Temporal Effects of Combined Birinapant and Paclitaxel on Pancreatic Cancer Cells Investigated via Large-Scale, Ion-Current-Based Quantitative Proteomics (IonStar)

Systems pharmacodynamic model of combined gemcitabine and trabectedin in pancreatic cancer cells. Part I.Çô Effects on signal transduction pathways related to tumor growth

Pancreatic ductal adenocarcinoma (PDAC) is often chemotherapy-resistant, and novel drug combinations would fill an unmet clinical need. Previously we reported synergistic cytotoxic effects of gemcitabine and trabectedin on pancreatic cancer cells, but underlying protein-level interaction mechanisms remained unclear. We employed a reliable, sensitive, comprehensive, quantitative, high-throughput IonStar proteomic workflow to investigate the time course of gemcitabine and trabectedin effects, alone and combined, upon pancreatic cancer cells. MiaPaCa-2 cells were incubated with vehicle (controls), gemcitabine, trabectedin, and their combinations over 72 hours. Samples were collected at intervals and analyzed using the label-free IonStar liquid chromatography-mass spectrometry (LC-MS/MS) workflow to provide temporal quantification of protein expression for 4,829 proteins in four experimental groups. To characterize diverse signal transduction pathways, a comprehensive systems pharmacodynamic (SPD) model was developed. The analysis is presented in two parts. Here, Part I describes drug responses in cancer cell growth and migration pathways included in the full model: receptor tyrosine kinase- (RTK), integrin-, G-protein coupled receptor- (GPCR), and calcium-signaling pathways. The developed model revealed multiple underlying mechanisms of drug actions, provides insight into the basis of drug interaction synergism, and offers a scientific rationale for potential drug combination strategies.

Species-Deconvolved Proteomics for In Situ Investigation of Tumor-Stroma Interactions after Treatment of Pancreatic Cancer Patient-Derived Xenografts with Combined Gemcitabine and Paclitaxel

Tumor-stroma interactions are critical in pancreatic ductal adenocarcinoma (PDAC) progression and therapeutics. Patient-derived xenograft (PDX) models recapitulate tumor-stroma interactions, but the conventional antibody-based immunoassay is inadequate to discriminate tumor and stromal proteins. Here, we describe a species-deconvolved proteomics approach embedded in IonStar that can unambiguously quantify the tumor (human-derived) and stromal (mouse-derived) proteins in PDX samples, enabling unbiased investigation of tumor and stromal proteomes with excellent quantitative reproducibility. With this strategy, we studied tumor-stroma interactions in PDAC PDXs that responded differently to Gemcitabine combined with nab-Paclitaxel (GEM+PTX) treatment. By analyzing 48 PDX animals 24 h/192 h after treatment with/without GEM+PTX, we quantified 7262 species-specific proteins under stringent cutoff criteria, with high reproducibility. For the PDX sensitive to GEM+PTX, the drug-dysregulated proteins in tumor cells were involved in suppressed oxidative phosphorylation and the TCA cycle, and in the stroma, inhibition of glycolytic activity was predominant, suggesting a relieved reverse Warburg effect by the treatment. In GEM+PTX-resistant PDXs, protein changes suggested extracellular matrix deposition and activation of tumor cell proliferation. Key findings were validated by immunohistochemistry (IHC). Overall, this approach provides a species-deconvolved proteomic platform that could advance cancer therapeutic studies by enabling unbiased exploration of tumor-stroma interactions in the large number of PDX samples required for such investigations.

High-quality and robust protein quantification in large clinical/pharmaceutical cohorts with IonStar proteomics investigation

Robust, reliable quantification of large sample cohorts is often essential for meaningful clinical or pharmaceutical proteomics investigations, but it is technically challenging. When analyzing very large numbers of samples, isotope labeling approaches may suffer from substantial batch effects, and even with label-free methods, it becomes evident that low-abundance proteins are not reliably measured owing to unsufficient reproducibility for quantification. The MS1-based quantitative proteomics pipeline IonStar was designed to address these challenges. IonStar is a label-free approach that takes advantage of the high sensitivity/selectivity attainable by ultrahigh-resolution (UHR)-MS1 acquisition (e.g., 120-240k full width at half maximum at m/z = 200) which is now widely available on ultrahigh-field Orbitrap instruments. By selectively and accurately procuring quantitative features of peptides within precisely defined, very narrow m/z windows corresponding to the UHR-MS1 resolution, the method minimizes co-eluted interferences and substantially enhances signal-to-noise ratio of low-abundance species by decreasing noise level. This feature results in high sensitivity, selectivity, accuracy and precision for quantification of low-abundance proteins, as well as fewer missing data and fewer false positives. This protocol also emphasizes the importance of well-controlled, robust experimental procedures to achieve high-quality quantification across a large cohort. It includes a surfactant cocktail-aided sample preparation procedure that achieves high/reproducible protein/peptide recoveries among many samples, and a trapping nano-liquid chromatography-mass spectrometry strategy for sensitive and reproducible acquisition of UHR-MS1 peptide signal robustly across a large cohort. Data processing and quality evaluation are illustrated using an example dataset ( http://proteomecentral.proteomexchange.org ), and example results from pharmaceutical project and one clinical project (patients with acute respiratory distress syndrome) are shown. The complete IonStar pipeline takes ~1-2 weeks for a sample cohort containing ~50-100 samples.

