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 & lowast;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. Copyright (c) 2024 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Supplementary Figures (S1A-B, S2, S3, S4A-C and Table S1 & S2.

PDF file - 132K, S1 Differential phosphorylation of ERalpha following E2 or growth factor stimulation. S2 Stable isotope dilution MRM (SID-MRM) quantitation of Ser294 and Ser167 using heavy-labeled synthetic peptides

Background: Midostaurin plus intensive chemotherapy (M+IC) is approved for FLT3 mutant-positive (FLT3-MP) acute myeloid leukaemia (AML). The presence of refractory/early relapse (R/ER) disease following M+IC treatment suggests the existence of FLT3-independent determinants of M+IC response (Stone et al. NEJM 2017). We have previously reported a phosphoproteomic signature significantly elevated in primary AML blasts that responded to midostaurin ex vivo (Casado et al., 2018, Leukemia). Aims: To build and test a phosphoproteomics-based model to predict M+IC response from FLT3-MP AML patient samples collected at diagnosis. Methods: We retrospectively analysed peripheral blood (PB, n=37) and/or bone marrow (BM, n=34) diagnosis samples of 47 FLT3-MP AML patients subsequently treated with M+IC (median age at diagnosis 61, range 19-79y) using liquid chromatography-tandem mass spectrometry and MS1-based peptide quantification for phosphoproteomics analysis. Data from patients with extreme response profiles were used for model building; the “good-responder” (GR) group had a disease-free survival (DFS)>24 months (n=20), whereas the R/ER group had DFS<6 months (n=14, including refractory patients). Multivariate analysis and machine learning were used to build a phosphoproteomic signature-based model capable of predicting M+IC response from diagnosis samples. The model was validated on an independent, blinded retrospective set of 13 diagnosis FLT3-MP AML samples (median age 60, age range 33-73y, 9xPB and 4xBM). Results: In this study, we identify a highly-predictive phosphoproteomic signature of M+IC response in FLT3-MP AML diagnosis samples, and test it on an independent, blinded patient cohort. First, multivariate analysis of phosphoproteomic data identified several biochemically different groups of AML cases (Fig. 1A), highlighting potential distinct mechanisms of drug response. GR1 and GR2 groups showed upregulation of DNA damage response (DDR), and downregulation of receptor tyrosine kinase (RTK) signalling, and either downregulation of immune response (IR) pathways (GR1), or upregulation of chromatin remodellers (GR2). GR3 showed upregulation of RTK signalling and IR pathways, and downregulation of DDR. A phosphoproteomic signature made of a subset of more than a hundred phosphopeptides discriminating between at least two of these four patient groups (R/ER, GR1-GR3) was used to build a response-prediction model. On the expanded training dataset, including patients with DFS between 6 months and 24 months (n=13), response stratification was achieved with log rank p<1x10-9 (not shown); median DFS was 17.7 weeks for the signature-negative patients, and was not reached for signature-positive patients. The model was then tested on a blinded independent cohort of 13 FLT3-MP patients (Fig. 1B and C), with those positive for our signature showing markedly increased survival than signature-negative patients (median DFS 0 weeks vs not reached, log-rank p<0.0008). The overall model accuracy, with “response” defined as DFS>6 months, was 100% for signature-negative samples (5/5) and 85% for signature-positive samples (6/7, data was censored before 6 months for one patient). Summary/Conclusion: Using MS1-based quantitation of phosphoproteomic data, we identified several potential mechanisms of sensitivity to M+IC. Accounting for response heterogeneity enabled the creation of a model based on a highly-predictive phosphoproteomic signature of M+IC response. In an independent blinded patient cohort of 13 FLT3-MP patients this model predicted M+IC response with 92% accuracy.Keywords: Survival prediction, Acute myeloid leukemia, flt3 inhibitor, Phosphorylation

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.

