Phosphotyrosine-based Phosphoproteomics for Target Identification and Drug Response Prediction in AML Cell Lines

Phosphoproteomics predict response to midostaurin plus chemotherapy in independent cohorts of FLT3-mutated acute myeloid leukaemia

BACKGROUND

Acute myeloid leukaemia (AML) is a bone marrow malignancy with poor prognosis. One of several treatments for AML is midostaurin combined with intensive chemotherapy (MIC), currently approved for FLT3 mutation-positive (FLT3-MP) AML. However, many patients carrying FLT3 mutations are refractory or experience an early relapse following MIC treatment, and might benefit more from receiving a different treatment. Development of a stratification method that outperforms FLT3 mutational status in predicting MIC response would thus benefit a large number of patients.

METHODS

We employed mass spectrometry phosphoproteomics to analyse 71 diagnosis samples of 47 patients with FLT3-MP AML who subsequently received MIC. We then used machine learning to identify biomarkers of response to MIC, and validated the resulting predictive model in two independent validation cohorts (n = 20).

FINDINGS

We identified three distinct phosphoproteomic AML subtypes amongst long-term survivors. The subtypes showed similar duration of MIC response, but different modulation of AML-implicated pathways, and exhibited distinct, highly-predictive biomarkers of MIC response. Using these biomarkers, we built a phosphoproteomics-based predictive model of MIC response, which we called MPhos. When applied to two retrospective real-world patient test cohorts (n = 20), MPhos predicted MIC response with 83% sensitivity and 100% specificity (log-rank p < 7∗10-5, HR = 0.005 [95% CI: 0-0.31]).

INTERPRETATION

In validation, MPhos outperformed the currently-used FLT3-based stratification method. Our findings have the potential to transform clinical decision-making, and highlight the important role that phosphoproteomics is destined to play in precision oncology.

FUNDING

This work was funded by Innovate UK grants (application numbers: 22217 and 10054602) and by Kinomica Ltd.

Kinase activities in pancreatic ductal adenocarcinoma with prognostic and therapeutic avenues

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.

Mass spectrometry analysis of phosphotyrosine-containing proteins

Tyrosine phosphorylation is a crucial posttranslational modification that is involved in various aspects of cell biology and often has functions in cancers. It is necessary not only to identify the specific phosphorylation sites but also to quantify their phosphorylation levels under specific pathophysiological conditions. Because of its high sensitivity and accuracy, mass spectrometry (MS) has been widely used to identify endogenous and synthetic phosphotyrosine proteins/peptides across a range of biological systems. However, phosphotyrosine-containing proteins occur in extremely low abundance and they degrade easily, severely challenging the application of MS. This review highlights the advances in both quantitative analysis procedures and enrichment approaches to tyrosine phosphorylation before MS analysis and reviews the differences among phosphorylation, sulfation, and nitration of tyrosine residues in proteins. In-depth insights into tyrosine phosphorylation in a wide variety of biological systems will offer a deep understanding of how signal transduction regulates cellular physiology and the development of tyrosine phosphorylation-related drugs as cancer therapeutics.

Inferring kinase activity from phosphoproteomic data: Tool comparison and recent applications

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.

Application of omics in the diagnosis, prognosis, and treatment of acute myeloid leukemia

Acute myeloid leukemia (AML) is the most frequent leukemia in adults with a high mortality rate. Current diagnostic criteria and selections of therapeutic strategies are generally based on gene mutations and cytogenetic abnormalities. Chemotherapy, targeted therapies, and hematopoietic stem cell transplantation (HSCT) are the major therapeutic strategies for AML. Two dilemmas in the clinical management of AML are related to its poor prognosis. One is the inaccurate risk stratification at diagnosis, leading to incorrect treatment selections. The other is the frequent resistance to chemotherapy and/or targeted therapies. Genomic features have been the focus of AML studies. However, the DNA-level aberrations do not always predict the expression levels of genes and proteins and the latter is more closely linked to disease phenotypes. With the development of high-throughput sequencing and mass spectrometry technologies, studying downstream effectors including RNA, proteins, and metabolites becomes possible. Transcriptomics can reveal gene expression and regulatory networks, proteomics can discover protein expression and signaling pathways intimately associated with the disease, and metabolomics can reflect precise changes in metabolites during disease progression. Moreover, omics profiling at the single-cell level enables studying cellular components and hierarchies of the AML microenvironment. The abundance of data from different omics layers enables the better risk stratification of AML by identifying prognosis-related biomarkers, and has the prospective application in identifying drug targets, therefore potentially discovering solutions to the two dilemmas. In this review, we summarize the existing AML studies using omics methods, both separately and combined, covering research fields of disease diagnosis, risk stratification, prognosis prediction, chemotherapy, as well as targeted therapy. Finally, we discuss the directions and challenges in the application of multi-omics in precision medicine of AML. Our review may inspire both omics researchers and clinical physicians to study AML from a different angle.

