Metabolic tumor burden: A new promising way to reach precise personalized therapy in PDAC

Easy-Amanida: An R Shiny application for the meta-analysis of aggregate results in clinical metabolomics using Amanida and Webchem

Meta-analysis is a useful tool in clinical research, as it combines the results of multiple clinical studies to improve precision when answering a particular scientific question. While there has been a substantial increase in publications using meta-analysis in various clinical research topics, the number of published meta-analyses in metabolomics is significantly lower compared to other omics disciplines. Metabolomics is the study of small chemical compounds in living organisms, which provides important insights into an organism's phenotype. However, the wide variety of compounds and the different experimental methods used in metabolomics make it challenging to perform a thorough meta-analysis. Additionally, there is a lack of consensus on reporting statistical estimates, and the high number of compound naming synonyms further complicates the process. Easy-Amanida is a new tool that combines two R packages, "amanida" and "webchem", to enable meta-analysis of aggregate statistical data, like p-value and fold-change, while ensuring the compounds naming harmonization. The Easy-Amanida app is implemented in Shiny, an R package add-on for interactive web apps, and provides a workflow to optimize the naming combination. This article describes all the steps to perform the meta-analysis using Easy-Amanida, including an illustrative example for interpreting the results. The use of aggregate statistics metrics extends the use of Easy-Amanida beyond the metabolomics field.

Evaluation of circulating tumor DNA as a prognostic biomarker for metastatic pancreatic adenocarcinoma

Purpose

Pancreatic cancer is an aggressive solid tumor with a severe prognosis. Although tumor biomarkers are often used to identify advanced pancreatic cancer, this is not accurate, and the currently used biomarkers are not indicative of prognosis. The present study evaluated circulating tumor DNA (ctDNA) as a biomarker for prognosis prediction and disease monitoring in metastatic pancreatic adenocarcinoma (PAC).

Methods

From 2017 to 2018, 40 patients with metastatic PAC were enrolled, and tumor tissue and blood samples were collected from 40 and 35 patients, respectively. CtDNA was sequenced by next-generation sequencing (NGS) with a 425-gene capture panel. The association of clinical characteristics, laboratory indicators, and dynamic ctDNA with patient outcomes was analyzed.

Results

Mutations in KRAS (87.5%, N = 35) and TP53 (77.5%, N = 31) were most common in 40 tumor tissue. Patients’ ECOG score, CA19-9, CEA, neutrophil-lymphocyte ratio (NLR), platelet- lymphocyte ratio (PLR) levels and mutations in ≥ 3 driver genes were strongly correlated with patients’ overall survival (OS). Patients’ gender, ECOG score, CA19-9, and CEA levels were associated with progression-free survival (PFS) (P<0.05). In 35 blood samples, univariate analysis showed a significant association between ECOG score, CA19-9, KRAS or CDKN2A mutation in ctDNA and OS and between CA19-9, CDKN2A or SMAD4 mutation in ctDNA and PFS. Cox hazard proportion model showed that patients’ CDKN2A mutation in ctDNA (HR=16.1, 95% CI=4.4-59.1, P<0.001), ECOG score (HR=6.2, 95% CI=2.4-15.7, P<0.001) and tumor location (HR=0.4, 95% CI=0.1-0.9, P=0.027) were significantly associated with OS. Patients’ CDKN2A mutation in ctDNA (HR=6.8, 95% CI=2.3-19.9, P=0.001), SMAD4 mutation in ctDNA (HR=3.0, 95% CI=1.1-7.9, P=0.031) and metastatic organ (HR=0.4, 95% CI=0.2-1.0, P=0.046) were significantly associated with PFS. Longitudinal changes in gene mutation allelic frequency (MAF) value were evaluated in 24 patients. Detection of progression disease (PD) by ctDNA was 0.9 months earlier than by radiological imaging (mean PFS: 4.6m vs 5.5m, P=0.004, paired t-test).

Conclusions

The ctDNA has the potential as a specific survival predictive marker for metastatic PAC patients. Longitudinal ctDNA tracking could potentially help identify disease progression and be a valuable complement for routine clinical markers and imaging.

