The human genome encodes 518 protein kinases that are pivotal for drug discovery in various therapeutic areas such as cancer and autoimmune disorders. The majority of kinase inhibitors target the conserved ATP-binding pocket, making it difficult to develop selective inhibitors. To characterize and prioritize kinase-inhibiting drug candidates, efficient methods are desired to determine target engagement across the cellular kinome. In this study, we present CellEKT (Cellular Endogenous Kinase Targeting), an optimized and robust chemical proteomics platform for investigating cellular target engagement of endogenously expressed kinases using the sulfonyl fluoride-based probe XO44 and two new probes ALX005 and ALX011. The optimized workflow enabled the determination of the kinome interaction landscape of covalent and non-covalent drugs across over 300 kinases, expressed as half maximum inhibitory concentration (IC50), which were validated using distinct platforms like phosphoproteomics and NanoBRET. With CellEKT, target engagement profiles were linked to their substrate space. CellEKT has the ability to decrypt drug actions and to guide the discovery and development of drugs.

Bladder cancer often recurs, necessitating innovative treatments to reduce recurrence. We investigated non-thermal plasma's potential as a novel anti-cancer therapy, focusing on plasma-activated solution (PAS), created by exposing saline to non-thermal plasma. Our study aims to elucidate the biological effects of PAS on bladder cancer cell lines in vitro, as well as the combination with mitomycin C (MMC), using clinically relevant settings. PAS treatment exerts a potent cytotoxic effect through the production of intracellular reactive oxygen species, resulting in DNA damage and subsequent induction of G1 cell cycle arrest/senescence. This is induced via upregulation of cell cycle checkpoint signalling and DNA damage repair pathways using LC-M/MS-based phospho-proteomics. Importantly, combining PAS with MMC reveals a synergistic effect (Combination Index of 0.59-0.67), suggesting the potential of utilizing PAS in combination therapies. Our findings demonstrate PAS's mode of action and suggest its potential as a promising treatment for bladder cancer, warranting further clinical studies.

Aberrant cellular signaling pathways are a hallmark of cancer and other diseases. One of the most important signaling mechanisms involves protein phosphorylation/dephosphorylation. Protein phosphorylation is catalyzed by protein kinases, and over 530 protein kinases have been identified in the human genome. Aberrant kinase activity is one of the drivers of tumorigenesis and cancer progression and results in altered phosphorylation abundance of downstream substrates. Upstream kinase activity can be inferred from the global collection of phosphorylated substrates. Mass spectrometry-based phosphoproteomic experiments nowadays routinely allow identification and quantitation of >10k phosphosites per biological sample. This substrate phosphorylation footprint can be used to infer upstream kinase activities using tools like Kinase Substrate Enrichment Analysis (KSEA), Posttranslational Modification Substrate Enrichment Analysis (PTM-SEA), and Integrative Inferred Kinase Activity Analysis (INKA). Since the topic of kinase activity inference is very active with many new approaches reported in the past 3 years, we would like to give an overview of the field. In this review, an inventory of kinase activity inference tools, their underlying algorithms, statistical frameworks, kinase-substrate databases, and user-friendliness is presented. The most widely-used tools are compared in-depth. Subsequently, recent applications of the tools are described focusing on clinical tissues and hematological samples. Two main application areas for kinase activity inference tools can be discerned. (1) Maximal biological insights can be obtained from large data sets with group comparisons using multiple complementary tools (e.g., PTM-SEA and KSEA or INKA). (2) In the oncology context where personalized treatment requires analysis of single samples, INKA for example, has emerged as tool that can prioritize actionable kinases for targeted inhibition.

BACKGROUND:Abdominal aortic aneurysm (AAA) is characterized by weakening and dilatation of the aortic wall in the abdomen. The aim of this study was to gain insight into cell-specific mechanisms involved in AAA pathophysiology by analyzing the (phospho)proteome of vascular smooth muscle cells derived from patients with AAA compared with those of healthy donors. METHODS:A (phospho)proteomics analysis based on tandem mass spectrometry was performed on vascular smooth muscle cells derived from patients with AAA (n=24) and healthy, control individuals (C-SMC, n=8). Following protein identification and quantification using MaxQuant, integrative inferred kinase activity analysis was used to calculate kinase activity scores. RESULTS:Expression differences between vascular smooth muscle cells derived from patients with AAA and healthy, control individuals were predominantly found in proteins involved in ECM (extracellular matrix) remodeling (THSD4 [thrombospondin type-1 domain-containing protein 4] and ADAMTS1 [A disintegrin and metalloproteinase with thrombospondin motifs 1]), energy metabolism (GYS1 [glycogen synthase 1] and PCK2 [phosphoenolpyruvate carboxykinase 2, mitochondrial]), and contractility (CACNA2D1 [calcium voltage-dependent channel subunit α-2/δ-1] and TPM1 [tropomyosin α-1 chain]). Phosphorylation patterns on proteins related to actin cytoskeleton organization dominated the phosphoproteome of vascular smooth muscle cells derived from patients with AAA . Besides, phosphorylation changes on proteins related to energy metabolism (GYS1), contractility (PARVA [α-parvin], PPP1R12A [protein phosphatase 1 regulatory subunit 12A], and CALD1 [caldesmon 1]), and intracellular communication (GJA1 [gap junction α-1 protein]) were seen. Kinase activity of NUAK1 (NUAK family SNF1-like kinase 1), FYN (tyrosine-protein kinase Fyn), MAPK7 (mitogen-activated protein kinase 7), and STK10 (serine/threonine kinase 10) was different in vascular smooth muscle cells derived from patients with AAA compared with those from healthy, control individuals. CONCLUSIONS:This study revealed changes in expression and phosphorylation levels of proteins involved in various processes responsible for AAA progression and development (eg, energy metabolism, ECM remodeling, actin cytoskeleton organization, contractility, intracellular communication, and cell adhesion). These newly identified proteins, phosphosites, and related kinases provide further insight into the underlying mechanism of vascular smooth muscle cell dysfunction within the aneurysmal wall. Our omics data thereby offer the opportunity to study the relevance, either as drug target or biomarker, of these proteins in AAA development.

Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease with a limited number of known driver mutations but considerable cancer cell heterogeneity. Phosphoproteomics provides a direct read‐out of aberrant signaling and the resultant clinically relevant phenotype. Mass spectrometry (MS)‐based proteomics and phosphoproteomics were applied to 42 PDAC tumors. Data encompassed over 19 936 phosphoserine or phosphothreonine (pS/T; in 5412 phosphoproteins) and 1208 phosphotyrosine (pY; in 501 phosphoproteins) sites and a total of 3756 proteins. Proteome data identified three distinct subtypes with tumor intrinsic and stromal features. Subsequently, three phospho‐subtypes were apparent: two tumor intrinsic (Phos1/2) and one stromal (Phos3), resembling known PDAC molecular subtypes. Kinase activity was analyzed by the Integrative iNferred Kinase Activity (INKA) scoring. Phospho‐subtypes displayed differential phosphorylation signals and kinase activity, such as FGR and GSK3 activation in Phos1, SRC kinase family and EPHA2 in Phos2, and EGFR, INSR, MET, ABL1, HIPK1, JAK, and PRKCD in Phos3. Kinase activity analysis of an external PDAC cohort supported our findings and underscored the importance of PI3K/AKT and ERK pathways, among others. Interestingly, unfavorable patient prognosis correlated with higher RTK, PAK2, STK10, and CDK7 activity and high proliferation, whereas long survival was associated with MYLK and PTK6 activity, which was previously unknown. Subtype‐associated activity profiles can guide therapeutic combination approaches in tumor and stroma‐enriched tissues, and emphasize the critical role of parallel signaling pathways. In addition, kinase activity profiling identifies potential disease markers with prognostic significance.

Aim: Abdominal aortic aneurysms (AAA) are defined as a dilatation of the aortic wall. Impaired contractile ability of vascular smooth muscle cells (vSMC) is a hallmark of AAA. This study investigates the underlying mechanism of altered in vitro contractility of vSMC derived from AAA patients (AAA-SMC) compared to control vSMC (C-SMC). Methods: Contractility of AAA-SMC (n=24) and C-SMC (n=8) was measured using Electric Cell-substrate Impedance Sensing. Large variability in AAA-SMC contraction was observed compared to C-SMC contraction, and AAA-SMC were therefore subdivided into low, intermediate (i.e. comparable to C-SMC contraction) and high contracting. To identify novel proteins involved in altered AAA-SMC contraction, a phosphoproteomic analysis was performed. Results: The proteomics data showed that Thrombospondin-1, PDZ and LIM domain protein 4 and ATPase plasma membrane Ca2+ transporting 1 expression correlated with vSMC contraction, but knockdown (KD) of these targets did not affect contraction. Next, the phosphoproteomics data identified NUAK family kinase 1 (NUAK1) as a potential regulator of contraction, since its kinase activity correlated with AAA-SMC contraction. NUAK1 regulates Myosin phosphatase targeting subunit 1 (MYPT1) activity by phosphorylation, as confirmed by the correlation between NUAK1 activity and phosphorylation levels of Ser445 and Ser910 on MYPT1. NUAK1 protein and RNA expression correlated with AAA-SMC contraction. Moreover, NUAK1 KD decreased contraction in AAA-SMC, combined with reduced phosphorylation levels of Myosin light chain (pMLC) and Vinculin gene expression, and increased F-actin cytoskeletal filament levels. Conclusions: NUAK1 regulates AAA-SMC contraction. Low NUAK1 expression decreased pMLC, potentially by higher MYPT1 activity, and subsequently reduced contraction. ### Competing Interest Statement The authors have declared no competing interest.

Abstract Introduction: Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease, commonly characterized by multiple aberrant signaling. Phosphoproteomics provides a direct read-out of these complex signaling networks and the resultant clinically relevant phenotype, as well as a functional scaffold to identify new targets. In the absence of an oncogenic driver, low dose (LD) kinase inhibitor (KI) combinations against multiple (parallel) activated kinases might provide higher efficacy and reduce toxicity as compared to single drug treatment. Aims: 1) To identify targets and test combinations of multiple KIs at LDs for potential synergism in preclinical models. 2) To chart the phosphoproteome of 42 PDAC tumors to reveal signalling pathways that may be involved in PDAC progression. Methods: By using a two step phosphopeptide enrichment with phosphotyrosine immunoprecipitation and immobilized metal affinity chromatography, followed by label free MS analysis, we analyzed phosphoproteome data of 7 immortalized and 2 primary PDAC cell lines, and of 42 PDAC tumors. We used integrative inferred kinase activity (INKA) scoring of the maxquant output to identify hyperactive kinases. For the cell line panel, five KIs were selected based on targeting coverage of the INKA profiles. LD were set as IC20s for 2,3,4 drug combinations. Cell growth inhibition was assessed by SRB assay. Median-effect analysis was used to assess synergy. Functional testing was performed in immortalized 2D, xenoderived 2D and 3D cultures. Effective low dose 3 drug combinations were further validated using patient-derived xenografts. Results: High INKA scoring of multiple activities per cell line without clear outliers of single kinases underscores the need for combination therapies. Multiple LD combinations showed effective growth inhibition (70 to 92%) and synergism, mostly 3 drug combinations, which required targeting of several RTKs and downstream signaling. Different responses were further observed between epithelial and mesenchymal cell lines. These top performing combinations were further validated in PDAC organoids and in vivo. Clinical utility of these kinase targets was then confirmed in 42 tumor phosphoprofiles, which were characterized in different subtypes with distinct therapeutic options. Phosphoproteome signals and kinases activities with potential prognostic value and mutational associations were further described. Conclusion: Our INKA pipeline, which can rank kinase activities in individual tumors, is optimally suited to specifically prioritize actionable kinases with targeting purposes. Tailored LD combination strategies exhibited promising efficacy in preclinical models and may ultimately improve treatment outcomes. Next Steps: Multicellular patient-derived models will be used to further study and target the TME in PDAC tumors. Citation Format: Andrea Vallés Martí, Giulia Mantini, Cynthia Waasdorp, Richard R. de Goeij- de Haas, Alex A. Henneman, Sander R. Piersma, Thang V. Pham, Jaco C. Knol, Joanne Verheij, Frederike Dijk, Hans Halfwerk, Elisa Giovannetti, Connie R. Jiménez, Maarten F. Bijlsma. Profile guided low dose drug combination strategies and kinase activities with prognostic and therapeutic avenues in pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6485.

