Paul A. Stewart

Differentially Expressed Transcripts and Dysregulated Signaling Pathways and Networks in African American Breast Cancer

African Americans (AAs) have higher mortality rate from breast cancer than that of Caucasian Americans (CAs) even when socioeconomic factors are accounted for. To better understand the driving biological factors of this health disparity, we performed a comprehensive differential gene expression analysis, including subtype- and stage-specific analysis, using the breast cancer data in the Cancer Genome Atlas (TCGA). In total, 674 unique genes and other transcripts were found differentially expressed between these two populations. The numbers of differentially expressed genes between AA and CA patients increased in each stage of tumor progression: there were 26 in stage I, 161 in stage II, and 223 in stage III. Resistin, a gene that is linked to obesity, insulin resistance, and breast cancer, was expressed more than four times higher in AA tumors. An uncharacterized, long, non-coding RNA, LOC90784, was down-regulated in AA tumors, and its expression was inversely related to cancer stage and was the lowest in triple negative AA breast tumors. Network analysis showed increased expression of a majority of components in p53 and BRCA1 subnetworks in AA breast tumor samples, and members of the aurora B and polo-like kinase signaling pathways were also highly expressed. Higher gene expression diversity was observed in more advanced stage breast tumors suggesting increased genomic instability during tumor progression. Amplified resistin expression may indicate insulin-resistant type II diabetes and obesity are associated with AA breast cancer. Expression of LOC90784 may have a protective effect on breast cancer patients, and its loss, particularly in triple negative breast cancer, could be having detrimental effects. This work helps elucidate molecular mechanisms of breast cancer health disparity and identifies putative biomarkers and therapeutic targets such as resistin, and the aurora B and polo-like kinase signaling pathways for treating AA breast cancer patients.

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

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.

Differential phosphopeptide expression in a benign breast tissue, and triple-negative primary and metastatic breast cancer tissues from the same African-American woman by LC-LTQ/FT-ICR mass spectrometry

African-American women have a higher risk for developing triple-negative breast cancer (TNBC). Lacking the expression of receptors for estrogen and progesterone, and without human epidermal growth factor 2 receptor gene amplification, TNBC is a very aggressive type of breast cancer with a high likelihood of metastasis and recurrence. Specific therapeutic targets for this aggressive disease remain to be identified. Phosphorylation, a post-translational modification that adds one or more phosphate groups to a protein, plays a key role in the activation and deactivation of a proteins cellular function. Here, we report the first systematic phosphoproteomic analysis of a benign breast tissue, a primary breast cancer tissue, and a metastatic breast cancer tissue from the same African-American woman. Differential phosphoprotein levels were measured with reversed-phase nano-liquid chromatography coupled to a hybrid linear quadrupole ion trap/Fourier transform ion cyclotron resonance mass spectrometer (LC-LTQ/FT-ICR MS). Five proteins were found to be highly phosphorylated in the metastatic site whereas six proteins were highly phosphorylated in the cancer site of the TNBC patient. Identified phosphoproteins are known to be involved in breast cancer signal transduction pathways and these results may identify new diagnostic and therapeutic targets for TNBC.

Characterization of the Phosphoproteome in Androgen-Repressed Human Prostate Cancer Cells by Fourier Transform Ion Cyclotron Resonance Mass Spectrometry

Androgen-repressed human prostate cancer, ARCaP, grows and is highly metastatic to bone and soft tissues in castrated mice. The molecular mechanisms underlying the aberrant responses to androgen are not fully understood. Here, we apply state-of-the-art mass spectrometry methods to investigate the phosphoproteome profiles in ARCaP cells. Because protein biological phosphorylation is always substoichiometric and the ionization efficiency of phosphopeptides is low, selective enrichment of phosphorylated proteins/peptides is required for mass spectrometric analysis of phosphorylation from complex biological samples. Therefore, we compare the sensitivity, efficiency, and specificity for three established enrichment strategies: calcium phosphate precipitation (CPP), immobilized metal ion affinity chromatography (IMAC), and TiO(2)-modified metal oxide chromatography. Calcium phosphate precipitation coupled with the TiO(2) approach offers the best strategy to characterize phosphorylation in ARCaP cells. We analyzed phosphopeptides from ARCaP cells by LC-MS/MS with a hybrid LTQ/FT-ICR mass spectrometer. After database search and stringent filtering, we identified 385 phosphoproteins with an average peptide mass error of 0.32 ± 0.6 ppm. Key identified oncogenic pathways include the mammalian target of rapamycin (mTOR) pathway and the E2F signaling pathway. Androgen-induced proliferation inhibitor (APRIN) was detected in its phosphorylated form, implicating a molecular mechanism underlying the ARCaP phenotype.

