Proteomic Analysis of Combined Gemcitabine and Birinapant in Pancreatic Cancer Cells

Exploring the Molecular Therapeutic Mechanisms of Gemcitabine through Quantitative Proteomics

Gemcitabine (GEM) is widely employed in the treatment of various cancers, including pancreatic cancer. Despite their clinical success, challenges related to GEM resistance and toxicity persist. Therefore, a deeper understanding of its intracellular mechanisms and potential targets is urgently needed. In this study, through mass spectrometry analysis in data-dependent acquisition mode, we carried out quantitative proteomics (three independent replications) and thermal proteome profiling (TPP, two independent replications) on MIA PaCa-2 cells to explore the effects of GEM. Our proteomic analysis revealed that GEM led to the upregulation of the cell cycle and DNA replication proteins. Notably, we observed the upregulation of S-phase kinase-associated protein 2 (SKP2), a cell cycle and chemoresistance regulator. Combining SKP2 inhibition with GEM showed synergistic effects, suggesting SKP2 as a potential target for enhancing the GEM sensitivity. Through TPP, we pinpointed four potential GEM binding targets implicated in tumor development, including in breast and liver cancers, underscoring GEM's broad-spectrum antitumor capabilities. These findings provide valuable insights into GEM's molecular mechanisms and offer potential targets for improving treatment efficacy.

Integrative proteomics and n-glycoproteomics reveal the synergistic anti-tumor effects of aspirin- and gemcitabine-based chemotherapy on pancreatic cancer cells

OBJECTIVE AND DESIGN

Pancreatic cancer is a highly malignant tumor that is well known for its poor prognosis. Based on glycosylation, we performed integrated quantitative N-glycoproteomics to investigate the synergistic anti-tumor effects of aspirin and gemcitabine on pancreatic cancer cells and explore the potential molecular mechanisms of chemotherapy in pancreatic cancer.

METHODS AND RESULTS

Two pancreatic cancer cell lines (PANC-1 and BxPC-3) were treated with gemcitabine, aspirin, and a combination (gemcitabine + aspirin). We found that the addition of aspirin enhanced the inhibitory effect of gemcitabine on the activity of PANC-1 and BxPC-3 cells. Quantitative N-glycoproteome, proteome, phosphorylation, and transcriptome data were obtained from integrated multi-omics analysis to evaluate the anti-tumor effects of aspirin and gemcitabine on pancreatic cancer cells. Mfuzz analysis of intact N-glycopeptide profiles revealed two consistent trends associated with the addition of aspirin, which showed a strong relationship between N-glycosylation and the synergistic effect of aspirin. Further analysis demonstrated that the dynamic regulation of sialylation and high-mannose glycoforms on ECM-related proteins (LAMP1, LAMP2, ITGA3, etc.) was a significant factor for the ability of aspirin to promote the anti-tumor activity of gemcitabine and the drug resistance of pancreatic cancer cells.

CONCLUSIONS

In-depth analysis of N-glycosylation-related processes and pathways in pancreatic cancer cells can provide new insight for future studies regarding pancreatic cancer therapeutic targets and drug resistance mechanisms.

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.

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.

Natural Products in Preventing Tumor Drug Resistance and Related Signaling Pathways

Drug resistance is still an obstacle in cancer therapy, leading to the failure of tumor treatment. The emergence of tumor drug resistance has always been a main concern of oncologists. Therefore, overcoming tumor drug resistance and looking for new strategies for tumor treatment is a major focus in the field of tumor research. Natural products serve as effective substances against drug resistance because of their diverse chemical structures and pharmacological effects. We reviewed the signaling pathways involved in the development of tumor drug resistance, including Epidermal growth factor receptor (EGFR), Renin-angiotensin system (Ras), Phosphatidylinositol-3-kinase/protein kinase B (PI3K/Akt), Wnt, Notch, Transforming growth factor-beta (TGF-β), and their specific signaling pathway inhibitors derived from natural products. This can provide new ideas for the prevention of drug resistance in cancer therapy.

NF-κB and Pancreatic Cancer; Chapter and Verse

Pancreatic Ductal Adenocarcinoma (PDAC) is one of the world's most lethal cancers. An increase in occurrence, coupled with, presently limited treatment options, necessitates the pursuit of new therapeutic approaches. Many human cancers, including PDAC are initiated by unresolved inflammation. The transcription factor NF-κB coordinates many signals that drive cellular activation and proliferation during immunity but also those involved in inflammation and autophagy which may instigate tumorigenesis. It is not surprising therefore, that activation of canonical and non-canonical NF-κB pathways is increasingly recognized as an important driver of pancreatic injury, progression to tumorigenesis and drug resistance. Paradoxically, NF-κB dysregulation has also been shown to inhibit pancreatic inflammation and pancreatic cancer, depending on the context. A pro-oncogenic or pro-suppressive role for individual components of the NF-κB pathway appears to be cell type, microenvironment and even stage dependent. This review provides an outline of NF-κB signaling, focusing on the role of the various NF-κB family members in the evolving inflammatory PDAC microenvironment. Finally, we discuss pharmacological control of NF-κB to curb inflammation, focussing on novel anti-cancer agents which reinstate the process of cancer cell death, the Smac mimetics and their pre-clinical and early clinical trials.