Identification of Prognosis-Related Molecular Subgroups and Construction of a Prognostic Prediction Model Using Immune-Related Genes in Pancreatic Cancer

Background

Pancreatic cancer patients with similar clinicopathological status exhibit substantially different therapeutic responses, which might be caused by the vast molecular heterogeneity of tumors. In this study, we attempted to identify specific molecular subgroups and construct a prognostic prediction model based on the expression level of immune-related genes in pancreatic cancer. The transcriptome profiling, single nucleotide variation, copy number variation, clinicopathological information, and follow-up data of pancreatic cancer patients were obtained from The Cancer Genome Atlas database. Thereafter, the immune-related genes with prognostic significance were identified for further consensus cluster analysis. The molecular characteristics and clinicopathological information were compared between the identified subgroups, and a weighted correlation network analysis was performed to identify the hub genes associated with the subgroups. Finally, the prognostic prediction model based on immune-related genes was established using the least absolute shrinkage and selection operator (LASSO) analysis.

Results

A total of 67 immune-relevant genes with prognostic significance were selected and used for the consensus cluster analysis. The total samples were divided into two groups, C1 and C2. The subgroup C1 had a significantly worse prognosis than C2, as well as lower levels of immune cell infiltration, which indicate an immunosuppressed state. The mutational rate of the cancer-related genes including KRAS, TP53, and RNF43 was higher in the C1 subgroup. The C1 subgroup was associated with more advanced tumor grade and T stage and with higher mortality. Using LASSO regression, we developed a prognostic prediction model based on the expression levels of 19 immune-related genes, which we validated in three external data sets. In addition, we identified four potential therapeutic and prognostic biomarkers (TNNT1, KCNN4, SH2D3A, and PHLDA2).

Conclusion

We identified two novel molecular subgroups of pancreatic cancer and developed a prognostic prediction model based on the expression levels of immune-related genes, which could be used in a clinical setting and could aid in unraveling the molecular processes leading to the development of pancreatic cancer.

Apolipoprotein C-II induces EMT to promote gastric cancer peritoneal metastasis via PI3K/AKT/mTOR pathway

BACKGROUND

Peritoneal metastasis (PM) occurs frequently in patients with gastric cancer (GC) and confers poor survival. Lipid metabolism acts as a non-negligible regulator in epithelial-mesenchymal transition (EMT), which is crucial for the metastasis of GC. As apolipoprotein C2 (APOC2) is a key activator of lipoprotein lipase for triglyceride metabolism, the exact mechanism of APOC2 remains largely unknown in GC.

METHODS

Tandem mass tags identified differentially expressed proteins between human PM and GC tissues, and showed that APOC2 overexpressed in PM tissues, which was further confirmed by immunoblotting, immunohistochemistry, and ELISA. Global gene expression changes were identified in APOC2 knockdown cells via RNA-sequencing. The role of APOC2 in lipid metabolism of GC cells was assessed via the Seahorse XF analyzer and lipid staining assays. The biological role of APOC2 in GC cells was determined by 3D Spheroid invasion, apoptosis, colony formation, wound healing, transwell assay, and mouse models. The interaction between APOC2 and CD36 was analyzed by co-immunoprecipitation and biolayer interferometry. The underlying mechanisms were investigated using western blot technique.

RESULTS

APOC2 overexpressed in GC PM tissues. Upregulation of APOC2 correlated with a poor prognosis in GC patients. APOC2 promoted GC cell invasion, migration, and proliferation via CD36-mediated PI3K/AKT/mTOR signaling activation. Furthermore, APOC2-CD36 axis upregulated EMT markers of GC cells via increasing the phosphorylation of PI3K, AKT, and mTOR. Knockdown either APOC2 or CD36 inhibited the malignant phenotype of cancer cells, and delayed GC PM progression in murine GC models.

CONCLUSION

APOC2 cooperates with CD36 to induce EMT to promote GC PM via PI3K/AKT/mTOR pathway. APOC2-CD36 axis may be a potential target for the treatment of aggressive GC.

The Essential Factors of Establishing Patient-derived Tumor Model

Establishing an applicable preclinical model is vital for translational cancer research. Patient-derived xenograft has been important preclinical model systems and widely used for cancer research. Patient-derived xenograft models that represent the tumors of the patients are necessary to better translate research discoveries and to test potential therapeutic approaches. However, research in this field is hampered by the limited engraftment rate. In this review, we go over a large number of researches on patient-derived xenograft transplantation and firstly systematically summarize the main factors in methodology to successfully establish models. These results will be applied to the development of patient-derived xenograft leading to better preclinical research.

