Resistance Mechanisms to SYK Inhibition in Acute Myeloid Leukemia

Author Manuscript Published OnlineFirst on November 26, 2019; DOI: 10.1158/2159-8290.CD-19-0209 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Resistance mechanisms to SYK inhibition in acute myeloid leukemia

AUTHORS: Anjali Cremer1, Jana M. Ellegast1, Gabriela Alexe1,2,3, Elizabeth S. Frank1, Linda Ross1, S. Haihua Chu1, Yana Pikman1, Amanda Robichaud1, Amy Goodale2, Björn Häupl4,5, Sebastian Mohr4, Arati V. Rao6, Alison Walker7, James S. Blachly7, Federica Piccioni2, Scott A. Armstrong1, John C. Byrd7, Thomas Oellerich4,5* and Kimberly Stegmaier1,2*

AFFILIATIONS

1. Department of Pediatric Oncology, Dana-Farber Cancer Institute and Boston Children’s

Hospital, Harvard Medical School, Boston, MA 02215, USA. 2. The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. 3. Bioinformatics Graduate Program, Boston

University, Boston, MA 02215, USA. 4. University Hospital Frankfurt, Department of

Hematology/Oncology, Theodor-Stern-Kai 7, 60590 Frankfurt/Main, Germany. 5. German

Cancer Consortium/German Cancer Research Center, Im Neuenheimer Feld, 69120

Heidelberg, Germany. 6. Gilead Sciences Inc., 333 Lakeside Drive, Foster City, CA 94404 7.

Department of Internal Medicine, Division of Hematology, Department of Medicine, The Ohio

State University, Columbus, Ohio 43210, USA.

*Lead Contacts to whom correspondence should be addressed: Kimberly Stegmaier,

Department of Pediatric Oncology, Dana-Farber Cancer Institute, 450 Brookline Ave.,

Boston, MA 02215, USA. Tel.: 617-632-4438; Fax: 617-632-4850; E-mail: kimberly_stegmaier@ dfci.harvard.edu.

Thomas Oellerich, University Hospital Frankfurt, Department of Hematology/Oncology,

Theodor-Stern Kai 7, 60595 Frankfurt/Main, Germany. Tel.: +49 (0) 69-6301-6148; Fax: +49-

69-6301-4135; E-mail: [email protected].

Running title: Resistance mechanisms to SYK inhibition in AML

Conflicts of interest:

K.S. currently has funding from and previously consulted for Novartis on topics unrelated to this manuscript and has consulted for Rigel Pharmaceuticals. S.A.A. is a consultant and/or

Downloaded from cancerdiscovery.aacrjournals.org on September 26, 2021. © 2019 American Association for Cancer Research. Author Manuscript Published OnlineFirst on November 26, 2019; DOI: 10.1158/2159-8290.CD-19-0209 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

shareholder for Epizyme Inc, Imago Biosciences, Cyteir Therapeutics, C4 Therapeutics,

Syros Pharmaceuticals, OxStem Oncology, Accent Therapeutics and Mana Therapeutics.

S.A.A. has received research support from Janssen, Novartis, and AstraZeneca. J.S.B. has performed consulting for AbbVie, AstraZeneca, and KITE Pharma. J.C.B. is a consultant for

Acerta/AstraZeneca, Pharmacyclics, Jazz, and Verastem. A.R. is employed by Gilead. A.W. has received clinical trial research funding from Gilead. All other authors declare no potential conflict of interest.

ABSTRACT

Spleen tyrosine kinase (SYK) is a non-mutated therapeutic target in acute myeloid leukemia

(AML). Attempts to exploit SYK therapeutically in AML have shown promising results in combination with chemotherapy, likely reflecting induced mechanisms of resistance to single agent treatment in vivo. We conducted a genome-scale ORF resistance screen and identified activation of the RAS/MAPK/ERK pathway as one major mechanism of resistance to SYK inhibitors. This finding was validated in AML cell lines with innate and acquired resistance to

SYK inhibitors. Furthermore, patients with AML with select mutations activating these pathways displayed early resistance to SYK inhibition. To circumvent SYK inhibitor therapy resistance in AML, we demonstrate that a MEK and SYK inhibitor combination is synergistic in vitro and in vivo. Our data provide justification for use of ORF-screening to identify resistance mechanisms to kinase inhibitor therapy in AML lacking distinct mutations and to direct novel combination-based strategies to abrogate these.

STATEMENT OF SIGNIFICANCE

The integration of functional genomic screening with the study of mechanisms of intrinsic and acquired resistance in model systems and human patients identified resistance to SYK inhibitors through MAPK signaling in AML. The dual targeting of SYK and the MAPK pathway offers a combinatorial strategy to overcome this resistance.

INTRODUCTION

After a frustrating decade of limited progress in the treatment of patients with acute myeloid leukemia (AML), 2017-2018 was a remarkable turning point. The Federal Drug

Administration (FDA) approved for marketing new agents for patients with this disease: liposomal daunorubicin/cytarabine, enasidenib, ivosidenib, gemtuzumab ozogamacin, venetoclax, midostaurin, and gilteritinib. A next wave of drugs is coming down the pike targeting genes not mutated in AML with several showing evidence of early clinical activity.

A challenge that lies ahead is to leverage these new targeted agents toward maximal clinical efficacy.

One targeted approach for patients with AML recently showing promising signs of activity is the inhibition of spleen tyrosine kinase (SYK). SYK is a cytoplasmic tyrosine kinase best known for its role in B-cell development but also characterized to play a role in myeloid signaling more broadly (1-3). Multiple lines of preclinical evidence suggest the therapeutic targeting of SYK in AML. In rare instances, SYK is hyper-activated in myeloid malignancies through gene fusions, such as TEL-SYK (4,5), while in other instances, it is activated through integrin and Fc receptor signaling (3,6). Genetic suppression, as well as chemical perturbation of SYK activity, resulted in impaired growth of AML cells in vitro and in mouse models of AML and induced differentiation in some AML contexts (6,7). Adding further credence to an important role for SYK in AML, two independent studies reported high levels of SYK phosphorylation in AML bone marrow specimens as a poor prognostic marker relative to therapeutic outcome (8,9). Finally, candidate biomarkers of response to SYK inhibitors have included FLT3 mutations and high levels of HOXA9 and MEIS1 expression (9-11).

Notably, SYK inhibitors have been shown to be active in the high-risk FLT3-mutated AML subset (10-13) where SYK was demonstrated to physically interact with and activate FLT3. In addition, in a recent report from the BEAT AML program, mutations in NPM1 alone or in

combination with FLT3-ITD or DNMT3A were also predictive of response to SYK inhibition in primary patient samples treated in vitro (11).

Two orally bioavailable SYK inhibitors, entospletinib and TAK-659, have entered clinical trials for patients with AML with both studies demonstrating early evidence of response, including a modest number of complete responses with single agent treatment (14-16). More strikingly, in one study combining the SYK inhibitor entospletinib with standard chemotherapy

(cytarabine and daunorubicin), patients with FLT3 mutations, MLL rearrangements, and

NPM1 mutations, had a higher than predicted complete response rate compared to historical controls (15). Intriguingly, NPM1 mutated AML is another subset reported to have high expression of HOXA9 and MEIS1, and in further support of the preclinical studies, HOXA9 and MEIS1 expression were associated with a trend a toward higher incidence of complete remission in this clinical trial (17).

While these early clinical trial results are encouraging, targeted therapy is typically associated with the emergence of resistance, and combination therapy is almost always needed for a durable therapeutic response (18). The most frequent mechanism of acquired resistance is the development of, or selection for, secondary mutations in the drug target

(19,20). Patients can, however, also acquire mutations in genes that are upstream or downstream effectors of the targeted signaling pathways leading to its reactivation. Finally, different signaling hubs can be activated to compensate for inhibition of the drug target (21).

For example small molecule inhibitors of oncogenic BRAF (V600E) in colon cancer are circumvented through the activation of feedback loops that engage the epidermal growth factor receptor (EGFR) (22), which leads to the reactivation of mitogen-activated protein kinase (MAPK) and phosphoinositide 3- kinase (PI3K) pathways. In the case of BRAF, in order to optimize clinical efficacy of this targeted therapy, multiple preclinical studies with genome-wide screens were conducted to decipher resistance mechanisms. Mechanisms

elucidated in those studies precisely predicted response to treatment in human patients and paved the way for new drug combinations entering the clinic (23).

Resistance mechanisms to SYK inhibition in AML have not been explored, and SYK is only rarely mutated in human disease, including in AML. For genes such as SYK, which do not typically have mutations but are constitutively active in a subset of patients, the mechanism of resistance is poorly understood. Here, we identified resistance mechanisms to SYK inhibition by using an integrated proteogenomic approach, including the study of primary patient samples as part of a clinical trial. We show that SYK inhibition leads to the downregulation of central key signaling nodes, such as the MTOR/AKT, JAK/STAT and

RAS/MAPK/ERK pathways, but that resistance mechanisms to SYK inhibition are mainly conveyed by re-activation of the RAS/MAPK/ERK signaling pathway and can be overcome by combining a MEK inhibitor with a SYK inhibitor.

RESULTS

Entospletinib inhibits downstream targets of SYK

Entospletinib is an orally available, selective inhibitor of SYK (21), which was investigated in clinical trials in chronic lymphoid leukemia and non-Hodgkin lymphoma (21,22), as well as recently in AML (14,15). We first characterized response to entospletinib in a panel of AML cell lines and identified MV4-11, MOLM13 and the derivative cell line MOLM14 as among the most sensitive (Supplementary Fig. S1A and S1B and Supplementary Table S1). The IC50 values of these two cell lines match the plasma concentration of entospletinib in patients, given a 400 mg BID dosing schedule (15,24).