Abstract Background: NSCLC cells carrying EGFR mutations can gain resistance to cognate TKIs through amplification of Chr22q11.2 (Chr22amp), a chromosome segment containing CRKL. This also specifically associates with exquisite sensitivity to inhibitors of Aurora Kinase B (AZD2811), potentially mediated by other Chr22 genes. Furthermore, a phenotypic rewiring occurs in the response to AZD2811, from a senescent polyploidy in wildtype (WT) cells to apoptosis in Chr22amp cells. Here, we aimed to elucidate the underlying signaling alterations in this background by phosphoproteomic pathway analysis. Methods: The EGFR mutant cell line PC9 and 8 TKI resistant derivatives were profiled (4 Chr22amp and 4 WT). Kinetics of response to AZD2811 (100nM) and osimertinib (160 nM) were identified by flow cytometry. Samples (n=3) were prepared for phosphoproteomics, after 6, 24, and 48 h AZD2811 and 1 h osimertinib, with time matched controls. Cells were washed and lysed in urea, then digested with trypsin. Phosphorylated peptides were enriched with TiO2 and analyzed by Orbitrap LC-MS/MS. Computational analyses quantified peptides across samples. KScanTM bioinformatics identified differential phosphopeptides between Chr22amp and WT to determine kinase substrate profiles by KSEA, putative downstream targets (PDT) and differential compound target activity markers (CTAM). Results: Single cell time-course analysis of phenotypic response to AZD2811 in Chr22amp cells showed that >60% of cells become Annexin V+ by 48 h post-treatment. We took earlier timepoints of 6, 24 and 48 h post treatment. We focused the phosphoproteomic analysis on three comparisons of Chr22amp amplified cells to: 1) the basal signaling state compared to WT; 2) the signaling response to osimertinib in parental PC9; and 3) the altered kinetics of signaling in response to AZD2811 compared to WT. At the basal level, Chr22amp had CK1e, CDK2, p38a substrates differentially enriched, and MTOR inhibitor and Aurora B inhibitor modulated sites (p<10-3). The response to osimertinib was largely differential in the maintenance of ERK1/2 signaling to P90RSK1 but not MEK1 in Chr22amp cells. In cells treated with AZD2811, alterations in signaling were associated with Aurora B in all cells as expected. However in amplified cells, we observed key differences at 24h such in cell death and metabolic processes in specific hierarchical clusters of temporally modulated sites, underpinned by relative down regulation of multiple signaling nodes such as ARAF (z = 4.87, p<10-2), ERN1 (z = 4.56, p<10-2), and CDK2 (z = 4.30, p<10-2). Conclusions: Here, we identified significant pathway deregulation in Chr22amp cells that subverted EGFR inhibition and enhanced sensitivity to AZD2811. Intriguingly, we detected enhanced Aurora B activity in Chr22amp cells at basal levels, and surprising impact of AZD2811 on the EGFR pathway. Citation Format: Arran Dokal, Jordi Bertran-Alamillo, Edmund Wilkes, Hilary Lewis, Ana Gimenez-Capitan, Calum Greenhalgh, Ruth Osuntola, Maruan Higazi-Vega, Shona Ellison, Vinothini Rajeeve, Giulia Fabbri, Urszula Polanska, J. Elizabeth Pease, Pedro Rodriguez-Cutillas, Jelena Urosevic, Miguel Angel Molina-Vila, David Britton, Jon Travers. Precision phosphoproteomic analysis in Chr22q11.2 amplified NSCLC cells reveals distinct signaling corruption and response to Aurora kinase B inhibition [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1107.

The anti-CD20 monoclonal antibodies rituximab and obinutuzumab differ in their mechanisms of action, with obinutuzumab evoking greater direct B cell death. To characterize the signaling processes responsible for improved B cell killing by obinutuzumab, we undertook a phosphoproteomics approach and demonstrate that rituximab and obinutuzumab differentially activate pathways downstream of the B cell receptor. Although both antibodies induce strong ERK and MYC activation sufficient to promote cell-cycle arrest and B cell death, obinutuzumab exceeds rituximab in supporting apoptosis induction by means of aberrant SYK phosphorylation. In contrast, rituximab elicits stronger anti-apoptotic signals by activating AKT, by impairing pro-apoptotic BAD, and by releasing membrane-bound NOTCH1 to up-regulate pro-survival target genes. As a consequence, rituximab appears to reinforce BCL2-mediated apoptosis resistance. The unexpected complexity and differences by which rituximab and obinutuzumab interfere with signaling pathways essential for lymphoma pathogenesis and treatment provide important impetus to optimize and personalize the application of different anti-CD20 treatments.