SUMMARY:Identification and quantification of phosphorylation sites are essential for biological interpretation of a phosphoproteomics experiment. For data independent acquisition mass spectrometry-based (DIA-MS) phosphoproteomics, extracting a site-level report from the output of current processing software is not straightforward as multiple peptides might contribute to a single site, multiple phosphorylation sites can occur on the same peptides, and protein isoforms complicate site specification. Currently only limited support is available from a commercial software package via a platform-specific solution with a rather simple site quantification method. Here, we present sitereport, a software tool implemented in an extendable Python package called msproteomics to report phosphosites and phosphopeptides from a DIA-MS phosphoproteomics experiment with a proven quantification method called MaxLFQ. We demonstrate the use of sitereport for downstream data analysis at site level, allowing benchmarking different DIA-MS processing software tools. AVAILABILITY AND IMPLEMENTATION:sitereport is available as a command line tool in the Python package msproteomics, released under the Apache License 2.0 and available from the Python Package Index (PyPI) at https://pypi.org/project/msproteomics and GitHub at https://github.com/tvpham/msproteomics.

Accurate retention time (RT) prediction is important for spectral library-based analysis in data-independent acquisition mass spectrometry-based proteomics. The deep learning approach has demonstrated superior performance over traditional machine learning methods for this purpose. The transformer architecture is a recent development in deep learning that delivers state-of-the-art performance in many fields such as natural language processing, computer vision, and biology. We assess the performance of the transformer architecture for RT prediction using datasets from five deep learning models Prosit, DeepDIA, AutoRT, DeepPhospho, and AlphaPeptDeep. The experimental results on holdout datasets and independent datasets exhibit state-of-the-art performance of the transformer architecture. The software and evaluation datasets are publicly available for future development in the field.

Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease with a limited set of known driver muta-tions but considerable cancer cell heterogeneity. Phosphoproteomics provides a readout of aberrant signaling and has the potential to identify new targets and guide treatment decisions. Using two-step sequential phosphopeptide enrichment, we generate a comprehensive phosphoproteome and proteome of nine PDAC cell lines, encompassing more than 20,000 phosphosites on 5,763 phospho-proteins, including 316 protein kinases. By using integrative inferred kinase activity (INKA) scoring, we identify multiple (parallel) activated kinases that are subsequently matched to kinase inhibitors. Compared with high-dose single-drug treatments, INKA-tailored low-dose 3-drug combinations against multiple targets demonstrate superior ef-ficacy against PDAC cell lines, organoid cultures, and patient-derived xenografts. Overall, this approach is particularly more effective against the aggressive mesenchymal PDAC model compared with the epithelial model in both preclinical settings and may contribute to improved treatment outcomes in PDAC patients.

BACKGROUND: Diastolic dysfunction is central to diseases such as heart failure with preserved ejection fraction and hypertrophic cardiomyopathy (HCM). However, therapies that improve cardiac relaxation are scarce, partly due to a limited understanding of modulators of cardiomyocyte relaxation. We hypothesized that cardiac relaxation is regulated by multiple unidentified proteins and that dysregulation of kinases contributes to impaired relaxation in patients with HCM. METHODS: We optimized and increased the throughput of unloaded shortening measurements and screened a kinase inhibitor library in isolated adult cardiomyocytes from wild-type mice. One hundred fifty-seven kinase inhibitors were screened. To assess which kinases are dysregulated in patients with HCM and could contribute to impaired relaxation, we performed a tyrosine and global phosphoproteomics screen and integrative inferred kinase activity analysis using HCM patient myocardium. Identified hits from these 2 data sets were validated in cardiomyocytes from a homozygous MYBPC3c.2373insG HCM mouse model. RESULTS: Screening of 157 kinase inhibitors in wild-type (N=33) cardiomyocytes (n=24 563) resulted in the identification of 17 positive inotropes and 21 positive lusitropes, almost all of them novel. The positive lusitropes formed 3 clusters: cell cycle, EGFR (epidermal growth factor receptor)/IGF1R (insulin-like growth factor 1 receptor), and a small Akt (α-serine/threonine protein kinase) signaling cluster. By performing phosphoproteomic profiling of HCM patient myocardium (N=24 HCM and N=8 donors), we demonstrated increased activation of 6 of 8 proteins from the EGFR/IGFR1 cluster in HCM. We validated compounds from this cluster in mouse HCM (N=12) cardiomyocytes (n=2023). Three compounds from this cluster were able to improve relaxation in HCM cardiomyocytes. CONCLUSIONS: We showed the feasibility of screening for functional modulators of cardiomyocyte relaxation and contraction, parameters that we observed to be modulated by kinases involved in EGFR/IGF1R, Akt, cell cycle signaling, and FoxO (forkhead box class O) signaling, respectively. Integrating the screening data with phosphoproteomics analysis in HCM patient tissue indicated that inhibition of EGFR/IGF1R signaling is a promising target for treating impaired relaxation in HCM.