Next, we determined the effects of SYK inhibition with entospletinib on intracellular signaling pathways by combining stable isotope labeling by amino acids in cell culture (SILAC) and mass spectrometry (MS)-based phosphoproteomics. For this purpose, MV4-11 and MOLM13 cells were cultured for one hour with entospletinib at their respective IC50 concentrations,

lysed, and phosphorylated tyrosine residues (pYome) enriched prior to MS analysis. This quantitative phosphoproteome analysis allowed the quantification of up to 655 class I p-sites

(localization probability >75%) in two replicate measurements in MV4-11 (Fig. 1A and

Supplementary Table S2) and 522 p-sites in MOLM13 cells (Supplementary Fig. S1C and

Supplementary Table S3).

In both cell lines we detected reduced tyrosine phosphorylation for known effectors of SYK, such as STAT5 and CBL (6,25) (Fig. 1A and Supplementary Fig. S1C), consistent with on- target activity of entospletinib for SYK. We also detected reduced tyrosine phosphorylation of members of the RAS/MAPK/ERK signaling pathway, such as the downstream effectors

MAPK3 (ERK1) and MAPK1 (ERK2), as well as the upstream mediators GAB2 and PTPN11, consistent with prior reports that SYK inhibition negatively regulates RAS/MAPK/ERK signaling in AML (25). Concentration-dependent reductions in phosphorylation of SYK,

STAT5, PTPN11 and ERK1/2, were confirmed by immunoblotting in both MV4-11 (Fig. 1B) and MOLM14 cells (Supplementary Fig. S1D). When we investigated the corresponding genes from our quantitative phosphoproteome study with gene set enrichment analysis

(GSEA), we detected a strong correlation between entospletinib treatment and the downregulation of different gene set categories including RAS/MAPK/ERK signaling, growth factor signaling and chemokine/chemokine receptor signaling (Supplementary Fig. S1E and

S1F).

In parallel we analyzed the effects of SYK inhibition by entospletinib on genome-wide expression. MV4-11 cells were treated with vehicle or the IC50 concentration of entospletinib, and gene expression was profiled by RNA sequencing (RNA seq). Data were analyzed with

GSEA. We observed a strong correlation between entospletinib treatment and a previously published SYK-directed shRNA knockdown signature generated in AML cell lines (Figure 1C, upper panel) (NES = -4.1, FDR <0.01, p-value <0.01) (10) supporting the on-target activity of

entospletinib against SYK in AML. Moreover, we observed a strong correlation with mTOR signaling inhibition (NES = -2.7, FDR <0.01, p-value <0.01) (Figure 1C, lower panel), as described previously for other SYK inhibitors (25).

To further validate the anti-leukemic efficacy of entospletinib, we studied its activity in three different patient-derived xenograft (PDX) models of AML. These models had distinct AML- specific mutations (PDX #1: MLL-AF9, PDX #2: MLL-AF4, PDX #3: FLT3-ITD/NPM1).

Treatment with entospletinib was initiated after confirming engraftment of transplanted cells as evidenced by human CD45 (hCD45) positive cells in the peripheral blood by flow cytometry. Mice were sacrificed after 14 days of treatment and leukemia burden evaluated by detection of hCD45 positive cells. We observed a significant reduction in leukemia burden in all three PDX models (Fig. 1D and Supplementary Figure S1G). In mice injected with PDX #1 we detected 82.34% (3.059, n=5) hCD45 positive bone marrow cells in the vehicle treated group, in comparison to 65.38% (4.277, n=5) in the entospletinib treated group. Mice injected with PDX #2 showed 76.46% (0.575, n=5) hCD45 positive cells in the vehicle group versus only 50.78% (2.419, n=5) in the entospletinib treated group and PDX #3 had 20.9%

(1.427, n=5) hCD45 positive cells in the bone marrow in the vehicle treated group and

4.618% (0.7602, n=5) in the entospletinib treated group. We evaluated for effects on survival in PDX #1 and PDX #3 where we observed a significant survival advantage for mice treated with entospletinib in comparison to vehicle treated mice (PDX #1, p-value <0.0001, n=8) (Fig. 1E, left) and (PDX #3, p-value <0.0001. n=8) (Fig. 1E, right). Taken together, we validated the efficacy of entospletinib in AML cell line and mouse models and defined the signaling pathways regulated by SYK in AML.

Genome-scale lentiviral ORF screen identifies pathways that confer resistance to entospletinib in AML

To study mechanisms of resistance that may evolve during entospletinib treatment, we performed a genome-scale, lentiviral open reading frame (ORF) screen in MV4-11 and

MOLM14 cells. Cells were infected with a pooled lentiviral ORF library containing 17,255 barcoded ORFs that resulted in the overexpression of 10,315 distinct human genes with at least 99% nucleotide and protein match (26,27). Next, cells were selected using puromycin, passaged for three population doublings and an early time point (ETP) was harvested (Fig.

2A). ORF-expressing cells were passaged for 21 days in the presence of 3 M (IC90) entospletinib or a DMSO control (Supplementary Fig. S2A and S2B), gDNA isolated and barcodes amplified and sequenced as previously described (28) on a HiSeq2000 (Illumina).

The read counts were normalized to reads per million and then log2 transformed. The log2 fold-change of each ORF was determined relative to the initial time point for each biological replicate (Fig. 2B and 2C). ORF representation for entospletinib treatment was strongly correlated between the MV4-11 and MOLM14 cell lines (Pearson R = 0.61, p-value

< 2.2e-16) (Fig. 2D) and between replicates in each cell line (Pearson R = 0.74, p-value <

2.2e-16 for MV4-11 and Pearson R = 0.60, p-value < 2.2e-16 for MOLM14) (Supplementary

Fig. S2C and S2D). ORFs were considered to be hits if they displayed z-scores (standard deviations from the mean) for log2(FC) expression ≥ 2.0 with entospletinib vs. ETP. There were 78 ORF hits in the MV4-11 cell line, corresponding to 62 genes (Fig. 2B and 2E and

Supplementary Table S4) and 203 ORF hits corresponding to 168 genes in the MOLM14 cell line (Fig. 2C and 2E and Supplementary Table S5). After intersecting the top scoring ORF hits in both cell lines, 24 genes were found to be in common (Fig. 2D and 2E and

Supplementary Table S6). Importantly, top hits were specific to the drugs since they did not score in the DMSO control arm as promoting growth in the MV4-11 and MOLM14 cell line.

Genes that conferred resistance to entospletinib were significantly enriched for three major groups: growth factor receptors (e.g., EGFR, PDGFRA, and PDGFRB), chemokine/chemokine receptors (e.g., CSF2, CSF3 and IL3) and RAS/MAPK/ERK signaling.

Signaling via growth factor receptors, as well as signaling mediated by IL3 and GM-CSF,

occurs at least in part via the RAS signaling pathway (29). Data from the ORF screen suggested that overexpression of wildtype RAS ORFs (HRAS and KRAS), as well as pathway activators (CRKL) and downstream effectors (e.g., RAF1, MAPK1, and MAPK3)

(Fig. 2D and Supplementary Table S4-S6) conferred resistance to entospletinib in both screened AML cell lines. This genome-scale unbiased screen thus provided a strong rationale to further validate RAS as a driver of SYK inhibitor resistance.

RAS proteins, consisting of KRAS, NRAS, and HRAS, promote growth related signals from activated cell surface receptors, as well as other kinases. RAS mutations are common in a variety of different cancer types, including myeloid malignancies, and result in constitutive activation of the RAS protein, which is held in the active GTP-bound state (30). In AML, approximately 15% of adult and up to 30% of pediatric patients harbor a hot-spot mutation in the NRAS or KRAS genes, whereas mutations in HRAS almost never occur in these patients

(31-33). Therefore, further analysis focused on the RAS/MAPK/ERK signaling pathway, and specifically NRAS/KRAS, due to the frequency of RAS mutations in AML and its biological relevance in this disease.

We next over-expressed NRAS and KRAS ORFs in MV4-11 and MOLM14 cells and confirmed activation of the RAS/MAPK/ERK signaling pathway by detecting upregulation of phospho ERK 1/2 (Fig. 3A and 3B). We then treated these stably expressing cells with either

DMSO or entospletinib and determined that overexpression of wildtype NRAS or KRAS promoted resistance to entospletinib-mediated cell death (Fig. 3C and 3D) confirming the

ORF screen data.

To confirm our findings in another in vitro system, we infected primary MLL-AF9 rearranged murine bone marrow cells with retrovirus that contained an empty vector or mutated NRAS

(G12D). After confirming expression of mutated NRAS (G12D) by immunoblotting using a

mutant specific antibody, as well as upregulation of phospho ERK1/2 (Fig. 3E), we tested the sensitivity to entospletinib and a structurally distinct SYK inhibitor PRT062607 (34). In line with our previous results, the NRAS (G12D) (IC50= 2.2 M) expressing cells were 34-fold more resistant to entospletinib treatment than the control cells (IC50= 0.064 M) (Fig. 3F) and

7-fold more resistant to the SYK inhibitor PRT062607 (Supplementary Fig. S3A).