AbstractCholangiocarcinoma is a form of hepatobiliary cancer with an abysmal prognosis. Despite advances in our understanding of cholangiocarcinoma pathophysiology and its genomic landscape, targeted therapies have not yet made a significant impact on its clinical management. The low response rates of targeted therapies in cholangiocarcinoma suggest that patient heterogeneity contributes to poor clinical outcome. Here we used mass spectrometry–based phosphoproteomics and computational methods to identify patient-specific drug targets in patient tumors and cholangiocarcinoma-derived cell lines. We analyzed 13 primary tumors of patients with cholangiocarcinoma with matched nonmalignant tissue and 7 different cholangiocarcinoma cell lines, leading to the identification and quantification of more than 13,000 phosphorylation sites. The phosphoproteomes of cholangiocarcinoma cell lines and patient tumors were significantly correlated. MEK1, KIT, ERK1/2, and several cyclin-dependent kinases were among the protein kinases most frequently showing increased activity in cholangiocarcinoma relative to nonmalignant tissue. Application of the Drug Ranking Using Machine Learning (DRUML) algorithm selected inhibitors of histone deacetylase (HDAC; belinostat and CAY10603) and PI3K pathway members as high-ranking therapies to use in primary cholangiocarcinoma. The accuracy of the computational drug rankings based on predicted responses was confirmed in cell-line models of cholangiocarcinoma. Together, this study uncovers frequently activated biochemical pathways in cholangiocarcinoma and provides a proof of concept for the application of computational methodology to rank drugs based on efficacy in individual patients.Significance:Phosphoproteomic and computational analyses identify patient-specific drug targets in cholangiocarcinoma, supporting the potential of a machine learning method to predict personalized therapies.

Abstract Introduction: Responses to targeted drugs are highly variable across patients and current genomic biomarkers are often ineffective at stratification. Here, we tested the hypothesis that direct quantification of kinase activity markers (using phosphoproteomics) would predict kinase inhibitor efficacy in cancer with greater accuracy than proxies of pathway activation (such as genetic mutations). Experimental procedures We designed an approach (based on computational analysis of phosphoproteomics data) that (i) systematically identifies markers of kinase activity and (ii) measures these markers in primary cancer cells with precision and accuracy. To test the performance of the method, we carried out LC-MS/MS phosphoproteomics analysis of three different cancer cell lines treated with 60 different kinase inhibitors. In parallel, we determined the selectivity profile of the same 60 compounds against 460 kinases. An algorithm was designed to compare the in vitro specificity profiles of these kinase inhibitors with their effects on cellular phosphoproteomes. We then statistically assessed kinase activity enrichment in primary breast tumors from 86 cases and 36 primary acute myeloid leukaemia (AML) blast specimens, by measuring our markers of kinase and signaling activity in these tumors. Models to predict sensitivity to kinase inhibitors were constructed using methods based on multivariate regression (partial least squares) and machine learning (random forest and neural networks). Unpublished data We quantified 22,000 unique protein phosphorylation sites in three cell lines treated with 60 kinase inhibitors each in quadruplicate, resulting in the acquisition of 15.8M quantitative data points. Using our newly developed computational tools, we identified 6,206 kinase activity markers for 106 kinases and 1,508 network edges (kinase-kinase relationships). These markers of kinase activity and network circuitry correlated with the impact that several kinase inhibitors had in reducing the viability of primary AML cases. Machine learning models, that used our markers of kinase activity as input, predicted responses to inhibitors with high accuracy (RMSE <0.15). As an example, our approach was twice more accurate at determining response to midostaurin than the FDA approved FLT3-ITD mutation biomarkers. Quantification of our activity markers in 86 primary breast tumors revealed an anti-correlation between MAPK and PI3K activity markers. Consistent with this observation, MAPK pathway inhibitors decreased the viability of cells with wild-type PIK3CA to a greater extent than those harboring PIK3CA mutations in helical or kinase domains. Our data suggest that absence of PIK3CA mutations, in combination with our MAPK pathway activity markers, should be investigated as a predictive signature for MAPK pathway inhibitors. Conclusion: Our study provides insights into kinase network regulation and represents a unique resource to investigate the relationships between kinase network topology and drug response versus resistance. Crucially, we demonstrate that our approach predicts efficacy of targeted therapies with greater accuracy than mutational analysis. Citation Format: Pedro Rodriguez Cutillas, Mauran Hijazi, Ryan Smith, Conrad Bessant, David Britton. Chemical phosphoproteomics systematically identifies circuitries of kinase networks in cancer cells and predicts their response to kinase inhibitors [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2019 Oct 26-30; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2019;18(12 Suppl):Abstract nr C088. doi:10.1158/1535-7163.TARG-19-C088