Epidermal growth factor receptor (EGFR) is a well-exploited therapeutic target in metastatic colorectal cancer (mCRC). Unfortunately, not all patients benefit from current EGFR inhibitors. Mass spectrometry-based proteomics and phosphoproteomics were performed on 30 genomically and pharmacologically characterized mCRC patient-derived xenografts (PDXs) to investigate the molecular basis of response to EGFR blockade and identify alternative drug targets to overcome resistance. Both the tyrosine and global phosphoproteome as well as the proteome harbored distinctive response signatures. We found that increased pathway activity related to mitogen-activated protein kinase (MAPK) inhibition and abundant tyrosine phosphorylation of cell junction proteins, such as CXADR and CLDN1/3, in sensitive tumors, whereas epithelial-mesenchymal transition and increased MAPK and AKT signaling were more prevalent in resistant tumors. Furthermore, the ranking of kinase activities in single samples confirmed the driver activity of ERBB2, EGFR, and MET in cetuximab-resistant tumors. This analysis also revealed high kinase activity of several members of the Src and ephrin kinase family in 2 CRC PDX models with genomically unexplained resistance. Inhibition of these hyperactive kinases, alone or in combination with cetuximab, resulted in growth inhibition of ex vivo PDX-derived organoids and in vivo PDXs. Together, these findings highlight the potential value of phosphoproteomics to improve our understanding of anti-EGFR treatment and response prediction in mCRC and bring to the forefront alternative drug targets in cetuximab-resistant tumors.

The tyrosine kinase inhibitor sunitinib is an effective first-line treatment for patients with advanced renal cell carcinoma (RCC). Hypothesizing that a functional read-out by mass spectrometry-based (phospho, p-)proteomics will identify predictive biomarkers for treatment outcome of sunitinib, tumor tissues of 26 RCC patients were analyzed. Eight patients had primary resistant (RES) and 18 sensitive (SENS) RCC. A 78 phosphosite signature (p < 0.05, fold-change > 2) was identified; 22 p-sites were upregulated in RES (unique in RES: BCAR3, NOP58, EIF4A2, GDI1) and 56 in SENS (35 unique). EIF4A1/EIF4A2 were differentially expressed in RES at the (p-)proteome and, in an independent cohort, transcriptome level. Inferred kinase activity of MAPK3 (p = 0.026) and EGFR (p = 0.045) as determined by INKA was higher in SENS. Posttranslational modifications signature enrichment analysis showed that different p-site-centric signatures were enriched (p < 0.05), of which FGF1 and prolactin pathways in RES and, in SENS, vanadate and thrombin treatment pathways, were most significant. In conclusion, the RCC (phospho)proteome revealed differential p-sites and kinase activities associated with sunitinib resistance and sensitivity. Independent validation is warranted to develop an assay for upfront identification of patients who are intrinsically resistant to sunitinib.

Protein kinase inhibitors are amongst the most successful cancer treatments, but targetable kinases activated by genomic abnormalities are rare in T cell acute lymphoblastic leukemia. Nevertheless, kinases can be activated in the absence of genetic defects. Thus, phosphoproteomics can provide information on pathway activation and signaling networks that offer opportunities for targeted therapy. Here, we describe a mass spectrometry-based global phosphoproteomic profiling of 11 T cell acute lymphoblastic leukemia cell lines to identify targetable kinases. We report a comprehensive dataset consisting of 21,000 phosphosites on 4,896 phosphoproteins, including 217 kinases. We identify active Src-family kinases signaling as well as active cyclin-dependent kinases. We validate putative targets for therapy ex vivo and identify potential combination treatments, such as the inhibition of the INSR/IGF-1R axis to increase the sensitivity to dasatinib treatment. Ex vivo validation of selected drug combinations using patient-derived xenografts provides a proof-of-concept for phosphoproteomics-guided design of personalized treatments.

In Birt-Hogg-Dubé (BHD) syndrome, germline loss-of-function mutations in the Folliculin (FLCN) gene lead to an increased risk of renal cancer. To address how FLCN inactivation affects cellular kinase signaling pathways, we analyzed comprehensive phosphoproteomic profiles of FLCNPOS and FLCNNEG human renal tubular epithelial cells (RPTEC/TERT1). In total, 15,744 phosphorylated peptides were identified from 4329 phosphorylated proteins. INKA analysis revealed that FLCN loss alters the activity of numerous kinases, including tyrosine kinases EGFR, MET, and the Ephrin receptor subfamily (EPHA2 and EPHB1), as well their downstream targets MAPK1/3. Validation experiments in the BHD renal tumor cell line UOK257 confirmed that FLCN loss contributes to enhanced MAPK1/3 and downstream RPS6K1/3 signaling. The clinically available MAPK inhibitor Ulixertinib showed enhanced toxicity in FLCNNEG cells. Interestingly, FLCN inactivation induced the phosphorylation of PIK3CD (Tyr524) without altering the phosphorylation of canonical Akt1/Akt2/mTOR/EIF4EBP1 phosphosites. Also, we identified that FLCN inactivation resulted in dephosphorylation of TFEB Ser109, Ser114, and Ser122, which may be linked to increased oxidative stress levels in FLCNNEG cells. Together, our study highlights differential phosphorylation of specific kinases and substrates in FLCNNEG renal cells. This provides insight into BHD-associated renal tumorigenesis and may point to several novel candidates for targeted therapies.

Supplementary Data from Tumor Drug Concentration and Phosphoproteomic Profiles After Two Weeks of Treatment With Sunitinib in Patients with Newly Diagnosed Glioblastoma