Hyperactivation of RAS/MAPK signaling pathway confers innate resistance to SYK inhibition

Next, we explored whether activation of the RAS/MAPK/ERK signaling pathway, through mutations in NRAS or KRAS, confers innate resistance to SYK inhibition. Indeed, when we exposed a panel of 12 different AML cell lines to entospletinib, we observed a difference between sensitive RAS wildtype cell lines (MV4-11, CMK-86, UCSD-AML1, MOLM14,

MOLM13, and EOL-1) and cell lines that are RAS mutated (NOMO-1, TF-1, HL-60, THP-1, and NB4) (35) (Fig. 3G). The only wildtype RAS cell line that showed resistance to entospletinib treatment was U937. Interestingly, U937 harbors a PTPN11 (G60R) mutation

(36). Activating mutations of PTPN11 lead to a hyperactivation of the downstream

RAS/MAPK/ERK signaling pathway (37). We observed a similar finding with analysis of a large dataset of primary AML samples treated with various small molecule inhibitors published as part of the BEAT AML program (11). In this dataset primary patient samples that harbored NRAS mutations were less sensitive to ex vivo entospletinib treatment (Fig.

3H).

Acquired SYK inhibitor resistance is associated with RAS/MAPK activation

To extend our findings with regard to naturally acquired resistance mechanisms to SYK inhibitors, we generated resistant MV4-11 cells by gradually adapting cells to a maximum concentration of 5 µM of entospletinib and treating them chronically over several months. We measured the IC50 of entospletinib in the resistant cell line model and detected a shift in

sensitivity in comparison to the naive cells (IC50 MV4-11: 0.5 M, IC50 MV4-11 Resistant: 7.5

M) (Fig. 4A). These cells were also cross-resistant to the SYK inhibitor PRT62607 (Fig. 4B).

In order to elucidate which signaling pathways confer resistance in the cells chronically exposed to entospletinib in comparison to naive cells, we evaluated the main downstream effectors of pathways that scored in the ORF-Screen: JAK/STAT, AKT/MTOR and

RAS/MAPK/ERK. Strikingly, we detected an increase in phospho SYK signaling (Fig. 4C), indicating that in the resistant cells, activation of parallel signaling pathways results in a feedback activation of SYK phosphorylation sites. Moreover, we detected increased phosphorylation in each of the three pathways scoring in the screen (ERK1/2, STAT5,

STAT3 and SHP2) suggesting that cells resistant to entospletinib have adapted to drug exposure by upregulation of the primary target SYK, as well as other signaling pathways bypassing SYK (Fig. 4C), such as JAK/STAT (phospho STAT3) and RAS/MAPK/ERK signaling (phospho SHP2, phospho ERK1/2, and total RAS). We validated these findings in another population of MV4-11 cells independently grown to resistance with chronic entospletinib treatment (Supplementary Fig. S4A and S4B).

As a next step, we investigated whether these changes in signaling were associated with a change in gene expression using RNA Seq analysis of the MV4-11 resistant cells. We observed a significant upregulation of NRAS and KRAS (p-value < 0.05), as well as the guanine nucleotide exchange factors SOS2 and RASGRP4, which couple signals from protein tyrosine kinases to RAS, facilitating the GDP-GTP exchange (38,39) (Fig. 4D).

Moreover, we detected upregulated expression of downstream effectors (BRAF, MAP3K1

(encoding for MEKK1) and MAPK3 (encoding for ERK1) (Fig. 4D). We validated the upregulation of NRAS and KRAS by RT-PCR (Fig. 4E). In line with these observations, the gene sets for KRAS and MTOR signaling were enriched by GSEA in the SYK inhibitor resistant compared to the drug sensitive cells (Fig. 4F).

We next explored whether there was clonal heterogeneity in this resistant population of cells and selected four single cell clones (SCC) from the bulk resistant cells by serial dilution. Only two out of four clones showed a similar increase of phosphorylation signals (Supplementary

Fig. S4C) to that observed in the bulk population of resistant cells (upregulation of phospho

SYK, phospho ERK1/2, phospho SHP2 and RAS). Importantly, each of these single cell selected populations were resistant to entospletinib (Supplementary Fig. S4D) consistent with clonal heterogeneity in these cells. This data suggests that in our acquired SYK inhibitor resistance model upregulation of the RAS/MAPK/ERK pathway is a dominant resistance mechanism, but there are also other resistance mechanisms at play.

To further investigate mechanisms underlying resistance in these MV4-11 cells, we performed whole exome sequencing (WES) of the drug sensitive versus resistant cells. We did not observe new mutations in SYK, RAS or other RTKs (Supplementary Table S7). And, while we observed a mutation in the E3 ligase C-CBL, this occurred in only one of the two populations of cells and with a VAF of 24%, explaining at best resistance in a minor population of cells. Similarly, we did not identify any copy gain in RAS/MAPK/ERK pathway members to explain the increase in transcripts such as KRAS, NRAS, or BRAF nor the activation of the MAPK pathway (Supplementary Table S8).

Taken together, this data supports a model whereby sub-clonal, largely non-genetic events

(e.g., increase in RAS expression), contribute to the resistance observed in the bulk populations of MV4-11 resistant cells.

Activation of the RAS/MAPK/ERK pathway is associated with resistance in patients with AML

To explore if our findings are also of importance in patients, we retrospectively analyzed a cohort of patients with AML treated with entospletinib. Twenty-eight patients were treated with entospletinib as part of a phase 1b/2 study (200 or 400 mg BID) for 14 days prior to receiving concurrent conventional 7 + 3 chemotherapy. Three patients whose AML harbored

PTPN11 mutations as part of their mutational profile (Fig. 5A) were enrolled. Each of these patients either failed to attain a remission or relapsed within 1 month of complete morphologic remission (CR) with medullary and/or extramedullary disease. To functionally validate the mutations observed in primary patient AML cells, we used site-directed mutagenesis to generate activating PTPN11 mutations (E76G and T73I) that were observed in two of these patients. First, we confirmed overexpression in MV4-11 cells by immunoblotting (Fig. 5B). Next, we cultured the cells chronically for three weeks with entospletinib in vitro. The PTPN11 mutants did not confer a growth advantage to the control cells in DMSO (Supplementary Fig. S5A). However, both PTPN11 mutations rendered resistance to entospletinib in comparison to the control cells overexpressing GFP or wildtype

PTPN11 (Fig. 5C). Importantly, we observed a failure to repress phospho ERK1/2 with entospletinib treatment in the PTPN11 mutant context in line with SYK inhibitor resistance

(Fig. 5D). Moreover, for the one patient whose relapse skin biopsy had sample sufficient to obtain interpretable sequencing data, the original PTPN11 and NPM1 mutations were identified in addition to novel, previously uncharacterized PTPN11 and MAPK1 mutations

(Supplementary Fig. S5B). However, neither of these new mutations has been reported previously, and their functional relevance remains unknown. Therefore, both mutations will require future validation and functional characterization.

Inhibitors of the RAS/MAPK/ERK pathway synergize with SYK inhibition to overcome

RAS mediated resistance

Because RAS mutant AML cells are sensitive to MEK inhibition (40,41), we next tested whether MEK inhibition using PD0325901 (42) would recapture response to SYK inhibition in

RAS/MAPK/ERK activated AML cells resistant to entospletinib. The combination of entospletinib and PD0325901 demonstrated strong synergy based on the Chou-Talalay combination index (CI) model (43) in RAS-mutant cell lines. We could detect strong synergy in NRAS (G12D) mutant THP-1 (Fig. 6A) and in the primary murine MLL-AF9 leukemia bone marrow cells retrovirally transduced with a plasmid containing mutated NRAS (G12D) (Fig.

6A). We could also detect strong synergy if RAS is activated by rewiring of the signaling pathways as in the MV4-11 cells with acquired resistance to SYK inhibitors (Fig. 6A). In the

MV4-11 cells with acquired resistance, the combination of entospletinib and PD0325901 led to cell death (Supplementary Fig. S6A). Culturing of cells with PD0325901 in combination with entospletinib was also able to re-sensitize other RAS/MAPK/ERK activated cell lines to

SYK inhibition, resulting in increased cell death (Supplementary Fig. S6A). Next, we confirmed that synergy persists if RAS is activated by mutation of an upstream effector (U937 cell line expressing PTPN11 (G60R)) (Fig. 6A). Isobolograms revealed synergy across a wide range of concentrations. Strikingly, we also saw additivity/synergy of the combination of entospletinib and PD0325901 in the genetically engineered cell lines (Fig. 6A and

Supplementary Fig. S6B), harboring PTPN11 wildtype or PTPN11 mutations (E76G and

T73I) and in the RAS wildtype AML cell lines MV4-11 and MOLM14 (Supplementary Fig.

S6B).

We extended this evaluation to a primary AML sample (Fig. 6A) possessing NRAS (G12D),

KRAS (G12D) and PTPN11 (G60R) mutations obtained from a patient with myeloproliferative neoplasm where we similarly saw additivity/synergy depending on the concentrations tested.

We confirmed strong synergy in two additional samples obtained from patients with RAS wildtype AML (Fig. 6A). We next confirmed synergistic activity in three previously tested PDX models of AML in vitro (Supplementary Figure S6B).