Phosphohistidine signaling promotes FAK-RB1 interaction and growth factor-independent proliferation of esophageal squamous cell carcinoma

Current clinical therapies targeting receptor tyrosine kinases including focal adhesion kinase (FAK) have had limited or no effect on esophageal squamous cell carcinoma (ESCC). Unlike esophageal adenocarcinomas, ESCC acquire glucose in excess of their anabolic need. We recently reported that glucose-induced growth factor-independent proliferation requires the phosphorylation of FAK^His58. Here, we confirm His58 phosphorylation in FAK immunoprecipitates of glucose-stimulated, serum-starved ESCC cells using antibodies specific for 3-phosphohistidine and mass spectrometry. We also confirm a role for the histidine kinase, NME1, in glucose-induced FAK^poHis58 and ESCC cell proliferation, correlating with increased levels of NME1 in ESCC tumors versus normal esophageal tissues. Unbiased screening identified glucose-induced retinoblastoma transcriptional corepressor 1 (RB1) binding to FAK, mediated through a "LxCxE" RB1-binding motif in FAK's FERM domain. Importantly, in the absence of growth factors, glucose increased FAK scaffolding of RB1 in the cytoplasm, correlating with increased ESCC G1→S phase transition. Our data strongly suggest that this glucose-mediated mitogenic pathway is novel and represents a unique targetable opportunity in ESCC.

Comparative Proteomic Analysis Identifies Key Metabolic Regulators of Gemcitabine Resistance in Pancreatic Cancer

Pancreatic adenocarcinoma (PDAC) is highly refractory to treatment. Standard-of-care gemcitabine (Gem) provides only modest survival benefits, and development of Gem resistance (GemR) compromises its efficacy. Highly GemR clones of Gem-sensitive MIAPaCa-2 cells were developed to investigate the molecular mechanisms of GemR and implemented global quantitative differential proteomics analysis with a comprehensive, reproducible ion-current-based MS1 workflow to quantify ∼6000 proteins in all samples. In GemR clone MIA-GR8, cellular metabolism, proliferation, migration, and 'drug response' mechanisms were the predominant biological processes altered, consistent with cell phenotypic alterations in cell cycle and motility. S100 calcium binding protein A4 was the most downregulated protein, as were proteins associated with glycolytic and oxidative energy production. Both responses would reduce tumor proliferation. Upregulation of mesenchymal markers was prominent, and cellular invasiveness increased. Key enzymes in Gem metabolism pathways were altered such that intracellular utilization of Gem would decrease. Ribonucleoside-diphosphate reductase large subunit was the most elevated Gem metabolizing protein, supporting its critical role in GemR. Lower Ribonucleoside-diphosphate reductase large subunit expression is associated with better clinical outcomes in PDAC, and its downregulation paralleled reduced MIAPaCa-2 proliferation and migration and increased Gem sensitivity. Temporal protein-level Gem responses of MIAPaCa-2 versus GemR cell lines (intrinsically GemR PANC-1 and acquired GemR MIA-GR8) implicate adaptive changes in cellular response systems for cell proliferation and drug transport and metabolism, which reduce cytotoxic Gem metabolites, in DNA repair, and additional responses, as key contributors to the complexity of GemR in PDAC. These findings additionally suggest targetable therapeutic vulnerabilities for GemR PDAC patients.

Recent Developments and Applications of Quantitative Proteomics Strategies for High-Throughput Biomolecular Analyses in Cancer Research

Innovations in medical technology and dedicated focus from the scientific community have inspired numerous treatment strategies for benign and invasive cancers. While these improvements often lend themselves to more positive prognoses and greater patient longevity, means for early detection and severity stratification have failed to keep pace. Detection and validation of cancer-specific biomarkers hinges on the ability to identify subtype-specific phenotypic and proteomic alterations and the systematic screening of diverse patient groups. For this reason, clinical and scientific research settings rely on high throughput and high sensitivity mass spectrometry methods to discover and quantify unique molecular perturbations in cancer patients. Discussed within is an overview of quantitative proteomics strategies and a summary of recent applications that enable revealing potential biomarkers and treatment targets in prostate, ovarian, breast, and pancreatic cancer in a high throughput manner.