Dendritic cells (DCs) are key initiators of the adaptive immunity, and upon recognition of pathogens are able to skew T cell differentiation to elicit appropriate responses. DCs possess this extraordinary capacity to discern external signals using receptors that recognize pathogen-associated molecular patterns. These can be glycan-binding receptors that recognize carbohydrate structures on pathogens or pathogen-associated patterns that additionally bind receptors, such as Toll-like receptors (TLRs). This study explores the early signaling events in DCs upon binding of α2-3 sialic acid (α2-3sia) that are recognized by Immune inhibitory Sialic acid binding immunoglobulin type lectins. α2-3sias are commonly found on bacteria, e.g. Group B Streptococcus, but can also be expressed by tumor cells. We investigated whether α2-3sia conjugated to a dendrimeric core alters DC signaling properties. Through phosphoproteomic analysis, we found differential signaling profiles in DCs after α2-3sia binding alone or in combination with LPS/TLR4 co-stimulation. α2-3sia was able to modulate the TLR4 signaling cascade, resulting in 109 altered phosphoproteins. These phosphoproteins were annotated to seven biological processes, including the regulation of the IL-12 cytokine pathway. Secretion of IL-10, the inhibitory regulator of IL-12 production, by DCs was found upregulated after overnight stimulation with the α2-3sia dendrimer. Analysis of kinase activity revealed altered signatures in the JAK-STAT signaling pathway. PhosphoSTAT3 (Ser727) and phosphoSTAT5A (Ser780), involved in the regulation of the IL-12 pathway, were both downregulated. Flow cytometric quantification indeed revealed de- phosphorylation over time upon stimulation with α2-3sia, but no α2-6sia. Inhibition of both STAT3 and -5A in moDCs resulted in a similar cytokine secretion profile as α-3sia triggered DCs. Conclusively, this study revealed a specific alteration of the JAK-STAT pathway in DCs upon simultaneous α2-3sia and LPS stimulation, altering the IL10:IL-12 cytokine secretion profile associated with reduction of inflammation. Targeted control of the STAT phosphorylation status is therefore an interesting lead for the abrogation of immune escape that bacteria or tumors impose on the host.