Liquid chromatography-selected reaction monitoring (LC-SRM) mass spectrometry has developed into a versatile tool for quantification of proteins with a wide range of applications in basic science, translational research, and clinical patient assessment. This strategy uniquely complements traditional pathology approaches, like hematoxylin and eosin (H&E) staining and immunohistochemistry (IHC). The multiplexing capabilities offered by mass spectrometry are currently unmatched by other techniques. However, quantification of biomarkers in tissue specimens without the other data obtained from H&E-stained slides or IHC, including tumor cellularity or percentage of positively stained cells inter alia, may not provide as much information that is needed to fully understand tumor biology or properly assess the patient. Therefore, additional characterization of the tissue proteome is needed, which in turn requires the ability to assess protein markers across a wide range of expression levels from a single sample. This protocol provides an example of multiplexed analysis in breast tumor tissue quantifying specific biomarkers, specifically estrogen receptor, progesterone receptor, and the HER2 receptor tyrosine kinase, in combination with other proteins that can report on tissue content and other aspects of tumor biology.

Signaling pathways driven by protein and lipid kinases are altered in most human diseases. Therefore, pharmacological inhibitors of cell signaling are one of the most intensively pursued therapeutic approaches for the treatment of diseases such as cancer, neurodegeneration, and metabolic syndromes. Phosphoproteomics is a technique that measures the products of kinase activities and, with the appropriate bioinformatics techniques, the methodology can also provide measures of kinase pathway activation and network circuitry. Hence, due to recent technological advantages, LC-MS-based quantitative phosphoproteomics provides relevant information for the design and implementation of kinase inhibitor based therapies. Here, we review how phosphoproteome profiling is being used in translational research as a means to identify drug targets and biomarkers for personalizing therapies based on kinase inhibitors.

Liquid chromatography-selected reaction monitoring mass spectrometry (LC-SRM) is not only a proven tool for clinical chemistry, but also a versatile method to enhance the capability to quantify biomarkers for tumor biology research. As the treatment of cancer continues to evolve, the ability to assess multiple biomarkers to assign cancer phenotypes based on the genetic background and the signaling of the individual tumor becomes paramount to our ability to treat the patient. In breast cancer, the American Society of Clinical Oncology has defined biomarkers for patient assessment to guide selection of therapy: estrogen receptor, progesterone receptor, and the HER2/Neu receptor tyrosine kinase; therefore, these proteins were selected for LC-SRM assay development. Detailed molecular characterization of these proteins is necessary for patient treatment, so expression and phosphorylation assays have been developed and applied. In addition, other LC-SRM assays were developed to further evaluate tumor biology (e.g. Ki-67 for proliferation and vimentin for tumor aggressiveness related to the epithelial-to-mesenchymal transition). These measurements combined with biomarkers for tissue quality and histological content are implemented in a three-tier multiplexed assay platform, which is translated from cell line models into frozen tumor tissues banked from breast cancer patients.

