A new class of protein biomarkers based on subcellular distribution: application to a mouse liver cancer model

Cytoplasmic Shotgun Proteomic Points to Key Proteins and Pathways in Temozolomide-Resistant Glioblastoma Multiforme

Temozolomide (TMZ) is the first line of chemotherapy to treat primary brain tumors of the type glioblastoma multiforme (GBM). TMZ resistance (TMZR) is one of the main barriers to successful treatment and is a principal factor in relapse, resulting in a poor median survival of 15 months. The present paper focuses on proteomic analyses of cytosolic fractions from TMZ-resistant (TMZR) LN-18 cells. The experimental workflow includes an easy, cost-effective, and reproducible method to isolate subcellular fraction of cytosolic (CYTO) proteins, mitochondria, and plasma membrane proteins for proteomic studies. For this study, enriched cytoplasmic fractions were analyzed in replicates by nanoflow liquid chromatography tandem high-resolution mass spectrometry (nLC-MS/MS), and proteins identified were quantified using a label-free approach (LFQ). Statistical analysis of control (CTRL) and temozolomide-resistant (TMZR) proteomes revealed proteins that appear to be differentially controlled in the cytoplasm. The functions of these proteins are discussed as well as their roles in other cancers and TMZ resistance in GBM. Key proteins are also described through biological processes related to gene ontology (GO), molecular functions, and cellular components. For protein-protein interactions (PPI), network and pathway involvement analyses have been performed, highlighting the roles of key proteins in the TMZ resistance phenotypes. This study provides a detailed insight into methods of subcellular fractionation for proteomic analysis of TMZ-resistant GBM cells and the potential to apply this approach to future large-scale studies. Several key proteins, protein-protein interactions (PPI), and pathways have been identified, underlying the TMZ resistance phenotype and highlighting the proteins' biological functions.

Transcriptomic analysis of pancreatic adenocarcinoma specimens obtained from Black and White patients

In pancreatic cancer clinical trials, Black patients are under-represented while having higher morbidity and mortality rates as compared to other racial groups. Multiple factors, including socioeconomic and lifestyle factors may contribute to this disparity, but genomic contributions remain unclear. In an exploratory project to identify genes that may contribute to differences in survival between Black (n = 8) and White (n = 20) patients with pancreatic cancer, transcriptomic sequencing of over 24,900 genes was performed in human pancreatic tumor and non-tumor tissue obtained from Black and White patients. Over 4,400 genes were differentially expressed in tumor and non-tumor tissue, irrespective of race. To validate these results, the expression of four genes (AGR2, POSTN, TFF1, and CP) reported to be up-regulated in pancreatic tumor tissue as compared to non-tumor tissue were confirmed using quantitative PCR. Transcriptomic analysis that compared pancreatic tumor tissue from Black and White patients revealed differential expression in 1,200 genes, while a comparison of the non-tumor and tumor gene expression differences within each race revealed over 1,500 tumor-specific differentially expressed genes in pancreatic tumor and non-tumor tissue from Black patients. We identified TSPAN8 as a potential tumor-specific gene significantly overexpressed in pancreatic tumor tissue in Black patients as compared to White patients. Using Ingenuity Pathway Analysis software to compare the race-associated gene expression profiles, over 40 canonical pathways were identified to be potentially impacted by the gene expression differences between the races. Heightened expression of TSPAN8 was associated with poor overall survival, suggesting TSPAN8 as one potential genetic factor contributing to the differential outcomes in Black patients with pancreatic cancer, supporting the potential utility of larger genomic studies to further explore the role of TSPAN8 in pancreatic cancer.

Electrochemical biosensors based on peptide-kinase interactions at the kinase docking site

Kinases are important cancer biomarkers and are conventionally detected based on their catalytic activity. Kinases regulate cellular activities by phosphorylation of motif-specific multiple substrate proteins, resulting in a lack of selectivity of activity-based kinase biosensors. We present an alternative approach of sensing kinases based on the interactions of their allosteric docking sites with a specific partner protein. The new approach was demonstrated for the ERK2 kinase and its substrate ELK-1. A peptide derived from ELK-1 was bound to a gold electrode and ERK2 sensing was performed by electrochemical impedance spectroscopy. We performed a detailed analysis of the interaction between the ELK-1 peptide and the kinase on gold surfaces. Atomic force microscopy, variable angle spectroscopic ellipsometry, X-ray Photoelectron Spectroscopy, and polarization modulation IR reflection-absorption spectroscopy analysis of the gold surface revealed the adsorbed layer of the ERK2 on the peptide monolayer. The sensors showed a high level of target selectivity for ERK2 compared to the p38γ kinase and BSA. ERK2 was detected in its cellular concentration range, 0.5-2.0 μM, and the limit of detection was calculated to be 0.35 μM. Using the flexibility of peptide design, our method is generic for developing sensitive and substrate-specific biosensors and other disease-related enzymes based on their interactions.