Kinase hyperactivity is a common driver of acute myeloid leukemia (AML) and serves as a therapeutic target.1 The most frequent activating genetic aberrations in AML are internal tandem duplications (~23%) and tyrosine kinase domain mutations (~7%) of FMS-like tyrosine kinase 3 (FLT3-ITD and FLT3-TKD), and the presence of FLT3-ITD negatively affects survival.2 Combined with chemotherapy, FLT3-Tyrosine Kinase Inhibitor (FLT3-TKI) midostaurin improves overall survival in newly diagnosed FLT3-mutated AML, whereas the single agent gilteritinib proved superior to chemotherapy in relapsed/refractory FLT3-mutated AML.2 The presence of FLT3-ITD is predictive for response to FLT3-TKIs,3 yet 41%–56% of FLT3-WT patients respond to FLT3-TKIs, indicating alternative possibilities of FLT3 pathway activation or TKI off-target effects leading to unexpected treatment response.4 Others have identified genomic and global phosphorylation markers associated with FLT3-TKI response in FLT3-WT AML.5,6 As the primary targets of currently approved FLT3-TKIs are tyrosine (Y) kinases, we hypothesized that the direct evaluation of tyrosine kinome could reveal phosphorylation markers associated with FLT3-TKI response. Therefore, we performed both label-free pY-based and global phosphoproteomics7 in 35 primary AML samples (18 FLT3-WT, 17 FLT3-ITD, details provided in Supplemental Digital Table 1, https://links.lww.com/HS/A167) to identify differential phosphorylation underlying response to the FLT3-TKIs gilteritinib and midostaurin. We identified a total of 3.024 unique phosphosites (median 1.666 per sample, range 1.091–2.118; Supplemental Digital Figure 1, https://links.lww.com/HS/A167 and Supplemental Digital Table 2A, https://links.lww.com/HS/A167) in the pY and 27.821 unique phosphosites in the global phosphoproteome dataset. Two samples were excluded due to the low number (382 and 550) of identified phosphosites. Details are provided in the Supplemental Digital Materials and Methods, https://links.lww.com/HS/A167 and Supplemental Digital Table 2B, https://links.lww.com/HS/A167. We then assessed ex vivo response toward FLT3-TKIs by liquid culture and cell viability testing of AML blasts using flow cytometry (Supplemental Digital Figure 2, https://links.lww.com/HS/A167). Of 33, 19 AMLs yielded interpretable dose-response curves and were included in further analyses. As expected, FLT3-ITD samples were more responsive toward gilteritinib and midostaurin, compared with FLT3-WT samples (Figure 1A and B).3 We observed the most pronounced response of FLT3-ITD samples toward gilteritinib, exemplifying the known more potent and specific inhibition of FLT3 by gilteritinib compared with midostaurin, which has a broad inhibition profile (EC50 12.9 versus 635.03 nM, https://proteomicsdb.org, Figure 1C).Figure 1.: Response toward FLT3-TKIs and differential phosphorylation profiles and kinase activity scores associated with FLT3-TKI response. Ex vivo response of FLT3-WT and FLT3-ITD AML blasts toward (A) gilteritinib and (B) midostaurin. P values are calculated using least squares fit regression comparing FLT3-WT and FLT3-ITD samples. As workflow control, response of MV4:11, a homozygous FLT3-ITD AML cell line, is shown. (C) Protein target space of gilteritinib and midostaurin. The top 12 targets are shown, ranked on EC50, which is the drug concentration at which half of the target is competed. Data are retrieved from https://proteomicsDB.org. Individual LC50 values as determined in liquid culture towards (D) gilteritinib and (E) midostaurin. The P value is determined using Wilcoxon rank-sum test. Additional samples for which no phosphoproteomics data was available are shown to illustrate the diversity in FLT3-TKI response. Phosphotyrosine phosphorylation profiles of significant (P < 0.05) differentially phosphorylated phosphosites between responsive and resistant primary AML samples towards (F) gilteritinib and (G) midostaurin. For heatmaps, Euclidean distance with complete linkage for rows and columns was applied. (H) INKA scores based on the pY analyses between gilteritinib responsive and resistant samples, based on median LC50. (I) INKA score of MAPK1 based on the global phosphorylation analyses. P values are calculated by Wilcoxon rank-sum tests. (J) ELISA validation of pERK intensity as determined by pY phosphoproteomics, annotated with gilteritinib response. Correlation coefficient and P value were calculated using Pearson correlation. AML = acute myeloid leukemia; INKA = integrative inferred kinase activity; pY = phosphotyrosine.Responses toward gilteritinib and midostaurin could not be fully explained by the presence of FLT3-ITD, with responses observed in FLT3-WT samples and relative resistance—exemplified by relatively high LC50 values—in FLT3-ITD samples (Figure 1D and E). To explore associations between response and phosphorylation, we compared phosphoproteomic profiles independent of FLT3-ITD status. We defined responsive and resistant samples based on the variation in LC50 values between patients: median LC50 for gilteritinib and the lowest and highest 25th percentile for midostaurin. For the pY phosphoproteome, the FLT3-ITD-independent response toward gilteritinib was associated with differential phosphorylation of 28 phosphosites (P < 0.05, Figure 1F and Supplemental Digital Table 3A, https://links.lww.com/HS/A167). Phosphosites with higher phosphorylation in gilteritinib-resistant samples included MAPK1-Y185, MAPK1-T187 (ERK2) and MAPK3-Y202 and MAPK3-T204 (ERK1), in concordance with other data on FLT3-TKI resistance, but not of FLT3 itself.5,8–11 Posttranslational Modification Signature Enrichment Analysis (PTM-SEA, https://github.com/broadinstitute/ssGSEA2.0) indicated enrichment of EGFR1 (P < 0.05) and KIT (P < 0.2) pathway-associated phosphosites in resistant samples (Supplemental Digital Figure 3A, https://links.lww.com/HS/A167). Differential phosphorylation related to midostaurin response (Figure 1G and Supplemental Digital Table 3B, https://links.lww.com/HS/A167) was more diverse with 46 significant phosphosites, including high phosphorylation of STAT6-Y531 in midostaurin responsive samples. Gene ontology analyses (g:Profiler, https://biit.cs.ut.ee/gprofiler/gost) revealed general processes related to kinase binding and transmembrane signaling. Ras-Raf-MEK-ERK-related phosphosites were absent among the identified differentially phosphorylated sites, although overexpression of RGL4—a regulator of this cascade—has been related to midostaurin response.5 Comparison of global phosphoproteomic profiles between gilteritinib responsive and resistant samples did not yield any clear response-specific phosphorylation profiles (Supplemental Digital Figure 4A, https://links.lww.com/HS/A167). However, PTM-SEA revealed enrichment of phosphosites associated with KIT pathway activation (P < 0.05) and GSK3B activity (P < 0.1) in gilteritinib-resistant AML samples (Supplemental Digital Figure 3B, https://links.lww.com/HS/A167), which are independent from FLT3-ITD mutation status (Supplemental Digital Figure 3C and D, https://links.lww.com/HS/A167). Integrative inferred kinase activity (INKA12) analysis of the pY phosphoproteome identified high activity of MAPK1, MAPK3, and GSK3A-B in gilteritinib-resistant samples, and confirmed that there was no differential activity of FLT3 (Figure 1H). Similarly, INKA analysis of the global phosphoproteome indicated higher activity of MAPK1 in gilteritinib-resistant samples (Figure 1I and Supplemental Digital Figure 5A–E, https://links.lww.com/HS/A167), suggesting that activation of FLT3-independent pathways may abrogate FLT3-inhibition and alternative pathways for survival may be targetable. Validation of pERK1/2 levels using ELISA suggests that high ERK phosphorylation is indeed associated with impaired response toward gilteritinib (Figure 1J). No significantly different INKA scores were identified in the midostaurin responsive versus resistant comparison. To investigate which differential phosphorylated phosphosites between responsive and resistant samples were truly independent of FLT3-ITD status, we assessed phosphorylation differences between FLT3-WT and FLT3-ITD untreated de novo AML samples. As AML sample heterogeneity could hamper mutation-based analyses, we selected samples enriched with FLT3-ITD-positive blasts, on the basis of an allelic ratio of ≥ 0.5. In the pY data, 61 phosphosites were differentially (P < 0.05) phosphorylated between FLT3-WT and FLT3-ITD AML. Phosphorylation of STAT5A-Y90 and LYN-Y265 was significantly higher in FLT3-ITD AML compared with FLT3-WT AML (Figure 2A–D and Supplemental Digital Table 3C, https://links.lww.com/HS/A167). STAT5A is a known downstream component of FLT3-ITD signaling.13 While LYN may be activated by both FLT3-WT and FLT3-ITD, higher stochiometry of phosphorylation of FLT3-ITD may lead to higher binding of LYN.1,14 Surprisingly, phosphorylation of FLT3 itself seemed independent of the presence of in-sample FLT3-ITD (Figure 2E and Supplemental Digital Figure 6, https://links.lww.com/HS/A167), indicating that not overall activity, but differential downstream activation distinguishes FLT3-WT from FLT3-ITD samples. Six phosphosites overlapped between differentially phosphorylated phosphosites of the mutation- and gilteritinib-response comparison (Figure 2F). Mutation-independent, response-specific differential phosphorylation (n = 22) included higher phosphorylation of MAPK1-T185, VIM-T63, STAT1-Y701, and Src-family kinases YES1, FYN, and SRC in gilteritinib-resistant samples. Phosphorylation of PTPN18-Y319 was low in resistant samples (Figure 2G). Global phosphorylation patterns were not clearly distinct between FLT3-WT and FLT3-ITD (Supplemental Digital Figure 4B and C, https://links.lww.com/HS/A167), although PTM-SEA identified known activation of mTOR and Pi3K-AKT signaling in FLT3-ITD samples (Supplemental Digital Figure 3D, https://links.lww.com/HS/A167).15Figure 2.: Phosphoproteomic characterization of FLT3-WT and FLT3-ITD AML and parallel treatment with FLT3- and MEK/ERK inhibitors of primary AML samples. (A) Volcanoplot of differentially (P < 0.05) phosphorylated proteins between FLT3-WT and FLT3-ITD AML samples and log2 fold changes. Specific phosphorylation according to FLT3-ITD status in AML samples of (B) STAT5-Y90; (C) LYN-Y265;244; (D) SPTLC1-Y82; and (E) FLT3-Y842. P values are calculated by Wilcoxon rank-sum tests. (F) Overlapping and unique differentially phosphorylated phosphosites from the pY phosphoproteomics comparisons of responsive and resistant samples towards gilteritinib and midostaurin, and FLT3-WT versus FLT3-ITD-AR > 0.5. (G) Normalized phosphosite intensities determined using pY-based phosphoproteomics of selected unique phosphosites between gilteritinib resistant and responsive AML samples. (H) Kinase target space of ulixertinib and trametinib. All targets are shown, ranked on EC50, which is the drug concentration at which half of the target is competed. Data from https://proteomicsDB.org. (I, K, L) Combination treatment of AML samples with gilteritinib plus the LC10 of trametinib or ulixertinib and their individual INKA profiles from the pY and global phosphoproteomic analyses. (J) Combination treatment of AML7 with parallel increasing concentrations of gilteritinib and trametinib indicates synergism, exemplified by an overall Bliss synergy score of > 10. AML = acute myeloid leukemia; INKA = integrative inferred kinase activity; pY = phosphotyrosine.To further characterize FLT3-WT and FLT3-ITD AML on the protein expression level, we performed proteomics on 17 AML samples (9 FLT3-WT, 8 FLT3-ITD) with sufficient material for additional analyses. On the proteomic level, 4.092 unique proteins were identified, and 199 proteins were differentially (P < 0.05) expressed between FLT3-WT and FLT3-ITD samples (Supplemental Digital Figure 7A, https://links.lww.com/HS/A167 and Supplemental Digital Table 4, https://links.lww.com/HS/A167). Gene ontology analyses of proteins with a minimal fold change of 2 indicated that these proteins were primarily involved in leukocyte activation, oxidation-reduction processes, and protein activation cascades, including oncogenic MAPK signaling, stressing its important role in FLT3-ITD-biology (Supplemental Digital Figure 6B, https://links.lww.com/HS/A167). An integrated network with differentially expressed proteins and phosphorylated phosphosites for the FLT3-WT versus FLT3-ITD comparison—informed by the proteomic, global, and pY phosphoproteomic analyses—revealed relevant differential biology associated with FLT3-ITD-status, in particular cell activation (light green cluster), regulation of cell cycle (red cluster), and RNA splicing (light blue cluster) (Supplemental Digital Figure 8A, https://links.lww.com/HS/A167). Impaired drug response associated with alternative pathway activation may be overcome by simultaneous blocking of those pathways. To replicate previously observed ex vivo therapeutic benefit of parallel MEK inhibition,9 we assessed whether responses toward FLT3-TKIs would improve when treatment was combined with the MEK-inhibitor trametinib (Figure 2H). We only observed marginal decreases in LC50 for gilteritinib combined with fixed concentrations of trametinib (Figure 2I, K, L and Supplemental Digital Figure 9, https://links.lww.com/HS/A167). Surprisingly, we even observed an increase in LC50 towards both gilteritinib and midostaurin in several AML cases (Supplemental Digital Figures 9 and 10, https://links.lww.com/HS/A167), possibly explained by competitive antagonism or unexpected off-target effects. Using parallel increasing concentrations of gilteritinib or midostaurin and trametinib, synergy (exemplified by overall Bliss scores of > 10 [https://synergyfinder.fimm.fi/]) was only observed in a sample harboring an NRAS mutation (Figure 2J and Supplemental Digital Figures 9 and 10; https://links.lww.com/HS/A167), which may activate MEK-ERK signaling. Additionally, we explored the therapeutic effect of ulixertinib—a novel pan-ERK inhibitor. Combining gilteritinib with ulixertinib more efficiently enhanced response (Figure 2I, K, and L) than with trametinib. Combination of midostaurin with ulixertinib did not enhance responses (Supplemental Digital Figure 10, https://links.lww.com/HS/A167). Responses may be improved by optimizing concentrations and timing to prevent competitive antagonistic effects. Based on INKA ranking, AML patient-specific drug combinations could be explored1 such as inhibition of KIT in AML2393: the differential phosphorylation of 2 KIT sites in the gilteritinib resistant samples (Figure 2F and Supplemental Digital Figure 8B, https://links.lww.com/HS/A167)—in tandem with the enrichment of KIT pathway components (Supplemental Digital Figure 3B, https://links.lww.com/HS/A167)—may in part explain our observations. KIT itself is a known driver of leukemogenesis and not inhibited by gilteritinib. KIT-Y936 site phosphorylation is a docking site for several signal transduction molecules, including GRB2 and CBL.16 Binding of GRB2 to KIT can recruit GAB2 and may thereby mediate alternative activation of the MAPK signaling pathway and additionally activate the PI3K-Akt pathway in the gilteritinib-resistant samples.17 Future studies may therefore explore combination treatment of gilteritinib with a KIT inhibitor in FLT3-TKI-resistant AML. Nevertheless, the marginal benefit of combining trametinib with FLT3-TKIs is discordant with previous reports9 and warrants clarification to maximize the benefit of combinations with potentially toxic MEK inhibitors in clinical studies. Our study has a few limitations. First, sample selection is biased toward highly proliferative AMLs allowing for ample (≥ 4.5 mg) protein extraction for in-depth pY phosphoproteomics analysis. Second, although we selected samples with high blast counts and enriched for mononuclear cells during pre-processing, samples also contained variable numbers of normal leukocytes. The small (< 10%) fraction of normal leukocytes present in the samples may have led to identification of some normal leukocyte biology associated phosphorylation events in the (phospho)proteomics datasets. However, this should not affect the profiles associated with mutation status and drug response. Third, the observed associations are based on the ex vivo response of primary AMLs in liquid culture and require mechanistic validation using conditions mimicking the BM microenvironment,18 which may impact the observed responses. As we compare relative resistance among samples cultured in identical culture conditions, our experiments still provide valuable information regarding response mechanisms. Nevertheless, clinical translation and validation of our findings is warranted: to further clarify response mechanisms on the phosphorylation level, future studies analyzing BM of patients treated with monotherapy FLT3-TKIs are required. Considering the current developments in AML treatment, however, most clinical studies will combine TKIs with other (targeted) agents or chemotherapy,19 which should be taken into account in future research. In conclusion, we present an in-depth clinical phosphoproteome dataset, characterizing FLT3-ITD AML and FLT3-TKI responses. We observed distinct phosphorylation signatures and protein expression profiles associated with response towards gilteritinib and midostaurin. Our ex vivo drug combination studies indicate that further exploration of the role of ERK and simultaneous blocking the MEK-ERK axis is warranted to maximize the potential benefit of treatment combinations aiming to improve responses to FLT3-TKIs. The identification of key proteins and phosphorylation events in FLT3-ITD-AML serve as a reference for future exploration of phosphoproteomic biomarkers associated with FLT3-ITD AML and FLT3-TKI response. Disclosures JJWMJ received research funding from Novartis and Bristol Myers Squibb; speaker fees from Incyte and Pfzer; advisory board honoraria from AbbVie, Incyte, Novartis, and Pfizer. He is the President of the Apps for Care and Science Foundation that develops the HematologyApp and which has received funding from Abbvie, Amgen, Astellas, Celgene/Bristol Myers Squibb, Daiichi-Sankyo, Incyte, Janssen, Jazz, Novartis, Sanofi Genzyme, Takeda, Roche, and Servier. All the other authors have no conflicts of interest to disclose. Sources of funding This study was partly funded by Stichting Egbers and Cancer Center Amsterdam (CCA2014-1-15 and CCA2012-5-08). Cancer Center Amsterdam and Netherlands Organization for Scientific Research (NWO Middelgroot, #91116017) are acknowledged for support of the mass spectrometry infrastructure.