The origin of the expressed retrotransposed gene ACTBL2 and its influence on human melanoma cells' motility and focal adhesion formation

We have recently found that β-actin-like protein 2 (actbl2) forms complexes with gelsolin in human melanoma cells and can polymerize. Phylogenetic and bioinformatic analyses showed that actbl2 has a common origin with two non-muscle actins, which share a separate history from the muscle actins. The actin groups' divergence started at the beginning of vertebrate evolution, and actbl2 actins are characterized by the largest number of non-conserved amino acid substitutions of all actins. We also discovered that ACTBL2 is expressed at a very low level in several melanoma cell lines, but a small subset of cells exhibited a high ACTBL2 expression. We found that clones with knocked-out ACTBL2 (CR-ACTBL2) or overexpressing actbl2 (OE-ACTBL2) differ from control cells in the invasion, focal adhesion formation, and actin polymerization ratio, as well as in the formation of lamellipodia and stress fibers. Thus, we postulate that actbl2 is the seventh actin isoform and is essential for cell motility.

Pharmacodynamic modeling of synergistic birinapant/paclitaxel interactions in pancreatic cancer cells

Background

For most patients, pancreatic adenocarcinoma responds poorly to treatment, and novel therapeutic approaches are needed. Standard-of-care paclitaxel (PTX), combined with birinapant (BRP), a bivalent mimetic of the apoptosis antagonist SMAC (second mitochondria-derived activator of caspases), exerts synergistic killing of PANC-1 human pancreatic adenocarcinoma cells.

Methods

To investigate potential mechanisms underlying this synergistic pharmacodynamic interaction, data capturing PANC-1 cell growth, apoptosis kinetics, and cell cycle distribution were integrated with high-quality IonStar-generated proteomic data capturing changes in the relative abundance of more than 3300 proteins as the cells responded to the two drugs, alone and combined.

Results

PTX alone (15 nM) elicited dose-dependent G2/M-phase arrest and cellular polyploidy. Combined BRP/PTX (150/15 nM) reduced G2/M by 35% and polyploid cells by 45%, and increased apoptosis by 20%. Whereas BRP or PTX alone produced no change in the pro-apoptotic protein pJNK, and a slight increase in the anti-apoptotic protein Bcl2, the drug combination increased pJNK and decreased Bcl2 significantly compared to the vehicle control. A multi-scale, mechanism-based mathematical model was developed to investigate integrated birinapant/paclitaxel effects on temporal profiles of key proteins involved in kinetics of cell growth, death, and cell cycle distribution.

Conclusions

The model, consistent with the observed reduction in the Bcl2/BAX ratio, suggests that BRP-induced apoptosis of mitotically-arrested cells is a major contributor to the synergy between BRP and PTX. Coupling proteomic and cellular response profiles with multi-scale pharmacodynamic modeling provides a quantitative mechanistic framework for evaluating pharmacodynamically-based drug-drug interactions in combination chemotherapy, and could potentially guide the development of promising drug regimens.

Proteomic analysis of pancreatic ductal adenocarcinoma

INTRODUCTION

Pancreatic ductal adenocarcinoma (PDAC), which represents approximately 80% of all pancreatic cancers, is a highly aggressive malignant disease and one of the most lethal among all cancers. Overall, the 5-year survival rate among all pancreatic cancer patients is less than 9%; these rates have shown little change over the past 30 years. A more comprehensive understanding of the molecular mechanisms underlying this complex disease is crucial to the development of new diagnostic tools for early detection and disease monitoring, as well as to identify new and more effective therapeutics to improve patient outcomes.

AREA COVERED

We summarize recent advances in proteomic strategies and mass spectrometry to identify new biomarkers for early detection and monitoring of disease progression, predict response to therapy, and to identify novel proteins that have the potential to be 'druggable' therapeutic targets. An overview of proteomic studies that have been conducted to further our mechanistic understanding of metastasis and chemotherapy resistance in PDAC disease progression will also be discussed.

EXPERT COMMENTARY

The results from these PDAC proteomic studies on a variety of PDAC sample types (e.g., blood, tissue, cell lines, exosomes, etc.) provide great promise of having a significant clinical impact and improving patient outcomes.

