. Label-free and isobaric labeling quantitative mass spectrometry–based phosphoproteomics of these resistant models demonstrated that AML cells can restore phosphorylated ERK (pERK) in the presence of SHP099, thus developing adaptive resistance. Mechanistically, SHP2 inhibition induced tyrosine phosphorylation and feedback-driven activation of the FLT3 receptor, which in turn phosphorylated SHP2 on tyrosine 62. This phosphorylation stabilized SHP2 in its open conformation, preventing SHP099 binding and conferring resistance. Combinatorial inhibition of SHP2 and MEK or FLT3 prevented pERK rebound and resistant cell growth. The same mechanism was observed in a FLT3-mutated B-cell acute lymphoblastic leukemia cell line and in the inv(16)/KitD816Y AML mouse model, but allosteric inhibition of Shp2 did not impair the clonogenic ability of normal bone marrow progenitors. Together, these results support the future use of SHP2 inhibitor combinations for clinical applications.

Significance:

These findings suggest that combined inhibition of SHP2 and FLT3 effectively treat FLT3-ITD–positive AML, highlighting the need for development of more potent SHP2 inhibitors and combination therapies for clinical applications.

The SH2 domain–containing phosphotyrosine phosphatase 2 (SHP2) is a ubiquitously expressed nonreceptor protein tyrosine phosphatase (PTP), encoded by the PTPN11 gene. SHP2 comprises of two Src-homology-2 (SH2) domains at its N-terminus (N-SH2 and C-SH2), a central catalytic PTP domain and a C-terminal tail with two prominent activating phosphotyrosine sites, Tyr542 and Tyr580 (1). In the absence of a stimulus, SHP2 is kept in a closed, autoinhibited state by interactions between the N-SH2 domain and the PTP domain (2). SHP2 is activated by peptides containing appropriately spaced phosphotyrosine residues that bind the N-SH2 and C-SH2 domains of SHP2 in a bidentate manner, causing a conformational change of SHP2 into an open, active conformation. In this state, the interactions between the N-SH2 and the PTP domain are released, making the catalytic site available for substrate recognition and phosphate hydrolysis (2).

SHP2 is a central cellular signaling hub, typically relaying signals from upstream receptor tyrosine kinases (RTK), such as PDGFR (3) and EGFR (4). Downstream of these receptors, SHP2 is known as a key regulator of the RAS-ERK pathway (5).

Somatic PTPN11 mutations predominantly affect the N-SH2 and PTP domains, rendering SHP2 constitutively active, and are found in acute myeloid leukemia (AML), B-cell acute lymphoblastic leukemia (B-ALL), juvenile myelomonocytic leukemia (JMML), and solid tumors (6, 7). Furthermore, wild type (WT) SHP2 is essential for oncogenic transformation downstream of mutated RTKs (8). Combined, these findings support SHP2 as a potential drug target in the context of cancer treatment.

In 2016, Novartis developed a first-in-class potent and selective allosteric, noncovalent SHP2 inhibitor, SHP099. This molecule stabilizes SHP2 in its inactive conformation by binding to the interface between the N-SH2, C-SH2, and PTP domains (9). The therapeutic potential of SHP2 allosteric inhibitors is currently investigated for solid tumors in several clinical trials (10).

A recent study reported the effectiveness of SHP099 as a single agent in clinically relevant mouse models of AML, reducing leukemogenesis and leukemic blast stemness through downregulation of a Myc-driven gene signature (11). This observation was in contrast with published data showing that allosteric SHP2 inhibition (SHP2i) is only effective as combination treatment with MEK or RAS inhibitors in RTK mutation–driven cancers (12) and in a subset of KRAS-driven cancers (13, 14).

AML is a bone marrow malignancy characterized by a blockage of differentiation and an increased proliferation of myeloid hematopoietic progenitor cells (15). To investigate the sensitivity of human AML to allosteric SHP2i as a single agent, we focused on AML cell lines with internal tandem duplication (ITD) in the juxta-membrane domain of the RTK FLT3. This mutation is present in 25% of patients with AML and it leads to constitutive activation of its tyrosine kinase activity (16).

Here, we show that the initial phospho-ERK (pERK) downregulation observed in FLT3-ITD–positive AML cell lines treated with SHP099 is transient, and rapidly overpowered by adaptive resistance mechanisms. To analyze the global molecular changes induced by resistance to SHP099, we employed quantitative mass spectrometry (MS)-based proteomics and phosphoproteomics. We further investigated the role of SHP2 phosphorylation on Tyr62 using knockdown and single-site mutations to uncover a feedback activation of FLT3 that leads to the emergence of adaptive resistance through reactivation of the SHP2-RAS-ERK axis. Finally, we show that this adaptive resistance can be overcome by cotargeting components of the same pathway, such as the MEK or FLT3 kinases.

Cell culture

The MOLM-13 cell line was purchased from DSMZ line (catalog no. ACC-554, RRID: CVCL_2119), while the MV-4-11 and U-2 osteosarcoma (OS) cell lines were purchased from ATCC (catalog no. CRL-9591, RRID: CVCL_0064; catalog no. HTB-96, RRID: CVCL_0042). The HB11;19 cell line (RRID: CVCL_8227) was kindly provided by Dr. Yana Pikman. MOLM-13 and HB11;19 cells were cultured in RPMI1640 with GlutaMAX (Gibco) supplemented with 10% FBS and 1% penicillin-streptomycin (P-S). MV-4-11 cells were cultured in Iscove's Modified Dulbecco's Medium with GlutaMAX (Gibco) supplemented with 25 mmol/L HEPES (Gibco), 10% FBS, and 1% P-S. The human osteosarcoma cell line U-2 OS was cultured in DMEM with GlutaMAX (Gibco) supplemented with 10% FBS and 1% P-S. AML cell lines were starved in respective serum-free media, supplemented with 1% BSA (Sigma-Aldrich). Cell lines were kept in a humidified incubator at 37°C, with 5% CO2. All cell lines were regularly checked for Mycoplasma using the EZ PCR Mycoplasma detection Kit (Biological Industries). To avoid artefacts of long-term culture of immortalized cell lines, cell cultures were started up from an early passage vial and interrupted after 8–12 weeks.

Cell treatment and acquired resistance generation

SHP099, RMC-4550, cobimetinib, gilteritinib, and KW-2449 were purchased from Selleckchem. II-B08 was purchased from Sigma-Aldrich. Recombinant human, animal free EGF was purchased from Peprotech.

For acquired resistance generation, cells were initially treated with SHP099 at their respective IC50, followed by dose increase at each passage for over 3 weeks, stopping the increase at doses corresponding approximately 5× IC50 (2.5 μmol/L for MV-4-11 and 10 μmol/L for MOLM-13), and continuing cultivation at those doses of SHP099 for a maximum of 6 weeks.

Cell survival assay and IC50 determination

Approximately 25,000 cells were plated in a clear bottom 96-well plate and treated with increasing drug doses. Cell viability was measured after 72 hours, using the Cell counting Kit 8 (Tebu-Bio), according to manufacturer's instructions. Absorbance was measured using a FLUOstar Omega microplate reader (BMG Labtech). IC50 values were determined in GraphPad Prism v8–9 (RRID: SCR_002798), using nonlinear regression analysis (log inhibitor vs. response, four parameters).

inv(16)/KitD816Y AML mouse model

The inv(16)/KitD816Y AML mouse model has been described before (17). Animals were housed according to institutional guidelines at the University of Copenhagen (Copenhagen, Denmark) and experiments were approved by the Danish Animal Research Ethical Committee.

Colony-forming cell assay

Colony-forming assay (CFC) assay was performed as described previously (18). A total of 1,500 of MV-4-11 cells or 12,500 of primary murine bone marrow cells were seeded in a 12-well plate in 0.5 mL of MethoCult Methylcellulose-Based Media (StemCell Technologies: H4435 for human cells, M3434 for murine cells) supplemented with 1% P-S. Colonies were manually counted after 7–14 days.

Immunoblotting

A detailed protocol can be found in the Supplementary Materials and Methods. Information on number of replicates for each experiment is provided in Supplementary Table S1.

Determination of Ras-GTP levels

The active RAS pulldown and detection kit (Thermo Fisher Scientific, catalog no. 16117) was used according to manufacturer's instructions.

siRNA knockdown

A total of 1 × 106 cells per condition were transfected with 20 nmol of siRNA using the Neon Transfection System (Invitrogen) with the following setting: 1,400 V; 20 ms; 2 pulses. All assays 100 μL kit were carried out after 72 hours knockdown. Double-stranded siRNAs targeting human PTPN11 were purchased from Dharmacon as SmartPool. Stealth RNAi siRNA GFP Reporter Control duplex (Invitrogen) was used as negative control.

Site-directed mutagenesis

PTPN11 point mutations were generated from the parental pCMV-SHP2-WT (RRID: Addgene_8381) using site directed mutagenesis (QuikChange II XL, Agilent), according to manufacturer's instructions.

Plasmid overexpression

U-2 OS cells were transfected for 24 hours using Lipofectamine 2000 (Invitrogen) with either 1 μg (6-well plate) or 15 μg (15 cm plate) pCDNA 3.1 (Thermo Fisher Scientific, catalog no. V79020), WT SHP2 or mutants.