To confirm our results in an in vivo context, we evaluated the tolerability of the combination of entospletinib and PD0325901. We treated healthy C57BL/6 mice with entospletinib,

PD0325901, the combination or vehicle for 28 days and assessed the impact on different organs. We did not observe any toxic effect on the hematopoietic system (white blood count, neutrophils, hemoglobin, platelets) (Fig. 7A), nor on liver or kidney function (total bilirubin,

ALT, BUN, creatinine) (Supplementary Fig. S7A). Moreover, we did not observe any toxic effects on bone marrow (Supplementary Fig. S7B), liver or kidney tissue (Supplementary Fig.

S7C) in H&E staining, nor did the mice show any significant weight loss during treatment

(Supplementary Fig. S7D).

Next, to evaluate a PDX model in vivo (PDX #2, MLL-AF4 rearrangement), mice were treated with either vehicle, entospletinib, PD0325901 or the combination of entospletinib and

PD0325901 (Fig. 7B). Mice were sacrificed after 14 days of treatment, and the leukemia burden evaluated by detection of hCD45 positive cells by flow cytometry. We observed a significant reduction in leukemia burden in the bone marrow of the mice when comparing the vehicle treated mice with each other treatment group (Fig. 7B), also when comparing the entospletinib treated mice with the mice that received combination treatment (p-value  0.05)

(Fig. 7B). As had been observed with the PDX#1 and PDX#3, entospletinib alone prolonged survival of the mice (p-value  0.0001). However, there was a greater survival advantage in the combination treated group in comparison to all other treatment groups (Fig. 7C).

Finally, we investigated whether the combination treatment is also efficacious in a RAS mutant context in vivo. C57BL/6 mice, injected with very aggressive murine MLL-AF9/NRAS

(G12D) cells, were treated for 14 days with entospletinib, PD0325901, combination or vehicle. The combination therapy had a superior effect over single drug treatment with regard to reduction of GFP positive leukemic cells in the bone marrow, peripheral blood and spleen

(Figure 7D). Strong differences of spleen sizes between the mice in the different treatment groups were also observed (Figure 7E).

These results suggest that the combination of a MEK inhibitor and a SYK inhibitor is broadly effective in vitro and in vivo AML models and results in an improvement of survival, identifying this combination as a potential strategy that is well tolerated in vivo and might be able to block emergent resistance driven by activation of the RAS/MAPK/ERK signaling pathway. Furthermore, our findings suggest that targeting downstream effectors of RAS signaling in the MAPK signaling family is able to overcome resistance to SYK inhibition in

AML.

DISCUSSION

The most common mechanism of acquired resistance to targeted therapies is the development of secondary mutations in the drug target. SYK, however, is rarely mutated, which makes it different from other targets, such as FLT3. Because SYK inhibitors are now in clinical trials for the treatment of patients with AML where early promising data have been observed when combined with traditional therapies, we set out to identify the mechanisms of resistance by performing a proteogenomic characterization of SYK inhibitor resistance.

We identified the activation of RAS signaling as a major mechanism of resistance to SYK inhibition in AML, through mutations in RAS and PTPN11 in preclinical models of AML, as well as by rewired signaling at least in part through altered gene expression in AML cell lines.

Intriguingly, PTPN11 mutations were also observed in patients with AML with early relapse treated with a SYK inhibitor. RAS genes are proto-oncogenes that are mutated in approximately 15% of adult AML (31) and in over 30% of pediatric AML (33). In addition to mutations in RAS itself, RAS signaling can be deregulated by mutations in genes that

activate the signaling pathway, such as FLT3 and c-KIT, in another 25-40% of patients with

AML (44). Studies of patients with juvenile myelomonocytic leukemia (JMML) also underscore the importance of hyperactivated RAS signaling in myeloid malignancies. It is known that somatic mutations in PTPN11, which potentiate RAS signaling, occur in approximately 30% of patients with JMML (37,45) and in 7% of patients with AML (36) and in

10-15% of JMML and 1-3% of AML, respectively.

While the prognostic significance of RAS mutations remains debated in the context of standard cytotoxic chemotherapy (32,46,47), when selective pressure is applied to AML cells, RAS family mutations may bear therapeutic relevance. For example, in patients with

AML treated with the selective mutant IDH2 inhibitor enasidenib, NRAS or PTPN11 mutations are associated with decreased response rates (48). In addition, in a large study of primary patient samples treated ex vivo with over 100 small molecule inhibitors, mutations in

NRAS or KRAS were frequently associated with attenuated drug response (11). By putting selective pressure on AML cells with entospletinib treatment, RAS pathway mutations have also emerged as a biomarker of resistance in our study.

Despite the promising single-agent activity of very potent targeted therapies, such as the

FLT3 inhibitor quizartinib, most patients relapsed within three months (49,50). The main categories of resistance to targeted therapies are secondary mutations of the target, activation of other upstream stimulators or downstream effectors of the pathway, or the activation of parallel pathways (51). In the case of secondary mutations in the target itself, the development of new inhibitors may be called for. In the case of activation of parallel signaling pathways or the activation of other upstream activators or downstream effectors of the pathway, the combination of targeted drugs is critical to shutting down the escape mechanisms (52). The discovery of drug combinations that are able to prevent the emergence of resistance is an unmet clinical need in AML and targeted cancer therapy more

broadly. Genome-scale functional genomic screens provide a powerful, unbiased approach to identify drug resistance mechanisms (53) preemptively even before the clinical trial is initiated. Such an approach has been used successfully in the past to predict emerging mechanisms of resistance to targeted therapies (23,54). For example, a gain-of function screen identified COT as a driver of resistance to RAF inhibitor treatment in BRAF V600E mutant melanoma cell lines, as well as in patients that received treatment with MEK or RAF inhibitors (23). Here, we provide further support that these screening efforts can predict resistance mechanisms observed in the clinic.

As in a recent publication describing a complex pattern of leukemia heterogeneity and clonal evolution under selection pressure with gilteritinib, we too see clonal heterogeneity in AML cells grown under chronic entospletinib treatment (55). In two independent MV4-11 cell populations, we identified hyperactivation of the SYK/MAPK pathway. At the level of single cell clones, however, we observed variability in the degree of SYK/MAPK pathway activation despite all of the clones retaining entospletinib resistance. In line with this observation, while we did not see new RTK/MAPK mutations or copy number alterations in the resistant cells, in one of the two populations of MV4-11 drug resistant cells, we detected a sub-clonal mutation in the E3 ligase CBL, which would be predicted to result in entospletinib resistance. Similarly,

CBL mutations were reported in the context of gilteritinib resistance (55). Additional studies will be needed to determine the precise mechanism by which the drug resistant cells gain an increased expression of MAPK-related genes. In our preliminary studies we do not see alterations in H3K27Ac levels nor in chromatin accessibility to explain the gene expression changes (data not shown). Future studies will evaluate whether there are changes in repressive marks, chromatin looping, or the pattern of transcription factors binding at the promoter/enhancer regions for these genes.

Due to the structural and biochemical properties of RAS, it is not yet possible to target RAS directly (30), but it is known that RAS mutant cell lines are sensitive to MEK inhibition (40).

Because we identified the RAS signaling pathway as the major mechanism of resistance to

SYK inhibition in our study, we tested the hypothesis that the combination of a MEK and a

SYK inhibitor would show synergy in vitro. In fact, we demonstrated synergistic activity in combining PD0325901 with entospletinib in a panel of RAS mutated cell lines, primary patient samples, as well as a syngeneic MLL-AF9 primary leukemia cell model. Importantly, this drug combination maintained synergistic activity even in RAS wildtype cells and resulted in a greater reduction in leukemia burden compared to single agent treatments in a PDX AML model.

The selective, oral, allosteric MEK1/MEK2 inhibitor trametinib was recently FDA approved as part of the standard of care for patients with metastatic BRAF mutated melanoma (56). To date, data from two clinical trials testing MEK inhibitors in patients with AML have been published. In a Phase II study, selumetinib showed modest single-agent antileukemic activity in patients with advanced leukemia (57). Another study tested the single-agent activity of trametinib in patients with relapsed or refractory AML. In this study, patients with AML with

NRAS or KRAS mutations were enrolled in Cohort 1 and patients without RAS mutations into

Cohort 2. Patients in Cohort 1 had an overall response rate of 20% compared to 3% in

Cohort 2 (58). The most common drug-related toxicities were grade 1-2 diarrhea, fatigue, vomiting and skin rash (57,58). Both studies concluded that while some single agent activity was observed with a MEK inhibitor in AML, combination with another therapy in patients with

AML should be considered. Our results suggest that entospletinib, which is very well- tolerated in patients, is a drug to consider in combination with MEK inhibitors in AML, both in

RAS mutant and RAS wildtype disease, and should be further investigated in additional preclinical in vivo models.

PTPN11 activation has previously been identified as one major node in acquired and intrinsic resistance in cancer cells driven by RTKs (59). We identified PTPN11 as one of the proteins

whose phosphorylation was most significantly downregulated in our pYome-analysis with

SYK inhibitor treatment. After short-term treatment of AML cells with entospletinib, we detected decreased phosphorylation of PTPN11 at pY546, which is located in close proximity to the major phosphorylation site (Y542) of PTPN11. Indeed, PTPN11 activation is required for the full activation of RAS/MAPK/ERK signaling pathway (60). Furthermore, PTPN11 was identified as a direct activator of RAS and a potential target for cancers driven by hyperactive or oncogenic RAS (61). PTPN11 recruitment to duplicated Y599 on FLT3-ITD cooperates with SYK to enhance STAT5 phosphorylation and promote ERK phosphorylation (12). Our patient data suggests an association of PTPN11 mutations with early relapse, and we demonstrate that these mutations render resistance to SYK inhibitors in AML cell lines treated in vitro. While this data is provocative, more research is needed to definitively demonstrate that the PTPN11 mutations cause resistance in patients with AML treated with

SYK inhibitors. For example, serial samples of high quality to enable confident assessment of

VAF of PTPN11 mutations in patients treated with SYK inhibitors and functional validation of the novel PTPN11 and MAPK1 mutations identified in the current study are needed.