Glycometabolic rearrangements--aerobic glycolysis in pancreatic cancer: causes, characteristics and clinical applications

Pancreatic cancer is one of the most malignant tumors worldwide, and pancreatic ductal adenocarcinoma is the most common type. In pancreatic cancer, glycolysis is the primary way energy is produced to maintain the proliferation, invasion, migration, and metastasis of cancer cells, even under normoxia. However, the potential molecular mechanism is still unknown. From this perspective, this review mainly aimed to summarize the current reasonable interpretation of aerobic glycolysis in pancreatic cancer and some of the newest methods for the detection and treatment of pancreatic cancer. More specifically, we reported some biochemical parameters, such as newly developed enzymes and transporters, and further explored their potential as diagnostic biomarkers and therapeutic targets.

Comprehensive analysis of prognostic immune-related genes associated with the tumor microenvironment of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a malignant tumor with a specific tumor immune microenvironment (TIME). Therefore, investigating prognostic immune-related genes (IRGs) that are closely associated with TIME to predict PDAC clinical outcomes is necessary. In the present study, 459 samples of PDAC from the Genotype-Tissue Expression database, The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO) were included and a survival-associated module was identified using weighted gene co-expression network analysis. Based on the Cox regression analysis and least absolute shrinkage and selection operator analysis, four IRGs (2′-5′-oligoadenylate synthetase 1, MET proto-oncogene, receptor tyrosine kinase, interleukin 1 receptor type 2 and interleukin 20 receptor subunit β) were included in the prognostic model to calculate the risk score (RS), and patients with PDAC were divided into high- and low-RS groups. Kaplan-Meier survival and receiver operating characteristic curve analyses demonstrated that the low-RS group had significantly improved survival conditions compared with the high-RS group in TCGA training set. The prognostic function of the model was also validated using ICGC and GEO cohorts. To investigate the mechanism of different overall survival between the high- and low-RS groups, the present study included Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data and Cell Type Identification by Estimating Relative Subset of Known RNA Transcripts algorithms to investigate the state of the tumor microenvironment and immune infiltration inpatients in the cohort from TCGA. In summary, four genes associated with the TIME of PDAC were identified, which may provide a reference for clinical treatment.

Inflammation Associated Pancreatic Tumorigenesis: Upregulation of Succinate Dehydrogenase (Subunit B) Reduces Cell Growth of Pancreatic Ductal Epithelial Cells

Pancreatic ductal adenocarcinoma (PDAC) is amongst the most fatal malignancies and its development is highly associated with inflammatory processes such as chronic pancreatitis (CP). Since the succinate dehydrogenase subunit B (SDHB) is regarded as tumor suppressor that is lost during cancer development, this study investigated the impact of M1-macrophages as part of the inflammatory microenvironment on the expression as well as function of SDHB in benign and premalignant pancreatic ductal epithelial cells (PDECs). Immunohistochemical analyses on pancreatic tissue sections from CP patients and control individuals revealed a stronger SDHB expression in ducts of CP tissues being associated with a greater abundance of macrophages compared to ducts in control tissues. Accordingly, indirect co-culture with M1-macrophages led to clearly elevated SDHB expression and SDH activity in benign H6c7-pBp and premalignant H6c7-kras PDECs. While siRNA-mediated SDHB knockdown in these cells did not affect glucose and lactate uptake after co-culture, SDHB knockdown significantly promoted PDEC growth which was associated with increased proliferation and decreased effector caspase activity particularly in co-cultured PDECs. Overall, these data indicate that SDHB expression and SDH activity are increased in PDECs when exposed to pro-inflammatory macrophages as a counterregulatory mechanism to prevent excessive PDEC growth triggered by the inflammatory environment.

Selecting chemotherapy for pancreatic cancer: Far away or so close?