Cellular thermal shift assay

Cellular thermal shift assay (CETSA) was performed as described previously (19). A detailed protocol can be found in the Supplementary Data.

Evaluation of drug synergy

The synergy of small-molecule inhibitor combinations was calculated utilizing the SynergyFinder web application 2.0 (RRID: SCR_019318; ref. 20).

Multiple sequence alignment

Multiple sequence alignment was performed using the Clustal Omega tool (RRID: SCR_001591).

Modeling

For the modeling of the SHP2-mutant Y62D, we used the Dynamut web interface (RRID: SCR_021849; ref. 21). We used the deposited crystal structure of closed SHP2, in complex with the SHP099 inhibitor (PDB: 5EHR) and indicated the Y62D mutation on chain A as the mutation.

MS-based quantitative proteomics

Extended methods can be found in Supplementary Data.

Single-shot label-free proteomics and phosphoproteomics

After SDS-based lysis, proteins were on-bead digested through the PAC protocol (22). A total of 750 ng of peptide were loaded on evotips (Evosep) for proteome analysis. The remaining peptides were cleaned-up on Sep-Pak and subjected to automated Ti-IMAC phosphopeptide enrichment (200 μg peptide input). After elution, the phosphopeptides mixtures were loaded on evotips for MS analysis. Samples were analyzed on the Evosep One system (23) coupled to an Orbitrap Exploris 480 (Thermo Fisher Scientific; ref. 24). The mass spectrometer was operated in data-independent acquisition (DIA) mode. Raw MS data were analyzed using the Spectronaut software (25) with the direct DIA workflow (proteome) or with by using a project-specific spectral library (phosphoproteome).

Tandem mass tag phosphoproteomics

After guanidine-based lysis, proteins were digested in solution. Following Sep-Pak cleanup, peptides were tandem mass tag (TMT)-labeled (10-plex kit, Thermo Fisher Scientific), pulled and subjected to TiO2 enrichment (300 μg of peptides per sample: total 3 mg). After elution, phosphopeptide mixtures were separated by offline high-pH reversed-phase fractionation into 12 concatenated fractions. All fractions were acidified, dried, and subsequently solubilized in 5% acetonitrile, 0.1% trifluoroacetic acid prior to MS analysis. Samples were analyzed on an Easy nLC 1200 (Thermo Fisher Scientific) coupled to a Q Exactive HF-X (Thermo Fisher Scientific; ref. 26). The mass spectrometer was operated in data-dependent acquisition (DDA) mode. Raw MS data were analyzed using the MaxQuant software (RRID: SCR_014485; ref. 27).

Bioinformatics analysis

For all datasets, protein entries identified as potential contaminants were eliminated from the analysis. We kept only the phosphorylation sites with a localization score ≥ 0.75. After log2 transformation, MS intensities were realigned to the median signal. DEqMS (28) was used for the differential expression analysis of the DIA proteome, using the minimum number of peptide spectrum matches per run for variance estimation. Protein groups were considered regulated when presenting a Storey FDR (q-value) ≤ 0.05 between resistant or SHP099 treatment and DMSO (paired analysis). The same FDR strategy was applied to the phosphopeptides using the LIMMA package (RRID: SCR_010943; ref. 29) with a more stringent threshold (q-value ≤ 0.01). When possible, we calculated phosphorylation site occupancy on the mean of replicates as described before (30). For TMT phophoproteomics, the bioinformatics analysis was performed on the Phosphosite(STY).txt table of MaxQuant. Reverse identification were removed. Multiple phosphorylation sites quantified from the same peptide were labeled “protein_site1+site2.” We performed significance analysis using LIMMA for three comparisons (SHP/CTRL, RES/CTRL, RES/SHP), and we considered significantly regulated the phosphorylation sites with a q-value ≤ 0.05. Kinase activity prediction was performed with RoKAI (31) using kinase substrates from PhosphositePlus and Signor.

Data availability statement

All raw MS data were generated by the authors and deposited to the ProteomeXchange Consortium (RRID: SCR_004055), via the PRIDE partner repository (RRID: SCR_003411; ref. 32) with the dataset identifier PXD030338.

The R scripts related to this publication are freely accessible with the FASTA files and related tables at ZENODO (RRID: SCR_004129), under the Creative Commons Attribution 4.0 International license:

FLT3-ITD AML cell lines rapidly develop acquired resistance to SHP099

To characterize the cellular and molecular responses to the allosteric SHP2 inhibitor SHP099, we selected two commercial AML cell lines based on their strong genetic dependency on both PTPN11 and FLT3 in the Cancer Dependency Map (DepMap) database (RRID: SCR_017655; ref. 33): MV-4-11 and MOLM-13 (Fig. 1A; Supplementary Fig. S1A). Both cell lines are known to be sensitive to SHP2 (9) and FLT3 inhibition (34). We confirmed their sensitivity to SHP099, with a calculated half-maximal inhibitory concentration (IC50) of 0.5 μmol/L for MV-4-11 and 1.3 μmol/L for MOLM-13 (Fig. 1B, blue lines). The response observed for SHP099 was mimicked by treatment with RMC-4550 (Supplementary Fig. S1B, blue lines), another potent and selective SHP2 allosteric inhibitor (35).

Figure 1.

FLT3-ITD–positive AML cell lines develop acquired resistance to SHP099. A, Genetic dependence on PTPN11 and FLT3 for leukemia cell lines in the DepMap database. A score <−1 denotes gene essentiality. B, Survival assay of parental (blue) and acquired resistant (red) cells after 72 hours treatment with SHP099 for MV-4-11 and MOLM-13. Data are shown as mean ± SEM of two independent experiments with three replicates each (N = 6). IC50 values (bottom table) were calculated using nonlinear regression. C, Growth rates (relative to day 1) of MV-4-11 and MOLM-13 cells treated with increasing doses of SHP099 over a period of 3 weeks. The concentration range was 0.5–2.5 μmol/L or 1.5–10 μmol/L for MV-4-11 and MOLM-13, respectively. Data are shown as mean of N = 4 cell counts, performed by trypan blue exclusion, from one single experiment. D, Acquired resistance reversibility assessed by survival assay performed after 72 hours treatment with SHP099 on acquired resistant and washout cells. Cells were continuously grown in presence of SHP099 (red) or in drug-free media for 3 weeks to generate washout cells (green). IC50 was calculated using nonlinear regression. Data are shown as mean ± SEM of three independent experiments (N = 3). Significance was calculated on three independent experiments using a two-sided, unpaired, two-sample t test. *, P < 0.05.

Figure 1.

FLT3-ITD–positive AML cell lines develop acquired resistance to SHP099. A, Genetic dependence on PTPN11 and FLT3 for leukemia cell lines in the DepMap database. A score <−1 denotes gene essentiality. B, Survival assay of parental (blue) and acquired resistant (red) cells after 72 hours treatment with SHP099 for MV-4-11 and MOLM-13. Data are shown as mean ± SEM of two independent experiments with three replicates each (N = 6). IC50 values (bottom table) were calculated using nonlinear regression. C, Growth rates (relative to day 1) of MV-4-11 and MOLM-13 cells treated with increasing doses of SHP099 over a period of 3 weeks. The concentration range was 0.5–2.5 μmol/L or 1.5–10 μmol/L for MV-4-11 and MOLM-13, respectively. Data are shown as mean of N = 4 cell counts, performed by trypan blue exclusion, from one single experiment. D, Acquired resistance reversibility assessed by survival assay performed after 72 hours treatment with SHP099 on acquired resistant and washout cells. Cells were continuously grown in presence of SHP099 (red) or in drug-free media for 3 weeks to generate washout cells (green). IC50 was calculated using nonlinear regression. Data are shown as mean ± SEM of three independent experiments (N = 3). Significance was calculated on three independent experiments using a two-sided, unpaired, two-sample t test. *, P < 0.05.

Close modal

To investigate whether the two cell lines could develop acquired resistance to SHP099, we treated them for 3 weeks with increasing drug concentration (Fig. 1C, red lines). After chronic drug exposure, both cell lines became drug resistant. We confirmed that these resistant cell lines (MOLM-13/R and MV-4-11/R) showed increased IC50 values in survival assays (Fig. 1B, red lines) as well as cross-resistance to RMC-4550 (Supplementary Fig. S1B, red lines). To test for potential drug resistance reversibility, we removed SHP099 from the culture media of MV-4-11/R and MOLM-13/R cells for 3 weeks. After this washout period, both showed a significant decrease in SHP099 IC50 (Fig. 1D; Supplementary Fig. S1C and S1D), indicating that acquired resistance was reversible.

Proteomics and phosphoproteomics characterization of SHP099 resistance

To elucidate the molecular basis of SHP099 acquired resistance, we analyzed the proteome and phosphoproteome changes induced by SHP099 treatment in the MV-4-11 and MOLM-13 at early (1 and 24 hours) and late (72 hours and 21 days) timepoints (Fig. 2A).

Figure 2.