Recently, the first orally bioavailable highly potent allosteric PTPN11 inhibitor Shp099 was discovered (62,63). Shp099 treatment led to a reduction in leukemia burden in an orthotopic human primary leukemia derived FLT3-ITD AML model (62), suggesting that Shp099 could be a promising candidate for future drug combination studies in AML. Two recent reports identified a combination of a PTPN11 inhibitor with a MEK inhibitor as a targeted therapy approach not only in RAS wildtype, but also in KRAS mutant and KRAS amplified cancers

(52,64). Taken together this data suggests that the combination of Shp099 and entospletinib is a drug combination worth investigating in future experiments.

In conclusion, we report that RAS pathway activation, through RAS, PTPN11, and potentially

CBL mutations, or upregulation of expression of these target genes, renders resistance to

SYK inhibitors in AML in preclinical models and was observed in patients treated with the

SYK inhibitor entospletinib with early relapse. Furthermore, we provide preclinical data suggesting that combining SYK with MEK inhibitors has the potential to prevent the emergence of resistance to SYK inhibition and even shows upfront efficacy. Our approach exemplifies how a large-scale genomic screen may contribute to delineating clinical exclusion criteria for a targeted inhibitor, in this case RAS or PTPN11 mutations, and to determine effective drug combinations that may prevent the development of resistance.

METHODS

Cell Lines

U937, HL-60, MOLM13, UCSD-AML1, NB4, NOMO-1, and 293T cells were purchased from

ATCC. CMK-86 cells were purchased from JCRB Cell Bank. EOL-1 were purchased from

DSMZ. MV4-11, MOLM14 and THP-1 cells were provided by Scott Armstrong. MOLM13 and

MOLM14 cells were originally derived from the same patient. All cell lines were maintained in

RPMI 1640 (Cellgro) supplemented with 1% penicillin/streptomycin (PS; Cellgro) and 10%

FBS (Sigma-Aldrich) at 37°C with 5% CO2. The 293T cells were maintained in Dulbecco’s modified Eagle’s medium (Invitrogen) supplemented with 10% FBS (Invitrogen) and 1% PS.

All cell lines were tested negative for Mycoplasma and were authenticated using short tandem repeat (STR) profiling.

Western Blotting

Proteins were extracted using Lysis Buffer (Cell Signaling Technology) supplemented with

Complete, Ethylenediaminetetraacetic acid (EDTA)-free Protease Inhibitor Cocktail (Roche

Diagnostics) and Phosphatase Inhibitor Cocktail (Roche Diagnostics). Protein samples were separated by SDS-PAGE and subsequently transferred to polyvinylidene difluoride (PVDF) membranes, which were blocked in 5% BSA and incubated with primary antibodies against

RAS (G12D) (Cell Signaling Technology Cat. No. 14429), RAS (Cell Signaling Technology

Cat. No. 3965), NRAS (Santa Cruz Biotechnology Cat. No. sc-31), KRAS (Santa Cruz

Biotechnology Cat. No. sc-30), HRAS (Santa Cruz Biotechnology Cat. No. sc-35), phospho

STAT5 (Y694) (Cell Signaling Technology Cat. No. 9351), STAT5 (Cell Signaling Technology

Cat. No. 9363), phospho STAT3 (Cell Signaling Technology Cat. No. 9145), STAT3 (Cell

Signaling Technology Cat. No. 9139), phospho p44/42 MAPK (T202/Y204) (Cell Signaling

Technology Cat. No. 9101), p44/42 MAPK (Cell Signaling Technology Cat. No. 4696), phospho SHP2 (Y542) (Cell Signaling Technology 3751), SHP2 (Cell Signaling Technology

Cat. No. 3397), phospho SYK (Y525/Y526) (Cell Signaling Technology Cat. No. 2711), SYK

(Santa Cruz Biotechnology Cat. No. sc-1240), Vinculin (Abcam Cat. No. 18058), GAPDH

(Santa Cruz Biotechnology Cat. No. sc-47724), and -tubulin (Calbiochem, Cat. No. CP06).

Phospho protein signals were normalized to total protein content, which was normalized to indicated loading controls. For immunoblots showing phosphorylation and total protein content, the same protein lysates were run on separate blots to present one experiment and were normalized to its own loading control. Membranes were washed in TBS-T and incubated with the appropriate horseradish peroxidase-conjugated secondary antibodies.

Signal was detected by enhanced chemi-luminescence (ThermoFisher Scientific).

SILAC

For SILAC, cells were cultured in SILAC-RPMI devoid of arginine and lysine (Thermo Fisher

Scientific) supplemented with 10% h.i. dialyzed FCS (Sigma-Aldrich), 100 U/ml Penicillin /

13 15 100 mg/ml Streptomycin (Life Technologies) and amino acids with heavy isotopes ( C6 N4

13 15 12 14 L-arginine and C6 N2 L-lysine) or regular (light) amino acids ( C6 N4 L-arginine and

12 14 C6 N2 L-lysine) (Cambridge Isotope Laboratories).

Lentiviral/Retrovirus Production and Transduction

Lentivirus was generated by transfecting HEK-293T cells with the indicated vectors and the packaging plasmids, delta8.9 and VSVG, following the X-tremeGENE HP DNA Transfection

Reagent protocol (Roche). AML cells were infected with 2 mL of virus and 8 µg/mL polybrene.

Retrovirus was generated by using the packaging plasmids, Eco and RV. Primary murine bone marrow cells were infected by spin-infection in a centrifuge with 50 l of virus and 8

µg/mL polybrene in a 96-well plate, at 1500 rpm for 2 hours at room temperature (RT). Cells were selected with puromycin containing media 48 hours after infection.

Generation of MLL-AF9 Primary Mouse Leukemia Cells

Primary MLL-AF9 leukemia cells were provided by Scott H. Chu. Lin-Kit+Sca1+ hematopoietic stem cells were sorted from mice 6 to 8 weeks of age by harvesting of bone marrow cells from the femurs, tibias, hips, and spines as described previously (65) and retrovirally transduced with a MSCV-IRES-MLL-AF9-GFP construct. Cells were maintained in

15% Iscove’s modiﬁed Dulbecco medium supplemented with penicillin-streptomycin, murine

IL-6, murine SCF, and murine IL-3 (as above) for 2 days before ﬂuorescence-activated cell sorting (FACS) for GFP positive cells.

Generation of Entospletinib Resistant Cells

To generate cells that were resistant to SYK inhibition, MV4-11 were treated for 5 months with gradually increasing concentrations of entospletinib (500 nM to 5 μM). Cells were

considered entospletinib-resistant when they were able to remain 90-100% viable in the presence of this 10-fold higher than IC50 concentration of entospletinib.

Site-directed Mutagenesis

The different point mutations in the PTPN11 L sequence was generated according to the manufacturer’s instruction using a QuikChange XL Site-directed Mutagenesis kit

(Stratagene). All sequences were confirmed by Sanger sequencing. The following primers were used: PTPN11 T73I, Forward:

AGGGGAGAAATTTGCCATTTTGGCTGAGTTGGTCCA, Reverse:

TGGACCAACTCAGCCAAAATGGCAAATTTCTCCCCT. PTPN11 E76G, Forward:

ATTTGCCACTTTGGCTGGGTTGGTCCAGTATTACAT, Reverse: ATGTAATACTGGACCA-

ACCCAGCCAAAGTGGCAAAT.

In Vivo Transplantation

All in vivo studies were conducted under the auspices of protocols approved by the Dana-

Farber Cancer Institute Animal Care and Use Committee. Six- to eight-week old female

NSG-mice or C57BL/6-mice (The Jackson Laboratory) were injected with 1x106 cells. Bone marrow was harvested from femur, tibia and spine, and red blood cells were lysed (Sigma) prior to staining with human CD45 (Invitrogen Cat. No. MHCD4528) and murine CD45

(Biolegend Cat. No. 103113) antibodies and analysis by flow cytometry.

In Vivo Therapeutic Studies

Animals were randomly assigned to experimental treatment groups without blinding at any stage of the study. Treatment was started after we assessed engraftment by flow cytometry

(human CD45+ leukemic cells or GFP positive cells in the peripheral blood 1%). For efficacy studies leukemia burden was assessed using flow cytometry analysis of the percent of human CD45+ leukemic cells in the bone marrow on day 14 after treatment start. PD0325901

was administered once daily by oral gavage. We started dosing at 5 mg/kg body weight but needed to reduce the dose to 2.5 mg/kg body weight after three days of treatment due to weight loss of the mice in the PD0325901 and combo treatment arms. We continued with a five days on, two days off schedule for the remaining 10 days. For the entospletinib survival study, mice were weighed daily and were sacrificed when they reached a 20% weight loss threshold and/or showed lethargy/physical signs of illness. All animals assigned to treatment groups were included in the survival analysis.

In Vivo Toxicity Study

Healthy C57BL/6 mice were treated for 28 days with vehicle, entospletinib, PD0325901, or a combination of both drugs. We assessed CBCs at 14 days, and BUN, creatinine, total bilirubin, and ALT at 28 days. Moreover, weight of the mice was monitored over time. Bones, kidney and liver were harvested at the end of the study and H&E staining of representative animals from each group was performed.

