Evaluation of Direct Infusion-Multiple Reaction Monitoring Mass Spectrometry for Quantification of Heat Shock Proteins

Protein quantification with liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM) has emerged as a powerful platform for assessing panels of biomarkers. In this study, direct infusion, using automated, chip-based nanoelectrospray ionization, coupled with MRM (DI-MRM) is used for protein quantification. Removal of the LC separation step increases the importance of evaluating the ratios between the transitions. Therefore, the effects of solvent composition, analyte concentration, spray voltage, and quadrupole resolution settings on fragmentation patterns have been studied using peptide and protein standards. After DI-MRM quantification was evaluated for standards, quantitative assays for the expression of heat shock proteins (HSPs) were translated from LC-MRM to DI-MRM for implementation in cell line models of multiple myeloma. Requirements for DI-MRM assay development are described. Then, the two methods are compared; criteria for effective DI-MRM analysis are reported on the basis of the analysis of HSP expression in digests of whole cell lysates. The increased throughput of DI-MRM analysis is useful for rapid analysis of large batches of similar samples, such as time course measurements of cellular responses to therapy

Comparison of Quantitative Mass Spectrometry Platforms for Monitoring Kinase ATP Probe Uptake in Lung Cancer

Recent developments in instrumentation and bioinformatics have led to new quantitative mass spectrometry platforms including LC–MS/MS with data-independent acquisition (DIA) and targeted analysis using parallel reaction monitoring mass spectrometry (LC–PRM), which provide alternatives to well-established methods, such as LC–MS/MS with data-dependent acquisition (DDA) and targeted analysis using multiple reaction monitoring mass spectrometry (LC–MRM). These tools have been used to identify signaling perturbations in lung cancers and other malignancies, supporting the development of effective kinase inhibitors and, more recently, providing insights into therapeutic resistance mechanisms and drug repurposing opportunities. However, detection of kinases in biological matrices can be challenging; therefore, activity-based protein profiling enrichment of ATP-utilizing proteins was selected as a test case for exploring the limits of detection of low-abundance analytes in complex biological samples. To examine the impact of different MS acquisition platforms, quantification of kinase ATP uptake following kinase inhibitor treatment was analyzed by four different methods: LC–MS/MS with DDA and DIA, LC–MRM, and LC–PRM. For discovery data sets, DIA increased the number of identified kinases by 21% and reduced missingness when compared with DDA. In this context, MRM and PRM were most effective at identifying global kinome responses to inhibitor treatment, highlighting the value of a priori target identification and manual evaluation of quantitative proteomics data sets. We compare results for a selected set of desthiobiotinylated peptides from PRM, MRM, and DIA and identify considerations for selecting a quantification method and postprocessing steps that should be used for each data acquisition strategy

Activity-Based Protein Profiling Shows Heterogeneous Signaling Adaptations to BRAF Inhibition

Patients with BRAF V600E mutant melanoma are typically treated with targeted BRAF kinase inhibitors, such as vemurafenib and dabrafenib. Although these drugs are initially effective, they are not curative. Most of the focus to date has been upon genetic mechanisms of acquired resistance; therefore, we must better understand the global signaling adaptations that mediate escape from BRAF inhibition. In the current study, we have used activity-based protein profiling (ABPP) with ATP-analogue probes to enrich kinases and other enzyme classes that contribute to BRAF inhibitor (BRAFi) resistance in four paired isogenic BRAFi-naïve/resistant cell line models. Our analysis showed these cell line models, which also differ in their PTEN status, have considerable heterogeneity in their kinase ATP probe uptake in comparing both naïve cells and adaptations to chronic drug exposure. A number of kinases including FAK1, SLK, and TAOK2 had increased ATP probe uptake in BRAFi resistant cells, while KHS1 (M4K5) and BRAF had decreased ATP probe uptake in the BRAFi-resistant cells. Gene ontology (GO) enrichment analysis revealed BRAFi resistance is associated with a significant enhancement in ATP probe uptake in proteins implicated in cytoskeletal organization and adhesion, and decreases in ATP probe uptake in proteins associated with cell metabolic processes. The ABPP approach was able to identify key phenotypic mediators critical for each BRAFi resistant cell line. Together, these data show that common phenotypic adaptations to BRAF inhibition can be mediated through very different signaling networks, suggesting considerable redundancy within the signaling of <i>BRAF</i> mutant melanoma cells

APOSTL: An Interactive Galaxy Pipeline for Reproducible Analysis of Affinity Proteomics Data