Proteomics and phosphoproteomics analysis of allosteric SHP2i. A, Experimental label-free proteomics and phosphoproteomics workflow. B and C, 3D PCA of the normalized row-wise scaled protein intensities (B) and phosphorylation occupancies (C). Res, cells with acquired resistance to SHP099; S9, SHP099-treated parental cells. Dashed circles indicate similar samples discussed in the text. Dark and light blue, same replicates of the SHP099 treatment; green, all time points of the resistant cells; black, all time points of the DMSO control. Arrows in C illustrate the time course of SHP099 treatment in parental (blue) and resistant cells (green). D, Kinase activity prediction results from RoKAI performed using the log2-transformed differences of phosphorylation site occupancies between cells treated with SHP099 and their DMSO control at the same time point. The kinases plotted here have a FDR ≤ 0.05 in minimum one of the comparisons and a minimum of three identified substrates. These are ordered by decreasing mean activity. Plot sizes correspond to −log10(FDR) and they are color coded by Z-score (red, positive = upregulation; blue, negative = downregulation). Points with dashed and solid borders indicate FDR ≤ 0.05 and ≤ 0.01, respectively.

Figure 2.

Proteomics and phosphoproteomics analysis of allosteric SHP2i. A, Experimental label-free proteomics and phosphoproteomics workflow. B and C, 3D PCA of the normalized row-wise scaled protein intensities (B) and phosphorylation occupancies (C). Res, cells with acquired resistance to SHP099; S9, SHP099-treated parental cells. Dashed circles indicate similar samples discussed in the text. Dark and light blue, same replicates of the SHP099 treatment; green, all time points of the resistant cells; black, all time points of the DMSO control. Arrows in C illustrate the time course of SHP099 treatment in parental (blue) and resistant cells (green). D, Kinase activity prediction results from RoKAI performed using the log2-transformed differences of phosphorylation site occupancies between cells treated with SHP099 and their DMSO control at the same time point. The kinases plotted here have a FDR ≤ 0.05 in minimum one of the comparisons and a minimum of three identified substrates. These are ordered by decreasing mean activity. Plot sizes correspond to −log10(FDR) and they are color coded by Z-score (red, positive = upregulation; blue, negative = downregulation). Points with dashed and solid borders indicate FDR ≤ 0.05 and ≤ 0.01, respectively.

Close modal

By DIA of biological quadruplicates per treatment condition, 5,495 protein groups were quantified by label-free quantification across all samples (Supplementary Table S2). The principal component analysis (PCA) of the proteome differences is presented in Fig. 2B. SHP099 treatment induced a shift in PC1. Resistant cell proteomes separated from all other conditions in both cell lines but with different patterns: MV-4-11/R separated from the parental cells on the same component as of SHP099-treated cells (PC1), while MOLM-13/R differed from the parental cells on the third principal component (PC3), suggesting that these proteomes presented different features with respect to parental MOLM-13 cells.

In the phosphoproteome, we quantified 17,024 phosphorylation sites across all samples (Supplementary Table S3). Because phosphopeptide upregulation and downregulation can be due to protein-level remodeling, we calculated phosphorylation site occupancy, alongside phosphopeptide relative quantities, for the sites that were located on proteins also detected in the proteome, with quantitation of their cognate nonphosphorylated peptide (30). PCA of phosphorylation sites occupancies (Fig. 2C) shows that SHP099 treatment drove phosphoregulations early in MOLM-13, as SHP099-treated parental cells were separated from parental cells in the PC1. Yet, phosphorylation remodeling was less pronounced in MV-4-11 with separation alongside the PC2 explaining only 7% of variance. For MOLM-13, resistant cells appeared to revert toward the parental cells along the course of SHP099 treatment. To identify the activated kinases responsible for the phosphorylation site occupancies, we performed kinase activity prediction (31), which showed a decrease of predicted MEK/ERK activity at 1 hour that was not maintained over time (Fig. 2D).

We performed the statistical analysis of protein and phosphorylation site remodeling using a LIMMA-based approach (29) where variance was estimated as a function of intensities for the phosphopeptides and number of peptide-spectrum matches for the protein groups (DEqMS; ref. 28). This analysis identified 1,434 and 3,230 protein groups that were differentially regulated upon resistance acquisition in the MOLM-13 and MV-4-11, respectively, whereas 1,522 and 1,013 proteins were regulated after SHP099 treatment in MOLM-13 and MV-4-11 parental cell lines (Fig. 3A). Consistent with the PCA plots (Fig. 2B), all proteins regulated upon short time SHP099 treatment in the parental MV-4-11 cells were found regulated between MV-4-11/R and MV-4-11, whereas in MOLM-13 the set of proteins regulated upon short-term SPH099 treatment differed from long-term MOLM-13 proteome remodeling. Only a partial overlap was observed between the two cell lines, which indicates that cell line–specific mechanisms may be responsible for SHP099 resistance. Nevertheless, many proteins passed the statistical threshold in one of the two cell lines while presenting common expression kinetics over the course of the experiment (Fig. 3B).

Figure 3.

Significant proteome and phosphoproteome remodeling upon allosteric SHP2i. A, Proportional Euler diagram of significantly regulated proteins (q-value ≤ 0.05) upon SHP099 treatment in MV-4-11 and MOLM-13 parental and resistant cells. B, Heatmap of the row-wise scaled protein relative MS signals for the proteins significantly regulated between DMSO and resistance in minimum one cell line (q-value ≤ 0.01) and with a Pearson correlation between cell lines > 0.9 (ordered by hierarchical clustering with the hclust function, default parameters). This heatmap only shows the 50 most correlated proteins. C, Log2-transformed raw normalized MS signal measured for MAPK1 and MAPK3 in the proteome (right) and phospho-enriched peptides (only significantly regulated sites). Each point corresponds to a sample. Missing values were not replaced. Mean and SEs are indicated with gray bars.

Figure 3.

Significant proteome and phosphoproteome remodeling upon allosteric SHP2i. A, Proportional Euler diagram of significantly regulated proteins (q-value ≤ 0.05) upon SHP099 treatment in MV-4-11 and MOLM-13 parental and resistant cells. B, Heatmap of the row-wise scaled protein relative MS signals for the proteins significantly regulated between DMSO and resistance in minimum one cell line (q-value ≤ 0.01) and with a Pearson correlation between cell lines > 0.9 (ordered by hierarchical clustering with the hclust function, default parameters). This heatmap only shows the 50 most correlated proteins. C, Log2-transformed raw normalized MS signal measured for MAPK1 and MAPK3 in the proteome (right) and phospho-enriched peptides (only significantly regulated sites). Each point corresponds to a sample. Missing values were not replaced. Mean and SEs are indicated with gray bars.

Close modal

Among the phosphorylation sites regulated by SHP099 treatment, we observed a strong dephosphorylation of ERK2/MAPK1-Y187 after 1 hour of SHP099 treatment in both cell lines, while this phosphosite only returned to baseline level of parental cell lines at longer timepoints. The same was observed for ERK1/T202+T207, two phosphothreonines measured on the same peptide that were under the level of detection in the parental cells 1 hour post-SHP099 treatment in both cell lines (Fig. 3C).

To validate the phosphoproteome data discussed above and investigate further the phosphoregulations underlying SHP099 resistance acquisition, we performed a TMT-based quantitative phosphoproteomics experiment (Supplementary Fig. S2A; Supplementary Table S4). TMT labeling allows sample multiplexing with more precise quantification (Supplementary Fig. S2b) and returns fewer missing values than classical label-free strategies (36). We focused on phosphorylation changes induced by SHP099 treatment in both parental and resistant MV-4-11 cells after 1 hour, when pERK rebound was observed in resistant cells (Fig. 3C). Overall, we quantified 18,832 phosphosites. This deepened the phosphoproteome of SHP099-treated MV-4-11 and MV-4-11/R, reaching in total 32,256 phosphorylation sites (Supplementary Fig. S2C). ERK2/MAPK1-Y187 and ERK1/MAPK3-Y204 were both regulated in a similar fashion in the DIA and TMT data, like many other significantly regulated sites (Fig. 4A). More importantly, the TMT-based phosphoproteome dataset included two significantly regulated sites on SHP2: Tyr62 and Tyr542 (Fig. 4B and C; Supplementary Fig. S3A). Tyr542 followed the dynamics of pERK, by being downregulated in SHP099 treated parental cells compared with control and rescued in the acquired resistant cells. Conversely, Tyr62 displayed a specific dynamic behavior characterized by an absence of regulation upon SHP099 treatment in parental cells and an increase only in resistant cells. Importantly, total SHP2 protein level was not regulated in any condition studied, indicating that the observed phosphorylation changes did not result from protein-level regulation (Supplementary Fig. S3B). Phosphorylation on Tyr542 on SHP2 has an adaptor function for the recruitment of GRB2 (37) and subsequent activation of the RAS-RAF-MEK-ERK signaling axis. Moreover, this phosphosite has been shown to interact with the N-SH2 domain, thus relieving basal inhibition of the PTP domain (38). SHP2 Tyr62 phosphorylation, less well studied than Tyr542, was shown to correlate with an activated state of the PTP domain (39).

Figure 4.

Distinct expression patterns of SHP2 phosphorylation sites. A, Heatmap of the row-wise scaled normalized signal in the DIA and TMT datasets (dataset independent normalization). The selected sites are the top 20 lowest q values in the DIA data between SHP099 treatment and DMSO at 1 hour in the MV-4-11. We only kept the sites with less than two missing values (white). B, Representative MS2 spectra of LC/MS-MS identification of SHP2 pTyr62. C, Log2-transformed raw normalized MS signal measured for ERK2/MAPK1-T185+T187 and SHP2 in the TMT phosphoproteome (only significantly regulated sites).