Primary Patient Samples

The three patients reported in this manuscript with AML were treated with entospletinib in a clinical trial at Ohio State University. Patient samples used for synergy studies, were obtained from patients treated at the Dana-Farber Cancer Institute after obtaining informed consent under Dana-Farber Cancer Institute Internal Review Board–approved protocols.

AML blasts from patients were maintained in SFEM (Stemcell Technologies) with SCF,

FLT3L, IL-3, IL-6, and GCSF (PeproTech). All patients provided written informed consent and the studies were conducted in accordance with the Declaration of Helsinki and after

Human Research Ethics Committee approval.

Chemicals

All compounds for in vitro experiments were obtained from Selleck. PD0325901 for in vivo experiments was obtained from Selleck. Entospletinib chow for in vivo experiments was provided by Gilead.

Validation of ORF Hits

Lentiviral infected cells expressing candidate ORF hits from the primary screen were seeded in TC25 flasks in technical duplicate and were treated with DMSO or 3 µM entospletinib.

Cumulative population doublings were calculated by manually counting cells every 3-4 days for a total of 21 days.

ORFeome Library Titration

Accurate virus volumes to use in large-scale were determined in each cell line in order to achieve 30-40% infection efficiency, corresponding to a multiplicity of infection (MOI) of ~

0.5-1. Spin-infections were performed in 12-well plate format with 3 x 106 and 1.5 x 106 cells per well for MV4-11 and MOLM-14 cells, respectively, with different virus volumes (0,100,

200, 300, 400, 500 μL) with a final concentration of 8 μg/mL polybrene. Cells were spin- infected for 2 hours at 2000 rpm at 30 °C. Twenty-four hours later, cells were harvested and

2 x 105 MV4-11 cells and 3.5 x 105 MOLM-14 cells from each infection were seeded into duplicate wells in 6-well plates, each with complete medium and one treated with puromycin.

Seventy-two to -96 hours after selection, cells were counted to determine the amount of virus that yielded ~30 - 40% infection efficiency, and this amount was used for screening.

Genome-scale ORF Screens

The ORFeome barcoded library contains 17,255 barcoded ORFs overexpressing 10,135 distinct human genes with at least 99% nucleotide and protein match. Screening-scale infections of the ORFeome library were performed with a number of cells to achieve a representation of at least 1000 cells per ORF (~2 x 107 surviving cells containing 17,255

ORFs). Infections were performed with the pre-determined virus volume in the 12-well format, as the viral titration described above, and pooled 24 hours post-infection.

Approximately 24 hours after infection, all wells within a replicate were pooled and 48 hours after infection, cells were selected with puromycin. After selection was completed, 3 x 107 cells were divided into drug treated (3 µM entospletinib for MV4-11 and MOLM14) and vehicle treated arms. Cells were passaged in drug or fresh media containing drug every 3-4 days, and throughout the screen we maintained an average representation of 1,000 cells per

ORF construct. Cells were harvested 21 days after initiation of treatment. For both ORF screens, genomic DNA (gDNA) was isolated using Maxi (2 × 107 to 1 × 108 cells) or Midi (5 ×

106 to 3 × 107 cells) kits according to the manufacturer's protocol (Qiagen). PCR and sequencing were performed as previously described (28). We performed two replicates for the ETP and two replicates for the late time point for each cell line.

RNA-sequencing

RNA was extracted from cells with the RNeasy Kit and on-column DNA digestion (Qiagen).

For RNA-sequencing of MV4-11 cells, polyA mRNA was isolated and libraries were prepared using the TruSeq Stranded mRNA Kit (Illumina) according to the manufacturer’s protocol. All samples were sequenced on a NextSeq500 instrument with single-end 75bp reads to a depth of 30-50M reads/sample.

Gene Set Enrichment Analysis (GSEA)

GSEA v3.0 software was used to identify functional associations of the genome-wide molecular profiles. Significance cut-offs were assessed based on the GSEA standard recommendations: absolute Normalized Enrichment Score (NES) ≥ 1, p-value ≤ 0.05,

Benjamini-Hochberg false discovery rate ≤ 0.25.

Cell Viability and Synergy Studies

Cells were resuspended at 15,000 cells/mL and seeded at 40 µl/well into 384-well plates.

Cells were then treated with a single agent or combination of compounds and analyzed for cell viability on days 0 and 3 post-treatment using the Cell-TiterGlo luminescent assay kit

(Promega) according to the manufacturer’s protocol. Luminescence was read on a Fluostar

Omega Reader (BMG Labtech).

Quantification and Statistical Analyses

GSEA v2.1.0, GraphPad PRISM 7, R 3.2.3 and Python 2.7.2 software packages were used to perform the statistical analyses. Statistical tests used are specified in the figure legends.

Errors bars represent standard deviation, unless otherwise stated. The threshold for statistical significance is p-value ≤ 0.05, unless otherwise specified.

Data availability

RNAseq data was deposited at GEO under accession number GSE129698. WES data was deposited at NCBI-SRA under accession number PRJNA532457 (WES Data1) and

PRJNA565774 (WES Data2).

For detailed information on RNA-sequencing Data Processing, Synergy Analysis, Enrichment of Phospho-Peptides, Mass Spectrometry, Serial Dilution Assay, Whole Exome Sequencing

Analysis, Somatic Variant Calling/Analysis and Data Processing see Supplementary

Methods.

Authors’ Contributions

Conception and design: A. Cremer, K. Stegmaier, T. Oellerich.

Development of methodology: A. Cremer, L. Ross, F. Piccioni, K. Stegmaier

Acquisition of data (provided cells, acquired and managed patients, managed animals, provided facilities, etc.): A. Cremer, J.M. Ellegast, J.S. Blachly, A. Walker, L. Ross, A.

Goodale, A. Robichaud, B. Häupl, S. Mohr, Y. Pikman, S.H. Chu, A. Rao, T. Oellerich, K.

Stegmaier, S.A. Armstrong, J.C. Byrd

Analysis and interpretation of data (e.g., statistical analysis, computational analysis):

A. Cremer, J.M. Ellegast, G. Alexe, J.S. Blachly, E. Frank, B. Häupl

Writing and review of the manuscript: A. Cremer, K. Stegmaier, T. Oellerich, J.C. Byrd and all other authors.

Study supervision: K. Stegmaier, T Oellerich, and J.C. Byrd

Acknowledgments

We thank Dr. H. Uckelmann and Dr. S. Cremer for critical discussion and reading of the manuscript. We thank Dr. B. Huang for advice on in vivo use of PD0325901 and Dr. M.

Fleming for histopathology slide review. WES was performed by CCGD at DFCI. This work was supported by German Cancer Aid (Deutsche Krebshilfe) (A. Cremer), Swiss National

Science Foundation (J.M. Ellegast) and the National Cancer Institute R35 CA210030-01 (K.

Stegmaier), R35 CA197734 (J.C. Byrd), 1P50 CA206963-01 (K. Stegmaier) and K08

CA222684 (Y. Pikman), K. Stegmaier was a Leukemia and Lymphoma Society Scholar.

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Figure legends

Figure 1: Characterization of effects of entospletinib in AML in vitro and in vivo. A.

Intensities of peptide peaks versus mean log2 normalized (entospletinib/DMSO) ratios for p- sites identified by a mass-spectrometric pYome analysis in one out of two representative replicates. Red and black dots indicate p-sites downregulated in entospletinib/DMSO treated or upregulated, respectively, in MV4-11 cells (absolute mean log2 ratio entospletinib/DMSO ≥

0.5, p-value ≤ 0.05, t-test with Welch correction), ns = not significant. Selected p-sites are labeled. B. Validation of selected differential tyrosine phosphorylation events by

immunoblotting in MV4-11. Vinculin is used as a loading control. C. GSEA demonstrating enrichment of genes downregulated by entospletinib vs. DMSO in MV4-11 cells. D.

Percentage of hCD45 positive cells/live bone marrow cells after 14 days of entospletinib or vehicle treatment, respectively, in NSG mice injected with three different AML-PDX models

(*p-value ≤ 0.05, ***p-value ≤ 0.001, Mann-Whitney test). E. Survival study in NSG mice, injected with PDX#1 (left) and PDX#3 (right), randomly assigned to entospletinib or vehicle treatment, n=8 mice per arm (p-value ≤ 0.0001, Mantel-Cox test)

Figure 2: Genome-scale lentiviral ORFeome library screen identifies drivers of entospletinib resistance in AML. A. Schematic description of the genome-scale ORFeome library screen. B. Scatter plots presenting the z-scores for average log2 fold change ORF representation for entospletinib vs. ETP (y-axis) and for DMSO vs. ETP (x-axis) in MV4-11 and (C) MOLM14 cells. Dotted gray lines at z-score = 2 on either axis demonstrating the

ORF hits that are not scoring in the DMSO arm as promoting growth over time: DMSO vs.

ETP z-scores < 2 nominate genes not associated with enhanced growth in the DMSO, while entospletinib vs. ETP z-scores ≥ 2 nominate genes associated with resistance to entospletinib. Genes with z-scores < 2 for DMSO and ≥ 2 for entospletinib were nominated

as candidate genes conferring resistance and classified as significant ORFs. D. Scatter plots presenting the z-scores for average log2fold change core ORF representation for entospletinib vs. ETP in MOLM14 (y-axis) and for entospletinib vs. ETP in MV4-11 (x-axis).