Network-based elucidation of colon cancer drug resistance mechanisms by phosphoproteomic time-series analysis

Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. Leveraging progress in proteomic technologies and network-based methodologies, we introduce Virtual Enrichment-based Signaling Protein-activity Analysis (VESPA)-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and use it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogating tumor-specific enzyme/substrate interactions accurately infers kinase and phosphatase activity, based on their substrate phosphorylation state, effectively accounting for signal crosstalk and sparse phosphoproteome coverage. The analysis elucidates time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring, experimentally confirmed by CRISPR knock-out assays, suggesting broad applicability to cancer and other diseases.

Supramolecular Organo/hydrogel-Fabricated Long Alkyl Chain α-Amidoamides as a Smart Soft Material for pH-Responsive Curcumin Release

Low-molecular-mass gelators, due to their excellent biocompatibility, low toxicological profile, innate biodegradability and ease of fabrication have garnered significant interest as they self-assemble through non-covalent interactions. In this study, we have designed and synthesized a series of six α-amidoamides by varying the hydrophobic alkyl chain length (C12-C22), which were well characterized using different spectral techniques. These α-amidoamides formed self-assembled aggregates in a DMSO/water solvent system affording organo/hydrogels at 0.66% w/v, which is the minimum gelation concentration (MGC) making them as remarkable supergelators. The various functionalities present in these gelators such as amides and alkyl chain length pave the way toward excellent gelation mechanism through hydrogen bonding and van der Waals interaction as evidenced from FTIR spectroscopy. Notably, as the chain length increased, organo/hydrogels became more thermally stable. Rheological results showed that the stability and strength of these gelators were considerably impacted by variations in chain length. The SEM morphology revealed dense sheet architectures of the organo/hydrogel samples. Organo/hydrogels have a significant impact on the advancement of innovative drug delivery systems that respond to various stimuli, ushering in a new era in pharmaceutical technology. Inspired by this, we encapsulated curcumin, a chemopreventive medication, into the gel core and further released via gel-to-sol transition induced by pH variation at 37 °C, without any alteration in structure-activity relationship. The drug release behavior was observed by UV-vis spectroscopy. Moreover, cell viability and cell invasion experiments demonstrate that the gel formulations exhibit high biocompatibility and low cytotoxicity. Among the tested formulations, 5e+Cur exhibited remarkable efficacy in controlling A549 cell migration, suggesting significant potential for applications in the pharmaceutical industry.

Illuminating phenotypic drug responses of sarcoma cells to kinase inhibitors by phosphoproteomics

Kinase inhibitors (KIs) are important cancer drugs but often feature polypharmacology that is molecularly not understood. This disconnect is particularly apparent in cancer entities such as sarcomas for which the oncogenic drivers are often not clear. To investigate more systematically how the cellular proteotypes of sarcoma cells shape their response to molecularly targeted drugs, we profiled the proteomes and phosphoproteomes of 17 sarcoma cell lines and screened the same against 150 cancer drugs. The resulting 2550 phenotypic profiles revealed distinct drug responses and the cellular activity landscapes derived from deep (phospho)proteomes (9-10,000 proteins and 10-27,000 phosphorylation sites per cell line) enabled several lines of analysis. For instance, connecting the (phospho)proteomic data with drug responses revealed known and novel mechanisms of action (MoAs) of KIs and identified markers of drug sensitivity or resistance. All data is publicly accessible via an interactive web application that enables exploration of this rich molecular resource for a better understanding of active signalling pathways in sarcoma cells, identifying treatment response predictors and revealing novel MoA of clinical KIs.

Candidate biomarkers for treatment benefit from sunitinib in patients with advanced renal cell carcinoma using mass spectrometry-based (phospho)proteomics

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.