Abstract Introduction: Standard therapy for acute myeloid leukaemia (AML) generally includes intense induction with daunorubicin (D) on days 1-3 and cytarabine (A) on days 1-7, followed by consolidation should complete remission (CR) be achieved. Assessment of bone marrow morphology, including percentage of blasts, remains the standard approach to gauge treatment response, however more sensitive molecular based approaches are capable of detecting subclinical levels of leukemic blasts (minimal residual disease, MRD). MRD often remains during and after standard treatment and is the main cause of relapse, a major problem in the management of AML. Resistance of the residual blasts to treatment can be attributed to the activity of pro-survival enzymes, some of which can be pharmacologically inhibited, however, finding the right inhibitor for the right patient presents a major challenge due to the plethora of different enzymes and combinations thereof. Liquid chromatography - tandem mass spectrometry (LC-MS/MS) proteomics enables global and unbiased quantification of protein expression and enzymatic activity in samples. We applied this technology to AML blasts at relapse compared to diagnosis, and in cell lines treated with standard chemotherapy to detect modulated biochemical pathways that contribute to resistance. Thorough investigation into the expression and activity of the protein drug targets enabled selection of inhibitors which proved effective when cells were treated in culture. This approach represents an effective way to better understand the biochemistry of cells following chemotherapy and identify suitable drug targets in biopsies to guide effective inhibitor selection. Methods: LC-MS/MS proteomics and phosphoproteomics was used to investigate global protein expression and kinase activity in primary AML samples at diagnosis and matched relapse (18 cases), and in 3 AML/APL cell lines before and after chemotherapy. Briefly, we collected frozen biopsy specimens from the Barts tissue bank and after thawing the AML blasts were incubated in media for 2 hr at 37oC. Cell lines (HL60, MV411 and P31/FUJ) were treated ± D and/or A (2, 6, or 24 hr). After incubation, cells were centrifuged and washed in PBS, then proteins extracted in urea lysis buffer. Proteins were digested with trypsin, and resulting peptides analysed directly by LC-MS/MS for proteomics or subjected to phosphopeptide enrichment using TiO2 for phosphoproteomics. Commercial (Mascot) and in-house (Pescal, KSEA) software were utilised to identify and quantify proteins, determine kinase activities and investigate intracellular signalling. Cell Viability of blasts ± treatments were recorded using the Guava ViaCount Reagent and Cytometer. Results: On average, >3000 proteins and >9,000 phosphorylation sites were identified per sample. One of the drug targets that correlated strongest with % blasts was CD99 (r=0.79). Blasts showed high abundance & activity of enzymes involved in DNA repair (e.g. PARP1, ATR and PRKDC) at diagnosis and relapse, several significantly increasing in relapse (e.g. PLK3 and APEX1). We observed significant increase in phosphorylation of signalling proteins, such as KIT and STAT5, in relapse. Other signalling pathways regulating survival, apoptosis and metabolism were modulated after relapse but these were patient specific. AML cell lines were more sensitive to D than A. HL60 was the most sensitive cell line while P31/FUJ were least sensitive. Chemotherapy significantly increased the activity of ATM, ATR, PRKDC and MAPKAPK2. Phosphorylation of HSPB1 increased significantly in the presence of D and/or A, and inversely correlated with sensitivity of cells to these drugs. Simultaneous inhibition of ATM & ATR significantly reduced P31/FUJ & MV411 cell viability ± A, while MAPKAPK2 inhibition increased sensitivity of MV411 cells to A. Conclusion: We identified the most abundant and active protein drug targets in AML primary samples and cell lines. Investigating primary AML at diagnosis and relapse uncovered changes in biochemical pathways that regulate DNA repair, survival, apoptosis and metabolism, some of them being modulated by chemotherapy in AML cell lines. These changes were often patient specific, suggesting that to effectively implement targeted therapies, a personalised approach is required and we demonstrate drug selection can be directed by LC-MS/MS proteomics. Disclosures No relevant conflicts of interest to declare.