Integration of chronological omics data reveals mitochondrial regulatory mechanisms during the development of hepatocellular carcinoma

Mitochondria participate in multiple functions in eukaryotic cells. Although disruption of mitochondrial function has been associated with energetic deregulation in cancer, the chronological changes in mitochondria during cancer development remain unclear. With the aim to assess the role of mitochondria throughout cancer development, we analyzed samples chronologically obtained from induced hepatocellular carcinoma (HCC) in rats. In our analyses, we integrated mitochondrial proteomic data, mitochondrial metabolomic data and nuclear genome transcriptomic data. We used pathway over-representation and weighted gene co-expression network analysis (WGCNA) to integrate expression profiles of genes, miRNAs, proteins and metabolite levels throughout HCC development. Our results show that mitochondria are dynamic organelles presenting specific modifications in different stages of HCC development. We also found that mitochondrial proteomic profiles from tissues adjacent to nodules or tumor are determined more by the stage of HCC development than by tissue type, and we evaluated two models to predict HCC stage of the samples using proteomic profiles. Finally, we propose an omics integration pipeline to massively identify molecular features that could be further evaluated as key regulators, biomarkers or therapeutic targets. As an example, we show a group of miRNAs and transcription factors as candidates, responsible for mitochondrial metabolic modification in HCC.

Glycoproteogenomics: Setting the Course for Next-generation Cancer Neoantigen Discovery for Cancer Vaccines

Molecular-assisted precision oncology gained tremendous ground with high-throughput next-generation sequencing (NGS), supported by robust bioinformatics. The quest for genomics-based cancer medicine set the foundations for improved patient stratification, while unveiling a wide array of neoantigens for immunotherapy. Upfront pre-clinical and clinical studies have successfully used tumor-specific peptides in vaccines with minimal off-target effects. However, the low mutational burden presented by many lesions challenges the generalization of these solutions, requiring the diversification of neoantigen sources. Oncoproteogenomics utilizing customized databases for protein annotation by mass spectrometry (MS) is a powerful tool toward this end. Expanding the concept toward exploring proteoforms originated from post-translational modifications (PTMs) will be decisive to improve molecular subtyping and provide potentially targetable functional nodes with increased cancer specificity. Walking through the path of systems biology, we highlight that alterations in protein glycosylation at the cell surface not only have functional impact on cancer progression and dissemination but also originate unique molecular fingerprints for targeted therapeutics. Moreover, we discuss the outstanding challenges required to accommodate glycoproteomics in oncoproteogenomics platforms. We envisage that such rationale may flag a rather neglected research field, generating novel paradigms for precision oncology and immunotherapy.

Biological Applications for LC-MS-Based Proteomics

Since its inception, liquid chromatography-mass spectrometry (LC-MS) has been continuously improved upon in many aspects, including instrument capabilities, sensitivity, and resolution. Moreover, the costs to purchase and operate mass spectrometers and liquid chromatography systems have decreased, thus increasing affordability and availability in sectors outside of academic and industrial research. Processing power has also grown immensely, cutting the time required to analyze samples, allowing more data to be feasibly processed, and allowing for standardized processing pipelines. As a result, proteomics via LC-MS has become popular in many areas of biological sciences, forging an important seat for itself in targeted and untargeted assays, pure and applied science, the laboratory, and the clinic. In this chapter, many of these applications of LC-MS-based proteomics and an outline of how they can be executed will be covered. Since the field of personalized medicine has matured alongside proteomics, it has also come to rely on various mass spectrometry methods and will be elaborated upon as well. As time goes on and mass spectrometry evolves, there is no doubt that its presence in these areas, and others, will only continue to grow.