e16556 Background: Sunitinib, a multi-targeted antiangiogenic tyrosine kinase inhibitor has improved the outcome of patients with advanced RCC considerably. Unfortunately, ± 30% of patients are intrinsically resistant, which underscores the urgent need to develop a clinically applicable method to select sunitinib treatment for patients. Since RCC is not single oncogene driven, but rather by multiple aberrantly activated kinase signaling pathways, we hypothesized that a functional read-out by large scale (phospho)proteomics can identify predictive biomarkers for sunitinib. Methods: Frozen tumor tissue of 26 patients (pts) with RCC, treated with sunitinib upon recurrence or progression, was obtained. Pts were classified as primary resistant (progression-free survival (PFS) < 3 months, n = 8) or sensitive (PFS ≥ 3 months, n = 18). Mass spectrometry-based tyrosine (pTyr) phosphoproteomics was performed by pTyr-immunoprecipitation followed by LC-MS/MS. Discriminatory phosphosites (p-sites) were identified (p < 0.05, fold-change (FC) > 2). Expression proteomics was performed by LC-MS/MS and differentially expressed proteins identified (p < 0.05, FC > 2, ≥ 50% data presence in group with highest abundance). Tumor biology was further analyzed by INferred Kinase Activity analysis, posttranslational modifications signature enrichment analysis (PTM-SEA, Krug et al 2019) and gene ontology mining. Results: pTyr-phosphoproteomics was successfully performed in 23 of 26 samples. Seventy-eight differential p-sites were identified, of which 22 (4 unique; BCAR3, NOP58, EIF4A2, GDI1) were upregulated in resistant and 56 in sensitive pts (35 unique). Supervised cluster analysis of these p-sites resulted in near-complete separation of the groups. EIF4A1 and its homolog EIF4A2 were differentially expressed in resistant pts both at the (phospho-)proteome and, in an independent cohort, at transcriptome level. Significantly higher inferred kinase activity of MAPK3 (p = 0.026) and EGFR (p = 0.045) was found in sensitive pts. PTM-SEA showed 3 p-site-centric signatures that were significantly enriched (p < 0.05) in resistant pts, including FGF1 and prolactin pathways. Fifteen signatures were significantly enriched (p < 0.05) in sensitive pts, including insulin, VEGF and FGF2 treatment and KIT receptor pathway. Expression proteomics revealed significantly higher expression (p < 0.05) of proteins related to vesicle mediated transport in resistant pts, while this was not the case in sensitive pts. Conclusions: This MS-based (phospho)proteomics analysis of RCC tissues revealed discriminatory phosphosite and protein signatures and differential kinase and pathway activities that are associated with sensitive and resistant tumors. These findings warrant validation in an independent cohort and the clinical utility for treatment selection remains to be demonstrated.