Figure 4.

Distinct expression patterns of SHP2 phosphorylation sites. A, Heatmap of the row-wise scaled normalized signal in the DIA and TMT datasets (dataset independent normalization). The selected sites are the top 20 lowest q values in the DIA data between SHP099 treatment and DMSO at 1 hour in the MV-4-11. We only kept the sites with less than two missing values (white). B, Representative MS2 spectra of LC/MS-MS identification of SHP2 pTyr62. C, Log2-transformed raw normalized MS signal measured for ERK2/MAPK1-T185+T187 and SHP2 in the TMT phosphoproteome (only significantly regulated sites).

Close modal

pERK rebound in acquired resistant cells depends on MEK and SHP2

We further confirmed by immunoblotting that resistant cells reactivated pERK in the presence of the drug within 1 hour of treatment (Fig. 5A; Supplementary Fig. S4A). We noted that pERK rebound happened also after 72 hours of SHP099 treatment in parental cells, confirming the phosphoproteomics data (Fig. 3C). Importantly, in the drug washout condition, SHP099 treatment regained the ability to inhibit ERK phosphorylation to the same extent as parental cells (Fig. 5B; Supplementary S4B), confirming that resistance was reversible. In addition, we tested whether the pERK rebound observed in acquired resistant cells was dependent on MEK. We treated MV-4-11 and MOLM-13 acquired resistant cells with the clinical MEK inhibitor cobimetinib and observed a decrease in the levels of ERK phosphorylation, indicative of its MEK dependency (Fig. 5C; Supplementary Fig. S4C). To assess whether the observed MEK-dependent pERK upregulation was responsible for resistant cell survival, we treated parental and resistant cells with cobimetinib for 72 hours (Fig. 5D; Supplementary Fig. S4D). Cobimetinib treatment completely abolished resistant cell survival advantage, proving that resistant cells depend on the ERK pathway. To validate that SHP099 resistance was caused by RAS-ERK pathway reactivation, we performed RAS-GTP pulldown in the MV-4-11 parental and resistant cell lines (Supplementary Fig. S4E). While SHP099 decreased RAS-GTP loading in parental cells, RAS-GTP levels were fully rescued in acquired resistant cells, verifying RAS activation in presence of the drug.

Figure 5.

SHP099 resistance is dependent on MEK and SHP2. A, MV-4-11 (P) and MV-4–11/R (R) cells were serum starved for 14 hours and treated with SHP099 at 2.5 μmol/L for 1 and 72 hours in media containing 20% FBS. Lysates were immunoblotted with indicated antibodies. B, MV-4-11 (P), MV-4-11/R (R), and MV-4-11/R washout (W) cells were serum starved for 14 hours and treated with SHP099 at 2.5 μmol/L for 1 hour. Lysates were immunoblotted with indicated antibodies. C, MV-4-11 (P) and MV-4-11/R (R) cells were serum starved for 14 hours and treated with SHP099 at 2.5 μmol/L and cobimetinib (MEKi) at 10 nmol/L for 1 hour. Lysates were immunoblotted with indicated antibodies. D, MV-4-11 (P) and MV-4-11/R (R) cells were treated with SHP099 at 1 μmol/L and cobimetinib (COB) at 0.2 nmol/L for 72 hours. Survival was evaluated by CCK8 assay. Data are shown as mean ± SEM of two independent experiments with two replicates each (N = 4). E, MV-4-11 (P), MV-4-11/R (R), MOLM-13 (P), and MOLM-13/R (R) were serum starved for 14 hours and treated with SHP099 at 2.5 and 10 μmol/L, respectively, for 1 hour. Lysates were immunoblotted with indicated antibodies. The same lysates, as well as the same nitrocellulose membrane, were also probed with the antibodies shown in Fig. 7B, and therefore share the loading control (GAPDH). F, MV-4-11 (P) and MV-4-11/R (R) cells were serum starved for 14 hours and treated with SHP099 at 2.5 μmol/L and the active site SHP2 inhibitor II-B08 at 5 μmol/L for 1 hour. Lysates were immunoblotted with indicated antibodies. G, MV-4-11 (P) and MV-4-11/R (R) were subjected to PTPN11 knockdown (siRNA PTPN11) for 72 hours and lysates were immunoblotted with the indicated antibodies. siGFP, cells transfected with siRNA GFP. NT, nontransfected cells. H, MV-4-11 (P) and MV-4-11/R (R) were subjected to PTPN11 knockdown (siRNA PTPN11) and assessed for cell viability by trypan blue exclusion after 72 hours. Data are shown as mean ± SEM of two independent experiments (N = 2). Data were normalized to siRNA GFP for each cell line. Significance was determined using a two-sided, unpaired, two-sample t test. *, P < 0.05; **, P < 0.01; ***, P < 0.001.

Figure 5.

SHP099 resistance is dependent on MEK and SHP2. A, MV-4-11 (P) and MV-4–11/R (R) cells were serum starved for 14 hours and treated with SHP099 at 2.5 μmol/L for 1 and 72 hours in media containing 20% FBS. Lysates were immunoblotted with indicated antibodies. B, MV-4-11 (P), MV-4-11/R (R), and MV-4-11/R washout (W) cells were serum starved for 14 hours and treated with SHP099 at 2.5 μmol/L for 1 hour. Lysates were immunoblotted with indicated antibodies. C, MV-4-11 (P) and MV-4-11/R (R) cells were serum starved for 14 hours and treated with SHP099 at 2.5 μmol/L and cobimetinib (MEKi) at 10 nmol/L for 1 hour. Lysates were immunoblotted with indicated antibodies. D, MV-4-11 (P) and MV-4-11/R (R) cells were treated with SHP099 at 1 μmol/L and cobimetinib (COB) at 0.2 nmol/L for 72 hours. Survival was evaluated by CCK8 assay. Data are shown as mean ± SEM of two independent experiments with two replicates each (N = 4). E, MV-4-11 (P), MV-4-11/R (R), MOLM-13 (P), and MOLM-13/R (R) were serum starved for 14 hours and treated with SHP099 at 2.5 and 10 μmol/L, respectively, for 1 hour. Lysates were immunoblotted with indicated antibodies. The same lysates, as well as the same nitrocellulose membrane, were also probed with the antibodies shown in Fig. 7B, and therefore share the loading control (GAPDH). F, MV-4-11 (P) and MV-4-11/R (R) cells were serum starved for 14 hours and treated with SHP099 at 2.5 μmol/L and the active site SHP2 inhibitor II-B08 at 5 μmol/L for 1 hour. Lysates were immunoblotted with indicated antibodies. G, MV-4-11 (P) and MV-4-11/R (R) were subjected to PTPN11 knockdown (siRNA PTPN11) for 72 hours and lysates were immunoblotted with the indicated antibodies. siGFP, cells transfected with siRNA GFP. NT, nontransfected cells. H, MV-4-11 (P) and MV-4-11/R (R) were subjected to PTPN11 knockdown (siRNA PTPN11) and assessed for cell viability by trypan blue exclusion after 72 hours. Data are shown as mean ± SEM of two independent experiments (N = 2). Data were normalized to siRNA GFP for each cell line. Significance was determined using a two-sided, unpaired, two-sample t test. *, P < 0.05; **, P < 0.01; ***, P < 0.001.

Close modal

Next, we analyzed phosphorylation of the Tyr62 and Tyr542 on SHP2 by immunoblotting, confirming its upregulation and rescue in resistant cells, respectively (Fig. 5E; Supplementary S4F). To determine the functional role of the upregulation of SHP2 phosphorylation in resistant cells, we treated parental and resistant MV-4-11 cells with the active-site inhibitor II-B08, which blocks SHP2 phosphatase activity, in combination with SHP099 (Fig. 5F). This experiment showed that II-B08 reverted pERK rebound in resistant cells, indicating that SHP2 regained phosphatase activity was responsible for upregulation of pERK in acquired resistant cells. Interestingly, II-B08 induced pERK upregulation in parental cells, possibly due to concomitant SHP1 inhibition, which is known to activate the ERK pathway (40). To further confirm that SHP2 reactivation was responsible for acquired resistance, we performed SHP2 knockdown for 72 hours in MV-4-11 parental and resistant cells (Fig. 5G). Downregulation of pERK was comparable in the knockdown in both parental and resistant cells. This correlated with a significant reduction in cell survival, both in parental and resistant cells (Fig. 5H), proving that MV-4-11/R cells were still dependent on SHP2.

SHP2 phosphorylation on Tyr62 stabilizes SHP2 in the open conformation

Restored SHP2 phosphatase activity in presence of SHP099 in resistant cells indicated that the drug was not able to inhibit its target, suggesting that SHP2 was in its open, active conformation. To test whether increasing the drug concentration could shift the equilibrium toward the closed, inhibited conformation, we treated the parental and acquired resistant cell lines MV-4-11 and MOLM-13 with increasing concentrations of either SHP099 or RMC-4550 (Fig. 6A; Supplementary Fig. S5A). In MV-4-11/R cells, high doses of SHP099 partially prevented pERK rebound. In MOLM-13/R cells, only the more potent RMC-4550, but not SHP099, was able to partially inhibit ERK phosphorylation.