Phosphoproteomics guides effective low-dose drug combinations against pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease with a limited set of known driver mutations 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 efficacy 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.

Proteomic Characterization of Acute Myeloid Leukemia for Precision Medicine

Acute myeloid leukemia (AML) is a highly heterogeneous cancer of the hematopoietic system with no cure for most patients. In addition to chemotherapy, treatment options for AML include recently approved therapies that target proteins with roles in AML pathobiology, such as FLT3, BLC2, and IDH1/2. However, due to disease complexity, these therapies produce very diverse responses, and survival rates are still low. Thus, despite considerable advances, there remains a need for therapies that target different aspects of leukemic biology and for associated biomarkers that define patient populations likely to respond to each available therapy. To meet this need, drugs that target different AML vulnerabilities are currently in advanced stages of clinical development. Here, we review proteomics and phosphoproteomics studies that aimed to provide insights into AML biology and clinical disease heterogeneity not attainable with genomic approaches. To place the discussion in context, we first provide an overview of genetic and clinical aspects of the disease, followed by a summary of proteins targeted by compounds that have been approved or are under clinical trials for AML treatment and, if available, the biomarkers that predict responses. We then discuss proteomics and phosphoproteomics studies that provided insights into AML pathogenesis, from which potential biomarkers and drug targets were identified, and studies that aimed to rationalize the use of synergistic drug combinations. When considered as a whole, the evidence summarized here suggests that proteomics and phosphoproteomics approaches can play a crucial role in the development and implementation of precision medicine for AML patients.

Principles of phosphoproteomics and applications in cancer research

Phosphorylation constitutes the most common and best-studied regulatory post-translational modification in biological systems and archetypal signalling pathways driven by protein and lipid kinases are disrupted in essentially all cancer types. Thus, the study of the phosphoproteome stands to provide unique biological information on signalling pathway activity and on kinase network circuitry that is not captured by genetic or transcriptomic technologies. Here, we discuss the methods and tools used in phosphoproteomics and highlight how this technique has been used, and can be used in the future, for cancer research. Challenges still exist in mass spectrometry phosphoproteomics and in the software required to provide biological information from these datasets. Nevertheless, improvements in mass spectrometers with enhanced scan rates, separation capabilities and sensitivity, in biochemical methods for sample preparation and in computational pipelines are enabling an increasingly deep analysis of the phosphoproteome, where previous bottlenecks in data acquisition, processing and interpretation are being relieved. These powerful hardware and algorithmic innovations are not only providing exciting new mechanistic insights into tumour biology, from where new drug targets may be derived, but are also leading to the discovery of phosphoproteins as mediators of drug sensitivity and resistance and as classifiers of disease subtypes. These studies are, therefore, uncovering phosphoproteins as a new generation of disruptive biomarkers to improve personalised anti-cancer therapies.

Advancing wide implementation of precision oncology: A liquid nitrogen-free snap freezer preserves molecular profiles of biological samples

PURPOSE

In precision oncology, tumor molecular profiles guide selection of therapy. Standardized snap freezing of tissue biospecimens is necessary to ensure reproducible, high-quality samples that preserve tumor biology for adequate molecular profiling. Quenching in liquid nitrogen (LN₂ ) is the golden standard method, but LN₂ has several limitations. We developed a LN₂ -independent snap freezer with adjustable cold sink temperature. To benchmark this device against the golden standard, we compared molecular profiles of biospecimens.

METHODS

Cancer cell lines and core needle normal tissue biopsies from five patients' liver resection specimens were used to compare mass spectrometry (MS)-based global phosphoproteomic and RNA sequencing profiles and RNA integrity obtained by both freezing methods.

RESULTS

Unsupervised cluster analysis of phosphoproteomic and transcriptomic profiles of snap freezer versus LN₂ -frozen K562 samples and liver biopsies showed no separation based on freezing method (with Pearson's r 0.96 (range 0.92-0.98) and >0.99 for K562 profiles, respectively), while samples with +2 h bench-time formed a separate cluster. RNA integrity was also similar for both snap freezing methods. Molecular profiles of liver biopsies were clearly identified per individual patient regardless of the applied freezing method. Two to 25 s freezing time variations did not induce profiling differences in HCT116 samples.