With continuously increasing scale and depth of coverage in affinity proteomics (AP–MS) data, the analysis and visualization is becoming more challenging. A number of tools have been developed to identify high-confidence interactions; however, a cohesive and intuitive pipeline for analysis and visualization is still needed. Here we present Automated Processing of SAINT Templated Layouts (APOSTL), a freely available Galaxy-integrated software suite and analysis pipeline for reproducible, interactive analysis of AP–MS data. APOSTL contains a number of tools woven together using Galaxy workflows, which are intuitive for the user to move from raw data to publication-quality figures within a single interface. APOSTL is an evolving software project with the potential to customize individual analyses with additional Galaxy tools and widgets using the R web application framework, Shiny. The source code, data, and documentation are freely available from GitHub (https://github.com/bornea/APOSTL) and other sources

A Pilot Proteogenomic Study with Data Integration Identifies MCT1 and GLUT1 as Prognostic Markers in Lung Adenocarcinoma

<div><p>We performed a pilot proteogenomic study to compare lung adenocarcinoma to lung squamous cell carcinoma using quantitative proteomics (6-plex TMT) combined with a customized Affymetrix GeneChip. Using MaxQuant software, we identified 51,001 unique peptides that mapped to 7,241 unique proteins and from these identified 6,373 genes with matching protein expression for further analysis. We found a minor correlation between gene expression and protein expression; both datasets were able to independently recapitulate known differences between the adenocarcinoma and squamous cell carcinoma subtypes. We found 565 proteins and 629 genes to be differentially expressed between adenocarcinoma and squamous cell carcinoma, with 113 of these consistently differentially expressed at both the gene and protein levels. We then compared our results to published adenocarcinoma versus squamous cell carcinoma proteomic data that we also processed with MaxQuant. We selected two proteins consistently overexpressed in squamous cell carcinoma in all studies, MCT1 (SLC16A1) and GLUT1 (SLC2A1), for further investigation. We found differential expression of these same proteins at the gene level in our study as well as in other public gene expression datasets. These findings combined with survival analysis of public datasets suggest that MCT1 and GLUT1 may be potential prognostic markers in adenocarcinoma and druggable targets in squamous cell carcinoma. Data are available via ProteomeXchange with identifier PXD002622.</p></div

Comparison with Existing NSCLC Proteomic Datasets.

<p>Mean intensities are given in log₂ scale. (A) The correlations between reporter ion intensities and peptide intensities from Kikuchi et al. were low (R = 0.3, <i>P</i> < 2.2E-16; ρ = 0.26, <i>P</i> < 2.2E-16). (B) As with the Kikuchi et al. data, correlations between reporter ion intensities and peptide intensities from Li et al. were also low (R = 0.23, <i>P</i> < 2.2E-16; ρ = 0.21, <i>P</i> < 2.2E-16).</p

Gene and Protein Expression Agreement.

<p>Gene and Protein Expression Agreement.</p

Proteogenomic Data Recapitulates NSCLC Histology.

<p>(A) Clustering of all identified proteins (7,241) from quantitative TMT analysis group tissues by ADC/SCC histology. (B) Clustering of Affymetrix array probes with standard deviation > 1 (11,008 or 18% of total probes) also groups tissues by ADC/SCC histology.</p

GSK3 Alpha and Beta Are New Functionally Relevant Targets of Tivantinib in Lung Cancer Cells

Tivantinib has been described as a potent and highly selective inhibitor of the receptor tyrosine kinase c-MET and is currently in advanced clinical development for several cancers including non-small cell lung cancer (NSCLC). However, recent studies suggest that tivantinib’s anticancer properties are unrelated to c-MET inhibition. Consistently, in determining tivantinib’s activity profile in a broad panel of NSCLC cell lines, we found that, in contrast to several more potent c-MET inhibitors, tivantinib reduces cell viability across most of these cell lines. Applying an unbiased, mass-spectrometry-based, chemical proteomics approach, we identified glycogen synthase kinase 3 (GSK3) alpha and beta as novel tivantinib targets. Subsequent validation showed that tivantinib displayed higher potency for GSK3α than for GSK3β and that pharmacological inhibition or simultaneous siRNA-mediated loss of GSK3α and GSK3β caused apoptosis. In summary, GSK3α and GSK3β are new kinase targets of tivantinib that play an important role in its cellular mechanism-of-action in NSCLC

Differentially Expressed Proteins from Quantitative TMT Shared Between Proteomic Datasets.

<p>Italics denote entries that were also differentially expressed at the gene level. Log₂ fold-change was calculated as log₂(SCC/ADC).</p><p>Differentially Expressed Proteins from Quantitative TMT Shared Between Proteomic Datasets.</p