Figure 6.

Tyr62 phosphorylation stabilizes SHP2 open conformation. A, MV-4-11 (P) and MV-4-11/R (R) were serum starved for 14 hours and treated with increasing doses of SHP099 for 1 hour. SHP099 concentrations used were 2.5, 5, 10, and 20 μmol/L. Cell lysates were immunoblotted with the indicated antibodies. B,In silico modeling of the GoF mutation SHP2 Y62D using the Dynamut online platform. Intramolecular bonds in which the aspartic acid is involved are shown. C, U-2 OS cells were transiently transfected with either wild-type SHP2 (WT), SHP2 GoF Y62E, or SHP2 LoF Y62F. After 24 hours, cells were serum starved for 14 hours, after which, they were pretreated with increasing doses of SHP099 for 1 hour and stimulated with 100 ng/mL EGF for 10 minutes. Cell lysates were immunoblotted with indicated antibodies. D, U-2 OS cells were either NT or transfected with WT SHP2, SHP2 Y62F, or Y62E for 24 hours. Afterward, cells were serum starved for 14 hours and treated with either DMSO or SHP099 at 20 μmol/L for 1 hour. CETSA was then performed. Cell lysates were immunoblotted with indicated antibodies.

Figure 6.

Tyr62 phosphorylation stabilizes SHP2 open conformation. A, MV-4-11 (P) and MV-4-11/R (R) were serum starved for 14 hours and treated with increasing doses of SHP099 for 1 hour. SHP099 concentrations used were 2.5, 5, 10, and 20 μmol/L. Cell lysates were immunoblotted with the indicated antibodies. B,In silico modeling of the GoF mutation SHP2 Y62D using the Dynamut online platform. Intramolecular bonds in which the aspartic acid is involved are shown. C, U-2 OS cells were transiently transfected with either wild-type SHP2 (WT), SHP2 GoF Y62E, or SHP2 LoF Y62F. After 24 hours, cells were serum starved for 14 hours, after which, they were pretreated with increasing doses of SHP099 for 1 hour and stimulated with 100 ng/mL EGF for 10 minutes. Cell lysates were immunoblotted with indicated antibodies. D, U-2 OS cells were either NT or transfected with WT SHP2, SHP2 Y62F, or Y62E for 24 hours. Afterward, cells were serum starved for 14 hours and treated with either DMSO or SHP099 at 20 μmol/L for 1 hour. CETSA was then performed. Cell lysates were immunoblotted with indicated antibodies.

Close modal

We investigated which structural, partially reversible, determinant could be responsible for SHP2 conformational change. Tyrosine phosphorylation is a transient posttranslational modification able to regulate protein function through multiple mechanisms, including conformational changes. Tyr62 is highly conserved from zebrafish to humans (Supplementary Fig. S5B) and it is part of the N-SH2 domain of SHP2, which is a known mutational hotspot in multiple diseases, including Noonan syndrome and JMML (6, 7, 41, 42). Mutations in this region generally result in constitutively active SHP2 mutants by stabilizing the open conformation of SHP2, thus antagonizing SHP2i by SHP099 (43). Intriguingly, we noticed a poorly characterized disease-causing mutation involving the Tyr62 on SHP2 (Y62D), which is both a somatic mutation in AML (COSMIC ID: COSV61011774) and a germline mutation in 42 reported cases of Noonan syndrome (dbSNP ID: rs121918460 and rs121918459; ref. 44). We therefore hypothesized that SHP2 phosphorylation on the Tyr62 or the Y62D mutation would stabilize SHP2 in its open conformation, making SHP099 ineffective. To determine changes in SHP2 conformation induced by this mutation, we performed in silico structural homology modeling through the DynaMut web interface (21). This highlighted that most ionic and hydrophobic bonds within the neighboring residues in the three-dimensional (3D) structure are broken upon introduction of aspartic acid in the position 62 (Fig. 6B; Supplementary Fig. S5C). Through the calculation of the free energy changes (ΔΔG), the Dynamut tool predicted that the Y62D mutation was destabilizing (ΔΔG = −1.636 kcal/mol), suggesting that it would lead to opening of the structure.

To experimentally validate this in the context of SHP2i by SHP099, we generated a mammalian SHP2 mutant by site directed mutagenesis, replacing the Tyr62 either by a phosphomimetic glutamic acid (Y62E, gain of function, GoF) or a nonphosphorylatable phenylalanine (Y62F, loss of function, LoF; Supplementary Fig. S5D). We transfected these mutants in U-2 OS cells and monitored pERK upon EGF stimulation as a proxy of SHP2 activation/inhibition in absence or presence of SHP099 (Fig. 6C; Supplementary Fig. S5E). We observed the same trend for pERK downregulation in nontransfected cells and cells transfected with the WT SHP2, with the highest SHP099 concentration (25 μmol/L) not being able to fully inhibit pERK. When we transfected the LoF mutant, 5 μmol/L of SHP099 induced pERK downregulation. Conversely, transfection with the GoF mutant nearly abolished SHP099-driven pERK downregulation.

To investigate the ability of WT and mutant SHP2 to bind SHP099, we performed a CETSA (19) in U-2 OS cells, either nontransfected (NT), transfected with WT SHP2 or with LoF and GoF SHP2 mutants (Fig. 6D). The protein melting temperature of SHP2 in the NT, WT, and Y62F-transfected cells increased upon SHP099 treatment in comparison with DMSO, indicating that nonphosphorylated SHP2 was stabilized, thus bound to SHP099. Conversely, treatment with SHP099 in the Y62E-expressing cells showed no sign of protein stabilization upon drug introduction, implying that phosphomimetic Y62E SHP2 could not bind SHP099.

pERK rebound in acquired resistant cells is caused by feedback activation of the FLT3 receptor

Next, we investigated which kinase could be responsible for SHP2 Tyr62 phosphorylation. Querying the NetworKIN prediction tool (45), we found that this phosphorylation is a predicted target of both EGFR and INSR, suggesting that it depends on an activated RTK (Supplementary Fig. S6A).

As RTKs need an extracellular ligand to be activated, we investigated whether serum deprivation could abolish pERK rebound in acquired resistant cells. Surprisingly, a full serum starvation for 12 hours was not able to prevent pERK rebound in the MV-4-11/R (Fig. 7A). Because MV-4-11 is known to express two FLT3-ITD–mutated alleles, we hypothesized that FLT3 was the RTK responsible for SHP2 reactivation. In support of this, we found that the autophosphorylation site FLT3 Tyr-969 was higher in both MV-4-11 and MOLM-13 acquired resistant cells compared with parental (Fig. 7B). This suggested that chronic SHP099 treatment relieved a negative feedback loop, leading to FLT3 activation in acquired resistant cells. To confirm this, we treated the MV-4-11 and MOLM-13 parental and resistant cells with the clinical second-generation FLT3 inhibitor gilteritinib for 72 hours and calculated its IC50 (Fig. 7C; Supplementary S6B). Both MV-4-11 and MOLM-13 were significantly more sensitive to FLT3 inhibition compared with their SHP099-resistant counterpart, indicating that resistant cell survival is dependent on FLT3. Moreover, we observed a synergistic effect for the combined treatment of RMC-4550 and gilteritinib in a CFC assay, where parental MV-4-11 was treated for 7 days (Fig. 7D). This synergy was validated by using the second-generation FLT3 inhibitor KW-2449, which completely abolished the resistant cell survival advantage (Supplementary Fig. S6C and S6D). In addition, gilteritinib was able to revert pERK rebound both in MV-4-11 (Fig. 7E). These data were confirmed in MV-4-11 and MOLM-13 by using KW-2449 (Supplementary Fig. S6E and S6F).

Figure 7.

Feedback activation of the FTL3-SHP2 axis causes pERK rebound in SHP099-resistant cells. A, MV-4-11 (P) and MV-4-11/R (R) cells were serum starved for 14 hours and stimulated with different percentages of serum (FBS) for 1 hour, either in the presence of DMSO or 2.5 μmol/L SHP099. Serum amounts were 20%, 10%, 5%, and 2.5%. Cell lysates were immunoblotted with designated antibodies. B, MV-4-11 (P), MV-4-11/R (R), MOLM-13 (P), and MOLM-13/R (R) were serum starved for 14 hours and treated with SHP099 at 2.5 and 10 μmol/L, respectively, for 1 hour. Lysates were immunoblotted with indicated antibodies. The same lysates, as well as the same nitrocellulose membrane, were also probed with the antibodies shown in Fig. 5E, and therefore share the loading control (GAPDH). C, IC50 values for the FLT3 inhibitor gilteritinib in MV-4-11 (P), MV-4-11/R (R), MOLM-13 (P), and MOLM-13/R (R) cells. MV-4-11/R cells were treated with 2.5 μmol/L SHP099. MOLM-13/R cells were treated with SHP099 at 10 μmol/L. IC50 values were calculated using nonlinear regression. Data are shown as mean ± SEM of three (MV-4-11) or four (MOLM-13) independent experiments (N = 3–4). D, Effects of single drugs RMC-2449 (40 nmol/L) and gilteritinib (1 nmol/L) on clonogenic growth of parental MV-4-11 (at 7 days). Data are shown as mean ± SEM of three replicates (N = 3) from one single experiment. E, MV-4-11 (P) and MV-4-11/R (R) were serum starved for 14 hours and treated for 1 hour with SHP099 at 2.5 μmol/L, gilteritinib at 2 nmol/L, and the combination. Lysates were immunoblotted with the indicated antibodies. Significance was calculated using a two-sided, unpaired, two-sample t test. *, P < 0.05; **, P < 0.01; ***, P < 0.001.