Pancreatic cancer is a lethal disease with a very poor prognosis. In contrast to treatments for many other tumor types, cytotoxic agents are still the first-line drugs for pancreatic cancer in both the palliative and adjuvant settings. Some progress has been made in recent years, but most large phase 3 studies have not shown significant improvements in survival. Because the available drugs and regimens are limited in both type and effect, the selection of chemotherapy based on clinicopathologic characteristics may be consequential for pancreatic cancer. In the present report, we focused on 7 landmark clinical trials for pancreatic cancer. We observed that FOLFIRINOX (oxaliplatin, irinotecan, fluorouracil, and leucovorin) and NG (nab-paclitaxel and gemcitabine), 2 first-line regimens, exerted opposite effects on metastatic pancreatic cancer patients with different baseline carbohydrate antigen 19-9 (CA19-9) levels. This suggested that not only the performance status but possibly also CA19-9 levels should be considered when making a therapeutic choice for patients with advanced pancreatic cancer. Moreover, we found that patients with a diagnosis of pancreatic cancer who have undergone a surgical resection with a negative margin (R0) may benefit more from fluorouracil and/or oral prodrugs of fluorouracil-based adjuvant therapy than from gemcitabine. Conversely, gemcitabine or gemcitabine-based regimens may be more effective for patients with a positive resection margin (R1). Based on these findings, we propose flowcharts for selecting chemotherapy for both advanced and resected pancreatic cancer. Furthermore, we present possible mechanisms and interpretations underlying the selection of chemotherapy for pancreatic cancer and propose the tumor burden as a key variable in this process. Regardless of the possible bias and exact treatment selection process, this study offers an opportunity to improve patient outcomes by using agents currently used in the therapy of pancreatic cancer. Although these conclusions are based on indirect evidence, we provide insights and possibilities to drive the selection of chemotherapy for pancreatic cancer.

Metabolomic analysis identifies differentially produced oral metabolites, including the oncometabolite 2-hydroxyglutarate, in patients with head and neck squamous cell carcinoma

BACKGROUND

Metabolomics represents a promising approach for discovering novel targets and biomarkers in head and neck squamous cell carcinoma (HNSCC). Here we used metabolomics to identify oral metabolites associated with HNSCC.

METHODS

Tumor and adjacent normal tissue from surgical resections and presurgical oral washes as well as oral washes were collected from healthy participants. Metabolites extractions of these samples were analyzed by liquid chromatography-mass spectroscopy (LC/MS), LC/MS/MS and gas chromatography-MS (GC/MS).

RESULTS

Among 28 samples obtained from 7 HNSCC cases and 7 controls, 422 metabolites were detected (269 identified and 153 unidentified). Oral washes contained 12 and 23 metabolites in healthy controls and HNSCC patients, respectively, with phosphate and lactate being the most abundant. Small molecules related to energy metabolism were significantly elevated in HNSCC patients compared to controls. Levels of beta-alanine, alpha-hydroxyisovalerate, tryptophan, and hexanoylcarnitine were elevated in HNSCC oral washes compared to healthy controls (range 7.8-12.2-fold). Resection tissues contained 22 metabolites, of which eight were overproduced in tumor by 1.9- to 12-fold compared to controls. TCA cycle analogs 2-hydroxyglutarate (2-HG) and 3-GMP were detected exclusively in tumor tissues. Targeted quantification of 2-HG in a representative HNSCC patient showed increase in tumor tissue (14.7 μg/mL), but undetectable in normal tissue. Moreover, high levels of 2-HG were detected in HNSCC cell lines but not in healthy primary oral keratinocyte cultures.

CONCLUSIONS

Oral metabolites related to energy metabolism were elevated in HNSCC, and acylcarnitine and 2HG may have potential as non-invasive biomarkers. Further validation in clinical studies is warranted.

Metabolomics in pancreatic cancer biomarkers research

Pancreatic cancer is one of the worst prognoses of all malignancies. More than 40,000 deaths a year from this disease are observed in European Union alone. The only possibly curative treatment of pancreatic cancer is surgery, yet only 15-20% of patients have operable disease and even patients, which go through surgery and adjuvant chemotherapy, survival is less than 30%. The sensitive and specific biomarkers which could be used for the advance of early diagnostics are needed and constantly researched. Metabolomics is a technology which analyzes the concentrations of low-molecular-weight metabolites (the metabolome) has lately effectively developed due to the improvements in analytical technology. Metabolome analysis can be a one of the useful approaches for the biomarker discovery and disease diagnosis. Here we discuss recent discoveries in the field of pancreatic cancer metabolomics as well as the most promising biomarkers for diagnostics, prognosis and prediction.

The combination of systemic inflammation-based marker NLR and circulating regulatory T cells predicts the prognosis of resectable pancreatic cancer patients

BACKGROUND

The systemic inflammation response and immune impairment are closely related to the development and progression of various tumours, such as pancreatic cancer. In this study, we evaluated circulating inflammation factors and circulating regulatory T cells (Tregs) as markers of immunosuppression in a cohort of Chinese patients with resectable pancreatic cancer.