E. Venn diagram presenting the 24 core ORF hits that scored in both cell lines MV4-11 and

MOLM14 (p-value < 0.0001, Fisher’s exact test).

Figure 3: Validation of ORF screen results: overexpression of RAS confers resistance to SYK inhibition. Immunoblots confirming overexpression of the indicated ORF hits in

MV4-11 cells (A) and (B) MOLM14 cells by antibodies directed against the ORF or downstream effectors. Alpha-tubulin and vinculin were used as a loading control. C-D. Long- term viability assays in MOLM14 (left) and MV4-11 (right) cells overexpressing the indicated

ORFs and treated with vehicle or 3 M entospletinib. GFP is included as a negative control.

Data are presented as mean values of triplicate replicates ± standard deviation (SD). E.

Immunoblot confirming overexpression of NRAS (G12D), as well as activation of the

downstream effector phospho ERK1/2, in retrovirally transduced murine MLL-AF9 (MAF9) rearranged leukemia cells. Vinculin was used as a loading control. F: Viability analysis with increasing concentrations of entospletinib for 72 hours in MAF9 leukemia cells. Data are plotted as the percentage of luminescence (measured by CellTiter-Glo) relative to DMSO controls. G. Viability analysis with increasing concentrations of entospletinib in a panel of 12 different AML cell lines after 72 hours of treatment. H. Average difference in AUC drug response between mutant and wild-type cases of 239 primary patient AML samples was determined using a Student’s two-sided t-test from a linear model fit (x-axis). P-values were corrected using the Benjamini-Hochberg method over all genes, -log(FDR)10 values are plotted (y-axis). Sensitive samples are red, resistant samples are blue. Size of circles correlates with patient sample size. Count = number of patients. Data was generated using the online data tool www.vizome.org (11).

Figure 4: Characterization of MV4-11 cells with acquired entospletinib resistance. A-B.

Viability analysis after 72 hours of (A) entospletinib or (B) PRT062067 treatment in naive and resistant MV4-11 cells. Data are plotted as the percentage of luminescence (measured by

CellTiter-Glo) relative to DMSO controls. C. Immunoblot demonstrating upregulated phospho sites of downstream effectors of SYK in the resistant state. Specific antibodies to indicated phospho sites were used. GAPDH was used as a loading control. D. Heatmap demonstrating upregulation of expression of indicated genes in replicate resistant cells (right) in comparison to replicate naive cells (left). Significance was assessed based on log2 fold change

expression ≥ 0.5 and p-value ≤ 0.05 for the Mann-Whitney test. E. Confirmation of NRAS and

KRAS mRNA fold change upregulation in resistant vs. naive MV4-11 cells by qPCR (*** p- value ≤ 0.001, Mann-Whitney test). F. GSEA demonstrating enrichment of signatures upregulated in resistant cells in comparison to naive cells. Significance was assessed based on normalized enrichment score (NES) ≥ 1.5, p-value ≤ 0.05 and false discovery rate (FDR)

≤ 0.05.

Figure 5: Activation of RAS/MAPK/ERK pathway is associated with resistance to entospletinib in patients with AML. A. Characteristics of patients with PTPN11 mutant

AML who were treated with entospletinib in a clinical trial. B. Immunoblot of MV4-11 cells engineered to overexpress WT (wildtype) PTPN11 or different mutants of PTPN11 (E76G and T73I) observed in patients. Antibody directed against PTPN11 was used. GFP was used as a negative control; Vinculin was used as a loading control. C. Long-term viability assays in

MV4-11 cells overexpressing the indicated ORFs and treated with vehicle or 3 M

entospletinib. GFP and WT PTPN11 are included as negative controls. Data are presented as mean values of triplicate replicates ± standard deviation (SD). D. Immunoblot of MV4-11 cells, overexpressing WT (wildtype) PTPN11 or different mutants of PTPN11 (E76G and

T73I), left panel: untreated, right panel: treated with 700 nM entospletinib for 1 hour. Antibody directed against phospho ERK (T202/Y204) was used. GFP was used as a negative control;

Vinculin was used as a loading control.

Figure 6: Combination of a MEK inhibitor and a SYK inhibitor is synergistic in in vitro

AML models. A. Combination index scores across the indicated cell lines with entospletinib treatment for 72 hours. Synergy was assessed by Chou-Talalay combination index (CI) across the indicated cell lines. Normalized isobolograms depict CI scores over a range of concentrations. The coordinates of the CI scores are d1/Dx1 and d2/Dx2, where Dx1 is the concentration of drug 1 (PD0325901) that alone produces the fractional inhibition effect x,

and Dx2 is the concentration of drug 2 (entospletinib) that alone produces the fractional inhibition effect x. The red line displayed is the line of additivity. Points below the line are synergistic and above the line are antagonistic.

Figure 7: Combination of a MEK inhibitor and a SYK inhibitor is active in in vivo AML models and shows no toxicity in healthy mice. A. Complete blood count (CBC) was evaluated after 14 days of treatment in healthy C57BL/6 mice. WBC = whole blood count, Ne

= neutrophils, Hb = hemoglobin, Plt = platelets. B. Leukemia burden was assessed with flow cytometry by measuring human CD45 positive cells/total live cells by in the bone marrow of

NSG mice injected with PDX #2 and treated with vehicle, entospletinib, PD0325901, or the

combination of entospletinib and PD0325901 for 14 days. Data are plotted as mean values

±SEM (n=6 to 7 mice per arm) (**** p-value ≤ 0.0001, * p-value 0.05). Significance was determined by 2-way ANOVA. C. Survival study in NSG mice, injected with PDX#2, randomly assigned to entospletinib, PD0325901, combination or vehicle treatment, n=8 mice per arm

(****p-value ≤ 0.0001, *p-value ≤ 0.05, Mantel-Cox test). D. Leukemia burden was assessed by measuring GFP positive cells/total live cells by flow cytometry in the bone marrow (left), peripheral blood (middle) or spleen (right) of C57BL/6 mice injected with primary murine

MLL-AF9 leukemia bone marrow cells retrovirally transduced with a plasmid containing mutated NRAS (G12D) and treated with vehicle, entospletinib, PD0325901, or the combination of entospletinib and PD0325901 for 14 days. E. Spleen weights from the four treatment groups. Data are plotted as mean values ±SEM (n=4 to 6 mice per arm) (**** p- value ≤ 0.0001, *** p-value ≤ 0.001, ** p-value ≤ 0.01 * p-value ≤ 0.05). Significance was determined by 2-way ANOVA.

A B MV4-11 5 Highlights p-sites down DMSO500 nM750 nM1 μM2 μM p-sites up 75 PTPN11 (pY279) phospho SYK (Y525/Y526) CBL (pY654) 4 p-sites ns 75 PTPN11 (pY546) SYK

CBL (pY656) -log10(P-value) 50 phospho ERK1/2 (T202/Y204) CBL (pY630) 3 50 ERK1/2 STAT5 (pY663) 75 STAT3 (pY704) 2 phospho SHP2 (Y542) STAT3 (pY607) 75 SYK (pY296) SHP2 100 1 phospho GAB2 (Y452) 100 GAB2

0 phospho STAT5 (Y694) -3 -2 -1 0 1 2 75 STAT5 Mean log2 ratio (Entospletinib/DMSO) 75 100 Vinculin

C D PDX1 MLL-AF9 PDX2 MLL-AF4 PDX3 FLT3-ITD/NPM1 STEGMAIER_Targets_of_SYK.KD_Down * *** *** 100 100 100 0.0 NES = -4.10 BM BM P < 0.001 BM s s

-0.2 s FDR < 0.001 el l

el l 80 -0.4 el l 80 80 -0.6 e c liv e ve c li ve -0.8 c liv e 60 60 60 Enrichment score

40 40 40 hCD4 5+/ hCD4 5+/ DMSO Entospletinib hCD4 5+/ en t HALLMARK_MTORC1_SIGNALING en t 20 20 20 ercen t er c er c P P 0.0 NES = -2.70 P -0.2 P < 0.001 0 0 0 FDR < 0.001 -0.4 -0.6 Vehicle Vehicle Vehicle -0.8 Entospletinib Entospletinib Entospletinib Enrichment score

DMSO Entospletinib

E PDX1 MLL-AF9 PDX3 FLT3-ITD/NPM1

100 100

p-value < 0.0001 50 p-value < 0.0001 50 ercen t s urvi val

ercen t s ur vival Vehicle Vehicle P P Entospletinib Entospletinib 0 0 30 40 50 40 60 80 Days Days

Grow for 3 weeks PCR barcode amplification and Illumina sequencing Infect cells Select cells

Harvest ETP

B C MV4-11 MOLM14 15 15 RAS/MAPK/ERK signaling RAS/MAPK/ERK signaling Growth factors Growth factors Chemokine/Chemokine receptors 5 5 Chemokine/Chemokine receptors b vs. ETP vs. ini b b vs. ETP vs. ini b 2 MTOR/AKT signaling 2 Other significant ORFs Other significant ORFs Not significant ORFs ospl et ospl et -5 Not significant ORFs -5

-15 Ent ore s -15 z-s c s Ent z-score s -15 -52 5 15 -15 -52 5 15 z-scores DMSO vs. ETP z-scores DMSO vs. ETP