AimsThis combined proteomic and histopathological study was aimed to compare tissue characteristics of immunoglobulin (Ig)G4‐related sclerosing cholangitis (ISC) and primary sclerosing cholangitis (PSC) in a global, non‐biased manner.Methods and resultsTissue proteomes and phosphorylomes of frozen large bile duct samples were analysed by a conventional liquid chromatography‐tandem mass spectrometry (LC‐MS/MS) protocol and additional phosphopeptide enrichment methods. The proteomic examination identified 23 373 peptides and 4870 proteins, including 4801 phosphopeptides and 1121 phosphoproteins. The expression profiles of phosphopeptides discriminated ISC from PSC more clearly than those of non‐phosphopeptides. In the pathway analysis, ISC was found to have 11 more activated signal cascades, including three immunological pathways, all B cell‐ or immunoglobulin‐related. On immunostaining, two immunological markers (FYN‐binding protein and allograft inflammatory factor‐1) up‐regulated in ISC were expressed mainly in M2 macrophages, consistent with increased phagocytotic activity induced by the immunoglobulin (Ig)G‐Fcγ receptor interaction. In contrast, PSC had two more activated signal pathways related to extracellular matrix (ECM) remodelling. Filamin‐A involved in ECM remodelling was expressed aberrantly in injured bile ducts and associated cholangiocarcinomas in PSC, suggesting its possible roles in periductal fibrosis and carcinogenesis in PSC.ConclusionsThis study suggested crucial roles of B cells and macrophages in ISC, and more dynamic ECM remodelling in PSC.

Abstract Melanoma, the most lethal form of skin cancer, is marked by numerous genetic modifications, including point mutations as well as overexpression and deletion of genes. Intrinsic and acquired resistance to BRAF V600E targeted therapies (BRAFi) in metastatic melanoma patients further underscores the need for global profiling of melanoma circuitry at the functional level. Therefore, activity-based protein profiling (ABPP) and phosphoproteomics was carried out to decipher steady state differences in global signaling mechanisms in naïve and BRAFi resistant melanoma cell lines with BRAF V600E mutations. Four cell lines (A375, 1205Lu, WM164 and WM793) were selected to evaluate different molecular backgrounds of BRAF mutation based on their PTEN status (either WT or null). For each cell type, both the naïve and BRAFi resistant lines were analyzed via ABPP as well as chemical labeling with tandem mass tags (TMT) prior to discovery phosphoproteomics. While the ABPP approach mined for kinases, phosphoproteomics identified STY phosphorylated peptides providing information on signaling via kinase substrates. LC-MS/MS discovery proteomics (RSLC and Q Exactive, Thermo) identified and relatively quantified all peptides observed in ABPP and TMT phosphoproteomics experiments. MaxQuant was used for data evaluation; preliminary statistical analyses were performed in Perseus to select significant differences for pathway mapping (GeneGO, Metacore) and follow up experiments using siRNA or pharmacological inhibition. Adaptive responses to combination treatment were also explored using the SysQuant workflow for quantitative expression analysis and phosphoproteomics. These experiments served as a basis for comparison for a pilot project of 12 metastatic tumors from BRAF mutant melanoma patients selected for comparison of good and poor survival outcomes. ABPP measurements on different cell line models (A375, 1205Lu, WM793 and WM164) reveal significant differences in ATP uptake of proteins in the resistant cell line model compared to its naïve counterpart. For example, in the 1205Lu cell line several proteins including EGFR, p38alpha, DNA-PK formed an interconnected pathway. Overall we identified between 2,000-2,800 proteins in each cell line with ∼150 kinases. Isobaric labeling coupled to phosphoproteomics identified ∼1,600 quantifiable proteins with ∼4,000 phosphorylation sites. Phosphoproteomics revealed concomitant increase in phosphorylation levels of the substrates acted upon by kinases showing higher ATP uptake in ABPP measurements. For example, in 1205Lu BRAFi resistant cells, CDK1, CDK2 and DNA-PK showed higher ATP uptake and their substrates SSK1, DPYSL3, and vimentin showed higher phosphorylation levels. The complementary nature of the two functional proteomics approaches provided holistic overview of signalling network in melanoma and enabled selection of targets for follow-up studies. Citation Format: Ritin Sharma, Manali Phadke, David Britton, Ian Pike, Keiran Smalley, John M. Koomen. Kinases and adaptive signaling contribute to drug resistance in BRAF mutant melanoma. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2006. doi:10.1158/1538-7445.AM2015-2006