Polypharmacology-based ceritinib repurposing using integrated functional proteomics

Targeted drugs are effective when they directly inhibit strong disease drivers, but only a small fraction of diseases feature defined actionable drivers. Alternatively, network-based approaches can uncover new therapeutic opportunities. Applying an integrated phenotypic screening, chemical and phosphoproteomics strategy, here we describe the anaplastic lymphoma kinase (ALK) inhibitor ceritinib as having activity across several ALK-negative lung cancer cell lines and identify new targets and network-wide signaling effects. Combining pharmacological inhibitors and RNA interference revealed a polypharmacology mechanism involving the noncanonical targets IGF1R, FAK1, RSK1 and RSK2. Mutating the downstream signaling hub YB1 protected cells from ceritinib. Consistent with YB1 signaling being known to cause taxol resistance, combination of ceritinib with paclitaxel displayed strong synergy, particularly in cells expressing high FAK autophosphorylation, which we show to be prevalent in lung cancer. Together, we present a systems chemical biology platform for elucidating multikinase inhibitor polypharmacology mechanisms, subsequent design of synergistic drug combinations, and identification of mechanistic biomarker candidates.

Activity-Based Proteomics Reveals Heterogeneous Kinome and ATP-Binding Proteome Responses to MEK Inhibition in KRAS Mutant Lung Cancer

One way cancer cells can escape from targeted agents is through their ability to evade drug effects by rapidly rewiring signaling networks. Many protein classes, such as kinases and metabolic enzymes, are regulated by ATP binding and hydrolysis. We hypothesized that a system-level profiling of drug-induced alterations in ATP-binding proteomes could offer novel insights into adaptive responses. Here, we mapped global ATP-binding proteomes perturbed by two clinical MEK inhibitors, AZD6244 and MEK162, in KRAS mutant lung cancer cells as a model system harnessing a desthiobiotin-ATP probe coupled with LC-MS/MS. We observed strikingly unique ATP-binding proteome responses to MEK inhibition, which revealed heterogeneous drug-induced pathway signatures in each cell line. We also identified diverse kinome responses, indicating each cell adapts to MEK inhibition in unique ways. Despite the heterogeneity of kinome responses, decreased probe labeling of mitotic kinases and an increase of kinases linked to autophagy were identified to be common responses. Taken together, our study revealed a diversity of adaptive ATP-binding proteome and kinome responses to MEK inhibition in KRAS mutant lung cancer cells, and our study further demonstrated the utility of our approach to identify potential candidates of targetable ATP-binding enzymes involved in adaptive resistance and to develop rational drug combinations.One way cancer cells can escape from targeted agents is through their ability to evade drug effects by rapidly rewiring signaling networks. Many protein classes, such as kinases and metabolic enzymes, are regulated by ATP binding and hydrolysis. We hypothesized that a system-level profiling of drug-induced alterations in ATP-binding proteomes could offer novel insights into adaptive responses. Here, we mapped global ATP-binding proteomes perturbed by two clinical MEK inhibitors, AZD6244 and MEK162, in KRAS mutant lung cancer cells as a model system harnessing a desthiobiotin-ATP probe coupled with LC-MS/MS. We observed strikingly unique ATP-binding proteome responses to MEK inhibition, which revealed heterogeneous drug-induced pathway signatures in each cell line. We also identified diverse kinome responses, indicating each cell adapts to MEK inhibition in unique ways. Despite the heterogeneity of kinome responses, decreased probe labeling of mitotic kinases and an increase of kinases linked to autophagy were identified to be common responses. Taken together, our study revealed a diversity of adaptive ATP-binding proteome and kinome responses to MEK inhibition in KRAS mutant lung cancer cells, and our study further demonstrated the utility of our approach to identify potential candidates of targetable ATP-binding enzymes involved in adaptive resistance and to develop rational drug combinations.

Upregulation of minichromosome maintenance complex component 3 during epithelial-to-mesenchymal transition in human prostate cancer

Metastasis is often associated with epithelial-to-mesenchymal transition (EMT). To understand the molecular mechanisms of this process, we conducted proteomic analysis of androgen-repressed cancer of the prostate (ARCaP), an experimental model of metastatic human prostate cancer. The protein signatures of epithelial (ARCaPE) and mesenchymal (ARCaPM) cells were consistent with their phenotypes. Importantly, the expression of mini-chromosome maintenance 3 (MCM3) protein, a crucial subunit of DNA helicase, was significantly higher in ARCaPM cells than that of ARCaPE cells. This increased MCM3 protein expression level was verified using Western blot analysis of the ARCaP cell lineages. Furthermore, immunohistochemical analysis of MCM3 protein levels in human prostate tissue specimens showed elevated expression in bone metastasis and advanced human prostate cancer tissue samples. Subcutaneous injection experiments using ARCaPE and ARCaPM cells in a mouse model also revealed increased MCM3 protein levels in mesenchymal-derived tumors. This study identifies MCM3 as an upregulated molecule in mesenchymal phenotype of human prostate cancer cells and advanced human prostate cancer specimens, suggesting MCM3 may be a new potential drug target for prostate cancer treatment.

Relative protein quantification and accessible biology in lung tumor proteomes from four LC-MS/MS discovery platforms.