CONCLUSION

The novel snap freezer preserves high-quality biospecimen and allows identification of individual patients' molecular profiles, while overcoming important limitations of the use of LN₂ . This snap freezer may provide a useful tool in clinical cancer research and practice, enabling a wider implementation of (multi-)omics analyses for precision oncology.

Network-based elucidation of colon cancer drug resistance by phosphoproteomic time-series analysis

Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. By leveraging progress in proteomic technologies and network-based methodologies, over the past decade, we developed VESPA-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and used it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogation of tumor-specific enzyme/substrate interactions accurately inferred kinase and phosphatase activity, based on their inferred substrate phosphorylation state, effectively accounting for signal cross-talk and sparse phosphoproteome coverage. The analysis elucidated time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring that was experimentally confirmed by CRISPRko assays, suggesting broad applicability to cancer and other diseases.

Computational systems approach towards phosphoproteomics and their downstream regulation

Protein phosphorylation plays an essential role in modulating cell signalling and its downstream transcriptional and translational regulations. Until recently, protein phosphorylation has been studied mostly using low-throughput biochemical assays. The advancement of mass spectrometry (MS)-based phosphoproteomics transformed the field by enabling measurement of proteome-wide phosphorylation events, where tens of thousands of phosphosites are routinely identified and quantified in an experiment. This has brought a significant challenge in analysing large-scale phosphoproteomic data, making computational methods and systems approaches integral parts of phosphoproteomics. Previous works have primarily focused on reviewing the experimental techniques in MS-based phosphoproteomics, yet a systematic survey of the computational landscape in this field is still missing. Here, we review computational methods and tools, and systems approaches that have been developed for phosphoproteomics data analysis. We categorise them into four aspects including data processing, functional analysis, phosphoproteome annotation and their integration with other omics, and in each aspect, we discuss the key methods and example studies. Lastly, we highlight some of the potential research directions on which future work would make a significant contribution to this fast-growing field. We hope this review provides a useful snapshot of the field of computational systems phosphoproteomics and stimulates new research that drives future development.

Recent Advances in the Development of Anti-FLT3 CAR T-Cell Therapies for Treatment of AML

Following the success of the anti-CD19 chimeric antigen receptor (CAR) T-cell therapies against B-cell malignancies, the CAR T-cell approach is being developed towards other malignancies like acute myeloid leukemia (AML). Treatment options for relapsed AML patients are limited, and the upregulation of the FMS-like tyrosine kinase 3 (FLT3) in malignant T-cells is currently not only being investigated as a prognostic factor, but also as a target for new treatment options. In this review, we provide an overview and discuss different approaches of current anti-FLT3 CAR T-cells under development. In general, these therapies are effective both in vitro and in vivo, however the safety profile still needs to be further investigated. The first clinical trials have been initiated, and the community now awaits clinical evaluation of the approach of targeting FLT3 with CAR T-cells.

Proteomic and phosphoproteomic measurements enhance ability to predict ex vivo drug response in AML

Acute Myeloid Leukemia (AML) affects 20,000 patients in the US annually with a five-year survival rate of approximately 25%. One reason for the low survival rate is the high prevalence of clonal evolution that gives rise to heterogeneous sub-populations of leukemic cells with diverse mutation spectra, which eventually leads to disease relapse. This genetic heterogeneity drives the activation of complex signaling pathways that is reflected at the protein level. This diversity makes it difficult to treat AML with targeted therapy, requiring custom patient treatment protocols tailored to each individual's leukemia. Toward this end, the Beat AML research program prospectively collected genomic and transcriptomic data from over 1000 AML patients and carried out ex vivo drug sensitivity assays to identify genomic signatures that could predict patient-specific drug responses. However, there are inherent weaknesses in using only genetic and transcriptomic measurements as surrogates of drug response, particularly the absence of direct information about phosphorylation-mediated signal transduction. As a member of the Clinical Proteomic Tumor Analysis Consortium, we have extended the molecular characterization of this cohort by collecting proteomic and phosphoproteomic measurements from a subset of these patient samples (38 in total) to evaluate the hypothesis that proteomic signatures can improve the ability to predict response to 26 drugs in AML ex vivo samples. In this work we describe our systematic, multi-omic approach to evaluate proteomic signatures of drug response and compare protein levels to other markers of drug response such as mutational patterns. We explore the nuances of this approach using two drugs that target key pathways activated in AML: quizartinib (FLT3) and trametinib (Ras/MEK), and show how patient-derived signatures can be interpreted biologically and validated in cell lines. In conclusion, this pilot study demonstrates strong promise for proteomics-based patient stratification to assess drug sensitivity in AML.