BEZ235 enhances chemosensitivity of paclitaxel in hepatocellular carcinoma through inhibiting the PI3K/Akt/mTOR pathway

Desensitization of hepatocellular carcinoma (HCC) to paclitaxel chemotherapy is a major deterrent to successful treatment of the cancer. Abnormal activation of the PI3K/Akt/mTOR, pathway is a common outcome of chemotherapy for HCC. Therefore, we investigated whether BEZ235, a dual PI3K and mTOR inhibitor, could increase the sensitivity of HCC to paclitaxel. In vitro results showed that paclitaxel, combined with BEZ235, inhibited HCC cell proliferation and migration, arrested the cell cycle in the G₂/M phase, and promoted cell apoptosis by decreasing PI3K/Akt/mTOR activity. In vivo experiments confirmed that BEZ235 enhances the anti-tumor effect of paclitaxel by reducing PI3K/Akt/mTOR activity. Immunohistochemical staining showed that paclitaxel combined with BEZ235 reduced the numbers of Ki-67- and GPC3-positive HepG2 cells in tumor tissues. We conclude that BEZ235 enhanced the sensitivity of HCC to paclitaxel, and inhibition of PI3K/Akt/mTOR signaling might be a therapeutic strategy against paclitaxel-resistant HCC.

MS1 ion current-based quantitative proteomics: A promising solution for reliable analysis of large biological cohorts

The rapidly-advancing field of pharmaceutical and clinical research calls for systematic, molecular-level characterization of complex biological systems. To this end, quantitative proteomics represents a powerful tool but an optimal solution for reliable large-cohort proteomics analysis, as frequently involved in pharmaceutical/clinical investigations, is urgently needed. Large-cohort analysis remains challenging owing to the deteriorating quantitative quality and snowballing missing data and false-positive discovery of altered proteins when sample size increases. MS1 ion current-based methods, which have become an important class of label-free quantification techniques during the past decade, show considerable potential to achieve reproducible protein measurements in large cohorts with high quantitative accuracy/precision. Nonetheless, in order to fully unleash this potential, several critical prerequisites should be met. Here we provide an overview of the rationale of MS1-based strategies and then important considerations for experimental and data processing techniques, with the emphasis on (i) efficient and reproducible sample preparation and LC separation; (ii) sensitive, selective and high-resolution MS detection; iii)accurate chromatographic alignment; (iv) sensitive and selective generation of quantitative features; and (v) optimal post-feature-generation data quality control. Prominent technical developments in these aspects are discussed. Finally, we reviewed applications of MS1-based strategy in disease mechanism studies, biomarker discovery, and pharmaceutical investigations.

Characterization and Proteomic-Transcriptomic Investigation of Monocarboxylate Transporter 6 Knockout Mice: Evidence of a Potential Role in Glucose and Lipid Metabolism

Monocarboxylate transporter 6 [(MCT6), SLC16A5] is an orphan transporter with no known endogenous substrates or physiological role. Previous in vitro and in vivo experiments investigated MCT6 substrate/inhibitor specificity in Xenopus laevis oocytes; however, these data remain limited. Transcriptomic changes in the livers of mice undergoing different dieting schemes have suggested that Mct6 plays a role in glucose and lipid metabolism. The objectives of this study were 1) to develop a novel knockout (KO) mouse model (Mct6^-/-) using CRISPR/Cas9 technology, 2) to characterize the KO animal model by examining physiological and biochemical parameters, and 3) to understand the physiological role of MCT6 in vivo through global proteomic and liver transcriptomic profiling. mRNA tissue analysis demonstrated knockout of Mct6, which showed greater than 90% knockdown of Mct6 (Slc16a5) gene expression in all major tissues analyzed when normalized to Mct6^+/+ mice. Proteomic analyses identified greater than 4000 unique proteins in kidney, liver, and colon tissues, among which 51, 38, and 241 proteins were significantly altered, respectively (for each tissue), between Mct6^+/+ and Mct6^-/- mice. Additionally, Mct6^-/- mice demonstrated significant changes in 199 genes in the liver compared with Mct6^+/+ mice. In silico biological pathway analyses revealed significant changes in proteins and genes involved in glucose and lipid metabolism-associated pathways. This study is the first to provide evidence for an association of Mct6 in the regulation of glucose and lipid metabolism. SIGNIFICANCE STATEMENT: This paper focuses on elucidating the innate biological role of an orphan transporter in vivo, which has not been investigated thus far. Using efficient and high-throughput technologies, such as CRISPR/Cas9 gene editing, liquid chromatography-tandem mass spectrometry-based proteomic and RNA-sequencing transcriptomic analyses, our laboratory provides the first existence and characterization of a Mct6 knockout mouse model. The evidence gathered in this paper, as well as other laboratories, support the importance of MCT6 in regulating a variety of glucose and lipid metabolic pathways, which may indicate its significance in metabolic diseases.