Figure 7.

Feedback activation of the FTL3-SHP2 axis causes pERK rebound in SHP099-resistant cells. A, MV-4-11 (P) and MV-4-11/R (R) cells were serum starved for 14 hours and stimulated with different percentages of serum (FBS) for 1 hour, either in the presence of DMSO or 2.5 μmol/L SHP099. Serum amounts were 20%, 10%, 5%, and 2.5%. Cell lysates were immunoblotted with designated antibodies. B, MV-4-11 (P), MV-4-11/R (R), MOLM-13 (P), and MOLM-13/R (R) were serum starved for 14 hours and treated with SHP099 at 2.5 and 10 μmol/L, respectively, for 1 hour. Lysates were immunoblotted with indicated antibodies. The same lysates, as well as the same nitrocellulose membrane, were also probed with the antibodies shown in Fig. 5E, and therefore share the loading control (GAPDH). C, IC50 values for the FLT3 inhibitor gilteritinib in MV-4-11 (P), MV-4-11/R (R), MOLM-13 (P), and MOLM-13/R (R) cells. MV-4-11/R cells were treated with 2.5 μmol/L SHP099. MOLM-13/R cells were treated with SHP099 at 10 μmol/L. IC50 values were calculated using nonlinear regression. Data are shown as mean ± SEM of three (MV-4-11) or four (MOLM-13) independent experiments (N = 3–4). D, Effects of single drugs RMC-2449 (40 nmol/L) and gilteritinib (1 nmol/L) on clonogenic growth of parental MV-4-11 (at 7 days). Data are shown as mean ± SEM of three replicates (N = 3) from one single experiment. E, MV-4-11 (P) and MV-4-11/R (R) were serum starved for 14 hours and treated for 1 hour with SHP099 at 2.5 μmol/L, gilteritinib at 2 nmol/L, and the combination. Lysates were immunoblotted with the indicated antibodies. Significance was calculated using a two-sided, unpaired, two-sample t test. *, P < 0.05; **, P < 0.01; ***, P < 0.001.

Close modal

SHP099-induced pERK rebound is observed in other RTK-driven leukemia models

To validate whether the molecular mechanisms discussed above were limited to FLT3-driven AML, we investigated the B-ALL cell line HB11;19, as it showed the highest genetic dependence on both FLT3 and PTPN11 in the DepMap database (Fig. 1A). In B-ALL, mutations in FLT3 and PTPN11 are common and often present in relapse-fated clones (46). HB11;19 harbors the FLT3 point mutation D835H, located in its tyrosine kinase domain, leading to ligand-independent FLT3 kinase activation. Surprisingly, HB11;19 cells were resistant to SHP099 (Supplementary Fig. S7A). SHP099 resistance was likely due to the fast pERK reactivation and upregulation following SHP099 treatment (Fig. 8A). However, they were sensitive to the FLT3 inhibitor gilteritinib (Supplementary Fig. S7B). Combined treatment with SHP099 and gilteritinib resulted in a synergistic effect on cell survival (Fig. 8B; Supplementary Fig. S7C) and it was able to reverse pERK rebound (Fig. 8C).

Figure 8.

Combined FLT3-SHP2 inhibition in B-ALL and inv(16)/KitD816Y AML cells. A, HB11;19 cells were serum starved for 14 hours and treated with SHP099 at 5 μmol/L. Lysates were immunoblotted with indicated antibodies. B, HB11;19 cells were treated with DMSO, SHP099, gilteritinib, or the combination of both drugs for 72 hours. Survival was evaluated by CCK8 assay. Synergy was assessed through SynergyFinder (version 2.0) using Bliss. A synergy score larger than 10 indicates that the interaction between two drugs is likely to be synergistic. C, HB11;19 cells were serum starved for 14 hous and treated with DMSO, SHP099 at 5 μmol/L, gilteritinib at 5 nmol/L, or the combination SHP099-gilteritinib. Lysates were immunoblotted with indicated antibodies. D, Effects of RMC-4550 on clonogenic growth of inv(16)/KitD816Y murine AML cells compared with WT bone marrow (BM) cells. Data are shown as mean ± SEM of three to six replicates from two independent experiments (N = 3–6). E, Inv(16)/KitD816Y murine AML cells were treated with DMSO, RMC-4550 (100 nmol/L), the Kit inhibitor BLU-285 (10 nmol/L) or the combination. CFC assay was then performed for 10 days. Data are shown as mean ± SEM of three replicates from one experiment (N = 3). Significance was calculated using a two-sided, unpaired, two-sample t test. *, P < 0.05.

Figure 8.

Combined FLT3-SHP2 inhibition in B-ALL and inv(16)/KitD816Y AML cells. A, HB11;19 cells were serum starved for 14 hours and treated with SHP099 at 5 μmol/L. Lysates were immunoblotted with indicated antibodies. B, HB11;19 cells were treated with DMSO, SHP099, gilteritinib, or the combination of both drugs for 72 hours. Survival was evaluated by CCK8 assay. Synergy was assessed through SynergyFinder (version 2.0) using Bliss. A synergy score larger than 10 indicates that the interaction between two drugs is likely to be synergistic. C, HB11;19 cells were serum starved for 14 hous and treated with DMSO, SHP099 at 5 μmol/L, gilteritinib at 5 nmol/L, or the combination SHP099-gilteritinib. Lysates were immunoblotted with indicated antibodies. D, Effects of RMC-4550 on clonogenic growth of inv(16)/KitD816Y murine AML cells compared with WT bone marrow (BM) cells. Data are shown as mean ± SEM of three to six replicates from two independent experiments (N = 3–6). E, Inv(16)/KitD816Y murine AML cells were treated with DMSO, RMC-4550 (100 nmol/L), the Kit inhibitor BLU-285 (10 nmol/L) or the combination. CFC assay was then performed for 10 days. Data are shown as mean ± SEM of three replicates from one experiment (N = 3). Significance was calculated using a two-sided, unpaired, two-sample t test. *, P < 0.05.

Close modal

To assess whether we identified a general RTK-driven Shp2i resistance mechanism in leukemia, we tested allosteric Shp2i in the context of murine inv(16)/KitD816Y AML (17). Kit is a transmembrane RTK, crucial for the development of hematopoietic stem cells. KitD816Y is one of the most common substitutions in AML, which causes constitutive activation of the receptor (47). Ex vivo AML blasts were sensitive to RMC-4550, while normal bone marrow progenitor clonogenic potential was not affected to the same extent (Fig. 8D). Combined treatment with RMC-4550 and the Kit inhibitor BLUE-285 synergistically reduced AML clonogenic potential (Fig. 8E), suggesting that allosteric Shp2i might be associated with signaling adaptation in a variety of RTK-driven bone marrow malignancies.

Currently, resistance to targeted therapy is the major hurdle in personalized cancer treatment. Intrinsic resistance is usually genetic, while acquired resistance typically arises through a number of diverse and often nongenetic mechanisms (48). Here, we show that the allosteric inhibitor of the oncogenic phosphatase SHP2, SHP099, causes development of acquired resistance through nongenetic mechanisms in AML cells. Previous studies have shown the efficacy of SHP099 monotherapy in FLT3-ITD AML mouse models (11). However, in these investigations mice were only treated for a maximum of 35 days, which might not be enough for adaptive resistance to arise. Prior studies support our model, as they have shown that resistance to SHP099 was caused by feedback activation of RTKs in in vitro models of RTK-driven solid tumors, and FGFR was the RTK responsible for resistance (49).

To understand the mechanism of acquired resistance, we utilized a phosphoproteomics approach, which allowed us to identify the Tyr62 as a resistant-specific phosphorylation site on SHP2. Using site-directed mutagenesis and in silico modeling, we suggest that introducing a high negative charge at residue 62 acts by forcing SHP2 to keep an open conformation, corroborating prior findings of a role for this phosphorylation site in maintaining an activated state of the PTP domain (39). Consistent with this finding, we show that the resistant cell lines still depend on SHP2 and that acquired resistance is caused by on-target reactivation. We prove that the phosphomimetic Y62E GoF variant cannot bind SHP099, resulting in resistance toward SHP2i, while nonphosphorylatable Y62F LoF variant displays the same level of affinity for SHP099 as the WT and is strongly inhibited by SHP099.

In this work, we use fractional stoichiometry to assess changes in phosphorylation dynamics. We opt for the approach originally proposed by our lab (30), which allows preserving information about phosphorylation site localization. A possible alternative for occupancy calculation is the one proposed by Gygi and colleagues (50), which in turn has the advantage to analyze the phosphorylated and unphosphorylated counterparts in the same run, thus reducing the number of missing values.