Phosphoproteomic analysis of FLCN inactivation highlights differential kinase pathways and regulatory TFEB phosphoserines

In Birt-Hogg-Dubé (BHD) syndrome, germline mutations in the Folliculin (FLCN) gene lead to an increased risk of renal cancer. To address how FLCN affects cellular kinase signaling pathways, we analyzed comprehensive phosphoproteomic profiles of FLCN^POS and FLCN^NEG human renal tubular epithelial cells (RPTEC/TERT1). In total, 15744 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 FLCN^NEG 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 caused by fact that FLCN^NEG cells experience oxidative stress. Together, our study highlights differential phosphorylation of specific kinases and substrates in FLCN^NEG renal cells. This provides insight into BHD-associated renal tumorigenesis and may point to several novel candidates for targeted therapies.

Phosphoproteomic profiling of T cell acute lymphoblastic leukemia reveals targetable kinases and combination treatment strategies

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.

FLT3-ITD transduces autonomous growth signals during its biosynthetic trafficking in acute myelogenous leukemia cells

FMS-like tyrosine kinase 3 (FLT3) in hematopoietic cells binds to its ligand at the plasma membrane (PM), then transduces growth signals. FLT3 gene alterations that lead the kinase to assume its permanently active form, such as internal tandem duplication (ITD) and D835Y substitution, are found in 30–40% of acute myelogenous leukemia (AML) patients. Thus, drugs for molecular targeting of FLT3 mutants have been developed for the treatment of AML. Several groups have reported that compared with wild-type FLT3 (FLT3-wt), FLT3 mutants are retained in organelles, resulting in low levels of PM localization of the receptor. However, the precise subcellular localization of mutant FLT3 remains unclear, and the relationship between oncogenic signaling and the mislocalization is not completely understood. In this study, we show that in cell lines established from leukemia patients, endogenous FLT3-ITD but not FLT3-wt clearly accumulates in the perinuclear region. Our co-immunofluorescence assays demonstrate that Golgi markers are co-localized with the perinuclear region, indicating that FLT3-ITD mainly localizes to the Golgi region in AML cells. FLT3-ITD biosynthetically traffics to the Golgi apparatus and remains there in a manner dependent on its tyrosine kinase activity. Tyrosine kinase inhibitors, such as quizartinib (AC220) and midostaurin (PKC412), markedly decrease FLT3-ITD retention and increase PM levels of the mutant. FLT3-ITD activates downstream in the endoplasmic reticulum (ER) and the Golgi apparatus during its biosynthetic trafficking. Results of our trafficking inhibitor treatment assays show that FLT3-ITD in the ER activates STAT5, whereas that in the Golgi can cause the activation of AKT and ERK. We provide evidence that FLT3-ITD signals from the early secretory compartments before reaching the PM in AML cells.

The role of proteomics in the multiplexed analysis of gene alterations in human cancer

INTRODUCTION

Proteomics has played a pivotal role in identifying proteins perturbed in disease conditions when compared with healthy samples. Study of dysregulated proteins aids in identifying diagnostic markers and potential therapeutic targets. Cancer is an outcome of interplay of several such disarrayed proteins and molecular pathways which perturb cellular homeostasis, resulting in transformation. In this review, we discuss various facets of proteomic approaches, including tools and technological advancements, aiding in understanding differentially expressed molecules and signaling mechanisms.

AREAS COVERED

In this review, we have taken the approach of documenting the different methods of proteomic studies, ranging from labeling techniques, data analysis methods, and the nature of molecule detected. We summarize each technique and provide a glimpse of cancer research carried out using them, highlighting the advantages and drawbacks in comparison with others. Literature search using online resources, such as PubMed and Google Scholar were carried out for this approach.

EXPERT OPINION

Technological advancements in proteomics studies have come a long way from the study of two-dimensional mapping of proteins separated on gels in the early 1970s. Higher precision in molecular identification and quantification (high throughput), and greater number of samples analyzed have been the focus of researchers.