Pharmacodynamic Drug-Drug Interactions

Pharmacodynamic drug-drug interactions (DDIs) occur when the pharmacological effect of one drug is altered by that of another drug in a combination regimen. DDIs often are classified as synergistic, additive, or antagonistic in nature, albeit these terms are frequently misused. Within a complex pathophysiological system, the mechanism of interaction may occur at the same target or through alternate pathways. Quantitative evaluation of pharmacodynamic DDIs by employing modeling and simulation approaches is needed to identify and optimize safe and effective combination therapy regimens. This review investigates the opportunities and challenges in pharmacodynamic DDI studies and highlights examples of quantitative methods for evaluating pharmacodynamic DDIs, with a particular emphasis on the use of mechanism-based modeling and simulation in DDI studies. Advancements in both experimental and computational techniques will enable the application of better, model-informed assessments of pharmacodynamic DDIs in drug discovery, development, and therapeutics.

Glucose Drives Growth Factor-Independent Esophageal Cancer Proliferation via Phosphohistidine-Focal Adhesion Kinase Signaling

BACKGROUND & AIMS

Most targeted therapies against cancer are designed to block growth factor-stimulated oncogenic growth. However, response rates are low, and resistance to therapy is high. One mechanism might relate to the ability of tumor cells to induce growth factor-independent proliferation (GFIP). This project aims to understand how (1) cancer cells preferentially derive a major growth advantage by using critical metabolic products of glucose, such as phosphoenolpyruvate (PEP), to drive proliferation and (2) esophageal squamous cell carcinoma (ESCC) cells, but not esophageal adenocarcinoma cells, can induce GFIP by using glycolysis to activate phosphohistidine (poHis)-mediated signaling through focal adhesion kinase (FAK).

METHODS

The hypothesis to be tested is that ESCC GFIP induced by glucose is facilitated by PEP-mediated histidine phosphorylation (poHis) of FAK, leading to the possibility that ESCC progression can be targeted by blocking poHis signaling. Biochemical, molecular biological, and in vivo experiments including bromodeoxyuridine/5-ethynyl-2'-deoxyuridine labeling, radioisotope tracing, CRISPR gene editing, and analysis of signaling gene sets in human cancer tissues and xenograft models were performed to define the mechanisms underlying ESCC GFIP.

RESULTS

Glucose promotes growth factor-independent DNA replication and accumulation of PEP in ESCC cells. PEP is the direct phospho-donor to poHis58-FAK within a known "HG" motif for histidine phosphorylation. Glucose-induced poHis58 promotes growth factor-independent FAK-mediated proliferation. Furthermore, glucose activates phosphatidylinositol-3'-kinase/AKT via poHis58-FAK signaling. Non-phosphorylatable His58A-FAK reduces xenograft growth.

CONCLUSIONS

Glucose induces ESCC, but not esophageal adenocarcinoma GFIP via PEP-His58-FAK-AKT signaling. ESCC progression is controlled by actionable growth factor-independent, glucose-induced pathways that regulate proliferation through novel histidine phosphorylation of FAK.

Signal-Targeted Therapies and Resistance Mechanisms in Pancreatic Cancer: Future Developments Reside in Proteomics

For patients with metastatic pancreatic cancer that are not eligible for surgery, signal-targeted therapies have so far failed to significantly improve survival. These therapeutic options have been tested in phase II/III clinical trials mostly in combination with the reference treatment gemcitabine. Innovative therapies aim to annihilate oncogenic dependency, or to normalize the tumoural stroma to allow immune cells to function and/or re-vascularisation to occur. Large scale transcriptomic and genomic analysis revealed that pancreatic cancers display great heterogeneity but failed to clearly delineate specific oncogene dependency, besides oncogenic Kras. Beyond these approaches, proteomics appears to be an appropriate approach to classify signal dependency and to identify specific alterations at the targetable level. However, due to difficulties in sampling, proteomic data for this pathology are scarce. In this review, we will discuss the current state of clinical trials for targeted therapies against pancreatic cancer. We will then highlight the most recent proteomic data for pancreatic tumours and their metastasis, which could help to identify major oncogenic signalling dependencies, as well as provide future leads to explain why pancreatic tumours are intrinsically resistant to signal-targeted therapies. We will finally discuss how studies on phosphatidylinositol-3-kinase (PI3K) signalling, as the paradigmatic pro-tumoural signal downstream of oncogenic Kras in pancreatic cancer, would benefit from exploratory proteomics to increase the efficiency of targeted therapies.