Data-independent acquisition mass spectrometry (DIA-MS) for proteomic applications in oncology

Data-independent acquisition mass spectrometry (DIA-MS) is a next generation proteomic methodology that generates permanent digital proteome maps offering highly reproducible retrospective analysis of cellular and tissue specimens. The adoption of this technology has ushered a new wave of oncology studies across a wide range of applications including its use in molecular classification, oncogenic pathway analysis, drug and biomarker discovery and unravelling mechanisms of therapy response and resistance. In this review, we provide an overview of the experimental workflows commonly used in DIA-MS, including its current strengths and limitations versus conventional data-dependent acquisition mass spectrometry (DDA-MS). We further summarise a number of key studies to illustrate the power of this technology when applied to different facets of oncology. Finally we offer a perspective of the latest innovations in DIA-MS technology and machine learning-based algorithms necessary for driving the development of high-throughput, in-depth and reproducible proteomic assays that are compatible with clinical diagnostic workflows, which will ultimately enable the delivery of precision cancer medicine to achieve optimal patient outcomes.

Overexpression of Hepatocyte Chemerin-156 Lowers Tumor Burden in a Murine Model of Diethylnitrosamine-Induced Hepatocellular Carcinoma

The tumor inhibitory potential of the highly active chemerin-156 isoform was described in orthotopic models of hepatocellular carcinoma (HCC). The majority of HCC arises in the fibrotic liver, which was not reproduced in these studies. Here, a potential therapeutic activity of chemerin-156 was evaluated in diethylnitrosamine (DEN)-induced liver cancer, which mimics fibrosis-associated HCC. Mice were infected with adeno-associated virus (AAV) six months after DEN injection to overexpress chemerin-156 in the liver, and animals injected with non-recombinant-AAV served as controls. Three months later, the animals were killed. Both groups were comparable with regard to liver steatosis and fibrosis. Of note, the number of very small tumors was reduced by chemerin-156. Anyhow, the expression of inflammatory and profibrotic genes was similar in larger tumors of control and chemerin-156-AAV-infected animals. Although genes with a role in lipid metabolism, like 3-hydroxy-3-methylglutaryl-coenzym-A--reductase, were overexpressed in tumors of animals with high chemerin-156, total hepatic cholesterol, diacylglycerol and triglyceride levels, and distribution of individual lipid species were normal. Chemerin-156-AAV-infected mice had elevated hepatic and systemic chemerin. Ex vivo activation of the chemerin receptor chemokine-like receptor 1 increased in parallel with serum chemerin, illustrating the biological activity of the recombinant protein. In the tumors, chemerin-155 was the most abundant variant. Chemerin-156 was not detected in tumors of the controls and was hardly found in chemerin-156-AAV infected animals. In conclusion, the present study showed that chemerin-156 overexpression caused a decline in the number of small lesions but did not prevent the growth of pre-existing neoplasms.

Are ENT1/ENT1, NOTCH3, and miR-21 Reliable Prognostic Biomarkers in Patients with Resected Pancreatic Adenocarcinoma Treated with Adjuvant Gemcitabine Monotherapy?

Evidence on equilibrative nucleoside transporter 1 (ENT1) and microRNA-21 (miR‑21) is not yet sufficiently convincing to consider them as prognostic biomarkers for patients with pancreatic ductal adenocarcinoma (PDAC). Here, we investigated the prognostic value of ENT1/ENT1, miR-21, and neurogenic locus homolog protein 3 gene (NOTCH3) in a well-defined cohort of resected patients treated with adjuvant gemcitabine chemotherapy (n = 69). Using a combination of gene expression quantification in microdissected tissue, immunohistochemistry, and univariate/multivariate statistical analyses we did not confirm association of ENT1/ENT1 and NOTCH3 with improved disease-specific survival (DSS). Low miR-21 was associated with longer DSS in patients with negative regional lymph nodes or primary tumor at stage 1 and 2. In addition, downregulation of ENT1 was observed in PDAC of patients with high ENT1 expression in normal pancreas, whereas NOTCH3 was upregulated in PDAC of patients with low NOTCH3 levels in normal pancreas. Tumor miR‑21 was upregulated irrespective of its expression in normal pancreas. Our data confirmed that patient stratification based on expression of ENT1/ENT1 or miR‑21 is not ready to be implemented into clinical decision-making processes. We also conclude that occurrence of ENT1 and NOTCH3 deregulation in PDAC is dependent on their expression in normal pancreas.