Phosphoproteomics: a valuable tool for uncovering molecular signaling in cancer cells

INTRODUCTION

Many pathologies, including cancer, have been associated with aberrant phosphorylation-mediated signaling networks that drive altered cell proliferation, migration, metabolic regulation, and can lead to systemic inflammation. Phosphoproteomics, the large-scale analysis of protein phosphorylation sites, has emerged as a powerful tool to define signaling network regulation and dysregulation in normal and pathological conditions.

AREAS COVERED

We provide an overview of methodology for global phosphoproteomics as well as enrichment of specific subsets of the phosphoproteome, including phosphotyrosine and phospho-motif enrichment of kinase substrates. We review quantitative methods, advantages and limitations of different mass spectrometry acquisition formats, and computational approaches to extract biological insight from phosphoproteomics data. Throughout, we discuss various applications and their challenges in implementation.

EXPERT OPINION

Over the past 20 years the field of phosphoproteomics has advanced to enable deep biological and clinical insight through the quantitative analysis of signaling networks. Future areas of development include Clinical Laboratory Improvement Amendments (CLIA)-approved methods for analysis of clinical samples, continued improvements in sensitivity to enable analysis of small numbers of rare cells and tissue microarrays, and computational methods to integrate data resulting from multiple systems-level quantitative analytical methods.

Quantitative Analysis of Tyrosine Phosphorylation from FFPE Tissues Reveals Patient-Specific Signaling Networks

Human tissue samples commonly preserved as formalin-fixed paraffin-embedded (FFPE) tissues after diagnostic or surgical procedures in the clinic represent an invaluable source of clinical specimens for in-depth characterization of signaling networks to assess therapeutic options. Tyrosine phosphorylation (pTyr) plays a fundamental role in cellular processes and is commonly dysregulated in cancer but has not been studied to date in FFPE samples. In addition, pTyr analysis that may otherwise inform therapeutic interventions for patients has been limited by the requirement for large amounts of frozen tissue. Here we describe a method for highly sensitive, quantitative analysis of pTyr signaling networks, with hundreds of sites quantified from one to two 10-μm sections of FFPE tissue specimens. A combination of optimized magnetic bead-based sample processing, optimized pTyr enrichment strategies, and tandem mass tag multiplexing enabled in-depth coverage of pTyr signaling networks from small amounts of input material. Phosphotyrosine profiles of flash-frozen and FFPE tissues derived from the same tumors suggested that FFPE tissues preserve pTyr signaling characteristics in patient-derived xenografts and archived clinical specimens. pTyr analysis of FFPE tissue sections from breast cancer tumors as well as lung cancer tumors highlighted patient-specific oncogenic driving kinases, indicating potential targeted therapies for each patient. These data suggest the capability for direct translational insight from pTyr analysis of small amounts of FFPE tumor tissue specimens. SIGNIFICANCE: This study reports a highly sensitive method utilizing FFPE tissues to identify dysregulated signaling networks in patient tumors, opening the door for direct translational insights from FFPE tumor tissue banks in hospitals.

Combining Mass Spectrometry-Based Phosphoproteomics with a Network-Based Approach to Reveal FLT3-Dependent Mechanisms of Chemoresistance

FLT3 mutations are the most frequently identified genetic alterations in acute myeloid leukemia (AML) and are associated with poor clinical outcome, relapse and chemotherapeutic resistance. Elucidating the molecular mechanisms underlying FLT3-dependent pathogenesis and drug resistance is a crucial goal of biomedical research. Given the complexity and intricacy of protein signaling networks, deciphering the molecular basis of FLT3-driven drug resistance requires a systems approach. Here we discuss how the recent advances in mass spectrometry (MS)-based (phospho) proteomics and multiparametric analysis accompanied by emerging computational approaches offer a platform to obtain and systematically analyze cell-specific signaling networks and to identify new potential therapeutic targets.

Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs

Artificial intelligence and machine learning (ML) promise to transform cancer therapies by accurately predicting the most appropriate therapies to treat individual patients. Here, we present an approach, named Drug Ranking Using ML (DRUML), which uses omics data to produce ordered lists of >400 drugs based on their anti-proliferative efficacy in cancer cells. To reduce noise and increase predictive robustness, instead of individual features, DRUML uses internally normalized distance metrics of drug response as features for ML model generation. DRUML is trained using in-house proteomics and phosphoproteomics data derived from 48 cell lines, and it is verified with data comprised of 53 cellular models from 12 independent laboratories. We show that DRUML predicts drug responses in independent verification datasets with low error (mean squared error < 0.1 and mean Spearman's rank 0.7). In addition, we demonstrate that DRUML predictions of cytarabine sensitivity in clinical leukemia samples are prognostic of patient survival (Log rank p < 0.005). Our results indicate that DRUML accurately ranks anti-cancer drugs by their efficacy across a wide range of pathologies.

Focal adhesion kinase inhibition synergizes with nab-paclitaxel to target pancreatic ductal adenocarcinoma

Background

Pancreatic ductal adenocarcinoma (PDAC) is a very lethal disease, with minimal therapeutic options. Aberrant tyrosine kinase activity influences tumor growth and is regulated by phosphorylation. We investigated phosphorylated kinases as target in PDAC.

Methods

Mass spectrometry-based phosphotyrosine proteomic analysis on PDAC cell lines was used to evaluate active kinases. Pathway analysis and inferred kinase activity analysis was performed to identify novel targets. Subsequently, we investigated targeting of focal adhesion kinase (FAK) in vitro with drug perturbations in combination with chemotherapeutics used against PDAC. Tyrosine phosphoproteomics upon treatment was performed to evaluate signaling. An orthotopic model of PDAC was used to evaluate the combination of defactinib with nab-paclitaxel.

Results

PDAC cell lines portrayed high activity of multiple receptor tyrosine kinases to various degree. The non-receptor kinase, FAK, was identified in all cell lines by our phosphotyrosine proteomic screen and pathway analysis. Targeting of this kinase with defactinib validated reduced phosphorylation profiles. Additionally, FAK inhibition had anti-proliferative and anti-migratory effects. Combination with (nab-)paclitaxel had a synergistic effect on cell proliferation in vitro and reduced tumor growth in vivo.

Conclusions

Our study shows high phosphorylation of several oncogenic receptor tyrosine kinases in PDAC cells and validated FAK inhibition as potential synergistic target with Nab-paclitaxel against this devastating disease.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13046-021-01892-z.

The influence of delay in mononuclear cell isolation on acute myeloid leukemia phosphorylation profiles

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.

Ex vivo cultures and drug testing of primary acute myeloid leukemia samples: Current techniques and implications for experimental design and outcome

New treatment options of acute myeloid leukemia (AML) are rapidly emerging. Pre-clinical models such as ex vivo cultures are extensively used towards the development of novel drugs and to study synergistic drug combinations, as well as to discover biomarkers for both drug response and anti-cancer drug resistance. Although these approaches empower efficient investigation of multiple drugs in a multitude of primary AML samples, their translational value and reproducibility are hampered by the lack of standardized methodologies and by culture system-specific behavior of AML cells and chemotherapeutic drugs. Moreover, distinct research questions require specific methods which rely on specific technical knowledge and skills. To address these aspects, we herein review commonly used culture techniques in light of diverse research questions. In addition, culture-dependent effects on drug resistance towards commonly used drugs in the treatment of AML are summarized including several pitfalls that may arise because of culture technique artifacts. The primary aim of the current review is to provide practical guidelines for ex vivo primary AML culture experimental design.

Computational Analysis of Phosphoproteomics Data in Multi-Omics Cancer Studies

Multiple types of molecular data for the same set of clinical samples are increasingly available and may be analyzed jointly in an integrative analysis to maximize comprehensive biological insight. This analysis is important as separate analyses of individual omics data types usually do not fully explain disease phenotypes. An increasing number of studies have now been focusing on multi-omics data integration, yet not many studies have included phosphoproteomics data, an important layer for understanding signaling pathways. Multi-omics integration methods with phosphoproteomics data are reviewed in the context of cancer research as well as multi-omics methods papers that would be promising to apply to phosphoproteomics data. Analysis of individual data types is still the major approach even in large cohort proteogenomics studies. Hence, a section is dedicated on possible integrative methods for multi-omics and phosphoproteomics data. In summary, this review provides the readers with both currently used integrative methods previously applied to phosphoproteomics and multi-omics data integration and other algorithms for multi-omics data integration promising for future application to phosphoproteomics data.