The finding presented here could be applied in the clinical setting in several ways. First, monitoring phosphorylation levels of SHP2 Tyr62 could be used as a prognostic marker for patients developing resistance to the administered drug. It would also be important to monitor patients receiving SHP099 for longer periods to identify whether they develop mutations in that particular site, or in close proximity. Second, because we found that resistance is reversible and can be targeted by rational combination treatment, such regimens could be developed. Finally, our study shows that treatment strategies based on allosteric SHP2 inhibitors still need further development to avoid resistance in the clinics and it suggests that the next-generation SHP2 inhibitors should prevent phosphorylation of Tyr62.

In conclusion, this study determines the path of adaptive evolution to SHP2i resistance in an AML setting and emphasizes the need for further investigation of allosteric SHP2 inhibitors and whether acquired resistance arises in tumors other than AML.

A. Pfeiffer reports grants from Novo Nordisk Foundation during the conduct of the study and personal fees from Scandion Oncology outside the submitted work. A. Pfeiffer reports grants from Copenhagen Bioscience PhD Programme. G. Franciosa reports grants from Novo Nordisk Foundation during the conduct of the study. S.C. Blacklow reports grants and personal fees from ERASCA, Inc.; grants from Novartis, Inc.; personal fees from Scorpion Therapeutics, Odyssey Therapeutics, MPM Capital, Ayala Pharmaceuticals, and Droia Ventures outside the submitted work. L.J. Jensen reports personal fees from Intomics A/S outside the submitted work and is a founder and owner of Intomics A/S. J.V. Olsen reports grants from Novo Nordisk Foundation during the conduct of the study; non-financial support from Acrivon Therapeutics, grants from Thermo Fisher Scientific and Novo Nordisk a/s outside the submitted work. No disclosures were reported by the other authors.

A. Pfeiffer: Conceptualization, data curation, formal analysis, validation, investigation, methodology, writing–original draft, writing–review and editing. G. Franciosa: Conceptualization, formal analysis, supervision, validation, investigation, writing–original draft, writing–review and editing. M. Locard-Paulet: Formal analysis, investigation, visualization, writing–review and editing. I. Piga: Data curation, validation. K. Reckzeh: Formal analysis, validation. V. Vemulapalli: Investigation, writing–review and editing. S.C. Blacklow: Resources, supervision, writing–review and editing. K. Theilgaard-Mønch: Resources, supervision, writing–review and editing. L.J. Jensen: Resources, supervision, writing–review and editing. J.V. Olsen: Conceptualization, resources, supervision, funding acquisition, project administration, writing–review and editing.

The authors would like to acknowledge Dr. Tasian and Dr. Patrick Brown for generating the B-ALL HB19;19 cell line, as well as Dr. Yana Pikman for providing the aforementioned cell line.