An Updated Review of Smac Mimetics, LCL161, Birinapant, and GDC-0152 in Cancer Treatment

Inhibitor of apoptosis proteins (IAPs) are suggested as therapeutic targets for cancer treatment. Smac/DIABLO is a natural IAP antagonist in cells; therefore, Smac mimetics have been developed for cancer treatment in the past decade. In this article, we review the anti-cancer potency and novel molecular targets of LCL161, birinapant, and GDC-0152. Preclinical studies demonstrated that Smac mimetics not only induce apoptosis but also arrest cell cycle, induce necroptosis, and induce immune storm in vitro and in vivo. The safety and tolerance of Smac mimetics are evaluated in phase 1 and phase 2 clinical trials. In addition, the combination of Smac mimetics and chemotherapeutic compounds was reported to improve anti-cancer effects. Interestingly, the novel anti-cancer molecular mechanism of action of Smac mimetics was reported in recent studies, suggesting that many unknown functions of Smac mimetics still need to be revealed. Exploring these currently unknown signaling pathways is important to provide hints for the modification and combination therapy of further compounds.

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.

Integrative proteomic and functional analyses provide novel insights into the action of the repurposed drug candidate nitroxoline in AsPC-1 cells

We recently identified nitroxoline as a repurposed drug candidate in pancreatic cancer (PC) showing a dose-dependent antiproliferative activity in different PC cell lines. This antibiotic is effective in several in vitro and animal cancer models. To date, the mechanisms of nitroxoline anticancer action are largely unknown. Using shotgun proteomics we identified 363 proteins affected by nitroxoline treatment in AsPC-1 pancreatic cancer cells, including 81 consistently deregulated at both 24- and 48-hour treatment. These proteins previously unknown to be affected by nitroxoline were mostly downregulated and interconnected in a single highly-enriched network of protein-protein interactions. Integrative proteomic and functional analyses revealed nitroxoline-induced downregulation of Na/K-ATPase pump and β-catenin, which associated with drastic impairment in cell growth, migration, invasion, increased ROS production and induction of DNA damage response. Remarkably, nitroxoline induced a previously unknown deregulation of molecules with a critical role in cell bioenergetics, which resulted in mitochondrial depolarization. Our study also suggests that deregulation of cytosolic iron homeostasis and of co-translational targeting to membrane contribute to nitroxoline anticancer action. This study broadens our understanding of the mechanisms of nitroxoline action, showing that the drug modulates multiple proteins crucial in cancer biology and previously unknown to be affected by nitroxoline.

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.

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.

Arsenic trioxide inhibits Skp2 expression to increase chemosensitivity to gemcitabine in pancreatic cancer cells

The S-phase kinase associated protein 2 (Skp2), a member of the F-box protein family, regulates cell cycle progression and is highly expressed in pancreatic cancer (PC). Recently, we reported that arsenic trioxide (ATO) inhibited cell growth and invasion via downregulation of Skp2 in PC cells. Emerging evidence has revealed that Skp2 plays a crucial role in drug resistance in several kinds of cancers. Here, we determined whether ATO enhanced the sensitivity of PC cell lines to gemcitabine (GEM). We found that the combined treatment of ATO and GEM demonstrated strong antitumor effects in Patu8988 and Panc-1 PC cells. In addition, ATO potentiated the effects of GEM via downregulation of the Skp2 pathway in PC cells. Together, these findings suggested that Skp2 may be a promising therapeutic target to overcome resistance to GEM in PC.

Multi-Scale Network Model Supported by Proteomics for Analysis of Combined Gemcitabine and Birinapant Effects in Pancreatic Cancer Cells

Gemcitabine combined with birinapant, an inhibitor of apoptosis protein antagonist, acts synergistically to reduce pancreatic cancer cell proliferation. A large‐scale proteomics dataset provided rich time‐series data on proteome‐level changes that reflect the underlying biological system and mechanisms of action of these drugs. A multiscale network model was developed to link the signaling pathways of cell cycle regulation, DNA damage response, DNA repair, apoptosis, nuclear factor‐kappa β (NF‐κβ), and mitogen‐activated protein kinase (MAPK)‐p38 to cell cycle progression, proliferation, and death. After validating the network model under different conditions, the Sobol Sensitivity Analysis was applied to identify promising targets to enhance gemcitabine efficacy. The effects of p53 silencing and combining curcumin with gemcitabine were also tested with the developed model. Merging proteomics analysis with systems modeling facilitates the characterization of quantitative relations among relevant signaling pathways in drug action and resistance, and such multiscale network models could be applied for prediction of combination efficacy and target selection.

Physiologically-based pharmacokinetic and pharmacodynamic models for gemcitabine and birinapant in pancreatic cancer xenografts

The anticancer effects of combined gemcitabine and birinapant were demonstrated as synergistic in PANC-1 cells in vitro. In this study, pharmacokinetic information derived from experiments and the literature was utilized to develop full physiologically-based pharmacokinetic (PBPK) models that characterize individual drugs. The predicted intra-tumor drug concentrations were used as the driving force within a linked PBPK/PD model for treatment-mediated changes in tumor volume in a xenograft mouse model. The efficacy of the drug combination in vivo was evaluated mathematically as exhibiting additivity. The network model developed for drug effects in the in vitro cell cultures was applied successfully to link the in vivo tumor drug concentrations with tumor growth inhibition, incorporating more mechanistic features and accounting for disparate drug interaction outcomes in vitro and in vivo.