Discovery proteomics experiments include many options for sample preparation and MS data acquisition, which are capable of creating datasets for quantifying thousands of proteins. To define a strategy that would produce a dataset with sufficient content while optimizing required resources, we compared (1) single‐sample LC‐MS/MS with data‐dependent acquisition to single‐sample LC‐MS/MS with data‐independent acquisition and (2) peptide fractionation with label‐free (LF) quantification to peptide fractionation with relative quantification of chemically labeled peptides (sixplex tandem mass tags (TMT)). These strategies were applied to the same set of four frozen lung squamous cell carcinomas and four adjacent tissues, and the overall outcomes of each experiment were assessed. We identified 6656 unique protein groups with LF, 5535 using TMT, 3409 proteins from single‐sample analysis with data‐independent acquisition, and 2219 proteins from single‐sample analysis with data‐dependent acquisition. Pathway analysis indicated the number of proteins per pathway was proportional to the total protein identifications from each method, suggesting limited biological bias between experiments. The results suggest the use of single‐sample experiments as a rapid tissue assessment tool and digestion quality control or as a technique to maximize output from limited samples and use of TMT or LF quantification as methods for larger amounts of tumor tissue with the selection being driven mainly by instrument time limitations. Data are available via ProteomeXchange with identifiers PXD004682, PXD004683, PXD004684, and PXD005733.

Escape Excel: A tool for preventing gene symbol and accession conversion errors

Background Microsoft Excel automatically converts certain gene symbols, database accessions, and other alphanumeric text into dates, scientific notation, and other numerical representations. These conversions lead to subsequent, irreversible, corruption of the imported text. A recent survey of popular genomic literature estimates that one-fifth of all papers with supplementary gene lists suffer from this issue. Results Here, we present an open-source tool, Escape Excel, which prevents these erroneous conversions by generating an escaped text file that can be safely imported into Excel. Escape Excel is implemented in a variety of formats (http://www.github.com/pstew/escape_excel), including a command line based Perl script, a Windows-only Excel Add-In, an OS X drag-and-drop application, a simple web-server, and as a Galaxy web environment interface. Test server implementations are accessible as a Galaxy interface (http://apostl.moffitt.org) and simple non-Galaxy web server (http://apostl.moffitt.org:8000/). Conclusions Escape Excel detects and escapes a wide variety of problematic text strings so that they are not erroneously converted into other representations upon importation into Excel. Examples of problematic strings include date-like strings, time-like strings, leading zeroes in front of numbers, and long numeric and alphanumeric identifiers that should not be automatically converted into scientific notation. It is hoped that greater awareness of these potential data corruption issues, together with diligent escaping of text files prior to importation into Excel, will help to reduce the amount of Excel-corrupted data in scientific analyses and publications.

Abstract LB-140: Scaling discovery proteomics to large lung cancer cohorts using data independent acquisition

The success of label free protein quantification relies on larger cohort sizes to accurately profile protein expression patterns enabling specific targets to be translated to confirmatory studies. Thus, proteome profiling experiments must satisfy two primary requirements: maximizing breadth and depth of protein sampling as well as facilitating automated data processing. The approach presented here integrates discovery experiments used to create reference databases and rapid data independent acquisition (DIA) methods to examine large clinical cohorts using Samples used to evaluate the robustness of the study were collected from lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD). A sample total of 159 (50 LUSC, 46 LUAD, and 63 adjacent control lung tissues) were analyzed using UHPLC separations coupled to a hybrid quadrupole-orbital ion trap mass spectrometer (QExactive Plus) for DIA. Automated data processing was performed by matching peptides in spectral libraries developed from expression proteomics experiments using pooled tissue proteomes. Data were evaluated to determine differences between LUSC, LUAD, and adjacent control tissue, which were then compared to previous literature to prioritize candidate biomarkers and evaluate the performance of DIA LC-MS/MS for tumor proteome profiling. Hierarchical clustering visualized potential tumor classification schemes based on proteomic phenotypes. In a further study, analysis of cores from a tissue microarray produced quantitative data for > 3,000 proteins per core, indicating that the technology can be applied to minimal amounts of formalin fixed paraffin embedded tumor tissue. Because TMAs are assembled to address specific clinical questions, further analysis of these cohorts is an extremely valuable use for DIA; tumor proteome profiles can be rapidly accumulated and mapped to the clinical variables used to select samples for the TMA. Data independent acquisition provides a method for discovery proteomics that balances a sufficient depth of coverage with the ability to analyze large cohorts of patients (n = 100-400) within 1-2 months on a single instrument. Initial data have been produced for lung squamous cell carcinoma and lung adenocarcinoma, which indicate its utility for assessment of patient groups. Citation Format: Scott Peterman, Bin Fang, Melissa Hoffmann, Amol Prakash, Paul A. Stewart, Richard Liu, Matthew Smith, Joseph Johnson, Steven Eschrich, Guolin Zhang, Eric Haura, John Koomen. Scaling discovery proteomics to large lung cancer cohorts using data independent acquisition. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr LB-140.