Work at the NNF CPR was funded by a donation from the NNF (NNF14CC0001). G. Franciosa was funded by the NNF Exploratory Interdisciplinary Synergy Programme (NNF20OC0064594). A. Pfeiifer was funded by the Copenhagen Bioscience PhD Program from the NNF (NNF16CC0020906).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Neel
BG
,
Gu
H
,
Pao
L
.
The ’Shp'ing news: SH2 domain-containing tyrosine phosphatases in cell signaling
.
Trends Biochem Sci
2003
;
28
:
284
93
.
2.
Hof
P
,
Pluskey
S
,
Dhe-Paganon
S
,
Eck
MJ
,
Shoelson
SE
.
Crystal structure of the tyrosine phosphatase SHP-2
.
Cell
1998
;
92
:
441
50
.
3.
Batth
TS
,
Papetti
M
,
Pfeiffer
A
,
Tollenaere
MAX
,
Francavilla
C
,
Olsen
JV
.
Large-scale phosphoproteomics reveals Shp-2 phosphatase-dependent regulators of Pdgf receptor signaling
.
Cell Rep
2018
;
22
:
2784
96
.
4.
Vemulapalli
V
,
Chylek
LA
,
Erickson
A
,
Pfeiffer
A
,
Gabriel
K-H
,
LaRochelle
J
, et al
.
Time resolved phosphoproteomics reveals scaffolding and catalysis-responsive patterns of SHP2-dependent signaling
.
Elife
2021
;
10
:
e64251
.
5.
Dance
M
,
Montagner
A
,
Salles
JP
,
Yart
A
,
Raynal
P
.
The molecular functions of Shp2 in the Ras/Mitogen-activated protein kinase (ERK1/2) pathway
.
Cell Signal
2008
;
20
:
453
9
.
6.
Tartaglia
M
,
Niemeyer
CM
,
Fragale
A
,
Song
X
,
Buechner
J
,
Jung
A
, et al
.
Somatic mutations in PTPN11 in juvenile myelomonocytic leukemia, myelodysplastic syndromes and acute myeloid leukemia
.
Nat Genet
2003
;
34
:
148
50
.
7.
Bentires-Alj
M
,
Paez
JG
,
David
FS
,
Keilhack
H
,
Halmos
B
,
Naoki
K
, et al
.
Activating mutations of the noonan syndrome-associated SHP2/PTPN11 gene in human solid tumors and adult acute myelogenous leukemia
.
Cancer Res
2004
;
64
:
8816
20
.
8.
Nabinger
SC
,
Li
XJ
,
Ramdas
B
,
He
Y
,
Zhang
X
,
Zeng
L
, et al
.
The protein tyrosine phosphatase, Shp2, positively contributes to FLT3-ITD-induced hematopoietic progenitor hyperproliferation and malignant disease in vivo
.
Leukemia
2013
;
27
:
398
408
.
9.
Chen
YNP
,
Lamarche
MJ
,
Chan
HM
,
Fekkes
P
,
Garcia-Fortanet
J
,
Acker
MG
, et al
.
Allosteric inhibition of SHP2 phosphatase inhibits cancers driven by receptor tyrosine kinases
.
Nature
2016
;
535
:
148
52
.
10.
Wu
J
,
Zhang
H
,
Zhao
G
,
Wang
R
.
Allosteric inhibitors of SHP2: an updated patent review (2015–2020)
.
Curr Med Chem
2021
;
28
:
3825
42
.
11.
Pandey
R
,
Ramdas
B
,
Wan
C
,
Sandusky
G
,
Mohseni
M
,
Zhang
C
, et al
.
SHP2 inhibition reduces leukemogenesis in models of combined genetic and epigenetic mutations
.
J Clin Invest
2019
;
129
:
5468
73
.
12.
Dardaei
L
,
Wang
HQ
,
Singh
M
,
Fordjour
P
,
Shaw
KX
,
Yoda
S
, et al
.
SHP2 inhibition restores sensitivity in ALK-rearranged non-small-cell lung cancer resistant to ALK inhibitors
.
Nat Med
2018
;
24
:
512
7
.
13.
Mainardi
S
,
Mulero-Sánchez
A
,
Prahallad
A
,
Germano
G
,
Bosma
A
,
Krimpenfort
P
, et al
.
SHP2 is required for growth of KRAS-mutant non-small-cell lung cancer in vivo
.
Nat Med
2018
;
24
:
961
7
.
14.
Fedele
C
,
Ran
H
,
Diskin
B
,
Wei
W
,
Jen
J
,
Geer
MJ
, et al
.
SHP2 inhibition prevents adaptive resistance to MEK inhibitors in multiple cancer models
.
Cancer Discov
2018
;
8
:
1237
49
.
15.
De Kouchkovsky
I
,
Abdul-Hay
M
.
Acute myeloid leukemia: a comprehensive review and 2016 update
.
Blood Cancer J
2016
;
6
:
e441
.
16.
Lagunas-Rangel
FA
,
Chávez-Valencia
V
.
FLT3-ITD and its current role in acute myeloid leukaemia
.
Med Oncol
2017
;
34
:
114
.
17.
Zhao
L
,
Melenhorst
JJ
,
Alemu
L
,
Kirby
M
,
Anderson
S
,
Kench
M
, et al
.
KIT with D816 mutations cooperates with CBFB-MYH11 for leukemogenesis in mice
.
Blood
2012
;
119
:
1511
21
.
18.
Estruch
M
,
Reckzeh
K
,
Vittori
C
,
Centio
A
,
Ali
M
,
Engelhard
S
, et al
.
Targeted inhibition of cooperative mutation- and therapy-induced AKT activation in AML effectively enhances response to chemotherapy
.
Leukemia
2021
;
35
:
2030
42
.
19.
Martinez Molina
D
,
Jafari
R
,
Ignatushchenko
M
,
Seki
T
,
Larsson
EA
,
Dan
C
, et al
.
Monitoring drug target engagement in cells and tissues using the cellular thermal shift assay
.
Science
2013
;
341
:
84
7
.
20.
Ianevski
A
,
Giri
AK
,
Aittokallio
T
.
SynergyFinder 2.0: visual analytics of multi-drug combination synergies
.
Nucleic Acids Res
2020
;
48
:
W488
93
.
21.
Rodrigues
CHM
,
Pires
DEV
,
Ascher
DB
,
RenéRen
I
,
Rachou
R
,
Oswaldo Cruz
F
.
DynaMut: predicting the impact of mutations on protein conformation, flexibility and stability
.
Nucleic Acids Res
2018
;
46
:
W350
5
.
22.
Batth
TS
,
Tollenaere
MAX
,
Ruther
PL
,
Gonzalez-Franquesa
A
,
Prabhakar
BS
,
Bekker-Jensen
SH
, et al
.
Protein aggregation capture on microparticles enables multipurpose proteomics sample preparation
.
Mol Cell Proteomics
2019
;
18
:
1027
35
.
23.
Bache
N
,
Geyer
PE
,
Bekker-Jensen
DB
,
Hoerning
O
,
Falkenby
L
,
Treit
PV
, et al
.
A novel LC system embeds analytes in pre-formed gradients for rapid, ultra-robust proteomics
.
Mol Cell Proteomics
2018
;
17
:
2284
96
.
24.
Bekker-Jensen
DB
,
Martínez-Val
A
,
Steigerwald
S
,
Rüther
P
,
Fort
KL
,
Arrey
TN
, et al
.
A compact quadrupole-orbitrap mass spectrometer with FAIMS interface improves proteome coverage in short LC gradients
.
Mol Cell Proteomics
2020
;
19
:
716
29
.
25.
Bruderer
R
,
Bernhardt
OM
,
Gandhi
T
,
Miladinović
SM
,
Cheng
L-Y
,
Messner
S
, et al
.
Extending the limits of quantitative proteome profiling with data-independent acquisition and application to acetaminophen-treated three-dimensional liver microtissues
.
Mol Cell Proteomics
2015
;
14
:
1400
10
.
26.
Kelstrup
CD
,
Bekker-Jensen
DB
,
Arrey
TN
,
Hogrebe
A
,
Harder
A
,
Olsen
JV
.
Performance evaluation of the Q exactive HF-X for shotgun proteomics
.
J Proteome Res
2018
;
17
:
727
38
.
27.
Cox
J
,
Mann
M
.
MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification
.
Nat Biotechnol
2008
;
26
:
1367
72
.
28.
Zhu
Y
,
Orre
LM
,
Zhou Tran
Y
,
Mermelekas
G
,
Johansson
HJ
,
Malyutina
A
, et al
.
DEqMS: a method for accurate variance estimation in differential protein expression analysis
.
Mol Cell Proteomics
2020
;
19
:
1047
57
.
29.
Ritchie
ME
,
Phipson
B
,
Wu
D
,
Hu
Y
,
Law
CW
,
Shi
W
, et al
.
Limma powers differential expression analyses for RNA-sequencing and microarray studies
.
Nucleic Acids Res
2015
;
43
:
e47
.
30.
Olsen
JV
,
Vermeulen
M
,
Santamaria
A
,
Kumar
C
,
Miller
ML
,
Jensen
LJ
, et al
.
Quantitative phosphoproteomics reveals widespread full phosphorylation site occupancy during mitosis
.
Sci Signal
2010
;
3
:
ra3
.
31.
Yılmaz
S
,
Ayati
M
,
Schlatzer
D
,
Çiçek
AE
,
Chance
MR
,
Koyutürk
M
.
Robust inference of kinase activity using functional networks
.
Nat Commun
2021
;
12
:
1177
.
32.
Perez-Riverol
Y
,
Csordas
A
,
Bai
J
,
Bernal-Llinares
M
,
Hewapathirana
S
,
Kundu
DJ
, et al
.
The PRIDE database and related tools and resources in 2019: improving support for quantification data
.
Nucleic Acids Res
2019
;
47
:
D442
50
.
33.
Meyers
RM
,
Bryan
JG
,
McFarland
JM
,
Weir
BA
,
Sizemore
AE
,
Xu
H
, et al
.
Computational correction of copy number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells
.
Nat Genet
2017
;
49
:
1779
84
.
34.
Dumas
P-Y
,
Villacreces
A
,
Guitart
AV
,
El-Habhab
A
,
Massara
L
,
Mansier
O
, et al
.
Dual inhibition of FLT3 and AXL by gilteritinib overcomes hematopoietic niche-driven resistance mechanisms in FLT3-ITD acute myeloid leukemia
.
Clin Cancer Res
2021
;
27
:
6012
25
.
35.
Nichols
RJ
,
Haderk
F
,
Stahlhut
C
,
Schulze
CJ
,
Hemmati
G
,
Wildes
D
, et al
.
RAS nucleotide cycling underlies the SHP2 phosphatase dependence of mutant BRAF-, NF1- and RAS-driven cancers
.
Nat Cell Biol
2018
;
20
:
1064
73
.
36.
Hogrebe
A
,
von Stechow
L
,
Bekker-Jensen
DB
,
Weinert
BT
,
Kelstrup
CD
,
Olsen
JV
.
Benchmarking common quantification strategies for large-scale phosphoproteomics
.
Nat Commun
2018
;
9
:
1045
.
37.
Bennett
AM
,
Tang
TL
,
Sugimoto
S
,
Walsh
CT
,
Neel
BG
.
Protein-tyrosine-phosphatase SHPTP2 couples platelet-derived growth factor receptor beta to Ras
.
Proc Natl Acad Sci U S A
1994
;
91
:
7335
9
.
38.
Lu
W
,
Gong
D
,
Bar-Sagi
D
,
Cole
PA
.
Site-specific incorporation of a phosphotyrosine mimetic reveals a role for tyrosine phosphorylation of SHP-2 in cell signaling
.
Mol Cell
2001
;
8
:
759
69
.
39.
Ren
Y
,
Chen
Z
,
Chen
L
,
Fang
B
,
Win-Piazza
H
,
Haura
E
, et al
.
Critical role of Shp2 in tumor growth involving regulation of c-Myc
.
Genes Cancer
2010
;
1
:
994
1007
.
40.
Nakata
K
,
Suzuki
Y
,
Inoue
T
,
Ra
C
,
Yakura
H
,
Mizuno
K
.
Deficiency of SHP1 leads to sustained and increased ERK activation in mast cells, thereby inhibiting IL-3-dependent proliferation and cell death
.
Mol Immunol
2011
;
48
:
472
80
.
41.
Tartaglia
M
,
Mehler
EL
,
Goldberg
R
,
Zampino
G
,
Brunner
HG
,
Kremer
H
, et al
.
Mutations in PTPN11, encoding the protein tyrosine phosphatase SHP-2, cause Noonan syndrome
.
Nat Genet
2001
;
29
:
465
8
.
42.
Tartaglia
M
,
Martinelli
S
,
Stella
L
,
Bocchinfuso
G
,
Flex
E
,
Cordeddu
V
, et al
.
Diversity and functional consequences of germline and somatic PTPN11 mutations in human disease
.
Am J Hum Genet
2006
;
78
:
279
90
.
43.
Larochelle
JR
,
Fodor
M
,
Vemulapalli
V
,
Mohseni
M
,
Wang
P
,
Stams
T
, et al
.
Structural reorganization of SHP2 by oncogenic mutations and implications for oncoprotein resistance to allosteric inhibition
.
Nat Commun
2018
;
9
:
4508
.
44.
Martinelli
S
,
Nardozza
AP
,
Delle Vigne
S
,
Sabetta
G
,
Torreri
P
,
Bocchinfuso
G
, et al
.
Counteracting effects operating on Src homology 2 domain-containing protein-tyrosine phosphatase 2 (SHP2) function drive selection of the recurrent Y62D and Y63C substitutions in Noonan syndrome
.
J Biol Chem
2012
;
287
:
27066
77
.
45.
Horn
H
,
Schoof
EM
,
Kim
J
,
Robin
X
,
Miller
ML
,
Diella
F
, et al
.
KinomeXplorer: an integrated platform for kinome biology studies
.
Nat Methods
2014
;
11
:
603
4
.
46.
Waanders
E
,
Gu
Z
,
Dobson
SM
,
Antić
Ž
,
Crawford
JC
,
Ma
X
, et al
.
Mutational landscape and patterns of clonal evolution in relapsed pediatric acute lymphoblastic leukemia
.
Blood Cancer Discov
2020
;
1
:
96
111
.
47.
Furitsu
T
,
Tsujimura
T
,
Tono
T
,
Ikeda
H
,
Kitayama
H
,
Koshimizu
U
, et al
.
Identification of mutations in the coding sequence of the proto-oncogene c-kit in a human mast cell leukemia cell line causing ligand-independent activation of c-kit product
.
J Clin Invest
1993
;
92
:
1736
44
.
48.
Konieczkowski
DJ
,
Johannessen
CM
,
Garraway
LA
.
A convergence-based framework for cancer drug resistance
.
Cancer Cell
2018
;
33
:
801
15
.
49.
Lu
H
,
Liu
C
,
Huynh
H
,
Le
TBU
,
LaMarche
MJ
,
Mohseni
M
, et al
.
Resistance to allosteric SHP2 inhibition in FGFR-driven cancers through rapid feedback activation of FGFR
.
Oncotarget
2020
;
11
:
265
81
.
50.
Wu
R
,
Haas
W
,
Dephoure
N
,
Huttlin
EL
,
Zhai
B
,
Sowa
ME
, et al
.
A large-scale method to measure absolute protein phosphorylation stoichiometries
.
Nat Methods
2011
;
8
:
677
83
.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs International 4.0 License.

Supplementary data