METHODS

Samples were retrospectively collected from a series of 195 pathological stage I/II pancreatic cancer patients who underwent potentially curative surgery between June 2010 and April 2014. To examine the prognostic factors, circulating systemic inflammation-based markers and Tregs, detected by flow cytometry, were analysed.

RESULTS

Univariate analyses revealed that the neutrophil-lymphocyte ratio (NLR), TNM stage, differentiation, chemotherapy, CA19-9 levels and presence of Tregs are significantly associated with overall survival in patients with resectable pancreatic cancers. NLR (p = 0.001, HR = 0.538), TNM stage (p = 0.004, HR = 0.593), differentiation (p = 0.011, HR = 0.46), chemotherapy (p = 0.006, HR = 0.516) and Tregs (p = 0.001, HR = 0.558) are identified as independent prognostic markers by multivariate analyses. Interestingly, we also found that high NLR levels combined with a high proportion of Tregs (p < 0.001, HR = 3.521) correlate strongly with worse survival, with a greater than 3.5-fold increased risk of death compared with those with concurrent low levels of NLR and Tregs.

CONCLUSIONS

The preoperative NLR and circulating regulatory T cells are potentially independent prognostic factors for overall survival in resectable pancreatic cancer patients. High NLR levels combined with poor immune state before surgery, as measured by Tregs, are associated with an extremely poor prognosis.

The clinical utility of CA125/MUC16 in pancreatic cancer: A consensus of diagnostic, prognostic and predictive updates by the Chinese Study Group for Pancreatic Cancer (CSPAC)

The prognosis for pancreatic cancer (PC) is poor; however, the timely and accurate treatment of this disease will significantly improve prognosis. Serum biomarkers involve non-invasive tests that facilitate the early detection of tumors, predict outcomes and assess responses to therapy, so that the patient can be continuously monitored and receive the most appropriate therapy. Studies have reported that cancer antigen (CA)125 [also known as mucin 16 (MUC16)] has functional significance in the tumorigenic, metastatic and drug resistant properties of PC. Our aim was to use this biomarker in the diagnosis, detection of metastasis, prognosis and in the monitoring of the treatment effects of PC. Members of the Chinese Study Group for Pancreatic Cancer (CSPAC) reviewed the literature on CA125/MUC16 and developed an objective consensus on the clinical utility of CA125/MUC16 for PC. They confirmed the role of CA125/MUC16 in tumorigenesis and the progression of PC, and recommended monitoring CA125/MUC16 levels in all aspects of the diagnosis and treatment of PC, particularly those that involve the monitoring of treatments. In addition, they suggested that the combination of other biomarkers and imaging techniques, together with CA125/MUC16, would improve the accuracy of the clinical decision-making process, thereby facilitating the optimization of treatment strategies. Periodic clinical updates of the use of CA125/MUC16 have been established, which are important for further analyses and comparisons of clinical results from affiliates and countries, particularly as regards the in-depth biological function and clinical translational research of this biomarker.

Omics-Based Biomarkers: Application of Metabolomics in Neuropsychiatric Disorders

One of the major concerns of modern society is to identify putative biomarkers that serve as a valuable early diagnostic tool to identify a subset of patients with increased risk to develop neuropsychiatric disorders. Biomarker identification in neuropsychiatric disorders is proposed to offer a number of important benefits to patient well-being, including prediction of forthcoming disease, diagnostic precision, and a level of disease description that would guide treatment choice. Nowadays, the metabolomics approach has unlocked new possibilities in diagnostics of devastating disorders like neuropsychiatric disorders. Metabolomics-based technologies have the potential to map early biochemical changes in disease and hence provide an opportunity to develop predictive biomarkers that can be used as indicators of pathological abnormalities prior to development of clinical symptoms of neuropsychiatric disorders. This review highlights different -omics strategies for biomarker discovery in neuropsychiatric disorders. We also highlight initial outcomes from metabolomics studies in psychiatric disorders such as schizophrenia, bipolar disorder, and addictive disorders. This review will also present issues and challenges regarding the implementation of the metabolomics approach as a routine diagnostic tool in the clinical laboratory in context with neuropsychiatric disorders.