D E

MOLM14

15 RAS/MAPK/ERK signaling MV4-11 Growth factor receptors 5 Chemokine/Chemokine receptors b vs. ETP vs. ini b 2 Other significant core ORFs

pl et Not significant ORFs LM 14 38 24 144 -5 MO

-15 s Entos sc ore s z- -15 -52 5 15 2-tailed Fisher exact test z-scores Entospletinib vs. ETP Odds ratios: 52.5 MV4-11 p-value < 2.2 e-16

GFP NRAS GFP NRAS GFP KRAS GFP KRAS NRAS 20 20 KRAS phospho ERK1/2 (T202/Y204) 37 37 phospho ERK1/2 (T202/Y204) ERK1/2 ERK1/2 37 37 50 α TUBULIN 100 VINCULIN C

MOLM14 MV4-11 20 20 GFP DMSO GFP DMSO doubli ngs doubli ngs 15 GFP Entospletinib 15 GFP Entospletinib ion 10 ion NRAS DMSO 10 NRAS DMSO 5 NRAS Entospletinib NRAS Entospletinib popula t popula t 5 0 5 10 15 20 0 -5 5 10 15 20 Days -5 Days

tive Cumula tive -10 tive Cumula tive

D MOLM14 MV4-11 20 GFP DMSO 20 GFP DMSO doubli ngs 15 doubli ngs GFP Entospletinib GFP Entospletinib 15 ion ion KRAS DMSO 10 KRAS DMSO 10 KRAS Entospletinib KRAS Entospletinib popula t 5 popula t 5 ive 0 5 10 15 20 0 5 10 15 20 -5 tive Cumula tive Days Cumula t -5 Days E F 100 MAF9-GFP ro l MAF9-NRAS(G12D) con t MAF9-GFPMAF9-GFP-NRAS(G12D) 50 20 NRAS(G12D) DM SO

phospho ERK1/2 (T202/Y204) 37 ercen t P 0 ERK1/2 -3 -2 -1 0 1 2 37 Log [Entospletinib (µM)] Vinculin 100 Count Entospletinib 5 54

G H NPMP1M1 : FLT3_ITD NB4 4 U937 THP-1* F LT3_ITD HL-60 NPM1 100 NOMO1* 3

ro l TF-1 NPM1 : FLT3_ITD : DNMT3A

con t EOL-1* NPPM1 : DNMT3A UCSD-AML1 -log10(FDR) 2 50 DM SO MOLM13* MOLM14* ercen t

P CMK-86 NRAS MV4-11* 1 0 -2 -1 0 1 2 RAS mutant Log [Entospletinib (µM)] RAS WT * MLL rearranged 0

- 50 - 40 - 30 - 20 - 10 0 10 20 30 40 Average difference Downloaded from cancerdiscovery.aacrjournals.org on September 26, 2021. © 2019 American Association for Cancer Research. Author Manuscript Published OnlineFirst on November 26, 2019; DOI: 10.1158/2159-8290.CD-19-0209 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Figure 4 A MV4-11 naive C MV4-11 resistant 100

ro l Naive Resistant 75 phospho SYK (Y525/Y526) 75 SYK

DM SO c on t 50 50

en t phospho ERK1/2 (T202/Y204)

er c 50 P ERK1/2 0 0.1 1 10 phospho STAT5 (Y694) 75 Log [Entospletinib (µM)] B STAT5 MV4-11 naive 75 MV4-11 resistant 100 phospho STAT3 (Y705) 100 ro l 100 STAT3 75 phospho SHP2 (Y542)

DM SO c on t 50 75 SHP2 en t er c

P RAS 20 0 0.01 0.1 1 10 37 GAPDH Log [PRT062067 (µM)]

D E log2 (FC) expression Naive R1Naive NaiveR2 R3ResistantResistant R1 Resistant R2 R3 *** *** CT CT ∆

KRAS ∆ 6 2.0 2^ - 2^ - SOS2 1.5 sion 4 MAPK3 pre s

expression 1.0 BRAF 2 NA mR NA NRAS ex mR NA 0.5

MAP3K1 AS NR AS 0 KR 0.0 RASGRP4

MAPK1 0.0 0.5 1.0 MV4-11 naive MV4-11 naive MV4-11 resistant MV4-11 resistant F

HALLMARK_KRAS_SIGNALING_UP HALLMARK_MTORC1_SIGNALING 0.6 0.6 NES = 1.58 NES = 2.20 0.4 P = 0.002 0.4 P < 0.001 FDR = 0.018 FDR < 0.001 0.2 0.2 0.0 0.0 -0.2 -0.2 Enrichment score Enrichment score

Resistant Naive Resistant Naive

Patient Gender Age Diagnosis Karyotype Mutations (VAF) #1 Male 25 y AML Trisomy 8 PTPN11 E76G (22%), NPM1 (23%) #2 Male 57 y AML Normal PTPN11 N58Y (5%) S502L (8.3%), NPM1 (44%), IDH R132H (30%), DNTM3A R882P (44%) #3 Male 67 y AML Normal PTPN11 T73I (10.4%), NPM1 (26%), TET2 K148fs (36%), TET2 G1695fs (44%)

B C MV4-11 MV4-11 GFP Ento 15 GFP WT E76G T73I PTPN11 WT Ento 75 doublin gs PTPN11 E76G Ento SHP2 10

n io n PTPN11 T73I Ento Vinculin 5 100 popul at 0 5 10 15 20 Days -5 ative Cumul ative D

MV4-11 ---- + + + + Entospletinib

GFP WT E76G T73I GFP WT E76G T73I

phospho ERK1/2 (T202/Y204) 37

ERK1/2 37 Vinculin 100

Patient sample I U937 THP-1 d /Dx d2/Dx2 2 2 d2/Dx2 0.5 1.0 1.5 2.0 0.5 1.0 1.5 2.0 0.5 1.0 1.5 2.0 0 0 0 . . . 2.0 1.5 1.0 0.5 0 PD0325901 +Entospletinib . . . 2.0 1.5 1.0 0.5 0 . . . 2.0 1.5 1.0 0.5 0 Downloaded from Author manuscriptshavebeenpeerreviewedandacceptedforpublicationbutnotyetedited. Author ManuscriptPublishedOnlineFirstonNovember26,2019;DOI:10.1158/2159-8290.CD-19-0209 NRAS (G12D) PTPN11(G60R) PTPN11(G60R) KRAS (G12D) NRAS (G12D) d d d 1 1 1 /Dx /Dx /Dx 1 1 1 cancerdiscovery.aacrjournals.org

Patient sample II MV4-11 MAF9-NRAS(G12D) d2/Dx2 d2/Dx2 d2/Dx2 0.5 1.0 1.5 2.0 0.5 1.0 1.5 2.0 0.5 1.0 1.5 2.0 0 0 0 . . . 2.0 1.5 1.0 0.5 0 . . . 2.0 1.5 1.0 0.5 0 . . . 2.0 1.5 1.0 0.5 0 Cancer Research. NRAS (G12D) PTPN11 (E76G) FLT3-ITD on September 26, 2021. © 2019American Association for d d d 1 1 1 /Dx /Dx /Dx 1 1 1

MV4-11 resistant Patient sample III MV4-11 d2/Dx2 d2/Dx2 d2/Dx2 0.5 1.0 1.5 2.0 0.5 1.0 1.5 2.0 0.5 1.0 1.5 2.0 0 0 0 . . . 2.0 1.5 1.0 0.5 0 . . . 2.0 1.5 1.0 0.5 0 . . . 2.0 1.5 1.0 0.5 0 PTPN11 (T73I) MLL-AF4 FLT3-ITD d d 1 d /Dx 1 1 /Dx /Dx 1 1 1 Figure 6 Author Manuscript Published OnlineFirst on November 26, 2019; DOI: 10.1158/2159-8290.CD-19-0209 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Figure 7 A WBC Ne Hb Plt 15 2.5 17 2000 Vehicle Entospletinib 0.07% 2.0 16 1500 10 PD0325901 2.5 mg/kg 1.5 Combo K/mcl K/mcl 15 1000 g/dl K/mcl 1.0 5 0.5 14 500

0 0.0 13 0

B **** C * PDX #2 MLL-AF4 * 25 100 20 * 15 ve cells BM cells li ve survival

t 50 Vehicle 10 * p < 0.014 Combo vs. Entospletinib PD0325901 hCD4 5/ **** p < 0.0001 Combo vs. PD0325901 Percen % % Entospletinib 5 Combo 0 0 0 20 40 60 80 100 Days Vehicle Combo

EntospletinibPD0325901

D **** E *** ** **** * ** ** * * *** **** 100 ** 100 300 ** 100 * *

80 80 80 200 (miligra ms) 60 60

60 t /live cells BM cells t GFP /live /live cells PB cells t GFP /live 40 40 SPL cells t GFP /live 40 weig h 100 20 Percen

20 Percen 20 n plee n Percen S

0 0 0 0

Vehicle Combo Vehicle Combo Vehicle Combo Vehicle Combo PD0325901 PD0325901 PD0325901 EntospletinibPD0325901 Entospletinib Entospletinib Entospletinib

Resistance mechanisms to SYK inhibition in acute myeloid leukemia

Anjali Cremer, Jana M Ellegast, Gabriela Alexe, et al.

Cancer Discov Published OnlineFirst November 26, 2019.

Updated version Access the most recent version of this article at: doi:10.1158/2159-8290.CD-19-0209

Supplementary Access the most recent supplemental material at: Material http://cancerdiscovery.aacrjournals.org/content/suppl/2019/11/26/2159-8290.CD-19-0209.DC1

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