We present a novel tandem mass tag solid-phase amino labeling (TMT-SPAL) protocol using reversible immobilization of peptides onto octadecyl-derivatized (C18) solid supports. This method can reduce the number of steps required in complex protocols, saving time and potentially reducing sample loss. In our global phosphopeptide profiling workflow (SysQuant), we can cut 24 h from the protocol while increasing peptide identifications (20%) and reducing side reactions. Solid-phase labeling with TMTs does require some modification to typical labeling conditions, particularly pH. It has been found that complete labeling equivalent to standard basic pH solution-phase labeling for small and large samples can be achieved on C18 resins under slightly acidic buffer conditions. Improved labeling behavior on C18 compared to that with standard basic pH solution-phase labeling is demonstrated. We analyzed our samples for histidine, serine, threonine, and tyrosine labeling to determine the degree of overlabeling and observed higher than expected levels (25% of all peptide spectral matches (PSMs)) of overlabeling at all of these amino acids (predominantly at tyrosine and serine) in our standard solution-phase labeling protocol. Overlabeling at all of these sites is greatly reduced (4-fold, to 7% of all PSMs) by the low-pH conditions used in the TMT-SPAL protocol. Overlabeling seems to represent a so-far overlooked mechanism causing reductions in peptide identification rates with NHS-activated TMT labeling compared to that with label-free methods. Our results also highlight the importance of searching data for overlabeling when labeling methods are used.

Abstract INTRODUCTION: LC-MS/MS proteomics is an essential technology to help unravel the complex molecular events that lead to and propagate cancer. We have developed an analytical workflow to quantify the expression and phosphorylation status of thousands of proteins simultaneously in frozen resected human pancreatic tumor (T), relative to matched non-tumor (NT) tissue. These measurements enabled us to determine activity of anti-neoplastic drug targets and other signaling proteins. METHODS: Peptides resulting from tryptic digestion of proteins extracted from frozen tissue of pancreatic ductal adenocarcinoma and background pancreas (n=12), were labelled with tandem mass tags (TMT 8-plex), separated by strong cation exchange chromatography, then were analysed by LC-MS/MS directly or first enriched for phosphopeptides using Fe3+ and TiO2, prior to analysis. In-house, commercial and freeware bio-informatics platforms were used to identify relevant biological events from the complex dataset. RESULTS: Of 2,101 proteins identified, 152 demonstrated significant difference in expression between tumor and non-tumor tissue. They included proteins that are known to be up-regulated in pancreatic cancer (e.g. Mucin-1), but the majority were new candidate markers such as homeodomain interacting protein kinase 1 & Myosin light chain kinase. Of the 6,543 unique phosphopeptides identified (6,284 unique phosphorylation sites), 635 showed significant regulation, particularly those from proteins involved in cell migration (Rho GTPase signaling proteins such as Rho guanine nucleotide exchange factors & Serine/threonine-protein kinase MRCKα) as well as proteins involved in disassembly of cell-cell junctions (tight junction, adherens junction) and formation of cell-extracellular matrix (ECM) junctions (focal adhesions). We quantified activator & inhibitory phosphorylation sites on FYN, AKT1, HDAC1&2, GSK3α&β, RAF kinases, MAPKs (p38, ERK1&2), PKCs, Casein Kinases and >20 others, as well as their downstream substrates some of which were often found to be highly modulated (≥ 2 fold) in T versus NT in different cases. CONCLUSION: Application of our LC-MS/MS proteomic workflow to frozen resected human pancreatic T versus NT tissue elucidated molecular events likely contributing to pancreatic cancer in each case, particularly those contributing to cell migration, and in future may help clinicians predict the best targeted anti-cancer therapy bespoke for an individual patient. Citation Format: David Britton, Yoh Zen, Stefan Selzer, Vikram Mitra, Alberto Quaglia, Debashis Sarker, Leandro Castellano, Justin Stebbing, Julia Gee, Rob Nicholson, Nigel Heaton, Ian Pike. Quantification of pancreatic cancer proteome & phosphorylome: Indicates molecular events likely contributing to cancer & activation status of drug targets. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 1617. doi:10.1158/1538-7445.AM2014-1617