Mass-spectrometry (MS) based phosphoproteomics is increasingly used to explore aberrant cellular signaling and kinase driver activity, aiming to improve kinase inhibitor (KI) treatment selection in malignancies. Phosphorylation is a dynamic, highly regulated post-translational modification that may be affected by variation in pre-analytical sample handling, hampering the translational value of phosphoproteomics-based analyses. Here, we investigate the effect of delay in mononuclear cell isolation on acute myeloid leukemia (AML) phosphorylation profiles. We performed MS on immuno-precipitated phosphotyrosine (pY)-containing peptides isolated from AML samples after seven pre-defined delays before sample processing (direct processing, thirty minutes, one hour, two hours, three hours, four hours and 24 h delay). Up to four hours, pY phosphoproteomics profiles show limited variation. However, in samples processed with a delay of 24 h, we observed significant change in these phosphorylation profiles, with differential phosphorylation of 22 pY phosphopeptides (p < 0.01). This includes increased phosphorylation of pY phosphopeptides of JNK and p38 kinases indicative of stress response activation. Based on these results, we conclude that processing of AML samples should be standardized at all times and should occur within four hours after sample collection. SIGNIFICANCE: Our study provides a practical time-frame in which fresh peripheral blood samples from acute myeloid patients should be processed for phosphoproteomics, in order to warrant correct interpretation of in vivo biology. We show that up to four hours of delayed processing after sample collection, pY phosphoproteomic profiles remain stable. Extended delays are associated with perturbation of phosphorylation profiles. After a delay of 24 h, JNK activation loop phosphorylation is markedly increased and may serve as a biomarker for delayed processing. These findings are relevant for biomedical acute myeloid leukemia research, as phosphoproteomic techniques are of particular interest to investigate aberrant kinase signaling in relation to disease emergence and kinase inhibitor response. With these data, we aim to contribute to reproducible research with meaningful outcomes.